Authors have proposed State Estimation Based Inverse Dynamics Controller (SEBIDC), which utilizes an Artificial Neural Network (ANN) based state estimation scheme for nonlinear autonomous hybrid systems which are subjected to state disturbances and measurement noises that are stochastic in nature. A salient feature of the proposed scheme is that it offers better state estimates and hence a better control of non-measurable state variables with a non linear approach in correcting the a priori estimates by avoiding statistical linearization involved in existing approaches based on derivative free estimation methods. Simulation results guarantees significant reduction in Integral Square Error (ISE) and standard deviation (σ) of error, between the controlled variable and set point and control signal computation time when compared with best existing related work based on Unscented Kalman Filter (UKF) and Ensemble Kalman Filter (EnKF). Detailed analysis of the experimental results on real plant under different operating conditions such as servo and regulatory operations, initial condition mismatch, and different types of faults in the system, confirms robustness of proposed approach in these conditions and support the simulation results obtained. The main advantage of the proposed controller is that the control signal computation time is very much less than the selected sampling time of the process, so direct control of the plant is possible with this approach.
Direct digital control scheme for controlling hybrid dynamic systems using AN...XiaoLaui
An Estimator Based Inverse Dynamics Controller (EBIDC), which utilizes an Artificial Neural Network (ANN) based state estimation scheme for nonlinear autonomous hybrid systems which are subjected to state disturbances and measurement noises that are stochastic in nature, is proposed in this paper. A salient feature of the proposed scheme is that it offers better state estimates and hence a better control of non-measurable state variables with a non linear approach in correcting the a priori estimates by avoiding statistical linearization involved in existing approaches based on derivative free estimation methods. Simulation results guarantees significant reduction in Integral Square Error (ISE) and standard deviation (σ) of error, between the controlled variable and set point and control signal computation time when compared with best existing related work based on Unscented Kalman Filter (UKF) and Ensembeled Kalman Filter (EnKF). Detailed analysis of the experimental results on real plant under different operating conditions such as servo and regulatory operations, initial condition mismatch, and different types of faults in the system, confirms robustness of proposed approach in these conditions and support the simulation results obtained. The main advantage of the proposed controller is that the control signal computation time is very much less than the selected sampling time of the process, so direct control of the plant is possible with this approach.
Event triggered control design of linear networked systems with quantizationsISA Interchange
This paper is concerned with the control design problem of event-triggered networked systems with both state and control input quantizations. Firstly, an innovative delay system model is proposed that describes the network conditions, state and control input quantizations, and event-triggering mechanism in a unified framework. Secondly, based on this model, the criteria for the asymptotical stability analysis and control synthesis of event-triggered networked control systems are established in terms of linear matrix inequalities (LMIs). Simulation results are given to illustrate the effectiveness of the proposed method.
Tracy–Widom distribution based fault detection approach: Application to aircr...ISA Interchange
The fault detection approach based on the Tracy–Widom distribution is presented and applied to the aircraft flight control system. An operative method of testing the innovation covariance of the Kalman filter is proposed. The maximal eigenvalue of the random Wishart matrix is used as the monitoring statistic, and the testing problem is reduced to determine the asymptotics for the largest eigenvalue of the Wishart matrix. As a result, an algorithm for testing the innovation covariance based on the Tracy–Widom distribution is proposed. In the simulations, the longitudinal and lateral dynamics of the F-16 aircraft model is considered, and detection of sensor and control surface faults in the flight control system which affect the innovation covariance, are examined.
Modern Tools for the Small-Signal Stability Analysis and Design of FACTS Assi...Power System Operation
There has recently been an intense development of eigenanalysis tools for power systems dynamics and control. This paper provides an up-to-date review of the major algorithms available. Comments on other relevant but yet unpublished algorithms are also made.
The conventional state-space description is obtained through the elimination of the algebraic variables of the mathematical model. In many applications, the power system case included, the algebraic variables in the model are usually more numerous than the state variables.
The problem of multiple, decentralized controller synthesis in large, FACTS assisted power systems is addressed.
A report in CIGRE [1] recognized the large benefits of working with the unreduced set of differential-algebraic equations linearized at an operating point. The resulting power system Jacobian is large and highly sparse, allowing efficient use of sparsity oriented programming. The computation of all system eigenvalues, not practical for large power systems, was since then replaced by the computation of only the eigenvalues of interest.
A good part of the paper is dedicated to showing the high productivity gains achieved when using a well designed package for graphical display and animation of results.
The pressing needs for better utilization of existing transmission and generation equipment will eventually dictate a wider use of FACTS devices. The higher complexity and stricter requirements on power system controls, under both steady-state and dynamic conditions, calls for the immediate development and use of more sophisticated computer tools. The material in this paper may help the further development of one of these tools.
Many important developments occurred in the last fifteen
Comparative Study on the Performance of A Coherency-based Simple Dynamic Equi...IJAPEJOURNAL
Earlier, a simple dynamic equivalent for a power system external area containing a group of coherent generators was proposed in the literature. This equivalent is based on a new concept of decomposition of generators and a two-level generator aggregation. With the knowledge of only the passive network model of the external area and the total inertia constant of all the generators in this area, the parameters of this equivalent are determinable from a set of measurement data taken solely at a set of boundary buses which separates this area from the rest of the system. The proposed equivalent, therefore, does not require any measurement data at the external area generators. This is an important feature of this equivalent. In this paper, the results of a comparative study on the performance of this dynamic equivalent aggregation with the new inertial aggregation in terms of accuracy are presented. The three test systems that were considered in this comparative investigation are the New England 39-bus 10-generator system, the IEEE 162-bus 17-generator system and the IEEE 145-bus 50-generator system.
Research and Development the Adaptive Control Model Using the Spectrometer De...theijes
In this paper there are consider the automatic adaptive control system, selected adaptive control system with the standard model.The mathematical model of adaptive control system with the etalon-model is developed. Constructed and researched mathematical model of adaptive system with a reference model using MathLab Simulink software.There are researched adaptive control system responds to various external influences.
PI and fuzzy logic controllers for shunt active power filterISA Interchange
This paper presents a shunt Active Power Filter (APF) for power quality improvements in terms of harmonics and reactive power compensation in the distribution network. The compensation process is based only on source current extraction that reduces the number of sensors as well as its complexity. A Proportional Integral (PI) or Fuzzy Logic Controller (FLC) is used to extract the required reference current from the distorted line-current, and this controls the DC-side capacitor voltage of the inverter. The shunt APF is implemented with PWM-current controlled Voltage Source Inverter (VSI) and the switching patterns are generated through a novel Adaptive-Fuzzy Hysteresis Current Controller (A-F-HCC). The proposed adaptive-fuzzy-HCC is compared with fixed-HCC and adaptive-HCC techniques and the superior features of this novel approach are established. The FLC based shunt APF system is validated through extensive simulation for diode-rectifier/R–L loads.
A portable hardware in-the-loop device for automotive diagnostic control systemsISA Interchange
In-vehicle driving tests for evaluating the performance and diagnostic functionalities of engine control systems are often time consuming, expensive, and not reproducible. Using a hardware-in-the-loop (HIL) simulation approach, new control strategies and diagnostic functions on a controller area network (CAN) line can be easily tested in real time, in order to reduce the effort and the cost of the testing phase. Nowadays, spark ignition engines are controlled by an electronic control unit (ECU) with a large number of embedded sensors and actuators. In order to meet the rising demand of lower emissions and fuel consumption, an increasing number of control functions are added into such a unit. This work aims at presenting a portable electronic environment system, suited for HIL simulations, in order to test the engine control software and the diagnostic functionality on a CAN line, respectively, through non-regression and diagnostic tests. The performances of the proposed electronic device, called a micro hardware-in-the-loop system, are presented through the testing of the engine management system software of a 1.6 l Fiat gasoline engine with variable valve actuation for the ECU development version.
Direct digital control scheme for controlling hybrid dynamic systems using AN...XiaoLaui
An Estimator Based Inverse Dynamics Controller (EBIDC), which utilizes an Artificial Neural Network (ANN) based state estimation scheme for nonlinear autonomous hybrid systems which are subjected to state disturbances and measurement noises that are stochastic in nature, is proposed in this paper. A salient feature of the proposed scheme is that it offers better state estimates and hence a better control of non-measurable state variables with a non linear approach in correcting the a priori estimates by avoiding statistical linearization involved in existing approaches based on derivative free estimation methods. Simulation results guarantees significant reduction in Integral Square Error (ISE) and standard deviation (σ) of error, between the controlled variable and set point and control signal computation time when compared with best existing related work based on Unscented Kalman Filter (UKF) and Ensembeled Kalman Filter (EnKF). Detailed analysis of the experimental results on real plant under different operating conditions such as servo and regulatory operations, initial condition mismatch, and different types of faults in the system, confirms robustness of proposed approach in these conditions and support the simulation results obtained. The main advantage of the proposed controller is that the control signal computation time is very much less than the selected sampling time of the process, so direct control of the plant is possible with this approach.
Event triggered control design of linear networked systems with quantizationsISA Interchange
This paper is concerned with the control design problem of event-triggered networked systems with both state and control input quantizations. Firstly, an innovative delay system model is proposed that describes the network conditions, state and control input quantizations, and event-triggering mechanism in a unified framework. Secondly, based on this model, the criteria for the asymptotical stability analysis and control synthesis of event-triggered networked control systems are established in terms of linear matrix inequalities (LMIs). Simulation results are given to illustrate the effectiveness of the proposed method.
Tracy–Widom distribution based fault detection approach: Application to aircr...ISA Interchange
The fault detection approach based on the Tracy–Widom distribution is presented and applied to the aircraft flight control system. An operative method of testing the innovation covariance of the Kalman filter is proposed. The maximal eigenvalue of the random Wishart matrix is used as the monitoring statistic, and the testing problem is reduced to determine the asymptotics for the largest eigenvalue of the Wishart matrix. As a result, an algorithm for testing the innovation covariance based on the Tracy–Widom distribution is proposed. In the simulations, the longitudinal and lateral dynamics of the F-16 aircraft model is considered, and detection of sensor and control surface faults in the flight control system which affect the innovation covariance, are examined.
Modern Tools for the Small-Signal Stability Analysis and Design of FACTS Assi...Power System Operation
There has recently been an intense development of eigenanalysis tools for power systems dynamics and control. This paper provides an up-to-date review of the major algorithms available. Comments on other relevant but yet unpublished algorithms are also made.
The conventional state-space description is obtained through the elimination of the algebraic variables of the mathematical model. In many applications, the power system case included, the algebraic variables in the model are usually more numerous than the state variables.
The problem of multiple, decentralized controller synthesis in large, FACTS assisted power systems is addressed.
A report in CIGRE [1] recognized the large benefits of working with the unreduced set of differential-algebraic equations linearized at an operating point. The resulting power system Jacobian is large and highly sparse, allowing efficient use of sparsity oriented programming. The computation of all system eigenvalues, not practical for large power systems, was since then replaced by the computation of only the eigenvalues of interest.
A good part of the paper is dedicated to showing the high productivity gains achieved when using a well designed package for graphical display and animation of results.
The pressing needs for better utilization of existing transmission and generation equipment will eventually dictate a wider use of FACTS devices. The higher complexity and stricter requirements on power system controls, under both steady-state and dynamic conditions, calls for the immediate development and use of more sophisticated computer tools. The material in this paper may help the further development of one of these tools.
Many important developments occurred in the last fifteen
Comparative Study on the Performance of A Coherency-based Simple Dynamic Equi...IJAPEJOURNAL
Earlier, a simple dynamic equivalent for a power system external area containing a group of coherent generators was proposed in the literature. This equivalent is based on a new concept of decomposition of generators and a two-level generator aggregation. With the knowledge of only the passive network model of the external area and the total inertia constant of all the generators in this area, the parameters of this equivalent are determinable from a set of measurement data taken solely at a set of boundary buses which separates this area from the rest of the system. The proposed equivalent, therefore, does not require any measurement data at the external area generators. This is an important feature of this equivalent. In this paper, the results of a comparative study on the performance of this dynamic equivalent aggregation with the new inertial aggregation in terms of accuracy are presented. The three test systems that were considered in this comparative investigation are the New England 39-bus 10-generator system, the IEEE 162-bus 17-generator system and the IEEE 145-bus 50-generator system.
Research and Development the Adaptive Control Model Using the Spectrometer De...theijes
In this paper there are consider the automatic adaptive control system, selected adaptive control system with the standard model.The mathematical model of adaptive control system with the etalon-model is developed. Constructed and researched mathematical model of adaptive system with a reference model using MathLab Simulink software.There are researched adaptive control system responds to various external influences.
PI and fuzzy logic controllers for shunt active power filterISA Interchange
This paper presents a shunt Active Power Filter (APF) for power quality improvements in terms of harmonics and reactive power compensation in the distribution network. The compensation process is based only on source current extraction that reduces the number of sensors as well as its complexity. A Proportional Integral (PI) or Fuzzy Logic Controller (FLC) is used to extract the required reference current from the distorted line-current, and this controls the DC-side capacitor voltage of the inverter. The shunt APF is implemented with PWM-current controlled Voltage Source Inverter (VSI) and the switching patterns are generated through a novel Adaptive-Fuzzy Hysteresis Current Controller (A-F-HCC). The proposed adaptive-fuzzy-HCC is compared with fixed-HCC and adaptive-HCC techniques and the superior features of this novel approach are established. The FLC based shunt APF system is validated through extensive simulation for diode-rectifier/R–L loads.
A portable hardware in-the-loop device for automotive diagnostic control systemsISA Interchange
In-vehicle driving tests for evaluating the performance and diagnostic functionalities of engine control systems are often time consuming, expensive, and not reproducible. Using a hardware-in-the-loop (HIL) simulation approach, new control strategies and diagnostic functions on a controller area network (CAN) line can be easily tested in real time, in order to reduce the effort and the cost of the testing phase. Nowadays, spark ignition engines are controlled by an electronic control unit (ECU) with a large number of embedded sensors and actuators. In order to meet the rising demand of lower emissions and fuel consumption, an increasing number of control functions are added into such a unit. This work aims at presenting a portable electronic environment system, suited for HIL simulations, in order to test the engine control software and the diagnostic functionality on a CAN line, respectively, through non-regression and diagnostic tests. The performances of the proposed electronic device, called a micro hardware-in-the-loop system, are presented through the testing of the engine management system software of a 1.6 l Fiat gasoline engine with variable valve actuation for the ECU development version.
Recurrent fuzzy neural network backstepping control for the prescribed output...ISA Interchange
This paper proposes a backstepping control system that uses a tracking error constraint and recurrent fuzzy neural networks (RFNNs) to achieve a prescribed tracking performance for a strict-feedback nonlinear dynamic system. A new constraint variable was defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries. An adaptive RFNN was also used to obtain the required improvement on the approximation performances in order to avoid calculating the explosive number of terms generated by the recursive steps of traditional backstepping control. The boundedness and convergence of the closed-loop system was confirmed based on the Lyapunov stability theory. The prescribed performance of the proposed control scheme was validated by using it to control the prescribed error of a nonlinear system and a robot manipulator.
Decentralized supervisory based switching control for uncertain multivariable...ISA Interchange
In this paper, the design of decentralized switching control for uncertain multivariable plants is considered. In the proposed strategy, the uncertainty region is divided into smaller regions with a nominal model and specific control structure. The underlying design is based on the quantitative feedback theory (QFT). It is assumed that a MIMO-QFT controller exists for robust stability and performance of the individual uncertain sets. The proposed control structure is made up by these local decentralized controllers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor makes the decision by comparing the local models’ behaviors with the one of the plant and selects the controller corresponding to the best fitted model. A hysteresis switching logic is used to slow down the switching to guarantee the overall closed loop stability. It is shown that this strategy provides a stable and robust adaptive controller to deal with complex multivariable plants with input–output pairing changes during the plant operation, which can facilitate the development of a reconfigurable decentralized control. Also, the multirealization technique is used to implement a family of controllers to achieve bumpless transfer. Simulation results are employed to show the effectiveness of the proposed method.
Analytical Evaluation of Generalized Predictive Control Algorithms Using a Fu...inventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
SYSTEM IDENTIFICATION AND MODELING FOR INTERACTING AND NON-INTERACTING TANK S...ijistjournal
System identification from the experimental data plays a vital role for model based controller design. Derivation of process model from first principles is often difficult due to its complexity. The first stage in the development of any control and monitoring system is the identification and modeling of the system. Each model is developed within the context of a specific control problem. Thus, the need for a general system identification framework is warranted. The proposed framework should be able to adapt and emphasize different properties based on the control objective and the nature of the behavior of the system. Therefore, system identification has been a valuable tool in identifying the model of the system based on the input and output data for the design of the controller. The present work is concerned with the identification of transfer function models using statistical model identification, process reaction curve method, ARX model, genetic algorithm and modeling using neural network and fuzzy logic for interacting and non interacting tank process. The identification technique and modeling used is prone to parameter change & disturbance. The proposed methods are used for identifying the mathematical model and intelligent model of interacting and non interacting process from the real time experimental data.
A novel nonlinear missile guidance law against maneuvering targets is designed based on the principles of partial stability. It is demonstrated that in a real approach which is adopted with actual situations, each state of the guidance system must have a special behavior and asymptotic stability or exponential stability of all states is not realistic. Thus, a new guidance law is developed based on the partial stability theorem in such a way that the behaviors of states in the closed-loop system are in conformity with a real guidance scenario that leads to collision. The performance of the proposed guidance law in terms of interception time and control effort is compared with the sliding mode guidance law by means of numerical simulations.
Compensation of Data-Loss in Attitude Control of Spacecraft Systems rinzindorjej
In this paper, a comprehensive comparison of two robust estimation techniques namely, compensated closed-loop Kalman filtering and open-loop Kalman filtering is presented. A common problem of data loss in a real-time control system is investigated through these two schemes. The open-loop scheme, dealing with the data-loss, suffers from several shortcomings. These shortcomings are overcome using compensated scheme, where an accommodating observation signal is obtained through linear prediction technique -- a closed-loop setting and is adopted at a posteriori update step. The calculation and employment of accommodating observation signal causes computational complexity. For simulation purpose, a linear time invariant spacecraft model is however, obtained from the nonlinear spacecraft attitude dynamics through linearization at nonzero equilibrium points -- achieved off-line through Levenberg-Marguardt iterative scheme. Attempt has been made to analyze the selected example from most of the perspectives in order to display the performance of the two techniques.
Refining Underwater Target Localization and Tracking EstimatesCSCJournals
Improving the accuracy and reliability of the localization estimates and tracking of underwater targets is a constant quest in ocean surveillance operations. The localization estimates may vary owing to various noises and interferences such as sensor errors and environmental noises. Even though adaptive filters like the Kalman filter subdue these problems and yield dependable results, targets that undergo maneuvering can cause incomprehensible errors, unless suitable corrective measures are implemented. Simulation studies on improving the localization and tracking estimates for a stationary target as well as a moving target including the maneuvering situations are presented in this paper
IRJET- Excitation Control of Synchronous Generator using a Fuzzy Logic based ...IRJET Journal
This document presents a fuzzy logic-based backstepping approach for excitation control of synchronous generators. The backstepping control law is designed using control Lyapunov functions to ensure stability. Fuzzy logic is then used to determine the optimal values for three tuning gains in the backstepping control law. Membership functions are defined based on the CLF boundaries. Fuzzy rules are formulated to map error and change in error inputs to tuning gain outputs. Simulation studies on a single machine infinite bus system evaluate the performance of the proposed controller under two fault conditions, and validate that it improves stability compared to traditional controllers with constant gains.
DESIGN OF OBSERVER BASED QUASI DECENTRALIZED FUZZY LOAD FREQUENCY CONTROLLER ...ijfls
This paper proposes Fuzzy Quasi Decentralized Functional Observers (FQDFO) for Load Frequency Control of inter-connected power systems. From the literature, it is well noticed about the need of
Functional Observers (FO’s) for power system applications. In past, conventional Functional Observers are used. Later, these conventional Functional Observers are replaced with Quasi Decentralized
Functional Observers (QDFO) to improve the system performance. In order to increase the efficacy of the
system, intelligent controllers gained importance. Due to their expertise knowledge, which is adaptive in
nature is applied successfully for FQDFO. For supporting the validity of the proposed observer FQDFO,
it is compared with full order Luenberger observer and QDFO for a two-area inter connected power system by taking parametric uncertainties into consideration. Computational results proved the robustness of the proposed observer.
The document discusses various applications of artificial neural networks (ANNs) including electrical load forecasting, system identification, control systems, and pattern recognition. It provides details on ANN approaches for each application area. For electrical load forecasting, ANNs can be used to classify forecasting into time spans and discuss techniques like fuzzy logic and regression models. ANNs are also discussed for system identification to determine system parameters from input-output data and for control system applications like predictive control and feedback linearization. The document concludes with ANN approaches for pattern recognition tasks involving classification, clustering, and regression.
Flatness Based Nonlinear Sensorless Control of Induction Motor SystemsIAES-IJPEDS
This paper deals with the flatness-based approach for sensorless control of
the induction motor systems. Two main features of the proposed flatness
based control are worth to be mentioned. Firstly, the simplicity of
implementation of the flatness approach as a nonlinear feedback linearization
control technique. Secondly, when the chosen flat outputs involve non
available state variable measurements a nonlinear observer is used to
estimate them. The main advantage of the used observer is its ability to
exploite the properties of the system nonlinearties. The simulation results are
presented to illustrate the effectiness of the proposed approach for sensorless
control of the considered induction motor.
Overview of State Estimation Technique for Power System ControlIOSR Journals
This document provides an overview of state estimation techniques for power system control. It discusses static, tracking, and dynamic state estimation approaches and how they differ based on whether measurements and system models are time-variant or invariant. The document also describes how state estimation processes redundant measurements to filter noise and estimate true system states like voltage magnitudes and angles at each bus. It further discusses weighted least squares estimation, the use of Jacobian matrices to iteratively estimate states, and how state estimation provides critical real-time data for power system monitoring and control functions.
This document discusses state estimation in power systems. It begins by defining state estimation as assigning values to unknown system state variables based on measurements according to some criteria. It then discusses that the most commonly used criterion is the weighted least squares method. It provides an example of using measurements to estimate voltage angles as state variables and calculate other power flows. Finally, it discusses the weighted least squares state estimation technique in detail including developing the measurement function matrix and solving the weighted least squares optimization.
A hybrid bacterial foraging and modified particle swarm optimization for mode...IJECEIAES
This paper study the model reduction procedures used for the reduction of large-scale dynamic models into a smaller one through some sort of differential and algebraic equations. A confirmed relevance between these two models exists, and it shows same characteristics under study. These reduction procedures are generally utilized for mitigating computational complexity, facilitating system analysis, and thence reducing time and costs. This paper comes out with a study showing the impact of the consolidation between the Bacterial-Foraging (BF) and Modified particle swarm optimization (MPSO) for the reduced order model (ROM). The proposed hybrid algorithm (BF-MPSO) is comprehensively compared with the BF and MPSO algorithms; a comparison is also made with selected existing techniques.
Performance analysis of a liquid column in a chemical plant by using mpceSAT Publishing House
This document discusses the use of model predictive control (MPC) to control the composition of a liquid column in a chemical plant. It provides background on MPC and how it can be used for multivariable processes like liquid columns. The document describes modeling a liquid column process in MATLAB Simulink and comparing the performance of MPC and PID control of the column. The results show that MPC provides better control of the column composition and liquid level than PID control.
Trajectory Control With MPC For A Robot Manipülatör Using ANN ModelIJMER
In this study, in a computer the dynamic motion modelling of manipulator and control of
trajectory with an algorithm this has been tested. First after dynamic motion simulation of manipulator
has been made MPC Control. The result in this study we can observe that computed torque method gives
better results than MPC methods. So in trajectory control it is approved of using computed torque
method. In last part of this study the results are estimated forward development are exemined and
suggested. The model predictive control (MPC) technique for an articulated robot with n joints is
introduced in this paper. The proposed MPC control action is conceptually different with the trajectory
robot control methods in that the control action is determined by optimising a performance index over
the time horizon. A neural network (NN) is used in this paper as the predictive model.
Assessment of quality indicators of the automatic control system influence of...TELKOMNIKA JOURNAL
This work concentrates the analysis of the system of automatic control of the directive diagram of the moving active electronically scanned array with a limited number of transceiver modules. The analysis revealed a number of shortcomings that lead to a significant increase in standard deviations, quadratic integral estimates, and an increase in transient time. The identified disadvantages lead to a decrease in the efficiency of the antenna system, an increase in the error rate at the reception, the inability of the system to react to disturbances applied to any point of the system in the event of a mismatch of a given signal/noise level. In accordance with the analysis, the mathematical model of the automatic control system of the directional diagram of the moving active electronically scanned array was considered, considering this a new method of estimating the quality indicators of the automatic control diagram of the directional diagram of the active electronically scanned array in a random setting and disturbing action was developed. The difference between the proposed method and the existing method is in the construction of an automatic control system with differential coupling equivalent to the combination due to the introduction of derivatives of the random setting action of the open compensation connection.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes a research paper that proposes a new design methodology for a roll position demand autopilot for guided missiles. The design is based on state feedback using Ackermann pole placement and a reduced order observer. The open-loop unstable roll control model is stabilized using state feedback and pole placement. Actuator dynamics are included. Simulation results show the new design significantly improves responses over previous frequency domain designs by eliminating overshoots and reducing maximum roll rates and aileron deflections. A reduced order Das & Ghosal observer is used to estimate unmeasurable states like aileron angle and rate.
Robust control for a tracking electromechanical systemIJECEIAES
A strategy for the design of robust control of tracking electromechanical systems based on 𝐻∞ synthesis is proposed. Proposed methods are based on the operations on frequency characteristics of control systems designed and developed using the MATLAB robust control toolbox. Determination of the singular values for a transfer matrix of the control system reduces the disturbances and guarantees its stability margin. For selecting the weighted transfer functions, the basic recommendations are formulated. The efficiency of the proposed approach is verified by robust control of an elastically coupled two-mass system whose parameter values are adjusted by matching them with the parameters of one of the supplied robots. The simulation results confirm that the proposed strategy of design of robust control of twomass elastic coupling system using the 𝐻∞ synthesis is very efficient and significantly reduces the perturbation of parameters of the controlled plant.
Data-driven adaptive predictive control for an activated sludge processjournalBEEI
Data-driven control requires no information of the mathematical model of the controlled process. This paper proposes the direct identification of controller parameters of activated sludge process. This class of data-driven control calculates the predictive controller parameters directly using subspace identification technique. By updating input-output data using receding window mechanism, the adaptive strategy can be achieved. The robustness test and stability analysis of direct adaptive model predictive control are discussed to realize the effectiveness of this adaptive control scheme. The applicability of the controller algorithm to adapt into varying kinetic parameters and operating conditions is evaluated. Simulation results show that by a proper and effective excitation of direct identification of controller parameters, the convergence and stability of the implicit predictive model can be achieved.
Integral Backstepping Sliding Mode Control of Chaotic Forced Van Der Pol Osci...ijctcm
ABSTRACT
Forced Van der Pol oscillator exhibits chaotic behaviour and instability under certain parameters and this poses a great threat to the systems where it has been applied hence, the need to develop a control method to stabilize and control chaos in a Forced Van der Pol oscillator so as to avoid damage in the controlled system and also to prevent unmodeled dynamics from being introduced in the system. Sliding Mode control makes use of the regulatory variables derived from the controlled Lyapunov function to bring the new variables to stability. The essence of using Integral Backstepping was to prevent chattering which can occur in the control input and can cause instability to the system by igniting unmodeled dynamics. Simulation was done using MATLAB and the results were provided to show the effectiveness of the proposed control method. Integral Backstepping Sliding Mode control method was effective towards stability and chaos control. It was also robust towards matched and unmatched disturbance.
Recurrent fuzzy neural network backstepping control for the prescribed output...ISA Interchange
This paper proposes a backstepping control system that uses a tracking error constraint and recurrent fuzzy neural networks (RFNNs) to achieve a prescribed tracking performance for a strict-feedback nonlinear dynamic system. A new constraint variable was defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries. An adaptive RFNN was also used to obtain the required improvement on the approximation performances in order to avoid calculating the explosive number of terms generated by the recursive steps of traditional backstepping control. The boundedness and convergence of the closed-loop system was confirmed based on the Lyapunov stability theory. The prescribed performance of the proposed control scheme was validated by using it to control the prescribed error of a nonlinear system and a robot manipulator.
Decentralized supervisory based switching control for uncertain multivariable...ISA Interchange
In this paper, the design of decentralized switching control for uncertain multivariable plants is considered. In the proposed strategy, the uncertainty region is divided into smaller regions with a nominal model and specific control structure. The underlying design is based on the quantitative feedback theory (QFT). It is assumed that a MIMO-QFT controller exists for robust stability and performance of the individual uncertain sets. The proposed control structure is made up by these local decentralized controllers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor makes the decision by comparing the local models’ behaviors with the one of the plant and selects the controller corresponding to the best fitted model. A hysteresis switching logic is used to slow down the switching to guarantee the overall closed loop stability. It is shown that this strategy provides a stable and robust adaptive controller to deal with complex multivariable plants with input–output pairing changes during the plant operation, which can facilitate the development of a reconfigurable decentralized control. Also, the multirealization technique is used to implement a family of controllers to achieve bumpless transfer. Simulation results are employed to show the effectiveness of the proposed method.
Analytical Evaluation of Generalized Predictive Control Algorithms Using a Fu...inventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
SYSTEM IDENTIFICATION AND MODELING FOR INTERACTING AND NON-INTERACTING TANK S...ijistjournal
System identification from the experimental data plays a vital role for model based controller design. Derivation of process model from first principles is often difficult due to its complexity. The first stage in the development of any control and monitoring system is the identification and modeling of the system. Each model is developed within the context of a specific control problem. Thus, the need for a general system identification framework is warranted. The proposed framework should be able to adapt and emphasize different properties based on the control objective and the nature of the behavior of the system. Therefore, system identification has been a valuable tool in identifying the model of the system based on the input and output data for the design of the controller. The present work is concerned with the identification of transfer function models using statistical model identification, process reaction curve method, ARX model, genetic algorithm and modeling using neural network and fuzzy logic for interacting and non interacting tank process. The identification technique and modeling used is prone to parameter change & disturbance. The proposed methods are used for identifying the mathematical model and intelligent model of interacting and non interacting process from the real time experimental data.
A novel nonlinear missile guidance law against maneuvering targets is designed based on the principles of partial stability. It is demonstrated that in a real approach which is adopted with actual situations, each state of the guidance system must have a special behavior and asymptotic stability or exponential stability of all states is not realistic. Thus, a new guidance law is developed based on the partial stability theorem in such a way that the behaviors of states in the closed-loop system are in conformity with a real guidance scenario that leads to collision. The performance of the proposed guidance law in terms of interception time and control effort is compared with the sliding mode guidance law by means of numerical simulations.
Compensation of Data-Loss in Attitude Control of Spacecraft Systems rinzindorjej
In this paper, a comprehensive comparison of two robust estimation techniques namely, compensated closed-loop Kalman filtering and open-loop Kalman filtering is presented. A common problem of data loss in a real-time control system is investigated through these two schemes. The open-loop scheme, dealing with the data-loss, suffers from several shortcomings. These shortcomings are overcome using compensated scheme, where an accommodating observation signal is obtained through linear prediction technique -- a closed-loop setting and is adopted at a posteriori update step. The calculation and employment of accommodating observation signal causes computational complexity. For simulation purpose, a linear time invariant spacecraft model is however, obtained from the nonlinear spacecraft attitude dynamics through linearization at nonzero equilibrium points -- achieved off-line through Levenberg-Marguardt iterative scheme. Attempt has been made to analyze the selected example from most of the perspectives in order to display the performance of the two techniques.
Refining Underwater Target Localization and Tracking EstimatesCSCJournals
Improving the accuracy and reliability of the localization estimates and tracking of underwater targets is a constant quest in ocean surveillance operations. The localization estimates may vary owing to various noises and interferences such as sensor errors and environmental noises. Even though adaptive filters like the Kalman filter subdue these problems and yield dependable results, targets that undergo maneuvering can cause incomprehensible errors, unless suitable corrective measures are implemented. Simulation studies on improving the localization and tracking estimates for a stationary target as well as a moving target including the maneuvering situations are presented in this paper
IRJET- Excitation Control of Synchronous Generator using a Fuzzy Logic based ...IRJET Journal
This document presents a fuzzy logic-based backstepping approach for excitation control of synchronous generators. The backstepping control law is designed using control Lyapunov functions to ensure stability. Fuzzy logic is then used to determine the optimal values for three tuning gains in the backstepping control law. Membership functions are defined based on the CLF boundaries. Fuzzy rules are formulated to map error and change in error inputs to tuning gain outputs. Simulation studies on a single machine infinite bus system evaluate the performance of the proposed controller under two fault conditions, and validate that it improves stability compared to traditional controllers with constant gains.
DESIGN OF OBSERVER BASED QUASI DECENTRALIZED FUZZY LOAD FREQUENCY CONTROLLER ...ijfls
This paper proposes Fuzzy Quasi Decentralized Functional Observers (FQDFO) for Load Frequency Control of inter-connected power systems. From the literature, it is well noticed about the need of
Functional Observers (FO’s) for power system applications. In past, conventional Functional Observers are used. Later, these conventional Functional Observers are replaced with Quasi Decentralized
Functional Observers (QDFO) to improve the system performance. In order to increase the efficacy of the
system, intelligent controllers gained importance. Due to their expertise knowledge, which is adaptive in
nature is applied successfully for FQDFO. For supporting the validity of the proposed observer FQDFO,
it is compared with full order Luenberger observer and QDFO for a two-area inter connected power system by taking parametric uncertainties into consideration. Computational results proved the robustness of the proposed observer.
The document discusses various applications of artificial neural networks (ANNs) including electrical load forecasting, system identification, control systems, and pattern recognition. It provides details on ANN approaches for each application area. For electrical load forecasting, ANNs can be used to classify forecasting into time spans and discuss techniques like fuzzy logic and regression models. ANNs are also discussed for system identification to determine system parameters from input-output data and for control system applications like predictive control and feedback linearization. The document concludes with ANN approaches for pattern recognition tasks involving classification, clustering, and regression.
Flatness Based Nonlinear Sensorless Control of Induction Motor SystemsIAES-IJPEDS
This paper deals with the flatness-based approach for sensorless control of
the induction motor systems. Two main features of the proposed flatness
based control are worth to be mentioned. Firstly, the simplicity of
implementation of the flatness approach as a nonlinear feedback linearization
control technique. Secondly, when the chosen flat outputs involve non
available state variable measurements a nonlinear observer is used to
estimate them. The main advantage of the used observer is its ability to
exploite the properties of the system nonlinearties. The simulation results are
presented to illustrate the effectiness of the proposed approach for sensorless
control of the considered induction motor.
Overview of State Estimation Technique for Power System ControlIOSR Journals
This document provides an overview of state estimation techniques for power system control. It discusses static, tracking, and dynamic state estimation approaches and how they differ based on whether measurements and system models are time-variant or invariant. The document also describes how state estimation processes redundant measurements to filter noise and estimate true system states like voltage magnitudes and angles at each bus. It further discusses weighted least squares estimation, the use of Jacobian matrices to iteratively estimate states, and how state estimation provides critical real-time data for power system monitoring and control functions.
This document discusses state estimation in power systems. It begins by defining state estimation as assigning values to unknown system state variables based on measurements according to some criteria. It then discusses that the most commonly used criterion is the weighted least squares method. It provides an example of using measurements to estimate voltage angles as state variables and calculate other power flows. Finally, it discusses the weighted least squares state estimation technique in detail including developing the measurement function matrix and solving the weighted least squares optimization.
A hybrid bacterial foraging and modified particle swarm optimization for mode...IJECEIAES
This paper study the model reduction procedures used for the reduction of large-scale dynamic models into a smaller one through some sort of differential and algebraic equations. A confirmed relevance between these two models exists, and it shows same characteristics under study. These reduction procedures are generally utilized for mitigating computational complexity, facilitating system analysis, and thence reducing time and costs. This paper comes out with a study showing the impact of the consolidation between the Bacterial-Foraging (BF) and Modified particle swarm optimization (MPSO) for the reduced order model (ROM). The proposed hybrid algorithm (BF-MPSO) is comprehensively compared with the BF and MPSO algorithms; a comparison is also made with selected existing techniques.
Performance analysis of a liquid column in a chemical plant by using mpceSAT Publishing House
This document discusses the use of model predictive control (MPC) to control the composition of a liquid column in a chemical plant. It provides background on MPC and how it can be used for multivariable processes like liquid columns. The document describes modeling a liquid column process in MATLAB Simulink and comparing the performance of MPC and PID control of the column. The results show that MPC provides better control of the column composition and liquid level than PID control.
Trajectory Control With MPC For A Robot Manipülatör Using ANN ModelIJMER
In this study, in a computer the dynamic motion modelling of manipulator and control of
trajectory with an algorithm this has been tested. First after dynamic motion simulation of manipulator
has been made MPC Control. The result in this study we can observe that computed torque method gives
better results than MPC methods. So in trajectory control it is approved of using computed torque
method. In last part of this study the results are estimated forward development are exemined and
suggested. The model predictive control (MPC) technique for an articulated robot with n joints is
introduced in this paper. The proposed MPC control action is conceptually different with the trajectory
robot control methods in that the control action is determined by optimising a performance index over
the time horizon. A neural network (NN) is used in this paper as the predictive model.
Assessment of quality indicators of the automatic control system influence of...TELKOMNIKA JOURNAL
This work concentrates the analysis of the system of automatic control of the directive diagram of the moving active electronically scanned array with a limited number of transceiver modules. The analysis revealed a number of shortcomings that lead to a significant increase in standard deviations, quadratic integral estimates, and an increase in transient time. The identified disadvantages lead to a decrease in the efficiency of the antenna system, an increase in the error rate at the reception, the inability of the system to react to disturbances applied to any point of the system in the event of a mismatch of a given signal/noise level. In accordance with the analysis, the mathematical model of the automatic control system of the directional diagram of the moving active electronically scanned array was considered, considering this a new method of estimating the quality indicators of the automatic control diagram of the directional diagram of the active electronically scanned array in a random setting and disturbing action was developed. The difference between the proposed method and the existing method is in the construction of an automatic control system with differential coupling equivalent to the combination due to the introduction of derivatives of the random setting action of the open compensation connection.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes a research paper that proposes a new design methodology for a roll position demand autopilot for guided missiles. The design is based on state feedback using Ackermann pole placement and a reduced order observer. The open-loop unstable roll control model is stabilized using state feedback and pole placement. Actuator dynamics are included. Simulation results show the new design significantly improves responses over previous frequency domain designs by eliminating overshoots and reducing maximum roll rates and aileron deflections. A reduced order Das & Ghosal observer is used to estimate unmeasurable states like aileron angle and rate.
Robust control for a tracking electromechanical systemIJECEIAES
A strategy for the design of robust control of tracking electromechanical systems based on 𝐻∞ synthesis is proposed. Proposed methods are based on the operations on frequency characteristics of control systems designed and developed using the MATLAB robust control toolbox. Determination of the singular values for a transfer matrix of the control system reduces the disturbances and guarantees its stability margin. For selecting the weighted transfer functions, the basic recommendations are formulated. The efficiency of the proposed approach is verified by robust control of an elastically coupled two-mass system whose parameter values are adjusted by matching them with the parameters of one of the supplied robots. The simulation results confirm that the proposed strategy of design of robust control of twomass elastic coupling system using the 𝐻∞ synthesis is very efficient and significantly reduces the perturbation of parameters of the controlled plant.
Data-driven adaptive predictive control for an activated sludge processjournalBEEI
Data-driven control requires no information of the mathematical model of the controlled process. This paper proposes the direct identification of controller parameters of activated sludge process. This class of data-driven control calculates the predictive controller parameters directly using subspace identification technique. By updating input-output data using receding window mechanism, the adaptive strategy can be achieved. The robustness test and stability analysis of direct adaptive model predictive control are discussed to realize the effectiveness of this adaptive control scheme. The applicability of the controller algorithm to adapt into varying kinetic parameters and operating conditions is evaluated. Simulation results show that by a proper and effective excitation of direct identification of controller parameters, the convergence and stability of the implicit predictive model can be achieved.
Integral Backstepping Sliding Mode Control of Chaotic Forced Van Der Pol Osci...ijctcm
ABSTRACT
Forced Van der Pol oscillator exhibits chaotic behaviour and instability under certain parameters and this poses a great threat to the systems where it has been applied hence, the need to develop a control method to stabilize and control chaos in a Forced Van der Pol oscillator so as to avoid damage in the controlled system and also to prevent unmodeled dynamics from being introduced in the system. Sliding Mode control makes use of the regulatory variables derived from the controlled Lyapunov function to bring the new variables to stability. The essence of using Integral Backstepping was to prevent chattering which can occur in the control input and can cause instability to the system by igniting unmodeled dynamics. Simulation was done using MATLAB and the results were provided to show the effectiveness of the proposed control method. Integral Backstepping Sliding Mode control method was effective towards stability and chaos control. It was also robust towards matched and unmatched disturbance.
Design Nonlinear Model Reference with Fuzzy Controller for Nonlinear SISO Sec...IJECEIAES
The document describes a proposed design for a nonlinear model reference controller combined with type-1 and interval type-2 fuzzy control schemes for nonlinear single-input single-output (SISO) systems. The model reference controller is designed based on an optimal desired model and Lyapunov stability theory. Then a type-1 or interval type-2 fuzzy Takagi-Sugeno controller is combined with the model reference controller to improve its performance by reducing steady state error from the system response. The proposed controller is applied to control an inverted pendulum system. Simulation results show that the model reference controller with interval type-2 fuzzy control has better performance than with type-1 fuzzy control.
Comparison of Soft Computing Techniques applied in High Frequency Aircraft Sy...ijeei-iaes
This document compares the use of fuzzy logic control and adaptive tabu search algorithm for controlling a shunt active power filter in an aircraft electrical system operating at 400Hz. Simulation results show that a sinusoidal current control strategy optimized with fuzzy logic provides the best compensation performance, reducing source current THD to 2.22% and voltage THD to 1.01% with the fastest compensation time of 0.0066 seconds. While adaptive tabu search achieved slightly higher THDs, its compensation time was faster at 0.0065 seconds. Overall, fuzzy logic provided the best balance of harmonic reduction and speed for compensating the aircraft power system.
Cone Crusher Model Identification Using Block-Oriented Systems with Orthonorm...ijctcm
In this paper, block-oriented systems with linear parts based on Laguerre functions is used to approximation of a cone crusher dynamics. Adaptive recursive least squares algorithm is used to identification of Laguerre model. Various structures of Hammerstein, Wiener, Hammerstein-Wiener models are tested and the MATLAB simulation results are compared. The mean square error is used for models validation.It has been found that Hammerstein-Wiener with orthonormal basis functions improves the quality of approximation plant dynamics. The mean square error for this model is 11% on average throughout the considered range of the external disturbances amplitude. The analysis also showed that Wiener model cannot provide sufficient approximation accuracy of the cone crusher dynamics. During the process it is unstable due to the high sensitivity to disturbances on the output.The Hammerstein-Wiener model will be used to the design nonlinear model predictive control application
In this paper, block-oriented systems with linear parts based on Laguerre functions is used to
approximation of a cone crusher dynamics. Adaptive recursive least squares algorithm is used to
identification of Laguerre model. Various structures of Hammerstein, Wiener, Hammerstein-Wiener models
are tested and the MATLAB simulation results are compared. The mean square error is used for models
validation.It has been found that Hammerstein-Wiener with orthonormal basis functions improves the
quality of approximation plant dynamics. The mean square error for this model is 11% on average
throughout the considered range of the external disturbances amplitude. The analysis also showed that
Wiener model cannot provide sufficient approximation accuracy of the cone crusher dynamics. During the
process it is unstable due to the high sensitivity to disturbances on the output.The Hammerstein-Wiener
model will be used to the design nonlinear model predictive control application.
Neural Network-Based Actuator Fault Diagnosis for a Non-Linear Multi-Tank SystemISA Interchange
The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H1 framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks.
2-DOF Block Pole Placement Control Application To: Have-DASH-IIBITT MissileZac Darcy
In a multivariable servomechanism design, it is required that the output vector tracks a certain reference
vector while satisfying some desired transient specifications, for this purpose a 2DOF control law
consisting of state feedback gain and feedforward scaling gain is proposed. The control law is designed
using block pole placement technique by assigning a set of desired Block poles in different canonical forms.
The resulting control is simulated for linearized model of the HAVE DASH II BTT missile; numerical
results are analyzed and compared in terms of transient response, gain magnitude, performance
robustness, stability robustness and tracking. The suitable structure for this case study is then selected.
2-DOF Block Pole Placement Control Application To: Have-DASH-IIBITT MissileZac Darcy
In a multivariable servomechanism design, it is required that the output vector tracks a certain reference
vector while satisfying some desired transient specifications, for this purpose a 2DOF control law
consisting of state feedback gain and feedforward scaling gain is proposed. The control law is designed
using block pole placement technique by assigning a set of desired Block poles in different canonical forms.
The resulting control is simulated for linearized model of the HAVE DASH II BTT missile; numerical
results are analyzed and compared in terms of transient response, gain magnitude, performance
robustness, stability robustness and tracking. The suitable structure for this case study is then selected.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
An Optimal LFC in Two-Area Power Systems Using a Meta-heuristic Optimization...IJECEIAES
The document presents a study that develops an optimal load frequency control (LFC) method for two-area power systems using a harmony search (HS) algorithm to tune the parameters of a PI controller (Kp and Ki). The HS algorithm is used to minimize an objective function of integral absolute error (IAE) in order to enhance system performance regarding settling time, maximum deviation, and peak time. The two-area power system model and HS-PI controller design are implemented in MATLAB. Results show the HS-PI controller provides more robust performance compared to a particle swarm optimization-based PI controller under different load conditions.
Comparison of backstepping, sliding mode and PID regulators for a voltage inv...IJECEIAES
In the present paper, an efficient and performant nonlinear regulator is designed for the control of the pulse width modulation (PWM) voltage inverter that can be used in a standalone photovoltaic microgrid. The main objective of our control is to produce a sinusoidal voltage output signal with amplitude and frequency that are fixed by the reference signal for different loads including linear or nonlinear types. A comparative performance study of controllers based on linear and non-linear techniques such as backstepping, sliding mode, and proportional integral derivative (PID) is developed to ensure the best choice among these three types of controllers. The performance of the system is investigated and compared under various operating conditions by simulations in the MATLAB/Simulink environment to demonstrate the effectiveness of the control methods. Our investigation shows that the backstepping controller can give better performance than the sliding mode and PID controllers. The accuracy and efficiency of the proposed backstepping controller are verified experimentally in terms of tracking objectives.
Hierarchical algorithms of quasi linear ARX Neural Networks for Identificatio...Yuyun Wabula
This document summarizes a research paper on hierarchical algorithms for training a quasi-linear ARX neural network model for identification of nonlinear systems. The key points are:
1) A hierarchical algorithm is proposed that first estimates the system using a linear sub-model and least squares estimation to obtain linear parameters. It then trains a neural network nonlinear sub-model to refine the errors of the linear sub-model.
2) The linear parameter estimates are fixed and used as biases for the neural network, which is trained to minimize the residual errors of the linear sub-model.
3) This hierarchical approach separates the identification into linear and nonlinear parts, allowing analysis of the system linearly while also capturing nonlinearities. The neural
2-DOF BLOCK POLE PLACEMENT CONTROL APPLICATION TO:HAVE-DASH-IIBTT MISSILEZac Darcy
In a multivariable servomechanism design, it is required that the output vector tracks a certain reference
vector while satisfying some desired transient specifications, for this purpose a 2DOF control law
consisting of state feedback gain and feedforward scaling gain is proposed. The control law is designed
using block pole placement technique by assigning a set of desired Block poles in different canonical forms.
The resulting control is simulated for linearized model of the HAVE DASH II BTT missile; numerical
results are analyzed and compared in terms of transient response, gain magnitude, performance
robustness, stability robustness and tracking. The suitable structure for this case study is then selected.
DAMPING INTER-AREA OSCILLATIONS IN POWER SYSTEMS USING A CDM-BASED PID CONTRO...Power System Operation
The interconnected energy transport system is the
mechanism that provides the stability of energy, or
the balance, between the centers of production and
consumption throughout the country. The
interconnected system adapts energy production to
changes in consumption. For many years, because of
long transmission lines or high-value power
transmission in weak-connected power systems, one
of the problems encountered is the existence of lowfrequency
oscillations between generator groups [1,
2]. These inter-area oscillations are a significant
issue in power system stability and control areas.
Modern power systems possess three main
properties: (1) the measure of interconnected system
is getting larger; (2) there is a closer operating point
to the power system stability limit because of
environmental and economic conditions; (3) being
composed of new elements and keeping pace with
technological developments unfortunately cause new
uncertainties in the stability of power systems. These
properties increase the risk of formation of lowfrequency
oscillations [3].
Optimal design & analysis of load frequency control for two interconnecte...ijctet
This document summarizes a research paper that proposes using Particle Swarm Optimization (PSO) to tune the parameters of PID controllers for Automatic Load Frequency Control (ALFC) in a system of two interconnected power generation areas. It describes modeling the two-area system and the objectives of ALFC to maintain frequency and distribute load. PSO is introduced as a technique to explore the search space to maximize objective functions by updating particle velocities and positions. The paper presents simulation results comparing the tuned PID controller performance to untuned controllers.
Design and Implementation of Model Reference Adaptive Controller using Coeffi...IOSR Journals
This document describes the design and implementation of a model reference adaptive controller (MRAC) using the coefficient diagram method (CDM) for a nonlinear process. It begins by introducing MRAC for single-input single-output plants, developing the MRAC design and control law. It then provides basics of the CDM design approach. The document applies the CDM-MRAC to a spherical tank level process in simulation, comparing its performance to a linear PI controller. Simulation results show the CDM-MRAC has faster rise time and settling time than the PI controller.
5 adaptive control for power system stability improvementnazir1988
This document discusses adaptive control for power system stability improvement using model reference adaptive control (MRAC) based on the command generator tracker theory. It begins by introducing the need for power system stabilizers to improve generator dynamics and stability under different loading conditions. It then provides the mathematical models for synchronous generators both with and without voltage control. Analysis shows generator dynamics and stability vary significantly with loading. The document then describes applying direct adaptive control using MRAC-CGT to provide optimal damping for generators across all operating ranges by adapting the stabilizer parameters.
DESIGN OF OBSERVER BASED QUASI DECENTRALIZED FUZZY LOAD FREQUENCY CONTROLLER ...Wireilla
ABSTRACT
This paper proposes Fuzzy Quasi Decentralized Functional Observers (FQDFO) for Load Frequency Control of inter-connected power systems. From the literature, it is well noticed about the need of Functional Observers (FO’s) for power system applications. In past, conventional Functional Observers are used. Later, these conventional Functional Observers are replaced with Quasi Decentralized Functional Observers (QDFO) to improve the system performance. In order to increase the efficacy of the system, intelligent controllers gained importance. Due to their expertise knowledge, which is adaptive in nature is applied successfully for FQDFO. For supporting the validity of the proposed observer FQDFO, it is compared with full order Luenberger observer and QDFO for a two-area inter connected power system by taking parametric uncertainties into consideration. Computational results proved the robustness of the proposed observer.
Similar to State Estimation based Inverse Dynamic Controller for Hybrid system using Artificial Neural Network (20)
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
State Estimation based Inverse Dynamic Controller for Hybrid system using Artificial Neural Network
1. State Estimation based Inverse Dynamic Controller for Hybrid system
using Artificial Neural Network
Xiao Laui,
Research Scholar,
City University of Hong Kong, China.
Rui-Lain Chua,
Professor,
City University of Hong Kong, China.
Abstract:
Authors have proposed State Estimation Based Inverse Dynamics Controller (SEBIDC), which utilizes an Artificial
Neural Network (ANN) based state estimation scheme for nonlinear autonomous hybrid systems which are subjected to
state disturbances and measurement noises that are stochastic in nature. A salient feature of the proposed scheme is
that it offers better state estimates and hence a better control of non-measurable state variables with a non linear
approach in correcting the a priori estimates by avoiding statistical linearization involved in existing approaches based
on derivative free estimation methods. Simulation results guarantees significant reduction in Integral Square Error
(ISE) and standard deviation (σ) of error, between the controlled variable and set point and control signal computation
time when compared with best existing related work based on Unscented Kalman Filter (UKF) and Ensemble Kalman
Filter (EnKF). Detailed analysis of the experimental results on real plant under different operating conditions such as
servo and regulatory operations, initial condition mismatch, and different types of faults in the system, confirms
robustness of proposed approach in these conditions and support the simulation results obtained. The main advantage
of the proposed controller is that the control signal computation time is very much less than the selected sampling time
of the process, so direct control of the plant is possible with this approach.
Key words: Artificial Neural Network, Hybrid Dynamic Systems, State Estimation, Inverse Dynamics Controller.
1. Introduction
Systems in which it may be required to model inherent process discontinuities, where the continuous behavior is drastically
changed, or use actuators and sensors which are often fundamentally discontinuous, or use discrete events that can be a useful
abstraction to model various mode switching used in the specification and control of the basically continuous process, require
hybrid systems modeling and control approach ([1], [2], [3] and [4]). Kalman filter (KF) [5] and extended Kalman filter (EKF)
[6] are used as a state estimator in conventional state observers. For linear systems as uncertainties in state and measurement
equations can be modeled as Gaussian white noise processes Kalman filter can generate optimal estimates of state. Extended
Kalman filter (EKF), which is a natural extension of the linear filter to the nonlinear domain through analytical linearization,
can be used in state estimation of nonlinear systems. In this approach, the estimated states are obtained by using Taylor series
expansion of the nonlinear state transition operator. But, it requires analytical computation of Jacobians at each time step,
which is considered to be computationally demanding for complex nonlinear systems. Also it is required that nonlinear
function vector appearing in state dynamics and output dynamics should be smooth and at least once differentiable. However,
dynamical models of Hybrid systems involve discontinuities due to switching of the discrete state values. As Jacobians cannot
be computed for non-smooth functions, EKF cannot be realized in Hybrid systems [7]. In [8] a moving horizon based state
estimation approach has been reported for hybrid system estimation. But, the use of fixed arrival cost used in the moving
horizon estimator formulation result in sub-optimal state estimates. The authors in [9] have proposed UKF as an alternative to
EKF so that the main shortcoming of EKF when used for highly nonlinear system is eliminated. [10] has proposed a derivative
free non linear filtering technique for nonlinear hybrid systems. As far as the control element is concerned, [11] had proposed a
robust model predictive control (RMPC) scheme for a class of hybrid system such as piece wise affine system to ensure simple
and fast suboptimal solution for the control problem with reduced computation time. Similarly, the nonlinear model predictive
control (NMPC) in [7] and [12], and fault tolerant model predictive control in [13], for hybrid systems, used UKF approach in
its state estimation part. All the state estimation schemes for hybrid systems in literature involve analytical or statistical
linearization [14] and preclude their use from systems which require more accurate state estimates. Introduction of Artificial
Neural Network (ANN) in state estimation and control of different systems considerably improves the performance which is
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2. very clear from the works reported in [15], [16], [17] and [18]. But, in the case of [15], if a nonlinear scheme is used in
correction of a priori estimates, more accurate state estimates can be obtained in hybrid systems also and this has been
implemented in this work. The distinguished feature of the proposed approach is that it uses nonlinear ANN to correct the a
priori estimates and gives better estimate than its counterparts. Once trained for sufficient variety of input data, including ill-
conditioned system data, the ANN-based state estimator provides accurate estimates of the system states. Further, an ANN
based Controller (ANNC), using this estimator, is developed for controlling the non-measurable states of the hybrid system.
The proposed controller (ANNC) provides better performance and has following advantages over existing schemes.
Apart from analytical and statistical linearization in the correction part of EKF and UKF based controllers respectively;
proposed controller uses a nonlinear correction approach using ANN to correct the a priori estimates and hence offers a
better state estimates by avoiding the linearization. The correction part of ANNC is completely parameter independent,
and thereby gives better state estimates even under there is mismatch in the parameters.
ANN has built in noise rejection capability which makes the ANNC scheme robust in performance.
Comparative analysis of the proposed approach with best relating work based on UKF (statistical linearization approach) and
EnKF (Particle Filter) is made in terms of ISE in state estimate on the same benchmark model and it reveals that the proposed
approach is able to reduce the error in state estimates. Detailed performance evaluation of the proposed approach under servo –
regulatory operations and plant model parameter mismatch were conducted. Detailed analysis of the experimental results on
the real plant under different operating conditions such as initial condition mismatch, and different types of faults in the system
confirms efficacy of proposed approach. Here, an inverse dynamics controller is utilized for controlling the non-measurable
states of the system so that the computational burden is very much reduced when compared with the model predictive control
scheme implemented in [7], [12] and [13] without compromising the performance. Also the constraints handling capability for
this scheme is also achieved with this approach by introducing upper and lower limiting functions at the output of both
estimator and controller. The rest of the paper is organized as follows. In section 2, description of the ANN state estimation
algorithm developed for hybrid systems is given. The Section 3 explains the SEBIDC scheme. Simulation results and detailed
performance analysis of the proposed scheme and with comparison to the best related work is given in section 4. Experimental
results and its analysis are presented in Section 5. Finally, section 6 summarizes the paper.
2. ANN based Hybrid State Estimation
In this scheme, an ANN based correction is developed. As in the case of EKF and UKF, ANN based state estimation is also
recursive in nature. Even though it has the same framework of Kalman filter based state estimator, it is designed for
eliminating the analytical and statistical linearization [14] used in the case of EKF and UKF. This structure is suggested
because recurrent type of ANN is better for the complex dynamic system [18]. The schematic diagram of proposed ANNC is
as given in Fig. 1.
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3. Fig.1. Schematic representation of proposed ANNC
The detailed nonlinear auto regressive with exogenous input (NARX) structure used for the considered problem is given in Fig.
2 and the other NARX parameters used for this study are provided in Table 1.
The current output can be predicted as a function of present and past inputs and past outputs as given below, in which Y and X
represent the outputs and inputs of the network respectively and KNN is a nonlinear ANN function.
Y (k) = KNN{X (k), X (k-m), Y (k-1)… Y (k-n)} (1)
Table 1
ANN Parameters
Parameter Value
ANN Structure NARX
No. of hidden layers 1
Hidden Layer neurons 5
Hidden layer activation function ‘tan sigmoid’
Output layer activation function ‘purelin’
No of epochs 100
No of exogenous inputs 4
No. of delayed inputs 0
No of outputs 3
No. of feedback output delays 2
Training method Back propagation
Training function Levenberg–Marquardt
Process Noise, ( )w t
Set point
Input, ( )u t
Measurement Noise, ( )v t
Output, ( )y t
Prediction
Model
ANN
Correction
Function
ITD
OTD
SEBIDC
Innovation,
( 1)k kˆ( 1)x k k
xk/k-1
ˆ( )x k
( )u k
( )y k
Process
SampleHold
ˆ( 1)y k k
ˆ( )x k
Estimator
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4. Performance Function Mean Square Error
A sequence of current and past input vectors (X (k), X (k-1), X (k-m)) are obtained by passing X (k) through an input time
delay unit, ITD (0: m). Similarly output time delay unit, OTD (1: n) provides a sequence of past output vectors (Y (k-1), Y (k-
n)). For the considered problem, the input and the output are T
1 2 3
ˆ ˆ ˆX( ) [ ( 1), ( 1), ( 1), ( 1)] k h k k h k k h k k k k
T
1 2 3
ˆ ˆ ˆY( ) [ ( ), ( ), ( )]k h k h k h k respectively.
Similar to Kalman filter based state estimators and its nonlinear extensions, the proper value for the initial state vector is
assumed for the prediction model. The input and output measurements are made from the process and the input measurement
are presented to the state prediction model (F) along with the assumed initial state vector in order to compute the time updated
values for states.
ˆ ˆ( 1) F( ( 1), ( )) x k k x k u k (2)
With, ˆ ˆ( 1) (0) [ (0)] x k x E x , the assumed initial value of state vector.
The a priori state estimates, ˆ( 1)x k k can be given to the output model (H) so that a priori estimates of the output, ˆ( 1)y k k
can be obtained as
ˆ ˆ( 1) H ( 1) y k k x k k (3)
The innovation between plant output ( )y k and a priori output estimate ˆ( 1)y k k is calculated as
ˆ( 1) ( ) ( 1) k k y k y k k (4)
In the correction step of the algorithm, the a priori state estimates will be corrected using this innovation with the help of the
ANN to obtain a posteriori estimates of state vector ˆ( )x k k .
NNˆ ˆ ˆ( ) K ITD( ( 1), ( 1)),OTD( ( )) x k x k k k k x k (5)
These estimated states are fed back to the controller for calculating the new input signal to the plant.
Fig.2. NARX structure for the three-tank hybrid system
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5. 3. State Estimation Based Inverse Dynamics Controller (SEBIDC)
In hybrid systems, for the controller design, its mode switching property has also to be taken in to account. For such processes,
model based control schemes are proposed in the literature ([12], [13], [19], [20], [21] and [22]) for obtaining the satisfactory
control of the output variables. In this work a model based control namely inverse dynamics controller (IDC) have been
implemented for controlling the non-measurable states of Hybrid System. The objective of this section is to review the non-
linear dynamic control technique that can be applied to develop a non-measurable level control system that is valid over the
entire operating region of the hybrid three-tank system which is described below.
Consider the nonlinear system of the form,
( ) ( ) x A x B x u (6)
y Cx (7)
Where, A(x) = (n × 1) vector, B(x) = (n × m) matrix, C = (m × 1) vector.
Using the inverse dynamics of (6) and (7), input vector u can be represented as a function of v and x,
Let it be 1F ( , )u v x
Where, F1 is a nonlinear function and v is the input to the inverse system.
Implementation of the inverse dynamics controller of (6) and (7) is done by repeatedly differentiating the measurement
function until the input variable u appears.
Differentiating (7)
y Cx (8)
[ ( ) ( ). ] y C A x B x u (9)
* *
( ) ( ). y A x B x u (10)
Where, A*
(x) =C.A(x) and B*
(x) =B.A(x)
So the control law u can be written as per [16] as
1
* *
( )[ ( )]
u B x v A x (11)
A sufficient condition for the existence of an inverse system model to (6) and (7) is that B*
in (11) be non-singular. If this is the
case, then the inverse system model takes the form,
( ) ( )[ ( ) ( ) ] x A x B x F x G x v (12)
Where,
1
* *
( ) ( ) ( )
F x B x A x and
1
*
( ) ( )
G x B x
( ) ( )[ ( ) ( ) ] x A x B x F x G x v (13)
The input to the inverse system is v = y – yref
As the hybrid three-tank system, considered under this study can be directly represented in the form of (6) and (7), applying
this procedure will yield the control law as
1 1 1sp=A C h - + + +1in 1 1 3 5F h Q Q Q (14)
2 2 2sp=A C h - + + +2in 2 2 4 7F h Q Q Q (15)
Since h1, h2, Q1, Q2, Q3, Q4, Q5, and Q7, are non-measureable for the considered problem, the estimated values can be used so
that the controller becomes an estimator based inverse dynamics controller.
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6. 1 1 1sp
ˆ ˆ ˆ ˆ=A C h - + + +1in 1 1 3 5F h Q Q Q (16)
2 2 2sp
ˆ ˆ ˆ ˆ=A C h - + + +2in 2 2 4 7F h Q Q Q (17)
Where, C1 and C2 are controller tuning parameter, and its values are varied from 0 < C1,C2 < 1 on separate runs and the
integral square error between the controlled variable and the set point, is noted. The values of C1 and C2, which give the
minimum ISE, are selected as the tuning parameters. The h1sp and h2sp are the corresponding desired values of water levels.
4. Simulation Results and Performance Analysis
The schematic representation of hybrid three-tank system is given in Fig.3.The benchmark system used in [7] is used here with
same levels in three tanks (h1, h2 and h3) as continuous states and z1 and z2 variables as discrete states for evaluating the
performance the controller in comparison to best related work.
Fig.3. Schematic representation of the benchmark hybrid three tank system
Detailed modeling of the hybrid three-tank system is given as appendix. The algorithm has been implemented in MATLAB.
The detailed discussions about the results obtained in the simulation are given in the following subsections.
4.1 Performance in Servo Operation
Servo operation of the closed loop system when a change in set point occurs was conducted by introducing a step change with
magnitude 0.04 m at 100th
sampling instant. The results are given in Fig.4. Comparison with the best existing related work is
shown in Table 2. Comparison of the proposed approach to UKF based approach based on ISE shows that the new approach is
better as ISE reduced from 3.3278 to 0.5533 in level, h1 and from 3.7203 to 0.4568 in level, h2. Also, the average computation
time per iteration reduced from 60.65 seconds to 0.0777 seconds. Evolution of true and estimated states of hybrid three-tank
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7. system with ANNC (Servo operation) is shown in Fig.5. Evolution of true and estimated values of discrete variables of hybrid
three-tank system with ANNC (Servo Operation) is shown in Fig.6.
Fig.4. Servo response of hybrid three tank system with ANNC (a) Level in Tank 1, (b) Level in tank 2 (c) Manipulating variables
Fig.5. True and estimated states of hybrid three tank system with ANNC (Servo operation) (a) Level in Tank 1, (b) Level in Tank 2, (c) Level in tank 3
50 100 150 200 250 300
.2
.4
Level(h
1
)
(a)
Setpoint CV(Proposed)
50 100 150 200 250 300
0.2
0.4
Level(h
2
)
(b)
Setpoint CV(proposed)
50 100 150 200 250 300
0
0.5
1
(c)
Sampling Instants
Manipulating
Variables
Fin1 Fin2
50 100 150 200 250 300
0.2
0.4
Level(h1)
(a)
True Estimated
50 100 150 200 250 300
0.2
0.4
Level(h2)
(b)
True Estimated
50 100 150 200 250 300
0.2
0.4
0.6
Level(h
3
)
(c)
Sampling Instants
True Estimated
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8. Fig.6. Evolution of true and estimated values of discrete variables of hybrid three tank system with ANNC (Servo Operation)
4.2 Performance in Regulatory Operation
The results of the performance of the estimator in regulatory operation are given in Fig.7. Comparison with the best existing
related work is shown in Table 3. Comparison of proposed approach to UKF based approach based on standard deviation
shows that the new approach is better as standard deviation has reduced from 0.0130 to 0.0127 in level h2 and from 0.0484 to
0.0065 in level h3 with very close standard deviation in level h1. It may be noted that the maximum standard deviation in the
case of nonmeasured state variables is 0.0217 in the proposed approach and 0.0484 in the UKF based NMPC(45% reduction
with proposed approach).
The results of the performance of the controller in regulatory operation are given in Fig.8 and evolution of true and estimated
values of discrete variables of hybrid three tank system with ANNC (Regulatory Operation; Disturbance by varying the valve
position of fifth hand valve) in Fig.9. Comparison with the best existing related work is shown in Table 4. Comparison of
proposed approach to UKF based approach based on ISE shows that the new approach is better as ISE has reduced from
3.5869 to 0.0587 in level h1 and from 1.5212 to 0.0366 in level h2. Also, the average computation time per iteration has
reduced from 59.05 seconds to 0.0739 seconds.
100 200 300
-2
-1
0
1
2
Sampling Instants
z
1
(True)
100 200 300
-2
-1
0
1
2
Sampling Instants
z
2
(True)
100 200 300
-2
-1
0
1
2
Sampling Instants
z
1
(Estimate)
100 200 300
-2
-1
0
1
2
Sampling Instants
z
2
(True)
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9. Fig.7. Evolution of true and estimated states of hybrid three tank system with ANNC (a) Level in Tank 1, (b) Level in Tank 2, (c) Level in tank 3
Fig .8. Regulatory response of hybrid three tank system with ANNC (a) disturbance, (b) Level in Tank 1, (c) Level in tank 2 (d) Manipulating variables
20 40 60 80 100 120 140
0.2
0.3
0.4
Level(h
1
)
(a)
True Estimated
20 40 60 80 100 120 140
0.2
0.3
0.4
Level(h
2
)
(b)
True Estimated
20 40 60 80 100 120 140
0.2
0.3
0.4
Level(h3
)
(c)
Sampling Instants
True Estimated
20 40 60 80 100 120 140
0.2
0.3
0.4
Level(h
1
)
(b)
Setpoint C V(Proposed)
20 40 60 80 100 120 140
0.2
0.3
0.4
Level(h
2
)
(c)
Setpoint CV (Proposed)
20 40 60 80 100 120 140
0
0.5
1
(d)
Sampling Instants
Manipulating
Variables
Fin1
Fin2
20 40 60 80 100 120 140
0
0.5
1
Disturbance
(a)
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10. Fig.9. Evolution of true and estimated values of discrete variables of hybrid three tank system with ANNC (Regulatory Operation)
In order to obtain better insight of the ability of the proposed controller to achieve decoupling and offset-free control action,
hypothetical situation, in which state and measurement noise are not present, is simulated. Response given in Fig. 10 reveals
that the effect in level of tank 2, due to the disturbance in tank 1 is very much less compared with that given in [7]. As in the
case of UKF based NMPC in [7], proposed method also giving a slight offset.
Table 3
Regulatory Control Problem: Estimator performance Comparison
Controller σE(h1) σE(h2) σE(h3)
Proposed 0.0217 0.0127 0.0065
UKF based NMPC[7] 0.0213 0.0130 0.0484
UKF based NMPC[7] 0.0242 0.0141 0.0488
Table 4
Regulatory Control Problem: Controller Performance Comparison
Controller ISE(h1) ISE(h2) Avg. Computation time per iteration (S)
Proposed 0.0587 0.0366 0.0739
UKF based NMPC[7] 3.5869 1.5212 59.05
EnKF based NMPC[7] 3.3928 1.4011 206.44
50 100 150
-2
-1
0
1
2
Sampling Instants
z
1
(True)
50 100 150
-2
-1
0
1
2
Sampling Instants
z
2
(True)
50 100 150
-2
-1
0
1
2
Sampling Instants
z
1
(Estimate)
50 100 150
-2
-1
0
1
2
Sampling Instants
z
2
(True)
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11. Fig .10. Regulatory response of hybrid three tank system with ANNC (without state and measurement noise) (a) disturbance, (b) Level in Tank 1, (c) Level in
tank 2 (d) Manipulating variables
4.3 Plant Model Parameter Mismatch
The performance of the controller in case of plant model parameter mismatch is considered and the performance is given in
Fig. 11. From Table.5, it can be seen that the ISE is improved to 0.0276 from 0.9231 for level h1 and to 0.0228 from 1.0579 for
level h2 when compared to best existing related work based on UKF.
Fig.11. Response of hybrid three tank system with ANNC (Plant-Model mismatch ) (a) Level in Tank 1, (b) Level in Tank 2, (c) Level in tank 3
Table 5
Plant Model Parameter Mismatch: Controller Performance Comparison
Controller ISE(h1) ISE(h2)
Proposed 0.0276 0.0228
UKF based NMPC [7] 0.9231 1.0579
EnKF based NMPC[7] 0.8647 0.9688
20 40 60 80 100 120 140
0
0.5
1
Disturbance
(a)
20 40 60 80 100 120 140
0.2
0.3
0.4
Level(h
1
)
(b)
Setpoint CV(Proposed)
20 40 60 80 100 120 140
0.2
0.3
0.4Level(h
2
)
(c)
Setpoint CV(Proposed)
20 40 60 80 100 120 140
0
0.5
1
(d)
Sampling Instants
Manipulating
Variables
Fin1 Fin2
20 40 60 80 100
0.2
0.3
0.4
Level(h
1
)
(a)
Setpoint CV(Proposed)
20 40 60 80 100
0.2
0.3
0.4
Level(h
2
)
(b)
Setpoint CV(Proposed)
20 40 60 80 100
0
0.5
1
(c)
Sampling Instants
Manipulating
Variables
Fin1 Fin2
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12. 5. Experimental Results and Performance Analysis
Real-time experimental validations were carried out on the experimental setup. In addition to the experimental setup, other
tools used, which were for the real time implementation are the software Lab VIEW and the NI DAQ (USB6251). In the real
system, the performance of the controller in regulatory operation and servo operation based on ISE and average computation
time per iteration is shown in Fig. 12, Table 6, Fig. 13, and Table 7. In Table 8 and Fig. 14 response of the system in initial
condition mismatch is shown. The response of the system in +10% and -10% plant model parameter mismatch is given in
Tables 9 and 10 and Figures 15 and 16 respectively. Results of hand valve faults which can occur in real time application are
given in Table 11, Fig. 17, Table 12 and Fig. 18. The real time experimental results support the simulation results on
performance.
Table 6
Regulatory Control Problem: Controller Performance Comparison
Controller ISE(h1) ISE(h2)
Avg. Computation
time per iteration (S)
Proposed 0.0200 0.0193 0.1038
Table 7
Servo Control Problem: Controller Performance Comparison
Controller ISE(h1) ISE(h2)
Avg. Computation
time per iteration (S)
Proposed 0.0172 0.0144 0.1152
Table 8
Initial Condition Mismatch: Controller Performance Comparison
Controller ISE(h1) ISE(h2)
Avg. Computation
time per iteration (S)
Proposed 0.0214 0.0592 0.1017
Table 9
Plant Model Parameter Mismatch (+10%): Controller Performance Comparison
Controller ISE(h1) ISE(h2)
Avg. Computation
time per iteration (S)
Proposed 0.0045 0.0069 0.2112
Table 10
Plant Model Parameter Mismatch (-10%): Controller Performance Comparison
Controller ISE(h1) ISE(h2)
Avg. Computation
time per iteration (S)
Proposed 0.0122 0.0131 0.1037
Table 11
Hand Valve Faults -Leakage: Controller Performance Comparison
Controller ISE(h1) ISE(h2)
Avg. Computation
time per iteration (S)
Proposed 0.0600 0.0077 0.0768
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13. Table 12
Hand Valve Faults -Clogging: Controller Performance Comparison
Controller ISE(h1) ISE(h2)
Avg. Computation
time per iteration (S)
Proposed 0.0082 0.0078 0.0943
Fig.12. Regulatory response of hybrid three tank system with ANNC (a) Level in Tank 1, (b) Level in tank 2
Fig.13. Servo response of hybrid three tank system with ANNC (a) Level in Tank 1, (b) Level in tank 2
50 100 150 200 250 300 350 400
0.24
0.27
0.3
0.33
Level(h
1
)
(a)
CV1
(Proposed) SETPOINT1
100 200 300 400
0.24
0.27
0.3
0.33
Level(h
2
)
(b)
Sampling Instants
CV2
(Proposed) SETPOINT2
50 100 150 200
0.23
0.26
0.29
0.32
Level(h
1
)
(a)
50 100 150 200
0.23
0.26
0.29
0.32
Level(h
2
)
(b)
Sampling Instants
CV2
(Proposed) SETPOINT2
CV1
(Proposed) SETPOINT1
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14. Fig.14. Closed response of hybrid three tank system with ANNC (Initial Condition Mismatch ) (a) Level in Tank 1, (b) Level in Tank 2
Fig.15. Closed response of hybrid three tank system with ANNC (Plant-Model mismatch +10% ) (a) Level in Tank 1, (b) Level in Tank 2
Fig.16. Closed response of hybrid three tank system with ANNC (Plant-Model mismatch -10% ) (a) Level in Tank 1, (b) Level in Tank 2
50 100 150 200 250 300 350 400
0.26
0.3
0.34
0.38
Level(h
1
)
(a)
CV1
(Proposed) SETPOINT1
50 100 150 200 250 300 350 400
0.26
0.3
0.34
0.38
Level(h
2
)
(b)
Sampling Instants
CV2
(Proposed) SETPOINT2
50 100 150 200 250
0.28
.3
0.32
Level(h
1
)
(a)
CV1
(Proposed) SETPOINT1
50 100 150 200 250
0.28
0.3
0.32
Level(h
2
)
(b)
Sampling Instants
CV2
(Proposed) SETPOINT2
50 100 150 200 250
0.26
0.28
0.3
0.32
Level(h
1
)
(a)
CV1
(Proposed) SETPOINT1
50 100 150 200 250
0.26
0.28
0.3
0.32
Level(h
2
)
(b)
Sampling Instants
CV2
(Proposed) SETPOINT2
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15. Fig.17. Closed response of hybrid three tank system with ANNC (Handvalve fault-Leakage ) (a) Level in Tank 1, (b) Level in Tank 2
Fig.18. Closed response of hybrid three tank system with ANNC (Handvalve fault-Clogging ) (a) Level in Tank 1, (b) Level in Tank 2
6. Conclusion
An ANN estimation based control scheme which offers better state estimates and hence better control, with a non linear
approach in correcting the a priori estimates by avoiding statistical linearization involved in derivative free estimation is
proposed. On comparing with best exiting related work based on statistical linearization, 85%, 97% and 97% reduction in
Integral Square Error (ISE), between controlled variable and set point, for servo, regulatory and plant model parameter
mismatch operations respectively, and a 45% reduction in standard deviation (σ) of error between true and estimated values of
non-measurable states for regulatory control operation were obtained. In addition to these performance improvements, the
attracting feature of the proposed method is the time required for the computation of control signals and is very much less than
the sampling time of the process which ensures the capability of online implementation as direct control algorithm using the
proposed approach. The experimental studies conducted on a real plant illustrate the robust performance of the proposed
controller in offering better online control of hybrid dynamic system under real time operating constraints.
References
[1] Lennartson, M. Tittus, B. Egardt, and S. Pettersson, “Hybrid systems in process control”, IEEE Control Systems, 16,(5),
(2002), 45-56.
[2] Goebel R., Sanfelice R. G. Teel, A. R., Hybrid Dynamical Systems , IEEE Control Systems Magazine, 29(2), (2009), 28-
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100 200 300 400 500
0.26
0.28
Level(h
1
)
(a)
100 200 300 400 500
0.26
0.28
Level(h
2
)
(b)
Sampling Instants
CV2
(Proposed) SETPOINT2
CV1
(Proposed) SETPOINT1
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0.25
0.3
0.35
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1
)
(a)
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0.3
0.35
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2
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(b)
Sampling Instants
CV2
(Proposed) SETPOINT2
CV1
(Proposed) SETPOINT1
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Volume XII, Issue III, March/2020
ISSN NO:0886-9367
Page No:1935
16. [3] Goebel, R; Sanfelice, R, G.and Teel, A.R., Hybrid Dynamical Systems: Modeling, Stability, and Robustness, Princeton
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[6] J. H. Lee, N. L. Ricker, Extended Kalman filter based non-linear model predictive control, Ind. Eng. Chem. Res. 33(6),
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[7] J. Prakash, Sachin C. Patwardhan, Sirish L. Shah, State estimation and nonlinear predictive control of autonomous hybrid
system using derivative free state estimators, Journal of Process Control, 20, (2010), 787-799
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Controller, In Proc. 17th World Congress of IFAC, (2008), 12510-12515.
[13]Jagadeeshan P., Sachin C. P., Sirish L. S, Design and Implementation Fault Tolerant Model Predictive Control Scheme on
a Simulated Model of Three-Tank Hybrid System, Conference on Control and Fault Tolerant Systems, France, (2010),
173-178.
[14]Lefebvre, T.; Bruyninckx, H.; De Schutter, J., Comment on "A new method for the nonlinear transformation of means and
covariances in filters and estimators", IEEE Transactions on Automatic Control, 47(.8), (2002), 1406-1409.
[15]Kumar, S., Prakash, J. and Kanagasabapathy, P. “A critical evaluation and experimental verification of Extended Kalman
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Volume XII, Issue III, March/2020
ISSN NO:0886-9367
Page No:1936
17. APPENDIX: PROCESS DESCRIPTION
In the hybrid three-tank system, three tanks of uniform area (A1, A2 and A3) are connected to each other and to sump
through seven hand valves and two variable speed pumps as in figure 3. The seven valve discharge coefficients are represented
by k1 to k7. By conducting simple steady state experiments and necessary calculations, these hand valve coefficients are found,
which are given in table A.1. Now throughout this study the position of these hand valves should not be altered (For the
simulation results, all the parameter values are taken from [7]).
After calculating ki (where, i =1, 2,…,7), the system is modeled using volume balance equations as in (A.1) – (A.3),
in which Qi represent the flows through ith
hand valve and its equations are provided in (A.4) –(A.10). This is used as the
prediction model for calculating a priori state estimates in the estimation algorithms described in section 2.
1
1 1 3 5
1
1
[ ]
A
dh
Fin Q Q Q
dt
(A.1)
2
2 2 4 7
2
1
[ ]
A
dh
Fin Q Q Q
dt
(A.2)
3
1 2 3 4 6
3
1
[ ]
A
dh
Q Q Q Q Q
dt
(A.3)
1 1 1 3 1 3=k ( - ) 2g -Q sign h h h h (A.4)
2 2 3 2 3=k ( - ) 2g -2Q sign h h h h (A.5)
3 1 1 0 3 0k3 2g ( - h ) ( - h ) Q z a h b h (A.6)
4 2 4 2 0 3 0k 2g ( - h ) ( - h ) Q z c h b h (A.7)
15 5 1 d2g( h ) Q k h (A.8)
6 6 3 z=k 2g( h )Q h (A.9)
27 7 2 d=k 2g( h )Q h (A.10)
The system is modeled as hybrid system because the model contains both continuous and discrete states in it. The
flow through the middle inter connecting pipes can be better modeled with the help of discrete variables.
The levels in three tanks (h1, h2 and h3) make the continuous state and the variables z1 and z2 are the discrete state.
Depending on the flows, Q3 and Q4, discrete state variables, z1 and z2 may take the values 0, +1 or -1 as given below.
-1 if Q3 is away from Tank3
z1= 0; if Q3= 0 (A.11)
+1; if Q3 is towards Tank3
Similarly
-1 if Q4 is away from Tank3
z2= 0; if Q4=0 (A.12)
+1; if Q4 is towards Tank3
As Q3 and Q4 are determined by the three levels, it is obvious that the discrete mode switching depends on the
continuous variables; hence the system comes under the definition of AHS. The variables, a, b and c in (A.6) and (A.7) are
The International journal of analytical and experimental modal analysis
Volume XII, Issue III, March/2020
ISSN NO:0886-9367
Page No:1937
18. temporary variables to indicate the levels in three tanks as below or above threshold level. The threshold level, h0 is the height
between middle interconnecting pipes and the tank bottom as marked in figure 3. The height difference between the 5th
, 6th
and
7th
hand valves from the bottom of the corresponding tanks are given by hd1, hz and hd2 respectively. The pumps for delivering
inflows are variable speed pumps which deliver flow ranges from 0 to rated flow based on the input signal value (0-5V) given
to the pump. The experimental setup is given in figure A.1, whose specifications are given in table A.2.
Table A.1
Experimental Values Of Discharge Coefficients Of Hand Valves
Discharge coefficient Value(m2
) Discharge coefficient Value(m2
)
k1 2.6363x10-5
k4 3.4316 x10-5
k2 2.4891 x10-5
k6 2.2538 x10-5
k3 3.7984 x10-5
k5, k7 0
Table A.2
Values Of Different System Parameters
Parameter Value
Tank height 0.60m
Tank over flow height 0.55m
Rated flow rate of pumps 240 lph
Input voltage to pump 0 –5 V
Tank inner diameter 0.15m
Inter connecting pipes inner diameter 0.0125m
Figure A.1: Experimental Setup of autonomous hybrid three – tank system
The International journal of analytical and experimental modal analysis
Volume XII, Issue III, March/2020
ISSN NO:0886-9367
Page No:1938