This document proposes a fuzzy logic and neural network-based method for identifying the temperature condition of aircraft gas turbine engines. At early stages of operation when data is limited and uncertain, fuzzy logic and neural networks can be used to create an initial model of the engine's condition. As more normal distribution data is collected over 60-120 measurements, mathematical statistics methods are applied, including analyzing parameters within calculated fuzzy admissible and possible ranges. The method also identifies linear regression models for the engine's initial and actual conditions and compares them to monitor changes using fuzzy correlation and regression analysis. This combined fuzzy-statistical approach allows improved gas turbine engine condition monitoring and diagnostics.
This document proposes a method for identifying the temperature condition of aircraft gas turbine engines using fuzzy logic and neural networks. It involves a multi-stage process of evaluating engine parameters at different stages of operation. In the early stages when data is limited and uncertain, fuzzy logic and neural networks are used to create an initial model of the engine condition. As more data is obtained, mathematical statistics methods and regression analysis are applied to identify linear regression models of the engine temperature condition. A neural network structure is used to identify the fuzzy regression coefficients in the models based on statistical fuzzy data from experiments. This allows monitoring of the engine condition at different operational stages in a way that accounts for uncertainty and limitations in the early data.
The document describes a complex condition monitoring system for aircraft gas turbine engines that uses fuzzy logic and neural networks. It involves multiple levels of analysis including (AL1) fuzzy logic and neural network analysis of engine parameters, (AL2) fuzzy mathematical statistics analysis of parameter distributions including skewness and kurtosis coefficients, and (AL3) fuzzy correlation-regression analysis of engine models. The results of these analyses are then compared using fuzzy logic (AL4) to determine the engine's technical condition. The system provides stage-by-stage evaluation of the engine and was used to evaluate the condition of a new operating aviation engine.
The document presents an aircraft gas turbine engine (GTE) condition monitoring system that uses fuzzy logic and neural networks. At the preliminary stage of monitoring, when data is limited and uncertain, fuzzy logic and neural networks are used to estimate GTE condition. Fuzzy multiple linear and nonlinear regression models are developed to more adequately model GTE technical condition. Skewness and kurtosis coefficients of the distributions of GTE operational parameters are analyzed, showing their fuzzy character. Fuzzy correlation and regression analyses are also used. The system provides stage-by-stage estimation of engine technical condition. Testing on a new operating engine estimated its temperature condition accurately.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IRJET- Analysis of 3-Phase Induction Motor with High Step-Up PWM DC-DC Conver...IRJET Journal
This document discusses control methods for STATCOMs using fuzzy logic controllers and genetic algorithm-tuned PID controllers. STATCOMs are shunt FACTS devices that help solve power quality issues through fast reactive power control. Conventionally, PID controllers are used but require trial and error to tune parameters. The document proposes using fuzzy logic controllers and genetic algorithms to optimize PID parameters to improve STATCOM current control response. It describes STATCOM modeling, fuzzy logic controller design including fuzzification, inference, and defuzzification. Genetic algorithms are used to find optimal PID parameters. Simulation results in MATLAB show the proposed methods improve current control response over conventional PID control.
Fuzzy gain scheduling control apply to an RC Hovercraft IJECEIAES
The Fuzzy Gain Scheduling (FGS) methodology for tuning the ProportionalIntegral-Derivative (PID) traditional controller parameters by scheduling controlled gains in different phases, is a simple and effective application both in industries and real-time complex models while assuring the high achievements over pass decades, is proposed in this article. The Fuzzy logic rules of the triangular membership functions are exploited on-line to verify the Gain Scheduling of the Proportional-Integral-Derivative controller gains in different stages because it can minimize the tracking control error and utilize the Integral of Time Absolute Error (ITAE) minima criterion of the controller design process. For that reason, the controller design could tune the system model in the whole operation time to display the efficiency in tracking error. It is then implemented in a novel Remote Controlled (RC) Hovercraft motion models to demonstrate better control performance in comparison with the PID conventional controller.
Mathematical Modeling and Fuzzy Adaptive PID Control of Erection MechanismTELKOMNIKA JOURNAL
This paper describes an application of fuzzy adaptive PID controller to erection mechanism.
Mathematical model of erection mechanism was derived. Erection mechanism is driven by electrohydraulic
actuator system which is difficult to control due to its nonlinearity and complexity. Therefore fuzzy
adaptive PID controller was applied to control the system. Simulation was performed in Simulink software
and experiment was accomplished on laboratory equipment. Simulation and experiment results of erection
angle controlled by fuzzy logic, PID and fuzzy adaptive PID controllers were respectively obtained. The
results show that fuzzy adaptive PID controller can effectively achieve the best performance for erection
mechanism in comparison with fuzzy logic and PID controllers.
This document summarizes a study that used genetic algorithm (GA) and direct search (DS) techniques to optimize the parameters of an output derivative controller for speed control of a permanent magnet DC motor (PMDCM). The controller parameters were optimized based on different performance criteria including integral square error, integral absolute error, integral time-weighted absolute error, and integral time-weighted square error. Simulation results showed that both GA and DS improved the transient and steady-state performance of the motor compared to an uncontrolled system. GA reduced settling time more but introduced some overshoot, while DS achieved zero overshoot but slower settling. Both optimization techniques eliminated steady-state error and met system requirements. Plots of the cost function, mesh size, and
This document proposes a method for identifying the temperature condition of aircraft gas turbine engines using fuzzy logic and neural networks. It involves a multi-stage process of evaluating engine parameters at different stages of operation. In the early stages when data is limited and uncertain, fuzzy logic and neural networks are used to create an initial model of the engine condition. As more data is obtained, mathematical statistics methods and regression analysis are applied to identify linear regression models of the engine temperature condition. A neural network structure is used to identify the fuzzy regression coefficients in the models based on statistical fuzzy data from experiments. This allows monitoring of the engine condition at different operational stages in a way that accounts for uncertainty and limitations in the early data.
The document describes a complex condition monitoring system for aircraft gas turbine engines that uses fuzzy logic and neural networks. It involves multiple levels of analysis including (AL1) fuzzy logic and neural network analysis of engine parameters, (AL2) fuzzy mathematical statistics analysis of parameter distributions including skewness and kurtosis coefficients, and (AL3) fuzzy correlation-regression analysis of engine models. The results of these analyses are then compared using fuzzy logic (AL4) to determine the engine's technical condition. The system provides stage-by-stage evaluation of the engine and was used to evaluate the condition of a new operating aviation engine.
The document presents an aircraft gas turbine engine (GTE) condition monitoring system that uses fuzzy logic and neural networks. At the preliminary stage of monitoring, when data is limited and uncertain, fuzzy logic and neural networks are used to estimate GTE condition. Fuzzy multiple linear and nonlinear regression models are developed to more adequately model GTE technical condition. Skewness and kurtosis coefficients of the distributions of GTE operational parameters are analyzed, showing their fuzzy character. Fuzzy correlation and regression analyses are also used. The system provides stage-by-stage estimation of engine technical condition. Testing on a new operating engine estimated its temperature condition accurately.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IRJET- Analysis of 3-Phase Induction Motor with High Step-Up PWM DC-DC Conver...IRJET Journal
This document discusses control methods for STATCOMs using fuzzy logic controllers and genetic algorithm-tuned PID controllers. STATCOMs are shunt FACTS devices that help solve power quality issues through fast reactive power control. Conventionally, PID controllers are used but require trial and error to tune parameters. The document proposes using fuzzy logic controllers and genetic algorithms to optimize PID parameters to improve STATCOM current control response. It describes STATCOM modeling, fuzzy logic controller design including fuzzification, inference, and defuzzification. Genetic algorithms are used to find optimal PID parameters. Simulation results in MATLAB show the proposed methods improve current control response over conventional PID control.
Fuzzy gain scheduling control apply to an RC Hovercraft IJECEIAES
The Fuzzy Gain Scheduling (FGS) methodology for tuning the ProportionalIntegral-Derivative (PID) traditional controller parameters by scheduling controlled gains in different phases, is a simple and effective application both in industries and real-time complex models while assuring the high achievements over pass decades, is proposed in this article. The Fuzzy logic rules of the triangular membership functions are exploited on-line to verify the Gain Scheduling of the Proportional-Integral-Derivative controller gains in different stages because it can minimize the tracking control error and utilize the Integral of Time Absolute Error (ITAE) minima criterion of the controller design process. For that reason, the controller design could tune the system model in the whole operation time to display the efficiency in tracking error. It is then implemented in a novel Remote Controlled (RC) Hovercraft motion models to demonstrate better control performance in comparison with the PID conventional controller.
Mathematical Modeling and Fuzzy Adaptive PID Control of Erection MechanismTELKOMNIKA JOURNAL
This paper describes an application of fuzzy adaptive PID controller to erection mechanism.
Mathematical model of erection mechanism was derived. Erection mechanism is driven by electrohydraulic
actuator system which is difficult to control due to its nonlinearity and complexity. Therefore fuzzy
adaptive PID controller was applied to control the system. Simulation was performed in Simulink software
and experiment was accomplished on laboratory equipment. Simulation and experiment results of erection
angle controlled by fuzzy logic, PID and fuzzy adaptive PID controllers were respectively obtained. The
results show that fuzzy adaptive PID controller can effectively achieve the best performance for erection
mechanism in comparison with fuzzy logic and PID controllers.
This document summarizes a study that used genetic algorithm (GA) and direct search (DS) techniques to optimize the parameters of an output derivative controller for speed control of a permanent magnet DC motor (PMDCM). The controller parameters were optimized based on different performance criteria including integral square error, integral absolute error, integral time-weighted absolute error, and integral time-weighted square error. Simulation results showed that both GA and DS improved the transient and steady-state performance of the motor compared to an uncontrolled system. GA reduced settling time more but introduced some overshoot, while DS achieved zero overshoot but slower settling. Both optimization techniques eliminated steady-state error and met system requirements. Plots of the cost function, mesh size, and
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.
OPTIMAL PID CONTROLLER DESIGN FOR SPEED CONTROL OF A SEPARATELY EXCITED DC MO...ijscmcjournal
This paper presents a new approach to determine the optimal proportional-integral-derivative controller
parameters for the speed control of a separately excited DC motor using firefly optimization technique.
Firefly algorithm is one of the recent evolutionary methods which are inspired by the Firefly’s behavior in
nature. The firefly optimization technique is successfully implemented using MATLAB software. A
comparison is drawn from the results obtained between the linear quadratic regulator and firefly
optimization techniques. Simulation results are presented to illustrate the performance and validity of the
design method.
Configuration of pid controller for speed control of dc motor utilizing optim...Santosh Suman
This document discusses using optimization techniques and intelligent strategies to tune PID controllers for speed control of DC motors. It first provides background on DC motors and PID controllers. It then discusses using genetic algorithms, particle swarm optimization, differential evolution and fuzzy logic to tune the PID parameters for better performance in terms of settling time, overshoot, rise time and transient response. Tuning PID parameters is challenging but these optimization techniques and intelligent strategies can help overcome issues with conventional PID controllers for DC motor speed control applications.
Meta-heuristic optimization techniques are important tools to define the optimal solutions for many problems. In this paper, a new advanced artificial intelligence (AI) based direct torque control (DTC) speed drives are optimally designed and implemented in real time to achieve a high performance permanent-magnet synchronous-motor (PMSM) drive. Grey wolf (GW) algorithms are used with the standard PID-based DTC (PIDDTC) and with the DTC with fuzzy logic (FDTC) based speed controllers. DSPACE DS1202 is utilized in the real-time implementation. MATLAB SIMULINK is used to simulate the steady-state (S.S.) and dynamic responses. The overall system is tested at different operating conditions for both simulation and practical work and all results are presented. A comparison between experimental and simulation results is performed and also a comparison between different applied intelligent techniques is introduced.
Design of Adaptive Sliding Mode Control with Fuzzy Controller and PID Tuning ...IRJET Journal
This document presents a control system that combines fuzzy sliding mode control and PID tuning to control uncertain systems. A fuzzy logic controller is proposed using two inputs (error and derivative of error) and simple membership functions and rules. An adaptive sliding mode controller with PID tuning is also designed. The PID gains are systematically and continuously updated according to adaptive laws. This combined fuzzy sliding mode controller with PID tuning is applied to control a brushless DC motor. Simulation results show the system achieves good trajectory tracking performance while eliminating chattering through the use of a boundary layer.
MODELLING ANALYSIS & DESIGN OF DSP BASED NOVEL SPEED SENSORLESS VECTOR CONTRO...IAEME Publication
Unscented Kalman Filter (UKF), which is an update d version of EKF, is proposed as a state estimator for speed sensorless field oriented contr ol of induction motors. UKF state update computations, different from EKF, are derivative fr ee and they do not involve costly calculation of Jacobian matrices. Moreover, variance of each state is not assumed Gaussian, therefore a more realistic approach is provided by UKF. In order to examine the rotor speed (state V) estimation performance of UKF experimentally under varying spe ed conditions, a trapezoidal speed reference command is embedded into the DSP code. EKF rotor speed estimation successfully tracks the trapezoidal path. It has been observed that the est imated states are quite close to the measured ones. The magnitude of the rotor flux justifies that the estimated dq components of the rotor flux are estimated accurately. A number of simulations were carried out to verify the performance of the speed estimation with UKF. These simulated results are confirmed with the experimental results. While obtaining the experimental results, the real time stator voltages and currents are processed in Matlab with the associated EKF and UKF programs.
Data-based PID control of flexible joint robot using adaptive safe experiment...journalBEEI
This paper proposes the data-based PID controller of flexible joint robot based on adaptive safe experimentation dynamics (ASED) algorithm. The ASED algorithm is an enhanced version of SED algorithm where the updated tuning variable is modified to adapt to the change of the objective function. By adopting the adaptive term to the updated equation of SED, it is expected that the convergence accuracy can be further improved. The effectiveness of the ASED algorithm is verified to tune the PID controller of flexible joint robot. In this flexible joint control problem, two PID controllers are utilized to control both rotary angle tracking and vibration of flexible joint robot. The performance of the proposed data-based PID controller is assessed in terms of trajectory tracking of angular motion, vibration reduction and statistical analysis of the pre-defined control objective function. The simulation results showed that the data-based PID controller based on ASED is able to produce better control accuracy than the conventional SED based method.
This paper presents an enhanced nonlinear PID (NPID) controller to follow a preselected speed profile of brushless DC motor drive system. This objective should be achieved regardless the parameter variations, and external disturbances. The performance of enhanced NPID controller will be investigated by comparing it with linear PID control and fractional order PID (FOPID) control. These controllers are tested for both speed regulation and speed tracking. The optimal parameters values of each control technique were obtained using Genetic Algorithm (GA) based on a certain cost function. Results shows that the proposed NPID controller has better performance among other techniques (PID and FOPID controller).
Design of multiloop controller for multivariable system using coefficient 2IAEME Publication
The document describes the design of a multivariable controller for a coupled tank system using the Coefficient Diagram Method (CDM). CDM is a polynomial method for control design that is based on choosing coefficients for the closed-loop system's characteristic polynomial according to desired performance specifications like equivalent time constant, stability indices, and stability limit. The controller is designed by using CDM to determine the coefficients of the controller polynomials. The coupled tank process is modeled using mass balance equations and its parameters are provided. Controller design using CDM is demonstrated for multivariable processes like the coupled tank system to provide stable and robust performance while meeting time domain specifications.
This document proposes a new one-step method for tuning PI/PID controllers based on closed-loop experiments. It derives simple correlations between data from a proportional-only closed-loop step response experiment and PI/PID settings that provide good performance and robustness. Specifically:
1) A proportional-only controller is used to generate a step response with 10-60% overshoot. The gain, overshoot, peak time, and steady-state change are recorded.
2) Simulations show the proposed controller gain is proportional to the proportional gain used in the experiment, with the ratio dependent only on overshoot. Simple equations are derived relating overshoot and peak time to the PI/PID settings.
3
PID controller for microsatellite yaw-axis attitude control system using ITAE...TELKOMNIKA JOURNAL
The need for effective design of satellite attitude control (SAC) subsystem for a microsatellite is imperative in order to guarantee both the quality and reliability of the data acquisition. A proportional-integral-derivative (PID) controller was proposed in this study because of its numerous advantages. The performance of PID controller can be greatly improved by adopting an integral time absolute error (ITAE) robust controller design approach. Since the system to be controlled is of the 4th order, it was approximated by its 2nd order version and then used for the controller design. Both the reduced and higher-order pre-filter transfer functions were designed and tested, in order to improve the system performance. As revealed by the results, three out of the four designed systems satisfy the design specifications; and the PD-controlled system without pre-filter transfer function was recommended out of the three systems due to its structural simplicity, which eventually enhances its digital implementation.
This document summarizes a journal article that proposes a fuzzy logic approach for sensorless vector control of an induction motor using an efficiency optimization technique. It presents the following:
1) A dynamic model and state space model of the induction motor in a synchronous reference frame for vector control without sensors.
2) A fuzzy logic based online efficiency optimization controller that interfaces with the drive system to minimize power consumption.
3) The controller decrements the flux in steps until the measured input power is minimized. Membership functions and rules for the fuzzy controller are provided.
4) Performance of the drive is analyzed with and without the fuzzy controller using MATLAB/Simulink simulations. The fuzzy approach is found to improve efficiency
Hexacopter using MATLAB Simulink and MPU SensingIRJET Journal
This document describes the modeling and control of a hexacopter unmanned aerial vehicle using MATLAB simulation. It presents the mathematical modeling of the hexacopter dynamics using Newton-Euler angles and reference frames. PID controllers are developed for altitude, roll, pitch and yaw control. The rotor speeds required for thrust and attitude control are calculated from the PID outputs. Simulation parameters are provided and the results obtained from implementing the PID controllers on the hexacopter model in MATLAB are presented.
Experimental dataset to develop a parametric model based of DC geared motor i...IJECEIAES
This paper presents the application of a System Identification based on Particle Swarm Optimization (PSO) technique to develop parametric model of experimental dataset of DC Geared motor in feeder machine. The experimental was conducted to measure the input (voltage) and output (speed) data. The actual data is used to be optimized using PSO algorithm. The parameter emphasized is Time, Man Square Error (MSE) and Average Time. One of the best model has been chosen based on the optimum parameters.
Speed control of dc motor using relay feedback tuned piAlexander Decker
This document discusses speed control of a DC motor using different controller types, including a relay feedback tuned PI controller, fuzzy PI controller (FPIC), and self-tuned fuzzy PI controller (STFPIC). The FPIC and STFPIC are developed using fuzzy logic to overcome limitations of conventional PID controllers for nonlinear systems without an accurate mathematical model. An experimental setup is used to test the controllers' performance on a DC motor. Results show the model-independent STFPIC and FPIC controllers improve speed control performance compared to the relay-tuned PI controller.
Multi-objective Optimization Scheme for PID-Controlled DC MotorIAES-IJPEDS
DC Motor is the most basic electro-mechanical equipment and well-known for its merit and simplicity. The performance of DC motor is assessed based on several qualities that are most-likely contradictory each other, i.e. settling time and overshoot percentage. Most of controller’s optimization problems are multi-objective in nature since they normally have several conflicting objectives that must be met simultaneously. In this study, the grey relational analysis (GRA) was combined with Taguchi method to search the optimum PID parameter for multi-objective problem. First, a L9 (33) orthogonal array was used to plan out the processing parameters that would affect the DC motor’s speed. Then GRA was applied to overcome the drawback of single quality characteristics in the Taguchi method, and then the optimized PID parameter combination was obtained for multiple quality characteristics from the response table and the response graph from GRA. Signal-to-noise ratio (S/N ratio) calculation and analysis of variance (ANOVA) would be performed to find out the significant factors. Lastly, the reliability and reproducibility of the experiment was verified by confirming a confidence interval (CI) of 95%.
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.
Optimal backstepping control of quadrotor UAV using gravitational search opti...journalBEEI
Quadrotor unmanned aerial vehicle (UAV) has superior characteristics such as ability to take off and land vertically, to hover in a stable air condition and to perform fast maneuvers. However, developing a high-performance quadrotor UAV controller is a difficult problem as quadrotor is an unstable and underactuated nonlinear system. The effort in this article focuses on designing and optimizing an autonomous quadrotor UAV controller. First, the aerial vehicle's dynamic model is presented. Then it is suggested an optimal backstepping controller (OBC). Traditionally, backstepping controller (BC) parameters are often selected arbitrarily. The gravitational search algorithm (GSA) is used here to determine the BC parameter optimum values. In the algorithm, the control parameters are calculated using an integral absolute error to minimize the fitness function. As the control law is based on the theorem of Lyapunov, the asymptotic stability of the scheme can be ensured. Finally, several simulation studies are conducted to show the efficacy of the suggested OBC.
Speed controller design for three-phase induction motor based on dynamic ad...IJECEIAES
Three-phase induction motor (TIM) is widely used in industrial application like paper mills, water treatment and sewage plants in the urban area. In these applications, the speed of TIM is very important that should be not varying with applied load torque. In this study, direct on line (DOL) motor starting without controller is modelled to evaluate the motor response when connected directly to main supply. Conventional PI controller for stator direct current and stator quadrature current of induction motor are designed as an inner loop controller as well as a second conventional PI controller is designed in the outer loop for controlling the TIM speed. Proposed combined PI-lead (CPIL) controllers for inner and outer loops are designed to improve the overall performance of the TIM as compared with the conventional controller. In this paper, dynamic adjustment grasshopper optimization algorithm (DAGOA) is proposed for tuning the proposed controller of the system. Numerical results based on well-selected test function demonstrate that DAGOA has a better performance in terms of speed of convergence, solution accuracy and reliability than SGOA. The study results revealed that the currents and speed of TIM system using CPIL-DAGOA are faster than system using conventional PI and CPIL controllers tuned by SGOA. Moreover, the speed controller of TIM system with CPIL controlling scheme based on DAGOA reached the steady state faster than others when applied load torque.
Mimo system-order-reduction-using-real-coded-genetic-algorithmCemal Ardil
This document describes a method for reducing the order of multi-input multi-output (MIMO) systems using real-coded genetic algorithms. The method aims to minimize the integral square error between the transient responses of the original and reduced order models. It treats both the numerator and denominator parameters of the reduced order model as free parameters to be optimized. A real-coded genetic algorithm is used to search for the parameter values that minimize the error. The method is illustrated with an example and shown to produce results comparable to other established order reduction techniques while guaranteeing stability of the reduced model.
The document describes a new method for optimizing binary tree representations of logic functions to improve throughput. The method aims to reduce logic depth by minimizing delay through grouping Boolean terms with high literal matching. Experimental results on FPGA show the method achieves 10-13% higher maximum throughput and 44-45% lower resource usage compared to an existing method.
This document summarizes the design, development, and implementation of a temperature sensor using Zigbee concepts. The temperature sensor senses the temperature using an LM35 temperature sensor and transmits the data via a Zigbee module. The data is received by another Zigbee module and displayed on an LCD. The system was designed to be accurate, fast, and effective in sensing and transmitting temperature data wirelessly using Zigbee technology. A PIC microcontroller was used to control the temperature sensing, data transmission, and display.
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.
OPTIMAL PID CONTROLLER DESIGN FOR SPEED CONTROL OF A SEPARATELY EXCITED DC MO...ijscmcjournal
This paper presents a new approach to determine the optimal proportional-integral-derivative controller
parameters for the speed control of a separately excited DC motor using firefly optimization technique.
Firefly algorithm is one of the recent evolutionary methods which are inspired by the Firefly’s behavior in
nature. The firefly optimization technique is successfully implemented using MATLAB software. A
comparison is drawn from the results obtained between the linear quadratic regulator and firefly
optimization techniques. Simulation results are presented to illustrate the performance and validity of the
design method.
Configuration of pid controller for speed control of dc motor utilizing optim...Santosh Suman
This document discusses using optimization techniques and intelligent strategies to tune PID controllers for speed control of DC motors. It first provides background on DC motors and PID controllers. It then discusses using genetic algorithms, particle swarm optimization, differential evolution and fuzzy logic to tune the PID parameters for better performance in terms of settling time, overshoot, rise time and transient response. Tuning PID parameters is challenging but these optimization techniques and intelligent strategies can help overcome issues with conventional PID controllers for DC motor speed control applications.
Meta-heuristic optimization techniques are important tools to define the optimal solutions for many problems. In this paper, a new advanced artificial intelligence (AI) based direct torque control (DTC) speed drives are optimally designed and implemented in real time to achieve a high performance permanent-magnet synchronous-motor (PMSM) drive. Grey wolf (GW) algorithms are used with the standard PID-based DTC (PIDDTC) and with the DTC with fuzzy logic (FDTC) based speed controllers. DSPACE DS1202 is utilized in the real-time implementation. MATLAB SIMULINK is used to simulate the steady-state (S.S.) and dynamic responses. The overall system is tested at different operating conditions for both simulation and practical work and all results are presented. A comparison between experimental and simulation results is performed and also a comparison between different applied intelligent techniques is introduced.
Design of Adaptive Sliding Mode Control with Fuzzy Controller and PID Tuning ...IRJET Journal
This document presents a control system that combines fuzzy sliding mode control and PID tuning to control uncertain systems. A fuzzy logic controller is proposed using two inputs (error and derivative of error) and simple membership functions and rules. An adaptive sliding mode controller with PID tuning is also designed. The PID gains are systematically and continuously updated according to adaptive laws. This combined fuzzy sliding mode controller with PID tuning is applied to control a brushless DC motor. Simulation results show the system achieves good trajectory tracking performance while eliminating chattering through the use of a boundary layer.
MODELLING ANALYSIS & DESIGN OF DSP BASED NOVEL SPEED SENSORLESS VECTOR CONTRO...IAEME Publication
Unscented Kalman Filter (UKF), which is an update d version of EKF, is proposed as a state estimator for speed sensorless field oriented contr ol of induction motors. UKF state update computations, different from EKF, are derivative fr ee and they do not involve costly calculation of Jacobian matrices. Moreover, variance of each state is not assumed Gaussian, therefore a more realistic approach is provided by UKF. In order to examine the rotor speed (state V) estimation performance of UKF experimentally under varying spe ed conditions, a trapezoidal speed reference command is embedded into the DSP code. EKF rotor speed estimation successfully tracks the trapezoidal path. It has been observed that the est imated states are quite close to the measured ones. The magnitude of the rotor flux justifies that the estimated dq components of the rotor flux are estimated accurately. A number of simulations were carried out to verify the performance of the speed estimation with UKF. These simulated results are confirmed with the experimental results. While obtaining the experimental results, the real time stator voltages and currents are processed in Matlab with the associated EKF and UKF programs.
Data-based PID control of flexible joint robot using adaptive safe experiment...journalBEEI
This paper proposes the data-based PID controller of flexible joint robot based on adaptive safe experimentation dynamics (ASED) algorithm. The ASED algorithm is an enhanced version of SED algorithm where the updated tuning variable is modified to adapt to the change of the objective function. By adopting the adaptive term to the updated equation of SED, it is expected that the convergence accuracy can be further improved. The effectiveness of the ASED algorithm is verified to tune the PID controller of flexible joint robot. In this flexible joint control problem, two PID controllers are utilized to control both rotary angle tracking and vibration of flexible joint robot. The performance of the proposed data-based PID controller is assessed in terms of trajectory tracking of angular motion, vibration reduction and statistical analysis of the pre-defined control objective function. The simulation results showed that the data-based PID controller based on ASED is able to produce better control accuracy than the conventional SED based method.
This paper presents an enhanced nonlinear PID (NPID) controller to follow a preselected speed profile of brushless DC motor drive system. This objective should be achieved regardless the parameter variations, and external disturbances. The performance of enhanced NPID controller will be investigated by comparing it with linear PID control and fractional order PID (FOPID) control. These controllers are tested for both speed regulation and speed tracking. The optimal parameters values of each control technique were obtained using Genetic Algorithm (GA) based on a certain cost function. Results shows that the proposed NPID controller has better performance among other techniques (PID and FOPID controller).
Design of multiloop controller for multivariable system using coefficient 2IAEME Publication
The document describes the design of a multivariable controller for a coupled tank system using the Coefficient Diagram Method (CDM). CDM is a polynomial method for control design that is based on choosing coefficients for the closed-loop system's characteristic polynomial according to desired performance specifications like equivalent time constant, stability indices, and stability limit. The controller is designed by using CDM to determine the coefficients of the controller polynomials. The coupled tank process is modeled using mass balance equations and its parameters are provided. Controller design using CDM is demonstrated for multivariable processes like the coupled tank system to provide stable and robust performance while meeting time domain specifications.
This document proposes a new one-step method for tuning PI/PID controllers based on closed-loop experiments. It derives simple correlations between data from a proportional-only closed-loop step response experiment and PI/PID settings that provide good performance and robustness. Specifically:
1) A proportional-only controller is used to generate a step response with 10-60% overshoot. The gain, overshoot, peak time, and steady-state change are recorded.
2) Simulations show the proposed controller gain is proportional to the proportional gain used in the experiment, with the ratio dependent only on overshoot. Simple equations are derived relating overshoot and peak time to the PI/PID settings.
3
PID controller for microsatellite yaw-axis attitude control system using ITAE...TELKOMNIKA JOURNAL
The need for effective design of satellite attitude control (SAC) subsystem for a microsatellite is imperative in order to guarantee both the quality and reliability of the data acquisition. A proportional-integral-derivative (PID) controller was proposed in this study because of its numerous advantages. The performance of PID controller can be greatly improved by adopting an integral time absolute error (ITAE) robust controller design approach. Since the system to be controlled is of the 4th order, it was approximated by its 2nd order version and then used for the controller design. Both the reduced and higher-order pre-filter transfer functions were designed and tested, in order to improve the system performance. As revealed by the results, three out of the four designed systems satisfy the design specifications; and the PD-controlled system without pre-filter transfer function was recommended out of the three systems due to its structural simplicity, which eventually enhances its digital implementation.
This document summarizes a journal article that proposes a fuzzy logic approach for sensorless vector control of an induction motor using an efficiency optimization technique. It presents the following:
1) A dynamic model and state space model of the induction motor in a synchronous reference frame for vector control without sensors.
2) A fuzzy logic based online efficiency optimization controller that interfaces with the drive system to minimize power consumption.
3) The controller decrements the flux in steps until the measured input power is minimized. Membership functions and rules for the fuzzy controller are provided.
4) Performance of the drive is analyzed with and without the fuzzy controller using MATLAB/Simulink simulations. The fuzzy approach is found to improve efficiency
Hexacopter using MATLAB Simulink and MPU SensingIRJET Journal
This document describes the modeling and control of a hexacopter unmanned aerial vehicle using MATLAB simulation. It presents the mathematical modeling of the hexacopter dynamics using Newton-Euler angles and reference frames. PID controllers are developed for altitude, roll, pitch and yaw control. The rotor speeds required for thrust and attitude control are calculated from the PID outputs. Simulation parameters are provided and the results obtained from implementing the PID controllers on the hexacopter model in MATLAB are presented.
Experimental dataset to develop a parametric model based of DC geared motor i...IJECEIAES
This paper presents the application of a System Identification based on Particle Swarm Optimization (PSO) technique to develop parametric model of experimental dataset of DC Geared motor in feeder machine. The experimental was conducted to measure the input (voltage) and output (speed) data. The actual data is used to be optimized using PSO algorithm. The parameter emphasized is Time, Man Square Error (MSE) and Average Time. One of the best model has been chosen based on the optimum parameters.
Speed control of dc motor using relay feedback tuned piAlexander Decker
This document discusses speed control of a DC motor using different controller types, including a relay feedback tuned PI controller, fuzzy PI controller (FPIC), and self-tuned fuzzy PI controller (STFPIC). The FPIC and STFPIC are developed using fuzzy logic to overcome limitations of conventional PID controllers for nonlinear systems without an accurate mathematical model. An experimental setup is used to test the controllers' performance on a DC motor. Results show the model-independent STFPIC and FPIC controllers improve speed control performance compared to the relay-tuned PI controller.
Multi-objective Optimization Scheme for PID-Controlled DC MotorIAES-IJPEDS
DC Motor is the most basic electro-mechanical equipment and well-known for its merit and simplicity. The performance of DC motor is assessed based on several qualities that are most-likely contradictory each other, i.e. settling time and overshoot percentage. Most of controller’s optimization problems are multi-objective in nature since they normally have several conflicting objectives that must be met simultaneously. In this study, the grey relational analysis (GRA) was combined with Taguchi method to search the optimum PID parameter for multi-objective problem. First, a L9 (33) orthogonal array was used to plan out the processing parameters that would affect the DC motor’s speed. Then GRA was applied to overcome the drawback of single quality characteristics in the Taguchi method, and then the optimized PID parameter combination was obtained for multiple quality characteristics from the response table and the response graph from GRA. Signal-to-noise ratio (S/N ratio) calculation and analysis of variance (ANOVA) would be performed to find out the significant factors. Lastly, the reliability and reproducibility of the experiment was verified by confirming a confidence interval (CI) of 95%.
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.
Optimal backstepping control of quadrotor UAV using gravitational search opti...journalBEEI
Quadrotor unmanned aerial vehicle (UAV) has superior characteristics such as ability to take off and land vertically, to hover in a stable air condition and to perform fast maneuvers. However, developing a high-performance quadrotor UAV controller is a difficult problem as quadrotor is an unstable and underactuated nonlinear system. The effort in this article focuses on designing and optimizing an autonomous quadrotor UAV controller. First, the aerial vehicle's dynamic model is presented. Then it is suggested an optimal backstepping controller (OBC). Traditionally, backstepping controller (BC) parameters are often selected arbitrarily. The gravitational search algorithm (GSA) is used here to determine the BC parameter optimum values. In the algorithm, the control parameters are calculated using an integral absolute error to minimize the fitness function. As the control law is based on the theorem of Lyapunov, the asymptotic stability of the scheme can be ensured. Finally, several simulation studies are conducted to show the efficacy of the suggested OBC.
Speed controller design for three-phase induction motor based on dynamic ad...IJECEIAES
Three-phase induction motor (TIM) is widely used in industrial application like paper mills, water treatment and sewage plants in the urban area. In these applications, the speed of TIM is very important that should be not varying with applied load torque. In this study, direct on line (DOL) motor starting without controller is modelled to evaluate the motor response when connected directly to main supply. Conventional PI controller for stator direct current and stator quadrature current of induction motor are designed as an inner loop controller as well as a second conventional PI controller is designed in the outer loop for controlling the TIM speed. Proposed combined PI-lead (CPIL) controllers for inner and outer loops are designed to improve the overall performance of the TIM as compared with the conventional controller. In this paper, dynamic adjustment grasshopper optimization algorithm (DAGOA) is proposed for tuning the proposed controller of the system. Numerical results based on well-selected test function demonstrate that DAGOA has a better performance in terms of speed of convergence, solution accuracy and reliability than SGOA. The study results revealed that the currents and speed of TIM system using CPIL-DAGOA are faster than system using conventional PI and CPIL controllers tuned by SGOA. Moreover, the speed controller of TIM system with CPIL controlling scheme based on DAGOA reached the steady state faster than others when applied load torque.
Mimo system-order-reduction-using-real-coded-genetic-algorithmCemal Ardil
This document describes a method for reducing the order of multi-input multi-output (MIMO) systems using real-coded genetic algorithms. The method aims to minimize the integral square error between the transient responses of the original and reduced order models. It treats both the numerator and denominator parameters of the reduced order model as free parameters to be optimized. A real-coded genetic algorithm is used to search for the parameter values that minimize the error. The method is illustrated with an example and shown to produce results comparable to other established order reduction techniques while guaranteeing stability of the reduced model.
The document describes a new method for optimizing binary tree representations of logic functions to improve throughput. The method aims to reduce logic depth by minimizing delay through grouping Boolean terms with high literal matching. Experimental results on FPGA show the method achieves 10-13% higher maximum throughput and 44-45% lower resource usage compared to an existing method.
This document summarizes the design, development, and implementation of a temperature sensor using Zigbee concepts. The temperature sensor senses the temperature using an LM35 temperature sensor and transmits the data via a Zigbee module. The data is received by another Zigbee module and displayed on an LCD. The system was designed to be accurate, fast, and effective in sensing and transmitting temperature data wirelessly using Zigbee technology. A PIC microcontroller was used to control the temperature sensing, data transmission, and display.
A simplified-single-correlator-rake-receiver-for-cdma-communicationsCemal Ardil
This document summarizes a research paper that proposes a simplified single correlator RAKE receiver for CDMA communications. The receiver uses a single correlator and code generator, rather than multiple correlators as in conventional RAKE receivers. It spreads data using modified Walsh-Hadamard codes, which provide better uncorrelation between multipath signals. Simulation results showed the proposed receiver achieves lower bit error rates than conventional RAKE receivers when receiving multiple multipath signals.
Development of-new-control-techniques-for-vibration-isolation-of-structures-u...Cemal Ardil
The document discusses the development of new control techniques for vibration isolation of structures using smart materials. It summarizes previous research that showed isolation reduces acceleration and forces in structures but increases sliding displacement at low excitation frequencies. The paper then presents a study of a space frame structure on sliding bearings with a restoring force device. The results show the restoring force device reduces displacement of the structure and peak acceleration, bending moment, and base shear values compared to a structure without the device. The simulation demonstrates the effectiveness of the developed isolation method.
This document summarizes the application of computational intelligence techniques like genetic algorithms and particle swarm optimization for solving economic load dispatch problems. It first applies a real-coded genetic algorithm to minimize generation costs for a 6-generator test system with continuous fuel cost equations, showing superiority over quadratic programming. It then uses particle swarm optimization to minimize costs for a 10-generator system with each generator having discontinuous fuel options, showing better results than other published methods. The document provides background on economic load dispatch problems and optimization techniques like quadratic programming, genetic algorithms, and particle swarm optimization.
A black-box-approach-for-response-quality-evaluation-of-conversational-agent-...Cemal Ardil
The document discusses the challenges of evaluating conversational agent systems. It proposes a black-box approach to evaluate response quality using observation, classification, and scoring. This approach is used to assess three example systems: AnswerBus, START, and AINI. The evaluation of conversational agents is difficult because existing information retrieval metrics do not apply, and there is no single correct answer for many questions.
This document summarizes a research paper that proposes an approach for automatically authenticating handwritten documents based on low density pixel measurements. The approach focuses on analyzing subtle spatial features related to pen pressure, like the percentage of low density pixels in a signature, which could help distinguish genuine signatures from skilled forgeries that appear very similar. 10 features are extracted, including the low and high density pixel percentages and their ratio. An adaptive threshold is also introduced to make verification judgments. The method is tested on a dataset of 200 genuine and 200 forged signature images. Results show it can effectively detect skilled forgeries that current static feature-based methods struggle with.
This document summarizes an article that proposes an adaptive nonlinear filtering technique for image restoration. It begins by discussing common types of image noise and degradation models. It then discusses existing median filtering and adaptive filtering techniques that aim to remove noise while preserving edges. The paper proposes a new adaptive length median/mean algorithm that can simultaneously remove noise artifacts like impulses, strip lines, drop lines, band missing, and blotches. It detects corrupted pixels and evaluates new pixels to replace them. The algorithm switches between median and mean filtering depending on noise levels to better preserve details. The performance of the algorithm is evaluated based on metrics like mean square error and peak signal-to-noise ratio. The algorithm is found to outperform standard techniques in
This document discusses using fuzzy clustering to group real estate properties. It presents a case study clustering 46 real estate listings into 3 groups based on price, area, and region attributes. The fuzzy c-means clustering algorithm in MATLAB is used to assign membership levels and cluster centroids. The results identify 3 clusters - one for mid-priced properties in good regions and average areas, one for high-priced properties in excellent regions and large areas, and one for low-priced properties in poor regions and small areas. Graphs and tables show the clustered properties and centroids.
This document discusses using repeated simulations of a crisp neural network to obtain quasi-fuzzy weight sets (QFWS) that can be used to initialize fuzzy neural networks. The key points are:
1) A crisp neural network is repeatedly trained on input-output data to model an unknown function. The connection weights change with each simulation.
2) Recording the weights from multiple simulations produces quasi-fuzzy weight sets, where each weight is a fuzzy set rather than a single value.
3) These QFWS can provide initial solutions for training type-I fuzzy neural networks with reduced computational complexity compared to random initialization.
4) The QFWS follow fuzzy arithmetic and allow both numerical and linguistic data to
This document describes e-collaborative learning circles, which are small, diverse groups of 8-10 people that meet regularly online over weeks or months to collaborate on educational projects. The key points are:
- E-collaborative learning circles allow global partners like teachers and students to work together online to improve research, learning, and teaching skills.
- They follow a process that includes preparing, opening, working/learning together, planning outcomes, and closing. Students communicate to discuss projects and provide feedback.
- The goals are to enhance learning through international understanding and cultural exchange, while also developing language and ICT skills.
A comparison-of-first-and-second-order-training-algorithms-for-artificial-neu...Cemal Ardil
This document compares first and second order training algorithms for artificial neural networks. It summarizes that feedforward network training is a special case of functional minimization where no explicit model of the data is assumed. Gradient descent, conjugate gradient, and quasi-Newton methods are discussed as first and second order training methods. Conjugate gradient and quasi-Newton methods are shown to outperform gradient descent methods experimentally using share rate data. The backpropagation algorithm and its variations are described for finding the gradient of the error function with respect to the network weights. Conjugate gradient techniques are discussed as a way to find the search direction without explicitly computing the Hessian matrix.
This document describes a study that developed a neuro-fuzzy system for predicting electricity consumption. The neuro-fuzzy system combines the learning capabilities of neural networks with the linguistic rule interpretation of fuzzy inference systems. It was applied to predict future electricity consumption in Northern Cyprus based on past consumption data. The system was trained using a supervised learning algorithm to determine optimal parameters. Simulation results showed the neuro-fuzzy system achieved more accurate predictions of electricity consumption than a neural network model alone, using fewer training epochs.
This document discusses bit-interleaved parity (BIP) methods for monitoring bit error rates in SONET networks that employ automatic protection switching. It examines how BIP performs in declaring and clearing alarms compared to exact calculations.
The key points are:
1) BIP tends to declare alarms later than exact calculations as it diverges from the actual bit error probability at higher rates.
2) BIP also tends to clear alarms earlier than exact methods as it approaches a limit of 0.5 probability rather than 1.
3) For BIP to provide acceptable performance, the window size and thresholds used need to build in sufficient hysteresis to account for this late declaration and early clearing compared to
Energy efficient-resource-allocation-in-distributed-computing-systemsCemal Ardil
The document discusses energy efficient resource allocation in distributed computing systems. It proposes using a solution from cooperative game theory called the Nash Bargaining Solution (NBS) to allocate tasks to machines in a computational grid in a way that minimizes both power consumption and makespan.
The key points are:
- It formulates the problem of mapping tasks to machines as a multi-constrained multi-objective extension of the Generalized Assignment Problem (GAP) called Power-aware Task Allocation (PATA).
- It models a cooperative game where each machine is a player, and the goal is to minimize both the total power consumed and the overall makespan when allocating tasks.
- The proposed NBS
The document describes a multistage system for monitoring the technical condition of aircraft gas turbine engines. It proposes using soft computing methods like fuzzy logic and neural networks at early stages when data is limited or uncertain. At later stages, it uses statistical analysis methods like analyzing changes in skewness, kurtosis, and correlation coefficients of engine parameters. Fuzzy regression models are developed using these techniques to estimate engine condition. The system provides staged estimation of engine technical conditions from initial to later stages of operation.
Antenna Azimuth Position Control System using PIDController & State-Feedback ...IJECEIAES
This paper analyzed two controllers with the view to improve the overall control of an antenna azimuth position. Frequency ranges were utilized for the PID controller in the system; while Ziegler-Nichols was used to tune the PID parameter gains. A state feedback controller was formulated from the state-space equation and pole-placements were adopted to ensure the model design complied with the specifications to meet transient response. MATLAB Simulink platform was used for the system simulation. The system response for both the two controllers were analyzed and compared to ascertain the best controller with best azimuth positioning for the antenna. It was observed that state-feedback controller provided the best azimuth positioning control with a little settling time, some value of overshoot and no steady-state error is detected.
IRJET- Design and Analysis of Fuzzy and GA-PID Controllers for Optimized Perf...IRJET Journal
This document describes research into using different controller types, including fuzzy logic controllers and genetic algorithm optimized PID controllers, to control a STATCOM device for improved reactive power compensation performance. A STATCOM is a shunt Flexible AC Transmission System device that can help solve power quality issues. Conventionally, PID controllers are used but require trial and error to tune parameters. The document models a STATCOM system and explores using fuzzy logic control or genetic algorithms to automatically determine optimal PID parameters to achieve faster response compared to conventional PID control. Simulation results in MATLAB show that both fuzzy logic control and genetic algorithm optimized PID control improve the STATCOM current control response compared to manually tuned PID controllers.
This document summarizes a research project on process identification using relay feedback tests. The project aims to identify low-order models like FOPDT and SOPDT from relay feedback data to enable performance assessment and controller tuning. A new identification method is proposed that uses neural networks to estimate the apparent deadtime from steady-state cycles. This deadtime and other parameters allow classification of the process model and parameter estimation for assessment and auto-tuning.
Alienor method applied to induction machine parameters identification IJECEIAES
This paper presents an identification method to estimate simultaneously the electrical and mechanical induction machine (IM) parameters by using only the measured current and the corresponding phase voltage. This identification method is based on the output error and uses the multidimensional Alienor global optimization method as a minimization technique. Alienor method is essentially based on converting multivariable problem to monovariable one. To improve the Alienor method performance, the reducing transformation is proposed and compared with the genetic algorithm (GA). Firstly, the identification method is verified using the simulated data. Secondly, the validation is then confirmed by measured data from one machine. The corresponding computed transient and steady state currents agree well with the measured data. The results obtained show the superiority of the proposed Alienor method versus GA in terms of computing time.
Indirect Vector Control of Induction Motor Using Pi Speed Controller and Neur...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
PRACTICAL IMPLEMENTION OF GAOPF ON INDIAN 220KV TRANSMISSION SYSTEMecij
This paper presents the practical implementation of developed genetic algorithm based optimal power flow algorithms. These algorithms are tested on IEEE30 bus system and the results were presented in the paper [8]. The same algorithms now tested on 220KV Washi zone Indian power transmission system . The GAOPF with fixed penalty and Fuzzy based variable penalty tested on 220KV transmission system consists of 52 bus and 88lines. The fuel costs ,computational time and the system condition were studied and the results are presented in this paper .Also the available load transfer capability of the 220KV system for congestion management is also presented
COMPARATIVE ANALYSIS OF CONVENTIONAL PID CONTROLLER AND FUZZY CONTROLLER WIT...IJITCA Journal
All the real systems exhibits non-linear nature,conventional controllers are not always able to provide good and accurate results. Fuzzy Logic Control is used to obtain better response. A model for simulation is designed and all the assumptions are made before the development of the model. An attempt has been made to analyze the efficiency of a fuzzy controller over a conventional PID controller for a three tank level control system using fuzzification & defuzzification methods and their responses are compared. Analysis is done through computer simulation using Matlab/Simulink toolbox. This study shows that the application of Fuzzy Logic Controller (FLC) gives the best response with triangular membership function and centroid defuzzification method.
This document describes a model-based autotuning system that uses an artificial neural network (ANN) and relay feedback test. The system estimates process parameters like gain, ultimate gain, ultimate frequency, and apparent deadtime from relay feedback test data. It then classifies the process dynamics and tunes PI/PID controllers based on either a first-order plus deadtime or second-order plus deadtime process model. The tuning rules are derived from these models to provide superior control performance compared to Ziegler-Nichols tuning.
Robust PID Controller Design for Non-Minimum Phase Systems using Magnitude Op...IRJET Journal
This document discusses two approaches for designing a controller for non-minimum phase systems: 1) the magnitude optimum and multiple integration method, and 2) a numerical optimization approach. The magnitude optimum method uses areas calculated from the process step response to determine the PID controller parameters, eliminating the need to estimate process parameters directly. The numerical optimization approach formulates the controller design as an optimization problem to minimize sensitivity functions in the closed-loop system. Both approaches are presented as ways to design robust controllers for non-minimum phase systems.
j2 Universal - Modelling and Tuning Braking CharacteristicsJohn Jeffery
1) The document outlines tuning the braking characteristics of an aircraft model using flight test data. Key steps included generating test cases from the data, reconstructing the data for analysis, and performing a re-prediction analysis to identify discrepancies.
2) Regression analysis on acceleration errors was used to derive an improved braking friction coefficient table. Additional tuning of thrust reverser dynamics provided better matching of deceleration profiles.
3) Sanity checks confirmed the friction coefficients derived were feasible. The tuned model matched the flight data to tolerances required for pilot training simulation.
Metamodel-based Optimization of a PID Controller Parameters for a Coupled-tan...TELKOMNIKA JOURNAL
Liquid flow and level control are essential requirements in various industries, such as paper
manufacturing, petrochemical industries, waste management, and others. Controlling the liquids flow and
levels in such industries is challenging due to the existence of nonlinearity and modeling uncertainties of
the plants. This paper presents a method to control the liquid level in a second tank of a coupled-tank plant
through variable manipulation of a water pump in the first tank. The optimum controller parameters of this
plant are calculated using radial basis function neural network metamodel. A time-varying nonlinear
dynamic model is developed and the corresponding linearized perturbation models are derived from the
nonlinear model. The performance of the developed optimized controller using metamodeling is compared
with the original large space design. In addition, linearized perturbation models are derived from the
nonlinear dynamic model with time-varying parameters.
IRJET- Speed Control of Induction Motor using Hybrid PID Fuzzy ControllerIRJET Journal
This document presents a study on using a hybrid PID fuzzy controller with a BAT optimization algorithm to control the speed of an induction motor. It begins with background on PID controllers and fuzzy logic controllers. It then proposes using a BAT algorithm to select the Kp and Ki parameters of a PI controller to regulate motor speed. The results show that the proposed BAT-PID controller reduces speed fluctuations and settling time compared to a traditional PID controller. In conclusion, the hybrid fuzzy-PID controller with BAT optimization improves induction motor speed control.
Hybrid Stochastic Search Technique based Suboptimal AGC Regulator Design for ...Dr. Omveer Singh
This document proposes a hybrid genetic algorithm-simulated annealing (GASA) technique for designing suboptimal automatic generation control (AGC) regulators for a two-area power system model. GASA is applied to determine the constrained feedback gains of a PI regulator using available state variables as outputs. Simulation results show the proposed GASA regulator provides better dynamic performance than suboptimal PI regulators, with lower overshoots and faster settling times in response to load perturbations. The proposed approach provides an effective yet simple suboptimal AGC solution that does not require full state information.
IRJET- Speed Control of DC Motor using PID Controller - A ReviewIRJET Journal
This document reviews various methods for controlling the speed of a DC motor using a PID controller. It discusses tuning PID controllers using methods like the Ziegler-Nichols method, genetic algorithms, fuzzy-neuro techniques, and neural network PID controllers. These methods aim to optimize PID parameters to improve the motor's speed response by minimizing overshoot, rise time, and settling time. The document also examines using microcontrollers, pulse width modulation, backstepping control, and the Jaya optimization algorithm for PID tuning and DC motor speed control.
Optimum tuning of pid controller for a permanent magnet brushless dc motorIAEME Publication
This document summarizes an article from the International Journal of Electrical Engineering and Technology that compares using genetic algorithms and particle swarm optimization for tuning PID controllers for speed control of a permanent magnet brushless DC motor. It provides background on PID controllers and discusses modelling the motor system and defining fitness functions for optimization. The paper aims to evaluate which optimization method (genetic algorithm or particle swarm optimization) more efficiently improves the step response characteristics when tuning PID gains for controlling the motor speed.
Optimum tuning of pid controller for a permanent magnet brushless dc motorIAEME Publication
This document summarizes an article from the International Journal of Electrical Engineering and Technology that compares using genetic algorithms and particle swarm optimization for tuning PID controllers for speed control of a permanent magnet brushless DC motor. It provides background on PID control and discusses modelling the motor system and PID controller. Simulation results show that particle swarm optimization was more efficient at improving the step response characteristics compared to genetic algorithms for tuning the PID controller.
The document describes an experiment to validate a novel controller called a Piecewise Predictive Estimator (PPE) that aims to increase the frequency response of an aerospace electro-mechanical actuator (EMA) by reducing phase lag. The experiment showed that combining a PID controller with PPE increased the EMA's bandwidth from 22Hz to 25Hz without increasing noise levels, validating that PPE successfully enhances EMA performance by reducing phase lag at higher frequencies. The document also provides background on EMA modeling and design challenges, and discusses PID, LQR, and PPE controller design approaches.
Tuning PID Controller Parameters for Load Frequency Control Considering Syste...IJERA Editor
In this paper, parameters of PID controller and bias coefficient for Load Frequency Control (LFC) are designed using a new approach. In the proposed method, the power system uncertainties and nonlinear limitations of governors and turbines ,i.e. Valve Speed Limit (VSL)and Generation Rate Constraint (GRC), are taken into account in designing. Variations of uncertain parameters are considered between -40% and +40% of nominal values with 5% step .In order to design the proposed PID controller ,a new objective function is defined. MATLAB codes are developed for GA based PID controller tuning, the results of which are used to study the system step response. All these are through in Simulink based background.
Similar to Identification-of-aircraft-gas-turbine-engines-temperature-condition- (20)
This document summarizes a research paper that proposes using a Real-Coded Genetic Algorithm to design Unified Power Flow Controller (UPFC) damping controllers. The goal is to damp low frequency oscillations in power systems. The paper models a single-machine infinite-bus power system installed with a UPFC. It linearizes the system equations and formulates the controller design as an optimization problem to minimize oscillations. Simulation results comparing the proposed RCGA approach to conventional tuning are presented to demonstrate its effectiveness and robustness in damping power system oscillations.
The main-principles-of-text-to-speech-synthesis-systemCemal Ardil
This document discusses text-to-speech synthesis systems. It provides background on the history and development of such systems over three generations from 1962 to the present. It describes some of the main challenges in developing speech synthesis for different languages. The document then focuses on specifics of the Azerbaijani language and outlines the approach used in the text-to-speech synthesis system developed by the authors, which combines concatenative synthesis and formant synthesis methods.
The feedback-control-for-distributed-systemsCemal Ardil
The document summarizes a study on feedback control synthesis for distributed systems. The study proposes a zone control approach, where the state space is partitioned into zones defined by observable points. Control actions are piecewise constant functions that only change when the system transitions between zones. An optimization problem is formulated to determine the optimal constant control value for each zone. Gradient formulas are derived to solve this using numerical optimization methods. The zone control approach was tested on heat exchanger process control problems and showed more robust performance than alternative methods.
System overflow blocking-transients-for-queues-with-batch-arrivals-using-a-fa...Cemal Ardil
This document summarizes a research paper that analyzes the transient behavior of the overflow probability in a queuing system with fixed-size batch arrivals. It introduces a set of polynomials that generalize Chebyshev polynomials and can be used to assess the transient behavior. The key findings are:
1≤k ≤ B
k≥B+1
which is just the generating function of the Chebyshev
polynomials of the second kind.
Furthermore, if we consider the special case when B = 1 in
(9), and make the substitution x → 2x, we obtain
k =0
0≤k ≤ B
Pk −1
λ
μ
Sonic localization-cues-for-classrooms-a-structural-model-proposalCemal Ardil
The document describes a proposed structural model for sonic localization cues in classrooms. It discusses two primary cues for localization - interaural time difference (ITD) and interaural level difference (ILD) created by sounds reaching each ear. While these cues provide azimuth information, they do not provide elevation information. Elevation information is provided by spectral filtering effects of the head, torso and outer ears (pinnae) known as the head related transfer function (HRTF). The proposed structural model aims to produce well-controlled horizontal and vertical localization cues through a signal processing model of the HRTF that mimics how sounds interact with the body. The effectiveness of the model is tested through synthesized spatial audio experiments with human subjects
This document summarizes a new method for designing robust fuzzy observers for nonlinear systems based on Takagi-Sugeno fuzzy models. The method uses linear matrix inequalities to design observers that minimize the H-norm of the closed loop system, providing a measure of robustness and disturbance attenuation. The observer design method is similar to existing parallel distributed compensation controller design methods, making it possible to adapt controller design techniques for observer design. The observer estimates system states and outputs based on measured outputs and system inputs while attenuating effects of disturbances and uncertainties.
This document discusses evaluating the response quality of heterogeneous question answering systems. It begins by noting the lack of standard evaluation metrics for systems that use natural language understanding and reasoning to answer questions, as opposed to just information retrieval. It proposes a "black-box" approach to evaluate response quality by observing system responses, developing a classification scheme to categorize responses, and assigning scores. As a demonstration, it applies this approach to evaluate three example systems (AnswerBus, START, and NaLURI) on a set of questions about cyberlaw.
The document describes two methods for reducing the order of linear time-invariant systems: Routh approximation and particle swarm optimization (PSO). Routh approximation determines the denominator of the reduced order model using a Routh array, while retaining time moments or Markov parameters to determine the numerator. PSO reduces order by minimizing the integral squared error between responses of the original and reduced models, adjusting numerator and denominator coefficients. The methods are illustrated on examples, with Routh approximation providing stability guarantees when applied to stable systems.
Real coded-genetic-algorithm-for-robust-power-system-stabilizer-designCemal Ardil
This document summarizes a research paper that uses a real-coded genetic algorithm to optimize the design of power system stabilizers. The algorithm is applied to both single-machine and multi-machine power systems. The goal is to minimize rotor speed deviations and improve stability under disturbances. Simulation results show the proposed controller provides effective damping of low frequency oscillations across different operating conditions.
This summary provides the key points about the document in 3 sentences:
The document presents a method for obtaining the exact probability of error for block codes using soft-decision decoding and the eigenstructure of the code correlation matrix. It shows that under a unitary transformation, the performance evaluation of a block code becomes a one-dimensional problem involving only the dominant eigenvalue and its corresponding eigenvector. Simulation results demonstrate good agreement with the analysis, validating the method for computing the bit error rate of block codes based on the properties of the code correlation matrix.
This document presents an optimal supplementary damping controller design for Thyristor Controlled Series Compensator (TCSC) using Real-Coded Genetic Algorithm (RCGA). TCSC is capable of improving power system stability by modulating reactance during disturbances. The document proposes using a multi-objective fitness function consisting of damping factors and real parts of eigenvalues to optimize the parameters of a TCSC-based supplementary damping controller using RCGA. Simulation results presented show the effectiveness of the proposed controller over a wide range of operating conditions and disturbances.
This document presents a method for generating optimal straight line trajectories in 3D space using an algorithm called the Bounded Deviation Algorithm (BDA). BDA approximates a straight line trajectory between two points by iteratively inserting knot points to minimize the deviation between the actual trajectory and the joint space trajectory. The document provides the mathematical formulation and simulation results of applying BDA to generate a straight line trajectory for a 5-axis articulated robot between two specified points.
On the-optimal-number-of-smart-dust-particlesCemal Ardil
This document discusses optimizing the number of smart dust particles used to generate weather maps. It addresses two main challenges: 1) how to match signals from smart dust particles to receivers given atmospheric constraints, and 2) what is the optimal number of particles needed to generate precise and cost-effective 3D maps. The document presents an algorithm to optimally match particles to receivers in O(n*m) time by framing it as a maximal bipartite graph matching problem. It also develops mathematics to prove a conjecture that the optimal number of particles is approximately 1/ε, where ε is the drift error.
On the-joint-optimization-of-performance-and-power-consumption-in-data-centersCemal Ardil
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1. World Academy of Science, Engineering and Technology
International Journal of Mechanical, Industrial Science and Engineering Vol:1 No:2, 2007
Identification of Aircraft Gas Turbine Engine’s
Temperature Condition
Pashayev A., Askerov D., Ardil C., Sadiqov R., and Abdullayev P.
forward support vibration
V
FS
International Science Index 2, 2007 waset.org/publications/3415
Abstract—Groundlessness of application probability-statistic
a , a , a ,...
regression coefficients in
initial linear multiple
regression equation of GTE
condition model
a ′, a ′ , a ′ ,...
regression coefficients in
actual linear multiple
regression equation of GTE
condition model
~ ~ ~
a , a , a ,...
fuzzy regression coefficients
in linear multiple regression
equation of GTE condition
model
~ ~
X ,Y
measured fuzzy input and
output parameters of GTE
condition model
⊗
methods are especially shown at an early stage of the aviation GTE
technical condition diagnosing, when the volume of the information
has property of the fuzzy, limitations, uncertainty and efficiency of
application of new technology Soft computing at these diagnosing
stages by using the fuzzy logic and neural networks methods. It is
made training with high accuracy of multiple linear and nonlinear
models (the regression equations) received on the statistical fuzzy
data basis.
At the information sufficiency it is offered to use recurrent
algorithm of aviation GTE technical condition identification on
measurements of input and output parameters of the multiple linear
and nonlinear generalized models at presence of noise measured (the
new recursive least squares method (LSM)). As application of the
given technique the estimation of the new operating aviation engine
D30KU-154 technical condition at height H=10600 m was made.
fuzzy multiply operation
1
2
1
1
3
2
2
3
3
Keywords—Identification of a technical condition, aviation gas
turbine engine, fuzzy logic and neural networks.
NOMENCLATURE
Symbols
[mm/s]
Subscripts
H
flight altitude
[m]
ini
initial
M
Mach number
-
act
actual
*
TH
atmosphere temperature
[oC]
p*
H
atmosphere pressure
[Pa]
n LP
low pressure compressor
speed (RPM)
[%]
T
exhaust gas temperature
(EGT)
[oC]
GT
fuel flow
[kg/h]
pT
fuel pressure
[kg/cm2]
p
oil pressure
[kg/cm2]
T
oil temperature
[oC]
V
back support vibration
[mm/s]
*
4
M
M
BS
Authors are with National Academy of Aviation, AZ1045, Baku,
Azerbaijan, Bina, 25th km, NAA (phone: (99412) 439-11-61; fax: (99412)
497-28-29; e-mail: sadixov@mail.ru)..
O
I. INTRODUCTION
NE of the important maintenance conditions of the
modern gas turbine engines (GTE) on condition is the
presence of efficient parametric system of technical
diagnostic. As it is known the GTE diagnostic problem of the
following aircraft’s Yak-40, Yak-42, Tu-134, Tu-154(B, M)
etc. basically consists that onboard systems of the objective
control written down not all engine work parameters. This
circumstance causes additional registration of other
parameters of work GTE manually. Consequently there is the
necessity to create the diagnostic system providing the
possibility of GTE condition monitoring and elaboration of
exact recommendation on the further maintenance of GTE by
registered data either on manual record and onboard recorders.
Currently in the subdivisions of CIS airlines are operated
various automatic diagnostic systems (ASD) of GTE technical
conditions (Diagnostic D-30, Diagnostic D-36, Control-8-2U).
The essence of ASD method is mainly to form the flexible
ranges for the recorded parameters as the result of engine
operating time and comparison of recorded meaning of
parameters with their point or interval estimations (values).
107
2. World Academy of Science, Engineering and Technology
International Journal of Mechanical, Industrial Science and Engineering Vol:1 No:2, 2007
However, it should be noted that statistic data processing on
the above mentioned methods are conducted by the
preliminary allowance of the law normality of the recorded
parameters meaning distribution. This allowance affects on
the GTE technical condition monitoring reliability and cause
the error decision in the diagnostic and GTE operating process
[1-3]. More over some combination of the various parameters
changes of engine work can be caused by the different
reasons. Finally it complicates the definition of the defect
address.
Income data
insufficiently and fuzzy, GTE conditions is estimated by the
Soft Computing methods-fuzzy logic (FL) method and neural
networks. In spite of the rough parameters estimations of GTE
conditions the privilege of this stage is the possible creation of
initial image (initial condition) of the engine on the indefinite
information. One of estimation methods of aviation GTE
technical condition used in our and foreign practice is the
temperature level control and analysis of this level change
tendency in operation. Application of the various
mathematical models described by the regression equations
for aviation GTE condition estimation is present in [4, 5].
GTE condition monitoring
using fuzzy logic and neural
networks –(AL1)
GTE condition monitoring
using fuzzy admissible and
possible ranges of fuzzy
parameters - (AL21)
GTE condition monitoring
using fuzzy mathematical
statistics – (AL2)
International Science Index 2, 2007 waset.org/publications/3415
GTE condition monitoring using
fuzzy correlation- regression
analysis - (AL3)
GTE condition monitoring using fuzzy
mathematical statistics - (AL2)
GTE condition monitoring
using fuzzy skewness
coefficients of fuzzy
parameters distributions (AL22)
GTE condition monitoring
using fuzzy
correlation-regression
analysis – (AL3)
GTE condition monitoring
with comparison of results
AL1, AL2 and AL3 using
fuzzy logic - (AL4)
Rules for GTE operation (AL5)
Fuzzy correlation analysis of
GTE condition parameters (AL31)
Fuzzy comparison of GTE
condition parameter’s fuzzy
correlation coefficients with
it’s fuzzy initial values (AL32)
GTE condition monitoring
using fuzzy kurtosis
coefficients of fuzzy
parameters distributions (AL23)
Identification of GTE initial
fuzzy linear multiple
regression model - (AL33)
Identification of GTE actual
linear multiple regression
model - (AL34)
GTE condition monitoring
using complex skewness and
kurtosis coefficients fuzzy
analysis - (AL24)
Comparison of GTE fuzzy
initial and actual models (AL35)
GTE condition monitoring
using of comparison results
skewness-kurtosis-parameters ranges analysis by fuzzy
logic - (AL25)
GTE condition monitoring
using correlation-regression
analysis results by fuzzy
logic - (AL36)
Fig. 1 Flow chart of aircraft gas turbine engine fuzzy-parametric diagnostic algorithm
II. BASICS OF RECOMMENDED CONDITION MONITORING
SYSTEM
It is suggested that the combined diagnostic method of GTE
condition monitoring based on the evaluation of engine
parameters by soft computing methods, mathematical statistic
(high order statistics) and regression analysis.
The method provides for stage-by-stage evaluation of GTE
technical conditions (Fig. 1).
To creation of this method was preceded detail analysis of
15 engines conditions during 2 years (total engine operating
time was over 5000 flights).
Experimental investigation conducted by manual records
shows that at the beginning of operation during 40÷60
measurements accumulated meaning of recorded parameters
correctly operating GTE aren’t subordinated to the normal law
of distribution.
Consequently, on the first stage of diagnostic process (at
the preliminary stage of GTE operation) when initial data
Let's consider mathematical model of aviation GTE
temperature state, described by fuzzy regression equations:
~
~ x
Y = ∑a ⊗~ ;i =1, m
n
i
j =1
ij
j
~
~ x x
Y = ∑a ⊗~ ⊗~ ;r = 0,l; s = 0,l;r + s ≤ l
r
i
rs
s
1
2
r ,s
(1)
(2)
~ ~*
~ ~
where Yi = T4 - fuzzy output parameter, aij , ars - required
fuzzy parameters (fuzzy regression coefficients).
108
3. World Academy of Science, Engineering and Technology
International Journal of Mechanical, Industrial Science and Engineering Vol:1 No:2, 2007
~
X
~
T
Input-output
(knowledge base)
Блок масшScaler
табирования
Нечеткая
Fuzzy NN
НС
~
Y
~
E
+
Scaler
табирования
-
Fig. 2 Neural identification system
~
~
The definition task of fuzzy values aij and ars parameters
of the equation (1) and equations (2) is put on the basis of the
statistical experimental fuzzy data of process, that is input ~ j
x
International Science Index 2, 2007 waset.org/publications/3415
~
and ~1 , ~2 , output coordinates Y of model.
x x
we shall take advantage of a α -cut [8].
We allow, there are statistical fuzzy data received on the
basis of experiments. On the basis of these input and output
~ ~
data is made training pairs ( Х , Т ) for training a network. For
~
Let's consider the decision of the given tasks by using
fuzzy logic and neural networks [6-8].
Neural network (NN) consists from connected between
construction of process model on input Х NN input signals
(Fig. 2) move and outputs are compared with reference output
~
signals Т .
Correction algorithm
~
X
Input
signals
NN
Random-number
generator
~
Y
Target
signals
Deviations
Parameters
Training
quality
Fig. 3 System for network-parameter (weights, threshold) training (with feedback)
their sets fuzzy neurons. At use NN for the decision (1) and
(2) input signals of the network are accordingly fuzzy values
~
~
~
x x
x
x x
of variable X = ( ~ , ~ ,..., ~ ) , X = ( ~ , ~ ) and output Y .
As parameters of the network are fuzzy values of
~
~
parameters aij and ars . We shall present fuzzy variables in
1
2
n
1
2
the triangular form which membership functions are
calculated under the formula:
⎧1 − ( x − x) / α , if x − α < x < x;
⎪
⎪
μ ( x) = ⎨1 − ( x − x) / β , if x < x < x + β ;
⎪0,
otherwise.
⎪
⎩
~
At the decision of the identification task of parameters aij
~
and a rs for the equations (1) and (2) with using NN, the basic
problem is training the last. For training values of parameters
After comparison the deviation value is calculated:
~ 1 k ~ ~
Е = ∑ (У j − T j ) 2
2 j =1
With application a α -cut for the left and right part of
deviation value are calculated under formulas
1
1
Е = ∑ [y (α ) − t (α )] , Е = ∑ [y (α ) − t (α )] ,
2
2
Е=Е +Е ,
where
~
~
У (α ) = [y (α ), y (α ) ] ; T (α ) = [t (α ), t (α ) .
2
k
1
j =1
j1
1
1
j
j1
j2
2
k
j1
j =1
j1
j1
2
j
j1
j2
If for all training pairs, deviation value Е less given then
training (correction) parameters of a network comes to end
(Fig. 3). In opposite case it continues until value Е will not
reach minimum.
Correction of network parameters for left and right part is
109
4. World Academy of Science, Engineering and Technology
International Journal of Mechanical, Industrial Science and Engineering Vol:1 No:2, 2007
carried out as follows:
∂E
a =a +γ
,
∂a
н
rs 1
a
c
rs 1
=a
н
rs 2
rs
c
rs 2
following kind: y 41 = a111 x12 x22 ; y 42 = a112 x12 x21 ,
and the correction formulas
∂E
,
+γ
∂a
c
н
c
н
rs 1
rs 1
rs 2
rs 2
rs
rs 1
rs 2
~ ~ ~
Y = a00 + a10 ~1 + a01 ~2 + a11 ~1 ~2 + a20 ~12 + a02 ~22
x ~ x ~ xx ~ x ~ x
1
j1
j =1
111
j1
12
22
k
k
3
3
j3
003
j3
j3
j =1
113
j3
13
23
j =1
(3)
~
a i1
~
x1
~
x2
International Science Index 2, 2007 waset.org/publications/3415
∂Е2 k
∂Е
= ∑( уj 2 − t j 2 )x11 х21 ;
= ∑( у − t )x х ;
∂а112 j =1
∂а
For value α = 1 we shall receive
∂Е
∂Е
= ∑( у −t )x х ;
= ∑( у −t );
∂а
∂а
k
rs
where a , a , a , a - old and new values of left and right
~
parts NN parameters, a = [a , a ]; γ -training speed.
The structure of NN for identification the equation (1)
parameters are given on Fig. 4.
For the equation (2) we shall consider a concrete special
case as the regression equation of the second order
~
Yi
~
ai2
~
a ij
~
xj
Fig. 4 Neural network structure for multiple linear regression equation
Let's construct neural structure for decision of the equation
~
(2) where as parameters of the network are coefficients a ,
~ ~ ~ ~ ~
a , a , a , a , a . Thus the structure of NN will have four
inputs and one output (Fig. 5).
Using NN structure we are training network parameters.
For value α = 0 we shall receive the following expressions:
∂Е
∂Е
= ∑ ( у − t );
= ∑ ( у − t );
∂а
∂а
∂Е3 k
∂Е3 k
2
= ∑( уj 3 −t j3 )x13;
= ∑( уj 3 −t j3 )x13;
∂а103 j =1
∂а203 j =1
00
10
01
11
20
k
k
1
001
2
j =1
j1
j1
002
j =1
j2
j2
k
k
∂Е
∂Е1
= ∑ ( у j1 − t j 1 ) x21 ; 2 = ∑ ( у j 2 − t j 2 ) x22 ;
∂а012 j = 1
∂а011 j = 1
k
k
∂Е
∂Е1
= ∑ ( у j 1 − t j 1 ) x11 х21 ; 2 = ∑ ( у j 2 − t j 2 ) x12 х22 ;
∂а112 j = 1
∂а111 j = 1
k
k
∂Е
∂Е1
2
2
= ∑ ( у j1 − t j 1 ) x11 ; 2 = ∑ ( у j 2 − t j 2 ) x12 ;
∂а202 j = 1
∂а201 j = 1
1
021
k
∑(у
j =1
− t )x ;
2
j1
j1
21
∂Е
=
∂а
2
022
k
∑(у
j =1
−t ) x ;
2
j2
j2
2
22
(5)
As a result of training (4), (5) we find parameters of a
network satisfying the knowledge base with required training
quality.
The analysis show that during following 60-120
measurements happens the approach of individual parameters
of GTE work to normal distribution. So, on the second stage
as result of accumulation definite information by the means of
the mathematical statistic is estimated of GTE conditions.
Here the given and enumerated to the one GTE work mode
parameters are controlled is accordance with calculated
admissible and possible ranges.
k
k
∂Е
∂Е1
= ∑ ( у j 1 − t j 1 ) x11 ; 2 = ∑ ( у j 2 − t j 2 ) х12 ;
∂а102 j = 1
∂а101 j = 1
∂Е
=
∂а
k
∂Е3
∂Е3 k
2
= ∑( уj 3 −t j3 )x23;
= ∑( уj3 −t j3 )x23;
∂а013 j =1
∂а023 j =1
02
(4)
It is necessary to note, that at negative values of the
~ ~
coefficients a rs ( a rs < 0 ), calculation formulas of expressions
~
which include parameters a rs in (3) and correction of the
given parameter in (4) will change the form. For example, we
~
allow a rs < 0 , then formula calculations of the fourth
expression, which includes in (3) will be had with the
110
5. World Academy of Science, Engineering and Technology
International Journal of Mechanical, Industrial Science and Engineering Vol:1 No:2, 2007
~
x1
~
x1
~ x ~ x
~ xx
~ x
~ x
a10 ~1 + a 01~2 + a11~1~2 + a 20 ~12 + a02 ~22
~
a 00
~
x1
~
Y
~2
x1
~
x2
~
x2
~
x2
~ xx
a11~1~2
~2
x2
International Science Index 2, 2007 waset.org/publications/3415
Fig. 5 Structure of neural network for second-order regression equation
Further by the means of the Least Squares Method (LSM)
there are identified the multiple linear regression models of
GTE conditions changes. These models are made for each
correct subcontrol engine of the park at the initial operation
period. In such case on the basis analysis of regression
coefficients meaning (coefficients of influence) of engine’s
multiple regression models in park by the means of the
mathematical statistic are formed base and admissible range of
coefficients [3,9].
Let's consider mathematical model of aviation GTE
temperature state- described with help the multiple linear
regression model (application of various regression models for
the estimation of GTE condition is present in [4-7]).
n
y (k ) = ∑ a x (k ), (i = 1, m)
i
ij
j =1
j
(6)
where y i -output parameter of system; x j -input influence;
a ij -unknown
(estimated)
influence
factors
(regression
coefficients); n - number of input influences, k - number of
iteration.
Let the equations of measurements of input and output
coordinates of the model look like
z y (k ) = y i (k ) + ξ y (k )
i
i
z x (k ) = x j (k ) + ξ x (k )
j
j
(7)
where ξ y (k ) , ξ x (k ) -casual errors of measurements with
i
For the decision of similar problems the LSM well
approaches. However classical LSM may be used then when
values of arguments are known precisely x j . As arguments x j
are measured with a margin error use LSM in this case may
result in the displaced results and in main will give wrong
estimations of their errors. For data processing in a similar
case is expedient to use confluent methods analysis [10,11].
The choice confluent a method depends on the kind of
mathematical model and the priory information concerning
arguments values and parameters. In many cases recurrent
application LSM yields good results [3,9]. However, thus is
necessary the additional information about measuring
parameters (input and output coordinates of system). Practical
examples show that the dependences found thus essentially
may differ from constructed usual LSM.
Before to use the recurrent form LSM, taking into account
errors of input influences, for model parameters estimation
(6), we shall present it in the vector form
y (k ) = X (k ) ⋅ θ , (k = 1, l )
T
i
where
θ = a , a ,..., a
i
i1
1
xj
xj
[
K i (k ) =
(8)
where E the operator of statistical averaging; δ ( k , l ) Kronecker delta-function:
⎧1, k = l
δ (k , l ) = ⎨
⎩0, k ≠ l
of
estimated
factors;
m
(10)
]
+ Ki (k ) Z yi (k ) − X (k )θi (k − 1) ;
yi
xj
2
θi (k ) = θi (k − 1) +
xj
yi
-vector
X ( k ) = x ( k ), x (k ),..., x ( k ) -vector of input coordinates.
E[ξ (k )ξ ( j )] = D δ (k , j )
yi
im
The algorithm of model (4) parameters estimation in view of
an error of input coordinates has the following kind
j
E[ξ ( k )ξ (l )] = D δ (k , l )
i2
T
Gauss distribution and statistical characteristics
E[ξ ( k )] = E[ξ (k )] = 0
yi
(9)
i
T
T
Di (k − 1) X (k )
;
⎛ Dyi (k ) + θ iT (k − 1) Dx (k )θ (k − ⎞
⎜
⎟
⎜ − 1) + X T (k ) D (k − 1) X (k ) ⎟
i
⎝
⎠
Di (k ) = Di (k − 1) −
111
(D (k − 1) X (k ) X
)
(k ) Di (k − 1)
⎛ Dyi (k ) + θ (k − 1) Dx (k )θ (k − 1) + ⎞
⎜
⎟
⎜ + X T (k ) D (k − 1) X (k )
⎟
i
⎝
⎠
i
T
i
T
6. World Academy of Science, Engineering and Technology
International Journal of Mechanical, Industrial Science and Engineering Vol:1 No:2, 2007
where K i (x) -amplification coefficient of the filter; Di (k ) dispersion matrix of estimations errors; D (k ) -dispersion
matrix of input coordinates errors; D y (k ) -dispersion matrix
x
i
of output coordinates errors.
Let's consider the distinct regression equation of second
order with two variables
(11)
y=а +а х +а х +а х х +а х +а х
Output and input coordinates of the model (11) are registered
by the measuring equipment. Casual errors of measurements
have Gauss distribution and their statistical characteristics
(random variables means equally to zero) are known. It is
required to estimate (unknown) coefficients a00 , a10 , a01 ,
2
00
10
1
01
2
11
1
2
20
2
1
02
2
a11 , a20 , a02 of the regression equations (11).
Let x1 and x2 are defined with the errors which
dispersions are accordingly equal Dx and D x . Then input
International Science Index 2, 2007 waset.org/publications/3415
1
2
influence errors (with the purpose of this error definition we
shall take advantage of the linearation method [12] in view of
that variables is not enough correlated) can be defined with
help of expressions preliminary, having designated
x = x x ; x5 = x12 ; x = x ):
2
4
1
2
6
2
2
Dx 4
2
⎛ ∂x x ⎞
⎛ ∂x x ⎞
= ⎜ 1 2 ⎟ Dx1 + ⎜ 1 2 ⎟ Dx2 =
⎜ ∂x ⎟
⎜ ∂x ⎟
1 ⎠
2 ⎠
⎝
⎝
2
2
x2 Dx1
+
2
x1 Dx2 ,
D x6
⎛ ∂x 2 ⎞
2
Dx5 = ⎜ 1 ⎟ Dx1 = 4 x1 Dx1
⎜ ∂x1 ⎟
⎠
⎝
⎛ ∂x 2
=⎜ 2
⎜ ∂x
⎝ 2
2
⎞
2
⎟ Dx = 4 x2 Dx .
2
2
⎟
⎠
Then find average quadratic deviations of errors and errors
dispersion matrix of input coordinates, it is possible to
estimate coefficients of the equations (6) and (9), using of the
recurcive LSM (10).
On the third stage (for more than 120 measurements) by the
LSM estimation results are conducted the detail analyse of
GTE conditions. Essence of these procedures is in making
actual model (multiple linear regression equation) of GTE
conditions and in comparison actual coefficients of influence
(regression coefficients) with their base are admissible range.
The reliability of diagnostic results on this stage is high and
equalled to 0.95÷0.99. The influence coefficients meaning
going out the mention ranges make it’s possible to draw into
conclusion about the meaning changes of phases process
influence on the concrete parameters of GTE. The stable
going out one or several coefficient influence beyond of the
above-mentioned range witness about additional feature of
incorrectness and permit to accurate address and possible
reason of faults. In this case to receive the stable estimations
by LSM are used ridge-regression analysis.
For the purpose of prediction of GTE conditions the
regression coefficients are approximated by the polynomials
of second and third degree.
For example to apply the above mentioned method there
was investigated the changes of GTE conditions, repeatedly
putting into operation engine D-30KU-154 (Tu-154M)
(engine 03059229212434, ATB “AZAL”, airport “Bina”,
Baku, Azerbaijan), which during 2600 hours (690 flights) are
operated correctly. At the preliminary stage, when number of
measurements N ≤ 60 , GTE technical condition is described
by the fuzzy linear regression equation (1). Identification of
fuzzy linear model of GTE is made with help NN which
structure is given on Fig. 4. Thus as the output parameter of
GTE model is accepted the temperature
~
~ ~ ~ ~ ~ ~* ~ ~
~ p
~ p
( T4* )ini = a1 H + a2 M + a3TН + a4 nLP + a5 ~Т + a6 ~M +
~ ~
~ ~ ~ ~
~ ~
~ p
+ a T + a G + a V + a V + a ~*
7 М
8
T
9 FS
10 BS
11
Н
(12)
And at the subsequent stage for each current measurement’s
N > 60 , when observes the normal distribution of the engine
work parameters, GTE temperature condition describes by
linear regression equation (6) which parameters is estimated
by recurrent algorithm (10)
′
′
′ *
′
′
′
D = ( T4* )act = a1H + a2 M + a3TН + a4 nLP + a5 pТ + a6 pM +
′
′
′
′
′ Н
+ a7TМ + a8GT + a9VFS + a10VBS + a11 p*
(13)
As the result of the carried out researches for the varied
technical condition of the considered engine was revealed
certain dynamics of the regression coefficients values changes
which is given in Table I (see the Appendix).
For the third stage there were made the following admission
of regression coefficients (coefficients of influence of various
parameters) of various parameters on exhaust gas temperature
in linear multiple regression equation (2): frequency of engine
rotation (RPM of (low pressure) LP compressor)0.00456÷0.00496; fuel pressure-1.16÷1.25; fuel flow0.0240÷0.0252; oil pressure-11.75÷12.45; oil temperature1.1÷1.; vibration of the forward support-3.0÷5.4; vibration of
the back support-1.2÷1.9; atmosphere pressure-112÷128;
atmosphere temperature-(-0.84) ÷(-0.64); flight speed (Mach
number)-57.8÷60.6; flight altitude-0.00456÷0.00496. Within
the limits of the specified admissions of regression
coefficients was carried out approximation of the their
(regression coefficients) current values by the polynoms of the
second and third degree with help LSM and with use cubic
splines (Fig. 6).
III. CONCLUSION
1. The GTE technical condition combined diagnosing
approach is offered, which is based on engine work
parameters estimation with the help of methods Soft
Computing (fuzzy logic and neural networks) and the
confluent analysis.
2. It is shown, that application of Soft Computing (fuzzy logic
and neural networks) methods in recognition GTE technical
condition has the certain advantages in comparison with
traditional probability-statistical approaches. First of all, it is
connected by that the offered methods may be used
irrespective of the kind of GTE work parameters distributions.
As at early stage of the engine work, because of the limited
volume of the information, the kind of distribution of
112
7. World Academy of Science, Engineering and Technology
International Journal of Mechanical, Industrial Science and Engineering Vol:1 No:2, 2007
International Science Index 2, 2007 waset.org/publications/3415
parameters is difficult for establishing.
3. By complex analysis is established, that:
- between
aerogastermodynamic
and
mechanical
parameters of GTE work are certain relations, which degree
in operating process and in dependence of concrete diagnostic
situation changes dynamics is increases or decreases, that
describes the GTE design and work and it’s systems, as
whole.
- for various situations of malfunctions development’s is
observed different dynamics (changes) of connections
(correlation coefficients) between parameters of the engine
work in operating, caused by occurrence or disappearance of
factors influencing to GTE technical condition. Hence, in any
considered time of operation the concrete GTE technical
condition is characterized by this or that group of parameters
in which values is reflected presence of influencing factors.
The suggested methods make it’s possible not only to
diagnose and to predict the safe engine runtime. This methods
give the tangible results and can be recommended to practical
application as for automatic engine diagnostic system where
the handle record are used as initial information as well for
onboard system of engine work control.
England, 1995.
Pashayev A.M., Sadiqov R.A., Makarov N.V., Abdullayev P.S.
Estimation of GTE technical condition on flight information//Abstracts
of XI All-Russian interinstit.science-techn.conf. "Gaz turbine and
combined installations and engines" dedicated to 170 year MGTU nam.
N.E.BAUMAN, sec. 1. N.E.BAUMAN MGTU., 15-17 november.,
Moscow.-2000.- p.22-24.
[10] Granovskiy V.A. and Siraya T.N. Methods of experimental-data
processing in measurements [in Russian], Energoatomizdat, Moscow,
1990.
[11] Greshilov A.A., Analysis and Synthesis of Stochastic Systems.
Parametric Models and Confluence Analysis (in russian), Radio i Svyaz,
Moscow, 1990.
[12] Pugachev V.S., Probability theory and mathematical statistics [in
russian], Nauka, Moscow, 1979.
[9]
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
Sadiqov R.A. Identification of the quality surveillance equation
parameters //Reliability and quality surveillance.-M., 1999, № 6, p. 3639.
Sadiqov R.A., Makarov N.V., Abdullayev P.S. V International
Symposium an Aeronautical Sciences «New Aviation Technologies of
the XXI century»//A collection of technical papers., section №4-№24,
Zhukovsky, Russia, august, 1999.
Pashayev A.M., Sadiqov R.A., Makarov N.V., Abdullayev P. S.
Efficiency of GTE diagnostics with provision for laws of the distribution
parameter in maintenance. Full-grown. VI International STC "Machine
building and technosphere on border 21 century" //Collection of the
scientific works// Org. Donechki Gov.Tech.Univ., Sevastopol, Ukraine,
september, 1999, p.234-237.
Ivanov L.A. and etc. The technique of civil aircraft GTE technical
condition diagnosing and forecasting on registered rotor vibrations
parameters changes in service.- M: GOS NII GA, 1984.- 88p.
Doroshko S.M. The control and diagnosing of GTE technical condition
on vibration parameters. - M.: Transport, 1984.-128 p.
Abasov M.T., Sadiqov A.H., Aliyarov R.Y. Fuzzy neural networks in
the system of oil and gas geology and geophysics // Third International
Conference on Application of Fuzzy Systems and Soft computing/
Wiesbaden, Germany, 1998,- p.108-117.
Yager R.R., Zadeh L.A. (Eds). Fuzzy sets, neural networks and soft
computing. VAN Nostrand Reinhold. N.-Y. - № 4,1994.
Mohamad H. Hassoun. Fundamentals of artificial neutral networks / A
Bradford Book. The MIT press Cambridge, Massachusetts, London,
113
8. World Academy of Science, Engineering and Technology
International Journal of Mechanical, Industrial Science and Engineering Vol:1 No:2, 2007
APPENDIX
0,00496
60,6
-0,64
60,2
0,00488
-0,68
59,8
0,00480
-0,72
0,00472
T4_TH
T4_M
T4_H
59,4
59,0
-0,76
58,6
0,00464
-0,80
58,2
0,00456
57,8
0
50
100
150
200
250
300
350
400
0
50
100
150
200
250
300
350
-0,84
400
0
N
N
a)
50
100
150
b)
0,628
200
250
300
350
400
N
c)
12,45
1,25
1,24
12,35
0,622
1,23
0,610
T4_PM
1,21
T4_PT
T4_KND
12,25
1,22
0,616
1,20
12,15
12,05
1,19
0,604
11,95
1,18
0,598
1,17
0,592
1,16
0
50
100
150
200
250
300
350
400
11,85
0
50
100
150
International Science Index 2, 2007 waset.org/publications/3415
200
250
300
350
400
11,75
N
N
0
50
100
150
200
250
300
350
400
N
d)
e)
f)
1,40
0,0252
5,4
1,34
0,0250
5,0
0,0248
4,6
1,22
T4_VPO
T4_GT
T4_TM
1,28
0,0246
4,2
0,0244
1,10
3,8
0,0242
1,16
3,4
0,0240
0
50
100
150
200
250
300
350
400
0
50
100
150
200
250
300
350
3,0
400
0
50
100
150
N
N
g)
200
250
300
350
400
N
h)
i)
128
1,9
126
1,8
124
122
T4_PH
T4_VZO
1,7
1,6
120
118
1,5
116
1,4
114
1,3
112
1,2
0
50
100
150
200
250
300
350
0
50
100
150
400
200
250
300
350
400
N
N
k)
l)
*
Fig. 6 Change of regression coefficient’s values (influence) of parameters included in linear multiple regression equation D = ( T4 )act to T4* in
′
GTE operation: а) T4_H – influence H on T4 (coefficient a1′ ); b) T4_M - influence M on T4 (coefficient a2 ); c) T4_TH - influence TH
*
*
*
′
on T4 (coefficient a3 ); d) T4_KND – influence nLP on T4 (coefficient a′ ); e) T4_PT - influence pT on T4 (coefficient a5 ); f) T4_PM –
′
4
*
*
*
influence pM on T4 (coefficient a′ ); g) T4_TM - influence TM on T4 (coefficient a′ ); h) T4_GT – influence GT on T4 (coefficient
6
7
*
*
*
*
*
*
*
′
′
a8 ); i) T4_VPO - influence VFS on T4 (coefficient a′ ); j) T4_VZO - influence VBS on T4 (coefficient a10 ); k) T4_PH - influence pH on T4
9
(coefficient a11 ); N-number of measurements
′
114