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.
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.
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.
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.
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
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.
IRJET- Control of Induction Motor using Neural NetworkIRJET Journal
This document describes research into using neural networks to control induction motors. It begins by introducing the topic and noting limitations of traditional PI controllers for induction motor control. It then provides details on the experimental setup, which uses an artificial neural network (ANN) to mimic a PI controller for speed control of an induction motor drive system. Simulation results are presented comparing the performance of the ANN controller to a traditional PI controller under different dynamic operating conditions. The document concludes that the ANN mapping controller provides superior performance to the PI controller.
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.
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.
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.
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.
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
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.
IRJET- Control of Induction Motor using Neural NetworkIRJET Journal
This document describes research into using neural networks to control induction motors. It begins by introducing the topic and noting limitations of traditional PI controllers for induction motor control. It then provides details on the experimental setup, which uses an artificial neural network (ANN) to mimic a PI controller for speed control of an induction motor drive system. Simulation results are presented comparing the performance of the ANN controller to a traditional PI controller under different dynamic operating conditions. The document concludes that the ANN mapping controller provides superior performance to the PI controller.
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.
This paper focuses on the development of a prototype of thruster motor speed controller which exhibits robust performance for an Autonomous underwater vehicle. H infinity based speed controller with particle swarm optimized weights for a sensorless BLDC motor which is used as electrical thruster has been simulated in MATLAB/ SIMULINK and implemented using TI C2000 Delfino LaunchPad LaunchXL-F28377S and BoostXL DRV 8301. A performance comparison in reference tracking has been done with conventional PI controller and experimental results have been discussed in detail. The percentage variation in speed with respect to reference speed of proposed strategy has been observed to be 0.65% whereas it is 1.1% with PI controller. It has also been found that the proposed control strategy exhibits smooth starting with better reference tracking and less torque ripples.
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).
This document presents a sensor-less speed control method for induction motors using alpha-cut fuzzy logic field oriented control (FOC). It develops a non-linear induction motor model that accounts for errors in flux estimation. FOC is used to decouple the current components for flux generation and torque generation. Clarke and Park transformations are applied to control the d-q axis currents for flux and torque regulation using PI controllers. Simulations show the motor speed closely follows the reference speed even under load, maintaining stability. The stator current and electromagnetic torque responses indicate the control system effectively regulates load current and torque.
The speed estimation technique of induction machines has become a non-trivial task. For estimating the speed of an induction motor precisely and accurately an optimum state estimator is necessary. This paper deals with the performance analysis of induction motor drives using a recursive, optimum state estimator. This technique uses a full order state space Extended Kalman Filter (EKF) model where the rotor flux, rotor speed and stator currents are estimated. A major challenge with induction motor occurs at very low and at near zero speed. In such cases, information about the rotor parameters with respect to stator side become unobservable while using the synchronously rotating reference frame. To overcome this lost coupling effect, EKF observer linearizes the non-linear parameter in every sampling period and estimates the states and machine parameters simultaneously. The proposed algorithm is tuned to obtain least error in estimated speed. Any error found is further optimized using a non-linear fuzzy controller to obtain improved performance of the drive.
Comparison Analysis of Indirect FOC Induction Motor Drive using PI, Anti-Wind...IAES-IJPEDS
This paper presents the speed performance analysis of indirect Field Oriented Control (FOC) induction motor drive by applying Proportional Integral (PI) controller, PI with Anti-Windup (PIAW) and Pre- Filter (PF). The objective of this experiment is to have quantitative comparison between the controller strategies towards the performance of the motor in term of speed tracking and load rejection capability in low, medium and rated speed operation. In the first part, PI controller is applied to the FOC induction motor drive which the gain is obtained based on determined Induction Motor (IM) motor parameters. Secondly an AWPI strategy is added to the outer loop and finally, PF is added to the system. The Space Vector Pulse Width Modulation (SVPWM) technique is used to control the voltage source inverter and complete vector control scheme of the IM drive is tested by using a DSpace 1103 controller board. The analysis of the results shows that, the PI and AWPI controller schemes produce similar performance at low speed operation. However, for the medium and rated speed operation the AWPI scheme shown significant improvement in reducing the overshoot problem and improving the setting time. The PF scheme on the other hand, produces a slower speed and torque response for all tested speed operation. All schemes show similar performance for load disturbance rejection capability.
This document presents a method for tuning the parameters of a PID controller for a brushless DC motor using particle swarm optimization. PSO is used to find the optimal proportional, integral and derivative gains to minimize error metrics for the motor's step response. The BLDC motor is modeled in Simulink. PSO searches through potential solutions in a multi-dimensional space to determine PID parameters that produce the best step response with minimal overshoot, rise time, settling time and steady state error. The results show the PSO-tuned PID controller achieves better dynamic performance than other methods.
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.
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.
Type 1 versus type 2 fuzzy logic speed controllers for brushless DC motors IJECEIAES
This work presented two fuzzy logic (FL) schemes for speed-controlled brushless DC motors. The first controller is a Type 1 FL controller (T1FLC), whereas the second controller is an interval Type 2 FL controller (IT2FLC). The two proposed controllers were compared in terms of system dynamics and performance. For a fair comparison, the same type and number of membership functions were used for both controllers. The effectiveness of the structures of the two FL controllers was verified through simulation in MATLAB/SIMULINK environment. Simulation result showed that IT2FLC exhibited better performance than T1FLC.
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.
Estimation of motor inertia and friction components is a complex and challenging task in motion control applications where small size DC motors (<100W) are used for precise control. It is essential to estimate the accurate friction components and motor inertia, because the parameters provided by the manufacturer are not always accurate. This research proposes a Sensorless method of determining DC motor parameters, including moment of inertia, torque coefficient and frictional components using the Disturbance Observer (DOB) as a torque sensor. The constant velocity motion test and a novel Reverse Motion Acceleration test were conducted to estimate frictional components and moment of inertia of the motor. The validity of the proposed novel method was verified by experimental results and compared with conventional acceleration and deceleration motion tests. Experiments have been carried out to show the effectiveness and viability of the estimated parameters using a Reaction Torque Observer (RTOB) based friction compensation method.
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
DESIGN OF FAST TRANSIENT RESPONSE, LOW DROPOUT REGULATOR WITH ENHANCED STEADY...ijcsitcejournal
Design and implementation of control systems for power supplies require the use of efficient techniques that
provide simple and practical solutions in order to fulfill the performance requirements at an acceptable cost.
Application of manual methods of system identification in determining optimal values of controller settings is
quite time-consuming, expensive and, sometimes, may be impossible to practically carry out. This paper
describes an analytical method for the design of a control system for a fast transient response, low dropout
(LDO) linear regulated power supply on the basis of PID compensation. The controller parameters are
obtained from analytical model of the regulator circuit. Test results showed good dynamic characteristics
with adequate margin of stability. This study shows that PID parameter values sufficiently close to optimum
can easily be obtained from analytical study of the regulator system. The applied method of determining
controller settings greatly reduces design time and cost.
Decentralized supervisory based switching control for uncertain multivariable...ISA Interchange
In this paper, the design of decentralized switching control for uncertain multivariable plants is considered. In the proposed strategy, the uncertainty region is divided into smaller regions with a nominal model and specific control structure. The underlying design is based on the quantitative feedback theory (QFT). It is assumed that a MIMO-QFT controller exists for robust stability and performance of the individual uncertain sets. The proposed control structure is made up by these local decentralized controllers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor makes the decision by comparing the local models’ behaviors with the one of the plant and selects the controller corresponding to the best fitted model. A hysteresis switching logic is used to slow down the switching to guarantee the overall closed loop stability. It is shown that this strategy provides a stable and robust adaptive controller to deal with complex multivariable plants with input–output pairing changes during the plant operation, which can facilitate the development of a reconfigurable decentralized control. Also, the multirealization technique is used to implement a family of controllers to achieve bumpless transfer. Simulation results are employed to show the effectiveness of the proposed method.
Speed Control of Induction Motor by Using Intelligence TechniquesIJERA Editor
This paper gives the comparative study among various techniques used to control the speed of three phase induction motor. In this paper, indirect vector method is used to control the speed of Induction motor. Firstly Simulink Model is developed by using MATLAB/ Simulink software. PI controller, Fuzzy PI Hybrid controller, Genetic Algorithm (GA) are the techniques involved in control Induction motor and the results are compared. By converting three phase supply currents coming from stator to Flux and Torque components of current the speed responses such as rise time, overshoot, settling time and speed regulation at load have been observed and compared among the techniques. The PI controller parameters defined by an objective function are calculated by using Genetic Algorithms presented good performance compared to Fuzzy PI Hybrid controller which has parameters chosen by the human operator.
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.
Performance Analysis of Direct Torque Controlled BLDC motor using Fuzzy LogicIAES-IJPEDS
The Brushless DC motor (BLDC) control is used in many of the applications
as it is small in size and with low power which can drive in high speed and
lighter compared to other motors.The electric vehicles are built with BLDC
motors and also in ships, aerospace etc., The control of BLDC motors is done
with sensors like hall effect sensor for sensing the positions. The speed
control can be done with normal PI and PID controllers. Direct torque control
(DTC) of the BLDC motor is important in many applications. In this paper
BLDC motor is controlled with DTC using PI, PID and Fuzzy logic control.
The comparison of the performance of the motor is analyzed with the Matlab
simulation software.
A portable hardware in-the-loop device for automotive diagnostic control systemsISA Interchange
In-vehicle driving tests for evaluating the performance and diagnostic functionalities of engine control systems are often time consuming, expensive, and not reproducible. Using a hardware-in-the-loop (HIL) simulation approach, new control strategies and diagnostic functions on a controller area network (CAN) line can be easily tested in real time, in order to reduce the effort and the cost of the testing phase. Nowadays, spark ignition engines are controlled by an electronic control unit (ECU) with a large number of embedded sensors and actuators. In order to meet the rising demand of lower emissions and fuel consumption, an increasing number of control functions are added into such a unit. This work aims at presenting a portable electronic environment system, suited for HIL simulations, in order to test the engine control software and the diagnostic functionality on a CAN line, respectively, through non-regression and diagnostic tests. The performances of the proposed electronic device, called a micro hardware-in-the-loop system, are presented through the testing of the engine management system software of a 1.6 l Fiat gasoline engine with variable valve actuation for the ECU development version.
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.
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
This paper focuses on the development of a prototype of thruster motor speed controller which exhibits robust performance for an Autonomous underwater vehicle. H infinity based speed controller with particle swarm optimized weights for a sensorless BLDC motor which is used as electrical thruster has been simulated in MATLAB/ SIMULINK and implemented using TI C2000 Delfino LaunchPad LaunchXL-F28377S and BoostXL DRV 8301. A performance comparison in reference tracking has been done with conventional PI controller and experimental results have been discussed in detail. The percentage variation in speed with respect to reference speed of proposed strategy has been observed to be 0.65% whereas it is 1.1% with PI controller. It has also been found that the proposed control strategy exhibits smooth starting with better reference tracking and less torque ripples.
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).
This document presents a sensor-less speed control method for induction motors using alpha-cut fuzzy logic field oriented control (FOC). It develops a non-linear induction motor model that accounts for errors in flux estimation. FOC is used to decouple the current components for flux generation and torque generation. Clarke and Park transformations are applied to control the d-q axis currents for flux and torque regulation using PI controllers. Simulations show the motor speed closely follows the reference speed even under load, maintaining stability. The stator current and electromagnetic torque responses indicate the control system effectively regulates load current and torque.
The speed estimation technique of induction machines has become a non-trivial task. For estimating the speed of an induction motor precisely and accurately an optimum state estimator is necessary. This paper deals with the performance analysis of induction motor drives using a recursive, optimum state estimator. This technique uses a full order state space Extended Kalman Filter (EKF) model where the rotor flux, rotor speed and stator currents are estimated. A major challenge with induction motor occurs at very low and at near zero speed. In such cases, information about the rotor parameters with respect to stator side become unobservable while using the synchronously rotating reference frame. To overcome this lost coupling effect, EKF observer linearizes the non-linear parameter in every sampling period and estimates the states and machine parameters simultaneously. The proposed algorithm is tuned to obtain least error in estimated speed. Any error found is further optimized using a non-linear fuzzy controller to obtain improved performance of the drive.
Comparison Analysis of Indirect FOC Induction Motor Drive using PI, Anti-Wind...IAES-IJPEDS
This paper presents the speed performance analysis of indirect Field Oriented Control (FOC) induction motor drive by applying Proportional Integral (PI) controller, PI with Anti-Windup (PIAW) and Pre- Filter (PF). The objective of this experiment is to have quantitative comparison between the controller strategies towards the performance of the motor in term of speed tracking and load rejection capability in low, medium and rated speed operation. In the first part, PI controller is applied to the FOC induction motor drive which the gain is obtained based on determined Induction Motor (IM) motor parameters. Secondly an AWPI strategy is added to the outer loop and finally, PF is added to the system. The Space Vector Pulse Width Modulation (SVPWM) technique is used to control the voltage source inverter and complete vector control scheme of the IM drive is tested by using a DSpace 1103 controller board. The analysis of the results shows that, the PI and AWPI controller schemes produce similar performance at low speed operation. However, for the medium and rated speed operation the AWPI scheme shown significant improvement in reducing the overshoot problem and improving the setting time. The PF scheme on the other hand, produces a slower speed and torque response for all tested speed operation. All schemes show similar performance for load disturbance rejection capability.
This document presents a method for tuning the parameters of a PID controller for a brushless DC motor using particle swarm optimization. PSO is used to find the optimal proportional, integral and derivative gains to minimize error metrics for the motor's step response. The BLDC motor is modeled in Simulink. PSO searches through potential solutions in a multi-dimensional space to determine PID parameters that produce the best step response with minimal overshoot, rise time, settling time and steady state error. The results show the PSO-tuned PID controller achieves better dynamic performance than other methods.
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.
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.
Type 1 versus type 2 fuzzy logic speed controllers for brushless DC motors IJECEIAES
This work presented two fuzzy logic (FL) schemes for speed-controlled brushless DC motors. The first controller is a Type 1 FL controller (T1FLC), whereas the second controller is an interval Type 2 FL controller (IT2FLC). The two proposed controllers were compared in terms of system dynamics and performance. For a fair comparison, the same type and number of membership functions were used for both controllers. The effectiveness of the structures of the two FL controllers was verified through simulation in MATLAB/SIMULINK environment. Simulation result showed that IT2FLC exhibited better performance than T1FLC.
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.
Estimation of motor inertia and friction components is a complex and challenging task in motion control applications where small size DC motors (<100W) are used for precise control. It is essential to estimate the accurate friction components and motor inertia, because the parameters provided by the manufacturer are not always accurate. This research proposes a Sensorless method of determining DC motor parameters, including moment of inertia, torque coefficient and frictional components using the Disturbance Observer (DOB) as a torque sensor. The constant velocity motion test and a novel Reverse Motion Acceleration test were conducted to estimate frictional components and moment of inertia of the motor. The validity of the proposed novel method was verified by experimental results and compared with conventional acceleration and deceleration motion tests. Experiments have been carried out to show the effectiveness and viability of the estimated parameters using a Reaction Torque Observer (RTOB) based friction compensation method.
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
DESIGN OF FAST TRANSIENT RESPONSE, LOW DROPOUT REGULATOR WITH ENHANCED STEADY...ijcsitcejournal
Design and implementation of control systems for power supplies require the use of efficient techniques that
provide simple and practical solutions in order to fulfill the performance requirements at an acceptable cost.
Application of manual methods of system identification in determining optimal values of controller settings is
quite time-consuming, expensive and, sometimes, may be impossible to practically carry out. This paper
describes an analytical method for the design of a control system for a fast transient response, low dropout
(LDO) linear regulated power supply on the basis of PID compensation. The controller parameters are
obtained from analytical model of the regulator circuit. Test results showed good dynamic characteristics
with adequate margin of stability. This study shows that PID parameter values sufficiently close to optimum
can easily be obtained from analytical study of the regulator system. The applied method of determining
controller settings greatly reduces design time and cost.
Decentralized supervisory based switching control for uncertain multivariable...ISA Interchange
In this paper, the design of decentralized switching control for uncertain multivariable plants is considered. In the proposed strategy, the uncertainty region is divided into smaller regions with a nominal model and specific control structure. The underlying design is based on the quantitative feedback theory (QFT). It is assumed that a MIMO-QFT controller exists for robust stability and performance of the individual uncertain sets. The proposed control structure is made up by these local decentralized controllers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor makes the decision by comparing the local models’ behaviors with the one of the plant and selects the controller corresponding to the best fitted model. A hysteresis switching logic is used to slow down the switching to guarantee the overall closed loop stability. It is shown that this strategy provides a stable and robust adaptive controller to deal with complex multivariable plants with input–output pairing changes during the plant operation, which can facilitate the development of a reconfigurable decentralized control. Also, the multirealization technique is used to implement a family of controllers to achieve bumpless transfer. Simulation results are employed to show the effectiveness of the proposed method.
Speed Control of Induction Motor by Using Intelligence TechniquesIJERA Editor
This paper gives the comparative study among various techniques used to control the speed of three phase induction motor. In this paper, indirect vector method is used to control the speed of Induction motor. Firstly Simulink Model is developed by using MATLAB/ Simulink software. PI controller, Fuzzy PI Hybrid controller, Genetic Algorithm (GA) are the techniques involved in control Induction motor and the results are compared. By converting three phase supply currents coming from stator to Flux and Torque components of current the speed responses such as rise time, overshoot, settling time and speed regulation at load have been observed and compared among the techniques. The PI controller parameters defined by an objective function are calculated by using Genetic Algorithms presented good performance compared to Fuzzy PI Hybrid controller which has parameters chosen by the human operator.
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.
Performance Analysis of Direct Torque Controlled BLDC motor using Fuzzy LogicIAES-IJPEDS
The Brushless DC motor (BLDC) control is used in many of the applications
as it is small in size and with low power which can drive in high speed and
lighter compared to other motors.The electric vehicles are built with BLDC
motors and also in ships, aerospace etc., The control of BLDC motors is done
with sensors like hall effect sensor for sensing the positions. The speed
control can be done with normal PI and PID controllers. Direct torque control
(DTC) of the BLDC motor is important in many applications. In this paper
BLDC motor is controlled with DTC using PI, PID and Fuzzy logic control.
The comparison of the performance of the motor is analyzed with the Matlab
simulation software.
A portable hardware in-the-loop device for automotive diagnostic control systemsISA Interchange
In-vehicle driving tests for evaluating the performance and diagnostic functionalities of engine control systems are often time consuming, expensive, and not reproducible. Using a hardware-in-the-loop (HIL) simulation approach, new control strategies and diagnostic functions on a controller area network (CAN) line can be easily tested in real time, in order to reduce the effort and the cost of the testing phase. Nowadays, spark ignition engines are controlled by an electronic control unit (ECU) with a large number of embedded sensors and actuators. In order to meet the rising demand of lower emissions and fuel consumption, an increasing number of control functions are added into such a unit. This work aims at presenting a portable electronic environment system, suited for HIL simulations, in order to test the engine control software and the diagnostic functionality on a CAN line, respectively, through non-regression and diagnostic tests. The performances of the proposed electronic device, called a micro hardware-in-the-loop system, are presented through the testing of the engine management system software of a 1.6 l Fiat gasoline engine with variable valve actuation for the ECU development version.
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.
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
Mimo broadcast-scheduling-for-weighted-sum-rate-maximizationCemal Ardil
This document summarizes a research paper that proposes a scheduling algorithm for a MIMO broadcast system with multiple antennas at the transmitter and multiple users with antennas. The algorithm employs antenna selection at the receiver to select users for communication. It aims to address the issue that when user channels are not identically distributed (non-IID), some users may get more communication opportunities than others. The document proposes applying weights to the non-IID user channels so that each user has an equal opportunity to communicate on average. It investigates the effect of these weights on the overall weighted sum-rate achieved by the system.
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.
A combined-conventional-and-differential-evolution-method-for-model-order-red...Cemal Ardil
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(20)
The document proposes a mixed method for model order reduction of single-input single-output systems. The method combines a conventional technique using Mihailov stability criterion with a differential evolution technique. In the conventional part, the reduced denominator polynomial is derived using Mihailov stability criterion, while the numerator is obtained by matching continued fraction expansions. Then, the denominator polynomial is recalculated using differential evolution optimization to minimize integral squared error between the original and reduced models. The method is demonstrated on a numerical example and shown to produce superior results compared to using only the conventional method.
This document describes mathematical models for simulating the temperature fields of gas turbine blades during convective cooling. It presents boundary integral equation methods (BIEM) and finite difference methods (FDM) for calculating the stationary and quasi-stationary temperature distribution on a blade profile with radial cooling channels. The BIEM approach formulates the problem as a system of boundary integral equations involving temperature values and heat transfer coefficients on the blade surface and cooling channel boundaries. Numerical methods are developed to solve these equations, including discrete logarithmic potential operators and non-uniform surface discretizations. The reliability of the proposed methods is confirmed through computational and experimental analysis of heat transfer for a gas turbine nozzle blade.
A weighted-sum-technique-for-the-joint-optimization-of-performance-and-power-...Cemal Ardil
The document presents a self-adaptive weighted sum technique for jointly optimizing performance and power consumption in data centers. It formulates the problem as a multi-objective optimization to minimize total power consumption and task completion time. The proposed technique adapts weights during optimization to better explore non-convex regions of the solution space, unlike traditional weighted sum methods. It was tested on data from a satellite control network and showed improved results over greedy heuristics and competitive performance against optimal solutions for smaller problems.
An investigation-on-efficient-spreading-codes-for-transmitter-based-technique...Cemal Ardil
This document summarizes research on efficient spreading codes for transmitter-based techniques to mitigate interference in time division duplex code division multiple access (TDD/CDMA) downlink systems. It investigates bitwise and blockwise multiuser transmission schemes that transfer complexity to the transmitter. Different spreading codes are evaluated based on correlation properties to determine suitability for techniques like precoding, pre-rake, and rake diversity. Performance is measured by bit error rate with varying numbers of users to identify the most efficient codes for interference mitigation.
This document discusses a study investigating the combined use of Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) features in automatic speech recognition systems. It begins by outlining the challenges of automatic speech recognition and then describes the MFCC and LPC algorithms for extracting basic speech features. The study suggests combining MFCC and LPC-based recognition subsystems to improve reliability. Neural networks are used for training and recognition, and results show the combined approach improves recognition quality compared to individual methods.
A multi-layer-artificial-neural-network-architecture-design-for-load-forecast...Cemal Ardil
The document discusses a proposed artificial neural network architecture for short-term load forecasting in power systems. It begins with background on artificial neural networks and load forecasting. It then describes a multilayer neural network model trained using a modified backpropagation algorithm to forecast power system load 24 hours in advance based on historical load data. The results showed the neural network model could accurately forecast daily load patterns.
This document summarizes a study on modeling and simulating gas turbine cooled blades. It develops mathematical models and numerical methods to calculate the stationary and quasi-stationary temperature fields of a blade profile part with convective cooling. It considers the heat conduction equation and boundary conditions for the problem. It then outlines the following key points:
1) It uses the Boundary Integral Equation Method (BIEM) and Finite Difference Method (FDM) combination to solve the temperature field calculation problem.
2) It develops effective quadrature processes to evaluate singular integral operators in the boundary integral equations.
3) It extends the modeling technique to cases with blade inserts and for quasi-stationary temperature calculations.
4) It
This document summarizes a research article about numerical modeling of gas turbine engines. The researchers developed mathematical models and numerical methods to calculate the stationary and quasi-stationary temperature fields of gas turbine blades with convective cooling. They combined the boundary integral equation method and finite difference method to solve this problem. The researchers proved the validity of these methods through theorems and estimates. They were able to visualize the temperature profiles using methods like least squares fitting with automatic interpolation, spline smoothing, and neural networks. The reliability of the numerical methods was confirmed through calculations and experimental tests of heat transfer characteristics on gas turbine nozzle blades.
This document discusses using neuro-fuzzy networks to identify parameters for mathematical models of geofields. It proposes a new technique using fuzzy neural networks that can be applied even when data is limited and uncertain in the early stages of modeling. A numerical example is provided to demonstrate the identification of parameters for a regression equation model of a geofield using a fuzzy neural network structure. The network is trained on experimental fuzzy statistical data to determine values for the regression coefficients that satisfy the data. The technique is concluded to have advantages over traditional statistical methods as it can be applied regardless of the parameter distribution and is well-suited for cases with insufficient data in early modeling stages.
This document presents a new adaptive algorithm for an adaptive decision feedback equalizer (ADFE) that has lower computational complexity than existing algorithms. The proposed block-based normalized least mean square (BBNLMS) algorithm with set-membership filtering for the ADFE achieves similar bit error rate performance and convergence speed as conventional algorithms like set-membership normalized least mean square (SM-NLMS), but with significantly fewer computations. Simulation results show the new algorithm provides comparable equalization performance to SM-NLMS while realizing about a 70% reduction in computational operations, especially at high signal-to-noise ratios, making it suitable for high-speed decision feedback equalization applications.
A new-method-of-adaptation-in-integrated-learning-environmentCemal Ardil
This document describes a new method of adaptation in a partially integrated learning environment that includes an electronic textbook and integrated tutoring system. The method establishes interconnections between operations and concepts to determine relevant educational material based on tutorial problem results. The algorithm estimates concept mastery levels, student non-mastery on textbook pages, and creates a ranked list of textbook pages for repeated study. The method was integrated into software tools to dynamically determine relevant educational content for each student step.
Development of-effective-cooling-schemes-of-gas-turbine-blades-based-on-compu...Cemal Ardil
The document summarizes the development of effective cooling schemes for gas turbine blades based on computer simulation. It describes:
1) The development of mathematical models and numerical methods (BIEM and FDM) to accurately calculate the temperature fields of turbine blades with complex cooling channel configurations, taking into account various heat transfer factors.
2) The application of boundary integral equation methods (BIEM) to solve the boundary value problems describing the temperature distributions, which involves discretizing the boundary into elements and setting up a system of linear algebraic equations relating the unknown boundary temperatures.
3) The extension of the techniques to model transient/non-steady state heat transfer problems by using a substitution to reduce the problem to a stationary boundary value problem.
This document discusses frame and burst acquisition in a TDMA satellite communication network where transmissions may occur on different transponders. It presents the following key points:
1) A unique word pattern is used to aid in the acquisition process and detect the frame. Soft-decision detection of QPSK modulated signals is used in an additive white Gaussian channel.
2) The probability of detection is low when the false alarm rate is low, leading to a long acquisition time. Conversely, a high false alarm rate yields a high probability of detection and shorter acquisition time.
3) An analysis is presented of the unique word detection process at a traffic terminal, assuming carrier and bit timing have been partially recovered from a reference burst
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.
IRJET- A Review on SVM based Induction MotorIRJET Journal
This document summarizes several research papers on using support vector machines (SVMs) and other machine learning techniques for fault detection in induction motors. Specifically:
1. It discusses using an artificial immune system-optimized SVM for detecting broken rotor bars and stator faults in induction motors based on motor current data.
2. It describes using wavelet analysis, principal component analysis, and SVM classification to detect faults like frequency variations, unbalanced voltages, and interturn shorts based on motor current spectra.
3. It proposes using dq0 voltage components analyzed with fast Fourier transforms as features for an SVM classifier to detect stator winding shorts, achieving over 98% accuracy.
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.
Tuning of Proportional Integral Derivative Controller Using Artificial Neural...IRJET Journal
This document discusses tuning a proportional-integral-derivative (PID) controller using an artificial neural network (ANN). Specifically:
1. A PID controller is used to control various process variables like pressure, temperature, and speed. The PID controller gains (KP, KI, KD) are tuned by training an ANN to optimize the controller response.
2. An ANN is trained using the Levenberg-Marquardt algorithm to determine the optimal PID gains. The tuned PID controller results in reduced overshoot, peak value, and settling time compared to the untuned controller.
3. Simulation results show that with ANN tuning, overshoot is reduced from 27.1% to 7
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.
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.
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.
Speed Control and Parameter Variation of Induction Motor Drives using Fuzzy L...IRJET Journal
This document compares the speed control of an induction motor using auto-disturbance rejection controllers (ADRC) and fuzzy logic controllers. It presents the mathematical models and control schemes for both approaches. Simulation results show that the fuzzy logic controller has better performance characteristics than the ADRC controllers in responding to load disturbances, motor parameter variations, and model uncertainty. The fuzzy logic controller is able to settle to the reference speed faster after a disturbance and has a lower steady state error.
Automatic Generation Control of Multi-Area Power System with Generating Rate ...IJAPEJOURNAL
In a large inter-connected system, large and small generating stations are synchronously connected and hence all stations must have the same frequency. The system frequency deviation is the sensitive indicator of real power imbalance. The main objectives of AGC are to maintain constant frequency and tie-line errors with in prescribed limit. This paper presents two new approaches for Automatic Generation Control using i) combined Fuzzy Logic and Artificial Neural Network Controller (FLANNC) and ii) Hybrid Neuro Fuzzy Controller (HNFC) with gauss membership functions. The simulation model is created for four-area interconnected power system. In this four area system, three areas consist of steam turbines and one area consists of hydro turbine. The components of ACE, frequency deviation (F) and tie line error (Ptie) are obtained through simulation model and used to produce the required control action to achieve AGC using i) FLANNC and ii) HNFC with gauss membership functions. The simulation results show that the proposed controllers overcome the drawbacks associated with conventional integral controller, Fuzzy Logic Controller (FLC), Artificial Neural Network controller (ANNC) and HNFC with gbell membership functionsv
1. The document presents an automatic car parking mechanism using a neuro fuzzy controller tuned by a genetic algorithm.
2. The neuro fuzzy controller is based on a radial basis function neural network with Gaussian membership functions. The weights in the hidden layer are tuned by a genetic algorithm.
3. The genetic algorithm implements dynamic crossover and mutation probabilities to better optimize the neuro fuzzy controller parameters. This allows the controller to be self-tuning.
4. The proposed neuro fuzzy controller can be applied to automatically control the steering angle in a car parking process.
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.
Risk assessment of power system transient instability incorporating renewabl...IJECEIAES
This document presents a risk assessment method for power system transient stability that incorporates renewable energy sources. The method uses Gaussian process regression and feature selection algorithms to build a predictive model for online transient stability assessment. Offline data is collected from simulations at different operating conditions and contingencies. Feature selection algorithms identify the most important features related to critical fault clearing time as the stability index. The predictive model based on the selected features can then assess transient stability online by predicting critical fault clearing times based on new operating conditions. The method was tested on a 66-bus power system model with wind and solar power integrated at various buses.
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
Design and development of matlab gui based fuzzy logic controllers for ac motorIAEME Publication
This document summarizes the design and development of MATLAB-GUI based fuzzy logic controllers for AC motor speed control. It describes the hardware and software components of the speed control system, including an AC motor, tacho-generator, frequency to voltage converter, AD-DA board, ramp generator, comparator, and opto-isolator/triac. MATLAB is used to design the GUI and implement PID, fuzzy logic, and integrated fuzzy logic controllers. The performance of the controllers is compared for a step input of 7500 RPM. It is found that the integrated fuzzy logic controller has better performance in terms of rise time, settling time, and steady state error.
Electrocardiogram Beat Classification using Discrete Wavelet Transform, Highe...IRJET Journal
This document presents a method for classifying electrocardiogram (ECG) beats using discrete wavelet transform, higher order statistics, and multivariate analysis. The method extracts features from ECG data using discrete wavelet transform, principal component analysis of wavelet coefficients, higher order statistics, and independent component analysis. These features are then input to a support vector machine classifier to classify five types of heartbeats with an accuracy of 98.91%. The method demonstrates that combining linear features from discrete wavelet transform and principal component analysis with nonlinear features from higher order statistics and independent component analysis can effectively classify ECG beats.
Co-simulation of self-adjusting fuzzy PI controller for the robot with two-ax...TELKOMNIKA JOURNAL
This paper presents the co-simulation of the self-adjusting fuzzy PI controller to control a two-axes system. Each axis was driven by a permanent magnet linear synchronous motor (PMLSM). The position and speed controller used the fuzzy PI algorithm with parameters adjusted by a radial basis function neural network (RBFNN). The vector control was applied to the decoupled effect of the PMLSM. The field programmable gate array (FPGA) was used to control both axes of the system. The very high-speed integrated circuit-hardware description language (VHDL) was developed in the Quartus II software environment, provided by Altera, to analyze and synthesize designs. Firstly, the mathematical model of PMLSM and fuzzy PI was introduced. Secondly, the RBFNN adjusted the knowledge base of the fuzzy PI. Thirdly, the motion trajectory was introduced for testing the control algorithm. Fourthly, the implementation of the controller based on FPGA with the FSM method and the structure of co-simulation between Matlab/Simulink and ModelSim were set up. Finally, discussion about the results proved the effectiveness of the control system, determining the exact position and trajectory of the XY axis system. This research was successful in implementing a two-motor controller within one chip.
This document summarizes an article from the International Journal of Electrical Engineering and Technology that proposes applying an adaptive neuro fuzzy inference system (ANFIS) based intelligent hybrid neuro fuzzy controller for load frequency control of a three area power system considering parameter uncertainties. The controller is tested on the system for 1% and 10% step load perturbations in area 2. Its performance is compared to a fuzzy integral controller, with the neuro fuzzy controller showing better improvement of frequency transient responses to small disturbances. Simulations are performed in Matlab/Simulink.
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.
Embedded intelligent adaptive PI controller for an electromechanical systemISA Interchange
In this study, an intelligent adaptive controller approach using the interval type-2 fuzzy neural network (IT2FNN) is presented. The proposed controller consists of a lower level proportional - integral (PI) controller, which is the main controller and an upper level IT2FNN which tuning on-line the parameters of a PI controller. The proposed adaptive PI controller based on IT2FNN (API-IT2FNN) is implemented practically using the Arduino DUE kit for controlling the speed of a nonlinear DC motor-generator system. The parameters of the IT2FNN are tuned on-line using back-propagation algorithm. The Lyapunov theorem is used to derive the stability and convergence of the IT2FNN. The obtained experimental results, which are compared with other controllers, demonstrate that the proposed API-IT2FNN is able to improve the system response over a wide range of system uncertainties.
Similar to Complex condition-monitoring-system-of-aircraft-gas-turbine-engine (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.
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.
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1. World Academy of Science, Engineering and Technology
International Journal of Computer, Information Science and Engineering Vol:1 No:11, 2007
Complex Condition Monitoring System of Aircraft
Gas Turbine Engine
A. M. Pashayev, D. D. Askerov, C. Ardil, R. A. Sadiqov, P. S. Abdullayev
International Science Index 11, 2007 waset.org/publications/239
Azerbaijan National Academy of Aviation
AZ1045, Azerbaijan, Baku, Bina, 25th km
tel: (99412) 453-11-48, fax: (99412) 497-28-29, e-mail: a_parviz@azeronline.com
Abstract—Researches show that probability-statistical
methods application, especially at the early stage of the aviation
Gas Turbine Engine (GTE) technical condition diagnosing,
when the flight information has property of the fuzzy, limitation
and uncertainty is unfounded. Hence the efficiency of
application of new technology Soft Computing at these
diagnosing stages with the using of the Fuzzy Logic and Neural
Networks methods is considered. According to the purpose of
this problem training with high accuracy of fuzzy multiple
linear and non-linear models (fuzzy regression equations) which
received on the statistical fuzzy data basis is made.
For GTE technical condition more adequate model making
dynamics of skewness and kurtosis coefficients’ changes are
analysed. Researches of skewness and kurtosis coefficients
values’ changes show that, distributions of GTE work
and output parameters of the multiple linear and non-linear
generalised models at presence of noise measured (the new
recursive Least Squares Method (LSM)).
The developed GTE condition monitoring system provides
stage-by-stage estimation of engine technical conditions.
As application of the given technique the estimation of the
new operating aviation engine technical condition was made.
Keywords—aviation gas turbine engine, technical
condition, fuzzy logic, neural networks, fuzzy statistics
I. INTRODUCTION
O
NE of the important maintenance requirements of
the modern aviation GTE on condition is the
presence of efficient parametric technical diagnostic
system. As it is known the GTE diagnostic problem of
the aircraft’s is mainly in the fact that onboard systems of
the objective control do not register all engine work
parameters. This circumstance causes additional manual
registration of other parameters of GTE work.
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.
parameters have fuzzy character. Hence consideration of fuzzy
skewness and kurtosis coefficients is expedient.
Investigation of the basic characteristics changes’ dynamics
of GTE work parameters allows to draw conclusion on
necessity of the Fuzzy Statistical Analysis at preliminary
identification of the engines' technical condition.
Researches of correlation coefficients values’ changes
shows also on their fuzzy character. Therefore for models
choice the application of the Fuzzy Correlation Analysis results
is offered.
At the information sufficiency is offered to use recurrent
algorithm of aviation GTE technical condition identification
(Hard Computing technology is used) on measurements of input
Currently in the subdivisions of CIS airlines are
operated various automatic diagnostic systems (ASD) of
GTE technical conditions. 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).
However, is should be noted that statistic data
processing on the above-mentioned method are
conducted by the preliminary allowance of the recorded
parameters meaning normal distribution. This allowance
affects the GTE technical condition monitoring reliability
and causes of the error decision in the GTE diagnostic
and operating process [1-3]. More over some same
combination of the various parameters changes of engine
work can be caused by different malfunctions. Finally it
complicates the definition of the defect address.
II. BASICS OF RECOMMENDED CONDITION MONITORING
SYSTEM
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 is suggested.
3267
2. World Academy of Science, Engineering and Technology
International Journal of Computer, Information Science and Engineering Vol:1 No:11, 2007
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)
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)
International Science Index 11, 2007 waset.org/publications/239
GTE condition monitoring using
fuzzy correlation- regression
analysis - (AL3)
GTE condition monitoring using fuzzy
mathematical statistics - (AL2)
Income data
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
The method provides stage-by-stage evaluation of
GTE technical conditions (Fig.1).
Experimental investigation conducted by manual
records shows that at the beginning of operation during
40÷60 measurements accumulated values of recorded
parameters of good working order GTE aren’t distribute
normality.
Consequently, on the first stage of diagnostic process
(at the preliminary stage of GTE operation) when initial
data is insufficient and fuzzy, GTE condition is estimated
by the Soft Computing methods-fuzzy logic (FL) method
and neural networks (NN). 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.
As is known one of aviation GTE technical condition
estimation methods, used in our and foreign practice is
the vibrations level control and analysis of this level
change tendency in operation. Application of various
~
X
Scaler
mathematical models described by the regression
equations for aviation GTE condition estimation is
presented in [4,5].
Let's consider mathematical model of aviation GTE
vibration state, described by fuzzy regression equations:
n
~
Yi
~ x
aij ~j ;i 1,m
(1)
j 1
~
Yi
~ xr
ars ~1
~s ; r 1,l; s 1,l ;r s l
x2
~
where Yi fuzzy output parameter (e.g. GTE vibration),
~ or ~ , ~ input parameters (engine parameters x x
x
j
H ,M
1
*
, TH
2
~
*
, p * , nLP , T4 , GT , pT , pM , TM ) , aij and
H
~
ars -required
coefficients).
fuzzy
parameters
(fuzzy
~
T
Input-output
(knowledge base)
-
Fuzzy NN
(2)
r,s
+
~
Y
Scaler
-
Fig.2. Neural identification system
3268
~
E
regression
3. World Academy of Science, Engineering and Technology
International Journal of Computer, Information Science and Engineering Vol:1 No:11, 2007
~
~
The definition task of fuzzy values aij and a rs
parameters of the equations (1) and (2) is put on the basis
of the statistical experimental fuzzy data of process, that
~
x
is input ~ j and ~1, ~2 , output co-ordinates Y of model.
x x
Let's consider the decision of the given tasks by using
FL and NN [6-8]. NN consists of interconnected fuzzy
neurons sets. At NN use for the decision (1) and (2) input
signals of the network are accordingly fuzzy values of
~
~
~
x x
x
x x
variable X ( ~ , ~ ,..., ~ ) , X ( ~ , ~ ) and output Y .
1
2
n
1
2
is less than
If for all training pairs, deviation value
given then training (correction) parameters of a network
comes to end (fig. 3). In opposite case it continues until
value reach minimum.
Correction of network parameters for left and right
part is carried out as follows:
E
E
n
o
n
o
,
a rs1 a rs1
,
a rs 2 a rs 2
a rs
a rs
o
n
o
n
where ars1 , ars1 , ars 2 , ars 2 -old and new values of left and
Correction algorithm
~
X
Input
signals
NN
International Science Index 11, 2007 waset.org/publications/239
Random-number
generator
~
Y
Target
signals
Deviations
Parameters
Training
quality
Fig.3. System for network-parameter (weights, threshold) training (with feedback)
As parameters of the network are fuzzy values of
~
~
parameters aij and ars . We shall present fuzzy variables
in the triangular form which membership functions are
calculated under the formula:
1 ( x x ) / , if x
(x)
x
1 ( x x ) / , if x x
0,
x;
x
;
otherwise .
~
right parts NN parameters, a rs a rs1 , a rs 2 ; -training
speed.
The structure of NN for identification of the equation
(1) parameters is given on fig. 4.
For the equation (2) we shall consider a concrete
special case as the regression equation of the second order
~ ~
Y a00
~ x
a ~2
~ x ~ x ~ x x
a10 ~1 a01~2 a11~1 ~2
~ x
a ~2
20 1
At the decision of the identification task of parameters
~
~
aij and a rs for equations (1) and (2) with using NN, the
basic problem is training the last. For training values of
parameters we shall take advantage of a -cut [8].
We suppose, there are statistical fuzzy data received
on the basis of experiments. On the basis of these input
~ ~
and output data are made training pairs ( , ) for
training a network. For construction of process model on
~
the input of NN gives input signals
and further outputs
~
are compared with reference output signals
(fig.2).
After comparison the deviation value is calculated
~ 1 k ~ ~ 2
( j Tj )
2j 1
Let's construct neural structure for decision of the
equation (2) where as parameters of the network are
~
~
~
~
~
~
coefficients a00 , a10 , a 01 , a11 , a20 , a 02 . Thus the
structure of NN will have four inputs and one output (fig.
5).
Using NN structure we are training network
parameters. For value
0 we shall receive the
following expressions:
001
101
j(
)
j1
t j 1 ) x11 ;
(
111
j1
t j 1 ) x21 ;
(
,
201
j1
021
).
3269
t j 1 ) x11
j2
t j 2 ) x12
(
j2
2
t j 2 ) x12 ; (4)
(
j2
2
t j 2 )2 x22
j 1
112
(
j1
2
t j 1 ) x11 ;
(
j1
2
t j 1 ) x21 ;
(
k
j 1
k
2
022
22 ;
j 1
2
202
j 1
j 1
2
21 ;
j 1
k
t j 1( ),t j 2 (
t j 2 ) x22 ;
12 ;
k
(
1
)
j2
2
012
j 1
k
2
t j2 )
j 1
k
(
1
1
j2
2
102
j 1
1
j2
k
(
k
011
t j 2 );
(
j 1
002
k
1 k
y j2 ( ) t j2 ( ) ,
2j 1
~
y j 1 ( ), y j 2 ( ) ; T j (
2
t j 1 );
j 1
1
2
~
j1
k
2
where
(
1
1 k
y j1 ( ) t j1 ( ) ,
2j 1
2
k
k
1
With application -cut for the left and right part of
deviation value are calculated under formulas
1
(3)
02 2
j 1
4. World Academy of Science, Engineering and Technology
International Journal of Computer, Information Science and Engineering Vol:1 No:11, 2007
~
a i1
~
x1
~
x2
~
Yi
~
ai2
~
a ij
~
xj
International Science Index 11, 2007 waset.org/publications/239
Fig.4. Neural network structure for multiple linear regression equation
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 have the following kind: y41 a111x12 x22 ;
y42 a112 x12 x21 , and the correction formulas
e) if there is some fuzzy nonlinear regression ~ from
y
~ , but there is not functional dependence,
x
~ 1,
~ ~~
rxy
y/ x
where ~ , ~ - fuzzy relations which are determined by
the appropriate membership functions
(rxy ) and
(
y/ x
).
Values of ~ and ~ y / x may be estimated as follows
rxy
k
1
111
(
j1
t j1 )x12
~
rxy
22 ;
j 1
k
2
112
(
j2
t j 2 )x11
21 ;
003
j3
t j3 );
3
(
113
j 1
103
3
t j3 )x13
(
j3
k
(
j3
t j3 )x13;
3
203
j 1
k
013
j3
1
n
~
Rx
~2
x
1
n
~
x
~
Ry
~2
y
1
n
~
y
~
x
~
x
~;
y
(
j3
t j3 )x23;
3
023
2
t j3 )x13;
(
j3
2
t j3 )x23;
;
2
j 1
k
j 1
2
23;
j 1
k
3
~2
y
k
k
(
y/ x
1
~
y
~
R
1 we shall receive
For value
~2
~
~ ; y/ x
Ry
where
j 1
3
~
Rx
~
R
(5)
j 1
As the result of training (4), (5) we find parameters of
the network satisfying the knowledge base with required
training quality.
Choice of GTE technical condition model (linear or
nonlinear) may be made with the help of the complex
r
comparison analysis of fuzzy correlation coefficients ~xy
and fuzzy correlation ratios ~
values. Thus the
y/ x
following cases are possible:
a) if ~ is not dependent from ~ , ~ y / x ~ 0 ;
y
x
y
b) if there is the fuzzy functional linear dependence ~
from ~ , ~xy ~ ~ y / x ~ 1 ;
x r
c) if there is the fuzzy functional nonlinear dependence
~ from ~ , ~ ~ ~
~ 1;
y
x rxy
y/ x
;
~2
~ 2
yx )
(~
y
y/ x
n
-
y
x
residual dispersion ~ , which is formed by ~ influence;
~ ~ )2
(y y
~2
- general variation, which is taking
y
n
~
into account all fuzzy factors influences; y x - partial
y
x
fuzzy average value of ~ , which is formed by ~
~
influence ; y - general fuzzy average value of ~ .
y
The analysis show, that during following 60÷120
measurements occurs the approach of individual
parameters values of GTE work to normal distribution.
Therefore at the second stage, on accumulation of the
certain information, with the help of mathematical
statistics are estimated GTE conditions. Here the given
and enumerated to the one GTE work mode parameters
are controlled on conformity to their calculated
admissible and possible ranges.
y
x
d) if there is the fuzzy linear regression ~ from ~ , but
there is not functional dependence, ~xy ~ ~ y / x ~ 1 ;
r
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5. World Academy of Science, Engineering and Technology
International Journal of Computer, Information Science and Engineering Vol:1 No:11, 2007
~
~ ~ ~ ~ ~ ~
~ p
~ ~
( V BS )ini a 1 H a 2 M a 3 T * a 4 ~ * a 5 n LP
(6)
~ ~
~ ~
~ p
~ p
~ ~
~ ~
a 6 T4* a7 GT a 8 ~
a 9 ~ M a 10 T
a 11V FS
On the subsequent stage for each current measurement’s
N 60 , when observes the normal distribution of the
engine work parameters, GTE vibration condition
describes by linear regression equation which parameters
is estimated by recurrent algorithm [3,10-12]
D ( VBS )act a1 H a2 M a3T * a4 p* a5 nLP
(7)
*
a6T4 a7 GT a8 p a9 pM a10T
a11VFS
As the result of the carried out researches for the
varied technical condition of the considered engine was
revealed certain dynamics of the correlation and
regression coefficients values changes (see Appendix:
Fig.6 and Table 1).
The statistical characteristics of correlation
coefficients show the necessity of fuzzy NN application at
Further by the means of the Least Squares Method
(LSM) (confluent methods analysis, recursive LSM) there
are identified the multiple linear regression models of
GTE conditions changes [3,10,11]. These models are
made for each correct subcontrol engine of the fleet at the
initial operation period. In such case on the basis analysis
of regression coefficient’s values (coefficients of
influence) of all fleet engine’s multiple regression models
with the help of mathematical statistics are formed base
and admissible range of coefficients [3,9].
On the third stage (for more than 120 measurements)
by the LSM estimation conducts the detail analysis of
GTE conditions. Essence of this 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
admissible ranges. The reliability of diagnostic results on
~
x1
~
x1
~ x
a10 ~1
~ x
a 01 ~2
International Science Index 11, 2007 waset.org/publications/239
~
x2
~ x2
a 02 ~2
~
a 00
~
x1
~
x2
~ x2
a 20 ~1
~
Y
~2
x1
~
x2
~ xx
a11~1~2
~2
x2
Fig. 5. Structure of neural network for second-order regression equation
this stage is high and equal to 0.95÷0.99. The influence
coefficient's values going out the mentioned ranges allows
make conclusion about the meaning changes of physical
process influence on the concrete GTE work parameters.
The stable going out one or several coefficient's
influences beyond the above-mentioned range affirms
about additional feature of incorrectness and permits to
determine address and possible reason of faults. Thus, for
receiving stable (robust) estimations by LSM is used
ridge-regression analysis.
With the view of forecasting of GTE conditions the
regression coefficients are approximated by the
polynomials of second and third degree.
As an example on application of the above-mentioned
method changes of GTE conditions has been investigated
(repeatedly putting into operation engine D-30KU-154
during 2600 hours or 690 flights operated correctly). At
the preliminary stage, when number of measurements is
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 of NN
which structure is given on fig.2. Thus as the output
parameter of GTE model is accepted the vibration of the
engine back support
the flight information processing. In that case correct
application of this approach on describing up of the GTE
technical condition changes is possible by fuzzy linear or
nonlinear model [3,10,11].
For the third stage was made the following admission
of regression coefficients (coefficients of influence of
various parameters) of various parameters on back
support vibration in linear multiple regression equation
(1): frequency of engine rotation (RPM of (low pressure)
LP compressor ( nLP )-0.0133÷0.0196; fuel pressure
( pT )- 0.028÷0.037; fuel flow ( GT )-0.0005÷0.0011;
*
exhaust gas temperature ( T4 )-0.0021÷0.0032; oil
pressure ( pM )-0.289÷0.374; oil temperature ( TM )0.026÷0.084; vibration of the forward support ( VFS ) -
0.22÷0.53;
atmosphere
pressure
( p * )-1.44÷3.62;
H
*
atmosphere temperature ( T H )-(-0.041)÷(-0.029); flight
speed (Mach number- M ) -1.17÷1.77; flight altitude
( H )-0.0001÷0.0002. Within the limits of the specified
admissions of regression coefficients was carried out
approximation of the their (regression coefficients)
current values by the polynomials of the second and third
degree with help LSM and with use cubic splines
[1,3,10,11].
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International Journal of Computer, Information Science and Engineering Vol:1 No:11, 2007
International Science Index 11, 2007 waset.org/publications/239
III. CONCLUSIONS
1. The GTE technical condition combined diagnosing
approach is offered, which is based on engine work fuzzy
and non-fuzzy parameters estimation with the help of Soft
Computing methods (Fuzzy Logic and Neural Networks)
and the confluent analysis.
2. It is shown, that application of Soft Computing
methods in recognition GTE technical condition has
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 the early stage of the engine work,
because of the limited volume of the information, the kind
of distribution of parameters is difficult for establishing.
3. By complex analysis is established, that:
- between fuzzy thermodynamic and mechanical
parameters of GTE work there are certain fuzzy relations,
which degree in operating process and in dependence of
fuzzy diagnostic situation changes’ dynamics increases or
decreases.
- for various situations of malfunctions development is
observed different fuzzy dynamics (changes) of
connections (correlation coefficients) between engine
work fuzzy parameters in operating, caused by occurrence
or disappearance of factors influencing GTE technical
condition.
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.
REFERENCES
[1]
Pashayev A.M., Sadiqov R.A., Abdullayev P.S. “Fuzzy-Neural
Approach for Aircraft Gas Turbine Engines Diagnosing”, AIAA
1st Intelligent Systems Technical Conference (Chicago, Illinois,
Sep. 20-22, 2004), AIAA-2004-6222, 2004
[2] Sadiqov R.A., Makarov N.V., Abdullayev P.S., “Problems of
aviation GTE technical condition estimation in operation”,
Proceedings of V International Symposium an Aeronautical
Sciences «New Aviation Technologies of the XXI century», Sec.
4, “Perspective technologies of flight tests and problems of
flight safety increase”, MAKS’99, Zhukovsky, Russia, August,
1999..
[3] Pashayev A.M., Sadiqov R.A., Abdullayev P.S. Complex
identification technique of aircraft gas turbine engine's health,
ASME TurboEXPO 2003, Atlanta GA, USA, Paper GT200338704
[4] 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: GosNII GA, 1984.- 88p.
[5] Doroshko S.M. The control and diagnosing of GTE technical
condition on vibration parameters. - M.: Transport, 1984.-128 p.
[6] 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.
[7] Yager R.R., Zadeh L.A. (Eds). Fuzzy sets, neural networks and
soft computing. VAN Nostrand Reinhold. N.-Y. - 4,1994.
[8] Mohamad H. Hassoun. Fundamentals of artificial neutral networks
/ A Bradford Book. The MIT press Cambridge, Massachusetts,
London, England, 1995
[9] 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] Pashayev A.M., Sadiqov R.A., Abdullayev P.S. Neuro-Fuzzy
Identification of Aircraft Gas Turbine Engine's Technical
Condition. Proceedings of the International Conference on
Computational Intelligence (ICCI 2004), May 27-29, 2004,
Nicosia, North Cyprus, 2004, p.136-142
[11] Pashayev F.M., Askerov D.D., Sadiqov R.A., Abdullayev P.S.
Identification technique of aircraft gas turbine engine's health. 7th
Biennial Conference on Engineering Systems Design and
Analysis, July 19-22, 2004, Manchester, United Kingdom, Paper
ESDA2004-58168
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7. World Academy of Science, Engineering and Technology
International Journal of Computer, Information Science and Engineering Vol:1 No:11, 2007
APPENDIX
0,6
0,4
0,2
rXY
0,0
-0,2
H_VZO
M_VZO
TH_VZO
T4_VZO
PT_VZO
-0,4
PM_VZO
TM_VZO
GT_VZO
VPO_VZO
PH_VZO
International Science Index 11, 2007 waset.org/publications/239
KND_VZO
-0,6
60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340 350 360 370
65 75 85 95 105 115 125 135 145 155 165 175 185 195 205 215 225 235 245 255 265 275 285 295 305 315 325 335 345 355 365 375
N
Fig. 6 Change of correlation coefficient’s values (relation values) between parameters included in linear multiple regression
equation D (VBS ) act in GTE operation:
) H_VZO- relation between H and VBS (correlation coefficient rH ,V
*
TH
(correlation coefficient rM ,V ); c) TH_VZO-relation between
BS
BS
); b) M_VZO- relation between M and VBS
and VBS (correlation coefficient rT * ,V ); d) T4_VZO–
H
BS
*
relation between T4 and VBS (correlation coefficient rT * ,V ); e) PT_VZO- relation between pT on VBS (correlation
4
BS
coefficient r p ,V ); f) PM_VZO – relation between p M and VBS (correlation coefficient r p ,V ); g) TM_VZO– relation
T
BS
M
BS
between TM and VBS (correlation coefficient rT ,V ); h) GT_VZO- relation between GT and VBS ( correlation coefficient
M
BS
rGT ,V BS ); i) VPO_VZO– relation between V FS and VBS (correlation coefficient rV FS ,V BS ); j) PH_VZO - relation between
p H and VBS (correlation coefficient r p H ,V BS ); k) KND_VZO – relation between n LP and VBS ( correlation coefficient
rn LP ,V BS ); N-number of measurements.
TABLE I
BASIC CHARACTERISTICS OF FUZZY CORRELATION COEFFICIENTS
Correlation
coefficients
rX ,Y
Mean of rX ,Y
Minimum of
Maximum of
rX ,Y
rX ,Y
Standard deviation
of
rX ,Y
~
rX ,Y
(rX ,Y , , )
rH ,VBS
0.098569
0.027764
0.171722
0.039039
(0.126, 0.098, 0.046)
rM ,V BS
0.140608
0.070871
0.202407
0.034495
(0.157, 0.086, 0.045)
rT * ,V
-0.318031
-0.478252
-0.055527
0.111266
(-0.423, 0.056, 0.367)
H
BS
rT * ,V
BS
-0.126122
-0.244704
0.165399
0.131245
(-0.179, 0.066, 0.344)
r pT ,V BS
0.127778
0.038779
0.203882
0.040909
(0.142, 0.103, 0.062)
r p M ,V BS
0.266707
0.090810
0.368872
0.073460
(0.348, 0.257, 0.021)
rTM ,V BS
-0.217393
-0.451692
0.141657
0.167810
(-0.368, 0.084, 0.510 )
4
rGT ,VBS
0.256634
0.076418
0.402499
0.084452
(0.288, 0.211, 0.115)
rV FS ,V BS
0.124647
-0.189260
0.419226
0.205637
(0.222, 0.411, 0.641)
rp H ,V BS
-0.075837
-0.156228
0.004146
0.043771
(-0.091, 0.066, 0.095)
rn LP ,V BS
0.366038
0.230781
0.463275
0.066236
(0.396, 0.165, 0.067)
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