Abstract
In this work a new and systematic method for the determination of the optimal values of input and output scaling factors is proposed. The method uses Genetic Algorithms (GA’s) and Artificial Neural Networks (ANN’s). The method uses the GA for searching the optimal values of scaling factors whereas the ANN is used for the computation of fitness function in each generation.
On the other hand a new selftuning FLC architecture (NGTOSTFPID) is proposed. Here, the outputscaling factor is adjusted online by an independent fuzzy controller according to the trends of the controlled process. The rule base for tuning the outputscaling factor is defined on error (e) and change of error ( e) of the controlled variable. Also, the rule base of the main fuzzy controller is designed by a gradient decent technique without using any idea of a skilled operator. Furthermore, in order to eliminate the drawbacks of the FLC in transients, a feedforward integrator and a feedback derivative controller blocks are added to the controller architecture.
In order to demonstrate the validity of the proposed optimization method, the method is applied to conventional PI, conventional fuzzy PI, hybrid type fuzzy PID, self tuning type fuzzy PI and NGTOSTFPID controllers. Also the performances of these controllers are compared on a realtime servo system including a permanent magnet DC motor and load. All the controllers including neural network based system identifiers and genetic optimizers are coded under C++ and applied to the system via a Pentium PC. The performances of the controllers including the proposed selftuning FLC are compared in terms of several performance measures such as peak overshoot, settling time, rise time, integral of absolute error (IAE) and integral of the time weighted absolute error (ITAE), in addition to the responses due to ramp type trajectory and finally, in each case, the proposed scheme tuned by introduced optimization method shows a remarkably improved performance over the other abovementioned controllers.
Original language  English 

Qualification  Ph.D. 
Awarding Institution  
Supervisors/Advisors 

Publication status  Published  2002 
Fingerprint
Keywords
 fuzzy logic control
 genetic algorithms
 artificial neural networks
 scaling factor
 selftuning control
Cite this
}
Design of neuralgenetic based optimal fuzzy logic controller and application to a DC servosystem. / Kucukdemiral, Ibrahim Beklan.
2002.Research output: Thesis › Doctoral Thesis
TY  THES
T1  Design of neuralgenetic based optimal fuzzy logic controller and application to a DC servosystem
AU  Kucukdemiral, Ibrahim Beklan
N1  Date of award (from thesis module record): 24/5/2002, ET Thesis title not found in ethos. Validated as from a Turkish university and they don't appear to have a repository. ET 18/6/19
PY  2002
Y1  2002
N2  The design process of a fuzzy logic controller (FLC) usually can be divided into four sub stages. These are determination and tuning of input and output membership functions, determination and tuning operation of the input and output scaling factors, design operation of the rule base and choosing the implication, inference and defuzzification methods. Among them, much more attention should be paid for determination of the input and output scaling factors since, the outputscaling factor has the most influence on stability and oscillation tendency whereas the input scaling factors have the most influence on basic sensitivity of the controller with respect to the optimal choice of the operating areas of the input signals. However, relative importance of the input and output scalingfactors to the performance of a FLC system is yet to be fully established. In this work a new and systematic method for the determination of the optimal values of input and output scaling factors is proposed. The method uses Genetic Algorithms (GA’s) and Artificial Neural Networks (ANN’s). The method uses the GA for searching the optimal values of scaling factors whereas the ANN is used for the computation of fitness function in each generation. On the other hand a new selftuning FLC architecture (NGTOSTFPID) is proposed. Here, the outputscaling factor is adjusted online by an independent fuzzy controller according to the trends of the controlled process. The rule base for tuning the outputscaling factor is defined on error (e) and change of error ( e) of the controlled variable. Also, the rule base of the main fuzzy controller is designed by a gradient decent technique without using any idea of a skilled operator. Furthermore, in order to eliminate the drawbacks of the FLC in transients, a feedforward integrator and a feedback derivative controller blocks are added to the controller architecture. In order to demonstrate the validity of the proposed optimization method, the method is applied to conventional PI, conventional fuzzy PI, hybrid type fuzzy PID, self tuning type fuzzy PI and NGTOSTFPID controllers. Also the performances of these controllers are compared on a realtime servo system including a permanent magnet DC motor and load. All the controllers including neural network based system identifiers and genetic optimizers are coded under C++ and applied to the system via a Pentium PC. The performances of the controllers including the proposed selftuning FLC are compared in terms of several performance measures such as peak overshoot, settling time, rise time, integral of absolute error (IAE) and integral of the time weighted absolute error (ITAE), in addition to the responses due to ramp type trajectory and finally, in each case, the proposed scheme tuned by introduced optimization method shows a remarkably improved performance over the other abovementioned controllers.
AB  The design process of a fuzzy logic controller (FLC) usually can be divided into four sub stages. These are determination and tuning of input and output membership functions, determination and tuning operation of the input and output scaling factors, design operation of the rule base and choosing the implication, inference and defuzzification methods. Among them, much more attention should be paid for determination of the input and output scaling factors since, the outputscaling factor has the most influence on stability and oscillation tendency whereas the input scaling factors have the most influence on basic sensitivity of the controller with respect to the optimal choice of the operating areas of the input signals. However, relative importance of the input and output scalingfactors to the performance of a FLC system is yet to be fully established. In this work a new and systematic method for the determination of the optimal values of input and output scaling factors is proposed. The method uses Genetic Algorithms (GA’s) and Artificial Neural Networks (ANN’s). The method uses the GA for searching the optimal values of scaling factors whereas the ANN is used for the computation of fitness function in each generation. On the other hand a new selftuning FLC architecture (NGTOSTFPID) is proposed. Here, the outputscaling factor is adjusted online by an independent fuzzy controller according to the trends of the controlled process. The rule base for tuning the outputscaling factor is defined on error (e) and change of error ( e) of the controlled variable. Also, the rule base of the main fuzzy controller is designed by a gradient decent technique without using any idea of a skilled operator. Furthermore, in order to eliminate the drawbacks of the FLC in transients, a feedforward integrator and a feedback derivative controller blocks are added to the controller architecture. In order to demonstrate the validity of the proposed optimization method, the method is applied to conventional PI, conventional fuzzy PI, hybrid type fuzzy PID, self tuning type fuzzy PI and NGTOSTFPID controllers. Also the performances of these controllers are compared on a realtime servo system including a permanent magnet DC motor and load. All the controllers including neural network based system identifiers and genetic optimizers are coded under C++ and applied to the system via a Pentium PC. The performances of the controllers including the proposed selftuning FLC are compared in terms of several performance measures such as peak overshoot, settling time, rise time, integral of absolute error (IAE) and integral of the time weighted absolute error (ITAE), in addition to the responses due to ramp type trajectory and finally, in each case, the proposed scheme tuned by introduced optimization method shows a remarkably improved performance over the other abovementioned controllers.
KW  fuzzy logic control
KW  genetic algorithms
KW  artificial neural networks
KW  scaling factor
KW  selftuning control
M3  Doctoral Thesis
ER 