Retraction of “A Biologically Inspired Intelligent PID Controller Tuning for AVR Systems”

This paper proposes a hybrid approach involving Genetic Algorithm (GA) and Bacterial Foraging (BF) for tuning the PID controller of an AVR. Recently the social foraging behavior of E. coli bacteria has been used to solve optimization problems. We first illustrate the proposed method using four test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover, variation of step sizes, chemotactic steps, and the life time of the bacteria. Further, the proposed algorithm is used for tuning the PID controller of an AVR. Simulation results are very encouraging and this approach provides us a novel hybrid model based on foraging behavior with a possible new connection between evolutionary forces in social foraging and distributed non-gradient optimization algorithm design for global optimization over noisy surfaces.

[1]  Reiko Tanese,et al.  Distributed Genetic Algorithms , 1989, ICGA.

[2]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 2000, Springer Berlin Heidelberg.

[3]  Dong Hwa Kim,et al.  Adaptive Tuning of PID Controller for Multivariable System Using Bacterial Foraging Based Optimization , 2005, AWIC.

[4]  Dong Hwa Kim Robust Tuning of Embedded Intelligent PID Controller for Induction Motor Using Bacterial Foraging Based Optimization , 2004, ICESS.

[5]  Dong Hwa Kim,et al.  Loss Minimization Control of Induction Motor Using GA-PSO , 2005, KES.

[6]  Guy Albert Dumont,et al.  System identification and control using genetic algorithms , 1992, IEEE Trans. Syst. Man Cybern..

[7]  David E. Goldberg,et al.  Simplex crossover and linkage identification: single-stage evolution vs. multi-stage evolution , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[8]  Zbigniew Michalewicz,et al.  GAVaPS-a genetic algorithm with varying population size , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[9]  Peter J. Fleming,et al.  Evolutionary algorithms in control systems engineering: a survey , 2002 .

[10]  Chia-Feng Juang,et al.  A hybrid of genetic algorithm and particle swarm optimization for recurrent network design , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[13]  Joachim Holtz,et al.  High-performance current regulation and efficient PWM implementation for low-inductance servo motors , 1999 .

[14]  B. Roitberg Searching Behavior: the Behavioral Ecology of Finding Resources , 1992 .

[15]  D. Grünbaum Schooling as a strategy for taxis in a noisy environment , 1998, Evolutionary Ecology.

[16]  David J. Murray-Smith,et al.  Nonlinear model structure identification using genetic programming , 1998 .

[17]  Yoshikazu Fukuyama,et al.  A particle swarm optimization for reactive power and voltage control considering voltage security assessment , 2000 .

[18]  B. Turchiano,et al.  Genetic identification of dynamical systems with static nonlinearities , 2001, SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications (Cat. No.01EX504).

[19]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[20]  Dong Hwa Kim,et al.  Intelligent PID Controller Tuning of AVR System Using GA and PSO , 2005, ICIC.

[21]  W. J. Bell Searching Behaviour: The Behavioural Ecology of Finding Resources , 1991 .

[22]  Dong Hwa Kim,et al.  Intelligent Control of AVR System Using GA-BF , 2005, KES.

[23]  Dong Hwa Kim,et al.  Robust Tuning of PID Controller With Disturbance Rejection Using Bacterial Foraging Based Optimization , 2005 .

[24]  Daniel R. Lewin EVOLUTIONARY ALGORITHMS IN CONTROL SYSTEM ENGINEERING , 2005 .

[25]  David R. Jefferson,et al.  Selection in Massively Parallel Genetic Algorithms , 1991, ICGA.

[26]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[27]  B. Turchiano,et al.  GARA: A genetic algorithm with resolution adaptation for solving system identification problems , 2001, European Control Conference.

[28]  Sou-Chen Lee,et al.  A Calibration Method for Six-Accelerometer INS , 2006 .

[29]  Dong Hwa Kim Improvement of Genetic Algorithm Using PSO and Euclidean Data Distance , 2006 .