A PSO-Tuning Method for Design of Fuzzy PID Controllers

A novel tuning method is proposed for the design of fuzzy PID controllers for multivariable sys- tems. In the proposed method, a PID controller is expressed in terms of fuzzy rules, in which the input variables are the error signals and their derivatives, while the output variables are the PID gains. In this manner, the PID gains are adaptive and the fuzzy PID controller has more flexibility and capability than con- ventional versions with fixed gains. A particle swarm optimization (PSO) method is proposed for tuning of the fuzzy PID controller, in which the centers and the widths of the Gaussian membership functions, the num- ber of fuzzy control rules, and the PID gains are all parameters to be determined simultaneously. Meanwhile, based on the concept of multiobjective optimization, ways of defining the fitness function of the PSO to in- clude different performance criteria are also discussed. To show the feasibility and validity of the resulting fuzzy PID controller, illustrative experimental results for a multivariable seesaw system are included.

[1]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

[2]  P. N. Paraskevopoulos,et al.  Modern Control Engineering , 2001 .

[3]  Chia Ju Wu,et al.  Quasi time—optimal PID control of multivariable systems: a seesaw example , 1999 .

[4]  Zwe-Lee Gaing.  A particle swarm optimization approach for optimum design of PID controller in AVR system , 2004, IEEE Transactions on Energy Conversion.

[5]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[6]  J. G. Ziegler,et al.  Optimum Settings for Automatic Controllers , 1942, Journal of Fluids Engineering.

[7]  Nabil G. Chalhoub,et al.  Fuzzy Logic Control for an Integrated System of a Micro-Manipulator with a Single Flexible Beam , 2004 .

[8]  Shankar P. Bhattacharyya,et al.  A new approach to digital PID controller design , 2003, IEEE Trans. Autom. Control..

[9]  Ya Lei Sun,et al.  Hybrid fuzzy control of robotics systems , 2004, IEEE Transactions on Fuzzy Systems.

[10]  I. Cervantes,et al.  Vision-based PID control of planar robots , 2004, IEEE/ASME Transactions on Mechatronics.

[11]  Niahn-Chung Shieh,et al.  GA-Based Multiobjective PID Control for a Linear , 2003 .

[12]  Tzuu-Hseng S. Li,et al.  Design of a GA-based fuzzy PID controller for non-minimum phase systems , 2000, Fuzzy Sets Syst..

[13]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[14]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[15]  H. Yoshida,et al.  A particle swarm optimization for reactive power and voltage control considering voltage security assessment , 1999, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[16]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[17]  S. Bennett,et al.  Development of the PID controller , 1993, IEEE Control Systems.

[18]  Yuping Wang,et al.  Multiobjective programming using uniform design and genetic algorithm , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[19]  Kiyotaka Izumi,et al.  A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems , 2005, IEEE Transactions on Industrial Electronics.

[20]  David B. Fogel,et al.  Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (IEEE Press Series on Computational Intelligence) , 2006 .

[21]  Hisao Ishibuchi,et al.  A multi-objective genetic local search algorithm and its application to flowshop scheduling , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[22]  Shiyou Yang,et al.  A particle swarm optimization-based method for multiobjective design optimizations , 2005, IEEE Transactions on Magnetics.

[23]  M. Trabia,et al.  Comparison of Distributed PD-like and Importancebased Fuzzy Logic Controllers for a Two-link Rigid-flexible Manipulator , 2005 .

[24]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[25]  D.H. Werner,et al.  Particle swarm optimization versus genetic algorithms for phased array synthesis , 2004, IEEE Transactions on Antennas and Propagation.

[26]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[27]  P. J. Angeline,et al.  Using selection to improve particle swarm optimization , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[28]  Chin-Wang Tao,et al.  Robust fuzzy control for a plant with fuzzy linear model , 2005, IEEE Transactions on Fuzzy Systems.

[29]  Guanrong Chen,et al.  Design and analysis of a fuzzy proportional-integral-derivative controller , 1996, Fuzzy Sets Syst..

[30]  James F. Whidborne.  A genetic algorithm approach to designing finite-precision PID controller structures , 1999 .

[31]  Guanrong Chen,et al.  An improved robust fuzzy-PID controller with optimal fuzzy reasoning , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[32]  M. Cheng,et al.  A neural‐network‐based method for fuzzy parameter tuning of pid controllers , 2002 .

[33]  Man Gyun Na,et al.  Auto-tuned PID controller using a model predictive control method for the steam generator water level , 2001 .