Tuning optimal PID controller

In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters for nonlinear multiple-input multiple-output (MIMO) system using the particle swarm optimisation (PSO) algorithm is presented. Firstly, a nonlinear system was described based on Takagi-Sugeno (T-S) fuzzy models. Assuming that the antecedent parameters of T-S models were kept, the consequent parameters were identified online by using the weighted recursive least square (WRLS) method. Secondly, the identified parameters of fuzzy model were used to directly receive the model predicted output with direct iterative for the T-S model. The fast tuning of optimum PID controller parameters yields high-quality solution. In order to assist estimating the performance of the proposed PSO-PID controller, a new time-domain performance criterion function was also defined. Finally, the application results for continuous stirred tank reactor (CSTR) process show that the proposed algorithm is an effective control strategy with excellent tracing ability.

[1]  S. C. Shin,et al.  GA-based predictive control for nonlinear processes , 1998 .

[2]  Karl Johan Åström,et al.  PID Controllers: Theory, Design, and Tuning , 1995 .

[3]  E. Petlenkov.  NN-ANARX structure based dynamic output feedback linearization for control of nonlinear MIMO systems , 2007, 2007 Mediterranean Conference on Control & Automation.

[4]  Toru Yamamoto,et al.  A genetic tuning algorithm of PID parameters , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[5]  I. Lagrat,et al.  Fuzzy adaptive control of a class of MISO nonlinear systems , 2008 .

[6]  Fang Sheng,et al.  Genetic algorithm and simulated annealing for optimal robot arm PID control , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[7]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[8]  Sawsan Morkos Gharghory,et al.  Optimal Tuning of PID Controller using Adaptive Hybrid Particle Swarm Optimization Algorithm , 2012, Int. J. Comput. Commun. Control.

[9]  Jianming Zhang,et al.  Optimization design based on PSO algorithm for PID controller , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[10]  James F. Whidborne,et al.  Adaptive simulated annealing for designing finite-precision PID controller structures , 1998 .

[11]  Ya-Gang Wang,et al.  Adaptive PID controllers with robustness specifications , 2013, Int. J. Model. Identif. Control..

[12]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[13]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  Qing-Guo Wang,et al.  Auto-tuning of multivariable PID controllers from decentralized relay feedback , 1997, Autom..

[15]  Rubiyah Yusof,et al.  A multivariable self-tuning PID controller , 1993 .

[16]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[17]  Qidi Wu,et al.  Hybrid BBO and GA algorithms based on elites operation , 2013, Int. J. Model. Identif. Control..

[18]  Zwe-Lee Gaing,et al.  A particle swarm optimization approach for optimum design of PID controller in AVR system , 2004 .