The constraint optimization approach for robust PID design in AVR system

A constraint optimization approach is discussed for estimating the proportional-integral-derivative (PID) controller gains used for an AVR system. A new evaluation function is given to ensure the less variation in the control signal input to the system without compromising the overall dynamic responses. The robustness of the system is guaranteed by imposing the maximum sensitivity in solving the optimizing problem. The proposed method ensures the better performance even in the presence of the uncertainties in plant parameters. The simulation studies are presented to validate the design method.

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

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

[3]  Long Chen,et al.  Application of an improved PSO algorithm to optimal tuning of PID gains for water turbine governor , 2011 .

[4]  Aidan O'Dwyer,et al.  Handbook of PI and PID controller tuning rules , 2003 .

[5]  Ajith Abraham,et al.  Design of fractional-order PIlambdaDµ controllers with an improved differential evolution , 2009, Eng. Appl. Artif. Intell..

[6]  Utkal V. Mehta,et al.  On-line relay test for automatic tuning of PI controllers for stable processes , 2012 .

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

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

[9]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[10]  A. Ali,et al.  Design of Optimum PID Controller by Bacterial Foraging Strategy , 2006, 2006 IEEE International Conference on Industrial Technology.

[11]  Yoshikazu Fukuyama,et al.  Practical distribution state estimation using hybrid particle swarm optimization , 2001, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[12]  R. Gorez,et al.  ON AGGRESSIVENESS OF PI CONTROL , 2005 .

[13]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[14]  D. P. Atherton,et al.  Automatic tuning of optimum PID controllers , 1993 .

[15]  S. P. Ghoshal.  Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control , 2004 .

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

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

[18]  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).

[19]  Tore Hägglund,et al.  Advanced PID Control , 2005 .

[20]  Ching-Chang Wong,et al.  Optimal PID Controller Design for AVR System , 2009 .

[21]  Sakti Prasad Ghoshal,et al.  INTELLIGENT PARTICLE SWARM OPTIMIZED FUZZY PID CONTROLLER FOR AVR SYSTEM , 2007 .

[22]  Wei Wang,et al.  Optimal design of PI/PD controller for non-minimum phase system , 2006 .