GP and GA in the design of a constrained control system with disturbance rejection

The design of a robust controller for a constrained SISO linear system is considered. Initially, the study of a solution provided by genetic programming (GP) outlined that the GP search process does not achieve time-optimality. A genetic algorithm (GA) was chosen and implemented to confront the performance of the GP solution. The system presented In this work tuned a set of elements that form the controller structure, namely, a PID core, a feed-forward block, a filter on the derivative and a Butterworth Alter on the feedback. The proposed approach proved to reach time optimality thanks to the achievement of bang-bang control. The control synthesis method also showed versatility when considering load and feedback disturbances on the system. GA appears to be preferable for the computational cost and the quality of the solution obtained.

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

[2]  R. A. Krohling.  Genetic algorithms for synthesis of mixed H/sub 2//H/sub /spl infin// fixed-structure controllers , 1998, Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) Intell.

[3]  P. Wang,et al.  Optimal Design of PID Process Controllers based on Genetic Algorithms , 1993 .

[4]  Tetsuo Morimoto,et al.  AI approaches to identification and control of total plant production systems , 2000 .

[5]  John R. Koza,et al.  Survey of genetic algorithms and genetic programming , 1995, Proceedings of WESCON'95.

[6]  Chester L. Nachtigal,et al.  Instrumentation and Control: Fundamentals and Applications , 1990 .

[7]  S. Daley,et al.  Optimal-Tuning PID Control for Industrial Systems , 2000 .

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

[9]  Wolfgang Banzhaf,et al.  Genetic Programming: An Introduction , 1997 .

[10]  J. B. Gomm,et al.  Solution to the Shell standard control problem using genetically tuned PID controllers , 2002 .

[11]  Kevin M. Passino,et al.  Genetic adaptive identification and control , 1999 .

[12]  Renato A. Krohling,et al.  Designing PI/PID controllers for a motion control system based on genetic algorithms , 1997, Proceedings of 12th IEEE International Symposium on Intelligent Control.

[13]  Simon D. Kent,et al.  Genetic programming for prediction and control , 1997, Neural Computing & Applications.

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

[16]  Gustaf Olsson,et al.  Computer systems for automation and control , 1992 .

[17]  Kevin M. Passino,et al.  Intelligent control for brake systems , 1999, IEEE Trans. Control. Syst. Technol..

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

[19]  Matthew Walker.  Introduction to Genetic Programming , 2001 .

[20]  Tore Hägglund,et al.  The future of PID control , 2000 .

[21]  John R. Koza,et al.  Automatic Creation of Human-Competitive Programs and Controllers by Means of Genetic Programming , 2000, Genetic Programming and Evolvable Machines.

[22]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[23]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[24]  Renato A. Krohling,et al.  Design of optimal disturbance rejection PID controllers using genetic algorithms , 2001, IEEE Trans. Evol. Comput..