Comparison of classical control and intelligent control for a MIMO system

This paper presents several classical control schemes and intelligent control schemes of an experimental propeller setup, which is called the twin rotor multi-input multi-output (MIMO) system. The objective of this study is to decouple the twin rotor MIMO system into the horizontal plane and vertical plane, and perform setpoint control that makes the beam of the twin rotor MIMO system move quickly and accurately in order to track a trajectory or to reach specified positions. We utilize the conventional control and intelligent control techniques in the vertical and horizontal planes of the twin rotor MIMO system. In classical control, three of the most popular controller design techniques are utilized in this study. These are the Ziegler-Nichols Proportional-lntegral-Derivative (PID) rule, the gain margin and phase margin rule, and the pole placement method. Intelligent control is also proposed in this paper in order to improve the attitude tracking accuracy of the twin rotor MIMO system. Intelligent control designs are based on fuzzy logic system and genetic algorithm. Simulations show that the intelligent controllers have better performance than the classical controllers.

[1]  N. Minorsky.,et al.  DIRECTIONAL STABILITY OF AUTOMATICALLY STEERED BODIES , 2009 .

[2]  A. Callender,et al.  Time-Lag in a Control System , 1936 .

[3]  Andrea Bonarini,et al.  Evolutionary Learning of Fuzzy rules: competition and cooperation , 1996 .

[4]  Jih-Gau Juang,et al.  Hybrid Intelligent PID Control for MIMO System , 2006, ICONIP.

[5]  Jih-Gau Juang,et al.  Fuzzy Compensator Using RGA for TRMS Control , 2006, ICIC.

[6]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[7]  Yi-Sheng Zhou,et al.  Optimal design for fuzzy controllers by genetic algorithms , 2000 .

[8]  Jarmo T. Alander,et al.  An indexed bibliography of genetic algorithms with fuzzy logic , 1997 .

[9]  I-Lung Chien,et al.  Consider IMC Tuning to Improve Controller Performance , 1990 .

[10]  Charles L. Karr,et al.  Genetic algorithms for fuzzy controllers , 1991 .

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

[12]  Khaled Belarbi,et al.  Genetic algorithm for the design of a class of fuzzy controllers: an alternative approach , 2000, IEEE Trans. Fuzzy Syst..

[13]  Chang Chieh Hang,et al.  Self-tuning PID control of a plant with under-damped response with specifications on gain and phase margins , 1997, IEEE Trans. Control. Syst. Technol..

[14]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[15]  Benjamin C. Kuo,et al.  AUTOMATIC CONTROL SYSTEMS , 1962, Universum:Technical sciences.

[16]  Bo-Hyeun Wang,et al.  Automatic rule generation using genetic algorithms for fuzzy-PID hybrid control , 1996, Proceedings of the 1996 IEEE International Symposium on Intelligent Control.

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

[18]  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.

[19]  Jih-Gau Juang,et al.  Hybrid RNN-GA Controller for ALS in Wind Shear Condition , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[20]  Witold Pedrycz,et al.  Fuzzy evolutionary computation , 1997 .

[21]  George K. I. Mann,et al.  New methodology for analytical and optimal design of fuzzy PID controllers , 1999, IEEE Trans. Fuzzy Syst..

[22]  R. Subbu,et al.  Evolutionary design and optimization of aircraft engine controllers , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[23]  Weng Khuen Ho,et al.  Tuning of PID controllers based on gain and phase margin specifications , 1995, Autom..

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

[25]  Jih-Gau Juang,et al.  Nonlinear system identification by evolutionary computation and recursive estimation method , 2005, Proceedings of the 2005, American Control Conference, 2005..