An on-line PID control scheme for unknown nonlinear dynamic systems using evolution strategy

The paper presents an on-line PID control scheme with varying gains for unknown nonlinear dynamic systems. For the on-line control, it is necessary to have an on-line identifier of the system, so that an identifier is constructed in the form of an autoregressive moving average (ARMA) model. In order to tune the parameters of the identifier and the gains of the PID controller efficiently, we propose a modified evolution strategy. Experimental studies show that the proposed on-line control scheme has robust control performance under unknown disturbance and noise.

[1]  G. Langholz,et al.  Genetic-Based New Fuzzy Reasoning Models with Application to Fuzzy Control , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[2]  Tanja Urbancic,et al.  Genetic algorithms in controller design and tuning , 1993, IEEE Trans. Syst. Man Cybern..

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

[4]  Yoshiaki Ichikawa,et al.  Neural network application for direct feedback controllers , 1992, IEEE Trans. Neural Networks.

[5]  David B. Fogel,et al.  Evolving neurocontrollers using evolutionary programming , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[6]  T. Back.  Selective pressure in evolutionary algorithms: a characterization of selection mechanisms , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[7]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[8]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[9]  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.