Particle swarm optimization-based extremum seeking control

Authors:
Shi-Jie Yu;Hong Chen;Li Kong
Affiliations:
Electrical Engineering College, Wuhan University, Wuhan, China;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, China and Wuhan 2nd Ship Design and Research Institute, Wuhan, China;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
Venue:
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Year:
2010

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Abstract

This paper devises a particle swarm optimization-based extremum seeking control (ESC) scheme. In the scheme, the system states are guided to the optimal point by the controller based on the output measurement, and the explicit form of the performance function is not needed. By measuring the performance function value online, a sequence, generated by the particle swarm optimization algorithm, steers the regulator that drives the system states approaching to the set point that optimizes the performance. We also propose an algorithm that first reshuffles the sequence, and then inserts intermediate states into the sequence, to reduce the regulator gain and oscillation induced by stochastic, population-based searching algorithms. Simulation examples demonstrate the effectiveness and robustness of the proposed scheme.