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.
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Yu, SJ., Chen, H., Kong, L. (2010). Particle Swarm Optimization-Based Extremum Seeking Control. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_24
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DOI: https://doi.org/10.1007/978-3-642-14922-1_24
Publisher Name: Springer, Berlin, Heidelberg
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