Swarm intelligence-based extremum seeking control

This paper proposes an extremum seeking control (ESC) scheme based on particle swarm optimization (PSO). In the proposed scheme, the controller steers the system states to the optimal point based on the measurement, and the explicit form of the performance function is not needed. By measuring the performance function value online, a sequence, generated by PSO algorithm, guides the regulator that drives the state of system 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, in order to reduce the regulator gain and oscillation induced by population-based stochastic searching algorithms. The convergence of the scheme is guaranteed by the PSO algorithm and state regulation. Simulation examples demonstrate the effectiveness and robustness of the proposed scheme.

► This paper propses an extremum seeking control scheme based on particle swarm optimization (PSO). ► The system states are steered to the optimal point by the controller, and the explicit form of the performance function is not needed. ► We also propose an reshuffle-then-insertion algorithm to reduce the regulator gain and osillation induced by population-based stochastic searching algorithms.