Information Processing Letters
Particle swarm optimization for integer programming
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Robust and adaptive design of numerical optimization-based extremum seeking control
Automatica (Journal of IFAC)
Information Processing Letters
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Modified PSO Structure Resulting in High Exploration Ability With Convergence Guaranteed
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Brief Stability of extremum seeking feedback for general nonlinear dynamic systems
Automatica (Journal of IFAC)
Brief Adaptive extremum seeking control of nonlinear dynamic systems with parametric uncertainties
Automatica (Journal of IFAC)
Extremum-seeking control of state-constrained nonlinear systems
Automatica (Journal of IFAC)
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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.