Optimal Robust Design of Sliding-mode Control Based on Multi-Objective Particle Swarm Optimization for Chaotic Uncertain Problems

Abstract

The aim of this paper is to present an optimal robust Pareto design of sliding-mode control for chaotic uncertain problems. When designing and applying sliding mode control to challenging dynamic systems, it is crucial to gain optimal control effort and minimum tracking errors, simultaneously. In this regard, multi-objective particle swarm optimization (periodic CDPSO) benefiting from crucial factors such as divergence and convergence operators, the leader selection method, and the adaptive elimination technique is utilized to design the optimal control approach via obtaining the Pareto front of objective functions addressing the trade-off between the states errors and control effort. Afterward, the Pareto front acquired by the periodic CDPSO algorithm is contrasted with those obtained via other prominent algorithms in the literature including Sigma method, Modified NSGAII, and MOGA. Eventually, the numerical results elucidate the effectiveness of the proposed optimal control scheme in terms of optimal control effort and minimum tracking errors.