Optimizing sliding mode control with hybrid enhanced particle swarm optimization: a study on enhancing wheeled mobile robot performance

Abstract

Abstract This paper proposes a Sliding Mode Controller (SMC) optimized with a new Hybrid Particle Swarm Optimization (HEPSO) algorithm. HEPSO integrates three advanced strategies: adaptive inertia weights (AIW), unified factor enhancement (UFE), and global optimal particle training (GOPT) to enhance performance. Simulations based on the CEC2022 benchmark functions demonstrate that HEPSO achieves superior convergence speed and accuracy compared to other optimization variants. Additionally, the HEPSO-SMC framework is applied to wheeled mobile robot simulations, outperforming PSO-SMC, IPSO-SMC, and UPS-SMC in terms of effectiveness and robustness. This research enhances the tracking performance and robustness of SMC and proposes a more efficient and reliable control strategy for wheeled mobile robots through parameter optimization.