PSO-Based Optimal Tracking Control of Mobile Robots with Unknown Wheel Slipping

Abstract

Wheel slipping during trajectory tracking presents significant challenges for wheeled mobile robots (WMRs), degrading accuracy and stability on low-friction or dynamic terrain. Effective control requires addressing unknown slipping parameters while balancing tracking precision and energy efficiency. To address this challenge, a control framework integrating a sliding mode observer (SMO), an improved particle swarm optimization (PSO) algorithm, and a linear quadratic regulator (LQR) is proposed. First, a dynamic model incorporating longitudinal slipping is established. Second, an SMO is designed to estimate the slipping ratio in real-time, with chattering suppressed using a low-pass filter. Finally, an improved PSO algorithm featuring a nonlinear cosine-decreasing inertia weight strategy optimizes the LQR weighting matrices (Q/R) online to both minimize tracking errors and control energy consumption. Simulations including both circular and sine wave trajectories demonstrate that the SMO achieves rapid and accurate slipping ratio estimation, while the PSO-optimized LQR significantly enhances tracking accuracy, achieves smoother control inputs, and maintains stability under varying slipping conditions.