Adaptive and robust tracking control for nonholonomic wheeled mobile robots with parameter adjustment

Abstract

Accurate trajectory tracking is important for the nonholonomic wheeled mobile robot (WMR). However, the system uncertainty and external disturbance could degrade the tracking performance. To address this issue, this study proposes an adaptive and robust tracking controller integrated with a parameter adjustment strategy to achieve stable and accurate trajectory tracking for WMRs. Specifically, virtual linear and angular velocity controllers equipped with an adaptive parameter estimation mechanism are developed to generate reference signals in the kinematic model. Meanwhile, a fuzzy logic system (FLS) is employed to approximate system uncertainties, and a nonlinear disturbance observer is designed to estimate unknown external disturbances. Furthermore, to enhance tracking performance, a genetic algorithm (GA)-based control parameter adjustment method is introduced to optimize the design parameters. By Lyapunov stability theory, the position and velocity tracking errors of the WMR are proven to be uniformly ultimately bounded. The key contributions and novelties of this study are threefold. First, this paper designs a control structure where the inner and outer loops work collaboratively, which realizes a complete and accurate closed-loop control from pose error to torque output. Second, we introduce a nonlinear disturbance observer that not only estimates external disturbances but also actively compensates for FLS approximation errors, thereby significantly enhancing the system’s robustness. Third, to overcome the common limitation of manual parameter tuning in fuzzy control, this paper uses GA to systematically optimize the controller and observer parameters, ensuring an optimal balance between transient response and steady-state accuracy. Comparative experiments validate the superiority of the proposed controller and parameter adjustment strategy.