Robust Optimal Tracking Control Using Adaptive Disturbance Observer for Wheeled Mobile Robot

This paper presents an advanced control approach for wheeled mobile robots to address the challenges of slipping, skidding, and input disturbance. The proposed method combines the synchronous online adaptive algorithm with an adaptive nonlinear disturbance observer to achieve robust trajectory tracking. The synchronous online adaptive (SOA) algorithm is utilized to approximate the Hamilton-Jacobi-Bellman (HJB) solution for the robot’s nonlinear dynamics, facilitating the design of a controller that can effectively compensate for uncertainties and disturbances. To address slipping and skidding, an adaptive nonlinear disturbance observer (ANDO) is incorporated into the control scheme. The ANDO accurately estimates the unknown slipping and skidding disturbances, enabling precise compensation and enhancing the robot’s overall tracking performance. The proposed approach is validated through extensive simulation studies. The results demonstrate that the controller bases on the synchronous online adaptive algorithm, coupled with the adaptive nonlinear disturbance observer, achieves uniform boundedness for all signals in the closed-loop system and guarantees convergence of point tracking errors to an adjustable neighborhood of the origin.