Adaptive neural tracking and obstacle avoidance of uncertain mobile robots with unknown skidding and slipping

This paper presents a neural-network-based adaptive control approach for path tracking and obstacle avoidance of a class of mobile robots in the presence of unknown skidding, slipping, and torque saturation. The model of mobile robots consists of kinematics and dynamics considering skidding and slipping where all robot parameters as well as skidding and slipping effects are unknown. The proposed adaptive controller is designed using systematic and recursive design methodologies, without the assumption of perfect velocity tracking, where the function approximation technique using neural networks is employed to compensate unknown nonlinear functions including the model uncertainties and bounds of the skidding and slipping. From Lyapunov-stability analysis, it is shown that all signals of the controlled closed-loop system are semiglobally uniformly ultimately bounded, point tracking errors converge to an adjustable neighborhood of the origin outside the obstacle detection region, and the obstacle avoidance is guaranteed inside the region. The effectiveness of the proposed control system is demonstrated by simulation results.