Dual Adaptive Control for Trajectory Tracking of Mobile Robots

This paper presents a novel dual adaptive dynamic controller for trajectory tracking of nonholonomic wheeled mobile robots. The controller is developed entirely in discrete-time and the and the robot´s nonlinear dynamic functions are assumed to be unknown. A Gaussian radial basis function neural network is employed for function approximation, and its weights are estimated stochastically in real-time. In contrast to adaptive certainty equivalence controllers hitherto published for mobile robots, the proposed control law takes into consideration the estimates´ uncertainty, thereby leading to improved tracking performance. The proposed method is verified by realistic simulations and Monte Carlo analysis.