Optimal Tracking Control for Wheeled Mobile Robot via Adaptive Dynamic Programming with Concurrent Learning

This paper presents an optimal trajectory tracking control approach for wheeled mobile robots (WMRs) using the adaptive dynamic programming (ADP) method integrated with radial basis function neural network (RBFNN). Firstly, the kine-matic model and error dynamics of the wheeled mobile robots are derived, with the tracking error defined in a transformed coordinate system. Then, the control input is decomposed into feedforward and feedback components to track the reference trajectory. To circumvent the issue of solving the Hamilton-Jacobi-Bellman (HJB) equation, the critic learning approach based on RBFNN is employed to approximate the optimal value function. The modified critic network weight update rule is introduced, which combines stability indicators and concurrent learning terms. The feedback control strategy is formulated by combining the output of critic network with the error dynamics, ensuring convergence of tracking errors to the neighborhood of zero. Finally, the simulation experiments validate the effectiveness of the method.