Dynamic surface control–based adaptive neural tracking for full-state constrained omnidirectional mobile robots

Abstract

This article studies the neural network–based adaptive dynamic surface control for trajectory tracking of full-state constrained omnidirectional mobile robots. The barrier Lyapunov function method is adopted to handle the full-state constraints of the omnidirectional mobile robot, and thus state variables will never violate the restrictions. Then, the neural network is used to approximate the uncertain system dynamics, and the adaptive law is proposed to adjust the weights. Moreover, the dynamic surface control is adopted to avoid the derivation of virtual variables, and the complexity of the controller can be simplified in comparison with the classical backstepping technique. The auxiliary system is proposed as the compensator to address the input saturation of omnidirectional mobile robots. All signals including tracking errors, state variables, adaptive parameters, and control inputs in the closed-loop system are proved to be uniformly bounded, while the control gains are chosen properly. Numerical simulations are tested to validate the effectiveness and advancements of the given control strategy.