Neural Network Based Adaptive Dynamic Surface Control for Omnidirectional Mobile Robots Tracking Control with Full-state Constraints and Input Saturation

This paper presents an adaptive neural network dynamic surface controller for four-Macanum-wheeled omnidirectional mobile robots (MWOMRs) trajectory tracking with full state constraints and input saturation. First of all, an adaptive dynamic surface controller is proposed for the MIMO nonlinear systems with uncertainties and disturbances. The neural network is utilized to approximate the uncertain dynamics. A second-order tracking differentiator, instead of the traditional first-order filter, is introduced to overcome the problem of “explosion of complexity” in back-stepping technique and reduce the filtering error. By employing a barrier Lyapunov function and an auxiliary compensator based on Nussbaum function, the full state constraints and input saturation of the MWOMRs are not violated. Moreover, it is proved that all the signals in the closed-loop system with suitable parameters are semi-global uniformly bounded and the tracking error converges to an arbitrarily small compact set to zero. Finally, experiment results are presented to verify the effectiveness and robustness of the proposed adaptive control approach.