Tracking Control for Full-State Constrained Wheeled Robot System with Unknown Dead-Zone Input

This paper considers the tracking problem of wheeled robot systems, in which a novel adaptive neural network full-state constrained controller is designed. First, a novel robust adaptive strategy is designed to circumvent some adverse effects caused by unknown dead-zone input. Further, in order to ensure that full-state variables of the system are bounded all the time, a barrier Lyapunov function (BLF) is designed. Meanwhile, an adaptive neural network is introduced to estimate unknown parameters of system model. Then based on the Lyapunov stability theory, it is proved strictly that all the states in the system are uniformly ultimate bounded. Finally, a simulation example is given to verify the effectiveness of the proposed control method.