Globally Stable Adaptive Tracking Control for Uncertain Strict Feedback Systems Based on Neural Network Approximation
This paper addresses the problem of globally stable adaptive neural tracking control for a class of strict feedback nonlinear systems. Compared with the existing works, the salient properties of the proposed scheme are given as follows. First, a novel switching controller is developed, which consists of a traditional adaptive neural controller and an extra robust controller to pull back the transient outside of the approximation domain. Second, only two adaptive parameters need to be tuned online, and the computational burden is considerably alleviated in practice. Third, to design the desired switching controller via the backstepping technique, a novel switching function, which has continuous derivatives up to the nth order, is constructed. It is shown that the system output converges to a small neighborhood of the reference signal and the closed loop system is globally stable. Finally, an example is provided to verify the effectiveness of the proposed control method.