Global Robust Adaptive Neural Tracking Control of Strict-Feedback Systems

In general, the adaptive neural tracking controllers ensure the semiglobal uniform ultimately bounded (SGUUB) stability on the condition that the neural approximation remains valid for all time. However, such a condition is difficult to verify beforehand. The paper aims to conquer such a drawback for strict-feedback systems with mismatched structured and unstructured uncertainty here. The proposed scheme contains an adaptive neural controller, a robust controller, and a switching algorithm monitoring the exchange of the two aforementioned controllers. In particular, the switching algorithm is sufficiently smooth and hence can be incorporated with the backstepping tool. The overall controller ensures the global uniformly ultimate boundedness (GUUB) without requiring prior knowledge of bounds of the neural approximation errors. Simulation results demonstrate the validity of the proposed designs.