Neuroadaptive Control of Strict Feedback Systems With Full-State Constraints and Unknown Actuation Characteristics: An Inexpensive Solution

In this paper, we present a neuroadaptive control for a class of uncertain nonlinear strict-feedback systems with full-state constraints and unknown actuation characteristics where the break points of the dead-zone model are considered as time-variant. In order to deal with the modeling uncertainties and the impact of the nonsmooth actuation characteristics, neural networks are utilized at each step of the backstepping design. By using barrier Lyapunov function, together with the concept of virtual parameter, we develop a neuroadaptive control scheme ensuring tracking stability and at the same time maintaining full-state constraints. The proposed control strategy bears the structure of proportional-integral (PI) control, with the PI gains being automatically and adaptively determined, making its design less demanding and its implementation less costly. Both theoretical analysis and numerical simulation validate the benefits and the effectiveness of the proposed method.