Neuroadaptive Control of Strict Feedback Systems With Full-State Constraints and Unknown Actuation Characteristics: An Inexpensive Solution
针对一类不确定非线性严格反馈系统，提出一种神经自适应控制方案，利用神经网络处理建模不确定性和非光滑驱动特性，通过障碍李雅普诺夫函数保证全状态约束和跟踪稳定性，且控制结构类似PI控制，增益自适应确定，降低了设计和实现成本。
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.