非仿射纯反馈系统自适应神经网络快速预设性能控制

Adaptive neural fast prescribed performance control for non-affine pure-feedback systems

中文关键词  非仿射系统  预设性能  自适应神经网络  微分器  

英文关键词  non-affine systems  prescribed performance control  adaptive neural control  differentiator  

中文摘要

      鉴于在纯反馈系统控制器设计过程中广泛采用的反推法需要逐级设计虚拟控制律, 设计过程复杂, 本文通过变量替换将一类未知非仿射纯反馈系统变换为等效的积分链式系统. 利用有限时间收敛的微分器对转换系统的状态进行估计, 并构造时变的误差面. 通过对误差面的瞬态与稳态值进行性能约束并设计自适应预设性能控制器, 实现了对跟踪误差的预设性能控制. 最后, 基于Lyapunov理论进行了稳定性分析, 证明了闭环系统所有信号半全局最终一致有界. 仿真算例表明了控制方法的有效性.

英文摘要

      Considering the complexity of backstepping control for non-affine pure-feedback systems, an equivalent transformational integral chain system is obtained based on variable substitution. The states of the transformational system are estimated by a finite-time-convergent differentiator. A time-varying stable manifold involving the tracking error and its high-order derivatives is chosen to deal with the high-order dynamics. An adaptive prescribed performance controller is constructed to ensure the finite-time convergence of the error manifold to a predefined region. The boundedness and convergence of the closed-loop system are proved by Lyapunov theory. Numerical simulations are performed to verify the theoretical findings.