ABSTRACT

This article is focused on the adaptive neural network (ANN) asymptotic tracking control design for stochastic nonlinear systems with state constraints. The neural networks are utilised to deal with unknown uncertainties. The existence of state constraints and unknown virtual control coefficients (UVCC) bring many difficulties for control synthesis and analysis. With the aid of barrier Lyapunov function, the predefined state constraints are guaranteed. By fusing the lower bounds of UVCC into Lyapunov function construction, a novel ANN asymptotic tracking control method is devised by employing the bound estimation approach and backstepping technique. The presented asymptotic tracking controller can guarantee that the tracking error converges to zero in probability and the state constraints are not violated. The validity of the developed scheme is elucidated by simulation example.

摘要

本文重点研究具有状态约束的随机非线性系统的自适应神经网络 (ANN) 渐近跟踪控制设计。神经网络用于处理未知的不确定性。状态约束和未知虚拟控制系数（UVCC）的存在给控制综合和分析带来了许多困难。在势垒李雅普诺夫函数的帮助下，预定义的状态约束得到保证。通过将 UVCC 的下界融合到 Lyapunov 函数构造中，利用边界估计方法和反步技术设计了一种新的 ANN 渐近跟踪控制方法。所提出的渐近跟踪控制器可以保证跟踪误差在概率上收敛到零并且不违反状态约束。