论文标题
随时间变化状态约束非线性系统的强大自适应神经网络控制
Robust Adaptive Neural Network Control of Time-Varying State Constrained Nonlinear Systems
论文作者
论文摘要
本文介绍了一系列非常简单的未知非线性系统类的跟踪控制问题。在本文中，我们提出了一种设计策略，用于跟踪自适应框架中对时变状态约束非线性系统的控制。控制器是使用反向替代方法设计的。在设计它时，使用屏障Lyapunov功能（BLF），以使状态变量不会违反其约束。为了应对系统的未知动力，在线近似器是使用具有新颖自适应定律的神经网络设计的，以进行重量更新。为了使控制器稳健和计算廉价，提出了一个干扰观察者，以应对干扰以及神经网络近似误差以及虚拟控制输入的时间导数。通过模拟研究证明了所提出的方法的有效性。
This paper deals with the tracking control problem for a very simple class of unknown nonlinear systems. In this paper, we presents a design strategy for tracking control of time-varying state constrained nonlinear systems in an adaptive framework. The controller is designed using the backstepping method. While designing it, Barrier Lyapunov Function (BLF) is used so that the state variables do not contravene its constraints. In order to cope with the unknown dynamics of the system, an online approximator is designed using a neural network with a novel adaptive law for its weight update. To make the controller robust and computationally inexpensive, a disturbance observer is proposed to cope with the disturbance along with neural network approximation error and the time derivative of virtual control input. The effectiveness of the proposed approach is demonstrated through a simulation study.
