具有输入时滞的切换非线性系统的神经网络追踪控制

发布时间：2018-04-01 12:13

本文选题：切换非线性系统　切入点：Backstepping方法　出处：《渤海大学》2017年硕士论文

【摘要】：由于连续的动力学、离散动力学和逻辑决策等因素的作用,大量的实际设备都可以被描述成切换系统。本文针对几类切换非线性系统,结合Backstepping设计方法和基于神经网络的自适应控制方法,研究了相应控制器的设计方法及闭环系统的有界性、收敛性问题。主要工作概括如下:对于一类具有不确定、输入时滞、外部时变干扰的严格反馈切换非线性系统,提出了一种基于神经网络的控制方法。首先,通过构建一个滤波器和虚拟观测器来获得辅助信号。然后,通过运用Backstepping设计方法和神经网络方法,对于所有的子系统构建一个共同的李雅普诺夫函数和一个状态反馈控制。然后,证明闭环系统的所有信号是半全局一致终结有界的,并且追踪误差最终收敛到一个充分小的紧集。最后,通过一个仿真的例子,所提出的方法被验证是有效的。对于一类具有时滞和干扰的多输入多输出不确定非线性切换系统,提出了一个新的基于神经网络的自适应输出追踪控制方法。结合径向基函数神经网络的通用逼近能力和一个改良的多重李雅普诺夫函数方案以及自适应Backstepping递归设计,对所考虑系统提出了一个新颖的自适应神经输出追踪控制设计方法。然后,证明了闭环系统的所有信号是半全局一致终结有界的,并且追踪误差最终收敛到一个充分小的紧集。最后,通过一个仿真的例子,所提出的方法被验证是有效的。
[Abstract]:Because of the continuous dynamics, discrete dynamics and logic decision factors, a large number of actual equipment can be described as a switching system. In this paper several kinds of switched nonlinear systems, combined with Backstepping design method based on adaptive neural network control method, the design method of the controller and the boundedness of the closed-loop system, convergence the problem. The main work is as follows: for a class of uncertain input delay, strict feedback switched nonlinear systems with external disturbance, proposes a control method based on neural network. Firstly, through the construction of a filter and a virtual observer to obtain auxiliary signal. Then, by using the Backstepping design method and neural network method for the construction of all subsystems of feedback control of a common Lyapunov function and a closed state. Then, to prove All the signal loop system is semiglobal1y uniformly bounded, and the tracking error converges to a sufficiently small compact set. Finally, through a simulation example, the proposed method is verified to be effective. With time delay and multi input multi output interference for a class of uncertain nonlinear switched systems and put forward a new adaptive output tracking control method based on neural network. The universal approximation ability combined with radial basis function neural network and an improved multiple Lyapunov function scheme and adaptive Backstepping recursive design, to consider the system proposes a novel adaptive neural output tracking control design method. Then, it is proved that all signals in the closed-loop system are semi globally uniformly ultimately bounded, and the tracking error converges to a sufficiently small compact set. Finally, through a simulation For example, the proposed method is validated to be effective.

【学位授予单位】：渤海大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：O231

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