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Simulation and stability analysis of neural network based control scheme for switched linear systems

机译:基于神经网络的开关线性系统控制方案的仿真与稳定性分析

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摘要

This paper proposes a new adaptive neural network based control scheme for switched linear systems with parametric uncertainty and external disturbance. A key feature of this scheme is that the prior information of the possible upper bound of the uncertainty is not required. A feedforward neural network is employed to learn this upper bound. The adaptive learning algorithm is derived from Lyapunov stability analysis so that the system response under arbitrary switching laws is guaranteed uniformly ultimately bounded. A comparative simulation study with robust controller given in [Zhang L, Lu Y, Chen Y, Mastorakis NE. Robust uniformly ultimate boundedness control for uncertain switched linear systems. Computers and Mathematics with Applications 2008; 56: 1709-14] is presented.
机译:针对参数不确定和外部干扰的切换线性系统,提出了一种新的基于自适应神经网络的控制方案。该方案的关键特征是不需要不确定性可能的上限的先验信息。前馈神经网络用于学习此上限。自适应学习算法是从Lyapunov稳定性分析中得出的,因此可以保证在任意切换定律下的系统响应均匀地最终受限。 [Lang L,Lu Y,Chen Y,Mastorakis NE中给出的带有鲁棒控制器的比较仿真研究。不确定线性切换系统的鲁棒均匀极限控制。计算机与应用数学,2008; 56:1709-14]。

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