Department of Mathematics, Indian Institute of Technology Roorkee (IITR), Roorkee-247667, Uttarakhand, India.
ISA Trans. 2012 Jan;51(1):105-10. doi: 10.1016/j.isatra.2011.08.004. Epub 2011 Sep 18.
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.
本文针对具有参数不确定性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。该方案的一个关键特点是，不需要不确定性的可能上界的先验信息。采用前馈神经网络来学习这个上界。自适应学习算法是从 Lyapunov 稳定性分析中得出的，从而保证了在任意切换律下系统响应的一致有界性。与 [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] 中给出的鲁棒控制器进行了对比仿真研究。
