Adaptive output-feedback decentralized control of a class of second order nonlinear systems using recurrent fuzzy neural networks
Adaptive output-feedback decentralized control of a class of second order nonlinear systems using recurrent fuzzy neural networks
复制标题
DOI：
10.1016/j.neucom.2009.07.010
复制
发表时间：
2009-12
期刊：
影响因子：
6
通讯作者：
M. Hernández;Yu Tang
中科院分区：
文献类型：
--
作者：
M. Hernández;Yu Tang
本文提出了一种基于递归模糊神经网络（RFNN）的二阶非线性仿射互联系统的自适应输出反馈分散控制的设计。首先，设计了需要所有子系统状态测量的集中控制。然后，通过添加旨在补偿互连的控制组件，获得使用本地状态测量的分散控制。最后，设计了一种基于RFNN的自适应输出反馈分散控制。在这种控制器的设计中，不需要分离的状态估计器，因为控制器动力学嵌入在循环网络中。实际跟踪是通过调用李亚普诺夫稳定性分析来建立的。仿真和实验结果用于评估所提出的控制律的性能。
In this paper the design of an adaptive output-feedback decentralized control for the class of second order nonlinear affine interconnected systems based on recurrent fuzzy neural networks (RFNN) is addressed. First, a centralized control that needs the state measurements of all subsystems is designed. Then a decentralized control using the local state measurements is obtained by adding a control component aimed at compensating for the interconnections. Finally, an adaptive output-feedback decentralized control based on an RFNN is designed. In design of such controller, no separated state estimator is needed, since the controller dynamics is embedded in the recurrent network. Practical tracking is established by invoking Lyapunov stability analysis. Simulation and experimental results are presented to evaluate the performance of the proposed control law.