Model Reference Adaptive Neural Network Control for a Class of Switched Nonlinear Singular Systems

摘要：

In this paper, we address the reference model adaptive neural network control problem for a class of switched nonlinear singular systems under the case of single input and multiple inputs. Based on RBF neural network, the state tracking controller and a switching strategy are designed so that switched nonlinear singular system can asymptotically track the desired reference model. It shows that RBF neural network are used to approximate the positive nonlinear unknown function. The approximation errors of the RBF neural networks are introduced to the adaptive law in order to improve the performance of the whole systems. A simulation example is performed in support of the proposed neural control scheme.

作者: Xin Chen Fei Long Zhumu Fu

作者单位: Institute of Intelligent Information Processing;Department of Mathematics,College of Science,Guizhou Institute of Intelligent Information Processing; College of Computer Science and Information,Guizhou Information Engineering College,Henan University of Science and Technology,Luoyang,Henan,PR China.

会议类型: 国际会议

会议名称: The 3rd International Symposium on Systems and Control in Aeronautics and Astronautics(第三届航空航天系统与控制国际会议 ISSCAA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 57-62

在线出版日期: 2010-01-08（万方平台首次上网日期，不代表论文的发表时间）