Output Feedback Control for Discrete-time Nonlinear Systems and Its Applications

摘要：

A compound neural network (CNN) which includes a linear feed-forward neural network (LFNN) and a recurrent neural network (RNN) is constructed to identify nonaffine dynamic nonlinear systems. Because the current control input is not included in the input vector of the recurrent neural network, output feedback control laws of nonlinear systems can be easily obtained from one-step predictive models approximated by the CNN. To minimize the predictive error, the current approximation error is used in the predictive process. The computation work is small because no on-line training is required for the output feedback controller. This algorithm can be used to SISO and MIMO nonlinear system control in real time. Simulation studies have shown that this scheme is simple and has good control accuracy and robustness.

关键词： MIMO system Output feedback control Compound neural network Nonlinear discrete-time system

作者: Zhang Yan Li Weiwei Liang Xiuxia Yang Peng

作者单位: Dept. of Automation, Hebei University of Technology, Tianjin, 300130

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 449-453

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）