Stable Tracking Control to a Nonlinear Process via Neural Network Model

摘要：

A stable neural network control scheme for unknown non-linear systems is developed in this paper. While the control variable is optimised to minimize the performance index, convergence of the index is guaranteed asymptotically stable by a Lyapnov control law. The optimization is achieved using a gradient descent searching algorithm and is consequently slow. A fast convergence algorithm using an adaptive learning rate is employed to speed up the convergence. Application of the stable control to a single input single output (SISO) non-linear system is simulated. Simulation results demonstrate the effectiveness of the method.

关键词： neural network Lyapnov nonlinear system

作者: Peng Wang Yuliang Cong Xuebai Zang

作者单位: College of communication Engineering,Jilin University,Changchun,China College of communication Engineering Jilin University,Changchun,China College of computer,Jilin University,Changchun,China

会议类型: 国际会议

会议名称: 2010 International Conference on Computer,Mechatronics,Control and Electronic Engineering(2010计算机、机电、控制与电子工程国际会议 CMCE 2010)

会议地点: 长春

会议语种:英文

页码: 284-287

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）