Design of multivariable self-tuning PID controllers via Quasi-Diagonal Recurrent wavelet Neural Network

摘要：

Multlvariable PID controllers have recently emerged as a kind of convenient yet very powerful control technique for solving coupling nonlinear system. This article describes a new method for design of multivariable PID based on Quasi-Diagonal Recurrent Wavelet Neural Network (QDRWNN). Firstly, Due to the advantages of Wavelet Neural Network(WNN) and Diagonal Recurrent Neural Network(DRNN) such as the good learning ability, generalization of wavelet transform, dynamic mapping and converges quickly, we present a novel Neural Network QDRWNN. Secondly, the new Neural Network is used to identify the coupling nonlinear system on Hne and tune parameters of multivariable PID controllers automatically. Finally, an illustrative example is given to demonstrate the feasibility and validity of the proposed method.

关键词： Multivariable PID controller Quasi-Diagonal Recurrent wavelet Neural Nehvork (QDRWNN) Decoupling control.

作者: Kui Zhang Xinyan An

作者单位: Electronic and Electrical Engineering College Changzhou College of Information Technology Changzhou ,China

会议类型: 国际会议

会议名称: 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics(第二届智能人机系统与控制论国际学术会议 IHMSC 2010)

会议地点: 南京

会议语种:英文

页码: 434-438

在线出版日期: 2010-08-26（万方平台首次上网日期，不代表论文的发表时间）