An Auto-tuning Grey-Neuro-PID Controller

摘要：

In this paper, we propose to add Grey prediction model GM(1,2) into the self-tuning Neuro-PID controller based on radial basis function (RBF) algorithm to improve the performance of the controller. Initially, the prediction of system output by the simple GM(1,2) model is added to the RBF algorithm as one of the inputs to enhance the performance of RBF neural network system identifier. The output of this GM(1,2)-RBF on-line learning system model is subsequently used to establish a set of updating algorithms for the gains of self-tuning PID controller. The detailed description of the proposed system structure and the design algorithm is given in this paper. The proposed auto-tuning PID controller via GM(1,2)-RBF algorithm is put into tests by Matlab simulations and motor speed control experiments by using LabVIEW. The system responses of self-tuning PID controller based on GM(1,2)-RBF and RBF are compared. Both simulations and motor test results confirm that the proposed self-tuning PID controller based on GM(1,2)-RBF performs better than the one based on RBF.

作者: Shuen-Jeng Lin Chia-Chang Tong Neng-Kai Yang

作者单位: degree at Chien-kuo Technology University, Changhua, Taiwan Chien-kuo Technology University, Changhua, Taiwan

会议类型: 国际会议

会议名称: 2007年IEEE灰色系统与智能服务国际会议(2007 IEEE International Conference on Grey Systems and Intelligent Services)

会议地点: 南京

会议语种:英文

在线出版日期: 2007-11-18（万方平台首次上网日期，不代表论文的发表时间）