NONLINEAR NEURAL NETWORK PREDICTIVE CONTROL FOR POWER UNIT USING PARTICLE SWARM OPTIMIZATION

摘要：

A novel approach of nonlinear model predictive control(NMPC) is proposed using radial basis function neural network (RBFNN) and particle swarm optimization (PSO). A multi-step predictive model of the controlled process based on RBFNN is studied. The fuzzy c-mean (FCM) clustering algorithm was used to determine the position of centers of the hidden layer of RBFNN. A modified PSO with simulated annealing is used at the optimization process in NMPC. The unit control for a fossil fuel power unit (FFPU) load system is studied. The simulation results demonstrate the effectiveness of the proposed algorithm.

关键词： Fossil fuel power unit nonlinear model predictive control radial basis function neural network particle swarm optimization fuzzy c-mean clustering

作者: JIAN-MEI XIAO XI-HUAI WANG

作者单位: Department of Electrical Engineering and Automation, Shanghai Maritime University, Shanghai, 200135, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 2851-2856

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）