Predictive Control Simulation Study Based on T-S Fuzzy Model
A T-S fuzzy model was established for nonlinear system by a fuzzy identification method, fuzzy predictive control was developed by combining with generalized predictive control. In this paper, the fuzzy identification method was based on fast fuzzy clustering, premise parameter identification using multi-step random sampling method, fuzzy C clustering method, parameter identification of the conclusions using least squares. This identification method divided fuzzy clustering into two parts, compared with the previous method, it can greatly shorten the time and has a high recognition accuracy. In terms of the nonlinear system, T-S fuzzy model has a good description of features, combined with the moving optimization of generalized predictive control to achieve effective control of nonlinear systems. The simulation results show that the algorithm is effective.
DU Shi-Jie SHEN Qing-Bo
Department of Information and Control Engineering, Liaoning University of Petroleum &Chemical Technology,Fushun 113001, P.R.China
国际会议
The 30th Chinese Control Conference(第三十届中国控制会议)
烟台
英文
1-4
2011-07-01（万方平台首次上网日期，不代表论文的发表时间）