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Predictive control simulation study based on T-S fuzzy model

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2 Author(s)
Du Shi-Jie ; Dept. of Inf. & Control Eng., Liaoning Univ. of Pet. &Chem. Technol., Fushun, China ; Shen Qing-Bo

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.

Published in:

Control Conference (CCC), 2011 30th Chinese

Date of Conference:

22-24 July 2011