Output feedback Takagi-Sugeno fuzzy model predictive control through linear matrix inequalities approaches Online publication date: Tue, 12-Jul-2022
by Thalita B.S. Moreira; Marcus V.S. Costa; Fabricio G. Nogueira
International Journal of Modelling, Identification and Control (IJMIC), Vol. 40, No. 1, 2022
Abstract: The present paper proposes an output feedback control scheme combining the Takagi-Sugeno (T-S) fuzzy method with a model predictive control (RMPC) technique, using parallel distributed compensation (PDC) and linear matrix inequalities (LMIs). The study presents an algorithm of relaxed RMPC, considering a nonlinear varying parameters rule-based T-S fuzzy model. Moreover, a new stability criterion is proposed considering an online observer-based output feedback T-S fuzzy model predictive control (FMPC). The aforementioned criterion is implemented through LMIs constraints ensuring the system's robust stability. This procedure assembles the aforementioned techniques and applies them in a benchmark problem. The obtained results evidence the better performance of the proposed method in comparison with the benchmark controller, considering analysis of time responses and performances indices.
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