Abstract

This paper presents two strategies of nonlinear predictive control based on a Takagi-Sugeno fuzzy model. The first one introduces a fuzzy logic-based modeling methodology, where a nonlinear system is divided into a number of linear subsystems. So the linear model based predictive control (MPC) technique is used for each subsystem. In the second one, the fuzzy model is considered as a nonlinear model of the system and the control signal is obtained by minimizing either the cumulative differences or the instant difference between set-point and fuzzy model output. The efficiency of these two fuzzy model predictive control (FMPC) approaches is demonstrated through two examples.

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