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Output feedback fuzzy model predictive control with multiple objectives
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AI
DOI：10.1016/j.jfranklin.2023.11.026.png)
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Abstract
En 中文
We present a dynamic output feedback fuzzy model predictive control (FMPC) approach for the Takagi-Sugeno model with bounded disturbance in this paper. The proposed approach relies on the formulation, at each sampling instant, of a lexicographic optimization problem that takes into account multiple objectives including improving the control performance, enlarging the region of attraction (ROA) and tightening the estimation error set (EES). Since the controller parameters and Lyapunov matrices are kept as degrees of freedom for the lower-ordered optimization problems, larger ROA and less conservative EES are obtained. As a result, the control performance can be greatly improved with respect to previous works. Furthermore, we prove the recursive feasibility of the approach and guarantee the stability of the closed-loop system. The result is verified through a numerical example.
Keywords:
Output feedback
Fuzzy model predictive control
Takagi-Sugeno model
Lexicographic optimization
Stability
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J
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