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Robust stability constraints for fuzzy model predictive control
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DOI：10.1109/91.983278.png)
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摘要
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This paper addresses the synthesis of a predictive controller for a nonlinear process based on a fuzzy model of the Takagi-Sugeno (T-S) type, resulting in a stable closed-loop control system. Conditions are given that guarantee closed-loop robust asymptotic stability for open-loop bounded-input-bounded-output (BIBO) stable processes with an additive l(1)-norm bounded model uncertainty. The idea is closely related to (small-gain-based) l(1)-control theory, but due to the time-varying approach, the resulting robust stability constraints are less conservative. Therefore the fuzzy model is viewed as a linear time-varying system rather than a nonlinear one. The goal is to obtain constraints on the control signal and its increment that guarantee robust stability. Robust global asymptotic stability and offset-free reference tracking are guaranteed for asymptotically constant reference trajectories and disturbances.
Keyword:
l(1)-control theory
model predictive control (MPC)
multiple-input-multiple-output (MIMO) systems
robust stability
Takagi-Sugeno (T-S) fuzzy models
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期刊
IF:
11.9
论文数:
5.0K
被引数:
2.9W
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