Min-max model predictive control for constrained nonlinear systems via multiple LPV embeddings |
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Authors: | Min Zhao Ning Li ShaoYuan Li |
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Affiliation: | (1) Institute of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China |
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Abstract: | A min-max model predictive control strategy is proposed for a class of constrained nonlinear system whose trajectories can
be embedded within those of a bank of linear parameter varying (LPV) models. The embedding LPV models can yield much better
approximation of the nonlinear system dynamics than a single LTV model. For each LPV model, a parameter-dependent Lyapunov
function is introduced to obtain poly-quadratically stable control law and to guarantee the feasibility and stability of the
original nonlinear system. This approach can greatly reduce computational burden in traditional nonlinear predictive control
strategy. Finally a simulation example illustrating the strategy is presented.
Supported by the National Natural Science Foundation of China (Grant Nos. 60774015, 60825302, 60674018), the National High-Tech
Research & Development Program of China (Grant No. 2007AA041403), the Specialized Research Fund for the Doctoral Program of
Higher Education of China (Grant No. 20060248001), and partly by Shanghai Natural Science Foundation (Grant No. 07JC14016) |
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Keywords: | constrained nonlinear systems predictive control LPV embedding parameter dependent Lyapunov function LMI |
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