期刊
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
卷 -, 期 -, 页码 -出版社
WILEY
DOI: 10.1002/rnc.6178
关键词
stochastic model predictive control; discrete nonlinear stochastic system; multi-step control strategy; probabilistic constraint
资金
- National Key Research and Development Program of China [2021YFE0190900]
- National Natural Science Foundation of China [62073136, 61833011]
- Central University Basic Research Fund of China [2020MS016]
This article presents an efficient stochastic model predictive control method that decomposes discrete nonlinear systems into a linear stochastic model and a bounded model mismatch. By incorporating a multi-step control strategy, the conservativeness of the controller is reduced. The feasibility of the recursive nature of the proposed controller is thoroughly discussed, and the control performance is demonstrated through examples involving numerical nonlinear stochastic systems and a wind-blade pitch system.
Stochastic model predictive control (SMPC) is a popular approach to control uncertain systems by incorporating the probabilistic distribution of the uncertainty into the controller design. However, the performance of the SMPC deteriorates rapidly when the system becomes nonlinear. In this article, an efficient SMPC is derived after decomposing the discrete nonlinear systems into a linear stochastic model and a bounded model mismatch. The proposed controller includes a linearized-model-based SMPC and an RMPC for the model mismatch. The multi-step control strategy is incorporated to reduce the conservativeness by introducing more degrees of freedom. The feasibility of the recursiveness of the proposed controller is thoroughly discussed. The control performance is illustrated by examples involving both a numerical nonlinear stochastic system and a wind-blade pitch system.
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