A Scenario-Based Convex Formulation for Probabilistic Linear Constraints in MPC

被引:0
|
作者
Li, Jiwei [1 ]
Li, Dewei
Xi, Yugeng
机构
[1] Shanghai Jiao Tong Univ, Dept Automat, Shanghai, Peoples R China
来源
2015 10TH ASIAN CONTROL CONFERENCE (ASCC) | 2015年
关键词
MODEL-PREDICTIVE CONTROL; RANDOMIZED SOLUTIONS; SYSTEMS;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper develops a model predictive control strategy for stochastic linear systems with both multiplicative and additive uncertainty. As satisfaction of probabilistic constraints as well as performance optimization relies on description of the random system nature, we derive polyhedrons that contain system evolution matrices with prescribed probability. This is achieved by letting each polyhedron incorporates a number of stochastic scenarios of the corresponding evolution matrix. The process is efficient through a designed convex optimization and subsequent off-line scaling and verification. On the basis of the polyhedrons, probabilistic constraints can be transformed into linear constraints and be solved in reduced computation burden. The proposed MPC algorithm ensures the constraints and closed loop stability. The results are illustrated by a numerical example.
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收藏
页数:6
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