A Distributionally Robust Optimization Based Method for Stochastic Model Predictive Control

被引：97
|
作者：
Li, Bin [1 ]
Tan, Yuan [2 ]
Wu, Ai-Guo [3 ]
Duan, Guang-Ren [4 ]
机构：
[1] Sichuan Univ, Sch Aeronaut & Astronaut, Chengdu 610065, Peoples R China
[2] Southeast Univ, Sch Automat, Nanjing 210096, Peoples R China
[3] Harbin Inst Technol, Shenzhen 518055, Peoples R China
[4] Harbin Inst Technol, Ctr Control Theory & Guidance Technol, Harbin 150001, Peoples R China
来源：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2022年 / 67卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Optimization; Stochastic processes; Predictive control; Prediction algorithms; Convergence; Computational complexity; Chebyshev approximation; Chance constraints; distributionally robust optimization (DRO); stochastic model predictive control (SMPC); LINEAR-SYSTEMS; CHANCE CONSTRAINTS; MPC; APPROXIMATIONS; UNCERTAINTY; STABILITY;
D O I：
10.1109/TAC.2021.3124750
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Two stochastic model predictive control algorithms, which are referred to as distributionally robust model predictive control algorithms, are proposed in this article for a class of discrete linear systems with unbounded noise. Participially, chance constraints are imposed on both of the state and the control, which makes the problem more challenging. Inspired by the ideas from distributionally robust optimization (DRO), two deterministic convex reformulations are proposed for tackling the chance constraints. Rigorous computational complexity analysis is carried out to compare the two proposed algorithms with the existing methods. Recursive feasibility and convergence are proven. Simulation results are provided to show the effectiveness of the proposed algorithms.
引用
收藏
页码：5762 / 5776
页数：15
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