Constrained Adaptive Model-Predictive Control for a Class of Discrete-Time Linear Systems With Parametric Uncertainties

被引：27

作者：

Zhu, Bing ^{[1
]}

Zheng, Zewei ^{[1
]}

Xia, Xiaohua ^{[1
,2
]}

机构：

[1] Beihang Univ, Sch Automat Sci & Elect Engn, Res Div 7, Beijing 100191, Peoples R China

[2] Univ Pretoria, Dept Elect Elect & Comp Engn, ZA-0028 Pretoria, South Africa

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2020年 / 65卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Optimization; Adaptive systems; Uncertainty; Linear systems; Predictive control; Stability analysis; Numerical stability; Adaptive control; linear systems; model-predictive control (MPC); optimization; MPC;

D O I：

10.1109/TAC.2019.2939659

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this technical note, an adaptive model-predictive control (MPC) is proposed for a class of discrete-time linear systems with constant parametric uncertainties and control constraint. The proposed adaptive MPC originates from the principle of min-max optimization, which cannot be solved in a direct numerical way. An adaptive strategy is proposed to estimate the uncertain parameters, such that the estimated error converges, and the optimization in the MPC can be transferred into a solvable simple structure. Feasibility of the optimization and stability of the closed-loop system are proved theoretically, and a simulation example is presented to illustrate the theoretical result.

引用

页码：2223 / 2229

页数：7

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