Constrained data-driven RMPC with guaranteed stability

被引：0

作者：

Yang, Lingyi ^{[1
,2
]}

Lu, Jianbo ^{[1
,2
]}

Xu, Yunwen ^{[1
,2
]}

Li, Dewei ^{[1
,2
]}

Xi, Yugeng ^{[1
,2
]}

机构：

[1] Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China

[2] Minist Educ China, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China

来源：

2019 12TH ASIAN CONTROL CONFERENCE (ASCC) | 2019年

基金：

美国国家科学基金会;

关键词：

MODEL-PREDICTIVE CONTROL; MATRIX;

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：

Being able to tackle constraints, the adaptive model predictive control (AMPC) is one of the most frequently used methodologies in coping with model uncertainties. Fundamentally, the core procedure of AMPC is dependent on the model identification to increase the suboptimality of MPC. However, only the type of data which admits the specified requirements is accessible to the identification, which implies the failure of adaptation method to some extent. Aimed at this proposal, this paper proposes a new control technique which adds data-driven representation skills into the robust model predictive controller (RMPC). The advantages over other controllers were explicitly reflected in numerical simulation. Feasibility of the algorithm is able to be proven and the stability of the controlled system is verified.

引用

页码：1277 / 1282

页数：6

共 19 条

[1] Optimizing the end-point state-weighting matrix in model-based predictive control [J].