Offline Computation of the Explicit Robust Model Predictive Control Law Based on Deep Neural Networks

Author:

Ma Chaoqun1ORCID,Jiang Xiaoyu2,Li Pei3,Liu Jing1

Affiliation:

1. Ministry of Education Key Laboratory of Intelligent and Network Security, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China

2. Department of Information Communication, Army Academy of Armored Forces, Beijing 100072, China

3. Research Institute of Tsinghua University in Shenzhen, Shenzhen 518000, China

Abstract

A significant challenge in robust model predictive control (MPC) is the online computational complexity. This paper proposes a learning-based approach to accelerate online calculations by combining recent advances in deep learning with robust MPC. The use of soft constraint variables addresses feasibility issues in the robust MPC design, while the employment of a symmetrical structure deep neural network (DNN) approximates the robust MPC control law. The symmetry of the network structure facilitates the training process. The use of soft constraints expands the feasible region and also increases the complexity of the training data, making the network difficult to train. To overcome this issue, a dataset construction method is employed. The performance of the proposed method is demonstrated through simulated examples, and the proposed algorithm can be applied to control systems in various fields such as aerospace, three-dimensional printing, optical imaging, and chemical production.

Funder

Natural Science Foundations of china

Natural Science Basic Research Program of Shaanxi

Publisher

MDPI AG

Subject

Physics and Astronomy (miscellaneous),General Mathematics,Chemistry (miscellaneous),Computer Science (miscellaneous)

Reference40 articles.

1. Robust constrained model predictive control using linear matrix inequalities;Kothare;Automatica,1996

2. Efficient robust predictive control;Kouvaritakis;IEEE Trans. Autom. Control,2000

3. Angeli, D., Casavola, A., and Mosca, E. (2002, January 10–13). Ellipsoidal low-demanding MPC schemes for uncertain polytopic discrete-time systems. Proceedings of the 41st IEEE Conference on Decision and Control, Vols 1–4, Las Vegas, NV, USA.

4. Efficient robust constrained model predictive control with a time varying terminal constraint set;Wan;Syst. Control. Lett.,2003

5. An efficient off-line formulation of robust model predictive control using linear matrix inequalities;Wan;Automatica,2003

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3