Review on System Identification, Control, and Optimization Based on Artificial Intelligence

被引：0

作者：

Yu, Pan ^{[1
,2
]}

Wan, Hui ^{[2
]}

Zhang, Bozhi ^{[2
]}

Wu, Qiang ^{[2
]}

Zhao, Bohao ^{[2
]}

Xu, Chen ^{[2
]}

Yang, Shangbin ^{[2
]}

机构：

[1] Beijing Univ Technol, Sch Informat Sci & Technol, Beijing 100124, Peoples R China

[2] Beijing Univ Technol, Beijing Inst Artificial Intelligence, Beijing 100124, Peoples R China

来源：

MATHEMATICS | 2025年 / 13卷 / 06期

基金：

中国国家自然科学基金;

关键词：

artificial intelligence (AI); control engineering; model prediction control; optimization; parameter estimation; reinforcement learning; system identification; neural networks; NEURAL-NETWORK MODEL; PARTICLE SWARM OPTIMIZATION; ITERATIVE LEARNING CONTROL; NONLINEAR-SYSTEMS; TRAJECTORY TRACKING; BACKSTEPPING CONTROL; POWER ELECTRONICS; ADAPTIVE-CONTROL; ALGORITHM; APPROXIMATION;

D O I：

10.3390/math13060952

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

Control engineering plays an indispensable role in enhancing safety, improving comfort, and reducing fuel consumption and emissions for various industries, for which system identification, control, and optimization are primary topics. Alternatively, artificial intelligence (AI) is a leading, multi-disciplinary technology, which tries to incorporate human learning and reasoning into machines or systems. AI exploits data to improve accuracy, efficiency, and intelligence, which is beneficial, especially in complex and challenging cases. The rapid progress of AI facilitates major changes in control engineering and is helping advance the next generation of system identification, control, and optimization methods. In this study, we review the developments, key technologies, and recent advancements of AI-based system identification, control, and optimization methods, as well as present potential future research directions.

引用

页数：22

共 177 条

[91] Model-based Reinforcement Learning: A Survey [J].

Moerland, Thomas M. ;

Broekens, Joost ;

Plaat, Aske ;

Jonker, Catholijn M. .

FOUNDATIONS AND TRENDS IN MACHINE LEARNING, 2023, 16 (01) :1-118

[92] Cyber-physical systems in manufacturing [J].

Monostori, L. ;

Kadar, B. ;

Bauernhansl, T. ;

Kondoh, S. ;

Kumara, S. ;

Reinhart, G. ;

Sauer, O. ;

Schuh, G. ;

Sihn, W. ;

Ueda, K. .

CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2016, 65 (02) :621-641

[93] Analysis of Recurrent Neural Networks for Probabilistic Modeling of Driver Behavior [J].

Morton, Jeremy ;

Wheeler, Tim A. ;

Kochenderfer, Mykel J. .

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2017, 18 (05) :1289-1298

[94] Review of Metaheuristic Optimization Algorithms for Power Systems Problems [J].

Nassef, Ahmed M. ;

Abdelkareem, Mohammad Ali ;

Maghrabie, Hussein M. ;

Baroutaji, Ahmad .

SUSTAINABILITY, 2023, 15 (12)

[95] Neural network-based finite-time command-filtered adaptive backstepping control of electro-hydraulic servo system with a three-stage valve [J].

Niu, Shanshuai ;

Wang, Junzheng ;

Zhao, Jiangbo ;

Shen, Wei .

ISA TRANSACTIONS, 2024, 144 :419-435

[96] Integrating Machine Learning and Model Predictive Control for automotive applications: A review and future directions [J].

Norouzi, Armin ;

Heidarifar, Hamed ;

Borhan, Hoseinali ;

Shahbakhti, Mahdi ;

Koch, Charles Robert .

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2023, 120

[97] Supervised Imitation Learning of Finite-Set Model Predictive Control Systems for Power Electronics [J].

Novak, Mateja ;

Dragicevic, Tomislav .

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2021, 68 (02) :1717-1723

[98]

Ostafew CJ, 2014, IEEE INT CONF ROBOT, P4029, DOI 10.1109/ICRA.2014.6907444

[99] Direct adaptive controller for nonaffine nonlinear systems using self-structuring neural networks [J].

Park, JH ;

Huh, SH ;

Kim, SH ;

Seo, SJ ;

Park, GT .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2005, 16 (02) :414-422

[100] Neural-network-based iterative learning control of nonlinear systems [J].

Patan, Krzysztof ;

Patan, Maciej .

ISA TRANSACTIONS, 2020, 98 :445-453

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