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

[51] Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems [J].

Hu, Yunfeng ;

Zhang, Chong ;

Wang, Bo ;

Zhao, Jing ;

Gong, Xun ;

Gao, Jinwu ;

Chen, Hong .

IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2024, 11 (02) :344-361

[52] High-Precision Dynamic Control of Soft Robots With the Physics-Learning Hybrid Modeling Approach [J].

Huang, Xinjia ;

Rong, Yu ;

Gu, Guoying .

IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2025, 30 (03) :1658-1669

[53] NEURAL NETWORKS FOR CONTROL-SYSTEMS - A SURVEY [J].

HUNT, KJ ;

SBARBARO, D ;

ZBIKOWSKI, R ;

GAWTHROP, PJ .

AUTOMATICA, 1992, 28 (06) :1083-1112

[54] Accelerating Symmetric Rank-1 Quasi-Newton Method with Nesterov's Gradient for Training Neural Networks [J].

Indrapriyadarsini, S. ;

Mahboubi, Shahrzad ;

Ninomiya, Hiroshi ;

Kamio, Takeshi ;

Asai, Hideki .

ALGORITHMS, 2022, 15 (01)

[55]

Jabbari Z.M., 2024, J. Mech. Eng, V60, P177

[56]

Kabanikhin S. I., 2008, Inverse and ill-Posed Problems

[57] Efficient Representation and Approximation of Model Predictive Control Laws via Deep Learning [J].

Karg, Benjamin ;

Lucia, Sergio .

IEEE TRANSACTIONS ON CYBERNETICS, 2020, 50 (09) :3866-3878

[58]

Kashyap A., 2023, P 2023 INT C APPL IN, P1

[59]

Khaki-Sedigh A., 2023, An Introduction to Data-Driven Control Systems

[60] A Modified Car-Following Model Based on a Neural Network Model of the Human Driver Effects [J].

Khodayari, Alireza ;

Ghaffari, Ali ;

Kazemi, Reza ;

Braunstingl, Reinhard .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, 2012, 42 (06) :1440-1449

← 1 2 3 4 5 6 7 8 9 10 →