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

Pan Yu (), Hui Wan, Bozhi Zhang, Qiang Wu, Bohao Zhao, Chen Xu and Shangbin Yang
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Pan Yu: School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
Hui Wan: Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing 100124, China
Bozhi Zhang: Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing 100124, China
Qiang Wu: Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing 100124, China
Bohao Zhao: Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing 100124, China
Chen Xu: Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing 100124, China
Shangbin Yang: Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing 100124, China

Mathematics, 2025, vol. 13, issue 6, 1-22

Abstract: 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.

Keywords: artificial intelligence (AI); control engineering; model prediction control; optimization; parameter estimation; reinforcement learning; system identification; neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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