Keynote Speeches

Keynote Speech II

Artificial Intelligence-Based Self-Learning Control Methods

Derong Liu
Anhui University, Hefei, China

Artificial Intelligence-Based Self-Learning Control Methods have emerged as a transformative approach for optimal control of nonlinear systems in dynamic and complex environments. Unlike traditional control techniques that rely on predefined models and manual tuning, self-learning control leverages AI-driven algorithms, such as reinforcement learning and neural networks to adapt and optimize control strategies in real time. These methods enable autonomous decision-making, improved robustness, and enhanced adaptability, making them particularly useful in robotics, autonomous vehicles, industrial automation, and smart energy systems. This lecture explores the fundamental principles of AI-driven self-learning control, discusses key methodologies including adaptive dynamic programming and parallel control approaches, and examines their practical applications across various domains. Furthermore, this lecture also explores the trade-offs between learning efficiency, real-time adaptability, and deployment feasibility, offering insights into overcoming these challenges for real-world implementations. Additionally, the concept of meta-control is considered as a higher-level mechanism to dynamically adjust self-learning strategies, improving the efficiency and robustness of AI-driven control systems. By integrating AI with control theory, self-learning control is poised to revolutionize intelligent automation, enabling more efficient and autonomous systems.

Biosketch

Derong Liu received the PhD degree in electrical engineering from the University of Notre Dame, USA, in 1994. He became a Full Professor of Electrical and Computer Engineering and of Computer Science at the University of Illinois Chicago in 2006. He was selected for the “100 Talents Program” by the Chinese Academy of Sciences in 2008, and he served as the Associate Director of The State Key Laboratory of Management and Control for Complex Systems at the Institute of Automation, from 2010 to 2016. He is currently a professor at Anhui University, Hefei, China. He has published 13 books. He received the International Neural Network Society’s Dennis Gabor Award in 2018 and the IEEE CIS Neural Network Pioneer Award in 2022. He has been named a highly cited researcher by Clarivate since 2017. He was the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems from 2010 to 2015. He is the Editor-in-Chief of Artificial Intelligence Review (Springer). He is a Fellow of the IEEE, a Fellow of the International Neural Network Society, a Fellow of the International Association of Pattern Recognition, and a Member of Academia Europaea (The Academy of Europe).