报告题目：Physical Intelligence and Affine matched parameterization method for T-S fuzzy systems

    报告人：Sangmoon Lee教授,Ph.D. supervisor， Kyungpook National University

    报告时间：2025年5月13日上午9：00-11：00

    报告地点：腾讯会议：184-950-196

    报告对象：感兴趣的教师、研究生、本科生等

    主办单位：电力电子与运动控制安徽省高等学校省级重点实验室、海洋之神之线路检测中心

    报告人简介：

Sangmoon Lee received the B.S. degree in electronic engineering from Kyungpook National University, Daegu, South Korea, in 1999, and the M.S. and Ph.D. degrees in electronic engineering from Pohang University of Science and Technology (POSTECH), Pohangsi, South Korea, in 2001 and 2006, respectively.

He is currently working as a Professor with the School of Electronic and Electrical Engineering, Kyungpook National University. His research interests include linear parameter varying systems, model predictive control, learning MPC, fuzzy systems, nonlinear systems analysis, robotics, networked control systems, neural networks, reinforcement learning, RL based control, imitation learning and its industrial applications.

Prof. Lee has been a recipient of the Highly Cited Researchers Award by Clarivate Analytics (formerly, Thomson Reuters) in 2016, 2018 to 2020. He is an Associate Editor/Senior Editor of several international journals, including IEEE TRANSACTION ON FUZZY SYSTEMS, ISA Transactions, International Journal of Systems Science, International Journal of Control, Automation and Systems.

摘要：In general, intelligence is divided into physical intelligence and computational intelligence. Recently, the field of artificial intelligence research has focused on computational intelligence, which corresponds to cognition and decision.

Research on autonomous intelligence systems recognizes human behavior according to the environment and decide how to act. It should be extended to research on controlled physical intelligence. This physical intelligence is used in medical surgical assistant robots, service robots, and cooperative robots in the manufacturing industry. Cognitive technologies such as artificial vision and natural language processing are key technologies to realize these skills. To decide and classify according to behavioral intentions, learning-based control skills should be studied.

In addition, this presentation is related with a new parallel distributed compensation controller design approach for T–S (Takagi–Sugeno) fuzzy control systems with affine matched membership functions in the system and controller. In the new fuzzy control, affine transformed membership functions are adopted by scaling and biasing the original membership functions of the system. Stabilization and performance criterion of the closed-loop T–S fuzzy systems are obtained through a new parameterized linear matrix inequality which is rearranged by affine matched membership functions. The conservativeness of stabilization condition for the T–S fuzzy system is significantly relaxed by utilizing the constraints condition of the controller’s membership functions, which is determined from the difference of each transformed membership function. In addition, the controller gain is reconstructed by a decision variable separation technique with two different free weighting matrices without any scaling parameter. Through numerical examples, the superiority of the proposed method is verified.

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