报告题目：Predefined-Time Convergence and its Application to Stabilization Problems

报告人：Michael V. Basin 院士

报告时间：2022年4月20日15:00-16:00

报告地点：腾讯会议：456-365-749

报告对象：感兴趣的教师、研究生、本科生

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报告人简介：

 Michael V. Basin received his Ph.D. degree in Physical and Mathematical Sciences with major in Automatic Control and System Analysis from the Moscow Aviation University (MAI) in 1992. He is currently Full Professor with the Autonomous University of Nuevo Leon, Mexico, and Ningbo Institute of Industrial Equipment Technology, Ningbo, China. Starting from 1992, Dr. Basin published more than 400 research papers in international referred journals and conference proceedings. He is the author of the monograph “New Trends in Optimal Filtering and Control for Polynomial and Time-Delay Systems,” published by Springer.  His works are cited more than 6500 times (h index = 44).  Dr. Basin has supervised 15 doctoral and 9 master's theses. He has served as the Editor-in-Chief and serves as the Co-Editor-in-Chief of Journal of The Franklin Institute, the Senior Editor of IEEE/ASME Transactions on Mechatronics, an Associate Editor of Automatica, IEEE Transactions on Systems, Man and Cybernetics: Systems, IET-Control Theory and Applications, International Journal of Systems Science, Neural Networks. Dr. Basin was awarded a title of Highly Cited Researcher by Thomson Reuters, the publisher of Science Citation Index, in 2009 and listed in “100 000 Leading Scientists in the World”; he is a regular member of the Mexican Academy of Sciences. Prof. Basin has been honored as a Fellow of Prominent Talent (Qian Ren) Program of Zhejiang Province, China. His research interests include optimal filtering and control problems, stochastic systems, time-delay systems, identification, sliding mode control and variable structure systems, applications to mechatronic and transportation systems.

报告内容：

This paper designs a predefined-time convergent continuous control algorithm to stabilize a permanent-magnet synchronous motor system. Three cases have been considered: disturbance-free, in presence of a deterministic disturbance satisfying a Lipschitz condition, and in presence of both a stochastic white noise and a deterministic disturbance satisfying a Lipschitz condition. The designed control law is free from the restrictions of exponential control growth and exact initial conditions knowledge. This is the first predefined-time convergent continuous control algorithm applied to stabilization of a permanent-magnet synchronous motor system with both deterministic and stochastic disturbances, which enables one to a priori set the predefined convergence time even in presence of various disturbances of different nature. Numerical simulations are provided for a permanent-magnet synchronous motor system to validate the obtained theoretical results in each of the three considered cases. The simulation results demonstrate that the employed values of the predefined-time convergent control inputs are applicable in practice.

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