报告题目：Analysis and Design on Fuzzy Control Systems
报告人：石碰教授
报告时间：11月22日 14:50
报告地点：教学楼A402
报告人简介:
Peng Shi received the PhD degree in Electrical Engineering from the University of Newcastle, Australia in 1994; and the PhD degree in Mathematics from the University of South Australia in 1998. He was awarded two higher doctorate degrees, the Doctor of Science degree from the University of Glamorgan, UK in 2006, and the Doctor of Engineering degree from the University of Adelaide in 2015.
He is now a distinguished professor, chair of systems and control at the University of Adelaide. Australia. His research interests include system and control theory, computational intelligence, and operational research. He has published widely in those areas. He has been continuously recognised by Thomson Reuters as a Highly Cited Researcher from 2014 to 2017. He has received a number of awards, including the Best Transactions Paper Award from IEEE Systems, Man and Cybernetics Society in 2016.
He is a Fellow of Institute of Electrical and Electronic Engineers, the Institution of Engineering and Technology, the Institute of Mathematics and its Applications, and the Institution of Engineers, Australia. He was the Chair of Control Aerospace and Electronic Systems Chapter, IEEE South Australia Section, and an IEEE Distinguished Lecturer. He has served in the editorial board for a number of scientific journals as editor, subject editor, special issue editor, associate editor, including Automatica, IEEE Transactions on Automatic Control; IEEE Transactions on Fuzzy Systems; IEEE Transactions on Cybernetics; IEEE Transactions on Circuits and Systems; IEEE Access; IEEE Control Systems Letters; Information Sciences; Signal Processing; etc.
报告内容简介:
Analysis and design on nonlinear systems is often problematic due to their complexities. One effective way of representing nonlinear dynamic systems is the so-called Takagi-Sugeno fuzzy model, which is governed by a family of fuzzyIF-THEN rules that represent local linear input-output relations of the system. It incorporates a family of local linear models that smoothly blend together through fuzzy membership functions. Within these fuzzy models, local dynamics in different state space regions are represented by linear models. An overall fuzzy model of the system is created by fuzzily `blending' these linear models. Based on the fuzzy model, the control design is carried out by using the parallel distributed compensation scheme. The strategy is that a linear state feedback controller is designed for each local linear model. The obtained overall controller is nonlinear in general. This talk will present introduction of analysis and design for nonlinear systems by fuzzy modelling approach, including stability, stabilization and control.
