应我校航天学院张立宪教授邀请，IEEE Fellow、IFAC Fellow、自动控制学科顶级期刊《IEEE自动控制汇刊》高级编委、意大利米兰理工大学PatrizioColaneri教授于近日来我校访问并展开学术交流活动，欢迎广大师生积极参加。
报告一：Control of Switching Systems with Markovian Jumps
报告二：Probabilistic Consensus in Markovian Multi-Agent Networks
PatrizioColaneri was born in Palmoli, Italy, in 1956. He received the PhD degree (Dottorato di Ricerca) in Automatic Control in 1987. After a few years in industry and at the National Research Council of Italy, he joined the Politecnico di Milano where he is full professor of Automatica and served as head of the PhD school on ICT (2007-2009). He spent a semester at the Systems Research Center of the University of Maryland (1989) and at the Hamilton Institute of the National University of Ireland (2009). He also collaborates with the Johannes Kepler University in Linz since 2000. Dr. Colaneri was a YAP (Young Author Prize) finalist at the 1990 IFAC World Congress, Tallin, USSR. He is a member of the IFAC Technical Board, a subject editor of the International Journal of Robust and Nonlinear Control, a Senior Editor of the IEEE Transaction on Automatic Control and a Senior Editor of Nonlinear Analysis: Hybrid Systems. He was a member of the Council of EUCA (European Union Control Association), and has been serving for six years as Associate Editor of Automatica (certificate of outstanding service). In 2010 he was elevated to the degree of IEEE Fellow for contributions on periodic and switching control. Since 2011 he is also a Fellow of IFAC (International Federation of Automatic Control). His main interests are in the area of periodic systems and control, robust filtering and control, and switching control. He has authored/ co-authored more than 200 papers and five books, including “Control Theory and Design: an RH2 and RH∞ viewpoint”, published by Academic Press in 1997, “Periodic Systems: Filtering and Control”, Springer Verlag, 2009.
In the seminar, we introduce and focus on control strategies for continuous-time Markov jump linear systems, in particular to a new control strategy denominated minimax control. This strategy generalizes switching and linear parametervarying control strategies and is determined such as to preservestochastic stability and guaranteed performance. The specialclasses of Markov mode dependent and mode independent control are considered. The design methodology is characterized byminimax problems for which the existence of a saddle point is thecentral issue to be taken into account. As a natural application, Markov jump linear systems state feedback control design is discussed under this framework. The case of positive dual switchedsystems is particularly emphasized.
This paper addresses the probabilistic consensus problem in a network of Markovian agents.The dynamics of each agent is modeled as a finite-state Markov chain, with transition rates that are affected by the communication with the neighbors, so inducingan emulation effect. Consensus is reached when allthe agent probability vectors converge to a commonsteady-state probability vector, in the case of communication networks described by either a complete graph or astar-topology graph. Some parametersof the network model could be used as tuning knobsto steer the steady-state consensus wherever desired.