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Pedro Albertos教授讲座通知:Iterative Identification and Control Design
时间:2015-10-10 来源:综合办 编辑:zhbgs 访问次数:1209

讲座名称:Iterative Identification and Control Design
时间:Part 1:2015年10月15日(周四)14:00
Part 2:2015年10月22日(周四)14:00
地点:智能系统与控制研究所(教十八)304会议室

 

Pedro Albertos教授简介
Pedro Albertos, past president of IFAC (the International Federation of Automatic Control) in 1999-2002, IFAC Fellow and Advisor, and Senior Member of IEEE, is a world recognized expert in real-time control, leading several projects in the field. Full Professor since 1975, he is currently Emeritus Professor at Systems Engineering and Control Dept. UPV, Spain. He is Doctor Honoris-Causa from Oulu University (Finland) and Bucharest Polytechnic (Rumania) and Honorary Professor from the Northeastern University, Shenyang, RPC.
Invited Professor in more than 25 Universities, he delivered seminars in more than 30 universities and research centers. Authored over 300 papers, book chapters and congress communications, co-editor of 7 books and co-author of “Multivariable Control Systems” (Springer 2004) and “Feedback and Control for Everyone” (Springer 2010), he is also associated editor of Control Engineering Practice and Editor in Chief of the Spanish journal RIAI. His research interest includes multivariable control and non-conventional sampling control systems, with focus on time delays and multirate sampling patterns.

 

讲座内容简介
Process modeling and identification are complex activities requiring many resources, leading to different solutions. It is relevant to be aware of the kind of model needed for a specific activity. On the other hand, control performance obtained by model-based control design is constrained to the model used in the control design procedure. In this 4-hours seminar, some issues related to both the modeling (closed-loop identification, model reduction, model comparison) and the control design (stability and performance, control limitations, robustness measurements) are outlined and the new paradigm of iterative identification and control design is introduced. Some examples illustrate the effectiveness of this approach.