The DCL invites you to hear
Prof. Zhongsheng Hou
give a talk entitled
Modern Control Theory and Model-based Framework
August 16, 2016 11:00 am
TSRB 530
Abstract:
Modern control theory, with its model-based framework, has been fully grown with the main branches in system identification, adaptive control, robust control, optimal control, variable structure control, stochastic system theory, etc. Many of the techniques have been extensively applied in areas such as industrial processes, aerospace systems, and so on. But for those plants where 1) whose first principle models or identified models are available but with indescribable uncertainties, 2) whose models are complicated with high order and/or highly nonlinear dynamics, 3) whose models are unavailable, however, there is still no efficient control design solutions, even theoretically. Many industrial processes generate and store a huge amount of process data, which contains all the valuable information of the process operations and the equipment. How to use such process data, both on-line and off-line, to directly determine the controller structure, tune the controller parameter, design the output prediction, make the performance assessment, etc., would have great significance when the process models are unavailable. Therefore, the establishment of the data-driven control theory is urgent and important both for the completeness and field applications in modern control theory. This talk includes four parts. First, a brief survey is given on the existing challenges and problems of the model-based control theory, followed by definitions, classifications, tasks and some important issues of data-driven control. Then two typical data-driven control methods, the model free adaptive control and the iterative learning control, are introduced to illustrate the design strategy of data-driven control. The focus will be on the dynamic linearization data model, controller designing, and stability and robustness issues. The third part of the talk will discuss possible united framework for data-driven control methods, especially the dynamic-linearization-technique based controller designing. Finally, some conclusions are given, together with future perspectives on data-driven control.
Bio:
Prof. Zhongsheng Hou received his Bachelor’s and Master’s degrees from Jilin University of Technology, China, in 1983 and 1988 respectively. His PhD was received from Northeastern University, China, in 1994. He was a postdoctoral fellow with Harbin Institute of Technology, China, from 1995 to 1997 and a visiting scholar with Yale University, CT, from 2002 to 2003. In 1997 he joined the Beijing Jiaotong University, China where he is a distinguished professor and founding director of Advanced Control Systems Lab and Head of Department of Automatic Control. He is also the founding director of the technical committee in Data Driven Control, Learning and Optimization (DDCLO), Chinese Association if Automation. He is IEEE Senior Member, IFAC Technical Committee Member on Adaptive and Learning Systems and Transportation Systems.His research interests are in the fields of data-driven control, models free adaptive control, learning control. And intelligent transportation systems. Prof. Hou’s original contribution in Model Free Adaptive Control has been recognized by over 130 different field applications including wide-area power system, lateral control of autonomous vehicle, temperature control of silicon rod, etc. and the pioneering contributions in Data Driven Control & Learning Control have been recognized by multiple projects supported by the National Nature Science Foundation of China (NSFC), including two key projects of NSFC, 2009 and 2015 respectively, and the major international cooperation project of NSFC, 2012.
- In Campus Calendar
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No
- Groups
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School of Aerospace Engineering
- Invited Audience
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Public
- Categories
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Seminar/Lecture/Colloquium
- Keywords
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aerospace engineering, Control Theory
- Status
-
- Created By: Corinna Draghi
- Workflow Status: Published
- Created On: Aug 12, 2016 - 9:02am
- Last Updated: Oct 7, 2016 - 10:18pm
