学术论坛报告: Dr. Zhaojian Li——NextGen Modeling and Control: Integrating Real-Time Learning with Control Theory

发布者:机械工程学院发布时间:2019-12-23浏览次数:13

学术论坛通知:本期学术论坛由Michigan State UniversityDr. Zhaojian Li主讲

题目:NextGen Modeling and Control: Integrating Real-Time Learning with Control Theory

报告人:Dr. Zhaojian Li

时间: 20191226日,15:00-16:00

地点:机械楼第一会议室

  

报告摘要:

While complex engineering systems incorporate first principles based on physical models, they may not make full use of relevant information from real-time data. Exclusively data-driven approaches to complex engineering systems may lead to incorrect and uninformed decisions as they do not incorporate useful information from the engineering and physical models. A hybrid approach that uses real-time data, in conjunction with basic physical and engineering constraints, has the promise to overcome these limitations and can lead to significantly improved decision capabilities.

In the first part of this talk, I will present an online nonlinear system identification algorithm that can simultaneously identify local linear models as well as their validity zones with minimum calibration efforts. It is enabled by integrating an online clustering algorithm with recursive least squares. In the second part of this talk, I will present a safe Reinforcement Learning framework where we learn a safe policy safely by exploiting system dynamics for training supervision. It has the great potential to enable real-time learning with safety-critical systems like process control systems. I will also talk about a recent project on AI-enabled apple robotic harvesting.

报告人简介:

Dr. Zhaojian Li is an Assistant Professor in the Department of Mechanical Engineering at Michigan State University. He obtained M.S. (2013) and Ph.D. (2015) in Aerospace Engineering (flight dynamics and control) at the University of Michigan, Ann Arbor. As an undergraduate, Dr. Li studied at Nanjing University of Aeronautics and Astronautics, Department of Civil Aviation, in China. Dr. Li worked as an algorithm engineer at General Motors from January 2016 to July 2017. His research interests include Learning-based Control, Nonlinear and Complex Systems, and Robotics and Automated Vehicles. He is the author of more than 20 top journal articles and several patents. He is currently the Associate Editor for Journal of Evolving Systems, American Control Conference and ASME Dynamics and Control Conference.

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