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工学院专题学术讲座 | Wen-Hua Chen 陈文华: Dual Control for High Levels of Automation in Uncertain Environments

时间

2024年5月13日(周一)
10:40-12:00

地点

西湖大学云谷菲律宾纸飞机聊天app E10-205

主持

西湖大学工学院赵世钰博士

受众

全体师生

分类

学术与研究

工学院专题学术讲座 | Wen-Hua Chen 陈文华: Dual Control for High Levels of Automation in Uncertain Environments

时间:2024年5月13日(周一)上午10:40-12:00

Time: 10:40-12:00, Monday, May 13, 2024

地点西湖大学云谷菲律宾纸飞机聊天app E10-205

Venue: E10-205, Yungu Campus

主持人: 西湖大学工学院赵世钰博士

Host: Dr. Shiyu Zhao, School of Engineering

语言:英文

Language: English

主讲嘉宾/Speaker:

Prof. Wen-Hua Chen 陈文华

Professor in Autonomous Vehicles

Loughborough University

IEEE Fellow

主讲人简介/Biography:

Dr Wen-Hua Chen holds Professor in Autonomous Vehicles and the founding Director of Centre for Autonomous Systems in the Department of Aeronautical and Automotive Engineering at Loughborough University, UK. Prof. Chen has a considerable experience in control, signal processing and artificial intelligence and their applications in aerospace, automotive and agriculture systems. In the last 20 years, he has been working on the development and application of unmanned aircraft system and intelligent vehicle technologies, spanning autopilots, situational awareness, decision making, verification, remote sensing for precision agriculture and environment monitoring. He is a Chartered Engineer, and a Fellow of IEEE, the Institution of Mechanical Engineers and the Institution of Engineering and Technology, UK.  Prof Chen currently holds a 5-years Established Career Fellowship of the UK Engineering and Physical Sciences Research Council (EPSRC) in developing AI enabled control systems for robotics and autonomous systems.

讲座摘要/Abstract:

For a system operating in an unknown or changing environment, it is desirable to design a control system to keep it always operating at its best possible performance (i. e. in terms of productivity or efficiency). This talk introduces a new approach, namely dual control for exploitation and exploration (DCEE), to this type of self-optimisation control problems. In this framework, the control action not only drives a system moving towards a believed optimal operational condition, but also aims to reduce the uncertainty of this belief by actively exploring and learning the unknown environment. Autonomous search of the source of airborne dispersion using a robot and maximum power point tracking in solar farming are used as case studies to illustrate the proposed DCEE approach. Its link with reinforcement learning and active inference in neuroscience is also discussed.


讲座联系人/Contact:

陈老师[email protected]


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