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【自动化学院南山(国际)讲坛】报告通知(第二十三讲)
2021-04-07 18:30   自动化学院 审核人:   (点击: )

报告题目:Adaptive tracking control of an electronic throttle valve based on recursive terminal sliding model

人:Wang Hai副教授

人:范泉涌副教授

报告时间:2021年4月8日(周四)下午15:00

报告地点:Microsoft Teams链接:https://teams.microsoft.com/dl/launcher/launcher.html?url=%2F_%23%2Fl%2Fmeetup-join%2F19%3Ameeting_NDQ0YjQ4NTMtY2EwYS00MGVjLTkwOWQtMzg5ZTIwMzgwODJl%40thread.v2%2F0%3Fcontext%3D%257b%2522Tid%2522%253a%2522c00d4c1b-cf7b-4e93-b7c7-10113a9bc230%2522%252c%2522Oid%2522%253a%25227a7dc7fe-afe9-4a76-852e-002ec419ae6d%2522%257d%26anon%3Dtrue&type=meetup-join&deeplinkId=ad89b56c-1022-4aeb-b566-bf89eb9d8eb1&directDl=true&msLaunch=true&enableMobilePage=true&suppressPrompt=true

报告简介:In conventional automotive throttle systems, the motion of throttle plate is controlled only by the intent of drivers via a rod linkage or cable. However, many issues such as aging and wearing of mechanical structures inevitably cause safety hazard. With the introduction of electronic throttle valve (ETV) system, not only the safety is improved due to the removal of mechanical connections, but also the engine efficiency and practicability are significantly enhanced since the throttle position is optimized by electronic control unit based on the trade-off between the driver request and drivability, safety and emission constraints. The control task is to enable the throttle plate to precisely track the reference position generated by the pedal, which is a quite challenging work due to parametric variations and nonlinear dynamics excited by friction in transmission, return spring limp-home, gear backlash, and external perturbations during engine operation.

In this talk, we will introduce an adaptive recursive terminal sliding mode (RTSM)-based tracking control scheme for an ETV. The developed RTSM dynamical structure for the controller is composed of a fast nonsingular terminal sliding surface and a recursive integral terminal sliding function, such that not only is the reaching phase eliminated, but also a sequential finite-time zero-convergence of both the recursive sliding surfaces and position tracking error are guaranteed. Due to the difficulty in ensuring a satisfactory tracking performance for a broad range of operation conditions, an adaptive mechanism is further embedded to estimate both the lumped uncertainty bound and the sliding mode parameters. Real-time comparative experiments are conducted to verify that the proposed control enjoys a fast finite-time convergence and superior robustness with respect to uncertainties and disturbances.

报告人简历:

Hai Wang (Senior Member, IEEE) received his PhD degree from Swinburne University of Technology (SUT), Australia, in 2013, in electrical and electronic engineering. From 2014 to 2015, he was the Postdoc Research Fellow in the Faculty of Sciences, Engineering and Technology, at SUT, Australia. From 2015 to early 2019, he was with the School of Electrical and Automation Engineering at Hefei University of Technology, China, where he served as the Full Professor (Huangshan Young Scholar) and the Deputy Discipline Head of Automation. Hai is currently the Senior Lecturer of Electrical Engineering, Academic Chair of Instrumentation & Control Engineering and Industrial Computer Systems Engineering, and Director of Advanced Mechatronics, Robotics, and Controls Laboratory, at Murdoch University, Perth, Australia.

Hai has published nearly 60 peer-reviewed leading international journal papers (including 20+ IEEE Transactions), mostly in the areas of sliding mode control theory and its applications, and robotics & mechatronics. He currently serves as a Section EiC of Actuators, Associate Editor of IEEE Access, ASME-Journal of Autonomous Vehicles and Systems, IET-Energy Conversion and Economics, Guest Editors of Neural Computing and Applications, Computers and Electrical Engineering, etc. He is also the Chair of IEEE Industrial Electronics Society Western Australia Chapter. His research interests are in sliding mode control and observer, adaptive control, robotics and mechatronics, neural networks, nonlinear systems, and vehicle dynamics & control.

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