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Ph.D Dissertation Defense
"Data-driven Robust Control of Underactuated Mechanical Systems using Identification of Flat output and Artificial Intelligence"
University of California, Merced
Underactuated mechanical system (UMS) is a special class of mechanical systems that play an important role in a wide variety of engineering applications. UMSs typically show a great difficulty in analysis and control design because of complex nonlinearity and loss of capability to configure arbitrary motions in some directions. Flatness-based control and active disturbance rejection control (ADRC) approach has been an active research topic about dealing with control problems of UMSs. However, the details of mathematical models are normally required. In this thesis, a data-driven control framework for single-input-multiple-output UMS is developed based on flat outputs identification(FOID) of UMSs, techniques of sparse regression and ADRC. Robust tracking control of UMS can be achieved based on the framework without relying on the accurate information of its mathematical model. It is also found that the flat output identification can be also extended to MIMO UMS in special cases. Finally, we proposed the neural network framework that helps to identify the locally flat output for UMSs which can also be used as a powerful tool to verify the identification result of FOID method. The result of flat output identification and tracking control are verified by the simulations and experiments.
Shangjie Ma is a Ph.D. candidate in the Department of Mechanical Engineering. He joined the Applied Controls Lab, charged by Professor Jian-Qiao Sun, in Fall 2018. His research works on designing robust control algorithm for underactuated mechanical systems using data-driven algorithm and machine learning models. He received his B.Sc. degree in Mechanical Engineering from China University of Mining and Technology, China, and M.S. degree in Mechanical Engineering from University of California, San Diego.
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