Data-Driven Backstepping Control of Underactuated Mechanical Systems

[+] Author and Article Information
Jingwen Huang

Beijing University of Chemical Technology, Beijing, 100029, China

Tingting Zhang

Beijing University of Chemical Technology, Beijing, 100029, China

Jian-Qiao Sun

Department of Mechanical Engineering, School of Engineering, University of California, Merced, CA 95343, USA

1Corresponding author.

ASME doi:10.1115/1.4043154 History: Received November 20, 2018; Revised March 07, 2019


This paper studies control problems of underactuated mechanical systems with model uncertainties. The control is designed with the method of backstepping. The first-order low-pass filters are used to estimate the unknown quantities and to avoid the ``explosion of terms''. A novel method is also proposed to implement the control without the knowledge of the control coefficient, which makes the whole process of backstepping control data-driven. The stability of the proposed control in the Lyapunov sense is studied. It is numerically and experimentally validated, and compared with the well-known model-based LQR control. The data-driven backstepping control is found to provide comparable performances to that of the LQR control with the advantage of being model-free and robust.

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