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Switched energy and Neural Network-Based approach for Swing-Up and tracking control of double inverted pendulum
Journal article   Peer reviewed

Switched energy and Neural Network-Based approach for Swing-Up and tracking control of double inverted pendulum

Zhaowu Ping, Delai Xu, Hao Tang, Suoliang Ge, Jun-Guo Lu and Hai Wang
IEEE transactions on systems, man, and cybernetics. Systems, Early Access
2023

Abstract

Automation & Control Systems Computer Science Computer Science, Cybernetics Science & Technology Technology
The double inverted pendulum (DIP) system is a benchmark underactuated mechanical system. The control problem of the DIP system is challenging since it not only has high nonlinearity, but also has underactuation degree equal to two. In this article, a switched energy-based swing-up and neural network (NN) controller is proposed to solve the swing-up and tracking control problem of the DIP system when the desired position is time varying. The implementation of the proposed controller can be divided into three steps. In step one, the energy-based control method is adopted to drive the first pendulum from the downward position to the neighborhood of the upright position. In step two, the sliding mode control method is adopted to stabilize the first pendulum. Meanwhile, a method combining energy-based control and "equivalent cart" is adopted to swing up the second pendulum. In step three, on the basis of approximate nonlinear output regulation theory, the NN controller is used for achieving satisfactory position tracking performance. Finally, the effectiveness of our design is verified by experimental results.

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Collaboration types
Domestic collaboration
International collaboration
Citation topics
4 Electrical Engineering, Electronics & Computer Science
4.29 Automation & Control Systems
4.29.104 Adaptive Control
Web Of Science research areas
Automation & Control Systems
Computer Science, Cybernetics
ESI research areas
Engineering
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