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Online robust self-learning terminal sliding mode control for balancing control of reaction wheel bicycle robots

Published online by Cambridge University Press:  19 December 2024

Xianjin Zhu Open the ORCID record for Xianjin Zhu [Opens in a new window] ,
Wenfu Xu ,
Zhang Chen ,
Yang Deng ,
Qingyuan Zheng ,
Bin Liang  and
Yu Liu
Xianjin Zhu
Affiliation:
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
Wenfu Xu
Affiliation:
School of Mechatronics Engineering and Automation, Harbin Institute of Technology, Shenzhen, China
Zhang Chen
Affiliation:
Department of Automation, Tsinghua University, Beijing, China
Yang Deng
Affiliation:
Department of Automation, Tsinghua University, Beijing, China
Qingyuan Zheng
Affiliation:
Department of Automation, Tsinghua University, Beijing, China
Bin Liang
Affiliation:
Department of Automation, Tsinghua University, Beijing, China
Yu Liu*
Affiliation:
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China
*
Corresponding author: Yu Liu; Email: [email protected]
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Abstract

This paper proposes an online robust self-learning terminal sliding mode control (RS-TSMC) with stability guarantee for balancing control of reaction wheel bicycle robots (RWBR) under model uncertainties and disturbances, which improves the balancing control performance of RWBR by optimising the constrained output of TSMC. The TSMC is designed for a second-order mathematical model of RWBR. Then robust adaptive dynamic programming based on an actor-critic algorithm is used to optimise the TSMC only by data sampled online. The system closed-loop stability and convergence of the neural network weights are guaranteed based on the Lyapunov analysis. The effectiveness of the proposed algorithm is demonstrated through simulations and experiments.

Keywords

Control of robotic systemsadaptive dynamic programmingsliding mode controlreaction wheel bicycle robotsbalancing control
Type
Research Article
Information
Robotica , Volume 42 , Issue 10 , October 2024 , pp. 3416 - 3430
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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