Modeling and Adaptive Neural Network Control for a Soft Robotic Arm With Prescribed Motion Constraints

Yan Yang; Jiangtao Han; Zhijie Liu; Zhijia Zhao; Keum-Shik Hong

doi:10.1109/JAS.2023.123213

Volume 10 Issue 2

Feb. 2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(2): 501-511

Y. Yang, J. T. Han, Z. J. Liu, Z. J. Zhao, and K.-S. Hong, “Modeling and adaptive neural network control for a soft robotic arm with prescribed motion constraints,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 501–511, Feb. 2023. doi: 10.1109/JAS.2023.123213

Citation:

Y. Yang, J. T. Han, Z. J. Liu, Z. J. Zhao, and K.-S. Hong, “Modeling and adaptive neural network control for a soft robotic arm with prescribed motion constraints,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 501–511, Feb. 2023. doi: 10.1109/JAS.2023.123213

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Modeling and Adaptive Neural Network Control for a Soft Robotic Arm With Prescribed Motion Constraints

doi: 10.1109/JAS.2023.123213

1.
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 10083, and also with the Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China
2.
School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
3.
School of Mechanical Engineering, Pusan National University, Busan 46241, South Korea

Funds: This work was supported by the National Natural Science Foundation of China (62103039, 62073030), the Scientific and Technological Innovation Foundation of Shunde Graduate School, University of Science and Technology Beijing (USTB) (BK21BF003), the Korea Institute of Energy Technology Evaluation and Planning through the Auspices of the Ministry of Trade, Industry and Energy, Republic of Korea, (20213030020160), the Science and Technology Planning Project of Guangzhou City (202102010398, 202201010758), the Guangzhou University-Hong Kong University of Science and Technology Joint Research Collaboration Fund (YH202205), and Beijing Top Discipline for Artificial Intelligent Science and Engineering, University of Science and Technology Beijing

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Author Bio:
Yan Yang received the B.S degree in automation from Xi’an University of Science in 2012, and the Ph.D. degree in control theory and control engineering form Xidian University in 2019. She is currently a Lecture at the Institute of Artificial Intelligence, University of Science and Technology Beijing. In 2016, she was a Research Assistant with the Department of Electrical Engineering, University of Notre Dame, for twelve months.Her current research interests include discrete event systems and their supervisory control techniques, Petri nets, intelligent control for robots

Jiangtao Han received the B.Eng. degree in automation from Shandong University of Science and Technology in 2019. He is currently a master student in control engineering at the School of Automation and Electrical Engineering, University of Science and Technology Beijing.His research interests include soft robots, model-based control, and neural network and its applications

Zhijie Liu (Member, IEEE) received B.Eng. degree in automation from China University of Mining and Technology Beijing in 2014, and the Ph.D. degree in control theory and control engineering from Beihang University, in 2019. In 2017, he was a Research Assistant with the Department of Electrical Engineering, University of Notre Dame, USA, for twelve months. He is currently a Professor with the School of Automation and Electrical Engineering, University of Science and Technology Beijing. His research interests include adaptive control, modeling and vibration control for flexible structures, and distributed parameter systems. He is a recipient of the IEEE SMC Beijing Capital Region Chapter Young Author Prize Award in 2019. He is a Member of IEEE SMC Technical Committee on Autonomous Bionic Robotic Aircraft

Zhijia Zhao (Member, IEEE) received the B.Eng. degree in automatic control from North China University of Water Resources and Electric Power, in 2010, and the M.Eng. and Ph.D. degrees in automatic control from South China University of Technology, in 2013 and 2017, respectively. He is currently an Associate Professor in the School of Mechanical and Electrical Engineering, Guangzhou University. His research interests include adaptive and learning control, flexible mechanical systems, ocean cybernetics, and robotics

Keum-Shik Hong (Fellow, IEEE) received the B.S. degree in mechanical design and production engineering from Seoul National University, South Korea in 1979, the M.S. degree in mechanical engineering from Columbia University, USA in 1987, and the M.S. degree in applied mathematics and the Ph.D. degree in mechanical engineering from the University of Illinois at Urbana-Champaign, USA both in 1991. In 1993, he joined the School of Mechani- cal Engineering, Pusan National University (PNU), South Korea. His Integrated Dynamics and Control Engineering Laboratory was designated a National Research Laboratory by the Ministry of Science and Technology of Korea in 2003. In 2009, under the auspices of the World Class University Program of the Ministry of Education, Science and Technology of Korea, he established the Department of Cogno-Mechatronics Engineering, PNU. His current research interests include brain-computer interface, nonlinear systems theory, adaptive control, distributed parameter systems, autonomous vehicles, and innovative control applications in brain engineering.Dr. Hong has received many awards, including the Best Paper Award from the KFSTS of Korea in 1999, the F. Harashima Mechatronics Award in 2003, the IJCAS Scientific Activity Award in 2004, the Automatica Certificate of Outstanding Service in 2006, the Presidential Award of Korea in 2007, the ICROS Achievement Award in 2009, the IJCAS Contribution Award in 2010, the Premier Professor Award in 2011, the JMST Contribution Award in 2011, the IJCAS Contribution Award in 2011, and the IEEE Academic Award of ICROS in 2016. He served as an Associate Editor for Automatica from 2000 to 2006, and as the Editor-in-Chief for the Journal of Mechanical Science and Technology from 2008 to 2011, and is serving as the Editor-in-Chief for the International Journal of Control, Automation, and Systems. He was a past President of the Institute of Control, Robotics and Systems (ICROS), South Korea, and the President of the Asian Control Association (2020–2021). He was the Organizing Chair of the ICROS-SICE International Joint Conference 2009, Fukuoka, Japan. He is a Fellow of the Korean Academy of Science and Technology, an ICROS Fellow, a Member of the National Academy of Engineering of Korea, and many other societies
Corresponding author: Zhijia Zhao, e-mail: zhjzhaoscut@163.com
Received Date: 2022-06-07
Revised Date: 2022-07-20
Accepted Date: 2022-08-02

Available Online: 2022-11-03

Abstract

Abstract

This paper presents a dynamic model and performance constraint control of a line-driven soft robotic arm. The dynamics model of the soft robotic arm is established by combining the screw theory and the Cosserat theory. The unmodeled dynamics of the system are considered, and an adaptive neural network controller is designed using the backstepping method and radial basis function neural network. The stability of the closed-loop system and the boundedness of the tracking error are verified using Lyapunov theory. The simulation results show that our approach is a good solution to the motion constraint problem of the line-driven soft robotic arm.
- Adaptive control,
- cosserat theory,
- prescribed motion constraints,
- soft robotic arm

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References

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The soft robotic arm is modeled using screw theory to prevent chirality caused by local axis systems
A NN-based adaptive feedback controller is designed to handle uncertainty
The motion limits and performance functions of the soft robotic arm are turned into error bounds
An adaptive NN control is designed to ensure the tracking error no violation of constraints

Modeling and Adaptive Neural Network Control for a Soft Robotic Arm With Prescribed Motion Constraints

doi: 10.1109/JAS.2023.123213

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