A Novel Disturbance Observer Based Fixed-Time Sliding Mode Control for Robotic Manipulators With Global Fast Convergence

Dan Zhang; Jiabin Hu; Jun Cheng; Zheng-Guang Wu; Huaicheng Yan

doi:10.1109/JAS.2023.123948

Volume 11 Issue 3

Mar. 2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2024 > 11(3): 661-672

D. Zhang, J. Hu, J. Cheng, Z.-G. Wu, and H. Yan, “A novel disturbance observer based fixed-time sliding mode control for robotic manipulators with global fast convergence,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 661–672, Mar. 2024. doi: 10.1109/JAS.2023.123948

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D. Zhang, J. Hu, J. Cheng, Z.-G. Wu, and H. Yan, “A novel disturbance observer based fixed-time sliding mode control for robotic manipulators with global fast convergence,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 661–672, Mar. 2024. doi: 10.1109/JAS.2023.123948

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A Novel Disturbance Observer Based Fixed-Time Sliding Mode Control for Robotic Manipulators With Global Fast Convergence

doi: 10.1109/JAS.2023.123948

Funds: This work was partially supported by the National Natural Science Foundation of China (62322315, 61873237), Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars (LR22F030003), the National Key Rearch and Development Funding (2018YFB1403702), the Key Rearch and Development Programs of Zhejiang Province (2023C01224), and Major Project of Science and Technology Innovation in Ningbo City (2019B1003)

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Author Bio:
Dan Zhang (Senior Member, IEEE) received the B.E. degree in automation and the Ph.D. degree in control theory and control engineering from Zhejiang University of Technology in 2007 and 2013, respectively. He was a Research Fellow with Nanyang Technological University, Singapore, from 2013 to 2014, the National University of Singapore, Singapore, from 2016 to 2017, and the City University of Hong Kong, China, from 2017 to 2019. Currently, he is a Professor with Zhejiang University of Technology. His research interests include area of industrial cyber-physical systems, deep learning, and its applications. He serves as an Associate Editor for ISA Transactions, International Journal of Control, Automation and Systems, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, IET Electronics Letters, and Cyberphysical Systems

Jiabin Hu received the B.E. degree in electrical engineering and automation from Quzhou College in 2020, and the M.E. degree in control science and engineering from Zhejiang University of Technology in 2023. His research interests include robot control systems, sliding mode control, and fixed-time control

Jun Cheng received the B.S. degree from Hubei University for Nationalities in 2010, and the Ph.D. degree in instrumentation science and technology from University of Electronic Science and Technology of China in 2015. From 2015 to 2019, he was a staff with Hubei Minzu University. He was a Visiting Scholar with the Department of Electrical and Computer Engineering, National University of Singapore, Singapore, from 2013 to 2014, and the Department of Electrical Engineering, Yeungnam University, South Korea, in 2016 and 2018. Since 2019, he has been with Guangxi Normal University, where he is currently a Professor with the College of Mathematics and Statistics. His research interests include analysis and synthesis for stochastic hybrid systems, networked control systems, robust control, and nonlinear systems. Prof. Cheng has been a recipient of the Highly Cited Researcher Award listed by Clarivate Analytics in 2019 and 2020. He is an Associate Editor of the International Journal of Control, Automation, and Systems

Zheng-Guang Wu (Member, IEEE) received the B.S. and M.S. degrees in mathematics from Zhejiang Normal University in 2004 and 2007, respectively, and the Ph.D. degree in control science and engineering from Zhejiang University in 2011. He was named as a Highly Cited Researcher at Clarivate Analytics. Currently, he is a Professor with Zhejiang University. He has published over 100 papers in IEEE transactions. His current research interests include Markov jump systems, networked systems, smart grid, cyber-physical systems, and reinforcement learning. Dr. Wu is an Invited Reviewer of mathematical review at the American Mathematical Society. He serves as an Associate Editor/Editorial Board Member for some journals, such as the IEEE Transactions on Systems, Man and Cybernetics: Systems, Journal of The Franklin Institute, Neurocomputing, International Journal of Control, Automation, and Systems, IEEE Access, International Journal of Sensors, Wireless Communications and Control, Scientia Sinica Informationis, Journal of Systems Science and Complexity, and IEEE Control Systems Society Conference Editorial Board

Huaicheng Yan (Member, IEEE) received the B.Sc. degree in automatic control from Wuhan University of Technology in 2001, and the Ph.D. degree in control theory and control engineering from Huazhong University of Science and Technology, in 2007. Currently, he is a Professor with the School of Information Science and Engineering, East China University of Science and Technology. His research interests include networked control systems, multi-agent systems, and robotics. He is an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, International Journal of Robotics and Automation, and IEEE Open Journal of Circuits and Systems
Corresponding author: Dan Zhang, e-mail: danzhang@zjut.edu.cn
Received Date: 2023-05-29
Revised Date: 2023-07-16
Accepted Date: 2023-09-06

Available Online: 2024-01-17

Abstract

Abstract

This paper proposes a new global fixed-time sliding mode control strategy for the trajectory tracking control of uncertain robotic manipulators. First, a fixed-time disturbance observer (FTDO) is designed to deal with the adverse effects of model uncertainties and external disturbances in the manipulator systems. Then an adaptive scheme is used and the adaptive FTDO (AFTDO) is developed, so that the priori knowledge of the lumped disturbance is not required. Further, a new non-singular fast terminal sliding mode (NFTSM) surface is designed by using an arctan function, which helps to overcome the singularity problem and enhance the robustness of the system. Based on the estimation of the lumped disturbance by the AFTDO, a fixed-time non-singular fast terminal sliding mode controller (FTNFTSMC) is developed to guarantee the trajectory tracking errors converge to zero within a fixed time. The settling time is independent of the initial state of the system. In addition, the stability of the AFTDO and FTNFTSMC is strictly proved by using Lyapunov method. Finally, the fixed-time NFESM (FTNFTSM) algorithm is validated on a 2-link manipulator and comparisons with other existing sliding mode controllers (SMCs) are performed. The comparative results confirm that the FTNFTSMC has superior control performance.
- Disturbance observer (DO),
- fixed-time,
- non-singular sliding mode control,
- robotic manipulator,
- trajectory tracking

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References

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A new fixed-time non-singular fast terminal sliding mode control strategy for robotic manipulators is proposed
A new non-singular fast terminal sliding mode surface based on the arctan function is designed to overcome the singularity problem more simply and effectively
A new adaptive fixed-time disturbance observer is designed to guarantee faster estimation speed and higher estimation accuracy

A Novel Disturbance Observer Based Fixed-Time Sliding Mode Control for Robotic Manipulators With Global Fast Convergence

doi: 10.1109/JAS.2023.123948

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