Cartesian Space Control and Joint Tracking Control for a Robotic Arm System with Explicit-time Proportional Convergence

Wen Yan; Tao Zhao; Ben Niu; Zhiyi Shi; Edmond Q. Wu

doi:10.1109/JAS.2026.125963

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > In Press, Accepted Manuscript

W. Yan, T. Zhao, B. Niu, Z. Shi, and E. Q. Wu, “Cartesian space control and joint tracking control for a robotic arm system with explicit-time proportional convergence,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.125963

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W. Yan, T. Zhao, B. Niu, Z. Shi, and E. Q. Wu, “Cartesian space control and joint tracking control for a robotic arm system with explicit-time proportional convergence,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.125963

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Cartesian Space Control and Joint Tracking Control for a Robotic Arm System with Explicit-time Proportional Convergence

doi: 10.1109/JAS.2026.125963

Funds: This work was supported by the National Natural Science Foundation for Distinguished Young Scholars of China (T2325018), in part by the National Natural Science Foundation of China (U25A20455, 62376170), and in part by the Natural Science Foundation of Liaoning Province of China (2025-MS-013)

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Author Bio:
Wen Yan (Member, IEEE) received the M.S. degree in Control Engineering and the Ph.D. degree in Electronic Information both from College of Electrical Engineering, Sichuan University in 2021 and 2025, respectively. He is currently a Postdoctoral researcher at the School of Computer Science, Shanghai Jiao Tong University. His research interests include robotic system, human-machine interaction, explicit-time control, and fuzzy logic system

Tao Zhao received the B.S. degree in mathematics and applied mathematics and the Ph.D. degree in systems engineering both from Southwest Jiaotong University in 2010 and 2015, respectively. He is the head of the automation department. He is currently a Professor in the College of Electrical Engineering, Sichuan University. His current research interests include type-2 fuzzy set theory and system design, rough sets, and intelligent control

Ben Niu (Senior Member, IEEE) received his Ph.D. degree in Control Theory and Control Engineering from Northeastern University in 2013. He is currently a Professor at the School of Control Science and Engineering, Dalian University of Technology. His research interests include switched systems, stochastic systems, adaptive control, intelligent control, and their applications

Zhiyi Shi received the B.S. degree in the School of Automation from Nanjing University of Aeronautics and Astronautics in 2019. He is currently working toward the Ph.D. degree in the School of Automation and Intelligent Sensing, Shanghai Jiao Tong University. His research interests include deep reinforcement learning and intelligent manipulator control

Edmond Q. Wu (Senior Member, IEEE) received the Ph.D. degree in control theory and engineering from Southeast University in 2009. He is a Professor with the School of Computer Science, Shanghai Jiao Tong University. He is with the Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai Jiao Tong University. He is also currently with Science and Technology on Avionics Integration Laboratory, China National Aeronautical Radio Electronics Research Institute. His research interests include deep learning, fatigue recognition, and human-machine interaction. Dr. Wu is currently the Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Intelligent Vehicles, IEEE Transactions on Cognitive and Developmental Systems, and IEEE Transactions on Medical Robotics and Bionics
Corresponding author: Tao Zhao, e-mail: zhaotaozhaogang@scu.edu.cn; Edmond Q. Wu, e-mail: edmondqwu@sjtu.edu.cn
Received Date: 2025-11-14
Accepted Date: 2026-02-06

Available Online: 2026-03-26

Abstract

Abstract

The Lyapunov synthesis method is a common controller design strategy in robotic arm motion control. However, it is difficult for this method to achieve fixed-time control without a nonlinear feedback design, whose nonlinearity may cause chattering in the robotic motion. To address this problem, a novel explicit-time control method is proposed using proportional feedback. Not only can the proposed method be applied to the Cartesian space control of the robotic arm system, but it can also be used for the joint-space tracking control. More specifically, under bounded initial condition, the origin of system is attracted to a predefined neighborhood of zero within an explicit fixed-time boundary. Based on that, a robust fixed-time tracking controller of robot is designed by using this linear time-invariant feedback. Besides, compared with other related methods, the proposed method has smoother and lower control input under the same initial condition. In particular, this method enables the robotic arm to achieve a tracking accuracy of 0.1 millimeters and 0.1 degrees within as short as 1.5 seconds, while the repeat positioning accuracy approaches the hardware limit, reaching 0.001 millimeters (±0.03 millimeters) and 0.001 degrees (±0.05 degrees). Theoretical analysis, simulation and experiment verify the main results. Code, data and video are also available, the corresponding links are printed in the relevant places.
- Cartesian space control,
- explicit-time proportional convergence,
- joint tracking control,
- robotic system

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References(38)

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