Dynamic Neural Networks for Manipulability Optimization of Omnidirectional Mobile Redundant Manipulator Under Anti-Input Disturbance

Shijun Tang; Zhongbo Sun; Yunfeng Hu; Hong Chen

doi:10.1109/JAS.2025.125963

Volume 13 Issue 6

Jun. 2026

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > 13(6): 1409-1427

S. Tang, Z. Sun, Y. Hu, and H. Chen, “Dynamic neural networks for manipulability optimization of omnidirectional mobile redundant manipulator under anti-input disturbance,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 6, pp. 1409–1427, Jun. 2026. doi: 10.1109/JAS.2025.125963

Citation:

S. Tang, Z. Sun, Y. Hu, and H. Chen, “Dynamic neural networks for manipulability optimization of omnidirectional mobile redundant manipulator under anti-input disturbance,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 6, pp. 1409–1427, Jun. 2026. doi: 10.1109/JAS.2025.125963

Citation:

S. Tang, Z. Sun, Y. Hu, and H. Chen, “Dynamic neural networks for manipulability optimization of omnidirectional mobile redundant manipulator under anti-input disturbance,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 6, pp. 1409–1427, Jun. 2026. doi: 10.1109/JAS.2025.125963

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Dynamic Neural Networks for Manipulability Optimization of Omnidirectional Mobile Redundant Manipulator Under Anti-Input Disturbance

doi: 10.1109/JAS.2025.125963

Funds: The work was supported in part by the National Natural Science Foundation of China (62173048, 62106023), the Key Science and Technology Projects of Jilin Province, China (20230508095RC), and the Changchun Science and Technology Project (21ZY41)

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Author Bio:
Shijun Tang received the M.S. degree in electrical and electronic engineering from Changchun University of Technology in 2023. He is currently pursuing a Ph.D. degree in control theory and control engineering from Jilin University. His current research interests include neural networks, optimization, data-driven, and mobile robotic arms

Zhongbo Sun received the Ph.D. degree in control theory and control engineering with Jilin University in 2016. He is currently a Professor with the Department of Control Engineering, Changchun University of Technology. He is also an Academic Visitor with Peking University in 2019. He is also an Academic Visitor with Michigan State University, USA, in 2020. His research interests include robots, neural network, and nonlinear programming

Yunfeng Hu received the M.S. degree in basic mathematics and the Ph.D. degree in control theory and control engineering from Jilin University in 2008 and 2012, respectively. He is currently a Professor with the College of Communication Engineering, Jilin University. His current research interests include nonlinear control and automotive control

Hong Chen (Fellow, IEEE) received the Ph.D. degree in system dynamics and control engineering from the University of Stuttgart, Stuttgart, Germany, in 1997. Since 1999, she has been a Professor and thereafter a Tang Aoqing Professor with Jilin University. She is currently a Professor with Tongji University. Her current research interests include model predictive control (MPC), optimal and robust control, nonlinear control, and applications in mechatronic systems focusing on automotive systems
Corresponding author: Yunfeng Hu, e-mail: huyf@jlu.edu.cn
Received Date: 2025-03-05
Revised Date: 2025-06-30
Accepted Date: 2025-10-03

Abstract

Abstract

Manipulability optimization plays a crucial role in the motion control of omnidirectional mobile redundant manipulators (OMRM), effectively reducing the risk of singularity. However, existing methods often overlook obstacle avoidance or simplify obstacles as single points, limiting their practical applicability. To address these issues, this paper proposes a convex manipulability optimization scheme with physical and face-avoidance constraints (C-MOPOFC), where position tracking and matrix inversion are formulated as equality constraints, while physical limitations and obstacle avoidance are incorporated as inequality constraints. To enable real-time optimization, a resistant input disturbance recursive neural network (RID-RNN) is proposed, which solves the C-MOPOFC problem in an inverse-free manner, ensuring both real-time performance and robustness against disturbances. Additionally, it overcomes the limitations of traditional time-varying optimization solvers, which suffer from high computational complexity and weak disturbance suppression. Theoretical analysis proves that RID-RNN globally converges to the optimal solution of C-MOPOFC, even in the presence of noise. Finally, numerical simulations and physical experiments validate the proposed method, demonstrating its effectiveness in enhancing manipulability while ensuring safe operation in dynamic environments.
- Anti-input disturbance,
- manipulability optimization,
- omnidirectional mobile redundant manipulator (OMRM),
- recursive neural network

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References(53)

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