An adaptive lumped-mass dynamic model and its control application for continuum robots

Zhang, Xu; Yang, Chenghao; Song, Zhibin; Khanesar, Mojtaba A.; Branson, David T.; Dai, Jian S.; Kang, Rongjie

Abstract

Dynamic modeling for continuum robots remains challenging due to their large nonlinear deformation and the variation of dynamic parameters during movement. In this paper, a lumped-mass dynamic model (LMD) for a continuum robot is constructed including elastic and viscous parameters in the robotic joints. Then the appropriate dynamic parameters (e.g. spring and damping coefficients of the LMD) with respect to the motion status (e.g. position and velocity of the robot) are estimated using a Genetic Algorithm (GA). Based on the obtained data set, a Multi-Layer Perception (MLP) is trained to establish a direct mapping from the motion status to the dynamic parameters, so the LMD can tune its parameters in real-time when moving within the workspace, resulting an adaptive lumped-mass dynamic model (ALMD). Compared to the fixed-parameter LMD, the modeling error of the ALMD is reduced by up to 60.2 %. Finally, a feedforward controller is implemented to control a continuum robotic prototype using the presented ALMD, reducing the maximum tracking error by 67.5 %.

Citation

Zhang, X., Yang, C., Song, Z., Khanesar, M. A., Branson, D. T., Dai, J. S., & Kang, R. (2024). An adaptive lumped-mass dynamic model and its control application for continuum robots. Mechanism and Machine Theory, 201, Article 105736. https://doi.org/10.1016/j.mechmachtheory.2024.105736

• Journal Article Type: Article
• Acceptance Date: Jul 8, 2024
• Online Publication Date: Jul 14, 2024
• Publication Date: 2024-10
• Deposit Date: Jul 18, 2024
• Publicly Available Date: Jul 15, 2025
• Journal: Mechanism and Machine Theory
• Print ISSN: 0094-114X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 201
• Article Number: 105736
• DOI: https://doi.org/10.1016/j.mechmachtheory.2024.105736
• Keywords: Continuum robot; Lumped-mass model; Dynamics; Parameter estimation
• Public URL: https://nottingham-repository.worktribe.com/output/37312931
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0094114X24001630?via%3Dihub