Admittance-based adaptive cooperative control for multiple manipulators with output constraints

Li, Yong; Yang, Chenguang; Yan, Weisheng; Cui, Rongxin; Annamalai, Andy

Abstract

This paper proposes a novel adaptive control methodology based on the admittance model for multiple manipulators transporting a rigid object cooperatively along a predefined desired trajectory. First, an admittance model is creatively applied to generate reference trajectory online for each manipulator according to the desired path of the rigid object, which is the reference input of the controller. Then, an innovative integral barrier Lyapunov function is utilized to tackle the constraints due to the physical and environmental limits. Adaptive neural networks (NNs) are also employed to approximate the uncertainties of the manipulator dynamics. Different from the conventional NN approximation method, which is usually semiglobally uniformly ultimately bounded, a switching function is presented to guarantee the global stability of the closed loop. Finally, the simulation studies are conducted on planar two-link robot manipulators to validate the efficacy of the proposed approach.

Citation

Li, Y., Yang, C., Yan, W., Cui, R., & Annamalai, A. (2019). Admittance-based adaptive cooperative control for multiple manipulators with output constraints. IEEE Transactions on Neural Networks and Learning Systems, 30(12), 3621-3632. https://doi.org/10.1109/TNNLS.2019.2897847

• Journal Article Type: Article
• Acceptance Date: Jan 27, 2019
• Online Publication Date: Mar 1, 2019
• Publication Date: Dec 1, 2019
• Deposit Date: Mar 4, 2019
• Publicly Available Date: Apr 1, 2019
• Journal: IEEE Transactions on Neural Networks and Learning Systems
• Print ISSN: 2162-237X
• Electronic ISSN: 2162-2388
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 30
• Issue: 12
• Pages: 3621-3632
• DOI: https://doi.org/10.1109/TNNLS.2019.2897847
• Public URL: https://uwe-repository.worktribe.com/output/851168
• Publisher URL: http://doi.org/10.1109/TNNLS.2019.2897847
• Additional Information: Additional Information : (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.