Adaptive fuzzy control for coordinated multiple robots with constraint using impedance learning

Kong, Linghuan; He, Wei; Yang, Chenguang; Li, Zhijun; Sun, Changyin

Abstract

In this paper, we investigate fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of the unknown robotic dynamics and the unknown environment with which the robot comes into contact. First, an FNN learning algorithm is developed to identify the unknown plant model. Second, impedance learning is introduced to regulate the control input in order to improve the environment-robot interaction, and the robot can track the desired trajectory generated by impedance learning. Third, in light of the condition requiring the robot to move in a finite space or to move at a limited velocity in a finite space, the algorithm based on the position constraint and the velocity constraint are proposed, respectively. To guarantee the position constraint and the velocity constraint, an integral barrier Lyapunov function is introduced to avoid the violation of the constraint. According to Lyapunov's stability theory, it can be proved that the tracking errors are uniformly bounded ultimately. At last, some simulation examples are carried out to verify the effectiveness of the designed control.

• Journal Article Type: Article
• Acceptance Date: May 3, 2018
• Online Publication Date: Mar 6, 2019
• Publication Date: Aug 1, 2019
• Deposit Date: Mar 11, 2019
• Publicly Available Date: Mar 11, 2019
• Journal: IEEE Transactions on Cybernetics
• Print ISSN: 2168-2267
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 49
• Issue: 8
• Pages: 3052-3063
• DOI: https://doi.org/10.1109/TCYB.2018.2838573
• Public URL: https://uwe-repository.worktribe.com/output/851005
• Publisher URL: http://doi.org/10.1109/TCYB.2018.2838573
• 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.
• Contract Date: Mar 11, 2019