Asymmetric bounded neural control for an uncertain robot by state feedback and output feedback

Kong, Linghuan; He, Wei; Dong, Yiting; Cheng, Long; Yang, Chenguang; Li, Zhijun

Abstract

In this paper, an adaptive neural bounded control scheme is proposed for an n-link rigid robotic manipulator with unknown dynamics. With the combination of the neural approximation and backstepping technique, an adaptive neural network control policy is developed to guarantee the tracking performance of the robot. Different from the existing results, the bounds of the designed controller are known a priori, and they are determined by controller gains, making them applicable within actuator limitations. Furthermore, the designed controller is also able to compensate the effect of unknown robotic dynamics. Via the Lyapunov stability theory, it can be proved that all the signals are uniformly ultimately bounded. Simulations are carried out to verify the effectiveness of the proposed scheme.

• Journal Article Type: Article
• Acceptance Date: Feb 5, 2019
• Online Publication Date: Apr 4, 2019
• Publication Date: Mar 1, 2021
• Deposit Date: Apr 8, 2019
• Publicly Available Date: Apr 8, 2019
• Journal: IEEE Transactions on Systems, Man, and Cybernetics: Systems
• Print ISSN: 2168-2216
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 51
• Issue: 3
• Pages: 1735-1746
• DOI: https://doi.org/10.1109/TSMC.2019.2901277
• Keywords: adaptive control , asymmetrically bounded inputs , neural networks , robotic manipulator
• Public URL: https://uwe-repository.worktribe.com/output/849082
• Publisher URL: http://doi.org/10.1109/TSMC.2019.2901277
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• Contract Date: Apr 8, 2019