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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


Linghuan Kong

Wei He

Yiting Dong

Long Cheng

Zhijun Li


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
Journal IEEE Transactions on Systems, Man, and Cybernetics: Systems
Print ISSN 2168-2216
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Pages 1-12
Institution Citation Kong, L., He, W., Dong, Y., Cheng, L., Yang, C., & Li, Z. (in press). Asymmetric bounded neural control for an uncertain robot by state feedback and output feedback. IEEE Transactions on Systems Man and Cybernetics: Systems, 1-12.
Publisher URL
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.


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