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Adaptive composite neural network disturbance observer-based dynamic surface control for electrically driven robotic manipulators

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Abstract

This paper presents an adaptive backstepping control scheme for electrically driven robotic manipulator (EDRM) system with uncertainties and external disturbances by using neural network disturbance observer (NNDO) and dynamic surface control (DSC) design technique. NNDO is employed to estimate the uncertainties and external disturbances such that the priori information of the unknown dynamics will not be needed. To overcome the problem of “explosion of complexity” inherent in the backstepping design method, the DSC technique is integrated into the adaptive backstepping control design framework, where the NNDOs with adaptive composite law are utilized to compensate the uncertainties and external disturbances of EDRM. Based on the Lyapunov stability theory, it can be proven that the closed-loop system is stable in the sense that all the variables are guaranteed to be uniformly ultimately bounded. The results of simulation and experimental tests demonstrate the approximation capability of NNDO and the effectiveness of the proposed adaptive DSC scheme.

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Acknowledgements

The authors would like to acknowledge the funding received from the National Natural Science Foundation of China (61773351), the Program for Science & Technology Innovation Talents in Universities of Henan Province (20HASTIT031), the Training Plan for University’s Young Backbone Teachers of Henan Province (2017GGJS004), the Outstanding Foreign Scientists Support Project in Henan Province of China (GZS201908), the China Scholarship Council (201907045008) and the Harrison McCain Visiting Professor Award in the University of New Brunswick to conduct this research investigation.

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Authors and Affiliations

  1. School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, Henan, People’s Republic of China

    Jinzhu Peng & Shuai Ding

  2. Department of Mechanical Engineering, The University of New Brunswick, Fredericton, NB, E3B5A3, Canada

    Jinzhu Peng & Rickey Dubay

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  1. Jinzhu Peng
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  2. Shuai Ding
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  3. Rickey Dubay
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Correspondence to Jinzhu Peng.

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Peng, J., Ding, S. & Dubay, R. Adaptive composite neural network disturbance observer-based dynamic surface control for electrically driven robotic manipulators. Neural Comput & Applic 33, 6197–6211 (2021). https://doi.org/10.1007/s00521-020-05391-8

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