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Disturbance-observer-based adaptive dynamic surface control for nonlinear systems with input dead-zone and delay using neural networks

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Abstract

Disturbance-observer-based adaptive neural control approach is proposed for nonlinear systems. Considering the effect caused by long input delay and dead-zone, a novel auxiliary system has been introduced to degrade the design difficult. Based on the auxiliary system, a novel disturbance observer is developed to estimate the unknown time-varying external disturbance and the approximation error. What is more, the priori knowledge on the boundary of the disturbance and approximation error is not required for the disturbance observer. The “explosion of complexity” problem has been overcome by using dynamic surface control (DSC) scheme. By combing DSC scheme with backstepping technique, an adaptive neural dynamic surface controller is correctly devised to improve the disturbance rejection performance of the closed-loop system. Finally, the simulations of two examples show the superiority of the proposed scheme.

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Acknowledgements

This research was financially supported by the National Natural Science Foundation of China (Grants Nos. 11871117 and 61873041).

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  1. College of Mathetatical Science, Bohai University, Jinzhou, 121013, Liaoning, China

    Junchang Zhai, Huanqing Wang & Jiaqing Tao

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  1. Junchang Zhai
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Correspondence to Junchang Zhai.

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Zhai, J., Wang, H. & Tao, J. Disturbance-observer-based adaptive dynamic surface control for nonlinear systems with input dead-zone and delay using neural networks. Neural Comput & Applic 35, 4027–4049 (2023). https://doi.org/10.1007/s00521-022-07865-3

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