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Command-filtered adaptive neural network backstepping quantized control for fractional-order nonlinear systems with asymmetric actuator dead-zone via disturbance observer

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Abstract

An adaptive neural network backstepping quantized control of fractional-order nonlinear systems with asymmetric actuator dead-zone and unknown external disturbance is investigated in this paper. An adaptive NN mechanism is designed to estimate uncertain functions. A command filter is introduced to estimate the virtual control variable as well as its derivative, so that the “explosion of complexity” problem existed in the classical backstepping method can be avoided. To handle the unknown external disturbance, a fractional-order disturbance observer is developed. Moreover, a hysteresis-type quantizer is used to quantify the final input signal to overcome the system performance damage caused by the actuator dead-zone. The quantized input signal can ensure that all the involved signals stay bounded and the tracking error converges to an arbitrarily small region of the origin. Finally, two examples are presented to verify the effectiveness of the proposed method.

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Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61967001 and 12261009), and the Xiangsihu Young Scholars Innovative Research Team of Guangxi Minzu University (Grant No. 2019RSCXSHQN02).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, 710119, China

    Jinzhu Yu & Shenggang Li

  2. School of Mathematics and Physics, Center for Applied Mathematics of Guangxi, Guangxi Key Laboratory of Universities Optimization Control and Engineering Calculation, Guangxi Minzu University, Nanning, 530006, China

    Heng Liu

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  1. Jinzhu Yu
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  2. Shenggang Li
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  3. Heng Liu
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Correspondence to Heng Liu.

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Yu, J., Li, S. & Liu, H. Command-filtered adaptive neural network backstepping quantized control for fractional-order nonlinear systems with asymmetric actuator dead-zone via disturbance observer. Nonlinear Dyn 111, 6449–6467 (2023). https://doi.org/10.1007/s11071-022-08175-y

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