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A multilayer neural dynamic controller design method of quadrotor UAV for completing time-varying tasks

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Abstract

In order to track the time-varying trajectory efficiently and accurately, a multilayer neural dynamic controller is proposed and exploited for quadrotor unmanned aerial vehicles (UAVs). Previous UAV controllers based on neural dynamic methods used forces and torques as control variables. However, in fact, the real control variables of a UAV are the PWM waves for controlling motors. What is more, the motors are always modeled as one-order systems since the existence of inertia and friction, which means that using forces and torques as control variables is not proper and may lead to low precision and inefficient tracking for UAVs. To solve this problem, a new UAV controller which uses exactly what inputs to motors as control variables is designed, so that the designed controller is more practical for UAVs. In the design process, a multilayer neural dynamic controller is obtained by applying the neural dynamic method to position layer, velocity, torque layer and motor layer successively. Both theoretical analysis and computer simulation results verify the effectiveness, convergence, stability and accuracy of the proposed multilayer neural dynamic controller for tracking time-varying trajectories.

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Acknowledgements

The paper was submitted for review in May 19, 2020. This work was supported in part by the National Natural Science Foundation under Grants 61976096, 61603142 and 61633010, Guangdong Basic and Applied Basic Research Foundation under Grant 2020B1515120047, the Guangdong Foundation for Distinguished Young Scholars under Grant 2017A030306009, the Guangdong Special Support Program under Grant 2017TQ04X475, the Fundamental Research Funds for Central Universities under Grant x2zdD2182410, the Scientific Research Starting Foundation of South China University of Technology, the National Key Research and Development Program of China under Grant 2017YFB1002505, the National Key Basic Research Program of China (973 Program) under Grant 2015CB351703, Guangdong Key Research and Development Program under Grant 2018B030339001, Guangdong Natural Science Foundation Research Team Program 1414060000024.

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  1. Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China

    Zhijun Zhang, Tao Chen & Lunan Zheng

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  1. Zhijun Zhang
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Zhang, Z., Chen, T. & Zheng, L. A multilayer neural dynamic controller design method of quadrotor UAV for completing time-varying tasks. Nonlinear Dyn 104, 3597–3616 (2021). https://doi.org/10.1007/s11071-021-06445-9

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