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Adaptive robust backstepping attitude control for a multi-rotor unmanned aerial vehicle with time-varying output constraints

机译：时变输出约束的多旋翼无人机自适应鲁棒反推姿态控制

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摘要

Output constraints and uncertainties are the main factors that degrade the control performance of the multi-rotor unmanned aerial vehicle (MUAV). In this paper, an adaptive neural network backstepping dynamic surface control algorithm based on asymmetric time-varying Barrier Lyapunov Function is proposed for the attitude system of a novel MUAV under asymmetric time-varying output constraints, model uncertainties and external disturbances. The asymmetric time-varying Barrier Lyapunov Function, which will grow infinite when its arguments approach some limit, is introduced to keep the output under time-varying asymmetric constraints. Considering the derivation problem of the virtual control function in backstepping, the dynamic surface control is applied to simplify the algorithm. The adaptive neural network is used to approximate the dynamic model of the attitude system, and the minimal learning parameters are employed at the same time to reduce online computation burden. In order to balance out the external disturbance and further reduce the approximate error of the adaptive neural network, a robust term is designed to compensate the above negative impacts. The proposed algorithm guarantees that all the signals of the closed-loop system bounded by Lyapunov theory. Finally, some contrast simulation experiments are given to illustrate the effectiveness and superiority of the control scheme. (C) 2018 Elsevier Masson SAS. All rights reserved.
机译：输出限制和不确定性是降低多旋翼无人机（MUAV）的控制性能的主要因素。针对不对称时变输出约束，模型不确定性和外界干扰的新型MUAV姿态系统，提出了一种基于不对称时变屏障Lyapunov函数的自适应神经网络逆推动态曲面控制算法。引入了非对称时变屏障Lyapunov函数，当其参数接近某个极限时，该函数将变得无限大，以使输出保持在时变非对称约束下。考虑到反推中虚拟控制功能的推导问题，采用动态曲面控制简化算法。自适应神经网络用于近似姿态系统的动力学模型，同时采用最小的学习参数来减少在线计算负担。为了平衡外部干扰并进一步减少自适应神经网络的近似误差，设计了一个健壮的术语来补偿上述负面影响。所提出的算法保证了闭环系统的所有信号都受李雅普诺夫理论的约束。最后，通过一些对比仿真实验来说明控制方案的有效性和优越性。 （C）2018 Elsevier Masson SAS。版权所有。

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