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首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers >Neural network adaptive backstepping fault tolerant control for unmanned airships with multi-vectored thrusters
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Neural network adaptive backstepping fault tolerant control for unmanned airships with multi-vectored thrusters

机译：无人驾驶飞艇与多矢量推进器的神经网络自适应BackStepping容错控制

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摘要

This paper presents the fault tolerant control (FTC) of an unmanned airship with multiple vectored thrusters in the presence of model parameter uncertainties and unknown wind disturbances. A fault tolerant control based on constrained adaptive backstepping (CAB) approach, combined with a radial basis function neural network (RBFNN) approximation, is proposed for the airship with thruster faults. A wind observer is designed to estimate the bounded wind disturbances. An adaptive fault estimator is proposed to estimate the unknown actuator faults. A weighted pseudo inverse based control allocation is incorporated to reconstruct and optimize the practical control inputs of the failed airship under constraints of actuator saturation. Rigorous stability analysis shows that trajectory tracking errors of the airship position and attitude converge to the desired set through Lyapunov theory. Numerical simulations demonstrate the fault tolerant trajectory tracking capability of the proposed NN-CAB controller under the actuator faults, even in the presence of aerodynamic coefficient uncertainties, and unknown wind disturbances.
机译：本文介绍了在模型参数不确定性和未知风扰动的情况下具有多个矢量推进器的无人驾驶飞艇的容错控制（FTC）。基于受约束的自适应BackStepping（CAB）方法的容错控制与径向基函数神经网络（RBFNN）近似建议，用于采用推进器断层的飞艇。风琴观测器旨在估计有界风障碍。建议自适应故障估算器来估计未知的执行器故障。加权伪逆的控制分配被纳入重构和优化在执行器饱和度的约束下失败的飞艇的实际控制输入。严格的稳定性分析表明，飞艇位置和态度的轨迹跟踪误差与Lyapunov理论的预期集合收敛。数值模拟表明，即使在空气动力学系数不确定性和未知的风扰动的存在下，也表明了所提出的NN-CAB控制器在执行器断层下的容错轨迹跟踪能力。

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