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Fuzzy SMC for UAV to Resist Strong Wind with Disturbance Approximation

机译:无人机抗干扰能力强的模糊SMC

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In this paper, a quadrotor UAV dynamic model with the wind speed factor is established and sliding mode controllers are designed for attitude stabilization. Neural network and fuzzy control are proposed to improve the performance of anti-strong wind controller for unmanned aerial vehicle (UAV). Uncertainty of dynamic model can be approximated by radical basis function (RBF) neural network, and fuzzy control can dynamically correct the coefficients of the symbol function during the state convergence process. The simulation results show that the neural network constructed in this paper can track the uncertainty of the model, combining sliding mode with fuzzy control maintains the advantages of fast response and robustness. At the same time, the chattering problem is greatly reduced, which guarantees the anti-strong wind requirements of UAV and improves the stability of the controllers.
机译:本文建立了具有风速因子的四旋翼无人机动力学模型,并设计了用于姿态稳定的滑模控制器。提出了神经网络和模糊控制的方法,以提高无人机抗风能力。动力学模型的不确定性可以通过根基函数(RBF)神经网络来近似,并且模糊控制可以在状态收敛过程中动态地校正符号函数的系数。仿真结果表明,本文构建的神经网络可以跟踪模型的不确定性,将滑模与模糊控制相结合,保持了快速响应和鲁棒性的优点。同时,抖振问题大大减少,保证了无人机的抗强风要求,提高了控制器的稳定性。

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