针对倾转四旋翼无人机在复杂扰动下自主飞行的控制稳定性和精度问题，提出了一种基于径向基函数（Radial Basis Function, RBF）神经网络的增强型自抗扰控制（Active Disturbance Rejection Control, ADRC）参数自适应控制策略。首先，基于分部件机理建模方法建立覆盖全飞行模式的倾转四旋翼无人机非线性飞行动力学模型。其次，为了解决非线性自抗扰控制器参数自适应在线整定问题，利用具有强非线性函数逼近能力的RBF神经网络，将控制器实时解算的控制输入和倾转四旋翼无人机的状态输出作为神经网络的输入，基于神经网络的输出构建参数自适应调节规则，对自抗扰控制器扰动估计扩张状态观测器（Extended State Observer, ESO）部分和非线性状态误差反馈控制律（Nonlinear State Error Feedback, NLSEF）部分的参数在线动态调整，实现对模型不确定性及外界扰动有效的估计和补偿。最后，基于ADRC-RBF控制器构建倾转四旋翼无人机姿态自适应控制系统，在典型飞行模式下进行姿态控制仿真验证。仿真结果表明：相较于传统的ADRC控制器，本文设计的ADRC-RBF控制器具有更好的抗干扰性、自适应能力和稳定性。
An enhanced active disturbance rejection control (ADRC) parameter adaptive control strategy based on radial basis function (RBF) neural network is proposed for the control stability and accuracy of the autonomous flight of tilt-quadrotor UAVs under complex disturbances. Firstly, based on the component mechanism modelling method, a nonlinear flight dynamics model of the tilt-quadrotor UAV covering the whole flight mode is established. Secondly, the RBF neural network with strong nonlinear function approximation ability is used to solve the problem of online adaptive tuning of nonlinear active disturbance rejection controller parameters. The con-trol input solved by the controller in real time and the state output of the tilt-quadrotor UAV are used as the input of the neural net-work. Based on the output of the neural network, the parameter adaptive adjustment rules are constructed, and the parameters of the extended state observer (ESO) part and the nonlinear state error feedback control (NLSEF) part of the active disturbance rejection controller are dynamically adjusted online to realise the effective estimation and compensation of model uncertainty and external dis-turbance. Finally, the attitude adaptive control system of the tilt-quadrotor UAV is constructed based on the ADRC-RBF controller, and the attitude control simulation is carried out in the typical flight mode. The simulation results show that compared with the tradi-tional ADRC controller, the ADRC-RBF controller designed in this paper has better anti-interference, adaptive ability and stability.
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