交通监管无人机的控制器设计

本文提出一种新的级联控制策略。利用自适应RBF神经网络控制和双闭环积分滑模控制,实现对无人机的位置控制以及姿态控制。自适应RBF神经网络控制可在线的、有效的估计未知的模型参数和持续的外界干扰,双闭环积分滑模控制可保证姿态系统状态量沿着预先设定的滑模面渐进收敛到期望的姿态轨迹,仿真结果表明本文的控制方法具有对外界干扰及模型不确定性良好的鲁棒性、光滑的输出信号以及非线性函数的逼近特性。

In this paper, a new cascade control strategy was proposed to utilizes adaptive RBF neural network control and double closed-loop integral sliding mode control to achieve position control and attitude control of the quadroto匚 Adaptive RBF neural network control can estimate unknown model parameters and continuous external interference online and effectively;the double closed-loop integral sliding mode control can ensure that the state quantity of the attitude system gradually converges to the desired attitude trajectory along the preset sliding surface. The simulation results showed that the control method proposed in this paper had the robustness to the external disturbance and the model uncertainty, the smooth output signal and the approximation of the nonlinear function.