A novel robust terminal sliding-mode control (RTSMC) based on radial basis function (RBF) neural network is proposed firstly for controlling the attitude and position of the quadrotor, guaranteeing the system converged to stability point in a limited time. After establishing the nonlinear kinematics and dynamics models of the system, robust control is adopted in the RBF neural network terminal sliding-mode controller such that the impact of external interference is reduced effectively. Resorting to the Lyapunov function, the asymptotically stable condition for the considered system is obtained, in which the convergence of the system is deduced in a finite time. Furthermore, simulation results are given to show the faster convergence speed and strong robustness for the considered system with RBF and RTSMC. Finally, the effectiveness and robustness of the developed control strategy is validated experimentally.

中文翻译：

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基于神经网络的四旋翼鲁棒终端滑模控制

首先提出了一种基于径向基函数（RBF）神经网络的鲁棒终端滑模控制（RTSMC），用于控制四旋翼的姿态和位置，保证系统在有限时间内收敛到稳定点。在建立系统的非线性运动学和动力学模型后，在RBF神经网络终端滑模控制器中采用鲁棒控制，有效降低了外界干扰的影响。借助李雅普诺夫函数，得到了所考虑系统的渐近稳定条件，其中系统在有限时间内收敛。此外，仿真结果显示了所考虑的具有 RBF 和 RTSMC 的系统具有更快的收敛速度和更强的鲁棒性。最后，