This article proposes an adaptive integral sliding mode control (ISMC) strategy for quadrotor control that ensures faster and finite-time convergence along with chattering attenuation. Quadrotor dynamics are assumed to be unknown because of the high degree of parametric uncertainties, including external disturbances. The equivalent control law obtained by ISMC consists of quadrotor dynamics and, thus, cannot be applied to the quadrotor. A new fully connected recurrent neural network (FCRNN) controller has been proposed to mimic the equivalent control instead of estimating the Quadrotor dynamics separately. The proposed FCRNN architecture consists of output feedback to the input layer and the hidden layer, which enhances the approximation capability of FCRNN. All hidden layer neurons receive self-feedback and feedback from other hidden layer neurons, which further strengthens FCRNN’s potential to capture complex dynamic characteristics. As learning should happen in finite time, the finite-time stability of the overall system has been guaranteed using the Lyapunov stability theory, and the update laws for FCRNN weights in real time are derived using the same. To show the effectiveness of the proposed approach, a comprehensive analysis has been done against existing SMC strategy and against well-known function approximation techniques, e.g., the radial basis function network (RBFN) and RNN.

中文翻译：

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利用全连接循环神经网络的自适应积分滑模控制进行四旋翼飞行器位置和姿态控制


本文提出了一种用于四旋翼飞行器控制的自适应积分滑模控制 (ISMC) 策略，可确保更快的有限时间收敛以及颤振衰减。由于参数不确定性（包括外部干扰）高度不确定，四旋翼飞行器动力学被假定为未知。 ISMC获得的等效控制律由四旋翼动力学组成，因此不能应用于四旋翼飞行器。提出了一种新的全连接循环神经网络（FCRNN）控制器来模拟等效控制，而不是单独估计四旋翼动力学。所提出的FCRNN架构由输入层和隐藏层的输出反馈组成，增强了FCRNN的逼近能力。所有隐藏层神经元都接收自反馈和其他隐藏层神经元的反馈，这进一步增强了FCRNN捕获复杂动态特征的潜力。由于学习应在有限时间内进行，因此利用Lyapunov稳定性理论保证了整个系统的有限时间稳定性，并利用该理论推导了FCRNN权重的实时更新规律。为了证明所提出方法的有效性，对现有的 SMC 策略和众所周知的函数逼近技术（例如径向基函数网络（RBFN）和 RNN）进行了全面分析。