IEEE Trans Neural Netw Learn Syst. 2021 Dec;32(12):5595-5609. doi: 10.1109/TNNLS.2021.3071020. Epub 2021 Nov 30.
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.
本文提出了一种适用于四旋翼飞行器控制的自适应积分滑模控制（ISMC）策略，该策略确保了更快和有限时间的收敛，同时衰减了抖振。由于存在高度的参数不确定性，包括外部干扰，假设四旋翼飞行器的动力学是未知的。ISMC 获得的等效控制律由四旋翼飞行器的动力学组成，因此不能直接应用于四旋翼飞行器。提出了一种新的完全连接递归神经网络（FCRNN）控制器来模拟等效控制，而不是单独估计四旋翼飞行器的动力学。所提出的 FCRNN 架构由输出反馈到输入层和隐藏层组成，这增强了 FCRNN 的逼近能力。所有隐藏层神经元都接收来自自身和其他隐藏层神经元的反馈，这进一步增强了 FCRNN 捕获复杂动态特性的潜力。由于学习应该在有限的时间内发生，因此使用 Lyapunov 稳定性理论保证了整个系统的有限时间稳定性，并使用相同的方法实时推导出 FCRNN 权重的更新律。为了展示所提出方法的有效性，对现有的 SMC 策略和著名的函数逼近技术（例如，径向基函数网络（RBFN）和 RNN）进行了全面分析。
