航空机电作动器神经网络快速终端滑模控制

目前机电作动器由于具有干净、维护方便等优点，越来越受到航空业的青睐。航空机电作动器的特点是控制精度、稳定性和响应速度要求高，针对以上特点，提出了一种基于多层神经网络的快速终端滑模控制策略。为了提高航空作动器响应速度和跟踪精度，设计了快速终端滑模控制策略，不仅可以加快系统响应而且可以在无扰动情况下实现系统的有限时间稳定。针对系统参数不确定性和外部扰动，设计多层神经网络进行估计并通过前馈方法加以补偿。针对神经网络的重构误差，设计了非线性鲁棒项加以克服。利用李亚普洛夫稳定性定理证明了控制系统在有扰动情况下可以实现有界稳定。实验结果表明：所设计的控制器具有良好的参数自适应和抗干扰能力，同时具有更高的跟踪精度和更快的响应速度。

Fast terminal sliding mode control of neural networks for aeromechanical actuators

Electromechanical actuators are increasingly favored by the aviation industry because of their advantages such as cleanness and easy maintenance, and the aviation electromechanical actuators have characteristics of high control accuracy, high stability and fast response speed. To further improve the response speed and tracking accuracy of aviation actuators, this paper proposes a fast terminal sliding mode control strategy based on multi-layer neural network, which can speed up the system response and realize the system limited time stability without disturbances. Aiming at the uncertainty of system parameters and external disturbances, we design a multi-layer neural network for estimation and compensation by the feedforward method. A nonlinear robust term is designed to overcome the reconstruction error of the neural network. The Lyapunov stability theorem proves that the control system can achieve bounded stability under disturbances. The experimental results show good parameter adaptation and anti-jamming capabilities, higher tracking accuracy and faster response speed of the designed controller.