School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, China.
School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, China.
ISA Trans. 2018 Feb;73:208-226. doi: 10.1016/j.isatra.2017.12.011. Epub 2018 Jan 5.
The distributed finite-time formation tracking control problem for multiple unmanned helicopters is investigated in this paper. The control object is to maintain the positions of follower helicopters in formation with external interferences. The helicopter model is divided into a second order outer-loop subsystem and a second order inner-loop subsystem based on multiple-time scale features. Using radial basis function neural network (RBFNN) technique, we first propose a novel finite-time multivariable neural network disturbance observer (FMNNDO) to estimate the external disturbance and model uncertainty, where the neural network (NN) approximation errors can be dynamically compensated by adaptive law. Next, based on FMNNDO, a distributed finite-time formation tracking controller and a finite-time attitude tracking controller are designed using the nonsingular fast terminal sliding mode (NFTSM) method. In order to estimate the second derivative of the virtual desired attitude signal, a novel finite-time sliding mode integral filter is designed. Finally, Lyapunov analysis and multiple-time scale principle ensure the realization of control goal in finite-time. The effectiveness of the proposed FMNNDO and controllers are then verified by numerical simulations.
本文研究了多架无人机的分布式有限时间编队跟踪控制问题。控制目标是在存在外部干扰的情况下保持跟随直升机的位置编队。基于多时间尺度特征，将直升机模型分为二阶外环子系统和二阶内环子系统。利用径向基函数神经网络（RBFNN）技术，我们首先提出了一种新的有限时间多变量神经网络干扰观测器（FMNNDO）来估计外部干扰和模型不确定性，其中通过自适应律可以动态补偿神经网络（NN）逼近误差。接下来，基于 FMNNDO，使用非奇异快速终端滑模（NFTSM）方法设计了分布式有限时间编队跟踪控制器和有限时间姿态跟踪控制器。为了估计虚拟期望姿态信号的二阶导数，设计了一种新的有限时间滑模积分滤波器。最后，李雅普诺夫分析和多时间尺度原理保证了在有限时间内实现控制目标。通过数值仿真验证了所提出的 FMNNDO 和控制器的有效性。
