机械臂反演非奇异终端的神经滑模控制

为了解决具有外部干扰以及建模误差的多关节机械臂的轨迹跟踪问题,提出了一种机械臂反演非奇异终端的神经滑模控制方法。采用非奇异终端的滑模面,基于反演方法以及滑模控制的原理,设计了反演滑模控制器。针对由于外部干扰以及建模误差引起的反演滑模控制系统中不确定的因素上界,设计了径向基(radial basis function,简称RBF)神经网络的自适应律,对不确定因素上界进行了在线估计,并对控制系统的稳定性使用了Lyapunov定理进行证明。仿真分析结果表明,所提出的方法不仅可以减少系统中存在的抖振现象,而且具有较好的轨迹跟踪性能和较强的鲁棒性。

Manipulator Inversion of Non-Singular Terminal Neural Sliding Mode Control

This paper presents a neural sliding mode control method for the mechanical arm with a non-singular inversion terminal in order to realize the trajectory tracking of a multi-joint robot arm with external interference and modeling errors. First, an inversion-sliding-mode controller with a non-singular terminal sliding surface is designed based on the inversion method and the principle of sliding mode control. Then, the radial basis function (RBF) neural network adaptive law is designed against the uncertainty in the inversion sliding mode control system due to its modeling errors and external interference. The upper bound of this uncertainty is estimated online. Finally, the stability of the control system is proved using the Lyapunov Theorem. Simulation analysis and experimental results show that the proposed method can not only eliminate the chattering phenomenon in the system, but also improve its tracking performance and robustness.