IEEE Trans Cybern. 2021 Jul;51(7):3724-3737. doi: 10.1109/TCYB.2019.2924446. Epub 2021 Jun 23.
In this paper, two novel adaptive finite-time control schemes are proposed for position tracking of nonlinear teleoperation system, which dynamic uncertainties, actuator saturation, and time-varying communication delays are considered. First, a novel auxiliary variable is designed to provide more stable performance. The radial basis function (RBF) neural network is introduced to estimate dynamic uncertainties. Second, two adaptive finite-time control schemes are investigated. In control scheme I, the RBF neural network and the gain switching strategy are applied to compensate the actuator saturation. In control scheme II, an auxiliary compensation filter and the compensation adaptive update laws, which contain the finite-time structure, are developed for dealing with saturation. Third, the finite-time adaptive controller is designed in each of these two control schemes. Based on the multiple Lyapunov function method, the closed-loop teleoperation system with these two control methods is proved to be bounded and finite-time stability. Finally, the simulation experiments are performed and the comparisons with other control methods are shown. The effectiveness of the proposed control schemes is demonstrated.
本文提出了两种新颖的自适应有限时间控制方案，用于非线性遥操作系统的位置跟踪，其中考虑了动态不确定性、执行器饱和和时变通信延迟。首先，设计了一种新颖的辅助变量，以提供更稳定的性能。引入了径向基函数 (RBF) 神经网络来估计动态不确定性。其次，研究了两种自适应有限时间控制方案。在控制方案 I 中，应用 RBF 神经网络和增益切换策略来补偿执行器饱和。在控制方案 II 中，开发了辅助补偿滤波器和补偿自适应更新律，其中包含有限时间结构，用于处理饱和。第三，在这两种控制方案中，分别设计了有限时间自适应控制器。基于多个 Lyapunov 函数方法，证明了这两种控制方法下的闭环遥操作系统是有界和有限时间稳定的。最后，进行了仿真实验，并与其他控制方法进行了比较。验证了所提出的控制方案的有效性。
