IEEE Trans Cybern. 2022 Sep;52(9):9756-9769. doi: 10.1109/TCYB.2021.3063729. Epub 2022 Aug 18.
In this article, subject to time-varying delay and uncertainties in dynamics, we propose a novel adaptive fixed-time control strategy for a class of nonlinear bilateral teleoperation systems. First, an adaptive control scheme is applied to estimate the upper bound of delay, which can resolve the predicament that delay has significant impacts on the stability of bilateral teleoperation systems. Then, radial basis function neural networks (RBFNNs) are utilized for estimating uncertainties in bilateral teleoperation systems, including dynamics, operator, and environmental models. Novel adaptation laws are introduced to address systems' uncertainties in the fixed-time convergence settings. Next, a novel adaptive fixed-time neural network control scheme is proposed. Based on the Lyapunov stability theory, the bilateral teleoperation systems are proved to be stable in fixed time. Finally, simulations and experiments are presented to verify the validity of the control algorithm.
在本文中，针对动力学中的时变延迟和不确定性，我们为一类非线性双边遥操作系统提出了一种新的自适应固定时间控制策略。首先，应用自适应控制方案来估计延迟的上界，这可以解决延迟对双边遥操作系统稳定性有重大影响的困境。然后，利用径向基函数神经网络（RBFNNs）来估计双边遥操作系统中的不确定性，包括动力学、操作员和环境模型。引入新的自适应律来解决固定时间收敛设置下系统的不确定性问题。接下来，提出了一种新的自适应固定时间神经网络控制方案。基于李雅普诺夫稳定性理论，证明双边遥操作系统在固定时间内是稳定的。最后，通过仿真和实验验证了控制算法的有效性。
