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.

中文翻译：

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具有时滞的双边遥操作的自适应神经网络固定时间控制设计。

在本文中，受时变时滞和动力学不确定性的影响，我们针对一类非线性双边遥操作系统提出了一种新颖的自适应固定时间控制策略。首先，采用自适应控制方案来估计时延的上限，可以解决时延对双边遥操作系统的稳定性有重大影响的困境。然后，利用径向基函数神经网络（RBFNN）估算双边遥测系统中的不确定性，包括动力学，操作员和环境模型。引入了新颖的自适应定律，以解决固定时间收敛设置中系统的不确定性。接下来，提出了一种新颖的自适应固定时间神经网络控制方案。根据李雅普诺夫稳定性理论，事实证明，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该控制算法的有效性。