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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时滞双边遥操作的自适应神经网络定时控制设计


在本文中，针对时变延迟和动力学不确定性，我们针对一类非线性双边遥控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上限，可以解决时延对双边遥操作系统稳定性影响较大的困境。然后，利用径向基函数神经网络（RBFNN）来估计双边远程操作系统中的不确定性，包括动力学、操作员和环境模型。引入新的适应律来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。基于Lyapunov稳定性理论，证明双边遥操作系统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。