In this manuscript, a recurrent neural network is proposed for variable admittance control in human–robot cooperation tasks. The virtual damping and the virtual inertia of the designed robot’s admittance controller are adjusted online and simultaneously. A Jordan recurrent neural network is designed and trained for this purpose. The network is indirectly trained using the real-time recurrent learning algorithm and based on the velocity error between the reference velocity of the minimum jerk trajectory model and the actual velocity of the robot. The performance of the proposed variable admittance controller is presented in terms of the human required effort, the task completion time, the achieved accuracy at the target, and the oscillations during the movement. Its generalization ability is evaluated experimentally by conducting cooperative tasks along numerous straight-line segments using the KUKA LWR robot and by ten subjects. Finally, a comparison with previous developed variable admittance controllers, where only the variable damping or only the virtual inertia is adjusted, is presented.

在此手稿中，提出了一种递归神经网络，用于人机协作任务中的可变导纳控制。在线和同时调整设计的机器人导纳控制器的虚拟阻尼和虚拟惯性。为此设计并训练了Jordan递归神经网络。使用实时递归学习算法并基于最小冲击轨迹模型的参考速度与机器人实际速度之间的速度误差来间接训练网络。所提出的可变导纳控制器的性能以人类所需的努力，任务完成时间，在目标处实现的精度以及运动过程中的振荡来表示。通过使用KUKA LWR机器人沿着多个直线段执行协作任务并由十个对象对它的泛化能力进行了实验评估。最后，将其与以前开发的可变导纳控制器进行比较，在该控制器中仅调整了可变阻尼或仅调整了虚拟惯性。