This paper proposes an approach for variable admittance control in human–robot collaboration depending on the online training of neural network. The virtual inertia is an important factor for the system stability, and its tuning is investigated in improving the human–robot cooperation. The design of the variable virtual inertia controller is analyzed, and the choice of the neural network type and their inputs and output is justified. The error backpropagation analysis of the designed system is elaborated since the end-effector velocity error depends indirectly on the multilayer feedforward neural network output. The proposed controller performance is experimentally investigated, and its generalization ability is evaluated by conducting cooperative tasks with the help of multiple subjects using the KUKA LWR manipulator under different conditions and tasks than the ones used for the neural network training. Finally, a comparative study is presented between the proposed method and previous published ones.

本文提出了一种基于神经网络在线训练的人机协作中的可变导纳控制方法。虚拟惯性是影响系统稳定性的重要因素，并且对其惯性进行了研究，以改善人机协作。分析了可变虚拟惯性控制器的设计，并选择了神经网络类型及其输入和输出。由于末端执行器速度误差间接取决于多层前馈神经网络的输出，因此对设计的系统进行了误差反向传播分析。拟议的控制器性能经过实验研究，并通过在与神经网络训练所使用的条件和任务不同的条件和任务下，使用KUKA LWR机械手在多个对象的帮助下执行协作任务来评估其泛化能力。最后，对提出的方法与以前发表的方法进行了比较研究。