Control of elastic joint robot based on electromyogram signal by pre-trained Multi-Layer Perceptron

Nowadays, humans can play an important role in control of robots. Some researches have used signals that coming directly from humans for control interfaces. In this paper, electromyogram (EMG) signals from the muscles of the human's upper limb are used as the control interface between the user and a robot arm. A Multi-Layer Perceptron (MLP) is trained by additional unsupervised pre-training to decode upper limb motion from kinematic data and EMG recordings. On the other hand, the control structure differs from previous ones because using the voltage control strategy instead of the torque control strategy. The common control structure for elastic-joint robots employs two control loops whereas this controller has only one control loop and actuators are considered in the dynamic equation of the robot. The proposed control design is verified by stability analysis and experimental results demonstrate the effectiveness of this controller.