利用肌电信号离线控制机械臂

CONTROLLING MANIPULATOR OFF-LINE USING ELECTROMYOGRAPHY SIGNALS

研究的目的是利用人体上肢肌肉的肌电信号来控制机械臂的运动。人体手臂在水平面上做屈伸运动,采集肱二头肌和肱三头肌的肌电信号和肘关节角度信号,对肌电信号进行处理和特征提取,提取的特征值作为一个四层的神经网络模型的输入信号。运用改进后的误差反传学习算法最优化网络各层权值,并使用该神经网络模型来预测人体的肘关节角,使用该预测角来控制机械臂,机械臂的运动与人的肘关节角进行比较,试验结果表明肘关节运动角度与机械臂的运动角度方均根误差小于1°。

The objective is to control manipulator off-line by using electromyography signals. Electromyography signals are collected from the biceps and triceps muscles of normal subjects when they move their elbow flexion-extension with time-varying loads. The raw electromyography signals are processed and the new defined characteristic is picked up. A four-layer feed-forward neural network model with the characteristic as its input is developed. The weighted values of the model are optimized with the adjusted back-propagation algorithm. By training the model the transformation can be mapped： From the processed eletromyography signals to the elbow joint angles. The predicted angles are used to control the manipulator by inverse-control method. The angles of the manipulator are compared with those of the elbow joint. The experimental results show that the root mean square error between the joint angle of the manipulator and the actual joint angle measured by the goniometer is less than 1°.