摘  要：在气动人工肌肉的静态建模中，为寻找拉力与气压和位移的函数关系，该文利用训练后的BP神经网络预测气动人工肌肉输出力。将准静态实验获得的气压、位移和对应的输出拉力代入BP神经网络进行训练，得到气动人工肌肉的BP神经网络静态模型。预测结果表明，预测拉力与试验测得拉力相关系数达0.99以上，且通过BP神经网络预测拉力与实测拉力误差率在较大收缩范围内维持在较低水平，从而证明根据BP神经网络预测拉力的静态模型是可行的。

关键词：气动肌肉；驱动器；BP神经网络；输出力；预测

文献标志码：A       文章编号：1674-5124（2015）12-0115-04

Application of BP neural networks in force prediction of pneumatic muscle actuators

GU Baotong， LIU Kai， MA Tao

（College of Mechanical and Electrical Engineering，Nanjing University of Aeronautics and Astronautics，

Nanjing 210016，China）

Abstract: To find out the function relationship among force， pressure and displacement in static models of pneumatic muscle actuators， the trained BP neural network is used to predict the force of pneumatic muscle actuators in the paper. To be specific， these data obtained through quasi-static experiment are trained in the BP neural network to get a BP neural network-based static model for pneumatic muscle actuators. Prediction results show that the correlation coefficient between the predicted and experimental force is higher than 0.99 and the error rate is confined in a relative low level for a wide range of contraction. The static model is therefore proven feasible.

Keywords: pneumatic muscle； actuator； BP neural network； output force； prediction