搭建了气动人工肌肉静态测试平台，在 0. 1 ～ 0. 8 MPa 气压下进行多次测量试验，对气动人工肌肉进行特性分析，根据理论模型和测试数据建立了数学模型，模型求解精度较好。考虑外负载、气源气压和系统摩擦等因素对数学模型的影响，结合RBF网络的快速学习能力设计了一种基于 RBF 网络的 PID 控制策略。在外负载50 ～200N 的条件下，搭建了气动人工肌肉动态测试平台并进行了多组位置控制试验。结果表明，传统 PID 控制只能在一定的外负载范围内实现较好的位置控制，基于RBF 网络的 PID 控制能自适应调整 PID 参数，且响应速度快，调节时间短，超调量小，能更好地补偿其数学模型误差并实现精确的位置控制。

The static test platform of pneumatic artificial muscle was built，and a series of measurement tests were carried out under pressure of 0. 1 ～ 0. 8 MPa to analyze the characteristics of pneumatic artificial muscle． The mathematical model，which was built based on the theoretical model and test data，shows a high accuracy of solu-tion． In consideration of the influence of external load，gas pressure and system friction on the mathematical mod-el，a PID control strategy based on ＲBF network was designed with the fast learning ability of ＲBF network． Un-der the condition of external load F = 50 ～ 200 N，the dynamic test platform was built and a number of position control tests were implemented． The results show that the traditional PID control strategy can only achieve better control accuracy within a certain range of external loads，while the proposed strategy is able to adjust the PID pa-rameters adaptively． Moreover，the proposed PID control strategy has the advantages of higher response speed，shorter adjustment time and smaller overshoot，and it can better compensate the mathematical model error and a-chieve higher control accuracy．