The pneumatic artificial muscles are widely used in the fields of medical robots, etc. Neuralnetworks are applied to modeling and controlling of artificial muscle system. A single-joint artificialmuscle test system is designed. The recursive prediction error (RPE) algorithm which yields fasterconvergence than back propagation (BP) algorithm is applied to train the neural networks. Therealization of RPE algorithm is given. The difference of modeling of artificial muscles using neuralnetworks with different input nodes and different hidden layer nodes is discussed. On this basis thenonlinear control scheme using neural neworks for artificial muscle system has been introduced. Theexperimental results show that the nonlinear control scheme yields faster response and higher controlaccuracy than the traditional linear control scheme. The pneumatic artificial muscles are widely used in the fields of medical robots, etc. Neuralnetworks are applied to modeling and controlling of artificial muscle systems. A single-joint artificialmuscle test system is designed. The recursive prediction error (RPE) algorithm which yields fasterconvergence than The difference of modeling of artificial muscles using neural networks with different input nodes and different hidden layer nodes is discussed. On this basis thenonlinear control scheme using neural neworks for artificial muscle system has been introduced. Theexperimental results show that the nonlinear control scheme yields faster response and higher controlaccuracy than the traditional linear control scheme.