Nonlinear Controlling of Artificial Muscle System with Neural Networks

Abstract

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