Controlling a New Biped Robot Model Since Walking Using Neural Network

Abstract

In this paper, a new biped model are evaluated and then a stable neural network controller is used to control it. The biped model has slink and 6 degrees of freedom and actuated by plated pneumatic artificial muscle, which have a very high power to weight ratio and an inherent adaptable compliance. This NN controller allow accurate and dynamic following of prescribed trajectories, not simply control using "via" points specified by a teach pendant. It can significantly improve the accuracy requirements by retraining the basic PD/PID loop, but adding an inner adaptive loop that allows the controller to learn unknown parameters such as friction coefficient, thereby improving tracking accuracy. Simulation results show that NN controller tracking performance is much better than PD controller tracking performance.