Neural networks in feedforward control of a robot arm driven by antagonistically coupled drives

Abstract

The paper deals with a rapidly growing trend in robotics - anthropomimetics. Following a human paragon, bio-inspired control of the robot arm is presented using artificial neural networks. This work demonstrates results achieved by feedforward control comparing feedforward backpropagation networks and radial bases networks. Use of radial bases network prevails as an efficient tool to evade the exact mathematical modeling and conventional control of the complex mechanical system that is highly nonlinear and includes passive compliance.