Neural network-based control of balanced robotic manipulators with joint flexibility

Abstract

This paper addresses an improvement on the controlled performance of balanced manipulators in a practical level by implementing neural network based controller. The mass balancing of robotic manipulators has been shown to have favorable effects on the dynamic characteristics. However, it was also pointed out that for the manipulators having a certain degree of flexibility at the joints, due to the lowered structural natural frequencies and reduced velocity related terms, mass balancing results in vibratory motion at high speed operation. Such a vibratory tendency of the balanced flexible joint manipulator limits the admissible range of servo gains of the conventional controllers, making those controllers unsuitable for controlling the manipulator at high speeds. To avoid such difficulty, an artificial neural network (NN) controller is introduced to realize the dynamic control of the balanced flexible joint manipulators. A feedforward type of NN controller is proposed and its performance is evaluated through a series of numerical simulations. The proposed NN controller showed much better tracking performances over the conventional PD controller.