Use of neural networks to identify and compensate for friction in precision, position controlled mechanisms

A special neural network topology has been developed that compensates for friction in precision, position controlled mechanisms. A major contribution is that knowledge of the friction's form is used to determine the neural network's structure. This unique approach solves network sizing and weight initializing problems. The friction model is used for feedforward decoupling of friction-induced torque. The neural network also explicitly incorporates inertia compensation and linear feedback control. Another contribution is a demonstration of the trajectory dependence of static friction compensation with a discrete time controller. The authors include both the theoretical formulation and practical implementation results for the control of a commercial DC motor having a significant amount of static friction.<>