Friction compensation using neural networks applicable to high precision motion control systems in manufacturing

There is an increasing number of applications in high precision motion control systems in manufacturing, i.e., ultra-precision machining, assembly of small components and micro devices. It is very difficult to assure such accuracy due to many factors affecting the precision motion, such as frictions and disturbances in the drive system. The standard proportional-integral-derivative (PID) type servo control algorithms are not capable of delivering the desired precision under the influence of frictions and disturbances. In this paper neural network model reference adaptive controller (NNMRAC) is used to compensate the frictions and disturbances and the desired precision under the influence of frictions and disturbances is achieved. Therefore the proposed scheme can be applicable to a wide class of mechanical systems. The simulation results of single link robot arm with friction verify the effectiveness of the proposed scheme.