Friction compensation for motion control system using multilayer feedforward network

Friction has been shown to be one of the major contributing factors for problem associated with accuracy in motion control systems. Apart from making the system response slow, it causes steady state error or limit cycles near the reference position for the motion control system. In order to alleviate these problems, various control methods have been introduced and proposed for compensating the friction effect. Among the successful method is model-based friction compensation. Many sophisticated friction models have been proposed by researchers. Unfortunately, selecting and developing accurate models for friction compensation for a particular application has been historically challenging and trouble some due to complexity of parametrically modeling of the friction nonlinearities. Motivated by the need for simple and at the same time effective friction compensation in motion control system, AI-based friction model using multilayer feedforward network (MFN) is proposed and developed to estimate and compensate the frictional dynamics of a DC motor driven motion control system. The effectiveness of the developed MFN-based friction model to compensate the friction is evaluated experimentally on a rotary experimental motion system, and compared with mathematical model approach. The experimental results show that the friction compensation using the MFN-based friction model is more effectively to compensate for the effect of the friction than that on mathematical model of friction.