AI-Based Friction Modeling and Compensation in Motion Control System Using ANFIS

The inherent presence of friction in motion control system has been one of the major sources of performance degradation in terms of slow responses, steady state accuracy, poor tracking and or limit cycles near the reference position. Motivated by the need for simple and at the same time effective friction model, an AI-based (non-parametric) friction model using an adaptive Neuro-Fuzzy inference system (ANFIS) is proposed in this work to estimate the non-linear friction in a motion control system. The effectiveness of the developed model in representing and compensating for the frictional effects is evaluated experimentally on a rotary experimental motion system, and the performance benchmarked with a parametric based model. The results show ANFIS as a viable and better alternative to mathematical-based techniques in representing and compensating friction effects.