Adaptive friction compensation for precision machine tool drive

Abstract

In this paper, two robust adaptive control algorithms are proposed for friction compensation in high performance machine tools. The first controller utilizes an adaptive friction compensation scheme based on a postulated linear-in-the-parameters friction model. The proposed friction compensation algorithm explicitly accounts for time varying normal forces as well as dependence of the friction coefficient on velocity. The Stribeck friction characteristic and variations in static friction are treated as bounded disturbances, and compensated for by adaptive bounding functions. In the second controller, a special form of the Takagi–Sugeno fuzzy system is utilized to adaptively learn unknown friction behavior and compensate for it. This approach assumes that no a priori knowledge about frictional effects in the strut joints is available. The proposed controllers are compared based on simulation and experimental results of tracking performance at low speeds, since frictional effects dominate strut dynamics at low speeds. The results indicate that the large tracking errors caused by friction at the velocity reversals when conventional control algorithms are used, are reduced greatly by the adaptive controllers. Instability of the gradient-type adaptive algorithms is observed when the adaptation rate is high. Inclusion of a dead-zone modification of the adaptive laws eliminates the instability.