Fuzzy-enhanced Adaptive Control for Flexible Drive System with Friction Using Genetic Algorithms

When a mechatronic system is in slow speed motion, serious effect of nonlinear friction plays a key role in its control design. In this paper, a stable adaptive control for drive systems including transmission flexibility and friction, based on the Lyapunov stability theory, is first proposed. For ease of design, the friction is fictitiously assumed as an unknown disturbance in the derivation of the adaptive control law. Genetic algorithms are then suggested for learning the structure and parameters of the fuzzy-enhancing strategy for the adaptive control to improve system's transient performance and robustness with respect to uncertainty. The integrated fuzzy-enhanced adaptive control is well tested via computer simulations using the new complete dynamic friction model recently suggested by Canudas de Wit et al. for modeling the real friction phenomena. Much lower critical velocity of a flexible drive system that determines system's low-speed performance bound can be obtained using the proposed hybrid control strategy.