A learning variable structure controller of a flexible one-link manipulator

Tip regulation of a flexible one-link manipulator by learning variable structure control (LVSC) is investigated. The sliding surface is designed according to a selected reference model which relocates system poles to be negative real ones, hence link vibration is eliminated. The proposed LVSC incorporates a learning mechanism to improve regulation accuracy. Rigorous proof shows: the state´s tracking error sequence converges uniformly to zero; the uniformly bounded learning control sequence converges to the equivalent control almost everywhere