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

In this paper, tip regulation of a flexible one-link manipulator by Learnin g Variable Structure Control (LVSC) is investigated. Switching surface is d esigned according to a selected reference model which relocates system pole s to be negative real ones, hence link vibration is eliminated. The propose d LVSC incorporates a learning mechanism to improve regulation accuracy. Ri gorous proof shows: the tracking error sequence converges uniformly to zero ; the uniformly bounded learning control sequence converges to the equivale nt control almost everywhere. For practical considerations, the learning me chanism is further conducted in frequency domain by means of Fourier series expansion, hence achieves better regulation performance. Numerical simulat ions confirm the effectiveness and robustness of the proposed approach. [S0 022-0434(00)01804-9].