Toward the iterative learning control for belt-driven system using wavelet transformation

An iterative learning scheme using wavelet transformation to achieve excellent speed-tracking performance of a belt-driven system is presented in this article. The learning control scheme consists of a proportional feedback controller and a feedforward controller. The learning law updates the control profile from the previous output signal of the feedback controller, which has been processed via the wavelet transformation. Using wavelet transformation, the adverse effect due to unlearnable system dynamics can be drastically attenuated. The convergence of the learning control scheme is thus theoretically guaranteed and the convergent rate can be greatly improved in comparison to other iterative learning schemes. The proposed scheme is applied to the speed tracking for an ink-jet printer and excellent speed-tracking performance is achieved through experimental verification.