A design framework for nonlinear iterative learning control and repetitive control: Applied to three mechatronic case studies

Iterative learning control (ILC) and repetitive control (RC) can lead to high performance by attenuating repeating disturbances perfectly, yet these approaches may amplify non-repeating disturbances. The aim of this paper is to achieve both perfect, fast attenuation of repeating disturbances and limited amplification of non-repeating disturbances. This is achieved by including a deadzone nonlinearity in the learning filter, which distinguishes disturbances based on their different amplitudes to apply different learning gains. Convergence conditions for nonlinear ILC and RC are developed, which are used in combination with system measurements in a comprehensive design procedure. Experimental implementation demonstrates fast learning and small errors.