Experience Inclusion in Fourier Series Based Iterative learning Control for Manipulators

This paper proposes a Fourier series based learning controller with experience for the tracking control of robot manipulator. In conventional ILC algorithms, the initial input for each new trajectory tracking task is assumed to be zero. In this paper, we included the idea of using the past trajectory tracking experiences, in the selection of initial input for a new trajectory tracking task, designed in the Fourier domain. The advantage of the learning controller is that it does not require the parameters of the system, since it utilizes only the local input-output information. Improvement of tracking performance and initial error reduction with considerable improvement in convergence rate with iterations are obtained. Algorithms are verified through detailed simulation studies on a single DOF robot manipulator.