Repetitive and iterative learning control are two modern control strategies for tracking systems in which the signals are periodic in nature. This paper discusses repetitive and iterative learning control from an internal model principle point of view. This allows the formulation of existence conditions for multivariable implementations of repetitive and learning control. It is shown that repet...

In order to improve the speed and accuracy of the trajectory tracking control for 3R plane robot, a PD iterative self-learning control algorithm is proposed based on the PD iterative control algorithm. The error of target value and the actual value of this iteration is introduced into the PD learning gain to make the PD learning gain becomes a function of the error and to achieve the effect of ...

This paper proposes a repetitive control type optimal gait generation framework by executing learning control and parameter tuning. We propose a learning optimal control method of Hamiltonian systems unifying iterative learning control (ILC) and iterative feedback tuning (IFT). It allows one to simultaneously obtain an optimal feedforward input and tuning parameter for a plant system, which min...

This abstract discusses our investigations relating Iterative Learning Control (ILC) for periodic systems on the one hand, and the class of Recursive Identification (RI), Gradient Descent (GD), Stochastic Approximation (SA) and adaptive filtering algorithms on the other. The benefit of such is the straightforward transfer of results in the latter context which is useful to study different desig...

A notion of robust stability is developed for iterative learning control in the context of disturbance attenuation. The size of the unmodelled dynamics is captured via a gap distance, which in turn is related to the standardH gap metric, and the resulting robustness certificate is qualitatively equivalent to that obtained in classical robust H∞ theory. A bound on the robust stability margin for...

Learning control is a very effective approach for tracking control in processes occuring repetitively over a fixed interval of time. In this note, an iterative learning control (ILC) algorithm is proposed to accommodate a general class of nonlinear, nonminimum-phase plants with disturbances and initialization errors. The algorithm requires the computation of an approximate inverse of the linear...

In this paper stochastic approximation theory is used to produce Iterative Learning Control (ILC) algorithms which are less sensitive to stochastic disturbances, a typical problem for the learning process of standard ILC algorithms. Two algorithms are developed, one to obtain zero mean controlled error and one to minimise the mean 2-norm of the controlled error. The former requires a certain kn...

In this paper we review the recent advances in three sub-areas of iterative learning control (ILC): 1) linear ILC for linear processes, 2) linear ILC for nonlinear processes which are global Lipschitz continuous (GLC), and 3) nonlinear ILC for general nonlinear processes. For linear processes, we focus on several basic configurations of linear ILC. For nonlinear processes with linear ILC, we co...

Abstract. This paper presents an iterative learning control using an information database (ILCID) for linear as well as nonlinear continuous time systems. It is proposed that a proper and efficient selection of the initial control input using the experience of previously tracked trajectories can improve the convergence rate of an iterative learning controller without modifying its control struc...

Abstract: Most of iterative learning control (ILC) methods requires that the relative degree of the plant is less than 2 for a linear system or the plant is passive for a non-linear system. A new model reference parametric adaptive iterative learning control using the command generator tracker (CGT) theory is proposed in this paper. The method can be applied to control a plant with a higher rel...