This paper presents an approach to the use of neural networks to improve iterative learning control performance. The neural networks are used to estimate learning gain of an iterative learning law and to store the learned control input prooles for diierent reference trajectories. A neural network of piecewise linear approximation is presented to eeectively identify the system dynamics, and the ...

This paper addresses iterative learning control (ILC) for periodic systems using model predictive and optimization methods to redesign trajectories and reject periodic disturbances. Stability and optimality of these optimization methods is analysed and illustrated on simulations. The additional prospects of the optimization formulation (e.g. including energy costs, system identification) referr...

In this paper, we present a subspace method for learning nonlinear dynamical systems based on stochastic realization, in which state vectors are chosen using kernel canonical correlation analysis, and then state-space systems are identified through regression with the state vectors. We construct the theoretical underpinning and derive a concrete algorithm for nonlinear identification. The obtai...

The present paper deals with a systematic study of incremental learning algorithms. The general scenario is as follows. Let c be any concept; then every innnite sequence of elements exhausting c is called positive presentation of c. An algorith-mic learner successively takes as input one element of a positive presentation as well as its previously made hypothesis at a time, and outputs a new hy...