This open-access book treats recent developments in kernel-based identification, of interest to anyone engaged learning dynamic systems from data.

A new iterative learning controller is proposed for a general unknown discrete time-varying nonlinear non-affine system represented by NARMAX (Nonlinear Autoregressive Moving Average with eXogenous inputs) model. The proposed controller is composed of an iterative learning neural identifier and an iterative learning controller. Iterative learning control and iterative learning identification ar...

This report formulates a minimal model based on a control theoretic framework to best describe the dynamics of perfect adaptation shown by the hyper osmotic shock response system in yeast. Using principles from adaptive control and stability theory, we step by step apply system identification methods to build a simple second order linear system with only a few parameters, that can concisely mod...

Iterative learning and repetitive control aim to eliminate the effect of unwanted disturbances over repeated trials or cycles. The disturbance-free system model, if known, can be used in a model-based iterative learning or repetitive control system to eliminate the unwanted disturbances. In the case of periodic disturbances, although the unknown disturbance frequencies may be the same from tria...

The most important and time consuming part of an industrial application of control is the modelling. It may take 50 per cent or more of the entire project. Therefore a major challenge for a control systems supplier like ABB is to constantly try to decrease the engineering effort for modelling. This paper discusses some different aspects of modelling and identification originating from applicati...

System identification is a fundamentally experimental field of science in that it deals with modeling of system dynamics using measured data. Despite this fact many algorithms and theoretical results are only tested with simulations at the time of publication. One reason for this may be a lack of easily available live data. This paper therefore presents three sets of data, suitable for developm...

The repetitive peripheral magnetic stimulation (RPMS) is an innovative approach in treatment of central paresis, e. g. after stroke. In this article we present a neuromuscular model for the RPMS-induced muscle contraction. This model is the basis for our two recent goals in research: Position controlled movement induction and automated therapy evaluation by means of system identification. In or...

A general formula is given for the conditional mean in terms of higher order statistics. Using this formula, a general scheme for nonlinear system identi cation is introduced including a broad range of nonlinearities which depends on the probability density function of the input. As a special case of that general scheme, the polynomial system identi cation problem is treated. It is shown that o...