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...

Abstract In this chapter we review some basic ideas for nonlinear system identification. This is a complex area with vast and rich literature. One reason the richness that very many parameterizations of unknown have been suggested, each various proposed estimation methods. We will first describe details nonparametric techniques based on Reproducing Kernel Hilbert Space theory Gaussian regressio...

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...

Recently, there has been much interest in spectral approaches to learning manifolds— so-called kernel eigenmap methods. These methods have had some successes, but their applicability is limited because they are not robust to noise. To address this limitation, we look at two-manifold problems, in which we simultaneously reconstruct two related manifolds, each representing a different view of the...

This paper describes an indentometer device used to identify the linear dynamic and nonlinear properties of skin and underlying tissue using an in vivo test. The device uses a Lorentz force actuator to apply a dynamic force to the skin and measures the resulting displacement. It was found that the skin could be modeled as a Wiener system (i.e. a linear dynamic system followed by a static nonlin...

This paper examines the potential role of unit consistency as a system design principle. Unit-consistent generalized matrix inverses and unit-invariant matrix decompositions are derived in support of this principle. Applications of the methods described are illustrated with examples relating to nonlinear system identification and robustness to multiplicative noise for image database retrieval.