This paper deals with the modelling and identification of a six axes industrial Stäubli RX90 robot. A non-linear finite element method is used to generate the dynamic equations of motion in a form suitable for both simulation and identification. The latter requires that the equations of motion are linear in the inertia parameters. Joint friction is described by a friction model that describes t...

In experimental science and engineering, least squares are ubiquitous in analysis and digital data processing applications. Minimizing sums of squares of some quantities can be interpreted in very different ways and confusion can arise in practice, especially concerning the optimality and reliability of the results. Interpretations of least squares in terms of norms and likelihoods need to be c...

In this correspondence new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness via combined parameter regularization and new robust structural selective criteria. In parallel to parameter regularization, we use two classes of robust model selection criteria based on either experimental design criteria tha...

The use of cubic splines, instead of polynomials, in representing static nonlinearities in block structured models is considered. A system identification algorithm for the Hammerstein structure, a static nonlinearity followed by a linear filter, is developed in which the static nonlinearity is represented by a cubic spline. The identification algorithm, based on a separable least squares Levenb...

Polynomially structured low-rank approximation problems occur in • algebraic curve fitting, e.g., conic section fitting, • subspace clustering (generalized principal component analysis), and • nonlinear and parameter-varying system identification. The maximum likelihood estimation principle applied to these nonlinear models leads to nonconvex optimization problems and yields inconsistent estima...

In this paper the author proposes to use the Least Squares Lattice filter with forgetting factor to estimate time-varying parameters of the model for noise processes. We simulated an Auto-Regressive (AR) noise process in which we let the parameters of the AR vary in time. We investigate a new way of implementation of Least Squares Lattice filter in following the non stationary time series for s...

This paper presents an analysis of the constrained least squares filter and a feedback structure is derived which shows the noise cancelling proper— ties of the filter, Using an identification algorithm, it is shown how the constrained least squares filter can be replaced by a f in— ite impulse response filter which can be J.nple— mented on-line. The limitations of this F.I.R. filter are discus...

The identification difficulties for a dual-rate Hammerstein system lie in two aspects. First, the identification model of the system contains the products of the parameters of the nonlinear block and the linear block, and a standard least squares method cannot be directly applied to the model; second, the traditional single-rate discrete-time Hammerstein model cannot be used as the identificati...

Raman spectroscopy has been proven a noninvasive technique with high potential in pharmaceutical industry. In this study, micro Raman technique and chemometric tools were used for identification of azithromycin (AZM) tablets by different manufacturers and quantitative analysis of the active pharmaceutical ingredient (API) in the samples. Support vector machine (SVM), Bayes classifier and K-near...

Because frequency load identification method will confront with the ill-posed problem of finding the inverse of coefficient matrix and it can only identify one load source, a new uncorrelated multi-source load identification algorithm based on linear regression and least-squares of generalized matrix inverse is proposed. According to response signals of multi-spot, this new algorithm can identi...