Yuz Eissmann, Juan Ignacio

In this paper we consider the analysis of multiple-input multiple-output systems using the Participation Matrix, which provides a tool both for interaction measure and controller structure selection. For this matrix we present novel interpretations in the time and in the frequency domain, based on definition of Hilbert-Schmidt-Hankel norm. Moreover, the time domain interpretation is exploited to obtain an empirical estimate of the participation matrix directly from input-output data of the multivariable system.

Model estimation of industrial processes is often done in closed loop due, for instance, to production constraints or safety reasons. On the other hand, many processes are time-varying because of aging effects or changes in the environmental conditions. In this study, a recursive estimation algorithm for linear, continuous-time, slowly time-varying systems operating in closed loop, is developed. The proposed method consists in coupling linear filter approaches to handle the time-derivative, with closed-loop instrumental variable (IV) techniques to deal with measurement noise. Simulations show the advantages of using this IV-based method.

A precursor to any advanced control solution is the step of obtaining an accurate model of the process. Suitable models can be obtained from phenomenological reasoning, analysis of plant data or a combination of both. Here, we will focus on the problem of estimating (or calibrating) models from plant data. A key goal is to achieve robust identification. By robust we mean that small errors in the hypotheses should lead to small errors in the estimated models. We argue that, in some circumstances, it is essential that special precautions be taken to ensure that robustness is preserved. We present several practical case studies to illustrate the results.

The behavior of a fluid in pipes with irregular geometries is studied. Departing from the partial differential equations that describe mass and momentum balances a scalable lumped-parameter model is proposed. To this end the framework of port-Hamiltonian systems is instrumental to derive a modular system which upon interconnection describes segments with different cross sections and dissipation effects. In order to perform the interconnection between different segments the incompressibility hypothesis is relaxed in some infinitesimal section to admit density variations and energy transference between segments. Numerical simulations are performed in order to illustrate the model.

In this paper we consider general port-Hamiltonian formulations of multidimensional Maxwell’s viscoelastic fluids. Two different cases are considered to describe the energy fluxes in isentropic compressible and incompressible fluids. In the compressible case, the viscoelastic effects of shear and dilatational strains on the stress tensor are described individually through the corresponding constitutive equations. In the incompressible case, an approach based on the bulk modulus definition is proposed in order to obtain an appropriate characterization, from the port-Hamiltonian point of view, of the pressure and nonlinear terms in the momentum equation, associated with both dynamic pressure and vorticity of the flow.

Some nonlinear systems can be represented through linear parameter varying models. In this work, we address the estimation of continuous-time linear parameter varying models in output error form, using a refined instrumental variable method. A distinguished feature of a linear parameter varying model is that it has parameters that depend on an external signal called the scheduling variable. In this paper, we assume that the scheduling variable is noisy, a condition which is often met in practice, but not frequently considered in the literature. On the other hand, there are applications in which the noise-free version of the scheduling variable is smooth. Under such scenario we can simply filter the scheduling variable before estimating the linear parameter model. Nonetheless, there are cases where special smoothing techniques are required. In this study, we consider one of these special cases, and we use the well-known local regression method as smoothing technique. A numerical example based on a Monte Carlo simulation shows the benefits of the proposed approach.

Models of the human body are key in bio-engineering and medical applications. This study presents the identification, in time and frequency domains, of linear time invariant models of the human subglottal system, for the clinical assessment of vocal function. For time domain identification, the input-output data corresponds to the glottal volume velocity and the acceleration registered by a sensor on the neck skin of the patient. For frequency domain identification, the frequency response of the subglottal tract module is used. Maximum likelihood and prediction error methods are applied. Additionally, the Akaike and Bayes Information Criteria are used to select the models order.

In-flame optical characterization of soot is of vital importance to understand soot formation mechanisms as well as to develop and validate accurate soot models. The present work introduces an unconventional methodology adapted to laminar axisymmetric flames that avoids the issue of variable measurement volume with varying scattering angle in the existing light scattering techniques and thus enables the determination of aggregate size with a higher spatial resolution. Coupled with multi-wavelength line-of-sight attenuation measurements, the proposed Horizontal Planar Angular Light Scattering at 532 nm was found able to provide radial profiles of aggregate size, number and diameter of primary spheres, soot volume fraction, and number density in a laminar axisymmetric coflow ethylene/air diffusion flame established over a Gülder burner. The spatial variation of soot optical properties associated with soot maturity was considered in data analysis.

A simple and scalable finite-dimensional model based on the port-Hamiltonian framework is proposed to describe the fluid–structure interaction in tubes with time-varying geometries. For this purpose, the moving tube wall is described by a set of mass-spring-damper systems while the fluid is considered as a one-dimensional incompressible flow described by the average momentum dynamics in a set of incompressible flow sections. To couple these flow sections small compressible volumes are defined to describe the pressure between two adjacent fluid sections. The fluid-structure coupling is done through a power-preserving interconnection between velocities and forces. The resultant model includes external inputs for the fluid and inputs for external forces over the mechanical part that can be used for control or interconnection purposes. Numerical examples show the accordance of this simplified model with finite-element models reported in the literature.

We consider the discretization of continuous-time nonlinear systems described by normal forms. In particular, we consider the case when the input to the system is generated by a B-spline hold device to obtain an approximate discrete-time model. It is shown that the corresponding sampled-data model and its accuracy (measured in terms of the local truncation error) depend on the smoothness of the input and on the applied integration strategy, namely, the truncated Taylor series expansion. Moreover, the sampling zero dynamics of the discrete-time model are asymptotically characterized as the sampling period goes to zero, and it is shown that these zero dynamics converge to the asymptotic sampling zeros of the linear case.