Hugues Garnier

University of Lorraine | UdL · CRAN - Centre de Recherche en Automatique de Nancy

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 exte...

This paper presents a new approach for the simultaneous inertia and actuator alignment parameter estimation. One of the strengths of the proposed estimation algorithm consists in its ability to work directly with the star tracker attitude measurements and it is therefore also suitable for small and medium size satellites, where the gyroscope measur...

This paper develops a new method of data-driven modeling for a class of multiple-transmitter, single-receiver wireless power transfer (WPT) systems. A continuous-time, multiple-input, single-output (MISO) model with pure time delays is used to characterize the input--output behavior of the system, where the transfer functions associated with each i...

This paper considers the data-driven modeling of a class of phase-controlled wireless power transfer (WPT) systems, where the load may vary slowly with respect to time. The dominant mode analysis suggests that a model of the Hammerstein type, which consists of a static nonlinearity function, followed by a linear time-varying model with a pure time...

This paper addresses the design of satellite maneuvers for the inertia estimation. The experiment design is an important step in system identification, since the choice of the excitation signal has a great influence on the precision of the parameter estimates. In order to design an optimal maneuver, the proposed method uses a cubic B-spline represe...

The off-line estimation of the parameters of continuous-time, linear, time-invariant transfer function models can be achieved straightforwardly using linear prefilters on the measured input and output of the system. The on-line estimation of continuous-time models with time-varying parameters is less straightforward because it requires the updating...

The problem of estimating continuous-time model parameters of linear dynamical systems using sampled time-domain input and output data has received considerable attention over the past decades and has been approached by various methods. The research topic also bears practical importance due to both its close relation to first principles modeling an...

This brief investigates recursive instrumental variable (IV) identification of time-delayed continuous-time (CT) models from the sampled input-output data. The refined IV method is extended for the first time to identify recursively both the parameters and time delay of a CT transfer function model. It is shown that the proposed method is able to y...

This paper is concerned with control-oriented modeling of a class of wireless power transfer systems with a phase-shift-controlled inverter. Two methods are investigated: the analytical modeling method, which is based on physical laws, and the refined instrumental variable method, which is based on sampled data. It shows that the former provides ph...

This paper is concerned with identification of continuous-time output-error models with time-delay from relay feedback tests. Conventional methods for solving this problem consist in deriving analytical limit cycle expressions and fitting them to measured shape factors. However, they may fail to handle different limit cycles uniformly, due to the s...

This paper discusses several issues related to the identification of time-delayed continuous-time systems using the refined instrumental variable method. The proposed estimation procedure is iterative where, at each iteration, the rational system parameters and time-delay are estimated separately. The main contribution of this paper covers three as...

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...

This paper deals with two robot identification methods recently introduced. The first one is based on the use of the Inverse Dynamic Identification Model (IDIM) and the Instrumental Variable (IV). The second one is the Direct and Inverse Dynamic Identification Models (DIDIM) method, which is a closed-loop output error method minimizing the quadrati...

Identification of industrial robots is a prolific topic that has been deeply investigated over the last three decades. The standard method is based on the use of the inverse dynamic model and the least-squares estimation (IDIM-LS method) while robots are operating in closed loop by tracking exciting trajectories. Recently, in order to secure the co...

In this paper, an output error method is proposed for the identification of continuous-time systems with time delay from sampled data. The challenge of time delay system identification lies in the presence of nonlinear time delays in models, then starting value-based optimization methods may be trapped easily by local minima. In order to improve th...

In this paper we present a novel algorithm for identifying continuous-time autoregressive moving-average models utilizing irregularly sampled data. The proposed algorithm is based on the expectation–maximization algorithm and obtains maximum-likelihood estimates. The proposed algorithm shows a fast convergence rate, good robustness to initial value...

An instrumental variable (IV) solution for wideband frequency-domain identification is developed in this paper. One of the most successful IV approaches for time-domain data is known as the simple refined IV method for continuous-time model. The purpose of this paper is to present a numerically reliable simplified refined IV method able to identify...

The identification of aerodynamic coefficients, based on free-flight measurements, remains complex and challenging for vehicles such as space probes, unmanned aerial vehicles, or ammunition. In this paper, a detailed procedure for the identification of the drag, pitching moment slope, and pitch damping coefficients of a reentry space vehicle is pre...

The on-line recursive estimation of linear time-varying systems usually involves discrete-time models. In the case of continuous-time models, recursive off-line estimation has been considered in some detail but on-line approaches based on continuous-time models have received less attention, partly due to the increased complexity associated with the...

This study investigates the estimation of continuous-time Box-Jenkins model parameters from irregularly sampled data. The Box-Jenkins structure has been successful in describing systems subject to coloured noise, since it contains two submodels that feature the characteristics of both plant and noise systems. Based on plant-noise model decompositio...

The modeling of Lithium-ion batteries usually utilizes discrete-time system identification methods to estimate parameters of discrete models. However, in real applications, there is a fundamental limitation of the discrete-time methods in dealing with sensitivity when the system is stiff and the storage resolutions are limited. To overcome this pro...

This paper deals with the identification of physical parameters of a one-degree-of-freedom electromechanical system that operates in closed loop. Two models for the system are considered: the inverse dynamic model which is linear with respect to the physical parameters to be identified and the direct dynamic model which is linear in relation to a n...

The CONtinuous-Time System IDentification (CONTSID) toolbox to be run with Matlab includes estimation routines to determine and evaluate continuous-time models of dynamic systems directly from discrete-time input-output data. Version 7.0 of the CONTSID toolbox is now fully compatible with the latest version of the System Identification toolbox for...

This paper discusses the importance and relevance of direct continuous-time system identification and how this relates to the solution for model identification problems in practical applications. It first gives a tutorial introduction to the main aspects of one of the most successful existing approaches for directly identifying continuous-time mode...

Recent developments on engineered multifunctional nanomaterials have opened new perspectives in oncology. But assessment of both quality and safety in nanomedicine requires new methods for their biological characterization. This paper proposes a new model-based approach for the pre-characterization of multifunctional nanomaterials pharmacokinetics...

National audience The treatment of household refuse is one of the ecological goals for the end of this century. A methodology is proposed in this article which makes it possible to control efficiently and automatically a household refuse incinerator with grids. The automatic control is based on a structure of cascaded regulation loops. It ensures g...

This paper presents a new approach to identify continuous-time systems with arbitrary time-delay from irregularly sampled input–output data. It is based on the separable nonlinear least-squares method which combines in a bootstrap manner the iterative optimal instrumental variable method for transfer function model estimation with an adaptive gradi...

This paper considers the problem of continuous-time model identification with arbitrary time-delay from irregularly sampled data. The proposed method estimates the plant and the time-delay in a separable way, when estimating one of them, the other is assumed to be fixed. More precisely, the plant is estimated by the iterative instrumental variable...

This paper considers the problem of continuous-time model identification from non-uniformly sampled input-output data, having the measured output corrupted by colored noise. We concentrate on the continuous-time transfer function model identification. A Box-Jenkins model structure is used to describe the system, thus providing independent parameter...

Transient temperature response measurements of semiconductor devices such as high-powered Light-Emitting-Diodes (LEDs) can be used to detect possible thermal defects. The thermal transient responses of these LEDs appear to be stiff which can be represented by a model with both fast and slow dynamics. It is shown how direct continuous-time model est...

Transient temperature response measurements of semiconductor devices such as high-powered Light-Emitting-Diodes (LEDs) can be used to detect possible thermal defects. The thermal transient responses of these LEDs appear to be stiff which can be represented by a model with both fast and slow dynamics. It is shown how direct continuous-time model est...

The direct identification and estimation of continuous-time models from sampled data is now mature. This paper does not present any new methodology, nor does it compare the performance of existing methods. Its main aim is to discuss the advantages of direct, continuous-time model identification with the help of illustrative examples that are all ba...

The identification of the aerodynamic coefficients, based on free flight measurements, remains a difficult task for flying vehicles like space vehicles, munitions, Unmanned Aerial Vehicles. This is mainly due to the nonlinear structure of the mathematical model describing the behavior of the vehicle in flight, the absence of an input signal, the un...

This paper presents a direct identification method for linear parameter varying models described by partial differential equations in an input-output setting. The continuous space-time model is firstly rewritten as a multiple-input single-output model. The continuous filtering operations are reformulated as a discrete convolution product and a refi...

In this paper, the instrumental variable (IV) and expectation-maximization (EM) methods are combined to identify a continuous-time (CT) transfer function model from non-uniformly sampled data obtained from a closed-loop system. A simple version of Box-Jenkins (BJ) model is considered, where the noise process is parameterized as a CT autoregressive...

Special issue for International Journal of Control, Vol 87, n°7

This paper presents a refined instrumental variable method for identifying partial differential equation models of distributed parameter systems directly from discrete-time sampled input–output data. The proposed method is compared with conventional least-squares and other instrumental variable-based techniques. Monte Carlo simulation analysis resu...

This paper deals with the identification of linear parameter varying (LPV) models described by partial differential equations (PDE). A direct identification of continuous space-time LPV-EDP systems in an input-output setting is investigated in the case of an additive output noise. The continuous space-time LPV-PDE model is firstly proposed to be re...

The identification of the aerodynamic coefficients, based on free flight measurements, remains a difficult task for flying vehicles like space vehicles, munitions, UAV. This is mainly due to the nonlinear structure of the mathematical model describing the behavior of the vehicle in flight, the absence of an input signal, the unknown initial conditi...

This paper presents a general methodology for identifying the dynamical part of the continuous-time model of an articulated arm including flexibilities dedicated to visual servoing. Based on this model, a H1 control law is designed and implemented, allowing to reach high dynamics.

This paper looks at the problem of system identification from non-uniformly sampled input-output data. It describes how refined instrumental variable estimators can be derived to directly identify the parameters of continuous-time output error and Box-Jenkins transfer function models from irregularly sampled data. Monte Carlo simulation analysis is...

This paper presents an instrumental variable method for identifying advection-diffusion equation models in presence of output measurement noise. This partial differential equation is commonly used to describe the transport and dispersion of a solute in a river channel. The realistic measurement situation where spatial data are limited and non-unifo...

This study presents the first attempt of direct continuous-time model identification using instrumental variable method for Hammerstein-Wiener systems from sampled data. Under the assumption of monotonic function for the Wiener part, the whole non-linear model is first estimated as an augmented multiple-input single-output linear model, from which...

The aim of this paper is to propose an instrumental variable (IV) solution for wide-band frequency domain identification. The focus is first on the extension of the refined IV method for continuous-time model identification to the frequency domain. Then, an iterative IV scheme coupled with an appropriate frequency localising basis function is devel...

This paper deals with continuous-time system identification using fractional differentiation models. An adapted version of the simplified refined instrumental variable method is first proposed to estimate the parameters of the fractional model when all the differentiation orders are assumed known. Then, an optimization approach based on the use of...

The identification of rainfall/runoff relationship is a challenging issue, mainly because of the complexity to find a suitable model for a whole given catchment. Conceptual hydrological models fail to describe correctly the dynamic changes of the system for different rainfall events (e.g. intensity or duration). However, the need for such relations...

This chapter presents an estimation method for Hammerstein models under colored added noise conditions. The proposed method is detailed for both continuous-time and discrete-time models and is based on the refined instrumental variable method. In order to use a regression form, the Hammerstein model is reformulated as an augmented multi-input-singl...

Identification of real-world systems is often applied in closed loop due to stability, performance or safety constraints. However, when considering Linear Parameter-Varying (LPV) systems, closed-loop identification is not well-established despite the recent advances in prediction error approaches. Building on the available results, the paper propos...

Recent developments on multifunctional nano-systems have opened new perspectives for tumor control by proposing new nano-actuators and nano-sensors in in vivo anti-cancer treatments. But the delivery control of these nano-agents into the cancer cells is one of the major factors that directly affect the efficiency of nanotherapies. In this study, we...

Direct identification of continuous-time models from sampled data is now mature. The developed methods have proven successful in many practical applications and are available as user-friendly and computationally efficient algorithms in the CAPTAIN and CONTSID toolboxes for Matlab. Surprisingly many practitioners appear unaware that such methods not...

This paper describes the latest developments for the CONtinuous-Time System IDentification (CONTSID) toolbox to be run with Matlab. The toolbox supports time-domain identification methods for estimating continuous-time linear and nonlinear models directly from regularly or irregularly sampled data. It now includes additional routines for identifyin...

System Identification, Environmetric Modelling, and Control Systems Design is dedicated to Professor Peter Young on the occasion of his seventieth birthday. Professor Young has been a pioneer in systems and control, and over the past 45 years he has influenced many developments in this field. This volume is comprised of a collection of contribution...

This chapter presents an overview of the available methods for identifying input-output LPV models both in discrete time and continuous time with the main fo93b40 93a30 93c30cus on noise modeling issues. First, a least-squares approach and an instrumental variable method are presented for dealing with LPV-ARX models. Then, a refined instrumental va...

Résumé— This paper discusses experience to introduce data-based continuous-time model identification to engineer-ing students. Specifically, the paper describes how the CONtinuous-Time System IDentification (CONTSID) tool-box and its graphical user interface to be run with Matlab are used to teach time-domain identification methods for estimating c...

Controllers in the linear parameter-varying (LPV) framework are commonly designed in continuous time (CT) requiring accurate and low-order CT models of the system. Nonetheless, most of the methods dedicated to the identification of LPV systems are addressed in the discrete-time setting. In practice when discretizing models which are naturally expre...

This paper presents in a new unified way, optimal instrumental variable methods for identifying discrete-time transfer function models when the system operates in closed-loop. The conditions for the optimal design of prefilters and instruments depending on common model structures are analyzed and different approaches are developed according to whet...

Data-based continuous-time model identification of continuous-time dynamic systems is a mature subject. In this contribution, we focus first on a refined instrumental variable method that yields parameter estimates with optimal statistical properties for hybrid continuous-time Box-Jenkins transfer function models. The second part of the paper descr...

Controllers in the linear parameter-varying (LPV) framework are commonly designed in continuous time (CT) requiring accurate and low-order CT models of the system. However, identification of CT-LPV models is largely unsolved, representing a gap between the available LPV identification methods and the needs of control synthesis. In order to bridge t...

This paper describes optimal instrumental variable methods for identifying discrete-time transfer function models when the system operates in closed-loop. Several noise models required for the design of optimal prefilters and instruments are analyzed and different approaches are developed according to whether the controller is known or not. Moreove...

Schemes for system identification based on closed-loop experiments have attracted considerable interest lately. However, most of the existing approaches have been developed for discrete-time models. In this paper, the problem of continuoustime model identification is considered. A bias correction method without noise modelling associated with the P...

This paper presents a methodology for system identification of continuous-time state-space models from finite sampled input-output signals. The estimation problem of the consecutive time-derivatives and integrals of the input-output signals is considered. The appropriate frequency characteristcs of a linear filtering based on the Poisson moment fun...

Identification of Linear Parameter-Varying (LPV) models is often addressed in an Input-Output (IO) setting. However, statistical properties of the available algorithms are not fully understood. Most methods apply auto regressive models with exogenous input (ARX) which are unrealistic in most practical applications due to their associated noise stru...

L'identification de la relation pluie/débit dans un bassin versant pour la prédiction de débit est un problème stimulant de par la difficulté à caractériser un modèle les décrivant dans leur ensemble. Les modèles conceptuels, basés sur les lois et modèles hydrauliques simples sont sou- vent limités dans la précision de la prédiction qu'ils offrent....

The identification of linear parameter-varying systems in an input–output setting is investigated, focusing on the case when the noise part of the data generating system is an additive colored noise. In the Box–Jenkins and output-error cases, it is shown that the currently available linear regression and instrumental variable methods from the liter...

The identification of rainfall/runoff relationship is a challenging issue, mainly because of the complexity to find a suitable model for a whole given catchment. Conceptual hydraulic models are often too limited for long term forecasting and fail to describe correctly the dynamic changes of the system with respect to the characteristics for differe...

This article presents an instrumental variable method dedicated to non-linear Hammerstein systems operating in closed loop. The linear process is a Box-Jenkins model and the non-linear part is a sum of known basis functions. The performance of the proposed algorithm is illustrated by a numerical example.

The paper describes a simple, two-stage instrumental variable method of closed loop identification and estimation. This can be used with both continuous and discrete-time transfer function models and the enclosed system can be unstable. The paper also shows briefly how a third stage of estimation can be added that induces statistical efficiency whe...

In this paper, the problem of identifying linear discrete-time systems from noisy input and output data is addressed. Several existing methods based on higher-order statistics are presented. It is shown that they stem from the same set of equations and can thus be united from the viewpoint of extended instrumental variable methods. A numerical exam...

This paper describes the latest developments for the CONtinuous-Time System IDentification (CONTSID) toolbox to be run with Matlab which includes time-domain identification methods for estimating continuous-time models directly from sampled data. The main additions to the new version aim at extending the available methods to handle wider practical si...

This paper presents a data-based mechanistic modelling (DBM) approach to rainfallrunoff modelling based on the direct identification and estimation of continuous-time models from discrete-time series. It is argued that many mechanistic model parameters are more naturally defined in the context of continuous-time, differential equation models. As a...

This paper describes the latest developments for the CONtinuous-Time System IDentification (CONTSID) toolbox to be run with Matlab which includes time-domain identification methods for estimating continuous-time models directly from sampled data. The main additions to the new version aim at extending the available methods to handle wider practical...

This paper describes an optimal instrumental variable method for identifying discrete-time transfer function models of the Box-Jenkins transfer function form in the closed-loop situation. This method is based on the Refined Instrumental Variable (RIV) algorithm which, because of an appropriate choice of particular design variables, achieves minimum...

This article presents instrumental variable methods for direct continuous-time estimation of a Hammerstein model. The non-linear function is a sum of known basis functions and the linear part is a Box-Jenkins model. Although the presented algorithm is not statistically optimal, this paper further shows the performance of the presented algorithms an...

Presentation at the European Research Network System Identification (ERNSI)

This paper deals with identification of dynamic discrete-time errors-in-variables systems. The statistical accuracy of a least squares estimator based on third-order cumulants is analyzed. In particular, the asymptotic covariance matrix of the estimated parameters is derived. The results are supported by numerical simulation studies.

This paper deals with continuous-time system identification using fractional differentiation models in a noisy output context. The simplified refined instrumental variable for continuous-time systems (srivc) is extended to fractional models. Monte Carlo simulation analysis are used to demonstrate the performance of the proposed optimal instrumental...

Cet article présente une nouvelle méthode d'identification de modèles d'état à temps continu de systèmes mul-tivariables à partir de données d'entrée/sortie échantillon-nées. L'approche proposée consiste plus particulièrement en l'association de techniques de filtrage et de deux algorithmes des sous-espaces. Ces techniques de filtrage permettent de...

Cet article présente un panorama des méthodes directes d'identification de modèles linéaires paramétriques à temps continu à partir de données échantillonnées. Nous rappelons les principales méthodes d'estimation de fonctions de transfert à temps continu : estimateurs traditionnels de la variable instrumentale / filtres de variable d'état, de la va...

For many industrial production processes, safety and production restrictions are often strong reasons for not allowing identification experiments in open loop. In such situations, experimental data can only be obtained under closed-loop conditions. The main difficulty in closed-loop identification is due to the correlation between the disturbances...

Mathematical models of dynamic systems are required in most areas of scientific enquiry and take various forms, such as differential equations, difference equations, state-space equations and transfer functions. The most widely used approach to mathematical modelling involves the construction of mathematical equations based on physical laws that ar...

This chapter describes and evaluates a statistically optimal method for the identification and estimation3 of continuous-time (CT) hybrid Box-Jenkins (BJ) transfer function models from discrete-time, sampled data. Here, the model of the basic dynamic system is estimated in continuous-time, differential equation form, while the associated additive n...

In this paper, the problem of identifying stochastic linear continuous-time systems from noisy input/output data is addressed. The input of the system is assumed to have a skewed probability density function, whereas the noises contaminating the data are assumed to be symmetrically distributed. The third-order cumulants of the input/output data are...