The Experts below are selected from a list of 206730 Experts worldwide ranked by ideXlab platform

Hugues Garnier - One of the best experts on this subject based on the ideXlab platform.

  • accurate lithium ion battery parameter estimation with Continuous Time System identification methods
    Applied Energy, 2016
    Co-Authors: Xin Zhao, Hugues Garnier, Raymond De Callafon, T Q Nguyen, Chris Mi
    Abstract:

    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 problem, this paper adopts direct Continuous-Time System identification methods to estimate the parameters of equivalent circuit models for Lithium-ion batteries. Compared with discrete-Time System identification methods, the Continuous-Time System identification methods provide more accurate estimates to both fast and slow dynamics in battery Systems and are less sensitive to disturbances. A case of a 2nd-order equivalent circuit model is studied which shows that the Continuous-Time estimates are more robust to high sampling rates, measurement noises and rounding errors. In addition, the estimation by the conventional Continuous-Time least squares method is further improved in the case of noisy output measurement by introducing the instrumental variable method. Simulation and experiment results validate the analysis and demonstrate the advantages of the Continuous-Time System identification methods in battery applications.

  • Accurate battery parameter estimation with improved Continuous Time System identification methods
    2016
    Co-Authors: Bing Xia, Hugues Garnier, Xin Zhao, Raymond De Callafon, Truong Nguyen
    Abstract:

    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 problem, this paper adopts direct Continuous-Time System identification methods to estimate the parameters of equivalent circuit models for Lithium-ion batteries. Compared with discrete- Time System identification methods, the Continuous-Time System identification methods provide more accurate estimates to both fast and slow dynamics in battery Systems and are less sensitive to disturbances. A case of a second order equivalent circuit model is studied which shows that the Continuous-Time estimates are more robust to high sampling rates, measurement noises and rounding errors. In addition, the estimation by the conventional Continuous-Time least squares method is further improved in the case of noisy output measurement by introducing the instrumental variable method. Simulation and experiment results validate the analysis and demonstrate the advantages of the Continuous-Time System identification methods in battery applications.

  • accurate lithium ion battery parameter estimation with Continuous Time System identification methods
    European Conference on Cognitive Ergonomics, 2016
    Co-Authors: Xin Zhao, Hugues Garnier, Raymond De Callafon, T Q Nguyen, Chris Mi
    Abstract:

    The modeling of Lithium-ion batteries usually utilizes discrete-Time System identification methods to estimate parameters of discrete models. This paper adopts direct Continuous-Time System identification methods to estimate the parameters of equivalent circuit models for Lithium-ion batteries. Compared with discrete-Time System identification methods, the Continuous-Time System identification methods provide more accurate estimates to both fast and slow dynamics in battery Systems and are less sensitive to perturbations. A case of a 2nd-order equivalent circuit model is studied which shows that the Continuous-Time estimates are more robust to high sampling rates, measurement noises and rounding errors. Simulation and experiment results validate the analysis and demonstrate the superiority of the Continuous-Time System identification methods in battery applications.

  • Developments for the CONTSID toolbox
    2012
    Co-Authors: Hugues Garnier, Marion Gilson, Vincent Laurain
    Abstract:

    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 identifying Continuous-Time linear models in closed loop, as well as efficient routines for identifying both LPV and Hammerstein-Wiener Continuous-Time models.

  • Teaching data-based Continuous-Time model identification with the CONTSID toolbox
    2011
    Co-Authors: Hugues Garnier
    Abstract:

    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 Continuous-Time models directly from sampled data. The educational focus is to mix theoretical aspects with hands-on experience at numerous computer sessions dealing with simulated and real data examples.

Denis Efimov - One of the best experts on this subject based on the ideXlab platform.

Z K Liu - One of the best experts on this subject based on the ideXlab platform.

Yoshito Ohta - One of the best experts on this subject based on the ideXlab platform.

  • Brief paper: Stochastic System transformation using generalized orthonormal basis functions with applications to Continuous-Time System identification
    Automatica, 2011
    Co-Authors: Yoshito Ohta
    Abstract:

    This paper studies the System transformation using generalized orthonormal basis functions that include the Laguerre basis as a special case. The transformation of the deterministic Systems is studied in the literature, which is called the Hambo transform. The aim of the paper is to develop a transformation theory for stochastic Systems. The paper establishes the equivalence of Continuous and transformed-discrete-Time stochastic Systems in terms of solutions. The method is applied to the Continuous-Time System identification problem. It is shown that using the transformed signals the PO-MOESP subspace identification algorithm yields consistent estimates for System matrices. An example is included to illustrate the efficacy of the proposed identification method, and to make a comparison with the method using the Laguerre filter.

  • CDC - A study on stochastic System transformation using generalized orthonormal basis functions
    2007 46th IEEE Conference on Decision and Control, 2007
    Co-Authors: Yoshito Ohta
    Abstract:

    Generalized orthonormal basis functions induce System transformation called Hambo transform, which maps Continuous-Time deterministic models to discrete-Time deterministic models with the property that it preserves input-output norms. This paper shows that generalized orthonormal basis functions also induce stochastic System transformation, and studies its properties. It is shown that under suitable definitions the equivalence between Continuous-Time stochastic Systems and transformed discrete-Time stochastic Systems exists. The results are of fundamental importance to Continuous-Time System identification.

  • Error analysis of Continuous-Time System identification using laguerre basis
    SICE Annual Conference 2007, 2007
    Co-Authors: T. Chimbe, Yoshito Ohta
    Abstract:

    Continuous-Time System identification is desirable and necessary from several fine characteristics of Continuous-Time models. It was already shown that a Continuous-Time System is identified effectively by means of the transformation of the Continuous-Time System into a discrete-Time System using the generalized orthonormal basis. This paper proposes the error analysis of Continuous-Time System identification using the Laguerre basis, the special case of the generalized orthonormal basis. This paper also argues how a parameter of the Laguerre basis should be chosen correctly to improve accuracy of System identification.

  • on the approximation of maximal output admissible set and reachable set via forward euler discretization
    IFAC Proceedings Volumes, 2004
    Co-Authors: Akarawit Limpiyamitr, Yoshito Ohta
    Abstract:

    Abstract The concept of positive invariance is involved with several problems in control theory, such as constrained control, disturbance rejection and robustness analysis. This paper considers the two types of positively invariant set, so called maximal output admissible set and reachable set, determined for linear Continuous-Time System. In this paper, the inner approximation of maximal output admissible set and the outer approximation of reachable set are established. The main purpose is to show that there exists the inclusion between the aforementioned positively invariant sets of linear Continuous-Time System and its forward Euler approximated discrete-Time System. The inclusion also holds monotonically in the case of forward Euler approximated Systems, discretized by different sampling periods. Finally, the volume of each approximated positively invariant set is recovered at any accuracy when sampling period is decreased.

Andrey Polyakov - One of the best experts on this subject based on the ideXlab platform.

  • Lyapunov-based Consistent Discretisation of Stable Homogeneous Systems
    International Journal of Robust and Nonlinear Control, 2020
    Co-Authors: Tonametl Sanchez, Andrey Polyakov, Denis Efimov
    Abstract:

    In this paper we propose a discretisation scheme for asymptotically stable homogeneous Systems. This scheme exploits the information provided by a homogeneous Lyapunov function of the System. The main features of the scheme are: 1) the dis-cretisation method is explicit and; 2) the discrete-Time System preserves the asymptotic stability, the convergence rate, and the Lyapunov function of the original Continuous-Time System.

  • A Consistent Discretisation method for Stable Homogeneous Systems based on Lyapunov Function
    2020
    Co-Authors: Tonametl Sanchez, Andrey Polyakov, Denis Efimov
    Abstract:

    In this paper we propose a discretisation scheme for Continuous and asymptotically stable homogeneous Systems. This method is based on the dynamics of the System projected on a level surface of a homogeneous Lyapunov function. The discretisation method is explicit and preserves the convergence rate of the Continuous-Time System.

  • Consistent Discretization of Finite-Time and Fixed-Time Stable Systems
    SIAM Journal on Control and Optimization, 2019
    Co-Authors: Andrey Polyakov, Denis Efimov, Bernard Brogliato
    Abstract:

    Algorithms of implicit discretization for generalized homogeneous Systems having discontinuity only at the origin are developed. They are based on the transformation of the original System to an equivalent one which admits an implicit or a semi-implicit discretization schemes preserving the stability properties of the Continuous-Time System. Namely, the discretized model remains finite-Time stable (in the case of negative homogeneity degree), and practically fixed-Time stable (in the case of positive homogeneity degree). The theoretical results are supported with numerical examples.

  • Globally Stable Implicit Euler Time-Discretization of a Nonlinear Single-Input Sliding-Mode Control System
    2015
    Co-Authors: Bernard Brogliato, Andrey Polyakov
    Abstract:

    In this note we study the effect of an implicit Euler Time-discretization method on the stability of the discretization of a globally fixed-Time stable, scalar differential inclusion representing a simple nonlinear System with a set-valued signum controller. The controller nonlinearity is a cubic term and it is shown that the fully-implicit method preserves the global Lyapunov stability property of the Continuous-Time System, contrarily the explicit discretization which does not. It allows to obtain finite-Time convergence to the origin when the plant is undisturbed, while the cubic term provides the hyper-exponential convergence rate.