Identifiability: from qualitative analysis to model structure approximation

Doren, J. F. M. Van; Douma, Sippe G.; Hof, Paul M.J. Van den; Bosgra, О.H.

doi:10.3182/20090706-3-fr-2004.00110

Cited by 24 publications

(20 citation statements)

References 14 publications

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“…Hahn et al [26] extended definition (12) to include other forms of inputs, such as (series of ) step inputs, resulting in more general covariance matrices of which empirical Gramians form a subset. (Alternatively, these covariance matrices could be referred to as generalized empirical Gramians).…”

Section: Empirical Controllability Gramianmentioning

confidence: 99%

“…permeabilities can only be identified to a very limited extent. Further aspects of the identifiability of permeabilities in single-phase flow, and its relation to observability and controllability, are given in [11,12]. Moreover, the identifiability of reservoir parameters from measured data is closely related to the ability to compute 'sensitivities' of measured data to reservoir parameters.…”

Section: Introductionmentioning

confidence: 99%

“…For a recent discussion, see [13]; for specific papers see e.g. [8,9,11,12]. Although the identifiability of parameters, or related concepts, has been treated before, to our knowledge very few papers have been published on controllability and observability of state variables in reservoir flow.…”

Section: Introductionmentioning

confidence: 99%

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Controllability and observability in two-phase porous media flow

2013

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Reservoir simulation models are frequently used to make decisions on well locations, recovery optimization strategies etc. The success of these applications is, among other aspects, determined by the controllability and observability properties of the reservoir model. In this paper it is shown how the controllability and observability of two-phase flow reservoir models can be analyzed and quantified with aid of generalized empirical Gramians. The empirical controllability Gramian can be interpreted as a spatial covariance of the states (pressures or saturations) in the reservoir resulting from input perturbations in the wells. The empirical observability Gramian can be interpreted as a spatial covariance of the measured bottom hole pressures or well bore flow rates resulting from state perturbations. Based on examples in the form of simple homogeneous and heterogeneous reservoir models we conclude that the position of the wells and of the front between reservoir fluids, and to a lesser extent the position and shape of permeability heterogeneities that impact the front, are the most important factors that determine the local controllability and observability properties of the reservoir.

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Section: Empirical Controllability Gramianmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Controllability and observability in two-phase porous media flow

2013

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“…Even though θ cannot be identified, it is pointed out in [19] that a reparameterized model structure, or more specifically, a combination of parameters in θ , may be identified. The next remarkable fact is then established.…”

Section: Joint Observability/identifiability Analysismentioning

confidence: 99%

“…Hence, the dimensions of Σ l , U l and V l are 3 × 3, 6 × 3 and 6 × 3, respectively. It can be proven that the column space of U l is the subspace of the identifiable parameter space [19]. In other words, the vector ϑ obtained from reparameterizing θ is identifiable, where ϑ is given by…”

Section: Joint Observability/identifiability Analysismentioning

confidence: 99%

Adaptive estimation of state of charge for lithium-ion batteries

Fang

Wang

Şahinoğlu

et al. 2013

2013 American Control Conference

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State of charge (SoC) estimation is a fundamental challenge in designing battery management systems. An adaptive SoC estimator, named as the AdaptSoC, is developed in this paper. It is able to estimate the SoC when the model parameters are unknown, through joint SoC and parameter estimation. Design of the AdaptSoC builds up on (1) a reduced complexity battery model that is developed from the well known single particle model (SPM) and, (2) joint local observability/identifiability analysis of the SoC and the unknown model parameters. Shown to be strongly observable, the SoC is estimated jointly with the parameters by the AdaptSoC using the iterated extended Kalman filter (IEKF). Simulation and experimental results exhibit the effectiveness of the AdaptSoC. American Control Conference (ACC)This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Abstract-State of charge (SoC) estimation is a fundamental challenge in designing battery management systems. An adaptive SoC estimator, named as the AdaptSoC, is developed in this paper. It is able to estimate the SoC when the model parameters are unknown, through joint SoC and parameter estimation. Design of the AdaptSoC builds up on (1) a reduced complexity battery model that is developed from the wellknown single particle model (SPM) and, (2) joint local observability/identifiability analysis of the SoC and the unknown model parameters. Shown to be strongly observable, the SoC is estimated jointly with the parameters by the AdaptSoC using the iterated extended Kalman filter (IEKF). Simulation and experimental results exhibit the effectiveness of the AdaptSoC.

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Visual-Inertial Localization for Skid-Steering Robots with Kinematic Constraints

Zuo

Zhang

Chen

et al. 2022

Springer Proceedings in Advanced Robotics

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While visual localization or SLAM has witnessed great progress in past decades, when deploying it on a mobile robot in practice, few works have explicitly considered the kinematic (or dynamic) constraints of the real robotic system when designing state estimators. To promote the practical deployment of current state-of-the-art visual-inertial localization algorithms, in this work we propose a low-cost kinematics-constrained localization system particularly for a skid-steering mobile robot. In particular, we derive in a principle way the robot's kinematic constraints based on the instantaneous centers of rotation (ICR) model and integrate them in a tightly-coupled manner into the sliding-window bundle adjustment (BA)-based visual-inertial estimator. Because the ICR model parameters are time-varying due to, for example, track-to-terrain interaction and terrain roughness, we estimate these kinematic parameters online along with the navigation state. To this end, we perform in-depth the observability analysis and identify motion conditions under which the state/parameter estimation is viable. The proposed kinematics-constrained visual-inertial localization system has been validated extensively in different terrain scenarios.

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Identifiability: from qualitative analysis to model structure approximation

Cited by 24 publications

References 14 publications

Controllability and observability in two-phase porous media flow

Controllability and observability in two-phase porous media flow

Adaptive estimation of state of charge for lithium-ion batteries

Visual-Inertial Localization for Skid-Steering Robots with Kinematic Constraints

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