Nonlinear observers with approximately linear error dynamics: the multivariable case

Lynch, Alan F.; Bortoff, Scott A.

doi:10.1109/9.928597

Cited by 39 publications

(26 citation statements)

References 9 publications

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“…Typically, the first step for nonlinear geometric observer approach is to put the original system into an observable form by change of coordinates. As a next step starting from the observable form, various techniques, for instance output transformation [31], [32], state transformation [31], [33], time scale transformation [34]- [36], dynamics extension [37], and approximate transformation [38], can be considered to further simplify the system dynamics. The transformed system typical takes a certain special structure, e.g.…”

Section: A Nonlinear Geometric Observer Approachmentioning

confidence: 99%

Adaptive Estimation of the State of Charge for Lithium-Ion Batteries: Nonlinear Geometric Observer Approach

Wang

Fang

Şahinoğlu

et al. 2015

IEEE Trans. Contr. Syst. Technol.

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Fang, H.; Sahinoglu, Z.; Wada, T.; Hara, S. TR2014-094 September 2014Abstract This paper considers the state of charge (SoC) and parameter estimation of lithium-ion batteries. Different from various prior arts, where estimation is based on local linearization of a nonlinear battery model, nonlinear geometric observer approach is followed to design adaptive observers for the SoC and parameter estimation based on nonlinear battery models. A major advantage of the proposed approach is the possibility to establish the exponential stability of the resultant error dynamics of state and parameter estimation. The proposed adaptive observers are shown to be robust with respect to unmodeled process uncertainties. Analysis also shows the design tradeoff between the convergence rate and the robustness of the estimation error dynamics with respect to the measurement noise. Simulation and experimental results validate the effectiveness and main advantages of the proposed approach. Error analysis is presented and explains the experimental results. IEEE Transactions on Control Systems TechnologyThis 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-This paper considers the State of Charge (SoC) and parameter estimation of Lithium-ion batteries. Different from various prior art, where estimation is based on local linearization of a nonlinear battery model, nonlinear geometric observer approach is followed to design adaptive observers for the SoC and parameter estimation based on nonlinear battery models. A major advantage of the proposed approach is the possibility to establish the exponential stability of the resultant error dynamics of state and parameter estimation. The proposed adaptive observers are shown to be robust with respect to unmodeled process uncertainties. Analysis also shows the design tradeoff between the convergence rate and the robustness of the estimation error dynamics with respect to the measurement noise. Simulation and experimental results validate the effectiveness and main advantages of the proposed approach. Error analysis is presented and explains the experimental results.

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Section: A Nonlinear Geometric Observer Approachmentioning

confidence: 99%

Adaptive Estimation of the State of Charge for Lithium-Ion Batteries: Nonlinear Geometric Observer Approach

Wang

Fang

Şahinoğlu

et al. 2015

IEEE Trans. Contr. Syst. Technol.

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“…The literature is vast about nonlinear observer normal form approach. This approach can be derived to the pioneer works of ( [3,4]) for single output systems and ( [5,6]) for the case of MIMO dynamical nonlinear systems, many other researches were published in this topic (see [7][8][9][10][11] ). Some extended results can be found in [12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Adaptive observer for simultaneous estimation of state and parameter for a class of nonlinear systems

Zheng

Boutat

2014

Proceedings of the 33rd Chinese Control Conference

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“…Then, it has been adopted for the multiple outputs case in [20], [21]. After that, several relevant algorithms have been developed in [22], [23], [24], [25], [26], [27], [28], [29]. Besides, another interesting form, the so-called depending output observer normal form, was introduced by [30], and then was improved in [31] and [32].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear observer for the PM synchronous motor

Tami

Boutat

Zheng

2014

2014 American Control Conference

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Abstract-This paper deals with nonlinear states and parameters estimation of a permanent magnet synchronous motor (PMSM) without mechanical sensors. In the proposed method, a change of coordinates is established in order to transform the PMSM system into an observer form which enables us to apply an adaptive or a high gain observer. Then the load torque, rotor speed and some parameters can be estimated from both measurable currents and voltages. The efficiency of the proposed methods is illustrated by the simulation results.

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Nonlinear observers with approximately linear error dynamics: the multivariable case

Cited by 39 publications

References 9 publications

Adaptive Estimation of the State of Charge for Lithium-Ion Batteries: Nonlinear Geometric Observer Approach

Adaptive Estimation of the State of Charge for Lithium-Ion Batteries: Nonlinear Geometric Observer Approach

Adaptive observer for simultaneous estimation of state and parameter for a class of nonlinear systems

Nonlinear observer for the PM synchronous motor

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