H∞ and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si16.gif" overflow="scroll"><mml:mrow><mml:msub><mml:mi>l</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mi>l</mml:mi><mml:mi>∞</mml:mi></mml:msub></mml:mrow></mml:math> finite-horizon filtering with randomly occurring gain variations and quantization effects

Zhang, Jie; Ma, Lifeng; Liu, Yurong; Lyu, Ming; Alsaadi, Fuad E.; Bo, Yuming

doi:10.1016/j.amc.2016.11.014

Cited by 14 publications

(2 citation statements)

References 43 publications

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“…Therefore, we seek to minimize the L 2 − L ∞ induced norm from (ν, ν) to fault estimation error e f . Moreover, one can note that the L 2 − L ∞ induced norm is widely used in the literature for filtering (See [30,31,32,33,34]). Definition 3.…”

Section: − L ∞ Induced Norm Performance Criterionmentioning

confidence: 99%

Ultra Local Nonlinear Unknown Input Observers for Robust Fault Reconstruction

Farhad

Murguia

Esfahani

et al. 2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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This paper proposes a nonlinear estimator for the robust reconstruction of process and sensor faults for a class of uncertain nonlinear systems. The proposed fault estimation method augments the system dynamics with an ultra-local (in time) internal state-space representation (a finite chain of integrators) of the fault vector. Next, a nonlinear state observer is designed based on the known parts of the augmented dynamics. This nonlinear filter (observer) reconstructs the fault signal as well as the states of the augmented system. We provide sufficient conditions that guarantee stability of the estimation error dynamics: firstly, asymptotic stability (i.e., perfect fault estimation) in the absence of perturbations induced by fault model mismatch (mismatch between internal, ultralocal model for the fault and the actual fault characteristics), uncertainty, external disturbances, and measurement noise and, secondly, Input-to-State Stability (ISS) of the estimation error dynamics is guaranteed in the presence of these perturbations. In addition, to support performance-based estimator design, we provide Linear Matrix Inequality (LMI) conditions for L2-gain and L2 − L∞ induced norm and cast the synthesis of the estimator gains as a semi-definite program where the effect of model mismatch and external disturbances on the fault estimation error is minimized in the sense of L2-gain, for an acceptable L2 − L∞ induced norm with respect to measurement noise. The latter result facilitates a design that explicitly addresses the performance trade-off between noise sensitivity and robustness against model mismatch and external disturbances. Finally, numerical results for a benchmark system illustrate the performance of the proposed methodologies.

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Section: − L ∞ Induced Norm Performance Criterionmentioning

confidence: 99%

Ultra Local Nonlinear Unknown Input Observers for Robust Fault Reconstruction

Farhad

Murguia

Esfahani

et al. 2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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“…Over the past few decades, there has been a tremendous amount of research looking at the filtering or state estimation problems in both signal processing and control communities. According to the types of the system noises and the performance specifications, a number of filtering schemes have been thoroughly investigated in the literature and applied in engineering practice, among which the popular ones are H ∞ filtering, set-membership filtering, and minimum-variance filtering algorithms [6], [7], [23], [28], [32], [37], [41], [44]. In particular, the renowned Kalman filter, which aims to characterize the estimation performance in the sense of minimum error variance for exactly known linear systems, has been widely utilized in various scenarios ranging from engineering practice to machine learning.…”

Section: Introductionmentioning

confidence: 99%

Recursive locally minimum-variance filtering for two-dimensional systems: When dynamic quantization effect meets random sensor failure

Wang

Liang

et al. 2023

Automatica

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A note on guaranteed cost control for nonlinear stochastic systems with input saturation and mixed time‐delays

Wang

Liu

et al. 2017

Intl J Robust & Nonlinear

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Summary ThisIn the system under investigation, the control input is subject to the saturation constraint, and both discrete and distributed time‐delays are taken into consideration. The objective of the addressed problem is to design a state feedback controller such that the resulting closed‐loop system is asymptotically stable in probability and an upper bound is guaranteed on a prespecified quadratic cost function. Sufficient conditions are established for the existence of the desired guaranteed cost control strategy in terms of the solvability of certain Hamilton–Jacobi inequalities. Simulation results demonstrate the correctness and applicability of the proposed control scheme. Copyright © 2017 John Wiley & Sons, Ltd.

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H∞ and l2−l∞ finite-horizon filtering with randomly occurring gain variations and quantization effects

Cited by 14 publications

References 43 publications

Ultra Local Nonlinear Unknown Input Observers for Robust Fault Reconstruction

Ultra Local Nonlinear Unknown Input Observers for Robust Fault Reconstruction

Recursive locally minimum-variance filtering for two-dimensional systems: When dynamic quantization effect meets random sensor failure

A note on guaranteed cost control for nonlinear stochastic systems with input saturation and mixed time‐delays

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