Deadbeat unknown-input state estimation and input reconstruction for linear discrete-time systems

Ansari, Ahmad; Bernstein, Dennis S.

doi:10.1016/j.automatica.2019.01.011

Cited by 24 publications

(15 citation statements)

References 34 publications

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“…The mapping E : R nz → R n E is a static algebraic mapping capturing the nonlinearity of the dynamics. Let n r represent the number of rows in (1), and the matrices H(q), L(q), F (q) are polynomial functions with n r rows and n x , n z , n f columns in the variable q, which represents the shift operator. As such, these matrices may be cast as linear operators in the space of discrete-time signals.…”

Section: Model Description and Problem Statementmentioning

confidence: 99%

“…This methodology is found in a great variety of model-settings, e.g., from single non-linear systems [8] towards multi-agent, possibly large scale, systems [19,5,6]. A more integral approach for detection and isolation of faults is an unknowninput type estimator, which decouples the effect of unknown state measurements and disturbances (or faults) from the residual through an algebraic approach [16,9] or approaches using the generalized inverse [1,27]. Multiplicative faults are inherently more difficult to detect and isolate due to their non-linear appearance in the model, hence, it requires non-linear approaches, e.g., adaptive-type observers [23] or sliding-mode observers [21].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiple Faults Estimation in Dynamical Systems: Tractable Design and Performance Bounds

Ploeg¹,

Alirezaei²,

Wouw³

et al. 2020

Preprint

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In this article we propose a tractable nonlinear fault isolation filter along with explicit performance bounds for a class of linear dynamical systems in the presence of additive and multiplicative faults. The proposed filter architecture combines tools from model-based approaches in the control literature and regression techniques from machine learning. To this end, we view the regression operator through a system-theoretic perspective to develop operator bounds that are then utilized to derive performance bounds for the proposed diagnosis filter. In the special case of constant faults (both additive and multiplicative), the estimation (tracking) error of the faults signal provably converges to zero with an exponential rate. The performance of the proposed diagnosis filter is validated through an application on the lateral safety systems of SAE level 4 automated vehicles. The numerical results show that the theoretical bounds of this study are indeed close to optimal and potentially tight.

show abstract

Section: Model Description and Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiple Faults Estimation in Dynamical Systems: Tractable Design and Performance Bounds

Ploeg¹,

Alirezaei²,

Wouw³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The iterative/recursive algorithms are the typical parameter identification algorithms [9], which have a wide range of applications in seeking the roots of the equation and developing parameter estimation methods [10][11][12][13][14]. Ansari and Bernstein proposed the deadbeat unknown-input state estimation and input reconstruction for linear discrete-time systems [15]; Xu et al presented a hierarchical Newton and least squares iterative estimation algorithm for dynamic systems based on the impulse responses [16]. Recently, Xia et al presented an improved least-squares identification for multiple-output non- linear stochastic systems [17].…”

Section: Introductionmentioning

confidence: 99%

State filtering and parameter estimation for two‐input two‐output systems with time delay

Zhu

et al. 2021

IET Control Theory & Appl

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This paper focuses on presenting a new identification algorithm to estimate the parameters and state variables for two-input two-output dynamic systems with time delay based on canonical state space models. First, the related input-output equation is determined and transformed into an identification oriented model, which does not involve in the unmeasurable states, and then a residual based least squares identification algorithm is presented for the estimations. After the parameters being estimated, the system states are subsequently estimated by using the estimated parameters. Through theoretical analysis, the convergence of the algorithm is derived to provide assurance for applicability. Finally, a selected simulation example is given for a meaningful case study to show the effectiveness of the proposed algorithm.

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“…Identification of the state‐space systems has received considerable attention in the past few decades [24–26]. There are various identification methods, representatively such as the subspace identification algorithm, the recursive least‐squares algorithm, and the Kalman filter algorithm [27].…”

Section: Introductionmentioning

confidence: 99%

Bias compensation‐based parameter and state estimation for a class of time‐delay non‐linear state‐space models

Zhu

Nouri

2020

IET Control Theory & Appl

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Deadbeat unknown-input state estimation and input reconstruction for linear discrete-time systems

Cited by 24 publications

References 34 publications

Multiple Faults Estimation in Dynamical Systems: Tractable Design and Performance Bounds

Multiple Faults Estimation in Dynamical Systems: Tractable Design and Performance Bounds

State filtering and parameter estimation for two‐input two‐output systems with time delay

Bias compensation‐based parameter and state estimation for a class of time‐delay non‐linear state‐space models

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