Estimation and detection of unknown inputs using optimal FIR filter

Park, Sang Hwan; Kim, Pyung Soo; Kwon, Oh-Sang; Кwon, Wook Hyun

doi:10.1016/s0005-1098(00)00063-7

Cited by 32 publications

(17 citation statements)

References 13 publications

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“…Gaussian random process with covariance   [8]. If we assume that the unknown inputs are uncorrelated with each other, the covariance matrix   is to be diagonal matrix.…”

Section: The Optimal Fir Smoother For System With Unknown Inputsmentioning

confidence: 99%

“…The pair       of the augmented system (5) and (6) is observable so that all modes are observed at the output and stabilized observers can be constructed [8].…”

Section: The Optimal Fir Smoother For System With Unknown Inputsmentioning

confidence: 99%

“…Kalman filter may diverge for system with initial state uncertainty, modeling errors, and numerical errors. To overcome the shortcomings of the Kalman filtering approaches, FIR filtering approach was proposed in [8]. As shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…1, since the optimal FIR filter estimate makes use of the recent finite measurements, it guarantees BIBO stability, robustness to temporary modeling uncertainties and fast convergency [9]. Moreover, since the optimal FIR filter is derived with unbiased constraint, which make exact estimates for unknown inputs, the estimates of each unknown input is completely independent of the initial state and other unknown inputs [8]. However, the optimal FIR filter used in [8] was derived assuming that the system matrix is nonsingular and the covariance of the initial state is infinity.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, since the optimal FIR filter is derived with unbiased constraint, which make exact estimates for unknown inputs, the estimates of each unknown input is completely independent of the initial state and other unknown inputs [8]. However, the optimal FIR filter used in [8] was derived assuming that the system matrix is nonsingular and the covariance of the initial state is infinity. Also, the optimal FIR filter was derived without known input.…”

Section: Introductionmentioning

confidence: 99%

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Unknown Input Estimation using the Optimal FIR Smoother

Kwon¹

2014

Journal of Institute of Control, Robotics and Systems

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Abstract:In this paper, an unknown input estimation method via the optimal FIR smoother is proposed for linear discrete-time systems. The unknown inputs are represented by random walk processes and treated as auxiliary states in augmented state space models. In order to estimate augmented states which include unknown inputs, the optimal FIR smoother is applied to the augmented state space model. Since the optimal FIR smoother is unbiased and independent of any a priori information of the augmented state, the estimates of each unknown input are independent of the initial state and of other unknown inputs. Moreover, the proposed method can be applied to stochastic singular systems, since the optimal FIR smoother is derived without the assumption that the system matrix is nonsingular. A numerical example is given to show the performance of the proposed estimation method.

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“…Gaussian random process with covariance   [8]. If we assume that the unknown inputs are uncorrelated with each other, the covariance matrix   is to be diagonal matrix.…”

Section: The Optimal Fir Smoother For System With Unknown Inputsmentioning

confidence: 99%

“…The pair       of the augmented system (5) and (6) is observable so that all modes are observed at the output and stabilized observers can be constructed [8].…”

Section: The Optimal Fir Smoother For System With Unknown Inputsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%