Extension of unbiased minimum-variance input and state estimation for systems with unknown inputs

Hsieh, Chien-Shu

doi:10.1016/j.automatica.2009.05.004

Cited by 123 publications

(88 citation statements)

References 14 publications

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“…Hsieh [11] proposed a recursive filter that can estimate both the state and the unknown input, but the author pointed out that estimation of the unknown input may have an inherent bias. Cheng et al [2] proposed a recursive state estimator with global optimality by using input and output transformations.…”

Section: Introductionmentioning

confidence: 99%

Input and state estimation for linear systems with a rank-deficient direct feedthrough matrix

Wang

Zhao

et al. 2015

ISA Transactions

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Section: Introductionmentioning

confidence: 99%

Input and state estimation for linear systems with a rank-deficient direct feedthrough matrix

Wang

Zhao

et al. 2015

ISA Transactions

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“…The algorithms are often referred to as joint input-state estimation algorithms and combine both input and state estimation, e.g. [18,19,20,21,22,23]. Recursive combined deterministic-stochastic approaches allow online joint input-state estimation, thereby accounting for measurement errors, modelling errors, and additional unknown vibration sources.…”

Section: Introductionmentioning

confidence: 99%

Verification of joint input-state estimation for force identification by means of in situ measurements on a footbridge

Maes

Nimmen

Lourens

et al. 2016

Mechanical Systems and Signal Processing

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This paper presents a verification of a joint input-state estimation algorithm using data obtained from in situ experiments on a footbridge. The estimation of the input and the system states is performed in a minimum-variance unbiased way, based on a limited number of response measurements and a system model. A dynamic model of the footbridge is obtained using a detailed finite element model that is updated using a set of experimental modal characteristics. The joint inputstate estimation algorithm is used for the identification of two impact, harmonic, and swept sine forces applied to the bridge deck. In addition to these forces, unknown stochastic forces, such as wind loads, are acting on the structure. These forces, as well as measurement errors, give rise to uncertainty in the estimated forces and system states. Quantification of the uncertainty requires determination of the power spectral density of the unknown stochastic excitation, which is identified from the structural response under ambient loading. The verification involves comparing the estimated forces with the actual, measured forces. Although a good overall agreement is obtained between the estimated and measured forces, modeling errors prohibit a proper distinction between multiple forces applied to the structure for the case of harmonic and swept sine excitation.

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“…Furthermore, a common approach to facilitate the above UIOs design is to use state transformation techniques or complex matrix equations solving. On the other hand, in the present paper we adopt a straightforward but more compact method, which is originated from the discrete-time unknown input filtering (UIF) [7]- [9], to propose a system augmentation approach to solve the SISE problem. Unlike those mentioned above, where the estimators for the state and unknown inputs are derived independently, the proposed new method determines the augmented state estimator which jointly estimates the state and unknown inputs estimates.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Input and State Estimation for Linear Continuous-Time Systems: A System Augmentation Approach

Hsieh¹

2016

Proceedings of the 2016 International Conference on Mechatronics, Control and Automation Engineering

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Abstract-This paper presents an augmented state observer design for linear time-varying continuous-time systems with unknown inputs. An equivalent augmented state system, which is free of unknown inputs, is derived for the purpose of simultaneous input and state estimation (SISE). It is shown that, through the proposed system augmentation approach, the observer design for SISE problems can be easily solved using well-known techniques. The existence condition of SISE designs is also proposed. An illustrative example is given to show the effectiveness of the proposed results.

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Extension of unbiased minimum-variance input and state estimation for systems with unknown inputs

Cited by 123 publications

References 14 publications

Input and state estimation for linear systems with a rank-deficient direct feedthrough matrix

Input and state estimation for linear systems with a rank-deficient direct feedthrough matrix

Verification of joint input-state estimation for force identification by means of in situ measurements on a footbridge

Simultaneous Input and State Estimation for Linear Continuous-Time Systems: A System Augmentation Approach

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