Recursive solutions to deadbeat observers for time-varying multivariable systems

Minamide, Nariyasu; Ohno, Masahiro; Imanaka, H.

doi:10.1080/00207178708933810

Cited by 9 publications

(9 citation statements)

References 8 publications

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“…The nature and properties of the time-varying deadbeat condition are briefly summarized in Appendix B, along with an example problem. Considerations of the time-varying deadbeat condition appear sparse (Minamide et al [13] and Hostetter [14] present some fundamental results on the design of time-varying deadbeat observers), if not completely heretofore unknown in modern literature. If the repeated experiments (as derived and presented in [9,10]) are performed so as to compute a least-squares solution to the I/O behavior conjectured in Eq.…”

Section: Input-output Representations: Observer Markov Parametersmentioning

confidence: 99%

Observer/Kalman-Filter Time-Varying System Identification

Majji

Juang

Junkins

2010

Journal of Guidance, Control, and Dynamics

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An algorithm for computation of the generalized Markov parameters of an observer or Kalman filter for discretetime-varying systems from input-output experimental data is presented. Relationships between the generalized observer Markov parameters and the system Markov parameters are derived for the time-varying case. The generalized system Markov parameters thus derived are used by the time-varying eigensystem realization algorithm to obtain a time-varying discrete-time state-space model. A qualitative relationship of the time-varying observer with the Kalman filter in the stochastic environment and an asymptotically stable realized observer are discussed briefly to develop insights for the analyst. The minimum number of repeated experiments for accurate recovery of the system Markov parameters is determined from these developments. The time-varying observer gains realized in the process are subsequently shown to be in consistent coordinate systems for observer state propagation. It is also demonstrated that the observer gain sequence realized in the case of the minimum number of experiments corresponds naturally to a time-varying deadbeat observer. Numerical examples demonstrate the utility of the concepts developed in the paper.

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Section: Input-output Representations: Observer Markov Parametersmentioning

confidence: 99%

Observer/Kalman-Filter Time-Varying System Identification

Majji

Juang

Junkins

2010

Journal of Guidance, Control, and Dynamics

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“…Thus, it can be expected that the proposed approach would be appropriate for fast estimation and detection of signals with unknown times of occurrence, which arise in many problems such as fault detection and diagnosis of various systems, maneuver detection and target tracking of flying objects, etc. Since the derivation of the the proposed BLUF filter considers noises, it may be less sensitive to noises or disturbances than existing deterministic deadbeat observers, which are known to be sensitive [10,11].…”

Section: Propertiesmentioning

confidence: 99%

Best Linear Unbiased Fir Filters For Continuous‐Time State Space Models

Кwon

Kim

Han

2001

Asian Journal of Control

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This paper proposes a new linear finite impulse response (FIR) filter called the best linear unbiased FIR (BLUF) filter for the state estimation in continuous‐time state space models. The proposed BLUF filter for continuous‐time state space models is obtained by a formal limiting procedure of discretized systems. The BLUF filter is represented in an iterative form and then in a standard FIR form. It is shown that the proposed BLUF filter has deadbeat and unbiasedness properties. It is also shown that the BLUF filter is equivalent to the existing receding horizon Kalman FIR (RHKF) filter whose optimality is not clear to understand. The former is more systematic and logical while the latter is heuristic due to handling of infinite covariance of the initial state.

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“…It is noted that the suggested RHKF filter can be used as a very special deadbeat observer for noise-free systems (24) and (25). In this case, it is believed that this deadbeat observer is more robust against noises than existing ones [15], [16].…”

Section: Properties Of the Rhkf Filtermentioning

confidence: 99%

A receding horizon Kalman FIR filter for linear continuous-time systems

Кwon

Kim

Park

1999

IEEE Trans. Automat. Contr.

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A receding horizon Kalman finite-impulse response (FIR) filter is suggested for continuous-time systems, combining the Kalman filter with the receding horizon strategy. In the suggested filter, the horizon initial state is assumed to be unknown. It can always be obtained irrespective of unknown information on the horizon initial state. The filter may be the first stochastic FIR form for continuous-time systems that may have many good inherent properties. The suggested filter can be represented in an iterative form and also in a standard FIR form. The suggested filter turns out to be a remarkable deadbeat observer. The validity of the suggested filter is illustrated by numerical examples.Index Terms-Deadbeat property, FIR filter, Kalman filter, receding horizon strategy, state estimation. P. Mandl is with the

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Recursive solutions to deadbeat observers for time-varying multivariable systems

Cited by 9 publications

References 8 publications

Observer/Kalman-Filter Time-Varying System Identification

Observer/Kalman-Filter Time-Varying System Identification

Best Linear Unbiased Fir Filters For Continuous‐Time State Space Models

A receding horizon Kalman FIR filter for linear continuous-time systems

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