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

Кwon, Wook Hyun; Kim, Pyung Soo; Park, PooGyeon

doi:10.1109/9.802927

Cited by 66 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Example 2. To demonstrate the application validity of the suggested interval observer, the second example is presented on the linearized dynamic model of a U.S. Navy F-404 engine which powers the F/A-18 aircraft (Kwon et al, 1999). The corresponding dynamic model is a stable interval ostensible purely Metzler system (1), (2), written as A −1.4600 0 2.4280 −0.8357 −2.4 −0.3788 0.3107 0 −2.1300…”

Section: Illustrative Examplesmentioning

confidence: 99%

Interval observers design for systems with ostensible Metzler system matrices

Krokavec

Filasová

2023

Front. Aerosp. Eng.

View full text Add to dashboard Cite

This paper attempts to resolve the problem concerning the interval observers design for linear systems with ostensible Metzler system matrices. Because system dynamics matrices are partially different from strictly Metzler structures, a solution is achieved by constructing a composed system matrix representation, which combines pre-compensated interval matrix structures fixed with a prescribed region of D-stability and the reconstructed strictly Metzler matrix structure, related to the original interval system matrix parameter definition. A novel design procedure is presented, which results in a strictly positive observer gain matrix and guarantees that the lower estimates of the positive state variables are non-negative when considering the given system structure and the non-negative system state initial values. The design is computationally simple since it is reduced to the feasibility of the set of linear matrix inequalities.

show abstract

Section: Illustrative Examplesmentioning

confidence: 99%

Interval observers design for systems with ostensible Metzler system matrices

Krokavec

Filasová

2023

Front. Aerosp. Eng.

View full text Add to dashboard Cite

show abstract

“…FIR filters make use of finite input and output measurements on the most recent time interval, called the receding horizon, or a horizon which is a moving, fixed-size estimation window. Because of the complicated structure of the FIR filter, a modified receding horizon Kalman FIR filter for linear continuous and discretetime systems was proposed in [17,18]. It has been a general rule that local receding horizon Kalman filters (LRHKFs) are typically more robust against dynamic model uncertainties and numerical errors than standard local Kalman filters, which utilize all measurements [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Distributed receding horizon filtering for mixed continuous–discrete multisensor linear stochastic systems

Song

Shin

2010

Meas. Sci. Technol.

View full text Add to dashboard Cite

A new distributed receding horizon filtering algorithm for mixed continuous–discrete linear systems with different types of observations is proposed. The distributed fusion filter is formed by summation of the local receding horizon Kalman filters (LRHKFs) with matrix weights depending only on time instants. The proposed distributed filter has a parallel structure and allows parallel processing of measurements; thereby, it is more reliable than the centralized version if some sensors become faulty. Also, the selection of the receding horizon strategy makes the proposed distributed filter robust against dynamic model uncertainties. The key contribution of this paper is the derivation of the error cross-covariance equations between the LRHKFs in order to compute the optimal matrix weights. High accuracy and efficiency of the proposed distributed filter are demonstrated on the damper harmonic oscillator motion and the water tank mixing system.

show abstract

“…For general continuous-time stochastic systems, FIR filters without a priori initial state information were established by modification of the Kalman filter [4], where heuristic infinite covariance of the initial state information is used and the efficiency of the filters is unclear. In [5], an optimal unbiased FIR filter was derived assuming that the system matrix is nonsingular.…”

Section: Introductionmentioning

confidence: 99%

Fixed-lag Minimum Variance FIR Smoothers for Continuous-time Systems

Kwon

Han

Кwon

2006

2006 SICE-ICASE International Joint Conference

View full text Add to dashboard Cite

In this paper, we consider the fixed-lag finite impulse response (FIR) smoother problem for a class of continuous-time systems. The proposed fixed-lag FIR smoother is represented linearly with inputs and outputs on the recent time interval, and has coefficients without respect to any state information. It is optimal in view of the minimum variance performance criterion with unbiased constraints. The approach to the minimum variance fixed-lag FIR (MVF) smoother provides a closed-form solution and can be easily extended to an FIR filter by setting a lag size to zero. A simulation example is presented to validate the effectiveness of the proposed approach by comparing to a traditional fixed-lag Kalman smoother.

show abstract

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

Cited by 66 publications

References 14 publications

Interval observers design for systems with ostensible Metzler system matrices

Interval observers design for systems with ostensible Metzler system matrices

Distributed receding horizon filtering for mixed continuous–discrete multisensor linear stochastic systems

Fixed-lag Minimum Variance FIR Smoothers for Continuous-time Systems

Contact Info

Product

Resources

About