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Suboptimal Filter (suboptimal + filter)

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Selected Abstracts

Suboptimal filter for continuous-time linear systems with unknown parameters

ASIAN JOURNAL OF CONTROL, Issue 5 2008
Du Yong Kim
Abstract The filtering problem for continuous-time linear systems with unknown parameters is considered. A new suboptimal filter is herein proposed. It is based on the optimal mean-square linear combination of the local Kalman filters. In contrast to the optimal weights, the suboptimal weights do not depend on current observations; thus, the proposed filter can easily be implemented in real-time. Examples demonstrate high accuracy and efficiency of the suboptimal filter. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]

Central suboptimal H, filter design for nonlinear polynomial systems

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 10 2009
Michael Basin
Abstract This paper presents the central finite-dimensional H, filter for nonlinear polynomial systems, which is suboptimal for a given threshold , with respect to a modified Bolza,Meyer quadratic criterion including the attenuation control term with the opposite sign. In contrast to the previously obtained results, the paper reduces the original H, filtering problem to the corresponding optimal H2 filtering problem, using the technique proposed in (IEEE Trans. Automat. Control 1989; 34:831,847). The paper presents the central suboptimal H, filter for the general case of nonlinear polynomial systems based on the optimal H2 filter given in (Int. J. Robust Nonlinear Control 2006; 16:287,298). The central suboptimal H, filter is also derived in a closed finite-dimensional form for third (and less) degree polynomial system states. Numerical simulations are conducted to verify performance of the designed central suboptimal filter for nonlinear polynomial systems against the central suboptimal H, filter available for the corresponding linearized system. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Suboptimal filter for continuous-time linear systems with unknown parameters

ASIAN JOURNAL OF CONTROL, Issue 5 2008
Du Yong Kim
Abstract The filtering problem for continuous-time linear systems with unknown parameters is considered. A new suboptimal filter is herein proposed. It is based on the optimal mean-square linear combination of the local Kalman filters. In contrast to the optimal weights, the suboptimal weights do not depend on current observations; thus, the proposed filter can easily be implemented in real-time. Examples demonstrate high accuracy and efficiency of the suboptimal filter. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]

Discrete-Time Risk-Sensitive Filters with Non-Gaussian Initial Conditions and Their Ergodic Properties

ASIAN JOURNAL OF CONTROL, Issue 4 2001
Subhrakanti Dey
ABSTRACT In this paper, we study asymptotic stability properties of risk-sensitive filters with respect to their initial conditions. In particular, we consider a linear time-invariant systems with initial conditions that are not necessarily Gaussian. We show that in the case of Gaussian initial conditions, the optimal risk-sensitive filter asymptotically converges to a suboptimal filter initialized with an incorrect covariance matrix for the initial state vector in the mean square sense provided the incorrect initializing value for the covariance matrix results in a risk-sensitive filter that is asymptotically stable, that is, results in a solution for a Riccati equation that is asymptotically stabilizing. For non-Gaussian initial conditions, we derive the expression for the risk-sensitive filter in terms of a finite number of parameters. Under a boundedness assumption satisfied by the fourth order absolute moment of the initial state variable and a slow growth condition satisfied by a certain Radon-Nikodym derivative, we show that a suboptimal risk-sensitive filter initialized with Gaussian initial conditions asymptotically approaches the optimal risk-sensitive filter for non-Gaussian initial conditions in the mean square sense. Some examples are also given to substantiate our claims. [source]