An information theoretic approach to dynamical systems modeling and identification

Baram,; Sandell,

doi:10.1109/cdc.1977.271737

Cited by 29 publications

(53 citation statements)

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“…Baram and Sandell [1978] analyzed the convergence of the conditional probabilities p i and showed that the one corresponding to the KF designed for the "nearest" to the actual system (in a stochastic norm sense) converges to 1, while the others tend to 0. We refer to this norm as the Baram Proximity Index (BPI) 3 given by…”

Section: Bank Of Estimatorsmentioning

confidence: 99%

Stability Analysis of Robust Multiple Model Adaptive Control

Hassani

Hespanha

Athans

et al. 2011

IFAC Proceedings Volumes

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Abstract:The Robust Multiple Model Adaptive Control (RMMAC) methodology was first introduced in Fekri et al. [2006] for open-loop stable plants with parametric uncertainty and unmodeled dynamics subjected to external disturbances and measurement noise. This paper addresses the stability of RMMAC systems. We show, using concepts and analysis tools that borrow from Supervisory Control, that all closed-loop signals in a RMMAC system are bounded. It is further shown that robust performance is recovered in steady state.

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Section: Bank Of Estimatorsmentioning

confidence: 99%

Stability Analysis of Robust Multiple Model Adaptive Control

Hassani

Hespanha

Athans

et al. 2011

IFAC Proceedings Volumes

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“…If the true mode is known and added to the model set, the estimator will converge to the true mode with probability 1, and the optimal state estimate can be obtained [34]. However, the true model is unknown actually, and a common way is to add the optimal mode estimate, which is statistically close to the true mode [35].…”

Section: Hgmm Tracking Algorithmmentioning

confidence: 99%

A Cost-Effective Tracking Algorithm for Hypersonic Glide Vehicle Maneuver Based on Modified Aerodynamic Model

2016

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Abstract:In order to defend the hypersonic glide vehicle (HGV), a cost-effective single-model tracking algorithm using Cubature Kalman filter (CKF) is proposed in this paper based on modified aerodynamic model (MAM) as process equation and radar measurement model as measurement equation. In the existing aerodynamic model, the two control variables attack angle and bank angle cannot be measured by the existing radar equipment and their control laws cannot be known by defenders. To establish the process equation, the MAM for HGV tracking is proposed by using additive white noise to model the rates of change of the two control variables. For the ease of comparison several multiple model algorithms based on CKF are presented, including interacting multiple model (IMM) algorithm, adaptive grid interacting multiple model (AGIMM) algorithm and hybrid grid multiple model (HGMM) algorithm. The performances of these algorithms are compared and analyzed according to the simulation results. The simulation results indicate that the proposed tracking algorithm based on modified aerodynamic model has the best tracking performance with the best accuracy and least computational cost among all tracking algorithms in this paper. The proposed algorithm is cost-effective for HGV tracking.

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“…Before proving the lemma, let us introduce the Baram index (see Baram and Sandell (1978)) and summarize the main results in Hassani et al (2009a) and Hassani et al (2013a). It is shown in Hassani et al (2009a) and Hassani et al (2013a) that under some distinguishingly conditions, as t → ∞, one of the posterior probabilities governed by (24) (the a posterior probability associated with the KF designed based on the closet model to the true plant in a well defined sense), say p j , converges to 1 and the rest converge to 0.…”

Section: Finite Parameter Casementioning

confidence: 99%

Adaptive Wave Filtering for Dynamic Positioning of Marine Vessels using Maximum Likelihood Identification: Theory and Experiments

Hassani

Sørensen

Pascoal

2013

IFAC Proceedings Volumes

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This paper addresses a filtering problem that arises in the design of dynamic positioning systems for ships and offshore rigs subjected to the influence of sea waves. The dynamic model of the vessel captures explicitly the sea state as an uncertain parameter. The proposed adaptive wave filter borrows from maximum likelihood identification techniques. The general form of the logarithmic likelihood function is derived and the dominant wave frequency (the uncertain parameter) is identified by maximizing this function. To this effect, a bank of Kalman filters is used to evaluate the log-likelihood function for different values of the uncertain parameter. After each identification step a new set of Kalman filters is designed to estimate the dominant wave frequency with better accuracy. The proposed sea state identification technique enables adaptive Wave Filtering (WF) and Dynamic Positioning (DP) systems to operate in different operational conditions and hence, it is a step forward to the development of a so-called all-year marine estimation and control system. The results are experimentally verified by model testing a DP operated ship, the Cybership III, under different sea conditions in a towing tank equipped with a hydraulic wave maker.

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An information theoretic approach to dynamical systems modeling and identification

Cited by 29 publications

References 0 publications

Stability Analysis of Robust Multiple Model Adaptive Control

Stability Analysis of Robust Multiple Model Adaptive Control

A Cost-Effective Tracking Algorithm for Hypersonic Glide Vehicle Maneuver Based on Modified Aerodynamic Model

Adaptive Wave Filtering for Dynamic Positioning of Marine Vessels using Maximum Likelihood Identification: Theory and Experiments

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