Modified Multiple Model Adaptive Estimation (M3AE) for Simultaneous Parameter and State Estimation
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Modified Multiple Model Adaptive Estimation (M3AE) for Simultaneous Parameter and State Estimation

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Published by Storming Media .
Written in English

Subjects:

  • BUS049000

Book details:

The Physical Object
FormatSpiral-bound
ID Numbers
Open LibraryOL11850497M
ISBN 101423562097
ISBN 109781423562092

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Modified Multiple Model Adaptive Estimation (M3AE) for Simultaneous Parameter and State Estimation In many estimation problems, it is desired to estimate system Cited by: 1. Orbital Sciences Corporation, Dulles, VA A multiple model adaptive estimation (MMAE) scheme is derived to determine both the position and the attitude of Cited by: The standard Multiple Model Adaptive Estimation (MMAE) algorithm uses a bank of Kalman filters, each based on a different model of the system. Multiple-model adaptive estimation using a residual correlation Kalman filter bank Abstract: We propose a modified multiple model adaptive estimation (MMAE) algorithm that uses the time correlation of the Kalman filter residuals, in place of their scaled magnitude, to assign conditional probabilities for each of the modeled by:

This study proposes a novel multiple model adaptive control (MMAC) algorithm for a class of nonlinear discrete time systems. The controller consists of a linear indirect adaptive controller, a. This new modified MMAE is termed a Generalized Residual Multiple Model Adaptive Estimator (GRMMAE). A derivation is provided for the hypothesis conditional probability formula which the GRMMAE uses to calculate probabilities that each elemental filter contains the correct parameter by: 4 From Model Selection to Adaptive Estimation. Lucien Birg´e1. Pascal Massart2. Introduction. Manydifferentmodelselectioninformationcriteriacanbefoundinthe literatureinvariouscontextsincludingregressionanddensityestimation. Abstract„This paper addresses the problem of Multiple Model Adaptive Estimation (MMAE) for discrete-time, linear, time-invariant MIMO plants with parameter uncertainty and unmodeled dynamics. Model identi“cation is analyzed in a deterministic setting by adopting a Minimum Energy selection criterion.

over the traditional multiple-model adaptive estimator. The new approach shown here, called generalized multiple-model adaptive estimation (GMMAE), is based on calculating the time-domain autocorrelation function [7], which is used to form the covariance of a generalized residual involving any number of backward time Size: KB. Multiple-Model Adaptive Estimation for Measurements with Unknown Time Delay Kyuman Lee, Eric N. Johnson y Georgia Institute of Technology, GA If a sensor requires finite processing and communication time, then the current sensor reading actually corresponds to the states of a vehicle at some point in the past. Finally, a new modified multiple model adaptive estimation algorithm with exponential decay terms is proposed to overcome the inherent drawback of classical Cited by:   An augmented multiple-model adaptive estimation (MMAE) algorithm is presented for a time-varying system, where the model uncertainty may occur occasionally. Generally, it is difficult for a single filter to achieve superior performance for both the certain system and the uncertain by: 1.