Adaptive state estimation using MRAS techniques--Convergence analysis and evaluation

Three adaptive state observers for discrete-time systems derived from MRAS techniques are presented. While in a deterministic environment all of these schemes converge toward the linear asymptotic observer, when used in a stochastic environment for adaptive state estimation their performances presen...

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Bibliographic Details
Published in:IEEE transactions on automatic control 1980-12, Vol.25 (6), p.1169-1182
Main Authors: Dugard, L., Landau, I., Silveira, H.
Format: Article
Language:English
Subjects:
Adaptive control
Algorithm design and analysis
Approximation algorithms
Automatic control
Convergence
H infinity control
Lyapunov method
Stability
State estimation
Stochastic processes
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Summary:Three adaptive state observers for discrete-time systems derived from MRAS techniques are presented. While in a deterministic environment all of these schemes converge toward the linear asymptotic observer, when used in a stochastic environment for adaptive state estimation their performances present noticeable differences. The schemes considered in the paper are analyzed both in a deterministic and stochastic environment using the "equivalent feedback representation" (EFR) method and "ordinary differential equation" (ODE) method, respectively. Conditions for the convergence of the estimated parameters to the desired ones in a stochastic environment are given. The connections with adaptive Kalman filters are discussed. A comparative evaluation of these schemes in a deterministic and stochastic environment based on simulations concludes the paper.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.1980.1102531