Short-Time Linear Quadratic Form Technique for Estimating Fast-Varying Parameters in Feedback Loops

The precision of a closed‐loop controller system designed for an uncertain plant depends strongly upon the maximum extent to which it is possible to track the trend of time‐varying parameters of the plant. The aim of this study is to describe a new parameter estimation algorithm that is able to foll...

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Bibliographic Details
Published in:Asian journal of control 2015-11, Vol.17 (6), p.2289-2302
Main Authors: Homaeinezhad, Mohammad Reza, Tahbaz-zadeh Moghaddam, Iman, Khakpour, Zahra, Naseri, Hosein
Format: Article
Language:English
Subjects:
Algorithms
coulomb friction
Feedback control systems
Parameter estimation
short-time least squares (STLS)
short-time linear quadratic form (STLQF)
sliding mode controller
viscous damping
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Summary:The precision of a closed‐loop controller system designed for an uncertain plant depends strongly upon the maximum extent to which it is possible to track the trend of time‐varying parameters of the plant. The aim of this study is to describe a new parameter estimation algorithm that is able to follow fast‐varying parameters in closed‐loop systems. The short‐time linear quadratic form (STLQF) estimation algorithm introduced in this paper is a technique for tracking time‐varying parameters based on short‐time analysis of the regressing variables in order to minimize locally a linear quadratic form cost function. The established cost function produces a linear combination of errors with several delays. To meet this objective, mathematical development of the STLQF estimation algorithm is described. To implement the STLQF algorithm, the algorithm is applied to a planar mobile robot with fast‐varying parameters of inertia and viscous and coulomb frictions. Next, performance of the proposed algorithm is assessed against noise effects and variation in the type of parameters.
ISSN:1561-8625
1934-6093
DOI:10.1002/asjc.1141