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Short‐Time Linear Quadratic Form Technique for Estimating Fast‐Varying Parameters in Feedback Loops
Author(s) -
Homaeinezhad Mohammad Reza,
Tahbazzadeh Moghaddam Iman,
Khakpour Zahra,
Naseri Hosein
Publication year - 2015
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1141
Subject(s) - control theory (sociology) , inertia , quadratic equation , noise (video) , controller (irrigation) , function (biology) , algorithm , mathematics , tracking (education) , estimation theory , computer science , mathematical optimization , control (management) , psychology , pedagogy , physics , geometry , classical mechanics , artificial intelligence , evolutionary biology , agronomy , image (mathematics) , biology
Abstract 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.