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.

A novel way to identify fast-varying parameters of Non-linear systems in the closed-loop is described in this study. The new estimation algorithm introduced in this paper is based on short-time analysis of the regressing variables in order to minimize locally a linear quadratic form cost function and called "Short-Time Linear Quadratic Form (STLQF)" technique. The established cost function produces a linear combination of errors with several delays. Toward this objective, mathematical development of the STLQF estimation algorithm is described. For casting implementation of the STLQF algorithm into an applied framework, the algorithm is applied to a 3-DOF mobile robot with fast-varying parameters of inertia, coulomb and viscous frictions.

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