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Open AccessArticle

Plant Friendly Input Design for Parameter Estimation in an Inertial System with Respect to D-Efficiency Constraints

Faculty of Computer Science, Bialystok University of Technology, Wiejska 45A, 15-351 Bialystok, Poland
Entropy 2014, 16(11), 5822-5837; https://doi.org/10.3390/e16115822
Received: 27 March 2014 / Revised: 14 July 2014 / Accepted: 29 October 2014 / Published: 6 November 2014
(This article belongs to the Section Information Theory, Probability and Statistics)
System identification, in practice, is carried out by perturbing processes or plants under operation. That is why in many industrial applications a plant-friendly input signal would be preferred for system identification. The goal of the study is to design the optimal input signal which is then employed in the identification experiment and to examine the relationships between the index of friendliness of this input signal and the accuracy of parameter estimation when the measured output signal is significantly affected by noise. In this case, the objective function was formulated through maximisation of the Fisher information matrix determinant (D-optimality) expressed in conventional Bolza form. As setting such conditions of the identification experiment we can only talk about the D-suboptimality, we quantify the plant trajectories using the D-efficiency measure. An additional constraint, imposed on D-efficiency of the solution, should allow one to attain the most adequate information content from the plant which operating point is perturbed in the least invasive (most friendly) way. A simple numerical example, which clearly demonstrates the idea presented in the paper, is included and discussed. View Full-Text
Keywords: plant-friendly identification; D-optimality; D-efficiency; ellipsoidal confidence region plant-friendly identification; D-optimality; D-efficiency; ellipsoidal confidence region
MDPI and ACS Style

Jakowluk, W. Plant Friendly Input Design for Parameter Estimation in an Inertial System with Respect to D-Efficiency Constraints. Entropy 2014, 16, 5822-5837.

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