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Research Papers

Enhanced Polynomial Chaos-Based Extended Kalman Filter Technique for Parameter Estimation

[+] Author and Article Information
Jeremy Kolansky

Advanced Vehicle Dynamics Laboratory,
Mechanical Engineering Department,
Virginia Tech,
9L Randolph Hall, 460 Old Turner Street,
Blacksburg, VA 24061
e-mail: jkolansk@vt.edu

Corina Sandu

Fellow ASME
Director
Professor
Advanced Vehicle Dynamics Laboratory,
Mechanical Engineering Department,
Virginia Tech,
104 Randolph Hall, 460 Old Turner Street,
Blacksburg, VA 24061
e-mail: csandu@vt.edu

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS. Manuscript received June 5, 2014; final manuscript received July 24, 2015; published online November 29, 2017. Assoc. Editor: D. Dane Quinn.

J. Comput. Nonlinear Dynam 13(2), 021012 (Nov 29, 2017) (9 pages) Paper No: CND-14-1146; doi: 10.1115/1.4031194 History: Received June 05, 2014; Revised July 24, 2015

The generalized polynomial chaos (gPC) mathematical technique, when integrated with the extended Kalman filter (EKF) method, provides a parameter estimation and state tracking method. The truncation of the series expansions degrades the link between parameter convergence and parameter uncertainty which the filter uses to perform the estimations. An empirically derived correction for this problem is implemented, which maintains the original parameter distributions. A comparison is performed to illustrate the improvements of the proposed approach. The method is demonstrated for parameter estimation on a regression system, where it is compared to the recursive least squares (RLS) method.

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References

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Figures

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Fig. 1

Vehicle model diagram

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Fig. 2

CG height estimation

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Fig. 4

Pitch inertia estimation

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Fig. 5

Roll inertia estimation

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Fig. 6

CG height estimation with sensor noise

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Fig. 7

Mass estimation with sensor noise

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Fig. 8

Pitch inertia estimation with sensor noise

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Fig. 9

Roll inertia estimation with sensor noise

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