Parallel numerical optimization for fast adaptive nonlinear moving horizon estimation

Author

Poloni, Tomas ; Rohal´-Ilkiv, B. ; Johansen, Tor Arne

Author_Institution

Inst. of Autom., Meas. & Appl. Inf., Slovak Univ. of Technol., Bratislava, Slovakia

fYear

2013

fDate

10-12 April 2013

Firstpage

40

Lastpage

47

Abstract

This paper proposes a novel strategy using parallel optimization computations for nonlinear moving horizon state estimation, and parameter identification problems of dynamic systems. The parallelization is based on the multi-point derivative-based Gauss-Newton search, as one of the most efficient algorithms for the nonlinear least-square problems. A numerical experiment is performed to demonstrate the parallel computations with the comparison to sequential computations.

Keywords

Newton method; least squares approximations; nonlinear estimation; optimisation; parameter estimation; dynamic system; fast adaptive nonlinear moving horizon estimation; multipoint derivative-based Gauss-Newton search; nonlinear least-square problem; parallel computation; parallel numerical optimization; parameter identification; Computational modeling; Equations; Loss measurement; Multicore processing; Noise; Real-time systems; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Networking, Sensing and Control (ICNSC), 2013 10th IEEE International Conference on

Conference_Location

Evry

Print_ISBN

978-1-4673-5198-0

Electronic_ISBN

978-1-4673-5199-7

Type

conf

DOI

10.1109/ICNSC.2013.6548708

Filename

6548708