Authors:
Volker Smits
1
and
Oliver Nelles
2
Affiliations:
1
DEUTZ AG, Ottostr. 1, Cologne, Germany
;
2
Institute of Mechanics and Control - Mechatronics, University of Siegen, Paul-Bonatz-Str. 9-11, Siegen, Germany
Keyword(s):
Design of Experiment, Genetic Algorithm, Space-filling, System Identification of Multi-variate Nonlinear Dynamic Systems, Optimal Excitation Signals, APRBS, GOATS, iGOATS.
Abstract:
The focus of this paper is on space-filling optimization of excitation signals for nonlinear dynamic multi-variate systems. Therefore, the study proposes an extension of the Genetic Optimized Time Amplitude Signal (GOATS) to multi-variate nonlinear dynamic systems, an incremental version of GOATS (iGOATS), a new space-filling loss function based on Monte Carlo Uniform Distribution Sampling Approximation (MCUDSA), and a compression algorithm to significantly speed up optimizations of space-filling loss functions. The results show that the GOATS and iGOATS significantly outperform the state-of-the-art excitation signals Amplitude Pseudo Random Binary Signal (APRBS), Optimized Nonlinear Input Signal (OMNIPUS), and Multi-Sine in the achievable model performances. This is demonstrated on a two-dimensional artificially created nonlinear dynamic system. Beside the good expectable model quality, the GOATS and iGOATS are suitable for the usage for stiff systems, supplementing existing data, a
nd easy incorporation of constraints.
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