[Zurück]

M. Stadlbauer, C. Hametner, Maxime Deregnaucourt, S. Jakubek, T. Winsel:
"Online Measuring Method Using an Evolving Model Based Test Design for Optimal Process Stimulation and Modelling";
Vortrag: 2012 IEEE International Instrumentation and Measurement Technology Conference, Graz; 13.05.2012 - 16.05.2012; in: "Proceedings of 2012 IEEE International Instrumentation and Measurement Technology Conference", (2012), 6 S.

Abstract-For data driven modelling the information content
of system input data and measured output data is decisive for
the achievable model quality of the underlying process. Process
stimulation is targeted to maximize the information content per
data sample, in order to limit the measurement time. Especially
for processes with an increasing number of system inputs the
experimental effort is continuously rising. Therefore methods for
an efficient process excitation combined with advanced modelling
strategies are necessary. In this context online methods, where
the design of experiments and the model training are in parallel
to the ongoing experiment are a very promising approach for
an efficient generation of process models. The compliance with
constraints on the system input as well as on the system output
is essential in order to provide secure and stable operational
conditions during the experiment. In this paper a recursive
algorithm is proposed, which uses an evolving local model network
for the online generation of optimal dynamic experiments
under constraints. The effectiveness of the proposed method
is demonstrated on a nonlinear dynamic exhaust temperature
model of an engine and a comparison with a standard excitation
signal is given.

Design of experiments, online training, system identification, local model network.