Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):

M. Stadlbauer, C. Hametner, S. Jakubek:
"Analytic model based design of experiments for nonlinear dynamic systems with constraints";
Vortrag: 13th IASTED International Conference Control and Applications (CA 2011), Vancouver, Canada; 01.06.2011 - 03.06.2011; in: "Proceedings of the IASTED International Conference Control and Applications (CA 2011)", C. de Silva (Hrg.); ACTA Press, paper Nr. 729-050 (2011), S. 20 - 27.

Kurzfassung englisch:
In this paper a novel analytic method for optimal model
based design of experiments for nonlinear dynamic systems
with incorporation of input, input rate and output constraints
is presented. The purpose of experiment design is
to excite an underlying process in a way that all the relevant
information about the system becomes visible from measured
input and output data. For complex processes, especially
if the number of inputs is high the experimental effort
can become tremendous, in terms of measurement time and
costs involved with the execution of the experiment. Model
based design of experiments (DoE) has proven to be a powerful
method in order to make experiments as informative
as possible concerning the reduction of experimental effort
with simultaneous gathering of all relevant information of
the process.In this context the compliance to system limits
on inputs and outputs is decisive so that secure and stable
operation conditions of the real system are guaranteed during
the experiment. The analytic constrained optimization
of the method proposed in this paper is based on multilayer
perceptron (MLP) networks and its effectiveness is demonstrated
for a dynamic nonlinear system.

design of experiments, Fisher information matrix, nonlinear dynamic systems, neural network, model predictive control, constrained optimization

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