Avl List Gmbh

Nico Didcock

Stefan Jakubek

Christoph Hametner

Andreas Rainer

Amra Suljanovic

Markus Stadlbauer

Hans-Michael Kögeler

Christian Mayr

Maxime Deregnaucourt

Herbert Lanschützer

Karl Zettel

Alexander Wasserburger

Various aspects of the present disclosure are directed to methods for calibrating a technical system with respect to stochastic influences during real operation of the technical system. In one example embodiment of the present disclosure, the method includes the steps of: determining the values of a number of control variables, carrying out the calibration on the basis of a load cycle which results in a sequence of a number of operating points, executing the load cycle multiple times under the influence of at least one random influencing variable, with each realization of the load cycle resulting in a random sequence of the number i of operating points, defining a risk measure of the probability distribution, with which the probability distribution is mapped to a scalar variable, and optimizing the risk measure by varying the number of control variables in order to obtain optimal control variables for calibration.

Method for calibrating a technical system

Various aspects of the present disclosure are directed to, for example, a method for calculating a data envelope while calibrating a technical system. In some specific embodiments, the d-dimensional calibration space, which comprises the calibration variables required for the calibration, is divided into a first sub-calibration space having a dimension d&#x3c;d and at least one further sub-calibration space, and a d-dimensional data envelope is calculated at least for the first sub-calibration space using available data points and is checked during the calibration as an auxiliary condition.

A method for generating a model ensemble that estimates at least one output variable of a physical process as a function of at least one input variable, the model ensemble being formed from a sum of model outputs from a plurality of models that have been weighted with a weighting factor.

Method for generating a model ensemble for calibrating a control device

To determine a model for an output quantity (y) of a technical system that is dependent in a nonlinear manner on a number of input quantities in the form of an input quantity vector (u), a target output quantity range (COR) is defined and a model-based experimental design is determined with which the model is parameterized in the target output quantity range (COR) through the selection of associated input quantity vectors (u). A distance-based selection criterion is used for the selection of the input quantity vectors (u).

Method for determining a model of an output quantity of a technical system

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Nico Didcock