US 11,899,414 B2Granted

Method for calibrating a technical system

AssigneeAVL List GmbH

InventorsNico Didcock, Alexander Wasserburger, Christoph Hametner

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Abstract

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.

Claims11

Dependent claims

Method for calibrating a technical system including the steps of:

determining the values of a number of control variables of the technical system, with which the technical system is controlled or tuned, at specific operating points of the technical system,
carrying out the calibration on the basis of a load cycle for the technical system which results in a sequence of a number of operating points of the technical system,
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 cost function which contains a target function y with a model f for an output variable of the technical system, the model f being dependent on the number of control variables of the technical system and on the number i of random operating points of the technical system so that the value of the cost function for each realization of the load cycle is itself a random variable that has a probability distribution,
defining a risk measure p of the probability distribution, with which the probability distribution is mapped to a scalar variable, and
optimizing the risk measure ρ by varying the number of control variables in order to obtain the optimal control variables for calibration.

3 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/311,966Filed 9 Dec 2019
Priority: AT A 51095/201810 Dec 2018
Published: US 2022/0026863 A127 Jan 2022
Granted: 13 Feb 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification6 codes

G05B 13/041
Involving the use of models or simulators: in which a variable is automatically adjusted to optimise the performance
G05B 1/03
Electric: for comparing digital signals
G05B 19/401
Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to…
G06F 17/18
Complex mathematical operations: for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression…