Robert Bosch

Edgar Klenske

Lukas Froehlich

Christian Daniel

Heiner Markert

Martin Schiegg

Leonel Rozo

Julia Vinogradska

Volker Imhof

Stefan Angermaier

Melanie Zeilinger

Methods for ascertaining a control strategy for a technical system using a Bayesian optimization method. The control strategy is created based on model parameters of a control model and is executable. The method includes providing a quality function whose shape corresponds to a regression function and that evaluates a quality of a controlling of the technical system based on model parameters; carrying out a Bayesian optimization method based on the quality function in order to iteratively ascertain a model parameter set having model parameters within a model parameter domain that indicates the permissible value ranges for the model parameters; and determining the model parameter domain for at least one of the model parameters as a function of an associated maximum a posteriori estimated value of the quality function.

Method and device for determining model parameters for a control strategy for a technical system with the aid of a Bayesian optimization method

A method is described for selecting evaluation points for a Bayesian optimization method for optimizing a physical or chemical process that is modeled by a statistical model. The method includes the ascertainment of a posterior model of the statistical model in accordance with the results of one or multiple evaluations at previous evaluation points and the selection of a next evaluation point by optimizing an acquisition function over a search space, which is given by a specified limit for the predictive variance of the points in the search space given by the posterior model.

Controller and method for selecting evaluation points for a Bayesian optimization method

A method for optimizing a predefined policy for a robot, the policy being a Gaussian mixture model. The method begins with an initialization of a Gaussian process, the Gaussian process including at least one kernel k which, as an input parameter, obtains a distance that is ascertained between probability distributions, which are characterized in each case by the Gaussian mixture model and the Gaussian process, according to the probability product kernel. This is followed by an optimization of the Gaussian process in such a way that it predicts the costs as a function of the parameters of the Gaussian mixture model. This is followed by an ascertainment of optimal parameters of the Gaussian mixture model as a function of the Gaussian process, the parameters being selected, as a function of the Gaussian process, in such a way that the Gaussian process outputs the optimal cost function.

Method for optimizing a policy for a robot

A computer-implemented method for creating a control process for a technical system using a Bayesian optimization method, the control process being created and executable based on model parameters of a control model, the following steps being performed in order to optimize the control process: furnishing a quality function that corresponds to a trainable regression function, and that assesses a quality of a control process of the technical system based on model parameters; executing a Bayesian optimization method based on the quality function in order to iteratively ascertain an optimized model parameter set having model parameters, such that during execution of the Bayesian optimization method, a model parameter domain that indicates the permissible value ranges for the model parameters is expanded, by an amount equal to an expansion distance, with respect to those dimensions for which the model parameter ascertained in the current iteration lies at a range boundary.

Method and device for determining a control strategy for a technical system

A method is provided for ascertaining a NOconcentration and an NHslip downstream from an SCR catalytic converter of an internal combustion engine of a vehicle. State variables of an internal combustion engine as first input variables and an updated NHfill level of the SCR catalytic converter as a second input variable cooperate with at least one machine learning algorithm or at least one stochastic model. The at least one machine learning algorithm or at least one stochastic model calculates the NOconcentration and the NHslip downstream from the SCR catalytic converter as a function of the first input variables and the second input variables and output the same as output variables.

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Renningen, Germany

Edgar Klenske