Siemens Aktiengesellschaft

Continental Automotive Gmbh

Siemens Energy Global Gmbh & Co. Kg

Siemens Aktiengesell Schaft

Yunex Gmbh

Kai Heesche

Friedrich Graf

Siegmund Düll

Marc Christian Weber

Werner Hauptmann

Ralph Grothmann

Joachim Bamberger

Steffen Udluft

Volkmar Sterzing

Gurdev Dave Singh

Michael Metzger

Hans-Gerd Brummel

Raymond Scott Nordlund

Uwe Pfeifer

Hans-Georg Zimmermann

Christoph Tietz

Frank Lohrenz

Michel Tokic

Stefan Depeweg

Markus Kaiser

Clemens Hoffmann

Jochen Cleve

Holger Schöner

Daniel Schneegaß

Siegmund DÜLL

Raymond S Nordlund

A technical system controller is trained using a machine learning method. For this purpose, a chronological sequence of training data is detected for the machine learning method. The training data includes state data, which specifies states of the technical system, and control action data, which specifies control actions of the technical system. A chronological sequence of control action data is extracted specifically from the training data and is checked for a change over time. If a change over time is ascertained, a time window including the change is ascertained, and training data which can be found within the time window is extracted in a manner which is specific to the time window. The controller is then trained by the machine learning method using the extracted training data and is thus configured for controlling the technical system.

Controller for controlling a technical system, and method for configuring the controller

For reducing oscillations in a technical system plurality of different controller settings for the technical system is received. For a respective controller setting signal representing a time series of operational data of the technical system controlled by the respective controller setting is received, the signal is processed, whereby the processing includes a transformation into a frequency domain, and an entropy value of the processed signal is determined. Depending on the determined entropy values a controller setting from the plurality of controller settings is selected, and the selected controller setting is output for configuring the technical system.

Method, controller, and computer product for reducing oscillations in a technical system

A method for controlling a gas turbine, having a measurement step, a prediction step which is carried out after the measurement step, and a control step which is carried out after the prediction step. In the measurement step, a state variable of a combustion within a gas turbine is measured. In the prediction step, a future combustion dynamic is predicted using the measured state variable. In the control step, a control signal is output using the prediction of the future combustion dynamic.

Method for controlling a gas turbine by means of a future combustion dynamic

In order to control a technical system using a control model, a transformation function is provided for reducing and/or obfuscating operating data of the technical system so as to obtain transformed operating data. In addition, the control model is generated by a model generator according to a first set of operating data of the technical system. In an access domain separated from the control model, a second set of operating data of the technical system is recorded and transformed by the transformation function into a transformed second set of operating data which is received by a model execution system. The control model is then executed by the model execution system, by supplying the transformed second set of operating data in an access domain separated from the second set of operating data, control data being derived from the transformed second set of operating data.

Method and device for controlling a technical system using a control model

A method of controlling a complex system and a gas turbine being controlled by the method are provided. The method comprises providing training data, which training data represents at least a portion of a state space of the system; setting a generic control objective for the system and a corresponding set point; and exploring the state space, using Reinforcement Learning, for a control policy for the system which maximizes an expected total reward. The expected total reward depends on a randomized deviation of the generic control objective from the corresponding set point.

Randomized reinforcement learning for control of complex systems

Disclosed is a method for the computer-assisted modeling of a technical system. One or more output vectors are modeled dependent on one or more input vectors by the learning process of a neural network on the basis of training data of known input vectors and output vectors. Each output vector comprises one or more operating variables of the technical system, and each input vector comprises one or more input variables that influence the operating variable(s). The neural network is a feedforward network with an input layer, a plurality of hidden layers, and an output layer. The output layer comprises a plurality of output clusters, each of which consists of one or more output neurons, the plurality of output clusters corresponding to the plurality of hidden layers. Each output cluster describes the same output vector and is connected to another hidden layer.

Method for the computer-assisted modeling of a wind power installation or a photovoltaic installation with a feed forward neural network

A method for improving the usability of photovoltaic installations (PV installations) by taking account of shading information of adjacent PV installations for forecasting the power output by a relevant PV installation is provided. In particular, cloud movements and cloud shapes are taken into account. This improves the accuracy of the forecast. Here, it is advantageous that short-term forecasts in relation to e.g. the next 15 minutes are possible and a substitute energy source can be activated accordingly, in good time, prior to a dip in the power output by the PV installation. The invention can be used e.g. in the field of renewable energies, PV installations or smart grids.

Method and device for determining the power output by a photovoltaic installation

A predefined operating strategy permits a predefined operating mode of the motor vehicle. A first operating variable value of the operating mode and/or one additional variable value, which is independent of the operating mode are sensed and one estimated value is determined depending thereon, which is representative of one expected value of the one first operating variable of a second operating variable of the operating mode. One predicted value is determined depending on the sensed values of the one first operating variable, the one additional variable, and/or the one determined estimated value. The predicted value represents an expected operating state and is compared with one predefined comparison value, which is representative of one predefined operation state. The operating strategy is set as a function of the comparison and of the at least one estimated variable in such a way that the predefined operating state is as far as possible avoided.

Method and device for operating a motor vehicle

In the method, the transmission ratio of the speed transmission (i_act) is set automatically using stored adjustment characteristic curves, as a function of a variable (FP, tv) which characterizes the engine output, and a variable (v) which characterizes the vehicle speed. The transmission ratio is varied as required by means of a manual intervention by the driver. The transmission ratio of the speed transmission is set as a stepped deviation from a setpoint engine speed (n_eng).

Control method and system for an infinitely variable automatic automobile transmission

A controller has characteristic diagram memories and a fuzzy system which detects operating parameters of a motor vehicle and sets a transmission ratio with the aid of the characteristic diagram memory. A driver inputs plus and minus gear shift instructions via an input device. The driver instructions are evaluated during a driving operation by an adaptation circuit, and the shifting behavior of a transmission is adapted to a desired shifting behavior.

Controller for an automatic motor vehicle transmission

The device contains an electronic control system, which, in an automatic mode, automatically determines the transmission ratio in dependence of an operating condition, and contains selector devices, which, in the automatic mode, determine the driving stages of the transmission control and, in a manual mode, change the transmission ratio in steps. After a manual selection of the transmission ratio, driving and operating situations in which the automatic mode of the control system is desirable are detected and, if such a situation is detected, a return circuit causes the electronic control system to return to the automatic mode.

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Munich, Germany

Kai Heesche