Siemens Aktiengesellschaft

Yunex Gmbh

Siemens Gamesa Renewable Energy A/s

Siemens Aktiengesell Schaft

Siemens Energy Global Gmbh & Co. Kg

Bsh Bosch Und Siemens Hausgerate Gmbh

Steffen Udluft

Siegmund Düll

Volkmar Sterzing

Alexander Hentschel

Marc Christian Weber

Daniel Hein

Anton Maximilian Schäfer

Clemens Otte

Jatinder P Singh

Daniel Schneegaβ

Alexander Hans

Hans-Gerd Brummel

Stefan Depeweg

Per Egedal

Peder Bay Enevoldsen

Stephen J Yutkowitz

Raymond Scott Nordlund

Mrinal Munshi

Hans-Georg Zimmermann

Kai Heesche

Thomas A Runkler

Michael Müller

Thomas Poppe

Glenn E Sancewich

Michel Tokic

Harald Frank

Peter Blauert

Thomas Runkler

Sigurd Spieckermann

Hany F Bassily

Markus Kaiser

Michael Leipold

Holger Schöner

Daniel Scheegaβ

Judith Mosandl

Lina Weichbrodt

Phillip Swazinna

Simon Fehrer

Siegmund DÜLL

Raymond S Nordlund

Daniel Schneegass

Daniel Schneegaß

Manfred Fochem

Martin Appl

David Grossenbacher

To configure a control agent, predefined training data are read in, which specify state datasets, action datasets and resulting performance values of the technical system. Using the training data, a data-based dynamic model is trained to reproduce a resulting performance value using a state dataset and an action dataset. An action evaluation process is also trained to reproduce the action dataset using a state dataset and an action dataset after an information reduction has been carried out, wherein a reproduction error is determined. To train the control agent, training data are supplied, the trained action evaluation process and the control agent. Performance values output by the trained dynamic model are fed into a predefined performance function. Reproduction errors are fed as performance-reducing influencing variables into the performance function. The control agent is trained to output an action dataset optimising the performance function on the basis of a state dataset.

Method for configuring a control agent for a technical system, and control device

A computer-implemented method for reducing friction within a machine tool is provided, including: a) reading a plurality of surrogate models for approximating friction compensation within a given machine tool, b) reading a friction compensation parameter set, c) determining a friction compensation result value for each surrogate model using the compensation parameter set, d) determining a weighted average friction compensation value of the friction compensation result values using the respective weighting factor, e) deducing a quality indicator for the friction compensation parameter set based on the weighted average friction compensation value, f) outputting the friction compensation parameter set, if the quality indicator fulfils a given quality criterion, or repeating b) to e) until the quality indicator fulfills the given quality criterion, g) applying the outputted friction compensation parameter set to the machine tool for reducing friction within the machine tool.

Reduction of friction within a machine tool

A method for predicting a remaining time of a signal phase includes capturing traffic data and a signal phase specification distinguishing different signal phases of a traffic signal generator. The traffic data is fed as input data to an artificial neural network including first and second sub-networks and a combination network for combining output data of the two sub-networks. The artificial neural network is trained to reproduce a time still remaining until a phase change of the traffic signal generator based on the traffic data. Outputting of the output data of the first and second sub-networks is controlled in a manner complementary to one another according to the signal phase specification. Lastly, the output data of the combination network or the prediction data derived therefrom are transmitted to a transport device or to a road user as a prediction of the time remaining for influencing traffic.

Method and device for predicting the time remaining of a signal phase

Method and device for controlling a machine in accordance with to multiple control objectives in which machine control is based on automated learning of subordinate control skills, wherein the device provides multiple subordinate control skills which are each assigned to a different one of the multiple control objectives, the device provides multiple learning processes that are reinforcement learning processes that are each assigned to a different one of the multiple control objectives and are configured to optimize the corresponding subordinate control skill based on input data received from the machine, and where the device is configured to determine a superordinate control skill based on the subordinate control skills and to control the machine based on the superordinate control skill.

Machine control based on automated learning of subordinate control skills

A computer-implemented method for determining at least one remaining time value, to be determined, for a system is provided, having the following steps: a. providing at least one known input data record containing a multiplicity of input elements for at least one determined time; b. providing at least one associated known remaining time value for the at least one input data record; c. determining the at least one remaining time value to be determined by applying an error function to the at least one input data record and the at least one associated remaining time value; and d. providing an output data record containing the at least one determined remaining time value and an associated reliability value. The invention furthermore targets a corresponding determination unit and computer program product.

Method for determining at least one remaining time value, to be determined, for a system

Provided is a method for the computer-assisted control of a technical system, in particular in a plant for generating energy, to achieve a predetermined technical behavior of the technical system, wherein an operating data set for controlling the system is provided. A system model for describing the mode of operation of the technical system is provided, wherein on the basis of the operating data set and on the basis of the system model, an optimization data set is determined by an optimization method. Based on the optimization data set, relevant parameters of the technical system that allow a more advantageous control of the technical system than other parameters of the technical system are selected using a selection method, wherein with the selected relevant parameters, a control method for the technical system is determined, wherein the technical system is controlled with the aid of the control method.

Method for the computer-aided control of a technical system, more particularly a power generation installation

Provided is an apparatus and method for cooperative controlling wind turbines of a wind farm, wherein the wind farm includes at least one pair of turbines aligned along a common axis approximately parallel to a current wind direction and having an upstream turbine and a downstream turbine. The method includes the steps of: a) providing a data driven model trained with a machine learning method and stored in a database, b) determining a decision parameter for controlling at least one of the upstream turbine and the downstream turbine by feeding the data driven model with the current power production of the upstream turbine which returns a prediction value indicating whether the downstream turbine will be affected by wake, and/or the temporal evolvement of the current power production of the upstream turbine; c) based on the decision parameter, determining control parameters for the upstream turbine and/or the downstream turbine.

Method and apparatus for cooperative controlling wind turbines of a wind farm

In order to control a technical system, a system state of the technical system is continually detected. By a trained first control model, a subsequent state of the technical system is predicted on the basis of a sensed system state. Then, a distance value is determined for a distance between the predicted subsequent state and an actually occurring system state. Furthermore, a second control model is trained by the trained first control model to predict the distance value on the basis of a sensed system state and on the basis of a control action for controlling the technical system. A subsequent state predicted by the first control model is then modified on the basis of a distance value predicted by the trained second control model. The modified subsequent state is output in order to control the technical system.

Method and control device for controlling a technical system

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

For controlling a target system, e.g. a gas or wind turbine or another technical system, a pool of control policies is provided. The pool of control policies comprising a plurality of control policies and weights for weighting each of the plurality of control policies are received. The plurality of control policies is weighted by the weights to provide a weighted aggregated control policy. With that, the target system is controlled using the weighted aggregated control policy, and performance data relating to a performance of the controlled target system are received. Furthermore, the weights are adjusted on the basis of the received performance data to improve the performance of the controlled target system. With that, the plurality of control policies is reweighted by the adjusted weights to adjust the weighted aggregated control policy.

Controlling a target system

An interactive assistance system and method for computer-aided control optimization for a technical system is provided. For example, a gas or wind turbine, in particular for optimizing the action sequence or the control variables of the plant (e.g. gas supply, compression), wherein an input terminal is provided for reading at least one status parameter providing a first system status of the technical system, and at least one setting parameter for adapting a reward function. A simulation module having a pre-trained neuronal network, simulating the plant, serves to simulate an action sequence on the technical system, starting from the first system status and to the prediction of the resulting statuses of the technical system.

Method for computer-aided installation control optimization using a simulation module

The embodiments relate to a method for the computer-aided control and/or regulation of a technical system, particularly a power generation installation. The actions to be performed in the course of regulation or control are ascertained using a numerical optimization method (e.g., a particle swarm optimization). In this case, the numerical optimization method uses a predetermined simulation model that is used to predict states of the technical system and, on the basis thereof, to ascertain a measure of quality that reflects an optimization criterion for the operation of the technical system.

Computer-aided control and/or regulation of a technical system

The invention concerns a method for the computerized control and/or regulation of a technical system (T). Within the context of the method according to the invention, there is implemented in a preset regulating process (CO, CO) an exploration rule (EP) by means of which new, as yet unknown, states (x) of the technical system (T) are started, a simulation model (SM) of the technical system (T) checking whether the actions (a) of the exploration rule (EP) lead to sequential states (x') lying within predetermined thresholds. Only in that case is the corresponding action (a) performed according to the exploration rule (EP) on the technical system. The method according to the invention enables new states to be explored within the framework of the operation of a technical system, it being ensured through checking of appropriate thresholds that the exploration is carried out imperceptibly and does not lead to incorrect operation of the technical system. The method according to the invention is suitable in particular for controlling or regulating gas turbines or wind turbines.

Method for the computerized control and/or regulation of a technical system

A computer-implemented method for controlling and/or regulating a technical system, in which actions to be carried out on the technical system are first of all determined using an action selection rule which was determined through the learning of a data-driven model and, in particular, a neural network. On the basis of these actions a numerical optimization searches for actions which are better than the original actions according to an optimization criterion. If such actions are found, the technical system is regulated or controlled on the basis of these new actions, such that the corresponding actions are applied to the technical system in succession. The method is suitable, in particular, for regulating or controlling a gas turbine, wherein the actions are preferably optimized with respect to the criterion of low pollutant emission or low combustion chamber humming.

Method for controlling and/or regulating a technical system in a computer-assisted manner

The invention concerns a method for the computerized control and/or regulation of a technical system. Within the context of the method according to the invention, an action-selection rule (PO') is determined which has a low level of complexity and yet is well suited to the regulating and/or control of the technical system, there being used for determination of the action-selection rule (PO&#x2032;) an evaluation measure (EM) which is determined on the basis of a distance measure and/or a reward measure and/or an action-selection rule evaluation method. The action-selection rule is then used to control and/or regulate the technical system. The method according to the invention has the advantage of the action-selection rule being comprehensible to a human expert. Preferably, the method according to the invention is used for regulating and/or controlling a gas turbine and/or a wind turbine.

Growing community of inventors

Eichenau, Germany

Steffen Udluft