Abstract: In order to configure a control device, a predefined default configuration data set is read in. Furthermore, a deviation from the default configuration data set as well as a control performance are determined for each of a large number of generated test configuration data sets. In addition, a Pareto optimization is performed for the large number of test configuration data sets, wherein the deviation as well as the control performance are used as Pareto objective criteria. A configuration data set resulting from the Pareto optimization is then selected to configure the control device.

Abstract: Training data sets which are obtained by controlling the machine by different control systems are read in, the training data sets each including a state data set and an action data set. Furthermore, a performance evaluator is provided and determines, for a control agent, a performance for controlling the machine by the control agent. A control-system-specific control agent for the different control systems is respectively trained to reproduce an action data set on the basis of a state data set. In addition, a respective environment is delimited on the basis of a distance dimension in a parameter space of the control-system-specific control agents. Test control agents, for each of which a performance value is determined by the performance evaluator, are then generated within the environments. Depending on the determined performance values, a performance-optimizing control agent is finally selected from the test control agents and is used to control the machine.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A computer-implemented method for configuring a system model and a computer-implemented method for configuring a sensor model. There is also described a computer-implemented method for determining future switching behavior of a system unit, with the following steps: a) receiving the configured system model; b) receiving the configured sensor model, c) the configured sensor model being a probability distribution regarding how the sensor unit will behave in the specific time period; d) establishing at least one random sample of behavior of a sensor unit by sampling from the probability distribution; and e) determining the future switching behavior of the system unit and/or at least one associated statistical value on the basis of the established random sample by means of the trained system model. There is also described a corresponding computer program product.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method for configuration of a controlled drive application of a logistics system. The logistics system includes parallel conveying paths for piece goods. Each conveying path includes sub-conveying paths which are each accelerated or delayed to merge the piece goods on a single output conveying path with defined spacing. A system model of the logistics system is firstly determined by operating data of the logistics system which include sensor values of the logistics system and changes to control variables. A control function is determined, which includes configuration data for the drives, with at least one control action being performed on the precondition of one or more performance features that are to be achieved in the system model, during which control action the operating data is simulated for a plurality of time steps.

Abstract: A control device for a technical system, state-specific safety information about an admissibility of a control action signal is read in by a safety module is provided. Furthermore, a state signal indicating a state of the technical system is supplied to a machine learning module and to the safety module. In addition, an output signal of the machine learning module is supplied to the safety module. The output signal is converted into an admissible control action signal by the safety module on the basis of the safety information depending on the state signal. Furthermore, a performance for control of the technical system by the admissible control action signal is ascertained, and the machine learning module is trained to optimize the performance. The control device is then configured by the trained machine learning module.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.