Method and machine controller for controlling a machine

Abstract

By means of a plurality of training state vectors (TSV), each of which is assigned a training target value (TTV), a neural network (NN) is trained such that, when a training state vector (TSV) is input, a partial derivative (DN) of the neural network reproduces the assigned training target value (TTV) at least on average according to the input variables (X1, ..., XK) of the neural network. Furthermore, a temporal sequence of state vectors of the machine (M) is captured. For each state vector, it is detected which vector elements (SV1, ..., SVK) contain valid state values of the machine (M), and subsets (S1, ..., SM) of those input variables which are assigned valid state values are generated. For each of the subsets (S1, ..., SM), a partial derivative of the trained neural network (NN) is provided as an evaluation network (DN1, ..., DNM) according to the input variables of the respective subset. The state values assigned to the input variables of the respective subset (S1,..., SM) are then fed into the evaluation network (DN1, ..., DNM), wherein the remaining input variables are set to predefined extreme values (L1, ..., LK, U1, ..., UK). Depending thereon, a subset-specific output signal (AS1L, AS1U) is generated by the evaluation network (DN1, ..., DNM). Finally, a weighted sum of the subset-specific output signals (AS1L, AS1U, ...) is determined and output as a total output signal (ASG) for controlling the machine (M).

Description

[0001] Description

[0002] Methods and machine control for controlling a machine

[0003] Machine learning methods are increasingly used to control complex machines such as robots, motors, production plants, machine tools, milling machines, gas turbines, wind turbines, cooling systems, heating systems, or internal combustion engines. Using such learning methods, a machine control system can be trained, based on training data, to determine, from current operating signals, sensor values, or other machine state values, those control signals that specifically produce a desired or optimized machine behavior and thus improve its performance.

[0004] A variety of well-known training methods, such as reinforcement learning, are available for training such a learning-based machine control system. Reinforcement learning is also frequently referred to as reinforcement learning.

[0005] However, when using learning-based machine controls, it is often not guaranteed that the control actions will comply with predefined limits or other technical constraints in all situations.

[0006] Particularly in cases where the state values ​​of the machine being controlled are not correctly recorded, transmitted, stored, or preprocessed at times, or are missing or invalid for other reasons, the probability of suboptimal or undesired control actions increases. This frequently poses a problem, especially in safety-critical applications.

[0007] Typically, a temporary lack of valid state values ​​for the machine being controlled is mitigated by using mean values, median values, particularly frequent values, or default values. However, in many cases, these measures are insufficient to adequately compensate for the absence of valid state values.

[0008] The object of the present invention is to provide a method and a machine control system for controlling a machine that is more robust against invalid, in particular missing, state values ​​of the machine to be controlled. This object is achieved by a method with the features of claim 1, by a machine control system with the features of claim 11, by a computer program product with the features of claim 12, and by a computer-readable storage medium with the features of claim 13.

[0009] According to the invention, for controlling a machine, vector elements of state vectors quantifying an operating state of the machine are each assigned to an input variable of an artificial neural network. Using a plurality of training state vectors, each of which is assigned a training target value, the neural network is trained so that, when a training state vector is input, a partial derivative of an output signal of the neural network with respect to the input variables reproduces the assigned training target value, at least on average.

[0010] Such a partial derivative is often also referred to as the partial derivative of the neural network, insofar as the neural network can be understood as a function that maps the input variables to the output signal. A partial derivative of a neural network can therefore itself be considered a neural network. If the neural network generates multiple output signals, output-signal-specific partial derivatives can be determined for each of these signals in a corresponding manner.

[0011] Furthermore, a temporal sequence of state vectors quantifying the respective current operating state of the machine is continuously recorded. For each state vector, it is detected which vector elements contain valid state values ​​of the machine, and subsets of those input variables to which vector elements with valid state values ​​are assigned are generated.

[0012] For the subsets

[0013] - a partial derivative of the output signal of the trained neural network with respect to the input variables of the respective subset is provided as an evaluation network,

[0014] - The vector elements assigned to the input variables of the respective subset are fed into the evaluation network, whereby the remaining input variables are set to predefined extreme values, and

[0015] Depending on the specific parameters, the evaluation network generates a subset-specific output signal. Furthermore, a weighted sum of these subset-specific output signals is calculated and used as the overall output signal to control the machine.

[0016] To carry out the method according to the invention, a machine control, a computer program product and a preferably non-volatile computer-readable storage medium are provided.

[0017] The method and machine control according to the invention can be executed or implemented, for example, using one or more computers, processors, application-specific integrated circuits (ASICs), digital signal processors (DSPs), and / or so-called "field-programmable gate arrays" (FPGAs). Furthermore, the method according to the invention can be executed, at least partially, in a cloud and / or in an edge computing environment.

[0018] A particular advantage of the invention lies in the fact that, by evaluating multiple subsets of valid state variables, hidden correlations between valid and missing state values ​​can be implicitly taken into account. In many cases, this significantly reduces errors caused by invalid state values ​​compared to simply replacing them with predefined or extrapolated values. The deviation from an optimal control curve is often 20% to 80% lower with the method according to the invention than with known extrapolation-based methods.

[0019] Furthermore, it is easy to determine which subset of input variables influences the output signal and how. This allows for the identification of input variables or subsets of input variables that are particularly relevant to control. As a result, the control behavior of the machine control system can generally be better understood and validated, which is of considerable importance, especially in safety-critical applications.

[0020] Advantageous embodiments and further developments of the invention are specified in the dependent claims.

[0021] According to an advantageous embodiment of the invention, the partial derivatives can be determined by automatic differentiation, forward accumulation, backward accumulation, numerical differentiation, and / or Taylor approximation. Forward accumulation and backward accumulation are often also referred to as forward propagation and backward propagation, respectively, in the context of automatic differentiation. Standard methods for automatic differentiation are described, for example, on the Wikipedia page https: / / en.wikipedia.org / wiki / Automatic_differentiation (accessed on July 19, 2024) and in the references cited therein. Efficient implementations of methods for the partial derivation of neural networks are available, in particular, in the TensorFlow or Google JAX libraries.

[0022] According to a further advantageous embodiment of the invention, the extreme values ​​for each input variable can be specified in a variable-specific manner in the form of a minimum and a maximum value. This allows a subset-specific output signal to be generated for each of the remaining input variables, based on their minimum and maximum values. If the remaining input variables are considered as an m-dimensional vector, the evaluation network is, in effect, defined for all vertices of an m-dimensional cuboid, i.e., over 2 m Key points evaluated.

[0023] According to a further advantageous embodiment of the invention, a control information can be assigned as a training target value to each training state value. In this way, the partial derivation of the neural network can be trained to reproduce the control information. The overall output signal can then be output as a control signal.

[0024] Furthermore, a future state value can be assigned as a training target value to each training state value. In this way, the partial derivative of the neural network can be trained to reproduce the future state value, i.e., to a predicted value. The overall output signal can then be output as the predicted value. Considering predicted state values ​​allows, in many cases, predictive and therefore particularly efficient machine control.

[0025] Furthermore, a sensor value can be assigned as a training target value to each training state value. This allows the partial derivation of the neural network to be trained on reproducing the sensor value. The overall output signal can then be output as a virtual sensor value, i.e., as a calculated sensor value rather than a directly measured one. In this way, the measurement and sensor effort can often be reduced. In particular, sensor values ​​can be calculated for positions where attaching or querying sensors would be very costly. According to a further advantageous embodiment of the invention, a weighted sum of subset-specific output signals can be recursively determined by calculating weighted sums of subset-specific output signals from actual subsets of the respective set.

[0026] In particular, the weighted sum can be determined by summing over a variance decomposition for multivariate functions. Such a variance decomposition is also frequently referred to as analysis of variance (ANOVA). This type of variance decomposition is minimal in that the summands do not contain any unnecessary variables, i.e., variables that cannot already be expressed by other summands. This minimal decomposition can significantly reduce computational effort in many cases. Furthermore, a variance decomposition allows for an estimation of errors caused by missing state values. Efficient methods for applying and summing variance decompositions can be found, for example, in the publication “On decompositions of multivariate functions” by F. Kuo, I. Sloan, G. Wasilkowski, and H. Wozniakowski, Math. Comp., vol. 79, no. 270, pp. 953–966, 2010.However, the solutions described there require the evaluation of multidimensional integrals over the multivariate function to be decomposed. Since, within the scope of the present invention, a partial derivative of the neural network with respect to its input variables is trained, these integrals can be easily determined using partial derivatives of the trained neural network with respect to a subset of the input variables.

[0027] According to an advantageous embodiment of the invention, a list of subsets of the input variables can be read in. This allows the generation of subsets contained in the list to be suppressed during subset generation, or the weighted sum to be applied only to subsets not included in the list. In this way, certain subsets of input variables can be excluded when generating the overall output signal, which would, for example, negatively affect the control due to so-called spurious correlations in the data. Such subsets can often be identified based on physical relationships or expert knowledge.

[0028] According to a further advantageous embodiment of the invention, a subset-specific statistical variance of the subset-specific output signals can be determined for each subset. A statistical variance of the overall output signal can then be determined as a weighted sum of the subset-specific statistical variances. In particular, the statistical variance of the overall output signal and the statistical variances of the subset-specific output signals, together with weighted sums of subset-specific output signals, can be determined recursively. Based on the statistical variances, the reliability of the output signals can at least be estimated.

[0029] Furthermore, a Shapley value can be determined for each vector element based on the subset-specific output signals. In particular, the Shapley value can be determined simply and efficiently as a weighted sum of the subset-specific output signals. Shapley values ​​allow for an estimation of the sensitivity of the output signals with respect to the various input variables, which generally facilitates a better understanding and validation of the machine control behavior. Shapley values ​​are described, for example, on the Wikipedia page https: / / en.wikipedia.org / wiki / Shapley_value (accessed on July 19, 2024). Further methods for applying and calculating Shapley values ​​can be found, for example, in the publication “A unified approach to interpreting model predictions” by Scott M. Lundberg and Su-ln Lee in “Advances in neural information processing systems”, vol. 30, 2017.Compared to the calculation methods described in the latter publication, calculating Shapley values ​​using weighted sums over the subset-specific output signals proves to be considerably more efficient.

[0030] Furthermore, a Sobol index can be determined for each subset based on the subset-specific output signals. Sobol indices allow for an estimation of the global sensitivity of the output signals with respect to the various subsets of input variables. Sobol indices are described, for example, on the Wikipedia page https: / / en.wikipedia.org / wiki / Variance-based_sensitivity_analysis.

[0031] An embodiment of the invention is explained in more detail below with reference to the drawing. The drawings illustrate, in schematic form:

[0032] Figure 1 shows a machine control system while controlling a machine,

[0033] Figure 2 shows a training of a neural network for machine control, and

[0034] Figure 3 shows the determination of a control signal based on potentially incomplete operating signals from the machine. Insofar as the same or corresponding reference numerals are used in different figures, these reference numerals denote the same or corresponding entities, which may be implemented or designed, in particular, as described in connection with the respective figure.

[0035] Figure 1 illustrates, by way of example, a machine control system CTL according to the invention controlling a machine M, which in the present embodiment is configured as a robot, e.g., a manufacturing robot. Alternatively or additionally, the machine M can also be a motor, a production plant, a factory, a machine tool, a milling machine, a gas turbine, a wind turbine, a steam turbine, a chemical reactor, an internal combustion engine, a cooling system, or a heating system. In particular, a component or subsystem of a machine or plant can also be considered a machine M.

[0036] Machine M is coupled to the machine control CTL, which can be implemented as part of machine M or wholly or partially externally. For clarity, Figure 1 shows the machine control CTL externally. The machine control CTL comprises one or more processors PROC for executing the claimed process steps of the machine control CTL, as well as one or more memory units MEM coupled to the processor PROC for storing the data to be processed by the machine control CTL.

[0037] Machine M is equipped with a sensor system (SEN) that continuously measures various current operating parameters of machine M during operation and outputs them as status values. These status values ​​allow for the quantification of physical, control-related, chemical, and / or functional state variables, sensor data, environmental data, or other operating parameters that occur during or influence the operation of machine M. The state variables can relate to temperature, pressure, settings, actuator positions, valve positions, pollutant emissions, utilization, resource consumption, and / or the performance of machine M or its components. In a manufacturing plant, the status values ​​can also relate to product quality or other product characteristics.In a gas turbine, the state values ​​can relate to turbine power, rotational speed, vibration frequencies, vibration amplitudes, combustion dynamics, combustion pressure fluctuation amplitudes, or nitrogen oxide concentrations. The state values ​​currently measured by the SEN sensor system, as well as any other determined state values ​​of the machine M, are combined as vector elements into a current, multidimensional state vector SV, which quantifies the current operating state of the machine M. The vector elements of a state vector SV can each quantify the respective state values ​​using floating-point numbers, integers, or other data structures.

[0038] By continuously determining the state values, a temporal sequence of current state vectors SV is generated during the ongoing operation of machine M, which is continuously transmitted from machine M to the machine control CTL.

[0039] The machine control system CTL derives control signals CS for controlling the machine M from the state vectors SV, which quantify the operating state of the machine M.

[0040] The control signals CS are generated by an artificial neural network (NN) of the machine control system CTL. The neural network can, in particular, include a recurrent neural network, a convolutional neural network, a perceptron, a Bayesian neural network, an autoencoder, a variational autoencoder, and / or a deep learning architecture.

[0041] The neural network (NN) is trained beforehand to generate output signals based on input state vectors, which can then be used to control the machine M in an optimized manner. A variety of efficient machine learning methods are available for training such a neural network (NN). Through the training of the neural network (NN), the machine control system (CTL) is configured to optimally control the machine M. One aspect of this training process is explained in more detail below.

[0042] To optimize the control of machine M, the trained neural network (NN) generates output signals, for example in the form of numerical data vectors, based on the state vectors (SV). These output signals, or signals derived from them, are then transmitted as control signals (CS) from the machine controller (CTL) to machine M to control it in an optimized manner. Machine M is controlled by executing control actions induced by the control signals (CS). In this way, for example, a robot can be instructed to execute an optimized movement trajectory by corresponding control signals (CS). Similarly, in a gas turbine, the gas supply, gas distribution, and / or air supply can be adjusted by corresponding control signals (CS).

[0043] Figure 2 illustrates the training of the neural network NN of the machine controller CTL. The training aims to train the neural network NN to generate one or more output signals from input state vectors, which can then be used to control the machine M in an optimized manner. For the sake of clarity, only a single output signal will be explicitly considered in the following.

[0044] Assume that the state vectors comprise a number K of vector elements, each quantifying a state value of the machine M. To process such state vectors of dimension K, the neural network NN has a corresponding number K of input variables X1...XK. K can typically take a value of approximately 3-15.

[0045] Training is generally understood as the optimization of a mapping of input variables of a machine learning module, in this case the neural network NN, to one or more output signals. As mentioned above, for the sake of clarity, only a single output signal will be explicitly considered below. The mapping is parameterized by a typically high-dimensional parameter vector 0. The parameters of parameter vector 0 can, in particular, include weights of connections between neurons of the neural network NN and / or specify their network structures. In this sense, the neural network NN can be understood as a function NN(0; X1 ,...,XK) parameterized by 0 on its input variables X1 ,...,XK, where the function value represents the output signal.

[0046] The aforementioned model is optimized during a training phase according to predefined, learned, and / or to-be-learned criteria. These criteria could include, for example, the performance or success of a control action in control models, a prediction error in prediction models, or a classification error in classification models. Through training, the parameters of parameter vector 0 are adjusted or optimized so that the predefined criteria are met as well as possible, at least on average. The training can therefore be understood as an optimization problem.

[0047] For such optimization problems in the field of machine learning, a variety of efficient optimization methods are available. In particular, backpropagation methods, gradient-based optimization methods, gradient-free optimization methods, population-based optimization methods, particle swarm optimization, and / or genetic optimization methods can be used.

[0048] The training is performed using a large set of training data sets, which in this exemplary embodiment are stored in a database DB linked to the machine control CTL. The training data sets preferably originate from machine M, from a similar machine, and / or from a simulation of machine M.

[0049] The training datasets each comprise a K-dimensional training state vector TSV and an associated training target value TTV. The training state vectors TSV each contain K vector elements TSV1, ..., TSVK, each quantifying a state value of the machine M. Thus, each training state vector TSV quantifies the K different state values ​​and therefore a respective operating state of the machine M.

[0050] The training target values ​​TTV assigned to the training state vectors TSV are the quantities that determine what the neural network NN should be trained on.

[0051] Thus, a training state vector TSV can be assigned control information about a control action as a training target value TTV, which is optimized for the operating state quantified by this training state vector TSV. In this way, optimized control actions can be determined by the trained neural network based on the input state vectors, enabling the machine M to be controlled in an optimized manner.

[0052] Alternatively or additionally, a future state value can be assigned to each training state vector TSV as a training target value TTV. In this way, future state values ​​of the machine M can be predicted using the trained neural network based on the input state vectors. Furthermore, a sensor value can be assigned to each training state vector TSV as a training target value TTV. In this way, virtual sensor values ​​of the machine M can be determined using the trained neural network based on the input state vectors.

[0053] For training the neural network NN, a partial derivative DN of the neural network NN with respect to its input variables X1,...,XK is generated. As mentioned above, this corresponds to a partial derivative of the output signal of the neural network NN with respect to its input variables X1,...,XK. If the neural network NN is considered a function of its input variables X1,...,XK parameterized by the parameter vector 0, the partial derivative is given by

[0054] DN(Q; XI, ...,XK) 7 = — - — NN(Q; XI, XK) dXl dXK k 7

[0055] It should be noted that the partial derivative DN and the neural network NN are based on the same parameter vector 0.

[0056] The partial derivative DN is preferably generated by automatic differentiation. Efficient differentiation methods are available for this purpose, for example in the TensorFlow or Google JAX libraries. In the latter library, the partial derivative DN can then be easily generated using the `jax.grad` method.

[0057] To train the neural network NN, the training state vectors TSV are fed into the input variables X1, ..., XK. Each vector element TSV1, TSV2, ..., or TSVK of a given training state vector TSV is assigned to the input variables X1, X2, ..., or XK, respectively. That is, for n = 1, ..., K, the nth input variable Xn is set to the value of the respective nth vector element TSVn.

[0058] The vector elements TSV1, TSV2, ..., TSVK are converted into an output signal DAS by the generated partial derivative DN, which represents the partial derivative of a corresponding output signal of the neural network NN with respect to the input variables X1 , ..., XK.

[0059] The training aims to ensure that the generated output signals DAS closely match the training target values ​​TTV. To this end, a deviation D between the output signals DAS and the training target values ​​TTV is determined. This deviation D can be considered a reproduction error. In machine learning, such a reproduction error is often referred to as "looseness." The deviation D can be quantified, in particular, by a Euclidean distance or a squared distance between the respective representing vectors, e.g., according to D = (DAS - TTV). 2 .

[0060] The deviation D, as indicated by a dashed arrow in Figure 2, is fed back to the neural network NN. Based on the fed-back deviation D, the partial derivative DN, and thus the neural network NN, is trained to minimize the deviation D, at least on average. As mentioned above, a variety of efficient optimization methods are available for minimizing the deviation D.

[0061] By minimizing the deviation D, the neural network NN is trained to output a signal for a given state vector, the partial derivative of which predicts a corresponding target value as accurately as possible with respect to the input variables X1 , ..., XK.

[0062] Figure 3 illustrates the determination of a control signal based on potentially incomplete operating signals from machine M.

[0063] As mentioned above, during operation, machine M continuously transmits state vectors, each quantifying a current operating state of machine M, to the machine controller CTL. Each state vector comprises a number K of vector elements SV1, SV2, ..., SVK, each quantifying an operating parameter of machine M in the form of a state value. Each vector element SV1, SV2, ..., or SVK is assigned to the respective input variables X1, X2, ..., or XK of the neural network NN. Clearly, the state values ​​contained in each vector element SV1, SV2, ..., or SVK of the sequence of state vectors represent a time series of the respective operating parameter.

[0064] For the present embodiment, it is assumed that in an exemplary state vector, the vector element SV2 does not contain a valid state value, while all other vector elements SV1, SV3, ..., SVK contain valid state values.

[0065] The state vectors are fed into an input detector DET of the CTL machine control. The purpose of the input detector DET is to determine which of the vector elements SV1, ..., SVK contain valid state values ​​and which do not. For the state vector under consideration, the input detector DET detects that the vector element SV2 does not contain a valid state value, while all other vector elements SV1, SV3, SVK contain valid state values.

[0066] Depending on the test result, the input detector DET transmits a validity information VI to a subset generator GEN. The validity information VI specifies the vector elements that contain valid state values. For the state vector under consideration, these are SV1, SV3, ..., SVK.

[0067] Consequently, the subset generator GEN generates, depending on the validity information VI, all subsets S1, ..., SM of those input variables to which vector elements with valid state values ​​are assigned. For the state vector under consideration, this means all subsets of the input variables X1, X3, ..., XK.

[0068] Alternatively, a list of excluded subsets stored in the subset generator GEN can be used to suppress the generation of subsets of the input variables X1, ..., XK contained in the list. In this way, predetermined subsets that would negatively affect the control, for example due to so-called spurious correlations in the input variables, can be excluded from determining the control signal CS.

[0069] The subsets S1, ..., SM are transmitted by the subset generator GEN to an input / assignment module IM of the machine control CTL. Each subset can be transmitted in the form of a data structure, e.g., a list, which specifies the individual elements of the respective subset.

[0070] Furthermore, the vector elements SV1 , ..., SVK are forwarded from the input detector DET to the input assignment module IM.

[0071] In the input assignment module IM, a predefined minimum value L1, ..., or LK and a predefined maximum value U1, ..., or UK are stored for each vector element SV1, ..., or SVK. Optionally, the extreme values ​​L1, ..., LK, U1, ..., UK can also be stored in the input detector DET to check whether a given vector element lies within a valid range between the respective minimum and maximum values. A vector element outside this range can thus be easily identified as invalid. For each generated subset Sn, ne {1 , M}, the input assignment module creates

[0072] IM provides at least one subset-specific assignment of the input variables X1, ..., XK. The input variables contained in the respective subset Sn are set to the state values ​​contained in the assigned vector elements. It should be noted that each of these state values ​​is a valid state value, since each of the generated subsets S1, ..., SM is a subset of the input variables with valid state values.

[0073] In contrast, the input variables not contained in the respective subset Sn are set to their corresponding extreme values. For each input variable Xi, i.e., {1, ..., K}, not contained in the subset Sn, one assignment is generated in which the input variable Xi is set to its corresponding minimum value Li, and another assignment is generated in which the input variable Xi is set to its corresponding maximum value Ui. Since the input assignment module IM generates two assignments of the input variables X1, ..., XK for each input variable Xi not contained in the subset Sn, the number of assignments generated for the subset Sn is 2. m, where m >= 0 denotes the number of input variables not contained in the respective subset Sn. In this way, different subset-specific assignments for the vertices of an m-dimensional cuboid are generated for each subset Sn.

[0074] Furthermore, the generated subsets S1, ..., SM are transmitted from the subset generator GEN to the neural network NN, which was trained as described above. In parallel, for each of the subsets S1, ..., SM, the corresponding subset-specific assignments of the input variables X1, ..., XK are fed into the trained neural network NN by the input assignment module IM.

[0075] Furthermore, for each subset Sn, ne {1 , ... M} of the subsets S1 , ..., SM, a partial derivative DNn of the trained neural network NN with respect to the input variables of the respective subset Sn is provided as an evaluation network. For example, for a subset Sn containing only the input variables X1 and X3, this results in

[0076] DNn Q; XI, ...,XK) = — — MV(0; XI, ...,XK) . dXl dX3

[0077] Apparently, a partial derivative of a given evaluation network DNn with respect to the input variables not included in the respective subset Sn corresponds to the partial derivative of the trained neural network NN with respect to all input variables X1, ..., XK. Insofar as the neural network NN has been trained to reproduce the target value with respect to its partial derivative with respect to all input variables X1, XK, and XK, an output signal of a given evaluation network DNn represents an integral of the target value over the input variables not included in the respective subset Sn. The minimum values ​​Li and maximum values ​​Ui corresponding to these input variables define the respective integration limits.

[0078] In the case of K = 3, for example, a subset Sn containing the input variables X1 and X3, but not the input variable X2, is obtained. with f(X1,X2,X3) as the function representing the target value.

[0079] As mentioned above, the partial derivatives DN1, ..., DNM can be efficiently generated by automatic derivation, for example, using the jax.grad method of the Google JAX library. The partial derivatives DN1, ..., DNM, or components thereof, can be pre-generated for all subsets of the input variables X1, ..., XK and stored for later use. However, if highly efficient derivation routines are available, the partial derivatives DN1, ..., DNM can also be determined on demand during operation.

[0080] For each subset Sn, ne {1 , ... M} of the subsets S1 , ..., SM transmitted to the neural network NN, the assignments corresponding to this subset Sn are fed into the evaluation network DNn corresponding to this subset Sn. Consequently, the respective subset-specific evaluation network DNn generates a subset-specific and assignment-specific output signal for each of the corresponding assignments.

[0081] To illustrate this, consider the case where the original set of input variables, to which vector elements with valid state values ​​are assigned, was created as a subset S1. If exactly one vector element, here SV2, in the state vector under consideration does not contain a valid state value, then exactly two assignments for the subset S1 are fed into the corresponding evaluation network DN1 by the input assignment module IM. Specifically, the first assignment sets the input variable X2 to the minimum value L2, and the second assignment sets the input variable X2 to the maximum value U2. The remaining input variables X1, X3, ..., XK are set to the assigned and valid vector elements SV1, SV3, SVK in both assignments.

[0082] Consequently, the subset-specific evaluation network DN1 generates a subset-specific and assignment-specific output signal AS1 L for the first assignment and a subset-specific and assignment-specific output signal AS1U for the second assignment. As already indicated above, the difference AS1U - AS1L represents an integral of the target value over the input variable X2, which is not contained in the subset S1, with integration limits L2 and U2.

[0083] By integrating over input variables, here X2, for which no valid state value is currently available, the uncertainty about the invalid state value can be taken into account in a way that significantly reduces the prediction error of the inventive method, especially in the case of incomplete operating signals.

[0084] The subset-specific and assignment-specific output signals generated for the subsets Sn, ne {1 , ... M} and the corresponding assignments, here ASI L, AS1 U, ..., are transmitted by the respective corresponding evaluation network DNn to an aggregation module AGG of the machine control CTL.

[0085] The aggregation module AGG serves the purpose of determining a suitably weighted sum of the received output signals, here ASI L, AS1U, ..., as the total output signal ASG.

[0086] In the present embodiment, the weighted sum is determined by summation using a so-called ANOVA decomposition or dispersion decomposition for multivariate functions.

[0087] In an ANOVA decomposition, the function f(X) to be approximated, representing the target value, is represented with K variables as a sum of functions fs(Xs) that depend only on a subset S of the variables X1, ..., XK. Here, X denotes the variables X1, ..., XK, and Xs denotes those variables X1, ..., XK that are contained in the subset S. That is, (X) where the set of all variables X1 , ..., XK is from

[0088] For the sake of clarity, this has also been denoted by K. An ANOVA decomposition is minimal in the sense that the functions fs can be determined such that the summands contain no variables that are not already expressed by other summands. As mentioned above, integrals over the function f to be decomposed are required to determine an ANOVA decomposition. However, these integrals can be easily determined by the trained neural network NN, or by the partial derivatives DN1, ..., DNM. In this case, the output signals generated by a respective evaluation network DNn, n ∈ {1, ..., M} for the subset Sn correspond to the functions fsn(Xsn).

[0089] Using ANOVA decomposition, the function f representing the target value can be approximated in the case of missing or otherwise invalid input variables in a way that significantly reduces the prediction error compared to known methods. Only those summands or output signals fs are summed where the subset S does not contain any invalid input variables. If the set of input variables without a valid state value is denoted by J, the approximation of the function f can be determined according to...

[0090] In this case, the above sum is determined by the aggregation module AGG as the overall output signal ASG. The sum is preferably calculated recursively according to the following pseudocode:

[0091] Input f(X)

[0092] Output-, {fs Xs)} s ^K' and optional variances o 2 , {cr s}| E Optionally, subset-specific statistical variances can also be calculated. <? der teilmengenspezifischen Ausgabesignale sowie eine statistische Varianz a 2 The total output signal ASG is calculated. The integrals are determined – as described above – using the evaluation networks DN1 and DNM.

[0093] Additionally, the aggregation module AGG can be used to calculate a so-called Shapley value for a given vector element SVi, i.e., {1 , ... K}, according to will be calculated.

[0094] By using the ANOVA summands f s The calculation of Shapley values ​​can be significantly accelerated compared to other known methods. Shapley values ​​allow for an estimation of the sensitivity of the output signals with respect to various input variables, which generally makes it easier to understand and / or validate the control behavior of the CTL machine controller.

[0095] Furthermore, the aggregation module AGG can be used to determine a so-called Sobol index for a given subset Sn, ne {1 , ... M}. Sobol indices can be used to estimate the global sensitivity of the output signals with respect to the various subsets of input variables.

[0096] The total output signal ASG, determined for each potentially incomplete state vector, is transmitted by the aggregation module AGG to a control signal generator CSG of the machine control CTL. The control signal generator CSG derives control signals CS from the transmitted total output signals ASG to control the machine M.

[0097] If the neural network NN has been trained using information about optimized control actions as target values, the total output signal ASG can in many cases be directly output as the control signal CS.

[0098] If the neural network NN was trained using future state values ​​as target values, the total output signal ASG can be output as a predictive value for future operating states of the machine M. The control signal generator CSG can use such predictive values ​​to generate control signals CS that are also advantageous for future operating states of the machine M.

[0099] If the neural network NN has been trained using sensor values ​​as target values, the total output signal ASG can be output as a virtual sensor value, which in many cases can be used for state-specific optimization of the control signal CS.

[0100] The control signals CS are then transmitted to machine M, as described above, in order to control it in an optimized manner.

Claims

Patent claims 1. A computer-implemented method for controlling a machine (M), wherein a) vector elements (SV1,...,SVK) of state vectors quantifying an operating state of the machine are each assigned to an input variable (X1,...,XK) of an artificial neural network (NN), b) the neural network (NN) is trained by means of a plurality of training state vectors (TSV), each of which is assigned a training target value (TTV), so that when a training state vector (TSV) is input, a partial derivative (DN) of an output signal of the neural network with respect to the input variables (X1,...,XK) reproduces the assigned training target value (TTV) at least on average, c) a temporal sequence of state vectors quantifying a respective current operating state of the machine (M) is continuously recorded, d) for a respective state vector - detects which vector elements (SV1,...,SVK) contain valid state values ​​of the machine, and - Subsets (S1,...,SM) of those input variables are generated to which vector elements with valid state values ​​are assigned, e) for the subsets (S1,...,SM) each - a partial derivative of the output signal of the trained neural network (NN) with respect to the input variables of the respective subset (S1 ,...,SM) is provided as an evaluation network (DN1 ,...,DNM), - the vector elements assigned to the input variables of the respective subset (S1,...,SM) are fed into the evaluation network (DN1 ,...,DNM), whereby the remaining input variables are set to predefined extreme values ​​(L1,...,LK, U1,...,UK), and - depending on this, a subset-specific output signal (AS1 L, AS1U) is generated by the evaluation network (DN1,...,DNM), and f) a weighted sum of the subset-specific output signals (AS1 L, AS1U,...) is determined and output as a total output signal (ASG) to control the machine (M).

2. Method according to claim 1, characterized in that the partial derivatives (DN, DN1,...,DNM) are determined by automatic differentiation, by forward accumulation, by backward accumulation, by numerical differentiation and / or by Taylor approximation.

3. Method according to one of the preceding claims, characterized in that the extreme values ​​(L1,...,LK, U1,...,UK) for a respective input variable (X1,...,XK) are specified in an input variable-specific manner in the form of a minimum value (L1,...,LK) and a maximum value (U1,...,UK), and that for all minimum values ​​(L1,...,LK) and maximum values ​​(U1,...,UK) of the remaining input variables, a subset-specific output signal is generated.

4. Method according to one of the preceding claims, characterized in that a control information is assigned to a respective training state vector (TSV) as a training target value (TTV) and the total output signal (ASG) is output as a control signal (CS), that a future state value is assigned to a respective training state vector (TSV) as a training target value (TTV) and the total output signal (ASG) is output as a prediction value, or that a sensor value is assigned to a respective training state vector (TSV) as a training target value (TTV) and the total output signal (ASG) is output as a virtual sensor value.

5. Method according to one of the preceding claims, characterized in that a weighted sum over a respective set of subset-specific output signals is determined recursively over weighted sums over subset-specific output signals of real subsets of the respective set.

6. Method according to one of the preceding claims, characterized in that the weighted sum is determined by summation over a dispersion decomposition for multivariate functions.

7. Method according to one of the preceding claims, characterized in that a list of subsets of the input variables (X1,...,XK) is read in, and that - when generating the subsets (S1,...,SM), the generation of subsets contained in the list is suppressed, or - the weighted sum is only applied to subsets not included in the list.

8. Method according to one of the preceding claims, characterized in that a subset-specific statistical variance of the subset-specific output signals is determined for each of the subsets (S1 ,...,SM), and that a statistical variance of the total output signal (TOS) is determined as a weighted sum of the subset-specific statistical variances.

9. Method according to one of the preceding claims, characterized in that a Shapley value is determined for each of the vector elements (SV1 ,...,SVK) based on the subset-specific output signals.

10. Method according to one of the preceding claims, characterized in that a Sobol index is determined for each of the subsets (S1 ,...,SM) based on the subset-specific output signals.

11. Machine control system (CTL) configured to execute all process steps of a process according to any of the preceding claims.

12. Computer program product comprising instructions which, when the program is executed by a computer, cause a machine control according to claim 11 to execute a method according to any one of claims 1 to 10.

13. Computer-readable storage medium containing a computer program product according to claim 12.

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