Patent ID: 11868887
Assignee: ROBERT BOSCH GMBH
Field: Control (Instruments)
Classification: CPC G | IPC G

Claim 13:
14. A system for generating and using a model with which the system predicts states of a computer-controlled system for operation of the computer-controlled system, the prediction being performed by applying sensor measurements of the computer-controlled system to the trained model comprising:
a data interface; and
at least one processor, wherein one or more of the at least one processor in combination is configured to:
use the data interface to access model data defining a model that uses a stochastic differential equation (SDE) that includes a set of variables having a drift component and a diffusion component, wherein the drift component includes a predefined part whose parameters are fixed and a trainable part whose parameters are adjustable by a training;
use, by the at least one processor, at least one sensor of a computer-controlled system to obtain a set of observations of the computer-controlled system, each observation of the set of observations including a time series of measurements of physical quantities of the computer-controlled system and/or an environment of the computer-controlled system obtained using the at least one sensor;
train the model based on the set of observations, the training of the model including iteratively performing the following until obtainments of a final version of the adjustable parameters of the trainable part of the drift component:
using the model to make a time-series prediction of a state of the computer-controlled system at a current time point based on values of the set of variables of the SDE that represent the measurements of the physical quantities at a previous time point, wherein the making of the prediction includes:
evaluating the predefined part of the drift component to obtain a first drift;
evaluating the trainable part of the drift component to obtain a second drift;
combining the first drift and the second drift to obtain an overall drift; and
identifying, as the predicted state, a state corresponding to the overall drift with a stochasticity characterized by the diffusion component;

deriving a training signal by comparing the time-series prediction to an observation of the set of observations; and
adjusting at least the parameters of the trainable part of the drift component based on the training signal to optimize a contribution of the trainable part of the drift component to the overall drift; and
during the operation of the computer-controlled system:
continually obtain, from the at least one sensor of the computer-controlled system, the sensor measurements, the sensor measurements being of the physical quantities; and
predict a current state of the computer-controlled system by applying to the model, and from the continually obtained measurements, one or more of the obtained measurements of an earlier time.