Patent ID: 11886782
Assignee: ROBERT BOSCH GMBH
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 8:
9. A computer-implemented method for controlling or monitoring a physical system using a machine learned modelling of dynamics of the physical system, the method comprising the following steps:
accessing:
sensor data representing an observation of a current state of the physical system; and
model data defining a machine learned dynamics model which includes a machine learnable Lyapunov function, wherein the dynamics model is learned to infer a future state of the physical system and/or its environment based on the current state, wherein the learning of the dynamics model has been constrained to provide a globally stable modelling of the dynamics of the physical system by the dynamics model and the Lyapunov function having been jointly learned so that values of the learned Lyapunov function decrease along all trajectories of states inferred by the learned dynamics model;

inferring the future state of the physical system based on the observation of the current state of the physical system as obtained from the sensor data; and
using an output interface to an output device which is used in the control or monitoring of the physical system, to provide output data to the output device to enable the control or monitoring of the physical system based on the inferred future state,
wherein the machine learnable Lyapunov function is represented at least in part by an input-convex neural network.