TRAINING OF MACHINE LEARNING MODELS WITH HARDWARE-IN-THE-LOOP SIMULATIONS

Number: 20260278473
Published: 2026-09-17
Filed: 2026-05-18
Assignee: Disney Enterprises, Inc.
Inventors: Milluzzi; Andrew Jesse, Haslebacher; Faith Elizabeth Smith
CPC: G06N20/00; G06F9/455; G05B2219/23446
Verdict: Medium Notable software
First reported: 2026-W38 (2026-09-18)
Source: Google Patents · FreePatentsOnline

The keeper's note

Trains fault-tolerant control-system models by injecting simulated faults into hardware-in-the-loop sensor emulations — relevant to robotics/animatronic control tuning.

Abstract

A method for training a control system model includes introducing a simulated fault into a software simulation of a physical system and generating emulated sensor data based on the simulated fault, where the emulated sensor data emulates output from one or more sensors of the physical system. The method further includes obtaining output data from a test control system provided with the emulated sensor data, where the test control system emulates a control system of the physical system and tagging the output data with the simulated fault to create training data. The method further includes utilizing the training data to train the control system model, where the control system model is a machine learning model for use with the control system of the physical system during operation of the physical system.

Background

BACKGROUND

Control systems are generally used to control complex mechanical and electromechanical systems. For example, theme park attractions, such as rollercoasters, rides, or the like, including control that monitor and actuate ride vehicles, and other aspects of the attraction may be controlled by control systems. Various control systems may also be used to assist in repair and/or maintenance of such systems, by identifying components within such systems in need of repair or replacement. However, control systems often evaluate the current state of a mechanical or electromechanical system and may not identify component malfunctions before they occur or before components need to be replaced for a system to remain operational. Further, such control systems may be unable to identify rare events occurring within a system, such as external physical conditions, system components that fail infrequently, and the like.

Control systems, including software controllers, are frequently used to control and monitor complex mechanical systems. For example, amusement park attractions may include complex ride systems controlled by control systems. Such attractions may further include show systems provided along a length of a ride system and/or about a travel path of ride vehicles, which show systems may be controlled and monitored by control systems to provide proper presentation of shows provided by the show systems. Other types of complex mechanical systems, such as automate

Claims

1. A computer-implemented method for generating a control system model, comprising: introducing, by a processor, a simulated fault into a software simulation of a physical system, the simulated fault comprising a plurality of fault instances distributed over a simulation time; executing, by the processor, the software simulation to generate emulated sensor data, the emulated sensor data emulating output from at least one physical sensor of the physical system in response to the simulated fault; providing, by the processor, the emulated sensor data to a test control system, wherein the test control system emulates a control system of the physical system; executing, by the processor, the test control system to generate control system output signals in response to the emulated sensor data; generating, by the processor, training data by associating the test control system output signals with the simulated fault instances that produced the emulated sensor data; and training, by the processor, a control system model using the training data, wherein the control system model comprises a machine learning model trained to identify physical fault conditions in the physical system during operation of the physical system. || 11. A system for generating a control system model comprising: a physical system comprising an electromechanical system; a memory; and a processor configured by instructions to perform operations comprising: introducing a simulated fault into a software simulation of a physical system, the simulated fault comprising a plurality of fault instances distributed over a simulation time; executing the software simulation to generate emulated sensor data, the emulated sensor data emulating output from at least one physical sensor of the physical system in response to the simulated fault; providing the emulated sensor data to a test control system, wherein the test control system emulates a control system of the physical system; executing the test control system to generate control system output signals in response to the emulated sensor data; generating training data by associating the test control system output signals with the simulated fault instances that produced the emulated sensor data; and training a control system model using the training data, wherein the control system model comprises a machine learning model trained to identify physical fault conditions in the physical system during operation of the physical system.