Method and system for providing an optimized control of a complex dynamical system

US10953891B2 · kind B2 · utility

Assignee

• Siemens Aktiengesellschaft · DE

Inventors

• Dirk Hartmann · Aßling, DE
• Birgit Obst · Munich, DE
• Erik Olof Johannes Wannerberg · Munich, DE

Key dates

• Filing date: Apr 26, 2018
• Grant date: Mar 23, 2021
• Expiry date: Feb 11, 2039

Classification

• Technology area (CPC G): Physics
• CPC primary: G05B2219/33041
• WIPO field: Control
• WIPO sector: Instruments

Abstract

A method using machine learned, scenario based control heuristics including: providing a simulation model for predicting a system state vector of the dynamical system in time based on a current scenario parameter vector and a control vector; using a Model Predictive Control, MPC, algorithm to provide the control vector during a simulation of the dynamical system using the simulation model for different scenario parameter vectors and initial system state vectors; calculating a scenario parameter vector and initial system state vector a resulting optimal control value by the MPC algorithm; generating machine learned control heuristics approximating the relationship between the corresponding scenario parameter vector and the initial system state vector for the resulting optimal control value using a machine learning algorithm; and using the generated machine learned control heuristics to control the complex dynamical system modelled by the simulation model.