Virtual Actuator Calibration Using Reinforcement Learning

The patent creates a virtual copy of the physical system through a digital twin that replicates the behavior and characteristics of the actual machine. This virtual model allows calibration to be performed in silico, eliminating the need for time-consuming physical experiments while maintaining calibration accuracy. The digital twin serves as a faithful reproduction that enables virtual testing and optimization.

The calibration process is performed in advance using the digital twin before deploying control programs to the physical system. By conducting preliminary calibration in the virtual environment, the system avoids iterative trial-and-error testing on actual equipment, significantly reducing calibration time and allowing multiple calibration scenarios to be evaluated beforehand.

Instead of conducting physical experiments on laboratory setups, the patent uses a digital twin to replicate system behavior and determine variable interactions. The virtual model captures the complex relationships between input and output variables through simulated experiments, eliminating the need for physical test equipment and reducing resource consumption while maintaining accuracy.

The patent replaces physical experimental apparatus with a computational model. The digital twin substitutes mechanical and physical testing infrastructure with software-based simulation, allowing interaction analysis to be performed through calculations rather than physical experiments, thereby reducing costs and resource requirements.

The digital twin enables automated calibration processes that perform self-optimization across multiple operating points without requiring manual intervention for each variant. The system automatically evaluates different calibration scenarios, identifies optimal parameters, and adjusts control programs, reducing the burden on calibration engineers while comprehensively covering all operating conditions.

The calibration process becomes dynamic and adaptive, automatically adjusting to different operating points and conditions. The digital twin allows the system to efficiently explore the parameter space and identify optimal calibration settings for various operating scenarios, transforming the static, manual process into a dynamic, automated optimization routine that handles complexity internally.