Edge Controller Model Transfer for Real-Time Facility Control
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Solution Overview
Problem
Existing control systems for facilities face challenges in efficiently performing learning processing for control models due to limited processing resources at the edge, making it difficult to manage and control complex industrial facilities effectively.
Innovation Solution
A control system architecture that utilizes edge computing with controllers near the facility and a central control apparatus for learning processing, allowing the central apparatus to generate and update control models, which are then transmitted and used by edge controllers for facility control, enabling efficient distribution of processing loads and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If learning processing is performed at the edge controller, then control responsiveness is improved, but processing resources are insufficient
Solution Approach 1:
The control system is segmented into two parts: edge controllers that perform real-time control execution and a cloud-based platform that performs learning processing. This segmentation allows each component to specialize in its strength - the edge controller maintains responsiveness while the cloud platform provides sufficient processing resources for complex learning tasks.
Solution Approach 2:
A communication interface serves as an intermediary between the edge controller and cloud platform, enabling the edge controller to offload learning processing to the cloud while maintaining real-time control capabilities. The intermediary manages data exchange and coordination between the two systems.
2Measurement precision
If complex learning models are used, then control accuracy is improved, but processing load increases
Solution Approach 1:
Complex learning model processing is extracted from the edge controller and relocated to the cloud-based platform. This extraction allows the use of computationally intensive models for high accuracy control while keeping the edge controller's processing load manageable for real-time execution.
3Measurement precision
If more processing power is allocated to learning, then model accuracy is improved, but control responsiveness decreases
Solution Approach 1:
The system segments processing tasks by function and time scale: the cloud platform handles offline learning model generation with high computational power, while edge controllers handle online real-time control with lower processing requirements. This segmentation resolves the trade-off between model accuracy and control responsiveness.
Data Source
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AI summary
Provided is a control system comprising a control apparatus including a learning processing unit configured to generate a control model by learning, the control model being configured to calculate control data for controlling a facility according to state data detected by at least one sensor configured to measure a state of the facility, and a model transmission unit configured to transmit the generated control model to a controller configured to control the facility; and a controller including a model receiving unit configured to receive the learned control model from the control apparatus, a state receiving unit configured to receive the state data from the at least one sensor, a calculation unit configured to calculate control data for controlling the facility according to the state data, which is a processing target, by using the control model received by the model receiving unit, and a control unit configured to control the facility by using the calculated control data.