Edge Control Model Deployment for Resource-Limited Controllers
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Solution Overview
Problem
Existing control systems face challenges in efficiently performing learning processing for facility control due to limited processing resources at the controller level, which hinders effective control model generation and data calculation.
Innovation Solution
A control system architecture that utilizes edge computing with controllers connected to an upper control apparatus, allowing for learning processing to be performed by the upper control apparatus with more processing resources, generating control models that are then transmitted to the controllers for facility control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If learning processing is performed at the controller level, then control decisions can be made locally, but the processing resources are insufficient for effective model generation and calculation
Solution Approach 1:
The system divides the control architecture into two segments: an upper control apparatus that performs learning processing and model generation, and lower-level controllers that execute control decisions. This segmentation allows each component to specialize in its strengths while working together as a unified system.
Solution Approach 2:
The upper control apparatus acts as an intermediary that generates control models and transmits them to the controller. This intermediary provides the heavy computational resources needed for learning processing while enabling the controller to make informed local decisions.
2Productivity
If control models are generated by the upper control apparatus, then learning processing efficiency is improved, but the system complexity increases
Solution Approach 1:
The system merges the learning processing capability into a dedicated upper control apparatus that combines data collection, model training, and model transmission functions. This consolidation improves learning efficiency while managing complexity through functional integration.
3Measurement precision
If multiple control models with different characteristics are generated, then control accuracy can be optimized, but the data management complexity increases
Solution Approach 1:
The system generates control models with different local qualities or characteristics tailored to specific control scenarios. Each model is optimized for particular conditions or time periods, allowing the controller to select the most appropriate model for the current situation.
Solution Approach 2:
The control model selection is dynamic rather than static. The controller can switch between different control models based on current conditions, and the upper control apparatus can generate new models as needed, making the system adaptable to changing requirements.
Data Source
AI summary
Provided is a control system comprising a control apparatus including a learning processing unit for generating a control model by learning, the control model being for calculating control data for controlling a facility according to state data detected by at least one sensor for measuring a state of the facility, and a model transmission unit for transmitting the generated control model to a controller for controlling the facility; and a controller including a model receiving unit for receiving the learned control model from the control apparatus, a state receiving unit for receiving the state data from the at least one sensor, a calculation unit for calculating 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 for controlling the facility by using the calculated control data.