Prediction Control Command Range Expansion Under Safety Constraints
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Solution Overview
Problem
Existing prediction control systems face challenges in determining suitable command values for subject devices when operating outside known conditions, as preset constraint conditions often result in narrowed acceptable ranges, limiting the performance of prediction models and potentially leading to unsuitable or unsafe operations.
Innovation Solution
A control system that estimates the numerical value range of command values from learning data distributions, determines a second threshold value to extend the acceptable range, and uses this range as a constraint condition for operation, allowing for safer and more effective prediction control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a preset constraint condition (first threshold value) is used to limit the command value range for safety, then safety of operation is secured, but the acceptable range becomes narrower than necessary and prediction model performance is not sufficiently exerted
Solution Approach 1:
The system performs preliminary learning to collect actual command values used in normal operations, then uses this collected data to determine an appropriate constraint condition (second threshold value) before actual prediction control begins. This preliminary data collection allows the system to establish safety boundaries based on real operational data rather than overly conservative preset values, thereby expanding the acceptable command value range while maintaining safety.
2Reliability
If the constraint condition is set too conservatively to ensure safety, then safety is maintained, but command values that satisfy safety are rejected and prediction control cannot be appropriately performed
Solution Approach 1:
The system collects feedback from actual operational data during a learning phase, where real command values and their outcomes are recorded. This feedback is then used to adjust and optimize the constraint condition (second threshold value) to reflect actual safe operating ranges. The feedback loop enables the system to distinguish between truly unsafe values and merely suboptimal ones, allowing more command values to be accepted while maintaining safety, thus improving prediction model performance without compromising reliability.
Data Source
AI summary
A control system estimates a numerical value range within which a command value can fall from a distribution of second data relating to the command value in a learning data set used to construct a prediction model, and in such a manner that a first acceptable range prescribed by a preset first threshold value with respect to a command value for a subject device is extended, decides a second threshold value with respect to the command value for the subject device on the basis of the estimated numerical value range. Further, in an operational phase, on the basis of an output value from the prediction model, the control system decides a command value for the subject device within a second acceptable range prescribed by the decided second threshold value, and controls an operation of the subject device on the basis of the decided command value.