Feedback Control Blending AI Reliability With Stable Operation
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Solution Overview
Problem
Existing control systems using machine learning models struggle with stability, particularly with untrained data, leading to instability in the operation of control target devices.
Innovation Solution
A control apparatus that integrates both AI and non-AI processing to determine the reliability of output values, adjusting the selection ratio based on reliability to ensure stable operation by using AI sensors when reliable and non-AI sensors when AI is unreliable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a machine learning model is used for feedback control, then efficient results can be obtained, but stable operation cannot be ensured for untrained data
Solution Approach 1:
The control apparatus dynamically adjusts the selection ratio between AI processing output and non-AI processing output based on the reliability degree of the AI model. This dynamic adjustment allows the system to optimize control efficiency when the AI model is reliable while ensuring operation stability when the AI model confidence is low, thereby resolving the contradiction between productivity and reliability
Solution Approach 2:
The system changes the parameter of selection ratio based on the reliability degree of the AI model. When the reliability degree is high, the selection ratio for AI output is increased to improve efficiency. When the reliability degree is low, the selection ratio is adjusted to favor non-AI processing, ensuring stability. This parameter change strategy resolves the contradiction by adapting to different operational conditions
2Measurement precision
If AI processing is introduced for feedback control, then control accuracy can be improved, but system complexity increases
Solution Approach 1:
The control apparatus merges AI processing and non-AI processing into a unified feedback control system. By combining the strengths of both approaches and using a selection mechanism based on reliability degree, the system achieves improved control accuracy while managing complexity through integrated architecture rather than separate independent systems
Solution Approach 2:
The reliability degree calculation unit acts as an intermediary that evaluates the AI model's confidence and determines the appropriate selection ratio. This intermediary component manages the complexity by providing a systematic way to integrate AI processing results with non-AI processing results, reducing the overall system complexity through structured mediation
Data Source
AI summary
A control apparatus performs feedback control on a control target device. The control apparatus includes a processor. The processor is configured to acquire a first output value related to feedback control on the control target device by using AI processing, and acquire a second output value related to the feedback control by using non-AI processing. The processor is configured to correct a selection ratio of the first output value and the second output value based on a degree of reliability of the first output value, and output a control signal for performing feedback control on the control target device based on the selection ratio.