Hybrid Equipment Control Switching Between Feedback and AI
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Solution Overview
Problem
Existing control systems for equipment in facilities, such as industrial plants, face challenges in efficiently managing and optimizing the operation of control target equipment using either feedback control or AI-driven control methods, often requiring manual intervention and lacking adaptive switching mechanisms to handle disturbances and setpoints effectively.
Innovation Solution
A control apparatus and method that incorporates both feedback control and AI control units, with a switching mechanism to dynamically switch between them based on measurement values and operational performance, allowing for adaptive control strategies that optimize operation amounts and reduce manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feedback control or AI-driven control is used for equipment operation, then control precision can be improved, but the system lacks adaptive switching capability to handle different operational conditions effectively
Solution Approach 1:
The control system implements dynamic switching between feedback control and AI-driven control modes based on real-time operational conditions. The switching unit dynamically selects the appropriate control mode according to the current state of the control target equipment, enabling the system to adapt to different operational scenarios and maintain optimal control precision across varying conditions.
2Device complexity
If a single control method is used, then system complexity is reduced, but the ability to optimize performance under varying conditions is limited
Solution Approach 1:
The control apparatus integrates multiple control functions within a single system by incorporating both feedback control unit and AI-driven control unit. The switching unit enables this multi-functional system to select the most appropriate control mode based on operational requirements, thereby achieving high operational efficiency without requiring separate independent control systems.
3Measurement precision
If manual intervention is required for control switching, then control accuracy can be maintained, but operational efficiency and automation level are reduced
Solution Approach 1:
The control system performs automatic self-switching between feedback control and AI-driven control modes through the switching unit, which autonomously determines the appropriate control mode based on real-time measurement values and operational conditions. This eliminates the need for manual intervention while maintaining control accuracy, thereby enhancing both automation level and operational efficiency.
4Productivity
If AI-driven control is implemented, then operational optimization can be improved, but the requirement for learning data and model training increases system complexity
Solution Approach 1:
The control system segments the control functionality into distinct modules: feedback control unit, AI-driven control unit, and switching unit. This modular architecture allows the AI component to be implemented as a separate functional block that can be trained and optimized independently, reducing the overall system complexity while maintaining operational optimization capabilities.
Data Source
AI summary
Provided is a control apparatus including an acquisition unit configured to acquire a measurement value measured regarding control target equipment, a first control unit configured to output an operation amount of the control target equipment according to the measurement value by at least one of feedback control or feed-forward control, a second control unit configured to output an operation amount of the control target equipment according to the measurement value using a model learnt by using learning data, and a switching unit configured to perform switching between the first control unit and the second control unit by which the control target equipment is controlled.