State-Adaptive Control Model for Fast and Precise Setpoint Convergence
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Solution Overview
Problem
Existing control systems lack the ability to efficiently adjust control parameters based on the specific state of the control target, leading to suboptimal performance in achieving precise and rapid convergence to set points.
Innovation Solution
A control apparatus that utilizes a control model with multiple sub-control models, each associated with different aspects of the control target's state, to output recommendation control parameters tailored to the current state and deviation, incorporating learning mechanisms to enhance precision and speed in parameter adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single control model is used for all states, then the device complexity is reduced, but the manufacturing precision and control accuracy deteriorate
Solution Approach 1:
The control model is segmented into multiple sub-control models, each specialized for specific states or ranges of the control target. This segmentation allows each sub-model to optimize for its particular operating condition, improving overall control precision without requiring a single overly complex universal model.
Solution Approach 2:
Different sub-control models are assigned to different operational states or parameter ranges, allowing each model to have locally optimized characteristics. This ensures high precision control for each specific state while keeping individual model complexities manageable.
2Manufacturing precision
If multiple sub-control models are used for different states, then the manufacturing precision is improved, but the device complexity increases
Solution Approach 1:
The system dynamically selects or switches between sub-control models based on the current state of the control target. This dynamic adaptation allows the system to use the most appropriate model for each situation, achieving high precision without permanently maintaining complex structures for all possible states.
Solution Approach 2:
The control model system is designed to be universal, with a framework that can handle multiple states through sub-models. This multi-functionality allows the same control architecture to serve various operational conditions, reducing the need for separate dedicated systems for each state.
3Speed
If control parameters are adjusted rapidly, then the convergence speed is improved, but the stability deteriorates
Solution Approach 1:
The control system changes parameters dynamically based on the current state and deviation magnitude. For large deviations, parameters are adjusted to prioritize rapid convergence; for small deviations or near-setpoint conditions, parameters are adjusted to prioritize stability and precision, preventing oscillations.
Solution Approach 2:
The control approach transitions dynamically between different control strategies. When the control target is far from the set point, the system uses aggressive control actions for fast convergence; when near the set point, it switches to conservative actions for stable settling, thus balancing speed and stability throughout the control process.
Data Source
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AI summary
There is provided an apparatus including: a first acquisition unit which acquires a deviation between a process variable of a state in relation to a control target, and a set point; a second acquisition unit which acquires a control parameter supplied to the control target; a supply unit which supplies the deviation acquired by the first acquisition unit, and the control parameter acquired by the second acquisition unit, to a control model that has a plurality of sub-control models respectively associated with a plurality of aspects preset for the state, the control model using a sub-control model associated with an aspect in accordance with the process variable, to output a recommendation control parameter that is recommended to be supplied to the control target, in response to inputs of a deviation and a control parameter; and an output unit which outputs, in response to the supply being performed from the supply unit to the control model, the recommendation control parameter that is output from the control model.