Multi-Model Control Parameter Tuning for Process State Deviations
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Solution Overview
Problem
Existing control systems lack the ability to efficiently and precisely adjust control parameters based on the deviation and aspect of the process variable, leading to suboptimal control outcomes in complex industrial processes.
Innovation Solution
An apparatus and method that utilizes a control model with multiple sub-control models associated with different aspects of the process variable, adjusting control parameters through a combination of sub-control models and learning algorithms to optimize precision and speed in controlling industrial processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single control model is used for all process conditions, 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 process conditions or aspects. This segmentation allows each sub-model to optimize control precision for its designated condition range, resolving the contradiction between model simplicity and control accuracy.
Solution Approach 2:
The control model dynamically selects or switches between different sub-control models based on the current process state. This dynamic adaptation enables the system to maintain high precision across varying conditions without requiring a single overly complex static model.
2Manufacturing precision
If multiple sub-control models are used for different process aspects, then the manufacturing precision is improved, but the device complexity increases
Solution Approach 1:
The control model is designed with multi-functionality, where a single control model structure can adapt to different process conditions through dynamic selection or parameter adjustment. This universality reduces the need for entirely separate control models for each condition, thereby limiting the increase in overall system complexity.
3Productivity
If control parameters are adjusted rapidly, then the productivity is improved, but the stability of the process variable deteriorates
Solution Approach 1:
The control system dynamically adjusts control parameters based on the current process state and deviation magnitude. During transient phases, larger adjustments accelerate convergence to equilibrium, while near equilibrium, smaller adjustments maintain stability. This dynamic parameter adaptation resolves the contradiction between rapid convergence and stability.
Solution Approach 2:
The control model continuously monitors process variables and adjusts control parameters based on feedback from the system state. This feedback mechanism enables the system to automatically modulate the aggressiveness of control actions, achieving both rapid convergence when needed and stability when接近 the target state.
Data Source
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 acquired deviation and the acquired control parameter, 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 the recommendation control parameter that is output from the control model.