Extremum Seeking Reset Control for Abrupt HVAC Load Changes
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Solution Overview
Problem
Traditional extremum seeking control systems in HVAC applications experience undesirable delays when adapting to abrupt changes, leading to increased power consumption as they adjust to new optimal settings.
Innovation Solution
A controller using an extremum seeking control strategy with a circuit to detect abrupt changes and reset the control strategy, reducing the time needed to adapt to new optimal settings by overriding the manipulated variable with a reset parameter or reinitializing the ESC loop.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional extremum seeking control is used to dynamically search for optimal settings, then the system can adapt to changing conditions, but abrupt changes cause undesirable delays in adapting to new optimal settings
Solution Approach 1:
The system performs preliminary action by detecting abrupt changes in plant operation and preemptively resetting the extremum seeking control parameters. This allows the system to prepare for and quickly adapt to new optimal settings rather than gradually converging, thereby reducing the time loss when conditions change abruptly.
Solution Approach 2:
The system uses feedback by continuously monitoring plant operation for abrupt changes and using this information to trigger parameter resets. The feedback mechanism detects when the plant state has changed significantly and initiates a reset of the ESC parameters, enabling the system to adapt its search behavior based on real-time conditions.
2Stability of the object's composition
If traditional extremum seeking control adapts gradually to new optimal settings, then the control strategy remains stable, but additional power is consumed by the AHU during the adaptation period
Solution Approach 1:
By detecting abrupt changes and resetting parameters in advance, the system performs preliminary action that prevents prolonged periods of suboptimal operation. This reduces the time the AHU operates at higher power consumption levels while maintaining stability through controlled, triggered resets rather than uncontrolled gradual adaptation.
Solution Approach 2:
The system applies parameter changes by resetting the ESC parameters when abrupt changes are detected. This changes the operating parameters of the control strategy from gradual adaptation to immediate reset, allowing the system to quickly transition to new optimal settings and reduce energy consumption during transitions while maintaining overall stability.
3Reliability
If the extremum seeking control strategy continues operating during abrupt changes, then continuous control is maintained, but the convergence time to new optimal settings increases
Solution Approach 1:
The system performs preliminary action by detecting abrupt changes before full convergence is needed and preemptively resetting the ESC parameters. This maintains reliability through continuous monitoring and controlled resetting, while significantly reducing the convergence time to new optimal settings by not waiting for natural gradual adaptation.
Solution Approach 2:
The feedback mechanism continuously monitors plant operation and triggers parameter resets when abrupt changes are detected. This maintains reliable continuous control by ensuring the system is always responding to current conditions, while reducing convergence time by initiating resets immediately upon detection rather than waiting for natural convergence.
Data Source
AI summary
An extremum seeking control method optimizes a control process for a plant such as an air handling unit. The method compensates for abrupt changes in the operation of the plant by resetting the extremum seeking control strategy in response to a detection of the abrupt change.