Control system with combined extremum-seeking control and feedforward control
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Solution Overview
Problem
Existing extremum-seeking control (ESC) systems for optimizing performance in HVAC systems face limitations in convergence speed, particularly when optimal system outputs change rapidly due to dynamic conditions like ambient temperature and load variations, leading to suboptimal tracking of the optimal system performance.
Innovation Solution
A control system combining extremum-seeking control with feedforward control, which includes a feedforward controller and an extremum-seeking controller, uses measurable disturbances to generate feedforward and extremum-seeking contributions to the control input, optimizing the control input by combining these contributions and providing it to the system to rapidly adjust and maintain optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If extremum-seeking control is used to optimize system performance, then the system can dynamically search for optimal inputs, but the convergence speed to optimal output is slow when driving conditions change rapidly
Solution Approach 1:
The feedforward controller uses a lookup table or model to predict and apply control actions in advance based on measurable disturbances (ambient temperature, load conditions). This preliminary action allows the system to proactively adjust to changing conditions rather than reactively converging, significantly improving the speed at which optimal performance is achieved when driving conditions change.
2Speed
If feedforward control based on lookup tables or models is used, then the convergence speed improves, but the accuracy may be insufficient when conditions deviate from pre-defined scenarios
Solution Approach 1:
The system combines feedforward control with extremum-seeking feedback control. The feedforward controller provides fast initial response based on predicted disturbances, while the extremum-seeking controller continuously monitors the actual performance variable and adjusts the control input to drive the system to the true optimal point. This feedback mechanism corrects any inaccuracies from the feedforward predictions, ensuring both speed and accuracy are achieved simultaneously.
Data Source
AI summary
A control system is configured to operate a plant to achieve an optimal value for a performance variable of the plant. The system comprise a feedforward controller configured to receive a measurable disturbance to the plant and generate a feedforward contribution to a control input to the plant using the measurable disturbance. The system also comprises an extremum-seeking controller configured to receive the performance variable from the plant and generate an extremum-seeking contribution to the control input to drive the performance variable to the optimal value. The system further comprises a control input element configured to generate the control input by combining the extremum-seeking contribution and the feedforward contribution and provide the control input to the plant.