Fuel-Air Mixture Control Using Smith Prediction to Reduce Actuator Wear
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Solution Overview
Problem
Existing systems for regulating fuel-air mixtures in heating systems, such as gas boilers, often lead to heavy wear on actuators due to frequent and prolonged control activities, necessitating regular maintenance and replacement.
Innovation Solution
A model-predictive regulation procedure that identifies system behavior characterized by dead time and reinforcement factors, allowing for adaptive and self-learning control to minimize actuator adjustments and reduce wear.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a standard controller is used to adjust the actuator to maintain the measured pressure at the target value, then the pressure control is achieved, but the actuator experiences significant wear due to frequent and prolonged control activities
Solution Approach 1:
The system performs preliminary identification of dead time and gain factor in a first operational phase before normal control. This preliminary characterization of system behavior enables the predictive controller to anticipate actuator adjustments, reducing frequent and prolonged control activities that cause wear while maintaining pressure stability.
Solution Approach 2:
The controller transitions from a static standard controller to a dynamic model-predictive adaptive controller that incorporates identified system parameters (dead time and gain factor). This dynamic adaptation allows the system to optimize control frequency and duration based on actual system behavior, reducing actuator wear while maintaining control reliability.
2Measurement precision
If the actuator position is changed frequently to maintain pressure at target value, then the pressure control accuracy is improved, but the control deviation and overshoot occur due to dead time and gain factor
Solution Approach 1:
The system uses feedback from the differential pressure sensor to continuously monitor the actual pressure value. This feedback is combined with the identified system parameters (dead time and gain factor) in the predictive controller to anticipate and compensate for control delays, reducing oscillations and improving both measurement accuracy and pressure stability.
Solution Approach 2:
The system changes the control parameters by incorporating dead time and gain factor into the predictive control algorithm. This parameter-based adaptation allows the controller to adjust its behavior based on the actual system dynamics, reducing overshoot and control deviation while maintaining accurate pressure control.
3Duration of action of stationary object
If a model-based predictive controller is used to reduce actuator control frequency, then the actuator wear is reduced, but the system requires identification of dead time and gain factor
Solution Approach 1:
The system implements periodic identification of system parameters (dead time and gain factor) during normal operation. This periodic recalibration allows the predictive controller to maintain optimal performance and reduce actuator wear without requiring continuous complex identification procedures, balancing reduced actuator usage with manageable system complexity.
4Reliability
If the control system operates in continuous rapid actuator activation, then the pressure target value is maintained, but regular maintenance and replacement of actuator components are required
Solution Approach 1:
The system performs preliminary identification of system characteristics before normal operation, enabling the predictive controller to anticipate required actuator adjustments. This preliminary setup reduces the need for frequent actuator activations during operation, thereby reducing maintenance frequency while maintaining reliable pressure control.
Data Source
Figure 1
AI summary
The invention relates to a method for controlling a fuel-air mixture of a system, wherein: a manipulated variable for controlling an actuator (2) of the system in a first method phase for identification of the system behaviour using a standard controller, in order to adjust the actual value on average to a target value; a profile of the actual value and a profile the manipulated variable are recorded during the first method phase for identification of the system behaviour, and from these the gain factor is determined depending on the manipulated variable and the dead time; after the determination of the dead time and the gain factor in a second method phase for model-predictive adaptive control of the system the manipulated variable is determined using a model-based controller which in particular has a Smith predictor and takes account of the gain factor and the dead time in order to adjust the actual value to the target value, so that in the second method phase the manipulated variable has to be altered less frequently and less significantly by comparison with the first method phase.