Adaptive Illuminance Control Loop with Dynamic Step Sizing
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Solution Overview
Problem
Conventional artificial light regulation systems face issues with slow adjustment to setpoint illuminance due to small control steps, leading to increased computation power and bandwidth utilization, and risk of overshoot, especially when dealing with varying luminaire behavior and aging, which requires frequent maintenance adjustments.
Innovation Solution
A method and apparatus that introduce a control parameter with an adjustable value, allowing for self-learning adaptive regulation by estimating illuminance changes and automatically adjusting the parameter based on sensor measurements, enabling larger actuation steps while minimizing overshoot and creep behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If small control steps are used to adjust illuminance, then overshoot is avoided, but the adjustment speed becomes slow and computation power utilization increases
Solution Approach 1:
The patent implements dynamic control step sizing where the actuation step size is adjusted based on the current state of the system. When the illuminance is far from the setpoint, larger control steps are used to accelerate convergence. As the system approaches the setpoint, the step size automatically reduces to prevent overshoot. This dynamic adaptation resolves the contradiction by making the control step size flexible rather than fixed, allowing fast adjustment when needed and precise control when接近 the target.
Solution Approach 2:
The patent changes the control parameter (actuation step size) based on the difference between actual and setpoint illuminance values. By modifying this parameter dynamically according to system state, the system achieves both fast response when far from target and precise control when near target, resolving the speed-reliability contradiction through parameter adaptation.
2Speed
If large control steps are used to achieve faster adjustment, then speed improves, but overshoot risk increases and computation bandwidth utilization increases
Solution Approach 1:
The system dynamically adjusts control step size based on real-time feedback from illuminance sensors. When the difference between actual and setpoint illuminance is large, larger steps are permitted for faster response. When the difference becomes small, the system automatically reduces step size to prevent overshoot, thus achieving fast adjustment without sacrificing reliability.
Solution Approach 2:
The patent employs feedback control where sensor measurements of actual illuminance are continuously compared with the setpoint value. This feedback loop enables the system to adjust control step size appropriately - using larger steps when far from target and smaller steps when approaching target - thereby achieving fast adjustment while preventing overshoot through continuous monitoring and adaptation.
3Device complexity
If fixed parameters are used for control steps, then device complexity is reduced, but adaptability to luminaire aging and different luminaires is poor
Solution Approach 1:
The patent implements self-learning capability where the regulator automatically determines optimal control parameters through observation of the luminaire's response to control actions. The system performs measurements, analyzes the relationship between actuation steps and resulting illuminance changes, and autonomously adjusts parameters to suit the specific luminaire characteristics. This self-service approach enables adaptation to luminaire aging and different luminaire types without increasing device complexity or requiring manual intervention.
Solution Approach 2:
Through continuous feedback from sensor measurements, the system learns the specific characteristics of each luminaire and automatically adjusts control parameters accordingly. The feedback mechanism enables the regulator to adapt to aging luminaires and different luminaire designs by observing their response patterns and optimizing control behavior, maintaining low complexity while achieving high adaptability.
4Adaptability or versatility
If frequent maintenance adjustments are performed to suit different luminaires, then adaptability improves, but ease of operation and time consumption increase
Solution Approach 1:
The regulator performs self-learning and automatic parameter optimization without requiring user intervention. The system autonomously measures illuminance responses, determines the relationship between actuation steps and illuminance changes, and adjusts control parameters to suit different luminaires. This eliminates the need for manual maintenance adjustments, significantly improving ease of operation while maintaining high adaptability to different luminaire types and aging conditions.
Solution Approach 2:
The feedback-driven learning mechanism automatically adapts control parameters based on observed luminaire behavior, eliminating the need for frequent manual adjustments. The system continuously monitors illuminance changes in response to control actions and autonomously optimizes parameters, achieving high adaptability while reducing maintenance effort and time consumption.
Data Source
AI summary
In order to regulate an illuminance, an actual value of a sensor (3) is queried, said value being dependent on the illuminance to be regulated and being regulated to a setpoint value. In a control step, an electronic computing device (11) determines an actuating step for an illuminant (2) on the basis of said actual value and a parameter value of a control parameter. The electronic computing device (11) determines a new parameter value of the control parameter for a subsequent control step on the basis of a first actual value which is received before the corresponding control step, and a second actual value which is determined after the corresponding control step.