Position Encoder Using Distorted Reference Curves for Accuracy
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Solution Overview
Problem
Existing position encoders face challenges in achieving high measuring accuracy and resolution with inexpensive components, particularly due to noise and deviations in sensor output signals from ideal sinusoidal or cosinusoidal courses, which complicate calibration and memory storage, and require complex mathematical evaluations.
Innovation Solution
The method involves using distorted reference curves generated from error curves recorded during calibration, which reflect the deviations of actual sensor output signals, and applying these in a computing circuit to determine fine position values with dynamic weighting of mean values from individual sensor readings to improve reproducibility and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If ideal sinusoidal or cosinusoidal reference curves are used in the computing circuit, then mathematical evaluation is simplified, but actual sensor output signals deviate from ideal courses requiring complex calibration and storage
Solution Approach 1:
The patent transforms the reference curves from ideal mathematical functions to empirically determined distorted reference curves that match actual sensor behavior. This parameter transformation allows the system to use real sensor characteristics while maintaining the computational framework, resolving the contradiction between mathematical simplicity and measurement accuracy.
Solution Approach 2:
The patent performs preliminary calibration by recording actual sensor output signals at known reference positions and storing these as distorted reference curves before actual measurement. This preliminary action captures real sensor deviations, allowing the system to compensate for non-ideal behavior without complicating the measurement process.
2Measurement precision
If distorted reference curves are stored in the comparison value memory, then memory space requirements increase, but measurement accuracy is maintained
Solution Approach 1:
The patent segments the measurement range into multiple measurement segments, each with its own distorted reference curves. This segmentation allows the system to store reference data only for relevant segments rather than the entire range, reducing overall memory requirements while maintaining accuracy within each segment.
Solution Approach 2:
The patent applies different distorted reference curves to different measurement segments based on local sensor characteristics. Each segment receives customized reference data matching its specific conditions, optimizing memory usage by storing only locally relevant information rather than universal reference data.
3Adaptability or versatility
If dynamic weighting factors are used in averaging circuits, then adaptability to signal variations improves, but computational complexity increases
Solution Approach 1:
The patent implements dynamic weighting factors in the averaging circuits that automatically adjust based on signal characteristics. The weighting factors vary with signal conditions, allowing the system to adapt to different measurement scenarios without manual intervention, resolving the contradiction between adaptability and complexity through automated dynamic adjustment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for high measuring accuracy and resolution with cost-effective components by compensating for signal deviations and noise, while reducing memory requirements and simplifying mathematical evaluations, enabling precise position determination with adaptable error handling.
Implementation Method 1
which includes at least two sensors formed, for example, by Hall elements, which generate periodic sensor output signals
Data Source
AI summary
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