Washer Drum Motor Torque Estimation With Adaptive Linear Parameters
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Solution Overview
Problem
Prior art estimator methods for universal electric motors in laundry machines lack precision in estimating motor torque due to variable load and performance conditions, and require complex and costly calibration of mathematical coefficients.
Innovation Solution
A rotatable-drum laundry machine with an estimator system that estimates motor torque using the equation T ≅ m ⋅ I RMS + q, where m and q are linear parameters determined through an energy balance, sampled motor currents, voltages, and speeds, employing recursive algorithms and methods like least squares or linear interpolation to continuously adjust parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior art estimator methods are used to estimate motor torque, then the estimation can be performed without direct measurement, but the precision is insufficient due to variable load and performance conditions
Solution Approach 1:
The patent implements a feedback mechanism where the estimated torque is continuously compared with actual operating conditions, and the linear parameters m and q are dynamically adjusted based on energy balance calculations. This closed-loop approach allows the system to adapt to variable load and performance conditions, significantly improving both measurement precision and reliability compared to static prior art methods.
Solution Approach 2:
The patent changes the parameters of the estimation model by introducing dynamically adjustable linear parameters m and q that are optimized based on real-time energy balance considerations. This allows the estimation equation T ≅ m ⋅ I RMS + q to adapt to varying operating conditions, resolving the contradiction between precision and reliability under variable conditions.
2Measurement precision
If complex mathematical models with multiple coefficients are used for torque estimation, then more comprehensive motor characteristics can be captured, but the calibration complexity and cost increase significantly
Solution Approach 1:
The patent extracts only the essential parameters needed for accurate torque estimation by simplifying the mathematical model to use just two linear parameters (m and q) instead of multiple complex coefficients. This extraction of critical parameters maintains measurement precision while dramatically reducing calibration complexity and cost.
Solution Approach 2:
The patent changes the model parameters from multiple fixed coefficients requiring extensive calibration to two dynamically adjustable linear parameters that can be optimized through energy balance calculations, thereby reducing device complexity while maintaining or improving measurement precision.
3Device complexity
If fixed calibration coefficients are used in the estimation model, then the model structure remains simple, but the estimation accuracy deteriorates under varying operating conditions
Solution Approach 1:
The patent introduces dynamics into the estimation model by making the linear parameters m and q variable rather than fixed. These parameters are continuously optimized based on energy balance calculations that reflect current operating conditions, allowing the simple model structure to maintain high measurement precision across varying loads and performance states.
Solution Approach 2:
The patent enables parameter changes by allowing the linear coefficients m and q to adapt dynamically based on real-time energy balance considerations, thereby maintaining model structure simplicity while achieving accurate torque estimation under varying operating conditions.
Data Source
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AI summary
Rotatable-drum laundry machine (1) comprising: a casing (2); a laundry drum (3) mounted inside the casing (2) to rotate about an rotation axis (4); and an universal electric motor (6) connected to said laundry drum (3) to rotate laundry drum (3) about said rotation axis (4); rotatable-drum laundry machine being characterized in comprising an estimator system (9) configured to estimate the motor torque according to the equation: T≅m⋅IRMS+q wherein T is the motor torque indicating the torque that motor supplied to drum (3); m and q are a first and a second linear parameter, and IRMS is a first value indicating the root mean square current supplied to said universal electric motor (6).