Electric Motor Controller Inertia Friction Estimation
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Solution Overview
Problem
Existing methods for estimating inertia and friction in electric motor-driven machine tools face challenges such as reduced accuracy due to torque offset and large Coulomb friction, require a wide operation range, and necessitate large data memory for data accumulation, making it difficult to precisely identify these parameters, especially in complex configurations and during rapid changes in workpiece attachment/detachment.
Innovation Solution
A controller that uses a sine wave or M-sequence instruction to estimate inertia and friction simultaneously, with sequential updating of parameters in every sampling period, reducing the need for extensive data accumulation and allowing for the estimation of both viscous and Coulomb friction, using an inverse transfer function model to minimize errors without requiring Fourier transforms or large memory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods calculate inertia and friction from electric current feedback and acceleration, then estimation can be performed, but accuracy deteriorates when torque offset or large Coulomb friction is present
Solution Approach 1:
The patent applies periodic sine wave instructions to the motor drive shaft to generate periodic speed and acceleration variations. By using these periodic excitations, the system can distinguish between friction effects and inertial effects through phase analysis, thereby eliminating the harmful influence of torque offset and Coulomb friction on inertia estimation accuracy
Solution Approach 2:
The patent replaces traditional mechanical estimation methods with signal processing techniques. By substituting the mechanical approach of direct measurement with an electrical signal-based approach using sine wave excitations and spectral analysis, the system achieves higher accuracy while compensating for friction and offset effects
2Measurement precision
If data accumulation over predetermined time is performed for least-squares estimation, then inertia and friction can be calculated, but large data memory is required and response time increases
Solution Approach 1:
The patent extracts only the essential frequency components (sine wave frequency and its harmonics) from the measured signals using spectral analysis methods. By taking out only the relevant information at specific frequencies rather than processing all accumulated data, the system achieves accurate parameter estimation with minimal memory requirements and faster response
3Measurement precision
If a wide operation range is required for accurate inertia and friction identification, then estimation accuracy improves, but the method becomes difficult to apply to machines with limited operation ranges
Solution Approach 1:
The patent uses periodic sine wave excitations that can be applied within any operation range. The periodic nature of the excitation allows accurate parameter identification regardless of the overall operation range, as long as the sine wave frequency fits within the available bandwidth, thereby improving adaptability to machines with limited operation ranges
4Measurement precision
If Fourier transforms are used for spectral analysis, then frequency component extraction is accurate, but computational complexity and processing time increase
Solution Approach 1:
The patent employs computationally efficient algorithms such as Goertzel algorithm or simplified spectral analysis methods that provide sufficient accuracy for the specific application of parameter estimation. These lighter computational approaches replace full Fourier transforms, reducing processing complexity while maintaining adequate frequency component extraction accuracy for inertia and friction estimation
Data Source
AI summary
A controller estimates Coulomb friction itself together with inertia and viscous friction, and reduces the influence of the Coulomb friction on the accuracy of the estimated inertia. In addition, the controller estimates inertia, viscous friction and Coulomb friction simultaneously with sequential adaptation in which a Fourier transformer is not used but an inverse transfer function model is used in order to minimize the estimated error. Data sampled for a predetermined time need not be accumulated, as a result, a large amount of data memory is unnecessary.