Motor System Recognition Using Logarithmic Sweep Kernels
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Solution Overview
Problem
Current methods for recognizing motor systems rely on physical models, which are cumbersome and prone to errors, especially in accurately describing nonlinear components, making them inefficient for customized tactile feedback applications.
Innovation Solution
A method using a signal generator, motor system, and accelerometer to establish a functional series filter model, employing a logarithmic sweep signal and inverse signal convolution to derive kernel functions, allowing for the recognition of motor systems without relying on physical models and effectively estimating vibration waveforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical models including circuit structure and magnetic drive are used for motor system recognition, then the recognition process can be established, but the analysis becomes cumbersome and errors in physical model recognition lead to system identification errors
Solution Approach 1:
The patent replaces the mechanical/physical model-based recognition system with a signal processing-based system using Volterra filters. Instead of analyzing physical components like circuit structures and magnetic drives, the invention uses mathematical signal processing techniques to recognize motor system characteristics, thereby reducing analysis complexity while maintaining recognition accuracy.
Solution Approach 2:
The patent creates a virtual model of the motor system through kernel functions that replicate the system's input-output behavior without physically modeling the underlying mechanical components. This virtual copy allows for simplified analysis while preserving the essential recognition capabilities.
2Measurement precision
If physical models are used for motor system recognition, then the recognition framework can be built, but it is difficult to accurately describe nonlinear components of the motor
Solution Approach 1:
The patent transforms the recognition approach by changing from fixed physical model parameters to adaptive kernel function parameters. The Volterra filter kernels are determined through signal processing and can adapt to capture nonlinear characteristics, providing more accurate description of nonlinear components without requiring complex physical models.
Solution Approach 2:
The invention substitutes the physical model description with a signal-based mathematical model. The nonlinear characteristics of the motor are described through the impulse response and kernel functions derived from input-output signal measurements, rather than through physical component models, enabling more accurate nonlinear characterization.
Data Source
AI summary
A method for recognizing a motor system is provided. The method includes steps of: establishing a functional series filter model for describing a motor system; generating a logarithmic sweep signal; feeding the logarithmic sweep signal to the motor system and obtaining, by an accelerometer, a vibration acceleration output by the motor system; generating an inverse signal of the logarithmic sweep signal generated; convolving the vibration acceleration with the inverse signal to obtain a one-dimensional impulse response sequence; intercepting the one-dimensional impulse response sequence by using a window function to obtain impulse response sequences; solving kernel functions according to the impulse response sequences, substituting the kernel functions into a recognition formula of the motor system, and describing and identifying the motor system through the recognition formula.