A linear hybrid model for enhanced servo error pre-compensation of feed drives with unmodeled nonlinear dynamics
| Autor: | Molong Duan, Chinedum E. Okwudire, Cheng Hao Chou |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Mechanical Engineering Feed drive Linear model 02 engineering and technology Transfer function Industrial and Manufacturing Engineering Compensation (engineering) Computer Science::Robotics Nonlinear system 020303 mechanical engineering & transports 020901 industrial engineering & automation 0203 mechanical engineering Control theory Physics::Space Physics Hybrid model Servo |
| Zdroj: | CIRP Annals. 70:301-304 |
| ISSN: | 0007-8506 |
| DOI: | 10.1016/j.cirp.2021.04.070 |
| Popis: | Servo error pre-compensation (SEP) is commonly used to improve the accuracy of feed drives. Existing SEP approaches often involve the use of physics-based linear models (e.g., transfer functions) to predict servo errors, but suffer from inaccuracies due to unmodeled nonlinear dynamics in feed drives. This paper proposes a linear hybrid model for SEP that combines physics-based and data-driven linear models. The proposed model is shown to approximate nonlinearities unmodeled in physics-based linear models. In experiments on a precision feed drive, the proposed hybrid model improves the accuracy of servo error prediction by up to 38% compared to a physics-based model. |
| Databáze: | OpenAIRE |
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