Research and analysis of an enhanced genetic algorithm identification method based on the LuGre model

Wanjun Zhang, Feng Zhang, Jingxuan Zhang, Siyan Zhang, Jingyi Zhang, Jingyan Zhang, Honghong Sun, Kristian E Waters and Hao Ma

PLOS ONE, 2025, vol. 20, issue 6, 1-28

Abstract: Nonlinear friction in high-precision, ultra-low-speed servo systems severely degrades performance, causing low-speed crawling, static errors, and limit-cycle oscillations. This study introduces the LuGre friction model to describe these phenomena mathematically and proposes an improved genetic algorithm (GA) for precise parameter identification. Simulations demonstrate that LuGre-based feedforward compensation outperforms conventional proportional-integral-derivative (PID) control, effectively mitigating speed tracking errors and enhancing both speed and position accuracy. Experimental validation on a linear motor platform confirms the method’s efficacy, achieving a 25.1% improvement in tracking accuracy. The results highlight the practical relevance of this approach for precision servo systems. This work has achieved a practical identification framework for LuGre parameters, combining GA optimization with transient/steady-state data, feedforward compensation that directly injects estimated friction forces, bypassing feedback delays and experimental verification of the method’s industrial applicability.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0322844

DOI: 10.1371/journal.pone.0322844

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