| 基于ANN-NSWOA 的汽车侧门锁系统参数优化研究 |
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| 引用本文：张瑞瑞,张飞,李庆梅,朱宗磊,王四玲.基于ANN-NSWOA 的汽车侧门锁系统参数优化研究[J].上海第二工业大学(中文版),2026,43(1):55-63 |
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| 基金项目:浦东新区科技发展基金产学研专项 (未来车领域) 项目 (PKX2023-W05) 资助 |
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| 中文摘要:针对某侧门锁系统在解锁过程中电动机负载较高、响应滞后等问题, 利用 Adams 建立侧门锁动力学仿真模型, 分析电动机扭矩变化规律。以侧门锁系统弹簧参数为设计变量, 构建人工神经网络 (artificial neural network, ANN) 模型, 并对其预测精度及误差进行评估分析。以解锁电动机峰值扭矩最小、解锁时间最短为优化目标, 基于非支配排序鲸鱼优化算法 (non-dominated sorting whale optimization algorithm, NSWOA) 与熵权 -TOPSIS 法求解得到最优弹簧参数组合。验证结果表明: 在确保侧门锁正常工作的前提下, 电动机峰值扭矩降低了 20.42%, 解锁时间缩短了 32.78%, 侧门锁系统性能显著提升, 验证了该方法的有效性与可靠性, 可为侧门锁设计提供有益借鉴。 |
| 中文关键词:侧门锁 人工神经网络 非支配排序鲸鱼优化算法 熵权 -TOPSIS 法 |
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| Parameter Optimization of An Automotive Side Door Lock System Based on ANN-NSWOA |
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| Abstract:This study addresses the issue of high motor load and response delay in a side-door lock system during the unlocking process. A dynamics simulation model of the side-door lock is established in Adams to analyze the variation in motor torque. The spring parameters of the side-door lock system are treated as design variables to construct an artificial neural network (ANN) surrogate model, whose prediction accuracy and error are evaluated. With the aim of minimizing the peak motor torque and unlocking time during the unlocking process, the non-dominated sorting whale optimization algorithm (NSWOA) in conjunction with the entropy-weight TOPSIS method is employed to identify the optimal spring parameter combinations. The results of validation demonstrate that, without
compromising the normal operation of the side-door lock, the peak motor torque is reduced by 20.42%, and the unlocking time is shortened by 32.78%. This signifies a substantial enhancement in the performance of the side-door lock system, thereby underscoring the efficacy and reliability of the experimental methodology. Consequently, this study offers valuable insights and references for the
design of side-door lock systems. |
| keywords:side door lock artificial neural network non-dominated sorting whale optimization algorithm entropy-weight TOPSIS method |
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