| 郝 喆,于春战,张佳林,席攀峰,窦琢仑,孙治博.基于 SSA-GB-ELM 的并联式六维加速度传感器非线性解耦[J].电子测量与仪器学报,2023,37(10):106-114 |
| 基于 SSA-GB-ELM 的并联式六维加速度传感器非线性解耦 |
| Nonlinear decoupling of parallel six dimensional acceleration sensor based on grey box extreme learning machine optimized by sparrow search algorithm |
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| DOI： |
| 中文关键词: 非线性解耦 六维加速度传感器 极限学习机 麻雀搜索算法 最大类间方差 |
| 英文关键词:nonlinear decoupling six dimensional acceleration sensor extreme learning machine sparrow search algorithm maximum
inter-class variance |
| 基金项目:国家自然科学基金青年科学基金(52205043)项目资助 |
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| 摘要点击次数: 667 |
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| 中文摘要: |
| 六维加速度传感器的高精度测量可有效提高底盘防倾翻控制系统的控制效果,但并联式弹性元件的维间耦合会给传感
器带来非线性误差,采用极限学习机算法进行标定解耦可以有效提高传感器的测量精度。 但传统极限学习机非线性解耦算法
精度较低,使用麻雀搜索算法可以获得极限学习机的最佳初始权值、阈值。 同时,将最大类间方差法融入到麻雀算法优化的极
限学习机中,可以探索六维加速度传感器固有耦合关系,把传统极限学习机黑箱模型转换为灰箱模型,从而提出一种麻雀搜索
优化灰箱极限学习机 (sparrow search algorithm-gray box-extreme learning machine,SSA-GB-ELM)的解耦算法。 通过实验验证,使
用该算法的并联式六维加速度传感器非线性解耦精度显著提高,Ⅰ类误差最大为 0. 023%,Ⅱ类误差最大为 0. 046%,解耦时间
为 1. 095 s,可以高效解决六维加速度传感器非线性耦合问题。 |
| 英文摘要: |
| The high-precision measurement of the six dimensional acceleration sensor can effectively improve the control effect of the
chassis anti-rollover control system, but the inter-dimensional coupling of parallel elastic elements can bring nonlinear errors to the
sensor. The use of extreme learning machine algorithm for calibration and decoupling can effectively improve the measurement accuracy
of the sensor. However, the traditional extreme learning machine nonlinear decoupling algorithm has low accuracy. The use of the
sparrow search algorithm can obtain the optimal initial weights and thresholds of the extreme learning machine. At the same time, the
maximum between-class variance method is integrated into the sparrow algorithm optimized extreme learning machine, which can explore
the inherent coupling relationship of the six dimensional acceleration sensor. By converting the traditional black-box extreme learning
machine model into a gray-box model, a decoupling algorithm for sparrow search optimization gray box extreme learning machine (SSAGB-ELM) is proposed. Through experimental verification, the nonlinear decoupling accuracy of the parallel six dimensional acceleration
sensor using this algorithm is significantly improved, with a maximum error of 0. 023% for class I errors and 0. 046% for class II errors.
The decoupling time is 1. 095 seconds, which can effectively solve the nonlinear coupling problem of six dimensional acceleration
sensors. |
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