| 刘泽朝,李敬兆,郑昌陆,王国锋.基于DFFRLS-AUKF的单轨吊车动态倾角辨识方法研究[J].电子测量与仪器学报,2024,38(2):101-111 |
| 基于DFFRLS-AUKF的单轨吊车动态倾角辨识方法研究 |
| Dynamic inclination angle of monorails crane based on DFFRLS-AUKF research on identification method |
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| DOI： |
| 中文关键词: 单轨吊车 轨道曲率模型 递归最小二乘 自适应无迹卡尔曼滤波 动态倾角 |
| 英文关键词:monorail crane orbit curvature model recursive least squares adaptive unscented Kalman filter dynamic dip angle |
| 基金项目:科技部国家重点研发计划（2020YFB1314100）、国家自然科学基金(52374154)、安徽理工大学博士研究生创新基金（2022CX1008）项目资助 |
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| Author | Institution |
| Liu Zechao | School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232000, China |
| Li Jingzhao | School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232000, China |
| Zheng Changlu | Shanghai Shenchuan Electric Co., Ltd., Shanghai 201800, China |
| Wang Guofeng | Coal Industry Company, Huaihe Energy Holding Group Co., Huainan 232000, China |
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| 中文摘要: |
| 为保障单轨吊车在深部矿井复杂轨道工况环境下行驶的安全控制性能，需提高单轨吊车动态倾角辨识的精度及可靠性。因此，本文提出了基于DFFRLS-AUKF算法的单轨吊车动态倾角辨识方法。首先，利用自适应平滑滤波算法对实时采集的加速度和速度数据进行滤波处理，避免环境噪声的干扰，保证数据的完整性；其次，通过建立轨道曲率模型实现对轨道全工况的精准分析，在滤波处理后的数据基础上，再结合带有动态遗忘因子的递归最小二乘(DFFRLS)算法得到可靠地轨道曲率值；最终，在计算出的轨道曲率基础上，利用Sage-Husa噪声估计器对无迹卡尔曼滤波(UKF)进行改进，实现了对动态倾角辨识结果地自适应动态调整，提高了动态倾角辨识地精准度。实验表明，单轨吊车在单轨路段1和单轨路段2测试期间，所提的DFFRLS-AUKF算法与传统算法相比动态倾角辨识精度分别平均提升了25.25%和39.5%，表明了DFFRLS-AUKF算法在不同轨道工况下具有良好的精准性及可靠性，有效保障了单轨吊车在复杂轨道工况下行驶的安全性。 |
| 英文摘要: |
| To guarantee the safety control performance of monorail cranes operating in complex track conditions within deep mines, enhancing the accuracy and reliability of dynamic inclination recognition for monorail cranes is necessary. Therefore, this paper proposes a dynamic inclination recognition method of monorail crane based on DFFRLS-AUKF algorithm. Firstly, an adaptive smoothing filtering algorithm is used to filter the acceleration and velocity data collected in real-time to avoid the interference of environmental noise and ensure the integrity of the data. Secondly, the track curvature model is established to achieve the accurate analysis of the entire working conditions of the track, and based on the filtered data, a reliable track curvature value is obtained by combining the dynamic recursive least squares of forgetting factor (DFFRLS) algorithm with the dynamic forgetting factor. Finally, based on the calculated track curvature, the unscented Kalman filter (UKF) is improved by using the Sage-Husa noise estimator, which achieves the self-adaptation of the dynamic Adaptive adjustment of dynamic inclination recognition, and the accuracy of emotional inclination recognition is improved. Experiments show that the proposed DFFRLS-AUKF algorithm improves the dynamic inclination recognition accuracy by 25.25% and 39.5% on average compared with the traditional algorithm during the testing of monorail crane in monorail section 1 and monorail section 2, which demonstrates that the DFFRLS-AUKF algorithm has good accuracy and reliability under different track conditions, and effectively guarantees the safety of monorail crane driving under complex track conditions. |
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