首页|基于模糊熵加权融合的单轨运输机器人动态倾角辨识研究

基于模糊熵加权融合的单轨运输机器人动态倾角辨识研究

Research on Dynamic Inclination Angle Identification of Monorail Transportation Robot Based on Fuzzy Entropy Weighted Fusion

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  • 国家科技期刊平台
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  • NSTL
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针对现有传感器检测单轨运输机器人动态倾角时存在辨识精度低的问题,提出了一种基于模糊熵加权融合的单轨运输机器人动态倾角精准辨识方法.首先,基于构建的轨道曲率与倾角变化双模型,通过改进的遗忘递归最小二乘(IFFRLS)算法分别计算出轨道曲率值和倾角动态变化率;其次,再以轨道曲率值和倾角动态变化率作为输入值,通过无迹卡尔曼滤波(UKF)和扩展卡尔曼滤波(EKF)算法不断迭代更新分别计算出倾角动态角度;最终,采用全局模糊熵加权融合(GFEWF)将各角度值深度融合,提高动态倾角的检测精度.实验表明,基于双模型的全局模糊熵加权融合(GFEWF)与基于单一模型的UKF或EKF算法相比,其辨识的动态倾角精度在轨道段 1和轨道段2 分别提升了10.38%和25.60%.
For the problem of low identification accuracy in detecting the dynamic inclination angle of monorail robots,a precise identification method for the dynamic inclination angle of monorail transport robot based on fuzzy entropy weighted fusion is proposed.Firstly,based on the constructed dual model of orbit curvature and inclination angle change,the improved forgetting recursive least squares(IFFRLS)algorithm is used to calculate the dynamic change rate of orbit curvature and inclination angle respectively.Secondly,taking the orbit curvature value and the dynamic change rate of inclination angle as input values,the extended Kalman filter(EKF)and unscented Kalman filter(UKF)algorithms are used to iteratively update and calculate the dynamic angle of inclination angle respectively.Finally,the global fuzzy entropy weighted fusion(GFEWF)is used to deeply fuse the angle values to improve the detection accuracy of dynamic inclination angle.The experiments show that the global fuzzy entropy weighted fusion(GFEWF)algorithm based on double model improves the identified dynamic inclination accuracies in rail segment 1 and rail segment 2 by 10.38%and 25.60%on average,respectively,compared with the single model-based UKF or EKF algorithms.

geometric measurementmonorail transport robotdynamic inclination anglerecursive least squares algorithmunscented Kalman filterglobal fuzzy entropy

刘泽朝、李敬兆、郑昌陆、王国锋

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安徽理工大学 电气与信息工程学院,安徽 淮南 232001

上海申传电气股份有限公司,上海 201800

淮河能源控股集团有限责任公司,安徽 淮南 232001

几何量计量 单轨运输机器人 动态倾角 递归最小二乘算法 无迹卡尔曼滤波 全局模糊熵

国家重点研发计划国家自然科学基金安徽理工大学博士研究生创新基金

2020YFB1314100523741542022CX1008

2024

10.3969/j.issn.1000-1158.2024.07.03

计量学报
中国计量测试学会

计量学报

CSTPCD北大核心
影响因子：0.303
ISSN：1000-1158
年,卷(期)：2024.45(7)
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