Aiming at the shortcoming that the traditional industrial manipulator using off-line programming cannot change along with the change of external environment,the key technologies such as machine vision and manipulator ...Aiming at the shortcoming that the traditional industrial manipulator using off-line programming cannot change along with the change of external environment,the key technologies such as machine vision and manipulator control are studied,and a complete manipulator vision tracking system is designed.Firstly,Denavit-Hartenberg(D-H)parameters method is used to construct the model of the manipulator and analyze the forward and inverse kinematics equations of the manipulator.At the same time,a binocular camera is used to obtain the threedimensional position of the target.Secondly,in order to make the manipulator track the target more accurately,the fuzzy adaptive square root unscented Kalman filter(FSRUKF)is proposed to estimate the target state.Finally,the manipulator tracking system is built by using the position-based visual servo.The simulation experiments show that FSRUKF converges faster and with less error than the square root unscented Kalman filter(SRUKF),which meets the application requirements of the manipulator tracking system,and basically meets the application requirements of the manipulator tracking system in the practical experiments.展开更多
针对无迹卡尔曼滤波(unscented Kalman filter,UKF)算法估计锂电池荷电状态(state of charge,SOC)时精度低、稳定性差、产生的sigma点过多导致计算难度大等不足,提出一种基于自适应球形不敏变换方式的无迹卡尔曼滤波(unscented Kalman f...针对无迹卡尔曼滤波(unscented Kalman filter,UKF)算法估计锂电池荷电状态(state of charge,SOC)时精度低、稳定性差、产生的sigma点过多导致计算难度大等不足,提出一种基于自适应球形不敏变换方式的无迹卡尔曼滤波(unscented Kalman filter based on adaptive spherical insensitive transformation,ASIT-UKF)算法。该算法通过使用球形不敏变换方式选择权系数以及初始化一元向量对sigma点的产生进行选取。与UKF算法相比,ASIT-UKF算法产生的sigma点减少近50%,使得算法的计算复杂度大大降低。同时,将产生的所有sigma点进行单位球形面上的归一化处理,提高了数值的稳定性。考虑到实际运行中锂电池系统噪声干扰带来的不确定性,加入Sage-Husa自适应滤波器对不确定性噪声的干扰进行实时更新和修正,以达到提高在线锂电池SOC估计精度的目的。最后,将均方根误差和最大绝对误差计算公式引入到性能估计指标中。实验结果表明,ASIT-UKF算法在准确度、鲁棒性和收敛性方面具有优越的性能。展开更多
The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cub...The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.展开更多
基金supported by Natural Science Basic Research Program of Shaanxi(2022JQ-593)Key Research and Development Program of Shaanxi(2022GY-089)。
文摘Aiming at the shortcoming that the traditional industrial manipulator using off-line programming cannot change along with the change of external environment,the key technologies such as machine vision and manipulator control are studied,and a complete manipulator vision tracking system is designed.Firstly,Denavit-Hartenberg(D-H)parameters method is used to construct the model of the manipulator and analyze the forward and inverse kinematics equations of the manipulator.At the same time,a binocular camera is used to obtain the threedimensional position of the target.Secondly,in order to make the manipulator track the target more accurately,the fuzzy adaptive square root unscented Kalman filter(FSRUKF)is proposed to estimate the target state.Finally,the manipulator tracking system is built by using the position-based visual servo.The simulation experiments show that FSRUKF converges faster and with less error than the square root unscented Kalman filter(SRUKF),which meets the application requirements of the manipulator tracking system,and basically meets the application requirements of the manipulator tracking system in the practical experiments.
文摘针对无迹卡尔曼滤波(unscented Kalman filter,UKF)算法估计锂电池荷电状态(state of charge,SOC)时精度低、稳定性差、产生的sigma点过多导致计算难度大等不足,提出一种基于自适应球形不敏变换方式的无迹卡尔曼滤波(unscented Kalman filter based on adaptive spherical insensitive transformation,ASIT-UKF)算法。该算法通过使用球形不敏变换方式选择权系数以及初始化一元向量对sigma点的产生进行选取。与UKF算法相比,ASIT-UKF算法产生的sigma点减少近50%,使得算法的计算复杂度大大降低。同时,将产生的所有sigma点进行单位球形面上的归一化处理,提高了数值的稳定性。考虑到实际运行中锂电池系统噪声干扰带来的不确定性,加入Sage-Husa自适应滤波器对不确定性噪声的干扰进行实时更新和修正,以达到提高在线锂电池SOC估计精度的目的。最后,将均方根误差和最大绝对误差计算公式引入到性能估计指标中。实验结果表明,ASIT-UKF算法在准确度、鲁棒性和收敛性方面具有优越的性能。
基金supported by the National Natural Science Foundation of China(No. 61032001)Shandong Provincial Natural Science Foundation of China (No. ZR2012FQ004)
文摘The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.
