Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matri...Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.展开更多
In this article, the problem on the estimation of the convolution model parameters is considered. The recursive algorithm for estimating model parameters is introduced from the orthogonal procedure of the data, the co...In this article, the problem on the estimation of the convolution model parameters is considered. The recursive algorithm for estimating model parameters is introduced from the orthogonal procedure of the data, the convergence of this algorithm is theoretically discussed, and a sufficient condition for the convergence criterion of the orthogonal procedure is given. According to this condition, the recursive algorithm is convergent to model wavelet A- = （1, α1,..., αq）.展开更多
针对常见控制策略在大型液压设备控制方面存在控制精度低与算法太复杂的问题,提出了基于线性扩张状态观测器(linear extended state observer,LESO)的线性时变模型预测控制(linear time-varying model predictive control,LTV-MPC)策略...针对常见控制策略在大型液压设备控制方面存在控制精度低与算法太复杂的问题,提出了基于线性扩张状态观测器(linear extended state observer,LESO)的线性时变模型预测控制(linear time-varying model predictive control,LTV-MPC)策略。通过起竖液压系统状态空间方程,设计了LESO实时估计系统当前状态;通过LTV-MPC输出比例阀电压信号的最优解。通过仿真与试验,验证所提方法的有效性。结果表明:无干扰时,相较于其他控制策略,LESO-LTV-MPC控制误差为0.014%,具有较高的控制精度;施加大干扰时,LESO-LTV-MPC控制误差为0.223%,具有较强的鲁棒性。因此,该控制策略能够有效提升起竖液压系统的性能。展开更多
地震产生的周期荷载作用下,钢混桥墩结构表现出滞回行为。为描述滞回行为,研究者提出各类滞回模型,其中BWBN(Bouc-Wen-Baber-Noori)模型可以描述结构滞回行为的强度退化、刚度退化和捏拢效应等典型特征。此外,无迹卡尔曼滤波器UKF(unsce...地震产生的周期荷载作用下,钢混桥墩结构表现出滞回行为。为描述滞回行为,研究者提出各类滞回模型,其中BWBN(Bouc-Wen-Baber-Noori)模型可以描述结构滞回行为的强度退化、刚度退化和捏拢效应等典型特征。此外,无迹卡尔曼滤波器UKF(unscented Kalman filter)算法是识别BWBN模型参数的高效方法,但当参数初始值与真实值的偏差过大及缺乏对系统的整体估计时,UKF算法识别过程受到局限。本文改进生成样本点规则,提出改进UKF算法。数值模拟结果表明,在无噪声条件下,改进UKF算法识别得到的参数估计值与准确值的误差平均为1.51%,最大误差为4%;在2%均方根RMS(root mean square)高斯白噪声条件下,误差平均为5.43%,最大误差为18%;在5%RMS高斯白噪声条件下,误差平均为8.9%,最大误差为26%和22%。改进UKF算法识别非线性滞回系统状态估计和BWBN模型参数更加准确和稳定。展开更多
基金This work is supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX18_0467)Jiangsu Province,China.During the revision of this paper,the author is supported by China Scholarship Council(No.201906840021)China to continue some research related to data processing.
文摘Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.
基金Project supported by Scientific Research Fund of Chongqing Municipal Education Commission (kj0604-16)
文摘In this article, the problem on the estimation of the convolution model parameters is considered. The recursive algorithm for estimating model parameters is introduced from the orthogonal procedure of the data, the convergence of this algorithm is theoretically discussed, and a sufficient condition for the convergence criterion of the orthogonal procedure is given. According to this condition, the recursive algorithm is convergent to model wavelet A- = （1, α1,..., αq）.
文摘针对常见控制策略在大型液压设备控制方面存在控制精度低与算法太复杂的问题,提出了基于线性扩张状态观测器(linear extended state observer,LESO)的线性时变模型预测控制(linear time-varying model predictive control,LTV-MPC)策略。通过起竖液压系统状态空间方程,设计了LESO实时估计系统当前状态;通过LTV-MPC输出比例阀电压信号的最优解。通过仿真与试验,验证所提方法的有效性。结果表明:无干扰时,相较于其他控制策略,LESO-LTV-MPC控制误差为0.014%,具有较高的控制精度;施加大干扰时,LESO-LTV-MPC控制误差为0.223%,具有较强的鲁棒性。因此,该控制策略能够有效提升起竖液压系统的性能。
文摘地震产生的周期荷载作用下,钢混桥墩结构表现出滞回行为。为描述滞回行为,研究者提出各类滞回模型,其中BWBN(Bouc-Wen-Baber-Noori)模型可以描述结构滞回行为的强度退化、刚度退化和捏拢效应等典型特征。此外,无迹卡尔曼滤波器UKF(unscented Kalman filter)算法是识别BWBN模型参数的高效方法,但当参数初始值与真实值的偏差过大及缺乏对系统的整体估计时,UKF算法识别过程受到局限。本文改进生成样本点规则,提出改进UKF算法。数值模拟结果表明,在无噪声条件下,改进UKF算法识别得到的参数估计值与准确值的误差平均为1.51%,最大误差为4%;在2%均方根RMS(root mean square)高斯白噪声条件下,误差平均为5.43%,最大误差为18%;在5%RMS高斯白噪声条件下,误差平均为8.9%,最大误差为26%和22%。改进UKF算法识别非线性滞回系统状态估计和BWBN模型参数更加准确和稳定。
