A polynomial model, time origin shifting model(TOSM, is used to describe the trajectory of a moving target .Based on TOSM, a recursive laeast squares(RLS) algorithm with varied forgetting factor is derived for tracki...A polynomial model, time origin shifting model(TOSM, is used to describe the trajectory of a moving target .Based on TOSM, a recursive laeast squares(RLS) algorithm with varied forgetting factor is derived for tracking of a non-maneuvering target. In order to apply this algorithm to maneuvering targets tracking ,a tracking signal is performed on-line to determine what kind of TOSm will be in effect to track a target with different dynamics. An effective multiple model least squares filtering and forecasting method dadpted to real tracking of a maneuvering target is formulated. The algorithm is computationally more effcient than Kalman filter and the percentage improvement from simulations show both of them are considerably alike to some extent.展开更多
The quarter model of an active suspension is established in the form of controllable autoregressive moving average （CARMA） model. An accelerometer can be mounted on the wheel hub for measuring road disturbance; this...The quarter model of an active suspension is established in the form of controllable autoregressive moving average （CARMA） model. An accelerometer can be mounted on the wheel hub for measuring road disturbance; this signal is used to identify the CARMA model parameters by recursive forgetting factors least square method. The linear quadratic integral （LQI） control method for the active suspension is presented. The LQI control algorithm is fit for vehicle suspension control, for the control performance index can comprise multi controlled variables. The simulation results show that the vertical acceleration and suspension travel both are decreased with the LQI control in the low frequency band, and the suspension travel is increased with the LQI control in the middle or high frequency band. The suspension travel is very small in the middle or high frequency band, the suspension bottoming stop will not happen, so the vehicle ride quality can be improved apparently by the LQI control.展开更多
永磁同步电机(permanent magnet synchronous motor,PMSM)的磁链准确辨识是实现高性能电机控制的基础。针对传统递推最小二乘(recursive least squares,RLS)法受噪声影响小但存在数据饱和,影响辨识精度和动态性问题,以及遗忘最小二乘(re...永磁同步电机(permanent magnet synchronous motor,PMSM)的磁链准确辨识是实现高性能电机控制的基础。针对传统递推最小二乘(recursive least squares,RLS)法受噪声影响小但存在数据饱和,影响辨识精度和动态性问题,以及遗忘最小二乘(recursive least squares with forgetting factor,FRLS)法避免数据饱和但存在参数估计误差与动态跟踪性能矛盾的问题,文章提出一种基于折息最小二乘(recursive least squares with discount factor,DRLS)法的磁链辨识方法。该算法在FRLS法中引入加权因子构成折息因子,采用递推方法进行磁链辨识,减小参数估计误差,提高磁链辨识精度及动态跟踪能力。通过MATLAB仿真及半实物仿真试验,验证所提磁链识别方法的有效性。展开更多
字典学习是信号稀疏分解研究的热点问题.在稀疏分解字典学习中,初始字典的选择影响字典学习的效果.为减小初始字典对学习字典的影响,在递归最小二乘(recursive least squares,RLS)字典学习方法中引入遗忘因子的概念.比较了最优方向法、...字典学习是信号稀疏分解研究的热点问题.在稀疏分解字典学习中,初始字典的选择影响字典学习的效果.为减小初始字典对学习字典的影响,在递归最小二乘(recursive least squares,RLS)字典学习方法中引入遗忘因子的概念.比较了最优方向法、K奇异值分解方法和RLS等3种方法的字典学习效果.分析了RLS字典学习中不同的遗传因子对字典学习效果的影响,以及遗忘因子为不同函数时的字典学习效果.仿真结果表明:RLS字典学习方法减小了初始字典对学习结果的影响,故学习效果较好;而在RLS字典学习中不同遗忘因子的选择会影响字典学习效果.展开更多
针对Boost转换器控制性能受电感和电容变化影响的问题,提出了一种基于可变遗忘因子递推最小二乘法(recursive least squares method,RLS)的在线多参数辨识算法.考虑电感电流纹波,推导了精确的电感和电容辨识模型.在此基础上,研究了RLS...针对Boost转换器控制性能受电感和电容变化影响的问题,提出了一种基于可变遗忘因子递推最小二乘法(recursive least squares method,RLS)的在线多参数辨识算法.考虑电感电流纹波,推导了精确的电感和电容辨识模型.在此基础上,研究了RLS算法中遗忘因子动态取值问题.通过在算法的误差信号中恢复系统噪声的方法,动态计算遗忘因子的取值,解决了传统RLS算法难以兼顾稳态精度和参数跟踪能力的问题.仿真结果表明,该算法可以在动态条件下,精确且快速地跟踪电感和电容值的变化,且具有良好的鲁棒性.展开更多
电动助力转向(electric power steering,EPS)系统具有非线性和时变性,采用常系数补偿无法实现对转矩的准确跟踪,影响驾驶员手感。文章采用滑模控制器准确跟踪电流,并设计补偿算法,利用带遗忘因子的递推最小二乘(recursive least squares...电动助力转向(electric power steering,EPS)系统具有非线性和时变性,采用常系数补偿无法实现对转矩的准确跟踪,影响驾驶员手感。文章采用滑模控制器准确跟踪电流,并设计补偿算法,利用带遗忘因子的递推最小二乘(recursive least squares,RLS)算法对助力装置进行在线参数辨识,并将辨识得到的结果进行补偿控制,在参数缓慢变化的条件下实现EPS对转矩的准确跟踪。展开更多
文摘A polynomial model, time origin shifting model(TOSM, is used to describe the trajectory of a moving target .Based on TOSM, a recursive laeast squares(RLS) algorithm with varied forgetting factor is derived for tracking of a non-maneuvering target. In order to apply this algorithm to maneuvering targets tracking ,a tracking signal is performed on-line to determine what kind of TOSm will be in effect to track a target with different dynamics. An effective multiple model least squares filtering and forecasting method dadpted to real tracking of a maneuvering target is formulated. The algorithm is computationally more effcient than Kalman filter and the percentage improvement from simulations show both of them are considerably alike to some extent.
文摘The quarter model of an active suspension is established in the form of controllable autoregressive moving average （CARMA） model. An accelerometer can be mounted on the wheel hub for measuring road disturbance; this signal is used to identify the CARMA model parameters by recursive forgetting factors least square method. The linear quadratic integral （LQI） control method for the active suspension is presented. The LQI control algorithm is fit for vehicle suspension control, for the control performance index can comprise multi controlled variables. The simulation results show that the vertical acceleration and suspension travel both are decreased with the LQI control in the low frequency band, and the suspension travel is increased with the LQI control in the middle or high frequency band. The suspension travel is very small in the middle or high frequency band, the suspension bottoming stop will not happen, so the vehicle ride quality can be improved apparently by the LQI control.
文摘永磁同步电机(permanent magnet synchronous motor,PMSM)的磁链准确辨识是实现高性能电机控制的基础。针对传统递推最小二乘(recursive least squares,RLS)法受噪声影响小但存在数据饱和,影响辨识精度和动态性问题,以及遗忘最小二乘(recursive least squares with forgetting factor,FRLS)法避免数据饱和但存在参数估计误差与动态跟踪性能矛盾的问题,文章提出一种基于折息最小二乘(recursive least squares with discount factor,DRLS)法的磁链辨识方法。该算法在FRLS法中引入加权因子构成折息因子,采用递推方法进行磁链辨识,减小参数估计误差,提高磁链辨识精度及动态跟踪能力。通过MATLAB仿真及半实物仿真试验,验证所提磁链识别方法的有效性。
文摘字典学习是信号稀疏分解研究的热点问题.在稀疏分解字典学习中,初始字典的选择影响字典学习的效果.为减小初始字典对学习字典的影响,在递归最小二乘(recursive least squares,RLS)字典学习方法中引入遗忘因子的概念.比较了最优方向法、K奇异值分解方法和RLS等3种方法的字典学习效果.分析了RLS字典学习中不同的遗传因子对字典学习效果的影响,以及遗忘因子为不同函数时的字典学习效果.仿真结果表明:RLS字典学习方法减小了初始字典对学习结果的影响,故学习效果较好;而在RLS字典学习中不同遗忘因子的选择会影响字典学习效果.
文摘针对Boost转换器控制性能受电感和电容变化影响的问题,提出了一种基于可变遗忘因子递推最小二乘法(recursive least squares method,RLS)的在线多参数辨识算法.考虑电感电流纹波,推导了精确的电感和电容辨识模型.在此基础上,研究了RLS算法中遗忘因子动态取值问题.通过在算法的误差信号中恢复系统噪声的方法,动态计算遗忘因子的取值,解决了传统RLS算法难以兼顾稳态精度和参数跟踪能力的问题.仿真结果表明,该算法可以在动态条件下,精确且快速地跟踪电感和电容值的变化,且具有良好的鲁棒性.
文摘电动助力转向(electric power steering,EPS)系统具有非线性和时变性,采用常系数补偿无法实现对转矩的准确跟踪,影响驾驶员手感。文章采用滑模控制器准确跟踪电流,并设计补偿算法,利用带遗忘因子的递推最小二乘(recursive least squares,RLS)算法对助力装置进行在线参数辨识,并将辨识得到的结果进行补偿控制,在参数缓慢变化的条件下实现EPS对转矩的准确跟踪。
