A new method for speeding up the state augment operations involved in the compressed extended Kalman filter-based simultaneous localization and mapping （CEKF-SLAM） algorithm was proposed. State augment usually requi...A new method for speeding up the state augment operations involved in the compressed extended Kalman filter-based simultaneous localization and mapping （CEKF-SLAM） algorithm was proposed. State augment usually requires a fully-updated state eovariance so as to append the information of newly observed landmarks, thus computational volume increases quadratically with the number of landmarks in the whole map. It was proved that state augment can also be achieved by augmenting just one auxiliary coefficient ma- trix. This method can yield identical estimation results as those using EKF-SLAM algorithm, and computa- tional amount grows only linearly with number of increased landmarks in the local map. The efficiency of this quick state augment for CEKF-SLAM algorithm has been validated by a sophisticated simulation project.展开更多
Aimed at designing the unpower aerocraft attitude control system in a simple and practical way, the guaranteed cost control is adopted. To eliminate the steady-error, a novel tracking control approach--augmented state...Aimed at designing the unpower aerocraft attitude control system in a simple and practical way, the guaranteed cost control is adopted. To eliminate the steady-error, a novel tracking control approach--augmented state feedback tracking guaranteed cost control is proposed. Firstly, the unpower aerocraft is modeled as a linear system with norm bounded parameter uncertain, then the linear matrix inequality based state feedback guaranteed cost control law is combined with the augmented state feedback tracking control from a new point of view. The sufficient condition of the existence of the augmented state feedback tracking guaranteed cost control is derived and converted to the feasible problem of the linear matrix inequality. Finally, the proposed approach is applied to a specified unpower aerocraft. The six dimensions of freedom simulation results show that the proposed approach is effective and feasible.展开更多
A design and verification of linear state observers which estimate state information such as angular velocity and load torque for retraction control of the motorized seat belt (MSB) system were described. The motorize...A design and verification of linear state observers which estimate state information such as angular velocity and load torque for retraction control of the motorized seat belt (MSB) system were described. The motorized seat belt system provides functions to protect passengers and improve passenger's convenience. Each MSB function has its own required belt tension which is determined by the function's purpose. To realize the MSB functions, state information, such as seat belt winding velocity and seat belt tension are required. Using a linear state observer, the state information for MSB operations can be estimated without sensors. To design the linear state observer, the motorized seat belt system is analyzed and represented as a state space model which contains load torque as an augmented state. Based on the state space model, a linear state observer was designed and verified by experiments. Also, the retraction control of the MSB algorithm using linear state observer was designed and verified on the test bench. With the designed retraction control algorithm using the linear state observer, it is possible to realize various types of MSB functions.展开更多
The state estimation of a maneuvering target,of which the trajectory shape is independent on dynamic characteristics,is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a ta...The state estimation of a maneuvering target,of which the trajectory shape is independent on dynamic characteristics,is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a target is completely determined by its dynamic characteristics.However,this is not true in the applications of road-target,sea-route-target or flight route-target tracking,where target trajectory shape is uncoupled with target velocity properties.In this paper,a new estimation algorithm based on separate modeling of target trajectory shape and dynamic characteristics is proposed.The trajectory of a target over a sliding window is described by a linear function of the arc length.To determine the unknown target trajectory,an augmented system is derived by denoting the unknown coefficients of the function as states in mileage coordinates.At every estimation cycle except the first one,the interaction(mixing)stage of the proposed algorithm starts from the latest estimated base state and a recalculated parameter vector,which is determined by the least squares(LS).Numerical experiments are conducted to assess the performance of the proposed algorithm.Simulation results show that the proposed algorithm can achieve better performance than the conventional coupled model-based algorithms in the presence of target maneuvers.展开更多
基于单一线路两端的监控与数据采集系统(supervisory control and data acquisition system,SCADA)和相量采集装置(phasor measurement unit,PMU)多时段量测信息,建立了5种独立线路的约束最小二乘参数估计模型,其中,量测方程分别由线路...基于单一线路两端的监控与数据采集系统(supervisory control and data acquisition system,SCADA)和相量采集装置(phasor measurement unit,PMU)多时段量测信息,建立了5种独立线路的约束最小二乘参数估计模型,其中,量测方程分别由线路两端有功、无功和电压幅值的SCADA量测、电流与电压相量的PMU量测以及线路两端电压相角差的PMU虚拟量测组合形成,约束方程为参数变量的上下限约束。采用Matlab的lsqnonlin优化函数求解参数估计问题,并基于多条典型线路的模拟量测信息仿真分析了所有模型的适用条件。结果表明,在负荷较重、线路较长条件下,利用所建含PMU量测的4种模型,都可以有效估计出线路的阻抗参数。展开更多
脉冲星导航可靠、稳定、精度高,是实现火星探测器自主导航的有效手段之一.针对脉冲星导航中脉冲到达时间的微小误差会引起巨大的位置估计误差这一问题,提出了一种基于扩维Unsented卡尔曼滤波(ASUKF,Augmented State Unscented Kalman Fi...脉冲星导航可靠、稳定、精度高,是实现火星探测器自主导航的有效手段之一.针对脉冲星导航中脉冲到达时间的微小误差会引起巨大的位置估计误差这一问题,提出了一种基于扩维Unsented卡尔曼滤波(ASUKF,Augmented State Unscented Kalman Filter)的火星探测器脉冲星自主导航方法,建立了以位置、速度和脉冲到达时间的常值测量误差作为状态量的导航系统数学模型,可在对探测器位置、速度进行估计的同时有效估计并修正脉冲到达时间的常值测量误差,并降低随机测量误差的影响.仿真结果表明该方法的导航定位精度可达350 m以内,可以满足火星探测自主导航的需要.展开更多
基金Sponsored by the Beijing Education Committee Cooperation Building Foundation Project
文摘A new method for speeding up the state augment operations involved in the compressed extended Kalman filter-based simultaneous localization and mapping （CEKF-SLAM） algorithm was proposed. State augment usually requires a fully-updated state eovariance so as to append the information of newly observed landmarks, thus computational volume increases quadratically with the number of landmarks in the whole map. It was proved that state augment can also be achieved by augmenting just one auxiliary coefficient ma- trix. This method can yield identical estimation results as those using EKF-SLAM algorithm, and computa- tional amount grows only linearly with number of increased landmarks in the local map. The efficiency of this quick state augment for CEKF-SLAM algorithm has been validated by a sophisticated simulation project.
基金the Spaceflight Innovation Foundation (20060115)the National Natural Science Foundation(60674105)
文摘Aimed at designing the unpower aerocraft attitude control system in a simple and practical way, the guaranteed cost control is adopted. To eliminate the steady-error, a novel tracking control approach--augmented state feedback tracking guaranteed cost control is proposed. Firstly, the unpower aerocraft is modeled as a linear system with norm bounded parameter uncertain, then the linear matrix inequality based state feedback guaranteed cost control law is combined with the augmented state feedback tracking control from a new point of view. The sufficient condition of the existence of the augmented state feedback tracking guaranteed cost control is derived and converted to the feasible problem of the linear matrix inequality. Finally, the proposed approach is applied to a specified unpower aerocraft. The six dimensions of freedom simulation results show that the proposed approach is effective and feasible.
基金Project supported by the Second Stage of Brain Korea 21 Projects and Changwon National University in 2011-2012
文摘A design and verification of linear state observers which estimate state information such as angular velocity and load torque for retraction control of the motorized seat belt (MSB) system were described. The motorized seat belt system provides functions to protect passengers and improve passenger's convenience. Each MSB function has its own required belt tension which is determined by the function's purpose. To realize the MSB functions, state information, such as seat belt winding velocity and seat belt tension are required. Using a linear state observer, the state information for MSB operations can be estimated without sensors. To design the linear state observer, the motorized seat belt system is analyzed and represented as a state space model which contains load torque as an augmented state. Based on the state space model, a linear state observer was designed and verified by experiments. Also, the retraction control of the MSB algorithm using linear state observer was designed and verified on the test bench. With the designed retraction control algorithm using the linear state observer, it is possible to realize various types of MSB functions.
基金supported by the National Natural Science Foundation of China(61671181).
文摘The state estimation of a maneuvering target,of which the trajectory shape is independent on dynamic characteristics,is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a target is completely determined by its dynamic characteristics.However,this is not true in the applications of road-target,sea-route-target or flight route-target tracking,where target trajectory shape is uncoupled with target velocity properties.In this paper,a new estimation algorithm based on separate modeling of target trajectory shape and dynamic characteristics is proposed.The trajectory of a target over a sliding window is described by a linear function of the arc length.To determine the unknown target trajectory,an augmented system is derived by denoting the unknown coefficients of the function as states in mileage coordinates.At every estimation cycle except the first one,the interaction(mixing)stage of the proposed algorithm starts from the latest estimated base state and a recalculated parameter vector,which is determined by the least squares(LS).Numerical experiments are conducted to assess the performance of the proposed algorithm.Simulation results show that the proposed algorithm can achieve better performance than the conventional coupled model-based algorithms in the presence of target maneuvers.
文摘基于单一线路两端的监控与数据采集系统(supervisory control and data acquisition system,SCADA)和相量采集装置(phasor measurement unit,PMU)多时段量测信息,建立了5种独立线路的约束最小二乘参数估计模型,其中,量测方程分别由线路两端有功、无功和电压幅值的SCADA量测、电流与电压相量的PMU量测以及线路两端电压相角差的PMU虚拟量测组合形成,约束方程为参数变量的上下限约束。采用Matlab的lsqnonlin优化函数求解参数估计问题,并基于多条典型线路的模拟量测信息仿真分析了所有模型的适用条件。结果表明,在负荷较重、线路较长条件下,利用所建含PMU量测的4种模型,都可以有效估计出线路的阻抗参数。
