Monitoring and evaluating the health parameters of marine gas turbine engine help in developing predictive control techniques and maintenance schedules.Because the health parameters are unmeasurable,researchers estima...Monitoring and evaluating the health parameters of marine gas turbine engine help in developing predictive control techniques and maintenance schedules.Because the health parameters are unmeasurable,researchers estimate them only based on the available measurement parameters.Kalman filter-based approaches are the most commonly used estimation approaches;how-ever,the conventional Kalman filter-based approaches have a poor robustness to the model uncertainty,and their ability to track the mutation condition is influenced by historical data.Therefore,in this paper,an improved Kalman filter-based algorithm called the strong tracking extended Kalman filter(STEKF)approach is proposed to estimate the gas turbine health parameters.The analytical expressions of Jacobian matrixes are deduced by non-equilibrium point analytical linearization to address the problem of the conventional approaches.The proposed approach was used to estimate the health parameters of a two-shaft marine gas turbine engine in the simulation environment and was compared with the extended Kalman filter(EKF)and the unscented Kalman filter(UKF).The results show that the STEKF approach not only has a computation cost similar to that of the EKF approach but also outperforms the EKF approach when the health parameters change abruptly and the noise mean value is not zero.展开更多
Non-intrusive methods for eye tracking are important for many applications of vision-based human computer interaction. However, due to the high nonlinearity of eye motion, how to ensure the robust- ness of external in...Non-intrusive methods for eye tracking are important for many applications of vision-based human computer interaction. However, due to the high nonlinearity of eye motion, how to ensure the robust- ness of external interference and accuracy of eye tracking poses the primary obstacle to the integration of eye movements into today＇s interfaces. In this paper, we present a strong tracking finite-difference extended Kalman filter algorithm, aiming to overcome the difficulty in modeling nonlinear eye tracking. In filtering calculation, strong tracking factor is introduced to modify a priori covariance matrix and improve the accuracy of the filter. The filter uses finite-difference method to calculate partial derivatives of nonlinear functions for eye tracking. The latest experimental results show the validity of our method for eye tracking under realistic conditions.展开更多
为增强三电平逆变器馈电永磁同步电机驱动系统在模型失配和工况变化下的稳定性和鲁棒性,该文提出一种基于强跟踪扩展卡尔曼观测器的无模型预测电流控制(strong tracking extended Kalman observer based model-free predictive current ...为增强三电平逆变器馈电永磁同步电机驱动系统在模型失配和工况变化下的稳定性和鲁棒性,该文提出一种基于强跟踪扩展卡尔曼观测器的无模型预测电流控制(strong tracking extended Kalman observer based model-free predictive current control,STEKO-MFPCC)策略。首先,分析电机在模型失配工况下的集总扰动,建立系统超局部模型;其次,设计EKO估计超局部模型中非线性部分,同时引入强跟踪滤波策略,快速调整误差协方差矩阵,优化增益矩阵,提升传统扩展卡尔曼算法的动态性能;最后,构建含有中点电位和电流的预测方程,输出令代价函数最小的电压矢量。实验结果表明,相较传统MFPCC,所提方法有效抑制中点电位波动,提升电机转速响应速度,改善输出电流质量,在参数扰动工况下增强系统鲁棒性和抗扰动能力。展开更多
文摘Monitoring and evaluating the health parameters of marine gas turbine engine help in developing predictive control techniques and maintenance schedules.Because the health parameters are unmeasurable,researchers estimate them only based on the available measurement parameters.Kalman filter-based approaches are the most commonly used estimation approaches;how-ever,the conventional Kalman filter-based approaches have a poor robustness to the model uncertainty,and their ability to track the mutation condition is influenced by historical data.Therefore,in this paper,an improved Kalman filter-based algorithm called the strong tracking extended Kalman filter(STEKF)approach is proposed to estimate the gas turbine health parameters.The analytical expressions of Jacobian matrixes are deduced by non-equilibrium point analytical linearization to address the problem of the conventional approaches.The proposed approach was used to estimate the health parameters of a two-shaft marine gas turbine engine in the simulation environment and was compared with the extended Kalman filter(EKF)and the unscented Kalman filter(UKF).The results show that the STEKF approach not only has a computation cost similar to that of the EKF approach but also outperforms the EKF approach when the health parameters change abruptly and the noise mean value is not zero.
基金Supported by the National Natural Science Foundation of China (Grant No. 60572027)the Outstanding Young Researchers Foundation of Sichuan Province (Grant No. 03ZQ026-033)+1 种基金the Program for New Century Excellent Talents in University of China (Grant No. NCET-05-0794)the Young Teacher Foundation of Mechanical School (Grant No. MYF0806)
文摘Non-intrusive methods for eye tracking are important for many applications of vision-based human computer interaction. However, due to the high nonlinearity of eye motion, how to ensure the robust- ness of external interference and accuracy of eye tracking poses the primary obstacle to the integration of eye movements into today＇s interfaces. In this paper, we present a strong tracking finite-difference extended Kalman filter algorithm, aiming to overcome the difficulty in modeling nonlinear eye tracking. In filtering calculation, strong tracking factor is introduced to modify a priori covariance matrix and improve the accuracy of the filter. The filter uses finite-difference method to calculate partial derivatives of nonlinear functions for eye tracking. The latest experimental results show the validity of our method for eye tracking under realistic conditions.
文摘为增强三电平逆变器馈电永磁同步电机驱动系统在模型失配和工况变化下的稳定性和鲁棒性,该文提出一种基于强跟踪扩展卡尔曼观测器的无模型预测电流控制(strong tracking extended Kalman observer based model-free predictive current control,STEKO-MFPCC)策略。首先,分析电机在模型失配工况下的集总扰动,建立系统超局部模型;其次,设计EKO估计超局部模型中非线性部分,同时引入强跟踪滤波策略,快速调整误差协方差矩阵,优化增益矩阵,提升传统扩展卡尔曼算法的动态性能;最后,构建含有中点电位和电流的预测方程,输出令代价函数最小的电压矢量。实验结果表明,相较传统MFPCC,所提方法有效抑制中点电位波动,提升电机转速响应速度,改善输出电流质量,在参数扰动工况下增强系统鲁棒性和抗扰动能力。
