In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utili...In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.展开更多
Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when ...Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state estimation.Unfortunately, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.展开更多
The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysi...The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysis of the proposed distributed KF algorithm without independent and stationary signal assumptions,which implies that the theoretical results are able to be applied to stochastic feedback systems.Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and non-stationary random matrices involved in the error equation.We employ analysis techniques such as stochastic Lyapunov function,stability theory of stochastic systems,and algebraic graph theory to deal with the above issue.The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal,the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way.At last,we illustrate the property of the proposed distributed KF algorithm by a simulation example.展开更多
Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled...Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data.The framework integrates Kalman filtering and Q-learning.Unlike smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error.Unlike traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads.For evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art approach.Hence,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.展开更多
A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filte...A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.展开更多
光伏发电功率超短期预测为电网调度煤电、储能等其他可调电源提供支持。针对气象因素随机性和光伏电池阵列积灰、老化导致光伏发电功率预测精度不高的问题,提出双向长短期记忆网络(bi-directional long short term memory,Bi-LSTM)和卡...光伏发电功率超短期预测为电网调度煤电、储能等其他可调电源提供支持。针对气象因素随机性和光伏电池阵列积灰、老化导致光伏发电功率预测精度不高的问题,提出双向长短期记忆网络(bi-directional long short term memory,Bi-LSTM)和卡尔曼滤波器(Kalman filter)结合的混合预测方法。Bi-LSTM模型学习气象因素特征,结合天气预报数据可减小气象因素造成的随机性误差;Kalman可以减小光伏电池阵列积灰、老化等因素带来的累积性误差。实例验证表明:长期运行条件下混合模型比单一Kalman、Bi-LSTM模型预测精度分别提高3.78%、2.50%。展开更多
When the proton exchange membrane fuel cell(PEMFC)system is running,there will be a condition that does not require power output for a short time.In order to achieve zero power output under low power consumption,it is...When the proton exchange membrane fuel cell(PEMFC)system is running,there will be a condition that does not require power output for a short time.In order to achieve zero power output under low power consumption,it is necessary to consider the diversity of control targets and the complexity of dynamic models,which brings the challenge of high-precision tracking control of the stack output power and cathode intake flow.For system idle speed control,a modelbased nonlinear control framework is constructed in this paper.Firstly,the nonlinear dynamic model of output power and cathode intake flow is derived.Secondly,a control scheme combining nonlinear extended Kalman filter observer and state feedback controller is designed.Finally,the control scheme is verified on the PEMFC experimental platform and compared with the proportion-integration-differentiation(PID)controller.The experimental results show that the control strategy proposed in this paper can realize the idle speed control of the fuel cell system and achieve the purpose of zero power output.Compared with PID controller,it has faster response speed and better system dynamics.展开更多
This paper introduces a Kalman-type recursive state estimator for a class of discrete-time stochastic linear singular systems where the measurements are carried part by part periodically following a scheduling algorit...This paper introduces a Kalman-type recursive state estimator for a class of discrete-time stochastic linear singular systems where the measurements are carried part by part periodically following a scheduling algorithm.We consider that the system is in a network with limited allotted bandwidth,which refers to a situation where the total available bandwidth for data transmission through the network is limited.This limitation can occur for various reasons,such as network congestion,resource allocation policies,or bandwidth limitations imposed by network administrators.In such networks,the entire measurement vector cannot be transmitted to the estimator instantly.Thus,managing a network with a limited allotted bandwidth requires careful planning,monitoring,and implementing some scheduling strategies to optimize the use of measured data while estimating the system states.We show that a scheduling method,namely,round-robin protocol,is suitable for singular systems to deal with such a scenario.The upper bound of the prior error covariance is studied via a periodic Riccati equation(PRE).To retain the boundedness of prior error covariance,the stability of the PRE is examined by the observability properties of the round-robin-induced system.Finally,a simulation example is presented to show the effectiveness of the designed filtering scheme.展开更多
机动飞行条件下高速转子系统会同时受到环境载荷以及转子自身的共同激励而产生强烈的强迫响应。为研究其复杂的振动特性,本文采用Vold-Kalman滤波(Vold-Kalman Filter,VKF)对不同基础运动激励下转子系统的实测振动信号进行阶次跟踪滤波...机动飞行条件下高速转子系统会同时受到环境载荷以及转子自身的共同激励而产生强烈的强迫响应。为研究其复杂的振动特性,本文采用Vold-Kalman滤波(Vold-Kalman Filter,VKF)对不同基础运动激励下转子系统的实测振动信号进行阶次跟踪滤波。为验证VKF的有效性及参数设置的可靠性,通过转子动力特性计算生成系统响应的仿真信号,并通过加噪处理模拟测量信号,然后通过VKF提取目标阶次的时域波形。通过陀螺运动转子动力学试验,测得不同基础转动激起的系统振动响应,组合使用VKF和计算阶次跟踪(Computed Order Tracking,COT)提取并分离了转子转频信号和基础低频信号的时域和阶次信息。结果表明,单轴滚转或俯仰运动均会激起与其频率一致的低频振动响应,且滚转、俯仰角速度的大小会影响该低频信号的幅值大小;随着基础运动角速度的变化,转子前四阶振动分量没有发生明显的变化,而基础运动频率与转频之间的频带区域有显著变化。此方法有效地提升了机动飞行下转子支承系统振动信号处理与分析的准确度和效率,降低了信号噪声。展开更多
In order to reduce the error judgment of outliers in vehicle temperature prediction and improve the accuracy of single-station processor prediction data,a Kalman filter multi-information fusion algorithm based on opti...In order to reduce the error judgment of outliers in vehicle temperature prediction and improve the accuracy of single-station processor prediction data,a Kalman filter multi-information fusion algorithm based on optimized P-Huber weight function was proposed.The algorithm took Kalman filter(KF)as the whole frame,and established the decision threshold based on the confidence level of Chi-square distribution.At the same time,the abnormal error judgment value was constructed by Mahalanobis distance function,and the three segments of Huber weight function were formed.It could improve the accuracy of the interval judgment of outliers,and give a reasonable weight,so as to improve the tracking accuracy of the algorithm.The data values of four important locations in the vehicle obtained after optimized filtering were processed by information fusion.According to theoretical analysis,compared with Kalman filtering algorithm,the proposed algorithm could accurately track the actual temperature in the case of abnormal error,and multi-station data fusion processing could improve the overall fault tolerance of the system.The results showed that the proposed algorithm effectively reduced the interference of abnormal errors on filtering,and the synthetic value of fusion processing was more stable and critical.展开更多
The structural dynamic response reconstruction technology can extract unmeasured information from limited measured data,significantly impacting vibration control,load identification,parameter identification,fault diag...The structural dynamic response reconstruction technology can extract unmeasured information from limited measured data,significantly impacting vibration control,load identification,parameter identification,fault diagnosis,and related fields.This paper proposes a dynamic response reconstruction method based on the Kalman filter,which simultaneously identifies external excitation and reconstructs dynamic responses at unmeasured positions.The weighted least squares method determines the load weighting matrix for excitation identification,while the minimum variance unbiased estimation determines the Kalman filter gain.The excitation prediction Kalman filter is constructed through time,excitation,and measurement updates.Subsequently,the response at the target point is reconstructed using the state vector,observation matrix,and excitation influence matrix obtained through the excitation prediction Kalman filter algorithm.An algorithm for reconstructing responses in continuous system using the excitation prediction Kalman filtering algorithm in modal space is derived.The proposed structural dynamic response reconstruction method evaluates the response reconstruction and the load identification performance under various load types and errors through simulation examples.Results demonstrate the accurate excitation identification under different load conditions and simultaneous reconstruction of target point responses,verifying the feasibility and reliability of the proposed method.展开更多
基金supported in part by the National Natural Science Foundation of China(12171124,61933007)the Natural Science Foundation of Heilongjiang Province of China(ZD2022F003)+2 种基金the National High-End Foreign Experts Recruitment Plan of China(G2023012004L)the Royal Society of UKthe Alexander von Humboldt Foundation of Germany
文摘In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.
基金supported in part by the National Key Research and Development Program of China(2023YFB3906403)the National Natural Science Foundation of China(62373118,62173105)the Natural Science Foundation of Heilongjiang Province of China(ZD2023F002)
文摘Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state estimation.Unfortunately, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.
基金supported in part by Sichuan Science and Technology Program under Grant No.2025ZNSFSC151in part by the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No.XDA27030201+1 种基金the Natural Science Foundation of China under Grant No.U21B6001in part by the Natural Science Foundation of Tianjin under Grant No.24JCQNJC01930.
文摘The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysis of the proposed distributed KF algorithm without independent and stationary signal assumptions,which implies that the theoretical results are able to be applied to stochastic feedback systems.Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and non-stationary random matrices involved in the error equation.We employ analysis techniques such as stochastic Lyapunov function,stability theory of stochastic systems,and algebraic graph theory to deal with the above issue.The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal,the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way.At last,we illustrate the property of the proposed distributed KF algorithm by a simulation example.
文摘Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data.The framework integrates Kalman filtering and Q-learning.Unlike smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error.Unlike traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads.For evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art approach.Hence,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.
基金supported by National Natural Science Foundation of China (Nos.62265010,62061024)Gansu Province Science and Technology Plan (No.23YFGA0062)Gansu Province Innovation Fund (No.2022A-215)。
文摘A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.
文摘光伏发电功率超短期预测为电网调度煤电、储能等其他可调电源提供支持。针对气象因素随机性和光伏电池阵列积灰、老化导致光伏发电功率预测精度不高的问题,提出双向长短期记忆网络(bi-directional long short term memory,Bi-LSTM)和卡尔曼滤波器(Kalman filter)结合的混合预测方法。Bi-LSTM模型学习气象因素特征,结合天气预报数据可减小气象因素造成的随机性误差;Kalman可以减小光伏电池阵列积灰、老化等因素带来的累积性误差。实例验证表明:长期运行条件下混合模型比单一Kalman、Bi-LSTM模型预测精度分别提高3.78%、2.50%。
基金Supported by the Major Science and Technology Projects in Jilin Province and Changchun City(20220301010GX).
文摘When the proton exchange membrane fuel cell(PEMFC)system is running,there will be a condition that does not require power output for a short time.In order to achieve zero power output under low power consumption,it is necessary to consider the diversity of control targets and the complexity of dynamic models,which brings the challenge of high-precision tracking control of the stack output power and cathode intake flow.For system idle speed control,a modelbased nonlinear control framework is constructed in this paper.Firstly,the nonlinear dynamic model of output power and cathode intake flow is derived.Secondly,a control scheme combining nonlinear extended Kalman filter observer and state feedback controller is designed.Finally,the control scheme is verified on the PEMFC experimental platform and compared with the proportion-integration-differentiation(PID)controller.The experimental results show that the control strategy proposed in this paper can realize the idle speed control of the fuel cell system and achieve the purpose of zero power output.Compared with PID controller,it has faster response speed and better system dynamics.
基金supported by the Science and Engineering Research Board,New Delhi(No.MTR/2019/000494).
文摘This paper introduces a Kalman-type recursive state estimator for a class of discrete-time stochastic linear singular systems where the measurements are carried part by part periodically following a scheduling algorithm.We consider that the system is in a network with limited allotted bandwidth,which refers to a situation where the total available bandwidth for data transmission through the network is limited.This limitation can occur for various reasons,such as network congestion,resource allocation policies,or bandwidth limitations imposed by network administrators.In such networks,the entire measurement vector cannot be transmitted to the estimator instantly.Thus,managing a network with a limited allotted bandwidth requires careful planning,monitoring,and implementing some scheduling strategies to optimize the use of measured data while estimating the system states.We show that a scheduling method,namely,round-robin protocol,is suitable for singular systems to deal with such a scenario.The upper bound of the prior error covariance is studied via a periodic Riccati equation(PRE).To retain the boundedness of prior error covariance,the stability of the PRE is examined by the observability properties of the round-robin-induced system.Finally,a simulation example is presented to show the effectiveness of the designed filtering scheme.
文摘机动飞行条件下高速转子系统会同时受到环境载荷以及转子自身的共同激励而产生强烈的强迫响应。为研究其复杂的振动特性,本文采用Vold-Kalman滤波(Vold-Kalman Filter,VKF)对不同基础运动激励下转子系统的实测振动信号进行阶次跟踪滤波。为验证VKF的有效性及参数设置的可靠性,通过转子动力特性计算生成系统响应的仿真信号,并通过加噪处理模拟测量信号,然后通过VKF提取目标阶次的时域波形。通过陀螺运动转子动力学试验,测得不同基础转动激起的系统振动响应,组合使用VKF和计算阶次跟踪(Computed Order Tracking,COT)提取并分离了转子转频信号和基础低频信号的时域和阶次信息。结果表明,单轴滚转或俯仰运动均会激起与其频率一致的低频振动响应,且滚转、俯仰角速度的大小会影响该低频信号的幅值大小;随着基础运动角速度的变化,转子前四阶振动分量没有发生明显的变化,而基础运动频率与转频之间的频带区域有显著变化。此方法有效地提升了机动飞行下转子支承系统振动信号处理与分析的准确度和效率,降低了信号噪声。
基金supported by Natural Science Foundation of Gansu Province(No.20JR5RA407).
文摘In order to reduce the error judgment of outliers in vehicle temperature prediction and improve the accuracy of single-station processor prediction data,a Kalman filter multi-information fusion algorithm based on optimized P-Huber weight function was proposed.The algorithm took Kalman filter(KF)as the whole frame,and established the decision threshold based on the confidence level of Chi-square distribution.At the same time,the abnormal error judgment value was constructed by Mahalanobis distance function,and the three segments of Huber weight function were formed.It could improve the accuracy of the interval judgment of outliers,and give a reasonable weight,so as to improve the tracking accuracy of the algorithm.The data values of four important locations in the vehicle obtained after optimized filtering were processed by information fusion.According to theoretical analysis,compared with Kalman filtering algorithm,the proposed algorithm could accurately track the actual temperature in the case of abnormal error,and multi-station data fusion processing could improve the overall fault tolerance of the system.The results showed that the proposed algorithm effectively reduced the interference of abnormal errors on filtering,and the synthetic value of fusion processing was more stable and critical.
基金supported by the National Natural Science Foundation of China(Nos.12372066,U23B6009,52171261)the Aeronautical Science Fund(No.20240013052002)the Qing Lan Project。
文摘The structural dynamic response reconstruction technology can extract unmeasured information from limited measured data,significantly impacting vibration control,load identification,parameter identification,fault diagnosis,and related fields.This paper proposes a dynamic response reconstruction method based on the Kalman filter,which simultaneously identifies external excitation and reconstructs dynamic responses at unmeasured positions.The weighted least squares method determines the load weighting matrix for excitation identification,while the minimum variance unbiased estimation determines the Kalman filter gain.The excitation prediction Kalman filter is constructed through time,excitation,and measurement updates.Subsequently,the response at the target point is reconstructed using the state vector,observation matrix,and excitation influence matrix obtained through the excitation prediction Kalman filter algorithm.An algorithm for reconstructing responses in continuous system using the excitation prediction Kalman filtering algorithm in modal space is derived.The proposed structural dynamic response reconstruction method evaluates the response reconstruction and the load identification performance under various load types and errors through simulation examples.Results demonstrate the accurate excitation identification under different load conditions and simultaneous reconstruction of target point responses,verifying the feasibility and reliability of the proposed method.
