In the estimation and identification of nonlinear system state,aiming at the adverse effect of observation missing randomly caused by detection probability of used sensor which is less than 1,a novel federal extended ...In the estimation and identification of nonlinear system state,aiming at the adverse effect of observation missing randomly caused by detection probability of used sensor which is less than 1,a novel federal extended Kalman filter( FEKF) based on reconstructed observation in incomplete observations( ROIO) is proposed in this paper. On the basis of multi-sensor observation sets,the observation is exchanged at different times to construct a new observation set. Based on each observation set,an extended Kalman filter algorithm is used to estimate the state of the target,and then the federal filtering algorithm is used to solve the state estimation based on the multi-sensor observation data. The effect of the sensor probing probability on the filtering result and the effect of the number of sensors on the filtering result are obtained by the simulation experiment,respectively. The simulation results demonstrate effectiveness of the proposed algorithm.展开更多
The square-root unscented Kalman filter （SR- UKF） for state estimation probably encounters the problem that Cholesky factor update of the covariance matrices can＇t be implemented when the zero＇th weight of sigm...The square-root unscented Kalman filter （SR- UKF） for state estimation probably encounters the problem that Cholesky factor update of the covariance matrices can＇t be implemented when the zero＇th weight of sigma points is negative or the mnnerical computation error becomes large during the faltering procedure. Consequently the filter becomes invalid. An improved SR-UKF algorithm （ISR- UKF） is presented for state estimation of arbitrary nonlinear systems with linear measurements. It adopts a modified form of predicted covariance matrices, and modifies the Cholesky factor calculation of the updated covariance matrix originating from the square-root covariance filtering method. Discussions have been given on how to avoid the filter invalidation and further error accumulation. The comparison between the ISR-UKF and the SR-UKF by simulation also shows both have the same accuracy for state estimation. Finally the performance of the improved filter is evaluated under the impact of model mismatch. The error behavior shows that the ISR-UKF can overcome the impact of model mismatch to a certain extent and has excellent trace capability.展开更多
Building energy consumption accounts for nearly 40% of global energy consumption, HVAC （Heating, Ventilating, and Air Conditioning） systems are the major building energy consumers, and as one type of HVAC systems, t...Building energy consumption accounts for nearly 40% of global energy consumption, HVAC （Heating, Ventilating, and Air Conditioning） systems are the major building energy consumers, and as one type of HVAC systems, the heat pump air conditioning system, which is more energy-efficient compared to the traditional air conditioning system, is being more widely used to save energy. However, in northern China, extreme climatic conditions increase the cooling and heating load of the heat pump air conditioning system and accelerate the aging of the equipment, and the sensor may detect drifted parameters owing to climate change. This non-linear drifted parameter increases the false alarm rate of the fault detection and the need for unnecessary troubleshooting. In order to overcome the impact of the device aging and the drifted parameter, a Kalman filter and SPC （statistical process control） fault detection method are introduced in this paper. In this method, the model parameter and its standard variance can he estimated by Kalman filter based on the gray model and the real-time data of the air conditioning system. Further, by using SPC to construct the dynamic control limits, false alarm rate is reduced. And this paper mainly focuses on the cold machine failure in the component failure and its soft fault detection. This approach has been tested on a simulation model of the ＂Sino-German Energy Conservation Demonstration Center＂ building heat pump air-conditioning system in Shenyang, China, and the results show that the Kalman filter and SPC fault detection method is simple and highly efficient with a low false alarm rate, and it can deal with the difficulties caused by the extreme environment and the non-linear influence of the parameters, and what＇s more, it provides a good foundation for dynamic fault diagnosis and fault prediction analysis.展开更多
To solve the precision self-positioning problem for mobile robot,a positioning program based on ultra-wideband technology was proposed. Ultra-wideband pulse has very high bandwidth; ranging accuracy can achieve centim...To solve the precision self-positioning problem for mobile robot,a positioning program based on ultra-wideband technology was proposed. Ultra-wideband pulse has very high bandwidth; ranging accuracy can achieve centimeter-level theoretically. The mobile robot obtained the distance to the reference node by sending ultra-wideband pulse. According to the geometric relations among the references and the robot,establish equations to calculate the position coordinates. Then Kalman filter algorithm was applied for mobile robot tracking. Simulation results show that robot positioning and tracking based on ultra-wideband technology can achieve indoor and outdoor seamless docking.展开更多
A newly proposed distributed dynamic state estimation algorithm based on the maximum a posteriori(MAP) technique is generalised and studied for power systems. The system model involves linear time-varying load dynamic...A newly proposed distributed dynamic state estimation algorithm based on the maximum a posteriori(MAP) technique is generalised and studied for power systems. The system model involves linear time-varying load dynamics and nonlinear measurements. The main contribution of this paper is to compare the performance and feasibility of this distributed algorithm with several existing distributed state estimation algorithms in the literature. Simulations are tested on the IEEE 39-bus and 118-bus systems under various operating conditions. The results show that this distributed algorithm performs better than distributed quasi-steady state estimation algorithms which do not use the load dynamic model. The results also show that the performance of this distributed method is very close to that by the centralized state estimation method. The merits of this algorithm over the centralized method lie in its low computational complexity and low communication load. Hence, the analysis supports the efficiency and benefits of the distributed algorithm in applications to large-scale power systems.展开更多
In this paper, bridge alignment control with considering dynamic train loads was experimentally and theoretically investigated.Analytical process of bridge dynamics and the self-adaptive Kalman filter bridge alignment...In this paper, bridge alignment control with considering dynamic train loads was experimentally and theoretically investigated.Analytical process of bridge dynamics and the self-adaptive Kalman filter bridge alignment control method with considering the dynamic train loads were briefly introduced. The static measurement, the dynamic test, the field alignment measurement as well as the finite element analysis(FEA) of the second longest rail transit cable-stayed bridge in the world were carried out.Based on the results, the train dynamic load effect on the bridge alignment was obtained quantitatively. Subsequently, alignment control of the rail transit bridge with considering this effect using a self-adaptive Kalman filter method was analyzed. The results show that:(a) the dynamic train loads have effects on alignment control of the bridge and therefore cannot be neglected;(b) the self-adaptive Kalman filter method is applicable and reliable for alignment control of bridges during construction. The analytical method and whole process contribute to develop a related specification and further engineering applications.展开更多
The problem of navigation for the distributed satellites system using relative range mea- surements is investigated. Firstly, observability for every participating satellites is analyzed based on the nonlinear Kepleri...The problem of navigation for the distributed satellites system using relative range mea- surements is investigated. Firstly, observability for every participating satellites is analyzed based on the nonlinear Keplerian model containing J2 perturbation and the nonlinear measurements. It is proven that the minimum number of tracking satellites to assure the observability of the distributed satellites system is three. Additionally, the analysis shows that the J2 perturbation and the nonlinearity make little contribution to improve the observability for the navigation. Then, a quasi-consistent extended Kalman filter based navigation algorithm is proposed, which is quasi-consistent and can provide an on- line evaluation of the navigation precision. The simulation illustrates the feasibility and effectiveness of the proposed navigation algorithm for the distributed satellites system.展开更多
The statistical inference for generalized mixed-effects state space models （MESSM） are investigated when the random effects are unknown. Two filtering algorithms are designed both of which are based on mixture Kalma...The statistical inference for generalized mixed-effects state space models （MESSM） are investigated when the random effects are unknown. Two filtering algorithms are designed both of which are based on mixture Kalman filter. These algorithms are particularly useful when the longitudinal ts are sparse. The authors also propose a globally convergent algorithm for parameter estimation of MESSM which can be used to locate the initial value of parameters for local while more efficient algorithms. Simulation examples are carried out which validate the efficacy of the proposed approaches. A data set from the clinical trial is investigated and a smaller mean square error is achieved compared to the existing results in literatures.展开更多
We propose a new and efficient algorithm to detect, identify, and correct measurement errors and branch parameter errors of power systems. A dynamic state estimation algorithm is used based on the Kalman filter theory...We propose a new and efficient algorithm to detect, identify, and correct measurement errors and branch parameter errors of power systems. A dynamic state estimation algorithm is used based on the Kalman filter theory. The proposed algorithm also successfully detects and identifies sudden load changes in power systems. The method uses three normalized vectors to process errors at each sampling time: normalized measurement residual, normalized Lagrange multiplier, and normalized innovation vector. An IEEE 14-bus test system was used to verify and demonstrate the effectiveness of the proposed method. Numerical results are presented and discussed to show the accuracy of the method.展开更多
基金Illinois Council on Food and Agricultural Research,the Strategic Research Initiative Program in Information Systems andTechnology (CFAR- SRI- IT) and USDA Hatch Funds
基金Supported by the National Nature Science Foundation of China(No.61771006)the Open Foundation of Key Laboratory of Spectral Imaging Technology of the Chinese Academy of Sciences(No.LSIT201711D)+1 种基金the Outstanding Young Cultivation Foundation of Henan university(No.0000A40366) the Basic and Advanced Technology Foundation of Henan Province(No.152300410195)
文摘In the estimation and identification of nonlinear system state,aiming at the adverse effect of observation missing randomly caused by detection probability of used sensor which is less than 1,a novel federal extended Kalman filter( FEKF) based on reconstructed observation in incomplete observations( ROIO) is proposed in this paper. On the basis of multi-sensor observation sets,the observation is exchanged at different times to construct a new observation set. Based on each observation set,an extended Kalman filter algorithm is used to estimate the state of the target,and then the federal filtering algorithm is used to solve the state estimation based on the multi-sensor observation data. The effect of the sensor probing probability on the filtering result and the effect of the number of sensors on the filtering result are obtained by the simulation experiment,respectively. The simulation results demonstrate effectiveness of the proposed algorithm.
基金Shanghai Commission of Science and Technology,China(No.08JC1408200)Shanghai Leading Academic Discipline Project,China(No.B504)
文摘The square-root unscented Kalman filter （SR- UKF） for state estimation probably encounters the problem that Cholesky factor update of the covariance matrices can＇t be implemented when the zero＇th weight of sigma points is negative or the mnnerical computation error becomes large during the faltering procedure. Consequently the filter becomes invalid. An improved SR-UKF algorithm （ISR- UKF） is presented for state estimation of arbitrary nonlinear systems with linear measurements. It adopts a modified form of predicted covariance matrices, and modifies the Cholesky factor calculation of the updated covariance matrix originating from the square-root covariance filtering method. Discussions have been given on how to avoid the filter invalidation and further error accumulation. The comparison between the ISR-UKF and the SR-UKF by simulation also shows both have the same accuracy for state estimation. Finally the performance of the improved filter is evaluated under the impact of model mismatch. The error behavior shows that the ISR-UKF can overcome the impact of model mismatch to a certain extent and has excellent trace capability.
基金Supported by the National Natural Science Foundation Committee of China(61503259)China Postdoctoral Science Foundation Funded Project(2017M611261)+1 种基金Chinese Scholarship Council(201608210107)Hanyu Plan of Shenyang Jianzhu University(XKHY2-64)
文摘Building energy consumption accounts for nearly 40% of global energy consumption, HVAC （Heating, Ventilating, and Air Conditioning） systems are the major building energy consumers, and as one type of HVAC systems, the heat pump air conditioning system, which is more energy-efficient compared to the traditional air conditioning system, is being more widely used to save energy. However, in northern China, extreme climatic conditions increase the cooling and heating load of the heat pump air conditioning system and accelerate the aging of the equipment, and the sensor may detect drifted parameters owing to climate change. This non-linear drifted parameter increases the false alarm rate of the fault detection and the need for unnecessary troubleshooting. In order to overcome the impact of the device aging and the drifted parameter, a Kalman filter and SPC （statistical process control） fault detection method are introduced in this paper. In this method, the model parameter and its standard variance can he estimated by Kalman filter based on the gray model and the real-time data of the air conditioning system. Further, by using SPC to construct the dynamic control limits, false alarm rate is reduced. And this paper mainly focuses on the cold machine failure in the component failure and its soft fault detection. This approach has been tested on a simulation model of the ＂Sino-German Energy Conservation Demonstration Center＂ building heat pump air-conditioning system in Shenyang, China, and the results show that the Kalman filter and SPC fault detection method is simple and highly efficient with a low false alarm rate, and it can deal with the difficulties caused by the extreme environment and the non-linear influence of the parameters, and what＇s more, it provides a good foundation for dynamic fault diagnosis and fault prediction analysis.
基金High Technology Research and Development Program(863program) of China (No.2007AA041604)
文摘To solve the precision self-positioning problem for mobile robot,a positioning program based on ultra-wideband technology was proposed. Ultra-wideband pulse has very high bandwidth; ranging accuracy can achieve centimeter-level theoretically. The mobile robot obtained the distance to the reference node by sending ultra-wideband pulse. According to the geometric relations among the references and the robot,establish equations to calculate the position coordinates. Then Kalman filter algorithm was applied for mobile robot tracking. Simulation results show that robot positioning and tracking based on ultra-wideband technology can achieve indoor and outdoor seamless docking.
基金supported by the National Natural Science Foundation of China under Grant Nos.61120106011,61573221,61633014National Key Technology Support Program of China under Grant No.2014BAF07B03
文摘A newly proposed distributed dynamic state estimation algorithm based on the maximum a posteriori(MAP) technique is generalised and studied for power systems. The system model involves linear time-varying load dynamics and nonlinear measurements. The main contribution of this paper is to compare the performance and feasibility of this distributed algorithm with several existing distributed state estimation algorithms in the literature. Simulations are tested on the IEEE 39-bus and 118-bus systems under various operating conditions. The results show that this distributed algorithm performs better than distributed quasi-steady state estimation algorithms which do not use the load dynamic model. The results also show that the performance of this distributed method is very close to that by the centralized state estimation method. The merits of this algorithm over the centralized method lie in its low computational complexity and low communication load. Hence, the analysis supports the efficiency and benefits of the distributed algorithm in applications to large-scale power systems.
基金supported by the State Key Laboratory Breeding Base of Mountain Bridge and Tunnel Engineering(Chongqing Jiaotong University)fund(Grant No.CQSLBF-Y16-16)the Engineering Research Center of Bridge Structure and Material in the Mountainous Area Fund(Grant No.QLGCZX-JJ2015-6)+4 种基金the National Natural Science Foundation of China(Grant No.51408087)the Construction Technology Project of Ministry of Transport(Grant No.2015318814190)the Key Project of Foundation and Frontier Research of Chongqing(Grant No.cstc2015jcyjBX0022)the Application Foundation Research Project of Ministry of transport(Grant No.2013319814180)the "Xiaoping Science and Technology Innovation Team" fund for Chinese college students
文摘In this paper, bridge alignment control with considering dynamic train loads was experimentally and theoretically investigated.Analytical process of bridge dynamics and the self-adaptive Kalman filter bridge alignment control method with considering the dynamic train loads were briefly introduced. The static measurement, the dynamic test, the field alignment measurement as well as the finite element analysis(FEA) of the second longest rail transit cable-stayed bridge in the world were carried out.Based on the results, the train dynamic load effect on the bridge alignment was obtained quantitatively. Subsequently, alignment control of the rail transit bridge with considering this effect using a self-adaptive Kalman filter method was analyzed. The results show that:(a) the dynamic train loads have effects on alignment control of the bridge and therefore cannot be neglected;(b) the self-adaptive Kalman filter method is applicable and reliable for alignment control of bridges during construction. The analytical method and whole process contribute to develop a related specification and further engineering applications.
基金supported by the National Basic Research Program of China under Grant No.2014CB845303the National Center for Mathematics and Interdisciplinary Sciences,Chinese Academy of Sciences
文摘The problem of navigation for the distributed satellites system using relative range mea- surements is investigated. Firstly, observability for every participating satellites is analyzed based on the nonlinear Keplerian model containing J2 perturbation and the nonlinear measurements. It is proven that the minimum number of tracking satellites to assure the observability of the distributed satellites system is three. Additionally, the analysis shows that the J2 perturbation and the nonlinearity make little contribution to improve the observability for the navigation. Then, a quasi-consistent extended Kalman filter based navigation algorithm is proposed, which is quasi-consistent and can provide an on- line evaluation of the navigation precision. The simulation illustrates the feasibility and effectiveness of the proposed navigation algorithm for the distributed satellites system.
基金supported by the National Natural Science Foundation of China under Grant No.71271165
文摘The statistical inference for generalized mixed-effects state space models （MESSM） are investigated when the random effects are unknown. Two filtering algorithms are designed both of which are based on mixture Kalman filter. These algorithms are particularly useful when the longitudinal ts are sparse. The authors also propose a globally convergent algorithm for parameter estimation of MESSM which can be used to locate the initial value of parameters for local while more efficient algorithms. Simulation examples are carried out which validate the efficacy of the proposed approaches. A data set from the clinical trial is investigated and a smaller mean square error is achieved compared to the existing results in literatures.
文摘We propose a new and efficient algorithm to detect, identify, and correct measurement errors and branch parameter errors of power systems. A dynamic state estimation algorithm is used based on the Kalman filter theory. The proposed algorithm also successfully detects and identifies sudden load changes in power systems. The method uses three normalized vectors to process errors at each sampling time: normalized measurement residual, normalized Lagrange multiplier, and normalized innovation vector. An IEEE 14-bus test system was used to verify and demonstrate the effectiveness of the proposed method. Numerical results are presented and discussed to show the accuracy of the method.
