Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded...Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields.展开更多
Dear Editor,This letter focuses on how an attacker can design suitable improved zero-dynamics (ZD) attack signal based on state estimates of target system. Improved ZD attack is to change zero dynamic gain matrix of a...Dear Editor,This letter focuses on how an attacker can design suitable improved zero-dynamics (ZD) attack signal based on state estimates of target system. Improved ZD attack is to change zero dynamic gain matrix of attack signal to a matrix with determinant greater than 1.展开更多
For target tracking and localization in bearing-only sensor network,it is an essential and significant challenge to solve the problem of plug-and-play expansion while stably enhancing the accuracy of state estimation....For target tracking and localization in bearing-only sensor network,it is an essential and significant challenge to solve the problem of plug-and-play expansion while stably enhancing the accuracy of state estimation.This paper pro-poses a distributed state estimation method based on two-layer factor graph.Firstly,the measurement model of the bearing-only sensor network is constructed,and by investigating the observ-ability and the Cramer-Rao lower bound of the system model,the preconditions are analyzed.Subsequently,the location fac-tor graph and cubature information filtering algorithm of sensor node pairs are proposed for localized estimation.Building upon this foundation,the mechanism for propagating confidence mes-sages within the fusion factor graph is designed,and is extended to the entire sensor network to achieve global state estimation.Finally,groups of simulation experiments are con-ducted to compare and analyze the results,which verifies the rationality,effectiveness,and superiority of the proposed method.展开更多
Quantum phase estimation reveals the power of quantum resources to beat the standard quantum limit and has been widely used in many fields.To improve the precision of phase estimation,we discuss the optimal probe stat...Quantum phase estimation reveals the power of quantum resources to beat the standard quantum limit and has been widely used in many fields.To improve the precision of phase estimation,we discuss the optimal probe states for phase estimation with a fixed mean particle number.By searching for the maximum quantum Fisher information,we optimize the probe states,which are superior to the path-entangled Fock states.Comparing the mean particle number(n)with the dimension of the probe states in Fock space(N+1),when n≤N,our optimal probe states can provide a better performance than the n00n states.When n>N,our optimal probe states can also remain optimal if the dimension of the probe states is large enough.展开更多
With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing hig...With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods,this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification.To resolve the scalability issue of coupled transmission and distribution power systems,clustering is first carried out based on the distribution system states.As the data and models of the transmission system and distribution systems are not shared.For the transmission system,equating the power transmitted from the transmission system to the distribution system is the same as equating the distribution system.Further,the power transmitted from the transmission system to different types of distribution systems is equivalent to different polynomial equivalent load models.Then,a parameter identification method is proposed to obtain the parameters of the equivalent load model.Finally,a transmission and distribution collaborative state estimation model is constructed based on the equivalent load model.The results of the numerical analysis show that compared with the traditional master-slave splitting method,the proposed method significantly enhances computational efficiency while maintaining high estimation accuracy.展开更多
Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles.This study examines ten machine learning architectures,Including Dee...Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles.This study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 instances.Results indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s variance.When computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction times.The GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational speed.In contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery management.Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep discharging.These findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained resources.Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.展开更多
The research on ocean dynamics information plays a crucial role in understanding ocean phenomena, assessing marine environmental impacts, and guiding engineering designs. The Doppler information observed by radars ref...The research on ocean dynamics information plays a crucial role in understanding ocean phenomena, assessing marine environmental impacts, and guiding engineering designs. The Doppler information observed by radars reflects sea surface dynamics, to which ocean waves make important contributions. Low-incidence-angle real aperture radar(RAR)demonstrates great potential for independently observing vectorial Doppler information on the ocean surface. To systematically characterize and accurately estimate the wave-induced Doppler frequency shift(WVF) from lowincidence-angle RAR, this study conducts comprehensive influencing factor analysis and establishes sea-stateparameterized WVF models. First, a simulated WVF dataset is generated under a rotating low-incidence-angle RAR.The feature parameters of WVF are then determined by analysing contributing factors including wind waves, swells,and sea state parameters. Furthermore, two WVF models(WVF_Ku P9 with 9 inputs and WVF_Ku P4 with 4 inputs) are constructed by the Transformer encoder for different application scenarios. Both models achieve high accuracy for WVF estimation with root mean square errors(RMSE) of 1.874 Hz and 2.716 Hz, respectively. The reliability and superiority of the proposed models are validated through comparisons with the Ka DOP, which is a typical geophysical model function(GMF). The findings in this paper advance the understanding of WVF characteristics and generation mechanisms. The proposed estimation models can provide reliable estimates, offering critical references for lowincidence-angle RAR applications such as ocean surface current retrieval.展开更多
The state estimation of the flexible multibody systems is a vital issue since it is the base of effective control and condition monitoring.The research on the state estimation method of flexible multibody system with ...The state estimation of the flexible multibody systems is a vital issue since it is the base of effective control and condition monitoring.The research on the state estimation method of flexible multibody system with large deformation and large rotation remains rare.In this investigation,a state estimator based on multiple nonlinear Kalman filtering algorithms was designed for the flexible multibody systems containing large flexibility components that were discretized by absolute nodal coordinate formulation(ANCF).The state variable vector was constructed based on the independent coordinates which are identified through the constraint Jacobian.Three types of Kalman filters were used to compare their performance in the state estimation for ANCF.Three cases including flexible planar rotating beam,flexible four-bar mechanism,and flexible rotating shaft were employed to verify the proposed state estimator.According to the different performances of the three types of Kalman filter,suggestions were given for the construction of the state estimator for the flexible multibody system.展开更多
This paper aims to enhance the array Beamforming(BF) robustness by tackling issues related to BF weight state estimation encountered in Constant Modulus Blind Beamforming(CMBB). To achieve this, we introduce a novel a...This paper aims to enhance the array Beamforming(BF) robustness by tackling issues related to BF weight state estimation encountered in Constant Modulus Blind Beamforming(CMBB). To achieve this, we introduce a novel approach that incorporates an L1-regularizer term in BF weight state estimation. We start by explaining the CMBB formation mechanism under conditions where there is a mismatch in the far-field signal model. Subsequently, we reformulate the BF weight state estimation challenge using a method known as variable-splitting, turning it into a noise minimization problem. This problem combines both linear and nonlinear quadratic terms with an L1-regularizer that promotes the sparsity. The optimization strategy is based on a variable-splitting method, implemented using the Alternating Direction Method of Multipliers(ADMM). Furthermore, a variable-splitting framework is developed to enhance BF weight state estimation, employing a Kalman Smoother(KS) optimization algorithm. The approach integrates the Rauch-TungStriebel smoother to perform posterior-smoothing state estimation by leveraging prior data. We provide proof of convergence for both linear and nonlinear CMBB state estimation technology using the variable-splitting KS and the iterated extended Kalman smoother. Simulations corroborate our theoretical analysis, showing that the proposed method achieves robust stability and effective convergence, even when faced with signal model mismatches.展开更多
Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, man...Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute（HSRI） tire model; the dual extended Kalman filter （DEKF） technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions（high-friction, low-friction, and joint-friction）. On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.展开更多
Aiming at handling complicated maneuvers or other unpredicted emergencies for hypersonic glide vehicle tracking,three coupled dynamic models of state estimation based on the priori information between guidance variabl...Aiming at handling complicated maneuvers or other unpredicted emergencies for hypersonic glide vehicle tracking,three coupled dynamic models of state estimation based on the priori information between guidance variables and aerodynamics are presented. Firstly, the aerodynamic acceleration acting on the target is analyzed to reveal the essence of the target’s motion.Then three coupled structures for modeling aerodynamic parameters are developed by different ideas: the spiral model with a harmonic oscillator, the bank model with trigonometric functions of the bank angle and the guide model with the changing rule of guidance variables. Meanwhile, the comparison discussion is concluded to show the novelty and advantage of these models.Finally, a performance assessment in different simulation cases is presented and detailed analysis is revealed. The results show that the proposed models perform excellent properties. Moreover, the guide model produces the best tracking performance and the bank model shows the second; however, the spiral model does not outperform the maneuvering reentry vehicle(MaRV) model markedly.展开更多
In actual power systems,most of the high-voltage buses of the transformers are zero injection buses without load or generation.Power injections into these buses are strictly 0,so based on Kirchhoff's current law(K...In actual power systems,most of the high-voltage buses of the transformers are zero injection buses without load or generation.Power injections into these buses are strictly 0,so based on Kirchhoff's current law(KCL),equality constraints should be used to handle these buses in a state estimation model.It is a challenge to ensure that these zero injection constraints can be strictly satisfied without losing computational efficiency.展开更多
In this paper, a new nonlinear augmented observer is proposed and applied to satellite attitude control systems. The observer can estimate system state and actuator fault simultaneously. It can enhance the performance...In this paper, a new nonlinear augmented observer is proposed and applied to satellite attitude control systems. The observer can estimate system state and actuator fault simultaneously. It can enhance the performances of rapidly-varying faults estimation. Only original system matrices are adopted in the parameter design. The considered faults can be unbounded, and the proposed augmented observer can estimate a large class of faults. Systems without disturbances and the fault whose finite times derivatives are zero piecewise are initially considered, followed by a discussion of a general situation where the system is subject to disturbances and the finite times derivatives of the faults are not null but bounded. For the considered nonlinear system, convergence conditions of the observer are provided and the stability analysis is performed using Lyapunov direct method. Then a feasible algorithm is explored to compute the observer parameters using linear matrix inequalities （LMIs）. Finally, the effectiveness of the proposed approach is illustrated by considering an example of a closed-loop satellite attitude control system. The mance in estimating states and actuator faults. It also successfully. simulation results show satisfactory perfor- shows that multiple faults can be estimated展开更多
The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial...The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.展开更多
This paper is concerned with the problem of finitehorizon energy-to-peak state estimation for a class of networked linear time-varying systems.Due to the inherent vulnerability of network-based communication,the measu...This paper is concerned with the problem of finitehorizon energy-to-peak state estimation for a class of networked linear time-varying systems.Due to the inherent vulnerability of network-based communication,the measurement signals transmitted over a communication network might be intercepted by potential eavesdroppers.To avoid information leakage,by resorting to an artificial-noise-assisted method,we develop a novel encryption-decryption scheme to ensure that the transmitted signal is composed of the raw measurement and an artificial-noise term.A special evaluation index named secrecy capacity is employed to assess the information security of signal transmissions under the developed encryption-decryption scheme.The purpose of the addressed problem is to design an encryptiondecryption scheme and a state estimator such that:1)the desired secrecy capacity is ensured;and 2)the required finite-horizon–l_(2)-l_(∞)performance is achieved.Sufficient conditions are established on the existence of the encryption-decryption mechanism and the finite-horizon state estimator.Finally,simulation results are proposed to show the effectiveness of our proposed encryption-decryption-based state estimation scheme.展开更多
An adaptive unscented Kalman filter(AUKF)and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects.A strong tracking filter is employed to i...An adaptive unscented Kalman filter(AUKF)and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects.A strong tracking filter is employed to improve the tracking ability and robustness of unscented Kalman filter(UKF)when the process noise is inaccuracy,and wavelet transform is used to improve the estimate accuracy by the variance of measurement noise.An augmented square-root framework is utilized to improve the numerical stability and accuracy of UKF.Monte Carlo simulations and applications in the rapid trajectory estimation of hypersonic artillery shells confirm the effectiveness of the proposed method.展开更多
Acquisition of real-time and accurate vehicle state and parameter information is critical to the research of vehicle dynamic control system.By studying the defects of the former Kalman filter based estimation method,a...Acquisition of real-time and accurate vehicle state and parameter information is critical to the research of vehicle dynamic control system.By studying the defects of the former Kalman filter based estimation method,a new estimating method is proposed.First the nonlinear vehicle dynamics system,containing inaccurate model parameters and constant noise,is established.Then a dual unscented particle filter(DUPF)algorithm is proposed.In the algorithm two unscented particle filters run in parallel,states estimation and parameters estimation update each other.The results of simulation and vehicle ground testing indicate that the DUPF algorithm has higher state estimation accuracy than unscented Kalman filter(UKF)and dual extended Kalman filter(DEKF),and it also has good capability to revise model parameters.展开更多
A model-based estimator design and implementation is described in this paper to undertake combined estimation of vehicle states and tire-road friction coefficients.The estimator is designed based on a vehicle model wi...A model-based estimator design and implementation is described in this paper to undertake combined estimation of vehicle states and tire-road friction coefficients.The estimator is designed based on a vehicle model with three degrees of freedom(3-DOF) and the dual extended Kalman filter(DEKF) technique is employed.Effectiveness of the estimation is examined and validated by comparing the outputs of the estimator with the responses of the vehicle model in CarSim in three typical road adhesion conditions(high-friction,low-friction,and joint-friction roads).Simulation results demonstrate that the DEKF estimator algorithm designed is able to obtain vehicle states(e.g.,yaw rate and roll angle) as well as road friction coefficients with reasonable accuracy.展开更多
Without the known state equation, a new state estimation strategy is designed to be against malicious attacks for cyber physical systems. Inspired by the idea of data reconstruction, the compressive sensing （CS） is ...Without the known state equation, a new state estimation strategy is designed to be against malicious attacks for cyber physical systems. Inspired by the idea of data reconstruction, the compressive sensing （CS） is applied to reconstruction of residual measurements after the detection and identification scheme based on the Markov graph of the system state, which increases the resilience of state estimation strategy against deception attacks. First, the observability analysis is introduced to decide the triggering time of the measurement reconstruction and the damage level from attacks. In particular, the dictionary learning is proposed to form the over-completed dictionary by K-singular value decomposition （K-SVD）, which is produced adaptively according to the characteristics of the measurement data. In addition, due to the irregularity of residual measurements, a sampling matrix is designed as the measurement matrix. Finally, the simulation experiments are performed on 6-bus power system. Results show that the reconstruction of measurements is completed well by the proposed reconstruction method, and the corresponding effects are better than reconstruction scheme based on the joint dictionary and the traditional Gauss or Bernoulli random matrix respectively. Especially, when only 29% available clean measurements are left, performance of the proposed strategy is still extraordinary, which reflects generality for five kinds of recovery algorithms.展开更多
Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention ...Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter（VSEKF）-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model（GLM）,which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics（HPPC） test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour（Ah） integration method and the extended Kalman filter（EKF） method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.展开更多
基金supported by the National Natural Science Foundation of China(62303273,62373226)the National Research Foundation,Singapore through the Medium Sized Center for Advanced Robotics Technology Innovation(WP2.7)
文摘Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields.
基金supported in part by the National Natural Science Foundation of China(61873106,62303109)Start-Up Research Fund of Southeast University(RF1028623002)Shenzhen Science and Technology Program(JCYJ20230807114609019)
文摘Dear Editor,This letter focuses on how an attacker can design suitable improved zero-dynamics (ZD) attack signal based on state estimates of target system. Improved ZD attack is to change zero dynamic gain matrix of attack signal to a matrix with determinant greater than 1.
基金supported by the National Natural Science Foundation of China(62176214).
文摘For target tracking and localization in bearing-only sensor network,it is an essential and significant challenge to solve the problem of plug-and-play expansion while stably enhancing the accuracy of state estimation.This paper pro-poses a distributed state estimation method based on two-layer factor graph.Firstly,the measurement model of the bearing-only sensor network is constructed,and by investigating the observ-ability and the Cramer-Rao lower bound of the system model,the preconditions are analyzed.Subsequently,the location fac-tor graph and cubature information filtering algorithm of sensor node pairs are proposed for localized estimation.Building upon this foundation,the mechanism for propagating confidence mes-sages within the fusion factor graph is designed,and is extended to the entire sensor network to achieve global state estimation.Finally,groups of simulation experiments are con-ducted to compare and analyze the results,which verifies the rationality,effectiveness,and superiority of the proposed method.
基金supported by the National Natural Science Foundation of China(Grant No.12405026)the Natural Science Foundation of Hangzhou(Grant No.2024SZRYBA050001)。
文摘Quantum phase estimation reveals the power of quantum resources to beat the standard quantum limit and has been widely used in many fields.To improve the precision of phase estimation,we discuss the optimal probe states for phase estimation with a fixed mean particle number.By searching for the maximum quantum Fisher information,we optimize the probe states,which are superior to the path-entangled Fock states.Comparing the mean particle number(n)with the dimension of the probe states in Fock space(N+1),when n≤N,our optimal probe states can provide a better performance than the n00n states.When n>N,our optimal probe states can also remain optimal if the dimension of the probe states is large enough.
基金State Grid Jiangsu Electric Power Co.,Ltd.Technology Project(J2023121).
文摘With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods,this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification.To resolve the scalability issue of coupled transmission and distribution power systems,clustering is first carried out based on the distribution system states.As the data and models of the transmission system and distribution systems are not shared.For the transmission system,equating the power transmitted from the transmission system to the distribution system is the same as equating the distribution system.Further,the power transmitted from the transmission system to different types of distribution systems is equivalent to different polynomial equivalent load models.Then,a parameter identification method is proposed to obtain the parameters of the equivalent load model.Finally,a transmission and distribution collaborative state estimation model is constructed based on the equivalent load model.The results of the numerical analysis show that compared with the traditional master-slave splitting method,the proposed method significantly enhances computational efficiency while maintaining high estimation accuracy.
文摘Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles.This study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 instances.Results indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s variance.When computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction times.The GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational speed.In contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery management.Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep discharging.These findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained resources.Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.
基金The National Natural Science Foundation of China under contract No. 42274159the Project supported by Key Laboratory of Space Ocean Remote Sensing and Application,MNR under contract No.2023CFO016。
文摘The research on ocean dynamics information plays a crucial role in understanding ocean phenomena, assessing marine environmental impacts, and guiding engineering designs. The Doppler information observed by radars reflects sea surface dynamics, to which ocean waves make important contributions. Low-incidence-angle real aperture radar(RAR)demonstrates great potential for independently observing vectorial Doppler information on the ocean surface. To systematically characterize and accurately estimate the wave-induced Doppler frequency shift(WVF) from lowincidence-angle RAR, this study conducts comprehensive influencing factor analysis and establishes sea-stateparameterized WVF models. First, a simulated WVF dataset is generated under a rotating low-incidence-angle RAR.The feature parameters of WVF are then determined by analysing contributing factors including wind waves, swells,and sea state parameters. Furthermore, two WVF models(WVF_Ku P9 with 9 inputs and WVF_Ku P4 with 4 inputs) are constructed by the Transformer encoder for different application scenarios. Both models achieve high accuracy for WVF estimation with root mean square errors(RMSE) of 1.874 Hz and 2.716 Hz, respectively. The reliability and superiority of the proposed models are validated through comparisons with the Ka DOP, which is a typical geophysical model function(GMF). The findings in this paper advance the understanding of WVF characteristics and generation mechanisms. The proposed estimation models can provide reliable estimates, offering critical references for lowincidence-angle RAR applications such as ocean surface current retrieval.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272123 and 12302047)the Natural Science Foundation of Jiangsu Province(Grant No.BK20231185)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.SJCX24_0192).
文摘The state estimation of the flexible multibody systems is a vital issue since it is the base of effective control and condition monitoring.The research on the state estimation method of flexible multibody system with large deformation and large rotation remains rare.In this investigation,a state estimator based on multiple nonlinear Kalman filtering algorithms was designed for the flexible multibody systems containing large flexibility components that were discretized by absolute nodal coordinate formulation(ANCF).The state variable vector was constructed based on the independent coordinates which are identified through the constraint Jacobian.Three types of Kalman filters were used to compare their performance in the state estimation for ANCF.Three cases including flexible planar rotating beam,flexible four-bar mechanism,and flexible rotating shaft were employed to verify the proposed state estimator.According to the different performances of the three types of Kalman filter,suggestions were given for the construction of the state estimator for the flexible multibody system.
基金supported in Natural Science Foundation of Shandong Province,China(ZR2013FM018)。
文摘This paper aims to enhance the array Beamforming(BF) robustness by tackling issues related to BF weight state estimation encountered in Constant Modulus Blind Beamforming(CMBB). To achieve this, we introduce a novel approach that incorporates an L1-regularizer term in BF weight state estimation. We start by explaining the CMBB formation mechanism under conditions where there is a mismatch in the far-field signal model. Subsequently, we reformulate the BF weight state estimation challenge using a method known as variable-splitting, turning it into a noise minimization problem. This problem combines both linear and nonlinear quadratic terms with an L1-regularizer that promotes the sparsity. The optimization strategy is based on a variable-splitting method, implemented using the Alternating Direction Method of Multipliers(ADMM). Furthermore, a variable-splitting framework is developed to enhance BF weight state estimation, employing a Kalman Smoother(KS) optimization algorithm. The approach integrates the Rauch-TungStriebel smoother to perform posterior-smoothing state estimation by leveraging prior data. We provide proof of convergence for both linear and nonlinear CMBB state estimation technology using the variable-splitting KS and the iterated extended Kalman smoother. Simulations corroborate our theoretical analysis, showing that the proposed method achieves robust stability and effective convergence, even when faced with signal model mismatches.
基金supported by National Natural Science Foundation of China(Grant Nos. 51075176, 51105165)
文摘Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute（HSRI） tire model; the dual extended Kalman filter （DEKF） technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions（high-friction, low-friction, and joint-friction）. On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.
基金supported by the National High-tech R&D Program of China(863 Program)(2015AA7326042 2015AA8321471)
文摘Aiming at handling complicated maneuvers or other unpredicted emergencies for hypersonic glide vehicle tracking,three coupled dynamic models of state estimation based on the priori information between guidance variables and aerodynamics are presented. Firstly, the aerodynamic acceleration acting on the target is analyzed to reveal the essence of the target’s motion.Then three coupled structures for modeling aerodynamic parameters are developed by different ideas: the spiral model with a harmonic oscillator, the bank model with trigonometric functions of the bank angle and the guide model with the changing rule of guidance variables. Meanwhile, the comparison discussion is concluded to show the novelty and advantage of these models.Finally, a performance assessment in different simulation cases is presented and detailed analysis is revealed. The results show that the proposed models perform excellent properties. Moreover, the guide model produces the best tracking performance and the bank model shows the second; however, the spiral model does not outperform the maneuvering reentry vehicle(MaRV) model markedly.
文摘In actual power systems,most of the high-voltage buses of the transformers are zero injection buses without load or generation.Power injections into these buses are strictly 0,so based on Kirchhoff's current law(KCL),equality constraints should be used to handle these buses in a state estimation model.It is a challenge to ensure that these zero injection constraints can be strictly satisfied without losing computational efficiency.
基金supported by the National Basic Research Program of China(No.2012CB720003)the National Natural Science Foundation of China(No.61203151)
文摘In this paper, a new nonlinear augmented observer is proposed and applied to satellite attitude control systems. The observer can estimate system state and actuator fault simultaneously. It can enhance the performances of rapidly-varying faults estimation. Only original system matrices are adopted in the parameter design. The considered faults can be unbounded, and the proposed augmented observer can estimate a large class of faults. Systems without disturbances and the fault whose finite times derivatives are zero piecewise are initially considered, followed by a discussion of a general situation where the system is subject to disturbances and the finite times derivatives of the faults are not null but bounded. For the considered nonlinear system, convergence conditions of the observer are provided and the stability analysis is performed using Lyapunov direct method. Then a feasible algorithm is explored to compute the observer parameters using linear matrix inequalities （LMIs）. Finally, the effectiveness of the proposed approach is illustrated by considering an example of a closed-loop satellite attitude control system. The mance in estimating states and actuator faults. It also successfully. simulation results show satisfactory perfor- shows that multiple faults can be estimated
基金Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110303)the Beijing Municipal Science & Technology Project,China (Grant No. Z111100064311001)
文摘The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.
基金This work was supported in part by the National Natural Science Foundation of China(62273087,61933007,62273088,U21A2019,62073180)the Shanghai Pujiang Program of China(22PJ1400400)+3 种基金the Program of Shanghai Academic/Technology Research Leader of China(20XD1420100)the European Union’s Horizon 2020 Research and Innovation Programme(820776)(INTEGRADDE)the Royal Society of UKthe Alexander von Humboldt Foundation of Germany.
文摘This paper is concerned with the problem of finitehorizon energy-to-peak state estimation for a class of networked linear time-varying systems.Due to the inherent vulnerability of network-based communication,the measurement signals transmitted over a communication network might be intercepted by potential eavesdroppers.To avoid information leakage,by resorting to an artificial-noise-assisted method,we develop a novel encryption-decryption scheme to ensure that the transmitted signal is composed of the raw measurement and an artificial-noise term.A special evaluation index named secrecy capacity is employed to assess the information security of signal transmissions under the developed encryption-decryption scheme.The purpose of the addressed problem is to design an encryptiondecryption scheme and a state estimator such that:1)the desired secrecy capacity is ensured;and 2)the required finite-horizon–l_(2)-l_(∞)performance is achieved.Sufficient conditions are established on the existence of the encryption-decryption mechanism and the finite-horizon state estimator.Finally,simulation results are proposed to show the effectiveness of our proposed encryption-decryption-based state estimation scheme.
基金supported by the National Natural Science Foundation of China(61304254)the National Science Foundation for Distinguished Young Scholars of China(60925011)the Provincial and Ministerial Key Fund of China(9140A07010511BQ0105)
文摘An adaptive unscented Kalman filter(AUKF)and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects.A strong tracking filter is employed to improve the tracking ability and robustness of unscented Kalman filter(UKF)when the process noise is inaccuracy,and wavelet transform is used to improve the estimate accuracy by the variance of measurement noise.An augmented square-root framework is utilized to improve the numerical stability and accuracy of UKF.Monte Carlo simulations and applications in the rapid trajectory estimation of hypersonic artillery shells confirm the effectiveness of the proposed method.
基金Supported by the National Natural Science Foundation of China(10902049)the Chinese Postdoctoral Science Foundation(2012M521073)+3 种基金the Fundamental Research Funds for the Central Universitiesthe Jiangsu Planned Projects for Postdoctoral Research Funds(1302020C)the Nanjing University of Aeronautics and Astronautics Student Innovative Training Program(20120119101535)the Fundation of Graduate Innovation Center in Nanjing University of Aeronautics and Astronautics(kfjj201404)
文摘Acquisition of real-time and accurate vehicle state and parameter information is critical to the research of vehicle dynamic control system.By studying the defects of the former Kalman filter based estimation method,a new estimating method is proposed.First the nonlinear vehicle dynamics system,containing inaccurate model parameters and constant noise,is established.Then a dual unscented particle filter(DUPF)algorithm is proposed.In the algorithm two unscented particle filters run in parallel,states estimation and parameters estimation update each other.The results of simulation and vehicle ground testing indicate that the DUPF algorithm has higher state estimation accuracy than unscented Kalman filter(UKF)and dual extended Kalman filter(DEKF),and it also has good capability to revise model parameters.
基金Project (Nos.50775096 and 51075176) supported by the National Natural Science Foundation of China
文摘A model-based estimator design and implementation is described in this paper to undertake combined estimation of vehicle states and tire-road friction coefficients.The estimator is designed based on a vehicle model with three degrees of freedom(3-DOF) and the dual extended Kalman filter(DEKF) technique is employed.Effectiveness of the estimation is examined and validated by comparing the outputs of the estimator with the responses of the vehicle model in CarSim in three typical road adhesion conditions(high-friction,low-friction,and joint-friction roads).Simulation results demonstrate that the DEKF estimator algorithm designed is able to obtain vehicle states(e.g.,yaw rate and roll angle) as well as road friction coefficients with reasonable accuracy.
基金This work was supported by the Natural Science Foundation of China （NSFC）-Guangdong Joint Foundation Key Project （No. U1401253）, the NSFC （Nos. 61573153, 616721 74）, the Foundation of Guangdong Provincial Science and Technology Projects （No. 2013B010401001 ）, the Fundamental Research Funds for the Central Universities （No. 2015ZZ099）, the Guangzhou Science and Technology Plan Project （No. 201510010132）, the Maoming Science and Technology Plan Project （No. MM201 7000004）, and the National Natural Science Foundation of Guangdong Province （No. 2016A030313510）.
文摘Without the known state equation, a new state estimation strategy is designed to be against malicious attacks for cyber physical systems. Inspired by the idea of data reconstruction, the compressive sensing （CS） is applied to reconstruction of residual measurements after the detection and identification scheme based on the Markov graph of the system state, which increases the resilience of state estimation strategy against deception attacks. First, the observability analysis is introduced to decide the triggering time of the measurement reconstruction and the damage level from attacks. In particular, the dictionary learning is proposed to form the over-completed dictionary by K-singular value decomposition （K-SVD）, which is produced adaptively according to the characteristics of the measurement data. In addition, due to the irregularity of residual measurements, a sampling matrix is designed as the measurement matrix. Finally, the simulation experiments are performed on 6-bus power system. Results show that the reconstruction of measurements is completed well by the proposed reconstruction method, and the corresponding effects are better than reconstruction scheme based on the joint dictionary and the traditional Gauss or Bernoulli random matrix respectively. Especially, when only 29% available clean measurements are left, performance of the proposed strategy is still extraordinary, which reflects generality for five kinds of recovery algorithms.
基金Supported by National Key Technology R&D Program of Ministry of Science and Technology of China(Grant No.2013BAG14B01)
文摘Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter（VSEKF）-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model（GLM）,which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics（HPPC） test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour（Ah） integration method and the extended Kalman filter（EKF） method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.
