Cyber-physical systems(CPSs)are regarded as the backbone of the fourth industrial revolution,in which communication,physical processes,and computer technology are integrated.In modern industrial systems,CPSs are widel...Cyber-physical systems(CPSs)are regarded as the backbone of the fourth industrial revolution,in which communication,physical processes,and computer technology are integrated.In modern industrial systems,CPSs are widely utilized across various domains,such as smart grids,smart healthcare systems,smart vehicles,and smart manufacturing,among others.Due to their unique spatial distribution,CPSs are highly vulnerable to cyber-attacks,which may result in severe performance degradation and even system instability.Consequently,the security concerns of CPSs have attracted significant attention in recent years.In this paper,a comprehensive survey on the security issues of CPSs under cyber-attacks is provided.Firstly,mathematical descriptions of various types of cyberattacks are introduced in detail.Secondly,two types of secure estimation and control processing schemes,including robust methods and active methods,are reviewed.Thirdly,research findings related to secure control and estimation problems for different types of CPSs are summarized.Finally,the survey is concluded by outlining the challenges and suggesting potential research directions for the future.展开更多
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
This paper investigates set-valued state estimation of nonlinear systems with unknown-but-bounded(UBB)noises based on constrained polynomial zonotopes which is utilized to characterize non-convex sets.First,properties...This paper investigates set-valued state estimation of nonlinear systems with unknown-but-bounded(UBB)noises based on constrained polynomial zonotopes which is utilized to characterize non-convex sets.First,properties of constrained polynomial zonotopes are provided and the order reduction method is given to reduce the computational complexity.Then,the corresponding improved prediction-update algorithm is proposed so that it can be adapted to non-convex sets.Based on generalized intersection,the utilization of set-based estimation for attack detection is analyzed.Finally,an example is given to show the efficiency of our results.展开更多
This paper proposes a state estimation method for a class of norm bounded non linear sampled data descriptor systems using the Kalman filtering method. The descriptor model is firstly discretized to obtain a discrete ...This paper proposes a state estimation method for a class of norm bounded non linear sampled data descriptor systems using the Kalman filtering method. The descriptor model is firstly discretized to obtain a discrete time non singular one. Then a model of robust extended Kalman filter is proposed for the state estimation based on the discretized non linear non singular system. As parameters are introduced in for transforming descriptor systems into non singular ones there exist uncertainties in the state of the systems. To solve this problem an optimized upper bound is proposed so that the convergence of the estimation error co variance matrix is guaranteed in the paper. A simulating example is proposed to verify the validity of this method at last.展开更多
In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obta...In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust estimation（RE） and fault detection（FD）. A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing priori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range; with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.展开更多
The multirate multi-input systems have different updating periods and sampling periods such that the conventional identification algorithms cannot be used to identify such multirate systems. By using the auxiliary mod...The multirate multi-input systems have different updating periods and sampling periods such that the conventional identification algorithms cannot be used to identify such multirate systems. By using the auxiliary model identification idea, the multiinnovation stochastic gradient algorithm is developed to estimate the parameters of multirate systems. Finally, an illustrative example is given to verify the effectiveness of the proposed algorithm.展开更多
The paper develops a novel framework of consensus control with fault-estimation-in-the-loop for multi-agent systems(MASs)in the presence of faults.A dynamic event-triggered protocol(DETP)by adding an auxiliary variabl...The paper develops a novel framework of consensus control with fault-estimation-in-the-loop for multi-agent systems(MASs)in the presence of faults.A dynamic event-triggered protocol(DETP)by adding an auxiliary variable is utilized to improve the utilization of communication resources.First,a novel estimator with a noise bias is put forward to estimate the existed fault and then a consensus controller with fault compensation(FC)is adopted to realize the demand of reliability and safety of addressed MASs.Subsequently,a novel consensus control framework with fault-estimation-in-the-loop is developed to achieve the predetermined consensus performance with the l_(2)-l_(∞)constraint by employing the variance analysis and the Lyapunov stability approaches.Furthermore,the desired estimator and controller gains are obtained in light of the solution to an algebraic matrix equation and a linear matrix inequality in a recursive way,respectively.Finally,a simulation result is employed to verify the usefulness of the proposed design framework.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
This paper investigates the security issue of multisensor remote estimation systems.An optimal stealthy false data injection(FDI)attack scheme based on historical and current residuals,which only tampers with the meas...This paper investigates the security issue of multisensor remote estimation systems.An optimal stealthy false data injection(FDI)attack scheme based on historical and current residuals,which only tampers with the measurement residuals of partial sensors due to limited attack resources,is proposed to maximally degrade system estimation performance.The attack stealthiness condition is given,and then the estimation error covariance in compromised state is derived to quantify the system performance under attack.The optimal attack strategy is obtained by solving several convex optimization problems which maximize the trace of the compromised estimation error covariance subject to the stealthiness condition.Moreover,due to the constraint of attack resources,the selection principle of the attacked sensor is provided to determine which sensor is attacked so as to hold the most impact on system performance.Finally,simulation results are presented to verify the theoretical analysis.展开更多
In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be describ...In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be described by an input-output nonlinear discrete-time mathematical model, with unknown, but constant or slowly time-varying parameters. Then, two recursive estimation methods are used to solve the parametric estimation problem for the considered class of the interconnected nonlinear systems. These methods are based on the recursive least squares techniques and the prediction error method. Convergence analysis is provided using the hyper-stability and positivity method and the differential equation approach. A numerical simulation example of the parametric estimation of a stochastic interconnected nonlinear hydraulic system is treated.展开更多
This paper investigates the problem of robust H-infinity state estimation for a class of uncertain discretetime piecewise affine systems where state space instead of measurable output space partitions are assumed so t...This paper investigates the problem of robust H-infinity state estimation for a class of uncertain discretetime piecewise affine systems where state space instead of measurable output space partitions are assumed so that the filter implementation may not be synchronized with plant state trajectory transitions. Based on a piecewise quadratic Lyapunov function combined with S-procedure and some matrix inequality convexifying techniques, two different approaches are developed to the robust filtering design for the underlying piecewise affine systems. It is shown that the filter gains can be obtained by solving a set of linear matrix inequalities （LMIs）. Finally, a simulation example is provided to illustrate the effectiveness of the proposed approaches.展开更多
To improve the computational efficiency and accuracy of multi-objective reliability estimation for aerospace engineering structural systems,the Intelligent Vectorial Surrogate Modeling(IVSM)concept is presented by fus...To improve the computational efficiency and accuracy of multi-objective reliability estimation for aerospace engineering structural systems,the Intelligent Vectorial Surrogate Modeling(IVSM)concept is presented by fusing the compact support region,surrogate modeling methods,matrix theory,and Bayesian optimization strategy.In this concept,the compact support region is employed to select effective modeling samples;the surrogate modeling methods are employed to establish a functional relationship between input variables and output responses;the matrix theory is adopted to establish the vector and cell arrays of modeling parameters and synchronously determine multi-objective limit state functions;the Bayesian optimization strategy is utilized to search for the optimal hyperparameters for modeling.Under this concept,the Intelligent Vectorial Neural Network(IVNN)method is proposed based on deep neural network to realize the reliability analysis of multi-objective aerospace engineering structural systems synchronously.The multioutput response function approximation problem and two engineering application cases(i.e.,landing gear brake system temperature and aeroengine turbine blisk multi-failures)are used to verify the applicability of IVNN method.The results indicate that the proposed approach holds advantages in modeling properties and simulation performances.The efforts of this paper can offer a valuable reference for the improvement of multi-objective reliability assessment theory.展开更多
This paper investigates the problem of event-triggered H∞state estimation for Takagi-Sugeno (T-S) fuzzy affine systems. The objective is to design an event-triggered scheme and an observer such that the resulting est...This paper investigates the problem of event-triggered H∞state estimation for Takagi-Sugeno (T-S) fuzzy affine systems. The objective is to design an event-triggered scheme and an observer such that the resulting estimation error system is asymptotically stable with a prescribed H∞performance and at the same time unnecessary output measurement transmission can be reduced. First, an event-triggered scheme is proposed to determine whether the sampled measurements should be transmitted or not. The output measurements, which trigger the condition, are supposed to suffer a network-induced time-varying and bounded delay before arriving at the observer. Then, by adopting the input delay method, the estimation error system can be reformulated as a piecewise delay system. Based on the piecewise Lyapunov-Krasovskii functional and the Finsler's lemma, the event-triggered H∞observer design method is developed. Moreover, an algorithm is proposed to co-design the observer gains and the event-triggering parameters to guarantee that the estimation error system is asymptotically stable with a given disturbance attenuation level and the signal transmission rate is reduced as much as possible. Simulation studies are given to show the effectiveness of the proposed method.展开更多
This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines...This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.展开更多
We study the parameter estimation of a nonlinear chaotic system,which can be essentially formulated as a multidimensional optimization problem.In this paper,an orthogonal learning cuckoo search algorithm is used to es...We study the parameter estimation of a nonlinear chaotic system,which can be essentially formulated as a multidimensional optimization problem.In this paper,an orthogonal learning cuckoo search algorithm is used to estimate the parameters of chaotic systems.This algorithm can combine the stochastic exploration of the cuckoo search and the exploitation capability of the orthogonal learning strategy.Experiments are conducted on the Lorenz system and the Chen system.The proposed algorithm is used to estimate the parameters for these two systems.Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to the particle swarm optimization and the genetic algorithm when considering the quality of the solutions obtained.展开更多
为线性分离变化时间的系统的 H 混合评价问题在这份报纸被调查，在估计的信号是状态和输入的线性联合的地方。设计目的从骚乱要求最坏的精力获得到是的评价错误不到规定水平。混合评价问题的最佳的答案是僵绳点一二播放器零和微分游戏...为线性分离变化时间的系统的 H 混合评价问题在这份报纸被调查，在估计的信号是状态和输入的线性联合的地方。设计目的从骚乱要求最坏的精力获得到是的评价错误不到规定水平。混合评价问题的最佳的答案是僵绳点一二播放器零和微分游戏。根据微分比赛途径，为混合评价问题的必要、足够的可解决的条件以一个 Riccati 微分方程的答案被提供。而且，如果可解决的条件满足，一个可能的评估者被建议。评估者被印射矩阵的一个获得矩阵和产量描绘，在后者反映在未知输入和输出评价错误之间的内部关系的地方。最后，一个数字例子被提供说明建议途径。展开更多
This paper was concerned with the problem of robust sampled data state estimation for uncertain continuous time systems. A sampled data estimation covariance is given by taking intersample behaviour into account. T...This paper was concerned with the problem of robust sampled data state estimation for uncertain continuous time systems. A sampled data estimation covariance is given by taking intersample behaviour into account. The primary purpose of this paper is to design robust discrete time Kalman filters such that the sampled data estimation covariance is not more than a prespecified value, and therefore the error variances achieve the desired constraints. It is shown that the addressed problem can be converted into a similar problem for a fictitious discrete time system. The existence conditions and the explicit expression of desired filters were both derived. Finally, a simple example was presented to demonstrate the effectiveness of the proposed design procedure.展开更多
文摘Cyber-physical systems(CPSs)are regarded as the backbone of the fourth industrial revolution,in which communication,physical processes,and computer technology are integrated.In modern industrial systems,CPSs are widely utilized across various domains,such as smart grids,smart healthcare systems,smart vehicles,and smart manufacturing,among others.Due to their unique spatial distribution,CPSs are highly vulnerable to cyber-attacks,which may result in severe performance degradation and even system instability.Consequently,the security concerns of CPSs have attracted significant attention in recent years.In this paper,a comprehensive survey on the security issues of CPSs under cyber-attacks is provided.Firstly,mathematical descriptions of various types of cyberattacks are introduced in detail.Secondly,two types of secure estimation and control processing schemes,including robust methods and active methods,are reviewed.Thirdly,research findings related to secure control and estimation problems for different types of CPSs are summarized.Finally,the survey is concluded by outlining the challenges and suggesting potential research directions for the future.
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 by the National Natural Science Foundation of China(61703286,62394342,61890924,61991404)。
文摘This paper investigates set-valued state estimation of nonlinear systems with unknown-but-bounded(UBB)noises based on constrained polynomial zonotopes which is utilized to characterize non-convex sets.First,properties of constrained polynomial zonotopes are provided and the order reduction method is given to reduce the computational complexity.Then,the corresponding improved prediction-update algorithm is proposed so that it can be adapted to non-convex sets.Based on generalized intersection,the utilization of set-based estimation for attack detection is analyzed.Finally,an example is given to show the efficiency of our results.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61021002)
文摘This paper proposes a state estimation method for a class of norm bounded non linear sampled data descriptor systems using the Kalman filtering method. The descriptor model is firstly discretized to obtain a discrete time non singular one. Then a model of robust extended Kalman filter is proposed for the state estimation based on the discretized non linear non singular system. As parameters are introduced in for transforming descriptor systems into non singular ones there exist uncertainties in the state of the systems. To solve this problem an optimized upper bound is proposed so that the convergence of the estimation error co variance matrix is guaranteed in the paper. A simulating example is proposed to verify the validity of this method at last.
基金co-supported by the National Natural Science Foundation of China(No.61153002)the Aeronautical Science Foundation of China(No.20130153002)
文摘In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust estimation（RE） and fault detection（FD）. A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing priori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range; with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.
基金supported by the National Natural Science Foundation of China (60973043)
文摘The multirate multi-input systems have different updating periods and sampling periods such that the conventional identification algorithms cannot be used to identify such multirate systems. By using the auxiliary model identification idea, the multiinnovation stochastic gradient algorithm is developed to estimate the parameters of multirate systems. Finally, an illustrative example is given to verify the effectiveness of the proposed algorithm.
基金supported in part by the Australian Research Council Discovery Early Career Researcher Award(DE200101128)。
文摘The paper develops a novel framework of consensus control with fault-estimation-in-the-loop for multi-agent systems(MASs)in the presence of faults.A dynamic event-triggered protocol(DETP)by adding an auxiliary variable is utilized to improve the utilization of communication resources.First,a novel estimator with a noise bias is put forward to estimate the existed fault and then a consensus controller with fault compensation(FC)is adopted to realize the demand of reliability and safety of addressed MASs.Subsequently,a novel consensus control framework with fault-estimation-in-the-loop is developed to achieve the predetermined consensus performance with the l_(2)-l_(∞)constraint by employing the variance analysis and the Lyapunov stability approaches.Furthermore,the desired estimator and controller gains are obtained in light of the solution to an algebraic matrix equation and a linear matrix inequality in a recursive way,respectively.Finally,a simulation result is employed to verify the usefulness of the proposed design framework.
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 the National Natural Science Foundation of China(61925303,62173034,62088101,U20B2073,62173002)the National Key Research and Development Program of China(2021YFB1714800)Beijing Natural Science Foundation(4222045)。
文摘This paper investigates the security issue of multisensor remote estimation systems.An optimal stealthy false data injection(FDI)attack scheme based on historical and current residuals,which only tampers with the measurement residuals of partial sensors due to limited attack resources,is proposed to maximally degrade system estimation performance.The attack stealthiness condition is given,and then the estimation error covariance in compromised state is derived to quantify the system performance under attack.The optimal attack strategy is obtained by solving several convex optimization problems which maximize the trace of the compromised estimation error covariance subject to the stealthiness condition.Moreover,due to the constraint of attack resources,the selection principle of the attacked sensor is provided to determine which sensor is attacked so as to hold the most impact on system performance.Finally,simulation results are presented to verify the theoretical analysis.
基金supported by the Ministry of Higher Education and Scientific Research of Tunisia
文摘In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be described by an input-output nonlinear discrete-time mathematical model, with unknown, but constant or slowly time-varying parameters. Then, two recursive estimation methods are used to solve the parametric estimation problem for the considered class of the interconnected nonlinear systems. These methods are based on the recursive least squares techniques and the prediction error method. Convergence analysis is provided using the hyper-stability and positivity method and the differential equation approach. A numerical simulation example of the parametric estimation of a stochastic interconnected nonlinear hydraulic system is treated.
基金supported by the Research Grants Council of the Hong Kong Special Administrative Region of China under the Project CityU/113708partly by the National Natural Science Foundation of China (No.60825303, 60834003)+2 种基金partly by the 973 Project (No.2009CB320600)partly by the Postdoctoral Science Foundation of China (No.20100471059)partly by the Overseas Talents Foundation of the Harbin Institute of Technology
文摘This paper investigates the problem of robust H-infinity state estimation for a class of uncertain discretetime piecewise affine systems where state space instead of measurable output space partitions are assumed so that the filter implementation may not be synchronized with plant state trajectory transitions. Based on a piecewise quadratic Lyapunov function combined with S-procedure and some matrix inequality convexifying techniques, two different approaches are developed to the robust filtering design for the underlying piecewise affine systems. It is shown that the filter gains can be obtained by solving a set of linear matrix inequalities （LMIs）. Finally, a simulation example is provided to illustrate the effectiveness of the proposed approaches.
基金supported by the National Natural Science Foundation of China(No.51875465)the Shaanxi Province Postdoctoral Research Project Funding,Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University(No.CX2023002)+1 种基金the Civil Aircraft Scientific Research Projectthe Fund of Shanghai Engineering Research Center of Civil Aircraft Health Monitoring(No.GCZX-2022-01).
文摘To improve the computational efficiency and accuracy of multi-objective reliability estimation for aerospace engineering structural systems,the Intelligent Vectorial Surrogate Modeling(IVSM)concept is presented by fusing the compact support region,surrogate modeling methods,matrix theory,and Bayesian optimization strategy.In this concept,the compact support region is employed to select effective modeling samples;the surrogate modeling methods are employed to establish a functional relationship between input variables and output responses;the matrix theory is adopted to establish the vector and cell arrays of modeling parameters and synchronously determine multi-objective limit state functions;the Bayesian optimization strategy is utilized to search for the optimal hyperparameters for modeling.Under this concept,the Intelligent Vectorial Neural Network(IVNN)method is proposed based on deep neural network to realize the reliability analysis of multi-objective aerospace engineering structural systems synchronously.The multioutput response function approximation problem and two engineering application cases(i.e.,landing gear brake system temperature and aeroengine turbine blisk multi-failures)are used to verify the applicability of IVNN method.The results indicate that the proposed approach holds advantages in modeling properties and simulation performances.The efforts of this paper can offer a valuable reference for the improvement of multi-objective reliability assessment theory.
基金Research Grants Council of the Hong Kong Special Administrative Region of China (No. CityU-11211818)the Self-Planned Task of State Key Laboratory of Robotics and Systems of Harbin Institute of Technology (No. SKLRS201801A03)the National Natural Science Foundation of China (No. 61873311).
文摘This paper investigates the problem of event-triggered H∞state estimation for Takagi-Sugeno (T-S) fuzzy affine systems. The objective is to design an event-triggered scheme and an observer such that the resulting estimation error system is asymptotically stable with a prescribed H∞performance and at the same time unnecessary output measurement transmission can be reduced. First, an event-triggered scheme is proposed to determine whether the sampled measurements should be transmitted or not. The output measurements, which trigger the condition, are supposed to suffer a network-induced time-varying and bounded delay before arriving at the observer. Then, by adopting the input delay method, the estimation error system can be reformulated as a piecewise delay system. Based on the piecewise Lyapunov-Krasovskii functional and the Finsler's lemma, the event-triggered H∞observer design method is developed. Moreover, an algorithm is proposed to co-design the observer gains and the event-triggering parameters to guarantee that the estimation error system is asymptotically stable with a given disturbance attenuation level and the signal transmission rate is reduced as much as possible. Simulation studies are given to show the effectiveness of the proposed method.
基金supported in part by the National Natural Science Foundation of China(61933007, U21A2019, 62273005, 62273088, 62303301)the Program of Shanghai Academic/Technology Research Leader of China (20XD1420100)+2 种基金the Hainan Province Science and Technology Special Fund of China(ZDYF2022SHFZ105)the Natural Science Foundation of Anhui Province of China (2108085MA07)the Alexander von Humboldt Foundation of Germany。
文摘This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 60473042,60573067 and 60803102)
文摘We study the parameter estimation of a nonlinear chaotic system,which can be essentially formulated as a multidimensional optimization problem.In this paper,an orthogonal learning cuckoo search algorithm is used to estimate the parameters of chaotic systems.This algorithm can combine the stochastic exploration of the cuckoo search and the exploitation capability of the orthogonal learning strategy.Experiments are conducted on the Lorenz system and the Chen system.The proposed algorithm is used to estimate the parameters for these two systems.Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to the particle swarm optimization and the genetic algorithm when considering the quality of the solutions obtained.
基金Supported by NationalNatural Science Foundation of China （60774068, 60574050） and China Postdoctor Science Foundation （20070421064）
文摘为线性分离变化时间的系统的 H 混合评价问题在这份报纸被调查，在估计的信号是状态和输入的线性联合的地方。设计目的从骚乱要求最坏的精力获得到是的评价错误不到规定水平。混合评价问题的最佳的答案是僵绳点一二播放器零和微分游戏。根据微分比赛途径，为混合评价问题的必要、足够的可解决的条件以一个 Riccati 微分方程的答案被提供。而且，如果可解决的条件满足，一个可能的评估者被建议。评估者被印射矩阵的一个获得矩阵和产量描绘，在后者反映在未知输入和输出评价错误之间的内部关系的地方。最后，一个数字例子被提供说明建议途径。
基金Supported by National Natural Science Foundation of China （60574083, 60811120024）, Graduate Innovation Research Foundation of Jiangsu Province （CX08B-090Z）, and Doctoral Innovation Foundation of Nanjing University of Aeronautics and Astronautics （BCXJ08-03）
文摘This paper was concerned with the problem of robust sampled data state estimation for uncertain continuous time systems. A sampled data estimation covariance is given by taking intersample behaviour into account. The primary purpose of this paper is to design robust discrete time Kalman filters such that the sampled data estimation covariance is not more than a prespecified value, and therefore the error variances achieve the desired constraints. It is shown that the addressed problem can be converted into a similar problem for a fictitious discrete time system. The existence conditions and the explicit expression of desired filters were both derived. Finally, a simple example was presented to demonstrate the effectiveness of the proposed design procedure.
基金Supported by National Natural Science Foundation of P. R. China （60574083）, Key Laboratory of Process Industry Automation, State Education Ministry of China （PAL200514）
