This paper is concerned with distributed fault detection of second-order discrete-time multi-agent systems with adversary,where the adversary is regarded as a slowly time-varying signal.Firstly,a novel intrusion detec...This paper is concerned with distributed fault detection of second-order discrete-time multi-agent systems with adversary,where the adversary is regarded as a slowly time-varying signal.Firstly,a novel intrusion detection scheme based on the theory of unknown input observability( UIO) is proposed. By constructing a bank of UIO,the states of the malicious agents can be directly estimated. Secondly,the faulty-node-removal algorithm is provided.Simulations are also provided to demonstrate the effectiveness of the theoretical results.展开更多
In this paper,a novel finite-time distributed identification method is introduced for nonlinear interconnected systems.A distributed concurrent learning-based discontinuous gradient descent update law is presented to ...In this paper,a novel finite-time distributed identification method is introduced for nonlinear interconnected systems.A distributed concurrent learning-based discontinuous gradient descent update law is presented to learn uncertain interconnected subsystems’dynamics.The concurrent learning approach continually minimizes the identification error for a batch of previously recorded data collected from each subsystem as well as its neighboring subsystems.The state information of neighboring interconnected subsystems is acquired through direct communication.The overall update laws for all subsystems form coupled continuous-time gradient flow dynamics for which finite-time Lyapunov stability analysis is performed.As a byproduct of this Lyapunov analysis,easy-to-check rank conditions on data stored in the distributed memories of subsystems are obtained,under which finite-time stability of the distributed identifier is guaranteed.These rank conditions replace the restrictive persistence of excitation(PE)conditions which are hard and even impossible to achieve and verify for interconnected subsystems.Finally,simulation results verify the effectiveness of the presented distributed method in comparison with the other methods.展开更多
This paper is focused on the state estimation problem for nonlinear systems with unknown statistics of measurement noise.Based on the cubature Kalman filter,we propose a new nonlinear filtering algorithm that employs ...This paper is focused on the state estimation problem for nonlinear systems with unknown statistics of measurement noise.Based on the cubature Kalman filter,we propose a new nonlinear filtering algorithm that employs a skew t distribution to characterize the asymmetry of the measurement noise.The system states and the statistics of skew t noise distribution,including the shape matrix,the scale matrix,and the degree of freedom(DOF)are estimated jointly by employing variational Bayesian(VB)inference.The proposed method is validated in a target tracking example.Results of the simulation indicate that the proposed nonlinear filter can perform satisfactorily in the presence of unknown statistics of measurement noise and outperform than the existing state-of-the-art nonlinear filters.展开更多
为了解决子空间数据融合(Subspace data fusion,SDF)算法用于未知互耦影响下的分布式多阵列定位时定位精度低的问题,本文结合降维搜索思想提出了一种降互耦维度的子空间数据融合(Reduced mutual coupling dimension subspace data fusio...为了解决子空间数据融合(Subspace data fusion,SDF)算法用于未知互耦影响下的分布式多阵列定位时定位精度低的问题,本文结合降维搜索思想提出了一种降互耦维度的子空间数据融合(Reduced mutual coupling dimension subspace data fusion,RMCD⁃SDF)方法。该方法首先将互耦误差模型引入SDF算法,使其适应于天线阵列受到未知互耦误差影响的场景。在此基础上,为了降低同时搜索所有未知参数带来的超高计算复杂度,本文引入降维搜索思想并构造了RMCD⁃SDF算法谱函数。仿真结果显示,RMCD⁃SDF算法的定位性能在阵列受到未知互耦影响的场景下具有优势,与现有算法相比计算复杂度接近,但是具有更高的定位精度。在10 dB信噪比下本文算法的定位均方根误差相比经典的SDF算法降低了8.67 dB。展开更多
目的研究不明原因发热(fever of unknown origin,FUO)的病因分布及诊断策略。方法制定FUO诊断策略,纳入FUO患者102例,按照FUO诊断策略进行诊断,比较不同性别组、不同年龄组、不同热程组、有无合并症组FUO患者病因分布是否存在差异。同...目的研究不明原因发热(fever of unknown origin,FUO)的病因分布及诊断策略。方法制定FUO诊断策略,纳入FUO患者102例,按照FUO诊断策略进行诊断,比较不同性别组、不同年龄组、不同热程组、有无合并症组FUO患者病因分布是否存在差异。同时评估该诊断策略的确诊率、平均确诊时间、平均住院费用及患者对该诊断策略的依从性。结果 102例FUO患者中感染性疾病最常见,感染性疾病排在前6位的为泌尿系感染、血流感染、未定位感染、布氏杆菌病、肺炎+泌尿系感染、肺炎,非感染性疾病常见的有结缔组织病、其他类疾病。未明确诊断15例(14.7%),确诊87例(85.3%),确诊时间中位值为6(3,10)d,住院费用中位值为1.6(1.1,2.3)万元,依从诊断策略者90例(88.2%)。不同性别组及不同热程组FUO患者的病因分布差异无统计学意义;老年组感染性疾病构成比高于青年组和中年组(P<0.05),非感染性疾病所占比例低于青年组和中年组(P<0.05),无合并症FUO患者非感染性疾病所占比例高于有合并症者(P<0.05)。结论 FUO首发病因为感染性疾病,其中以泌尿系感染为主,其次为结缔组织病及其他疾病。老年FUO患者感染性疾病更为多见。中青年及无合并症FUO患者非感染性疾病更常见。FUO诊断策略确诊率高、确诊时间短、平均费用低,患者对诊断策略的依从性好,可进一步临床推广。展开更多
基金National Natural Science Foundations of China(Nos.61203147,61374047,61203126,60973095)
文摘This paper is concerned with distributed fault detection of second-order discrete-time multi-agent systems with adversary,where the adversary is regarded as a slowly time-varying signal.Firstly,a novel intrusion detection scheme based on the theory of unknown input observability( UIO) is proposed. By constructing a bank of UIO,the states of the malicious agents can be directly estimated. Secondly,the faulty-node-removal algorithm is provided.Simulations are also provided to demonstrate the effectiveness of the theoretical results.
基金This work was partially supported by the European Union’s Horizon 2020 research and innovation programme(739551)(KIOS CoE)from the Republic of Cyprus through the Directorate General for European Programmes,Coordination and Development.
文摘In this paper,a novel finite-time distributed identification method is introduced for nonlinear interconnected systems.A distributed concurrent learning-based discontinuous gradient descent update law is presented to learn uncertain interconnected subsystems’dynamics.The concurrent learning approach continually minimizes the identification error for a batch of previously recorded data collected from each subsystem as well as its neighboring subsystems.The state information of neighboring interconnected subsystems is acquired through direct communication.The overall update laws for all subsystems form coupled continuous-time gradient flow dynamics for which finite-time Lyapunov stability analysis is performed.As a byproduct of this Lyapunov analysis,easy-to-check rank conditions on data stored in the distributed memories of subsystems are obtained,under which finite-time stability of the distributed identifier is guaranteed.These rank conditions replace the restrictive persistence of excitation(PE)conditions which are hard and even impossible to achieve and verify for interconnected subsystems.Finally,simulation results verify the effectiveness of the presented distributed method in comparison with the other methods.
基金This work was supported in part by National Natural Science Foundation of China under Grants 62103167 and 61833007in part by the Natural Science Foundation of Jiangsu Province under Grant BK20210451.
文摘This paper is focused on the state estimation problem for nonlinear systems with unknown statistics of measurement noise.Based on the cubature Kalman filter,we propose a new nonlinear filtering algorithm that employs a skew t distribution to characterize the asymmetry of the measurement noise.The system states and the statistics of skew t noise distribution,including the shape matrix,the scale matrix,and the degree of freedom(DOF)are estimated jointly by employing variational Bayesian(VB)inference.The proposed method is validated in a target tracking example.Results of the simulation indicate that the proposed nonlinear filter can perform satisfactorily in the presence of unknown statistics of measurement noise and outperform than the existing state-of-the-art nonlinear filters.
