In this paper, a novel robust fault detection (FD) approach called parity space vector machine (PSVM) is proposed for linear discrete-time systems. Aiming to achieve a tradeoff between false alarm rate (FAR) and FD rate (FDR) simultaneously, we focus our study on an integrated design of parity space-based FD in the context of residual generation and residual evaluation. Without a prior knowledge of the distribution of the unknown inputs, we propose to construct a PSVM model and formulate the underlying FD problem as a distribution-free Bayes optimal classifier, where the FAR and FDR indicate the worst-case classification accuracies of future residuals for the fault free case and faulty case. Then a bank of parity space vectors and corresponding thresholds can be designed integratedly by applying the techniques of the minimum error minimax probability machine and, at the same time, an optimal tradeoff between FAR and FDR is achieved. Finally, the effectiveness of the proposed approach is demonstrated on a longitudinal control system of unmanned aerial vehicle and further comparison with a traditional parity space-based FD is also addressed.

This article is concerned with the development of a new event-triggered parity space fault detection (FD) scheme. A linear discrete-time system model with varying sampling periods is presented for handling the problem of event-triggered FD and a new parity relation is established. Based on this, an event-triggered residual generator is constructed and the generated residual is completely decoupled from event-triggered transmission error. The design of the parity matrix is formulated into an optimization problem and an optimal solution of the parity matrix is obtained by using singular value decomposition. The issue of residual evaluation is also considered in the event-triggering implementation. The novelties of this article are twofold. First, a new event-triggered parity relation is obtained and the parity space-based residual signal achieves complete decoupling with the event-triggered transmission error. Second, the calculation of the parity matrix is independent of event parameters. So the design of the parity space-based residual generator and event generator can be carried out independently. Finally, a simulation example is considered to demonstrate the effectiveness of the proposed method.

This paper deals with the problem of parity space-based fault diagnosability analysis for linear discrete time systems. The main contribution lies in the design of the fault diagnosability evaluation indexes by combining the distance difference information with the direction difference information between the residuals in different cases. Under the assumption that the unknown inputs are random white noises, the residual generation is achieved by parity space-based fault diagnosis approach. Based on this, the problem of fault diagnosability analysis is formulated as a bank of evaluation problems of the difference information between two residuals in fault-free case or in different faulty cases. Then the fault diagnosability evaluation indexes are proposed based on the integrated design of the distance similarity function and the direction similarity function between different residuals, and the improved fault isolation conditions are constructed to provide an auxiliary index for fault diagnosability analysis. A simulation is carried out on a fixed-wing unmanned aerial vehicle (UAV) flight control system, and the results demonstrate that the proposed method can achieve fault diagnosability analysis accurately for linear discrete time systems.

Researchers have identified several problems in measuring the strongest path connecting pairs of actors in valued graphs. To address these problems, it has been proposed that average path value be used to indicate optimal connections between dyads. However, a lack of proper computer algorithm and its implementation has hindered a wide-range application of the proposed solution. In this paper we develop a computer algorithm and fully implement it with four JAVA programs, which are available on request. These programs produce an optimal connection matrix, which is subsequently inputted into UCINET for further multidimensional scaling and clustering analysis. We demonstrate this procedure with a data matrix containing 38 organizations in information technology. We discuss the methodological implications of the application of our algorithm to future social network studies.

This paper presents a novel data-driven approach to fault diagnosability analysis for linear discrete-time systems. Current methods rely heavily on single evaluation functions based on distance similarity, which may not fully utilize directional information in different sets of data. To address this limitation, the paper proposes a comprehensive set of quantitative evaluation indices that consider both distance and direction similarity. This approach introduces data-driven residuals and reformulates the fault diagnosability evaluation as an assessment of the differences between the residuals in fault-free and faulty cases. Specifically, the paper presents detectability measures that provide the relative size of the residual evaluation function caused by faults and noise. Additionally, a set of isolability evaluation indices is proposed by combining distance and direction similarity between the residuals in different faulty cases. These indices can potentially serve as a reference for optimizing system configuration and designing fault diagnosis systems. To verify the effectiveness of the proposed method, we analyze the diagnosability of gas regulators using data collected from an experimental platform. The proposed detectability indices are 0.560, 0.780, and 1.067, while the integrated isolability measures are 4.5×10 ⁻⁷ , 0.0046, and 0.0044, providing more detailed and precise information than existing methods.

This paper investigates the formation control problem of multi-UAV systems in the presence of switched trajectories and time-varying communication topologies. A distributed formation control protocol is proposed to enable UAVs to track piecewise continuous trajectories while the underlying communication network switches among a finite set of directed graphs. Sufficient and necessary conditions for achieving accurate formation tracking under dual-switching scenarios are derived through stability analysis while the stability of the overall switched system is proven by using multiple Lyapunov functions. To ensure collision avoidance during both trajectory and topology transitions, control barrier functions (CBFs) are employed to construct safety sets, and a quadratic programming(QP)-based optimization framework is designed to modify control inputs in real time. Simulation results demonstrate that the proposed approach effectively coordinates formation tracking, topology switching, and inter-agent safety, offering a solution for UAV collaboration in dynamic and uncertain environments.