This article is devoted to providing a method for quantifying fault diagnosability of nonlinear networked control systems under weak transmission conditions. The receiving signals of fault detection and isolation systems are characterized under packet dropouts and communication constraints with the help of Bernoulli random variables and a round-robin protocol. Then, the statistical characteristics of system dynamics under the impact of different faults are characterized by defined statistical functions combining probability density functions. Furthermore, a quantitative fault diagnosability evaluation framework for nonlinear networked control systems is established based on Takagi–Sugeno fuzzy models and Kullback–Leibler divergence. Moreover, an evaluation metric is proposed, which can be applied to system dynamics following both Gaussian distribution and mixed distribution consisting of Bernoulli distribution and Gaussian distribution, eliminating the constraint of assuming purely Gaussian distribution. In addition, a necessary and sufficient condition is given to reveal the relations between the qualitative fault diagnosability evaluation results and the proposed metric.

The high-accuracy positioning of autonomous underwater vehicles (AUVs) is important in the Internet of Underwater Things (IoUT). AUV positioning relies on acoustic signal measurement systems. Owing to the variation in sound speed, the acoustic signal path is curved. The straight-line distance from an AUV to a measurement system cannot be simply calculated using the product of the propagation time and sound speed. Therefore, a time of arrival (TOA) positioning model is proposed to avoid calculating the straight-line distance, which is based on the continuous sound speed profile (SSP) ray tracing method. This model does not require knowing the depth of the AUV in advance. In addition, an improved Gauss-Newton iteration method based on the effective sound speed (ESS) is proposed to estimate the AUV position. The distance residual in the improved iteration is calculated on the basis of the time residual and the ESS, which is more accurate. Because the measured SSP is discrete data and cannot be directly used for the TOA positioning model, a Gauss kernel B-spline is proposed to obtain the continuous SSP. By optimizing the parameter σ, a high-accuracy continuous SSP can be obtained. Furthermore, theoretical analyses including the Cramér-Rao lower bound (CRLB) and complexity of the algorithm are also provided. The simulation results show that, compared with the 10th-degree polynomial and cubic standard B-spline, the Gauss kernel B-spline effectively improves the fitting accuracy. Compared with the traditional sound speed model and the current isogradient ray tracking model, the proposed TOA positioning model has better AUV positioning accuracy and is closer to CRLB.

The study presents an innovative approach for observability analysis in time-varying, nonlinear autonomous systems. Traditional criteria assert that state observability applies solely to nonlinear systems where the system input is not explicitly state-dependent. In contrast, this study presents a groundbreaking observability criterion that incorporates the impact of system input without increasing the dimension of the analysis. This new criterion is applied to the relative navigation system of a Mars spacecraft, resulting in an observability conclusion for monitoring a non-cooperative target using angles-only measurements. Note to Practitioners—Observability is a critical property of observation systems that guides designs and operations. This paper introduces a new method for conducting observability analysis in time-varying, nonlinear autonomous systems. Traditional criteria typically limit state observability to nonlinear systems where the system input is not explicitly state-dependent. In contrast, this study proposes an innovative observability criterion that incorporates the influence of system input without increasing the dimensionality of the analysis. This new criterion has been successfully applied to the relative navigation system of a Mars spacecraft, resulting in an observability assessment for monitoring a non-cooperative target using angles-only measurements. This work has the potential to advance the understanding and application of observability in complex autonomous systems, especially in scenarios involving dynamic changes and nonlinearities.

This article addresses the quantitative fault diagnosability evaluation for dynamic systems without distribution knowledge. The system dynamics in different cases are first characterized by mean vectors and covariance matrices. Then, fault detectability and isolability are defined based on the constructed characteristics. On this basis, the Mahalanobis distance (MD) is employed to propose a fault diagnosability analysis measure. Furthermore, model-based and data-driven algorithms for fault diagnosability evaluation are given according to the MD-based measure. In addition, the reliabilities of the evaluation results are considered by taking advantage of ambiguity sets of mean vectors and covariance matrices of the system dynamics. Finally, two examples are employed to verify the effectiveness of the proposed algorithms.

Fault diagnosability evaluation is important for monitoring and control system design. The evaluation results provide knowledge of the achievable performance of concerned systems from the fault diagnosis perspective. In this article, the diagnosability analysis of Markov jump systems with multiple time delays is addressed by using statistics. Specifically, cases with both completely and partially known transition probabilities are considered. First, the characteristics of the multiple time delays and the Markov process are extracted based on a constructed system dynamic. This step is followed by defining the fault detectability and isolability of the considered system. On this basis, Kullback–Leibler divergence-based fault diagnosability measures for different fault cases are given, and the relations between these measures are investigated. For cases with completely known transition probabilities, due to random variation in the structure, a quantitative fault diagnosability method is proposed by simultaneously considering the fault diagnosis performance and the importance of different system structures. For cases with partially known or even completely unknown transition probabilities, instead of a scalar measure, an interval measure and the corresponding evaluation method are developed to take advantage of as much available information as possible. Finally, the effectiveness of the developed measures is verified via simulation.