This article addresses the coordinated path-following (CPF) control under human supervision for marine vehicles (MVs) with unknown disturbances. A human-in-the-loop coordinated path-following (HCPF) control architecture is proposed based on the robust exact differentiator (RED) observer and the output feedback continuous twisting control (OFCTC) method. Specifically, a human manipulation is introduced into a virtual leader to regulate its path update speed in response to sudden circumstances for the follower MVs. All follower MVs synchronize indirectly with the human-in-the-loop virtual leader over a communication graph. Then, the path-following control problem is transformed into the controls of a second-order along-track error dynamic and a third-order cross-track error dynamic. By using the path-following errors only, the RED observers are developed to recover the unknown states of the error dynamics. Finally, the OFCTC laws are designed to achieve the individual path following in finite time regardless of the lumped disturbances. The stability of the closed-loop system is analyzed by employing the cascade theory. A salient feature of the proposed HCPF control architecture is that the CPF for MVs can be implemented under the supervision and intervention of human. Simulation results are given to illustrate the effectiveness of the proposed HCPF control method.

Modern vessels integrate a massive digital infrastructure and navigation-dependent operating systems, allowing for ship-to-shore and ship-to-ship collaborative communication. However, the heightened interconnection of various maritime infrastructures inevitably amplifies the risk of vessel navigation and communication. Existing intrusion detection techniques were usually built on individual network events, failing to account for the multi-event long-term dependency problem caused by the high latency and low bandwidth of ship communication networks, therefore cannot tackle sophisticated cyber-ship attacks, resulting in lower accuracy in intrusion detection. In this paper, we propose a dependable Intrusion Detection System(IDS) based on Segment-wise CoTransformer(SegCoT) to detect cyber-ship intrusion events, which primarily contains a two-stage Network Pattern Extraction Component (NPEC) and an Intrusion Event Identification Component (IEIC). The NPEC automates the extraction of long-term dependency of massive intrusion events employing a SegEvent-wise Attention (SEA). Furthermore, the extracted dependencies are leveraged by the IEIC for specific intrusion type detection from a spatio-temporal feature fusion perspective. Based on a cyber-ship dataset collected from real ocean-going vessels, the proposed model achieves 99% intrusion detection accuracy, outperforming the existing state-of-the-art approaches.

With the increasing promotion of digital technology, safety issues faced by rush ships in the maritime industry have once again received attention. Ship collision avoidance (SCA) is not only a hot topic in the shipping industry but also an eye-catching issue in the development of intelligent ships. This paper provides a bibliometric and systematic overview of the literature on SCA to assist researchers in understanding the frontiers and recent trends of SCA . Based on the bibliographic portrait, a classification and grading study was conducted on the literature to provide a systematic review of it. A screening process was conducted on 851 relevant articles related to SCA and published in 2004–2023 in the Web of Science (WoS) database, and 526 highly relevant and high-quality papers were selected. Then, CiteSpace, VOSviewer software, and data visualization techniques were used to conduct a bibliographic portrait analysis on the selected papers. The evidence from these systematic literature reviews revealed the close collaboration relationships among researchers, research institutions, and countries or regions in the field of SCA studying shortly. Furthermore, the frontiers of the research on SCA were focused on three aspects, i.e. research subjects, technological methods, and novel algorithms in intelligent SCA. COLREG is a problem that must be considered in intelligent SCA. The future trends in intelligent SCA were explored and SCA technology was introduced from artificial intelligence (AI) so as to meet the safety requirement of maritime autonomous surface ships. AI algorithms such as machine learning and deep learning are key technologies for SCA. This research provides a theoretical basis and implementation directions of the research on SCA. The hybrid encounters between traditional ships and intelligent ships, as well as multi-ship encounters in narrow waterways, will be the focal points of attention in the future.

Underwater acoustic Internet of Things Networks (UAIoTNs) play a crucial role in oceanographic and environmental monitoring, necessitating precise localization for optimal functionality. However, the underwater setting introduces significant challenges, encompassing the stratification effect arising from underwater heterogeneity, uncertainty in anchor positions due to currents, and variations in the signal transmission environment. These factors collectively impede the accurate estimation of location. Consequently, this paper addresses these challenges by analyzing and deriving a closed-form solution using a time-of-arrival (TOA)-based technique for 3D localization in UAIoTNs. The investigation establishes an underwater stratified propagation model, drawing inspiration from ray tracing theory and Snell’s law. Employing the Cramér-Rao lower bound (CRLB) framework, we explore scenarios both with and without considering perturbed anchors, utilizing the Banachiewicz-Schur theorem. To quantify the impact of the stratification effect and perturbed anchors on CRLB and mean square error (MSE), we further analyze and derive an MSE expression, employing Taylor-series linearization. Building on our analysis of the detrimental effects of stratification and inaccurate anchors, we introduce a multiple-weighted least squares (MWLS) algorithm to alleviate potential performance losses. This approach integrates a matrix operator in the update step, eliminating variable dependencies and resulting in a closed-form solution that circumvents the need for iterative processes. Our simulation results validate our analytical findings and demonstrate the effectiveness of the proposed method, showcasing improved localization accuracy across various scenarios when compared to state-of-the-art approaches.

In this article, a challenging and significant intelligent fault diagnosis task is investigated, in which multisource sensor signals with intense noise and outlier disturbances are jointly analyzed. Such a scenario has hardly been considered in industrial research. To this end, we develop a novel tensor-based nonlinear classifier called a unified pinball loss intuitionistic fuzzy support tensor machine, which can successfully solve the above tasks and improve the performance of fault diagnosis in practical applications. First, the noisy multisource signals are converted into time–frequency images and reconstructed into tensor samples to mine the time-domain, frequency-domain features, and coupled structure information in the spatial domain. Next, we design two nonlinear forms of nonmembership functions in the tensor space and obtain an intuitionistic fuzzy score for each training sample to enhance the robustness of the model. Subsequently, a pinball loss function is introduced to better handle noise sensitivity and resampling instability problems. Note that, we employ the tensor robust principal component analysis method to accurately recover the low-rank tensors corrupted by sparse noise from the original tensors. Finally, two numerical examples are presented to verify the feasibility and validity of the proposed method.

In this paper, a two-level visual servo strategy is elaborately devised for an unmanned surface vehicle (USV) equipped with a pan-tilt camera, so as to exactly track the desired trajectory around a visual target without direct position measurements. In the lower level, a barrier function-based adaptive pseudo-inverse (BFAP) controller is specially designed for the camera to keep the target in sight. Together with a finite-time position observer (FPO) and a finite-time extended state observer (FESO), a model-free finite-time trajectory-tracking control (MFTC) scheme is naturally synthesized for the USV on the higher level. Prominent advantages are presented as follows: 1) The BFAP controller can not only circumvent the singularity issue in a simpler manner, but also solve the field-of-view problem thoroughly in spite of unknown image depth; 2) The FPO provides a new vision-based method to locate the USV by rapidly calibrating a constant extrinsic parameter of the camera online, achieving higher positioning accuracy; and 3) The MFTC scheme allows all model information of the USV to be unknown, which is more favorable to practical implementations. Stability analyses are strictly made by the Lyapunov theory, and simulation studies conducted on the prototype CyberShip II comprehensively demonstrate remarkable performance of the proposed BFAP controller and MFTC scheme.

In this paper, aiming at a fleet of manned-unmanned surface vehicles (MUSVs), a novel visual servo swarming (VSS) mechanism is deliberately established by embodying visibility maintenance, swarm aggregation, collision avoidance and velocity matching. By making full use of line-of-sight ranges and angles between neighbors, a swarm of unmanned surface vehicles (USVs) with unknown inertia masses, internal dynamics and external disturbances can cooperate with a manned surface vehicle (MSV), thereby emerging flexibly collective behaviors in GPS-denied environments. To endow MUSVs with individually flexible behaviors, the VSS mechanism renders velocity matching of USVs with the MSV executing human-intelligence intention. Meanwhile, barrier Lyapunov functions are employed to reliably maintain visibility and avoid collisions among individuals, simultaneously. Distributed neural approximators using reduced-dimension inputs are devised to estimate unknown dynamics of USVs, while residual uncertainties are thoroughly suppressed by robust adaptations, thereby contributing to a model-free VSS (MVSS) scheme. Eventually, together with projection-based adaptive laws, the MVSS-based controller ensures uniform boundedness of estimation parameters and asymptotic convergence of regulation errors. Simulation results demonstrate remarkable efficacy in terms of collective performance.

As an outstanding discriminant analysis technique, Fisher discriminant analysis (FDA) gained extensive attention in supervised dimensionality reduction and fault diagnosis fields. However, it typically ignores the multimodality within the measured data, which may cause infeasibility in practice. In addition, it generally incorporates all process variables without emphasizing the key faulty ones when modeling the complex process, thus leading to degraded fault classification capability and poor model interpretability. To ease the above two drawbacks of conventional FDA, this brief presents an advantageously sparse local FDA (SLFDA) model, it first preserves the within-class multimodality by introducing local weighting factors into scatter matrix. Then, the responsible faulty variables are identified automatically through the elastic net algorithm, and the current optimization problem is subsequently settled through the feasible gradient direction method. Since then, the local data structure characteristics are exploited from both the sample dimension and variable dimension so that the fault diagnosis performance and model interpretability are significantly enhanced. In addition, we naturally extend SLFDA model to nonlinear variant (i.e., sparse kernel local FDA) by the kernel trick, which is substantially more resistant to strong nonlinearity. The simulation studies on Tennessee Eastman (TE) benchmark process and real-world diesel engine working process both validate that the novel diagnosis strategy is more accurate and reliable than the existing state-of-the-art methods.

Chaotic time series widely exists in nature and society (e.g., meteorology, physics, economics, etc.), which usually exhibits seemingly unpredictable features due to its inherent nonstationary and high complexity. Thankfully, multifarious advanced approaches have been developed to tackle the prediction issues, such as statistical methods, artificial neural networks (ANNs), and support vector machines. Among them, the interval type-2 fuzzy neural network (IT2FNN), which is a synergistic integration of fuzzy logic systems and ANNs, has received wide attention in the field of chaotic time series prediction. This paper begins with the structural features and superiorities of IT2FNN. Moreover, chaotic characters identification and phase-space reconstruction matters for prediction are presented. In addition, we also offer a comprehensive review of state-of-the-art applications of IT2FNN, with an emphasis on chaotic time series prediction and summarize their main contributions as well as some hardware implementations for computation speedup. Finally, this paper trends and extensions of this field, along with an outlook of future challenges are revealed. The primary objective of this paper is to serve as a tutorial or referee for interested researchers to have an overall picture on the current developments and identify their potential research direction to further investigation.

In order to improve the generalization capacity of neural networks for poorly known nonlinear dynamic system with long time-delay, a novel adaptive dynamic feedforward neural network on modified particle swarm optimization (PSO) algorithm is proposed. The adaptive time delay operator is adopted between input layer and the first hidden layer, and also the last hidden layer and output layer. Utilizing these dynamic time delay parameters, the proposed structure can adequately identify different classes of nonlinear systems expressed in the input-output representation form and pure time delay. Otherwise, to overcome the particles' premature convergence, the white noise and logistic mapping are used to enhance the particles' search performance. Furthermore, the parameters in the dynamic feedforward neural network are trained by the modified PSO method. The proposed neural network shows a satisfactory global search and quick convergence capability, avoiding the complexity of gradient calculation. Simulation results demonstrate that the proposed algorithm is effective and accurate in identifying long-time delay nonlinear systems through the comparison with other methods.