AUV play an important role in the exploration and utilization of the ocean. Underactuated autonomous underwater vehicles (AUVs) are widely used in these missions due to the associated low manufacturing costs, low power consumption, and high reliability. Among many control problems of underactuated AUVs, the research of trajectory tracking and formation control methods has become a hot topic in recent years. In order to obtain superior control performance, research on trajectory tracking and formation control of underactuated AUVs based on intelligent control methods should be further advanced and deepened. In this context, we present a survey of intelligent trajectory tracking and formation control of underactuated AUVs, which is beneficial to researchers in this field. To facilitate a comprehensive understanding of the subject matter, we review some preliminary knowledge in trajectory tracking and formation control of underactuated AUVs. We also highlight research problems and challenges, including external disturbances, system characteristics, and system faults. We review recent advances in intelligent trajectory tracking and formation control of underactuated AUVs and analyze and discuss their characteristics. Moreover, we put forward some prospects of advanced control techniques, especially artificial intelligence-based techniques for underactuated AUVs.

This paper mainly concerns the problem of performance evaluation for an Ultra-Wideband (UWB) positioning system. An evaluation index system suitable for UWB positioning system is established to assess the performance of UWB positioning system from multiple aspects, including accuracy, reliability and anti-interference ability. Analytic hierarchy process (AHP) and entropy weight method (EWM) are combined to yield a more objective weight for each evaluation index. Besides, fuzzy comprehensive evaluation (FCE) is used to conclude the evaluation result. An illustrative example is provided to demonstrate the effectiveness of proposed evaluation index system and evaluation method.

In this paper, a novel approach towards addressing the filtering problem for a class of networked control systems (NCSs) with mixed $\mathcal {H}_{\infty }$ and passive (MHP) performance index is proposed. The underlying NCSs may manifest different characteristics (termed quantization density herein) when subjected to channel switching. As such, it is possible to model the NCSs as a class of switched systems. A mode-dependent and quantization-error-dependent Lyapunov-like function is first adopted in order to conduct stability and MHP performance analysis with less conservatism. Based on the developed stability and MHP performance analysis, a set of MHP filters suitable for the controlled system is designed such that the filtering error system is globally uniformly asymptotically stable with a specified MHP performance index. The effectiveness and potential applications of the derived results are demonstrated via a numerical simulation.

Although Random Forest (RF) is a widely adopted ensemble method for classification, preserving diversity among decision trees and reducing the negative impact of underperforming trees remain major challenges. This paper proposes DWRF-MVC, a novel RF optimization framework that integrates DBSCAN clustering with a multi-metric performance-weighted voting mechanism. Using a dissimilarity metric derived from classic RF outcomes, the framework applies DBSCAN to cluster decision trees, selects top-performing representatives from each cluster, and retains noise points to maintain diversity. A weighted voting mechanism is then introduced to further improve model performance. Experimental results on 14 benchmark datasets show that DWRF-MVC achieves an average accuracy improvement of 7.45% over classical RF with a 91.79% reduction in the number of trees. Moreover, DWRF-MVC surpasses two cutting-edge methods by 1.82% and 3.83% in average accuracy, respectively.