Among existing studies on formation reconfiguration for multiple unmanned aerial vehicles (multi-UAVs), the majority are conducted on the assumption that the swarm scale is stationary. In fact, because of emergencies, such as communication malfunctions, physical destruction, and mission alteration, the scale of the multi-UAVs can fluctuate. In these cases, the achievements of formation reconfiguration for fixed-scale multi-UAVs are no longer applicable. As such, in this article, the formation reconfiguration problem of leader–follower multi-UAVs is investigated with a variable swarm scale taken into consideration. First, a streamlined topological structure is designed on the basis of the parity of the vertex numbers. Then, three formation reconfiguration strategies corresponding to the scenarios covering leader disengagement, follower detachment, and new member additions are developed with the aim of reducing the frequency of connection changes. Moreover, in terms of the leader election link of the leader disengagement scenario, a knowledge-based performance assessment model for UAVs is constructed with the help of the hierarchical belief rule base (BRB). Finally, the proposed formation reconfiguration strategies for leader disengagement, new member addition, and follower disengagement are demonstrated through simulations. The connection retention rate (CRR) for swarm communication topology under the three formation reconfiguration strategies can reach 67%, 90%, and 100%, respectively.

Health condition assessment is the basis for formulating and optimizing maintenance strategies of complex systems, which is crucial for ensuring the safe and stable operation of these systems. In complex system health condition assessment, it is not only necessary for the model to handle various uncertainties to ensure the accuracy of assessment results, but also to have a transparent and reasonable assessment process and interpretable, traceable assessment results. belief rule base (BRB) has been widely used as an interpretable modeling method in health condition assessment. However, BRB-based models currently face two issues: (1) inaccuracies in expert-provided parameters that can affect the model's accuracy, and (2) after model optimization, interpretability may be reduced. Therefore, this paper proposes a new method for complex system health condition assessment called interpretable BRB with reference value optimization (I-BRB). Firstly, to address the issue of inaccurate reference values, a reference value optimization algorithm with interpretability constraints is designed, which optimizes the reference values without compromising expert knowledge. Secondly, the remaining parameters are optimized using the projection covariance matrix adaptation evolution strategy (P-CMA-ES) with interpretability constraints to improve the model's accuracy. Finally, a case study evaluating the bearing components of a flywheel system is conducted to validate the proposed method. Experimental results demonstrate that I-BRB achieves higher accuracy in health condition assessment.

Autonomous underwater vehicles (AUVs), sophisticated devices that operate independently in complex underwater environments, are prone to malfunctions. Therefore, fault diagnosis is crucial for maintaining stable operations. The massive amounts of complex multidimensional data generated during AUV operations make it challenging to construct rule bases efficiently. The extended belief rule base (EBRB) offers a solution by employing data-driven approaches. Furthermore, as the data are sequentially generated, online training is more aligned with the practical realities of AUV operations. When processing large amounts of data, the EBRB system generates a substantial number of rules. This process can be very time-consuming. It may trigger the activation of many irrelevant rules, potentially leading to erroneous decisions. In response, a fault diagnosis method for autonomous underwater vehicles based on an online extended belief rule base with dynamic rule reduction (OEBRB-R) is introduced in this paper. This approach involves first establishing an OEBRB model that continually processes new incoming sensor data in real time. Subsequently, it employs a Mahalanobis distance-based k-means clustering technique to dynamically reduce the number of active rules. Finally, evidence reasoning (ER) algorithms are utilized to infer the type of fault present. Through case studies, the proposed method has been empirically validated for maintaining strong accuracy in real-time diagnostics while effectively reducing the model’s spatial complexity.