As an ordered extension of evidence theory, Random permutation set (RPS) theory has received increasing attention due to its advantage in dealing with order-structured uncertain information. However, a significant research gap remains in the current literature concerning the construction of RPS. Building on the interpretation of RPS as a layer-2 belief structure, this paper proposes a data-driven hierarchical method for generating RPS, called DDHRPS. Specifically, DDHRPS first generates BPA from statistical features of data on the layer-1 belief structure, and then refines them with propensity information derived from distance analysis between samples to single classes, ultimately forming RPS on the layer-2 belief structure. Moreover, a DDHRPS-based classification algorithm (DDHRPSCA) is presented. Experimental comparisons involving two kinds of classifiers, namely, two uncertainty-based classifiers and seven machine learning classifiers validate the effectiveness and superiority of DDHRPSCA in handling uncertain information in classification tasks.

This paper addresses the problem of interval estimation of states and faults for local nonlinear Takagi-Sugeno fuzzy systems. A novel estimation method integrating observer design and zonotopic analysis is proposed. Firstly, a new discrete-time intermediate observer is designed. By introducing two auxiliary variables, the design freedom of the observer is enhanced, leading to improved interval estimation performance. Secondly, to handle the local nonlinear dynamics within the system, a semi-infinite programming method based on zonotopic analysis is developed. This method enables the online computation of minimal outer-bounding zonotopes for the local nonlinear dynamics, achieving precise bounding of their effects. Furthermore, by incorporating the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{\infty }$</tex-math></inline-formula> and P-radius performance indices, a convex optimization condition is derived to ensure the stability of the error system and satisfy the required robustness performance. Finally, simulation results based on a nonlinear vehicle system are presented to demonstrate the effectiveness of the proposed approach.

Event causality identification (ECI) aims to detect causal relationships between events in textual contexts. Existing ECI models predominantly rely on supervised methodologies, suffering from dependence on large-scale annotated data. Although large language models (LLMs) enable zero-shot ECI, they are prone to causal hallucination—erroneously establishing spurious causal links. To address these challenges, we propose MEFA, a novel zero-shot ECI model based on multisource evidence fuzzy aggregation. First, we decompose causality reasoning into three main tasks (temporality determination, necessity analysis, and sufficiency verification) complemented by three auxiliary tasks. Second, leveraging meticulously designed prompts, we guide LLMs to generate uncertain responses and deterministic outputs. Finally, we quantify LLM's responses of subtasks and employ fuzzy aggregation to integrate these evidence for causality scoring and causality determination. Extensive experiments on three benchmarks demonstrate that MEFA outperforms second-best unsupervised baselines by 6.2% in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F1$</tex-math></inline-formula>-score and 9.3% in precision, while significantly reducing hallucination-induced errors. In-depth analysis verify the effectiveness of task decomposition and the superiority of fuzzy aggregation.

This paper proposes an adaptive fuzzy output-feedback event-triggered control approach for uncertain discrete-time nonlinear systems with mismatched disturbances. The noncausal problem in traditional discrete-time backstepping can be effectively solved by introducing a command filter into each design step, and the influence of filtering error can be reduced by building an error compensation mechanism. Based on the approximation characteristics of the fuzzy logic systems, the fuzzy state observer is established to obtain the estimation of the immeasurable states although the uncertain dynamics exist. Furthermore, the adaptive update laws are designed for the observer to estimate unknown parameters, and the adaptive controller is developed under the event-triggered mechanism with a dynamic threshold to alleviate the network communication load. The stability analysis of the closed-loop systems is accomplished by constructing weighted Lyapunov functions, excluding Zeno behavior. The validity of the proposed control approach is demonstrated by simulation results.

In this paper, for manipulating flexible objects, e.g., connecting a grounding wire with the power line, in live-maintaining of power substations, we propose an action-level vision-language model (VLM) for flexible object positioning, including a fuzzy-based dynamic motion primitive (DMP), an adaptive admittance model for reshaping the task manipulation trajectory, and a fuzzy logic system-based (FLS) adaptive controller with event-triggered mechanism (ETM) for dynamic uncertainties. First, a pretrained VLM is co-finetuned with vision data to construct an action-level VLM called vision-language-action model (VLAM). Such an end-to-end model can directly generate proper target poses on deformable linear objects (DLOs), without the need for complex segmentation modules. Second, an admittance control with stiffness and damping adaptation is proposed to enable the robot to effectively interact with unmodeled DLOs. Then, a learning from demonstration (LfD) strategy based on DMP with fuzzy clustering is designed to transfer the live-maintaining skill. Additionally, an FLS-based adaptive controller with ETM is proposed to compensate for complex nonlinear dynamics and reduce communication frequency. Finally, a mobile manipulator for power substation live-maintaining is exploited to validate the proposed approaches through extensive experiments.

Accurate and timely detection of emergency event holds significant practical value for mitigating potential risks and losses. However, such emergency event often exhibits continuous, gradual evolutionary patterns in the temporal dimension, and this non-stepwise transition introduces inherent ambiguity to the detection task. To address the ambiguity issues in emergency event detection tasks within complex systems, this paper proposes a Multi-Scale Fuzzy Learning method with Weak Supervision (MS-FuLW). The proposed method first constructs a dynamic label mapping mechanism based on fuzzy set theory, extending traditional binary labels into fuzzy labels to characterize the gradual transition process of emergency events. Next, a Multi Scale Convolutional Network (MSC) is designed to extract both local abrupt features and global trend features from time series data in parallel, thereby enhancing the model ability to capture complex patterns, along with a specialized fuzzy loss function to improve compatibility with fuzzy labels. Finally, a weakly super vised learning mechanism is introduced to optimize the fuzzy label modeling process and reduce dependency on precise anno tations. The experimental results show that compared to baseline methods, MS-FuLW steadily improves detection real-time performance by 5%–25% in real environments, providing an effective solution for real-time risk warning in complex systems.