Traffic congestion, accidents, and unpredictable driver behaviour remain significant challenges in urban transportation systems. Traditional traffic simulation models often fail to adapt to dynamic environments and lack accuracy handling edge-case scenarios. To address these limitations, hybrid TrafficAI, an innovative Generative AI-based framework that integrates advanced modules for traffic simulation, behaviour modelling and anomaly detection. The framework incorporates several key components. First, an Adaptive Multi-Modal Fusion Engine (AMFE) seamlessly integrates video, LiDAR, and textual data. This is achieved through dynamic feature alignment layers and context-aware gating mechanisms. Second, an Edge-Case Generative Module (ECGM) augments synthetic edge-case scenarios. Third, a Temporal-Spatial Attention Network (TSAN) captures short-term and long-term traffic dependencies. Finally, large language model-driven semantic reasoning modules extract contextual insights from unstructured textual data, such as traffic reports and incident logs. The framework employs a hybrid dual-stage optimization process, combining unsupervised generative pre-training with fine-tuned supervised calibration to ensure efficient convergence and reduced latency. By fusing multi-modal data, enhancing anomaly robustness with synthetic edge-case scenarios, and interpreting contextual semantics with LLM, hybrid TrafficAI achieves precise anomaly detection, trajectory prediction and adaptive decision-making. Experimental evaluations demonstrate significant performance improvements, including 91.45% accuracy, 93.45% mean Average Precision (mAP) for vehicle detection and a 0.910 Normalized Risk Score (NRS) for anomaly detection, consistently outperforming state-of-the-art benchmarks across latency, precision, recall and stability metrics. This framework sets a new benchmark for intelligent transportation systems (ITS) and real-time traffic management.

This article investigates the design of robust H∞ filters for Takagi–Sugeno (T–S) fuzzy systems with time-varying delays, with a critical challenge in many consumer electronics applications. We extend existing research by incorporating Markovian jump parameters to model system uncertainties and considering internal–like time–varying delays, which are prevalent in real-world scenarios such as wireless communication and networked control systems in consumer devices. To address the complexities introduced by time-varying delays, we employ a three-term approximation model that more accurately captures the system dynamics compared to traditional approaches. Furthermore, we incorporate the time derivative of the membership functions (MFs) into the filter design, and its impact on system stability and performance. To ensure filter stability, we derive novel stability conditions based on the mode–dependent Lyapunov-Krasovskii functional approach, incorporating constraints on the higher bounds of the MF time derivatives. Leveraging the small–gain theorem with scaling techniques, we propose the design of both full-order and reduced-order H∞ filters, expressed in terms of linear matrix inequalities (LMIs). These LMI–based solutions provide a systematic and computationally efficient approach for filter synthesis. Finally, we validate the effectiveness of our proposed filtering strategies through illustrative examples drawn from relevant consumer electronics applications, such as noise cancellation in audio devices, image stabilization in cameras, and vibration suppression in wearable devices. The results demonstrate the enhanced robustness and performance of the proposed filters in the presence of time-varying delays and uncertainties.

A Fuzzy Sliding Mode (FSM) tracking approach is proposed via Interval Type-2 (IT-2) Takagi-Sugeno Fuzzy Model (T-SFM) to complete Formation and Containment (F-and-C) tasks for Nonlinear Multi-Agent Systems (NMASs) with uncertainties and disturbances. Using Imperfect Premise Matching (IPM) approach, the IT-2 F-and-C Fuzzy Controllers (FCs) are designed in accordance with the IT-2 T-SFM. Different from the existing research, the individual tracking FC for each leader is proposed to complete the formation tasks and assignment of whole system dynamics. The stability analysis process is also simplified for ensuring the formation purpose. Benefiting from the advantage, the FSM control is conveniently combined into the FC design to upgrade the performance of leaders. For followers, the analysis methods of linear Multi-Agent (M-A) systems are extended to solve the containment analysis problem without the additional assumption by virtue of the IT-2 T-SFM representation. To deal with the disturbance effect, the passive constraints are combined into the analysis process. Finally, the proposed F-and-C FCs design method is applied to a Nonlinear Multi-Ships System (NMSS) to illustrate the advantage.

Tracking is a crucial problem for nonlinear systems as it ensures stability and enables the system to accurately follow a desired reference signal. Using Takagi–Sugeno (T–S) fuzzy models, this paper addresses the problem of fuzzy observer and control design for a class of nonlinear systems. The Takagi–Sugeno (T–S) fuzzy models can represent nonlinear systems because it is a universal approximation. Firstly, the T–S fuzzy modeling is applied to get the dynamics of an observational system in order to estimate the unmeasurable states of an unknown nonlinear system. There are various kinds of nonlinear systems that can be modeled using T–S fuzzy systems by combining the input state variables linearly. Secondly, the T–S fuzzy systems can handle unknown states as well as parameters known to the indirect adaptive fuzzy observer. A simple feedback method is used to implement the proposed controller. As a result, the feedback linearization method allows for solving the singularity problem without using any additional algorithms. A fuzzy model representation of the observation system comprises parameters and a feedback gain. The Lyapunov function and Lipschitz conditions are used in constructing the adaptive law. This method is then illustrated by an illustrative example to prove its effectiveness with different kinds of nonlinear functions. A well-designed controller is effective and its performance index minimizes network utilization—this factor is particularly significant when applied to wireless communication systems.