Object detection plays a pivotal role in intelligent transportation systems. In recent years, the rapid advancements in deep learning have propelled object detection to a new level. However, the complexity of traffic environments and the inherent limitations of Convolutional Neural Networks (CNNs) in dealing with feature uncertainty have collectively resulted in a stagnation in the development of traffic object detection. To surmount these challenges, this study explores the combination of fuzzy theory with deep learning object detectors. Specifically, the features are first transformed into the fuzzy domain through fuzzification, and then the improved lightweight fuzzy inference method is employed for information interaction and selection, effectively managing and mitigating feature uncertainty. By merging the fuzzy strategy with YOLOv6 framework, a novel Fuzzy-based YOLO-style detector (F-YOLO) was proposed. F-YOLO is distinguished by three key innovations: the fuzzy fusion module, the fuzzy attention mechanism and the GPBblock. These components respectively bolster multi-scale feature fusion in the neck, refine prediction efficiency in the head, and enhance the global contextual learning of the backbone. Comprehensive experiments conducted across various datasets, including those tailored for traffic object detection, establish F-YOLO's state-of-the-art (SOTA) performance in YOLO-style detectors. F-YOLO demonstrates robustness and wide applicability, notably surpassing comparison models by 20.7% on VisDrone2019 dataset, 1.3% on BDD100K, and 6.2% on UA-DETRAC. Furthermore, F-YOLO also achieved competitive results on the MS COCO dataset.

In this study, we propose an innovative prescribed-time consensus control strategy for nonlinear strict-feedback multi-agent systems (MASs) with privacy protection requirements. Firstly, compared with the existing privacy protection strategies, the mask function adopted in this paper remains unknown to all agents, including the sender, thus greatly improving the security level of information transmission. Secondly, the existing related research results basically overlook prescribed-time control in the context of privacy preservation, based on the backstepping method, a prescribed time performance function is adopted in this paper, so that the systems can make the tracking error within the defined accuracy range within a user-defined time. Finally, through the verification of MATLAB simulation experiments, the proposed control strategy not only effectively realizes the privacy-preserving consensus control of multi-agent systems, but also shows better control performance compared with the existing schemes. Note to Practitioners—This paper aims to develop a mask privacy protection prescribed-time control algorithm for information transmission between multiple agents. In the automation industry, the demand for privacy protection in multi-agent systems is critical, necessitating the implementation of robust measures during agent collaboration and data sharing to safeguard data confidentiality. Employing advanced privacy-preserving technologies is essential to prevent the exposure of sensitive information, thereby ensuring the security of corporate secrets and operational integrity, in compliance with the evolving stringent privacy regulations. In addition, prescribed-time control enables users to achieve preset accuracy within a predefined time, reducing industrial resource consumption and improving resource utilization in the automation industry.

The suspension system of the 12/14 bearingless switched reluctance motor (BSRM) exhibits inherent time-varying characteristics owing to the salient pole structure of its rotor. However, existing control strategies tend to overlook these timevarying characteristics, resulting in reduced suspension performances, including accuracy and resistance to interference. In response, this study explores a control strategy for addressing the time-varying nature of the suspension system. The timevarying characteristics of the suspension system are initially revealed through finite element analysis and mathematical model representation. Subsequently, a double closed-loop control system for suspension force is designed, including a time-invariant suspension force model (TISFM) and a data driven fuzzy sliding mode observer (DDFSMO). The TISFM is used to conduct a timeinvariant direct suspension force control system, whereas the DDFSMO is designed on the basis of the TISFM to compensate for the error between the TISFM and complete suspension force model (CSFM). The time-varying suspension control system is successfully converted into a time-invariant one by the combined action of the TISFM and DDFSMO. Finally, the validity of the proposed control strategy for time-varying suspension system is validated.

High-speed maglev transportation is a new intelligent transportation system that combines high speed and eco-friendliness. The intelligent control of high-speed maglev trains (HSMT) faces many challenges such as partial actuator failure, strong nonlinearity, and input constraints ( i.e. unidirectional limitation, saturation, and dead zones). Additionally, racing effect between multiple electromagnets, external disturbances and parameter uncertainty makes it more difficult to maintain precise airgap. This paper presents a novel intelligent fault-tolerant control method to tackle the challenges of levitation control of HSMT under conditions of partial actuator failure, unidirectional inputs, saturation, and dead zones. First, a model of multi-points levitation system is provided. Then, a novel fuzzy-based control method is designed. It is based on an adaptive fuzzy update law and auxiliary manifold surfaces, which utilizes real-time measured data to estimate and compensate for the partial actuator failure and input constraints online. To the best of our knowledge, the developed fuzzy-based control law is the first method for the multi-electromagnet levitation system of HSMT that simultaneously considers partial actuator failure and input constraints. The stability and convergence within finite time of the closed-loop airgap errors are proven through the Lyapunov analysis method. Finally, experimental comparisons between the proposed fuzzy-based method and conventional control methods were conducted. The experimental results show that, compared to the LQR method, the proposed method reduces the system’s maximum overshoot by 93.3% and the maximum steady-state error by 36.98%. Moreover, it maintains stable levitation under actuator failure, which enhances the safety, efficiency, and reliability of future maglev transportation systems.

Object detection in complex traffic scenarios is crucial for Intelligent Transportation Systems (ITS). At present, most real-time traffic object detection methods primarily rely on YOLO-style vision-only detectors, limiting their potential for further improvement. Vision-Language Object Detection (VLOD) has made promising progress currently, yet its adoption in the realm of ITS remains limited. Previous VLOD methods utilize text features in the classification task, without fully exploring their impact on the regression process for object localization. Besides, existing multi-modal fusion approaches fail to fuse text features with multi-scale image features at corresponding scales, which is detrimental to the representation capability of the model. In this work, we dive into the limitations above and introduce Zone-YOLO to improve the VLOD to a new level. Specifically, we propose Scale-Aware Modal Fusion (SAMF) to fully exploit the text and image features and learn to fuse the multi-modal representations seamlessly at different scales with channel- and modal-wise enhancement. Moreover, we present a novel Zone Prompt learning method to introduce text features into regression process and capture the zone-class-entity triple co-occurrence, which significantly improves the localization performance of the model. Extensive experiments show that Zone-YOLO outperforms the comparative methods by a considerable margin, achieving 55.1 AP, 72.1 AP50 and 71.2 APL on COCO. The competitive results on BDD100K and VisDrone2019 further demonstrate the superiority of Zone-YOLO on efficient traffic object detection.

This article proposes a prescribed-time fuzzy optimal control approach for flexible-joint (FJ) robot systems utilizing the reinforcement learning (RL) strategy. The uniqueness of this method lies in its ability to ensure optimal tracking performance for n-link flexible joint robots within the prescribed-time frame, while the actor and critic fuzzy logic system effectively approximate the optimal cost and evaluates system performance. First, the optimal controllers with the auxiliary compensation term are constructed by utilizing the online approximation of the modified performance index function and RL actor-critic structure. The designed controller can deal with unknown structure impacts and avoid model identification. Besides, in designing the prescribed-time scale function, the introduced constant term not only prevents singularity but also allows flexible setting of constraint regions. The proposed scheme is theoretically verified to satisfy the Bellman optimality principle and ensure the tracking error converges to the desired zone within the prescribed time. Finally, the practicability of the designed control scheme is further demonstrated by the 2-link FJ robot simulation example.

The swift expansion of the Industrial Internet of Things (IIoT) presents formidable challenges for both data transmission networks and processing units. In this paper, we delve into the issue of accelerating visual data transmission and processing within IIoT systems. Our approach aims to deploy visual saliency detection models in edge data collection devices to empower them with data pre-processing capabilities. To address the challenge of model lightweight deployment on edge devices, we have designed a lightweight saliency detection model. It utilizes our proposed Spatial Difference Attention-based Dynamic Fusion (SADF) module for adaptive detection of salient objects of varying scales. As our model outputs retain only task-relevant information from the images, it maximizes compression on the raw data, significantly reducing the volume of data subsequent tasks and transmission networks need to process. This enhances the speed of data processing. Finally, extensive experimentation across multiple datasets demonstrates that our proposed model achieves a balance between performance and efficiency. Ultimately, this paper presents an effective avenue for efficiently handling data within IIoT systems by combining edge computing nodes with intelligent models.

Emergency prediction and driver attention prediction are fundamental tasks within the realm of self-driving vehicles and assistant driving systems. The utilization of visual saliency detection in these tasks has garnered considerable attention, owing to its inherent advantages. However, current research on traffic saliency detection primarily focuses on emulating the human visual system for attention allocation in traffic scenes, neglecting the detection of complete salient objects. In this paper, we propose the Traffic Salient Object Detection Using a Feature Deep Interaction and Guidance Fusion Network (TFGNet). Different from previous methods, our method detects the complete objects that attract human attention in natural traffic scenes, rather than a certain point without object semantic information, which can provide assistance for target recognition tasks in the domain of intelligent driving. Moreover, we propose a traffic salient object detection framework based on feature interaction and guidance fusion, enabling the detection of salient objects across varying scales. Experimental results on multiple benchmark datasets demonstrate that, compared to the state-of-the-art methods, our method exhibits superior performance in terms of precision, recall, and error rate.

A maglev train is a sustainable public transport method with the characteristics of being green, pollution-free, and low noise, as well as providing environmental protection. However, the performance of existing maglev control strategies for maglev trains may be deteriorated by various challenges, including disregard of the coordination and synchronization between multiple electromagnets, control input unidirectionality, dead zones, saturation, finite-time stability ability, etc. In this article, an adaptive fuzzy-based suspension control method based on a multielectromagnets dynamic coupling model is proposed that can cope with dead-zone and saturation problems and guarantee the finite time of the airgap tracking errors of multiple electromagnets simultaneously. Specifically, a fuzzy-logic system is utilized to compensate for the nonlinear input unidirectionality, dead-zone, saturation, and unmodeled dynamics. Moreover, considering the coupling dynamic characteristics of adjacent electromagnet control modules, a fuzzy-based cooperative suspension controller with adaptive update law is designed. The finite-time stability of the presented control strategy is proven with the Lyapunov method. Finally, the suspension frame experimental results are illustrated to validate the effectiveness and robustness of the developed method, whose superior performance is shown by being experimentally compared with some baseline methods.

When the electromagnetic suspension (EMS) type maglev vehicle is traveling over a track, the airgap must be maintained between the electromagnet and the track to prevent contact with that track. Because of the open-loop instability of the EMS system, the current must be actively controlled to maintain the target airgap. However, the maglev system suffers from the strong nonlinearity, force saturation, track flexibility, and feedback signals with network time-delay, hence making the controller design even more difficult. In this article, the minimum levitation unit of the maglev vehicle system has been established. An amplitude saturation controller (ASC), which can ensure the generation of only saturated unidirectional attractive force, is thus proposed. The stability and convergence of the closed-loop signals are proven based on the Lyapunov method. Subsequently, ASC is improved based on the radial basis function neural networks, and a neural network-based supervisor controller (NNBSC) is thus designed. The ASC plays the main role in the initial stage. As the neural network learns the control trend, it will gradually transition to the neural network controller. Simulation results are provided to illustrate the specific merit of the NNBSC. The hardware experimental results of a full-scale IoT EMS maglev train are included to validate the effectiveness and robustness of the presented control method as regards to time delay.

The vertical security problem of maglev train is challenging for nonlinearity, external disturbances, unmeasurable airgap velocity and constrained output. To solve this problem, a semi-supervised controller based on deep belief network (DBN) algorithm is proposed in the presence of unknown external disturbances. Firstly, the extended state observer (ESO) is designed to ensure fast convergence of observation errors with high enough estimation precision. An output-constrained controller is designed by backstepping method, and the estimated value of ESO is introduced to ensure that the output airgap is constrained within a bounded range. Then, the stability of this method is proved based on the symmetric Barrier Lyapunov function. Subsequently, a semi-supervised controller is presented based on DBN algorithm and the output-constrained controller. The numerical simulation results show that this method can effectively deal with unmeasurable airgap velocity and generalized external disturbances, and guarantee the vertical security with output airgap within a bounded range. Finally, experiments are implemented on a full-scale maglev vehicle and the experimental results demonstrate that the developed deep learning controller can ensure the vertical security.

The maglev rail transit has entered a rapid development stage. In order to prevent potential safety hazards in the operation of maglev train, the related monitoring technology needs to be studied urgently. In this article, in view of the wide application of the Internet of Things (IoT) in intelligent transportation, a new method for realizing suspension control for medium-low-speed maglev trains using the IoT and an adaptive fuzzy controller is proposed. First, a mathematical model of the suspension system of medium-low-speed maglev trains is established. Then, the basic composition of the IoT and the circuit design of the key components of maglev trains are introduced. On this basis, an improved Apriori algorithm is used to extract the stored historical database and establish a trusted database. Then, according to the data of the trusted database, the suspension airgap control law is extracted, and the adaptive fuzzy rules of the maglev train suspension system are determined. An improved adaptive suspension controller is designed. Finally, the effectiveness of the method is verified by experiments utilizing a full-scale maglev train.