Accurate vehicle navigation plays a critical role in vehicle-to-everything (V2X) applications, including connected transportation systems, intelligent traffic management, and autonomous driving. To address the stringent demands of these scenarios, the integration of the global navigation satellite system (GNSS) with the visual-inertial navigation system (VINS) has emerged as a pivotal advancement. Despite these strides, navigation systems remain susceptible to abnormal data. This data, originating from unpredictable external environments and internal device fallibility, poses a threat of substantial errors and system drift. In this paper, we present RF-Nav, a robust fusion-based GNSS-VINS navigation system with enhanced data processing and dynamic factor correction. The framework innovates with a dual-pronged approach: it first applies adaptive gamma correction with bilateral filtering and contrast-limited adaptive histogram equalization (AGCBF-CLAHE) to refine raw images; then, it deploys a long short-term memory (LSTM) denoising network enhanced with an advanced wavelet threshold for IMU data refinement. This dual enhancement of visual and IMU data integrity is further bolstered by a dynamic factor confidence correction mechanism, rooted in factor graph optimization (FGO), designed to counteract the adverse effects of abnormal data. Extensive experiments on large-scale public and real-field dataset demonstrate that RF-Nav exhibits superior robustness and accuracy in various environments.

In this study, the prescribed-time fault-tolerant tracking control problem for quadrotor unmanned aerial vehicle(QUAV) under external disturbances is investigated. Firstly, an error reconstruction mechanism based on adjustable convergence rate is proposed. This mechanism dynamically adjusts the convergence characteristics of the QUAV system, ensuring that the position and attitude tracking errors strictly converge to the prescribed accuracy range before the preset time threshold. Secondly, the disturbance observer is adopted to estimate unknown disturbances and additive faults, thereby reducing the impact of unknown variables on the stability of the system model. Afterwards, an adaptive fault-tolerant control (FTC) strategy is designed. It compensates for the multiplicative faults of the actuator online through the parameter adaptive law, and actively offsets additive faults by combining them with the output of the disturbance observer. This approach forms a composite FTC architecture. Finally, numerical simulation results show that the proposed control scheme can ensure the accurate convergence of the position and attitude system within prescribed-time under multiple actuator faults and unknown external disturbances. This verifies the effectiveness and robustness of the control strategy.

The low resolution of thermal imaging from unmanned aerial vehicles (UAVs) poses a substantial obstacle to the understanding and analysis of ground targets. Utilizing readily available high-resolution visible images presents a promising solution to improve the quality of thermal UAV images. However, current methods primarily focus on simple degradation conditions, neglecting the complexity of real-world degradation scenarios, such as blur and noise, which fail to meet the demands of practical applications. In this paper, we introduce a Degradation-aware Cross-modality Mamba (DC-Mamba) framework to super-resolve (SR) thermal UAV images by integrating degradation information with cross-modality cues. Our approach begins with a self-supervised learning framework that extracts degradation information directly from input images. This information guides the restoration process through the designed degradation-aware modules, which enhance model sensitivity to distorted regions. Additionally, we incorporate a vision-focused state-space module (SSM) to capture long-term spatial dependencies, thereby improving feature adaptability. To address modality disparities, we develop a cross-modality feature integration framework that leverages visible cues at three levels (interaction, refinement, and enhancement) to improve thermal image reconstruction quality. Extensive experiments demonstrate that the proposed method outperforms current state-of-the-art SR methods, providing more realistic details and superior performance across multiple evaluation metrics.

This article explores the fixed-time tracking control problem for stochastic nonstrict systems with unknown backlash-like hysteresis properties. To this end, a novel criterion of semiglobally practical fixed-time stochastic stability is established and proved. First, a continuous-time dynamic model that can be solved explicitly is constructed to model the discontinuous backlash-like hysteresis nonlinear behavior approximatively. Second, the coupling relationship between stochastic disturbance and hysteresis nonlinearity is analyzed, and the role of the coupling terms is attributed to each subsystem using the inequality expansion and the summation order transformation techniques. Then, based on the above analysis, a memory-free stochastic fixed-time control law without constructing the hysteresis inverse is developed recursively in the framework of backstepping by means of the It$\hat {o}$stochastic differential equation theory and the adaptive fuzzy technique, which can achieve semiglobally practical fixed-time stability of stochastic systems with hysteresis properties and the tracking error can converge to a small neighborhood near the origin. Finally, simulation studies for a numerical simulation example and a cart moving on a plane example are shown to verify the feasibility of the rendered approach.

The majority of existing energy management strategies (EMSs), merely considering external driving conditions, often allocate demand power in an irrational way, resulting in a waste of energy and a short service life of power sources. Therefore, it is necessary to integrate driving behavior in EMS to reduce the fuel consumption and improve the lifespan of power sources. In this paper, a driving-behavior-aware adaptive deep-reinforcement-learning (DRL) based EMS is proposed for a three-power-source fuel cell hybrid electric vehicle (FCHEV). To fully utilize each power source, a hierarchical power splitting method is adopted by an adaptive fuzzy filter. Then, a high-performance driving behavior recognizer is employed, and Pontryagin’s minimum principle (PMP) method is used to compute the optimal equivalent factor (EF) of each driving behavior. To realize a trade-off between global learning and real-time implementation, an improved multi-learning-space DRL-based algorithm, applying driving-behavior-aware adaptive equivalent consumption minimization strategy (A-ECMS) and soft learning mechanism, is proposed and verified by a series of simulations. Simulation results show that, compared with the benchmark method ECMS, the proposed P-DQL method can reduce the hydrogen consumption by 49.9% on average, and the total cost to use by 31.4%, showing a promising ability to increase fuel economy and reduce hydrogen consumption and the total cost to use of FCHEV.

This article investigates the adaptive fuzzy finite-time control problem for a class of high-order stochastic nonlinear systems with a class of exponential type prescribed performance function. It is assumed that the nonlinear functions in the controlled plant are unknown, in which fuzzy logic systems (FLSs) are utilized due to the approximation ability of any unknown continuous functions with arbitrary approximation errors. Based on the FLSs and backstepping design technique, a novel adaptive fuzzy tracking control strategy is proposed to guarantee that the closed-loop nonlinear system is semiglobally finite-time stable in probability via Lyapunov stability theory and It$\hat{o}$formula. Compared with existing results, the transformed error signal was regarded as a stochastic variable. In addition, the expressions of the first and second-order partial derivatives of the transformed error signals are given in this article. Finally, a simulation example with different covariance values is given to show the effectiveness of the proposed control strategy.

In this article, the finite-time adaptive fuzzy state-feedback tracking control problem for the pure-feedback system with full state constraints is studied. In order to transform the pure-feedback form into system strict-feedback case, the mean value theorem is introduced. By employing finite-time-stablelike function and state transformation for output tracking error, the output tracking error converges to a predefined set in a fixed finite interval. To tackle the problem of state constraints, integral barrier Lyapunov functions are utilized to guarantee that the state variables remain within the prescribed constraints with feasibility check. Fuzzy logic systems are utilized in this article to approximate nonlinear uncertainties. In addition, all the signals in the system are guaranteed to be semiglobal ultimately uniformly bounded. Finally, two simulation examples are given to show the effectiveness of the proposed control strategy.

The Internet of Vehicles (IoV) has always attracted attention as the emerging communication network with the most development potential in the 5G era. However, the performance of IoV under 5G ultra-dense networks is an open issue, especially in practice the outage probability and ergodic capacity of the relay cooperative IoV network under aggregate interference are still unclear. Therefore, an opportunistic Decoding and Forwarding (DF) relay cooperative transmission algorithm was proposed in this paper when the destination node of IoV has aggregated interference. In addition, based on mathematical theoretical knowledge such as numerical analysis, the closed expressions of the outage probability and ergodic capacity of the IoV system under aggregated interference was derived. Finally, simulation experiments verify the effectiveness of the proposed scheme and the correctness of the theoretical analysis, which improves the transmission rate of the system.

With the development of vehicle electrification, automation and connectivity, collaborative optimization among the traffic throughput, driving comfort, fuel economy and driving safety targets is still a huge challenging barrier for a connected and automated fuel cell/battery hybrid electric vehicle. Hence, this paper proposes an optimal car-following energy management strategy (EMS) that combines energy management and adaptive cruise control considering the above targets. Specifically, based on vehicle-to-vehicle and vehicle-to-infrastructure information, an optimal following distance algorithm is developed to obtain the optimal following distance considering driving safety, driving comfort and traffic throughput. Then, based on the established vehicle longitudinal dynamics model, an adaptive cruise controller using back-stepping technique is designed to accurately track optimal following distance. Meantime, combining the obtained controller, optimal EMS based on equivalent consumption minimization strategy is proposed to coordinate the output power of fuel cell and battery to improve fuel economy. The simulations of short and long-term driving cycles indicate that the proposed method can reduce hydrogen consumption by 12.12%, jerk by 61.21%, and keep the desired following distance tracking error within 0.5m.

In this article, the problem of adaptive fuzzy control for stochastic high-order nonlinear systems with full-state constraints of the strict-feedback structure was investigated. The unknown nonlinear functions are approximated by using fuzzy logic systems (FLSs) at each step. By introducing the barrier Lyapunov functional candidate, a novel adaptive fuzzy backstepping control strategy is proposed to solve the control problem of stochastic nonlinear systems with full-state constraints. Finally, a numerical simulation example is given to show the effectiveness of the proposed control strategy.

In this paper, an energy management strategy for electric vehicles equipped with fuel cell (FC), battery (BAT), and supercapacitor (SC) is considered, aiming at improving the whole performance under a framework of vehicle to network application. In detail, based on wavelet transform and equivalent consumption minimization strategy (ECMS), the demand power of vehicles is optimized to enhance the lifespan of fuel cell, fuel economy, and dynamic performance of electric vehicles. The wavelet transform is used to separate the high-frequency power in order to provide a peak power and recycle the braking energy. The equivalent consumption minimization strategy is used to distribute the low-frequency power to fuel cell and battery for minimizing the hydrogen consumption. Obtained results are studied using an advanced vehicle simulator, and its effectiveness of the strategy is confirmed, which provides a fundamental control method for the IOV application.