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 Internet of Things (IoT) system and path planning algorithm provide a technological foundation for autonomous navigation of uncrewed aerial vehicles (UAVs). Geospatial data from the IoT system is transmitted to UAVs through lightweight protocols, and UAVs make optimal path decisions based on these data through optimization algorithms. The combination of the IoT, UAV, and path planning technology constitutes a UAV delivery system, which offers an efficient and economical solution for last-mile logistics in smart cities. Among these, rapid and accurate optimal path planning is crucial for the autonomous delivery of UAVs. Therefore, this article proposes a multistrategy improved particle swarm optimization (PSO) algorithm called MSIPSO. First, the algorithm incorporates a local deadlock jump strategy to increase the success rate of path planning in dense obstacle environments. Second, to mitigate the influence of parameter selection on the algorithm’s performance, adaptive nonlinear inertia weights and learning factors are introduced to improve the algorithm’s stability. Finally, multiple population differentiation evolution strategies are designed, with different position update equations tailored for populations of varying qualities, which enhances the search efficiency of the algorithm. The simulation results show that MSIPSO outperforms PSO, gray wolf optimizer (GWO), whale optimizer (WOA), elite archive-driven PSO (EAPSO) algorithm, and hybrid GWO and differential evolution (HGWODE) algorithm in terms of convergence speed, accuracy, and stability.

Addressing the problem of complex communication in intelligent transportation system (ITS) under aggregated interference, it is clear that reconfigurable intelligent surface (RIS)-assisted communication is an advantageous solution to the problem of high energy consumption, due to its low-cost and sustainable features. We first propose a transmission protocol architecture of dedicated short range communication (DSRC) compatible mode under cellular-vehicle to everything (C-V2X), and a transmission selection scheme, which selects whether to perform RIS-assisted communication or not based on the requirement of real-time outage probability. Next, single-RIS-assisted and multi-RIS-assisted ITS models are proposed separately, and precise expressions for outage probability, bit error rate, and channel capacity are successfully derived. Finally, we provide sufficient Monte Carlo simulations and analysis of the proposed systems, and the simulation results verified the effectiveness of the proposed algorithm and the correctness of the theoretical analysis.

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.

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.