In this article, a novel active front steering (AFS) control strategy including the upper controller and the lower controller is proposed to improve the yaw stability and maneuverability for steer-by-wire (SbW) vehicles. The adaptive recursive integral terminal sliding mode (ARITSM) control is adopted in the upper controller for guaranteeing the convergence performance of both the actual sideslip angle and the yaw rate with strong robustness and fast convergence rate. Then, a fast nonsingular terminal sliding mode (FNTSM) control with extreme learning machine (ELM) estimator to estimate its equivalent control is designed in the lower controller to track the desired front wheel steering angle calculated from the upper controller for driving the sideslip angle and the yaw rate to converge ideal value. It is shown that the upper controller takes two controlled variables (vehicle sideslip angle and yaw rate) and only one control input (front steering angle) into consideration, which can obtain a better performance compared with the case of using only one of these values. Since using the ELM technique in the lower controller to estimate the equivalent control of the FNTSM, not only the dependence of SbW system dynamics can be alleviated in the process of designing controller but also the excellent steering control performance can be achieved. Comparative simulations are carried out by utilizing Carsim and Matlab software to validate the excellent performance of the proposed control strategy for different steering maneuvers.

A continuous adaptive fast terminal sliding mode based speed regulation control is proposed for a permanent magnet synchronous motor (PMSM) drive, where an improved super-twisting algorithm with linear terms (STLT) is constructed in the super-twisting speed observer. First, the STLT observer introduces a first-order term on the basis of conventional super-twisting sliding mode observer to improve the speed estimation convergence rate and to reduce the estimation error. Second, an adaptive function is added to the reaching law for the continuous fast terminal sliding mode speed controller to produce a faster response speed and stronger control robustness. In addition, the total disturbance of the system is estimated via Luenberger disturbance observer, which contributes to the feedforward compensation control component. The closed-loop stability analysis is rigorously given by the Lyapunov stability theory. Finally, real-time experiments on a PMSM platform are carried out to show the effectiveness and superior performance of the proposed control approach.

The global market share of electric vehicles (EVs) is on the rise, resulting in a rapid increase in their charging demand in both spatial and temporal domains. A remedy to shift the extra charging loads at peak hours to off-peak hours, caused by charging EVs at public charging stations, is an online pricing strategy. This paper presents a novel combinatorial online pricing strategy that has been established upon a reward-based model to prevent network instability and power outages. In the proposed solution, the utility provides incentives to the charging stations for their contributions in the EVs charging load shifting. Then, a constraint optimization problem is developed to minimize the total charging demand of the EVs during peak hours. To control the EVs charging demands in supporting utility’s stability and increasing the total revenue of the charging stations, treated as a multi-agent framework, an online reinforcement learning model is developed which is based on the combination of an adaptive heuristic critic and recursive least square algorithm. The effective performance of the proposed model is validated through extensive simulation studies such as qualitative, numerical, and robustness performance assessment tests. The simulation results indicate significant improvement in the robustness and effectiveness of the proposed solution in terms of utility’s power saving and charging stations’ profit.

This paper presents a comprehensive survey of UAV-centric situational awareness (SA), delineating its applications, limitations, and underlying algorithmic challenges. It highlights the pivotal role of advanced algorithmic and strategic insights, including sensor integration, robust communication frameworks, and sophisticated data processing methodologies. The paper critically analyzes multifaceted challenges such as real-time data processing demands, adaptability in dynamic environments, and complexities introduced by advanced AI and machine learning techniques. Key contributions include a detailed exploration of UAV-centric SA’s transformative potential in industries such as precision agriculture, disaster management, and urban infrastructure monitoring, supported by case studies. In addition, the paper delves into algorithmic approaches for path planning and control, as well as strategies for multi-agent cooperative SA, addressing their respective challenges and future directions. Moreover, this paper discusses forthcoming technological advancements, such as energy-efficient AI solutions, aimed at overcoming current limitations. This holistic review provides valuable insights into the UAV-centric SA, establishing a foundation for future research and practical applications in this domain.

This article presents a cooperative dynamic task assignment framework for a certain class of autonomous underwater vehicles (AUVs) employed to control the outbreak of Crown-Of-Thorns Starfish (COTS) in Australia's Great Barrier Reef. The problem of monitoring and controlling the COTS is transcribed into a constrained task assignment problem in which eradicating clusters of COTS, by the injection system of COTSbot AUVs, is considered as a task. A probabilistic map of the operating environment, including seabed terrain, clusters of COTS, and coastlines, is constructed. Then, a novel heuristic algorithm called heuristic fleet cooperation (HFC) is developed to provide a cooperative injection of the COTSbot AUVs to the maximum possible COTS in an assigned mission time. Extensive simulation studies together with quantitative performance analysis are conducted to demonstrate the effectiveness and robustness of the proposed cooperative task assignment algorithm in eradicating the COTS in the Great Barrier Reef.