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At present, the problem of global warming is becoming more and more serious, and the transformation of energy structure is very important. The rotary engine has the advantages of small size, high power-to-weight ratio, and high fuel adaptability, which makes it promising for application in the fields of new energy vehicle range extender and unmanned aerial vehicle.

Effective smart cockpit interaction design can address the specific needs of children, offering ample entertainment and educational resources to enhance their on-board experience. Currently, substantial attention is focused on smart cockpit design to enrich the overall travel engagement for children. Recognizing the contrasts between children and adults in areas such as physical health, cognitive development, and emotional psychology, it becomes imperative to meticulously customize the design and optimization processes to cater explicitly to their individual requirements. However, a noticeable gap persists in both research methodologies and product offerings within this domain.

Nowadays, the rapid growth of civil aviation transportation demand has led to more frequent flight delays. The major problem of flight delays is restricting the development of municipal airports. To further improve passenger satisfaction, and reduce economic losses caused by flight delays, environmental pollution and many other adverse consequences, three machine learning algorithms are constructed in current study: random forest (RF), gradient boosting decision tree (GBDT) and BP neural network (BPNN). The departure flight delay prediction model uses the actual data set of domestic flights in the United States to simulate and verify the performance and accuracy of the three models. This model combines the visual analysis system to show the density of departure flight delays between different airports. Firstly, the data set is reprocessed, and the main factors leading to flight delays are selected as sample attributes by principal component analysis.

Electric vertical take-off and landing (eVTOL) is defined as vertical lift aircraft propelled by electric power and capable of carrying people. Based on the system of battery powered CY300 eVTOL, a fuel cell-battery hybrid system (FBHS) in steady-state operation as a potential propulsion system for CY300 eVTOL is proposed. In order to analyze the feasibility of FBHS-powered eVTOL system, a mathematical model is established to evaluate the proposed system performance considering various irreversible effects. Furthermore, considerable sensitivity analyses indicate that the payload of the proposed system is considerably benefited by a higher specific energy of the battery system, specific power of the fuel cell system and hydrogen storage ratio of the hydrogen tank. Hydrogen tank weight decreases the payload while enhances the hovering time.

Transporting baggage is critical in airport ground support services to ensure smooth flight operations. However, the scheduling of baggage transport vehicles faces challenges related to low efficiency and high costs. A multi-objective optimization vehicle scheduling model is proposed to address these issues, considering time and space costs, vehicle utilization, and passenger waiting time. An improved genetic algorithm (IGA) based on the large-scale neighborhood search algorithm is proposed to solve this model. The simulation experiment is conducted using actual flight data from an international airport. The IGA algorithm is compared with the standard genetic algorithm (SGA) based on experimental results, revealing that the former achieves convergence in a significantly shorter time. Moreover, the scheduling paths of baggage cars that violate flight service time window requirements are significantly lower in the final scheduling scheme under the IGA algorithm than in SGA.

With increasing interest in the urban air traffic market for electric Vertical Take-Off and Landing (eVTOL) vehicles, there are opportunities to enhance flight performance through new technologies and control methods. One such concept is the propulsion wing, which incorporates a cross-flow fan (CFF) at the wing's trailing edge to drive the vehicle's flight. This article presents a wind tunnel experiment aimed at analyzing the aerodynamic characteristics of the propulsive wing for the novel eVTOL vehicle. The experiment encompasses variations in angels of attack, free stream velocities and fan rotational speeds. The result verifies that cross-flow fans offer unique flow control capabilities, achieving a tested maximum lift coefficient exceeding 7.6. Since flow from the suction surface is ingested into the CFF, the flow separation at large angle of attack (up to 40°) is effectively eliminated.

Electric vertical take-off and landing aircraft (eVTOL) has become a main trend in general aviation. Battery, power electronic and motor have made big progress in the last decades, most companies and research institutions are focusing on the development of 2-5 seat (2 tons maximum takeoff weight) eVTOL products and strat to put into market. This paper tried to defines the design and analysis process of electric propulsion system taking 2-ton eVTOL as an example. Firstly, the parameters of the aircraft is defined and the power and energy consumption was analyzed based on flight phase. Secondly, the electric propulsion system architecture is defined based on the aircraft design. The design and evaluation of propeller, motor, motor controller and battery were carried out respectively. The optimal design of propeller in hover stage and cruise stage is conducted.

The confidence of the onboard adaptive model in estimating surge margin significantly affects the operating stability in an aircraft engine’s active surge margin control process. Unfortunately, the existing onboard adaptive models lack high confidence, although wide-ranging in estimation, due to the unknown surge boundaries in component characteristics. Therefore, this paper first accurately estimates the actual surge margin during the engine operating near-surge boundary using a pressure correlation measurement technology. Then, innovatively, the estimated surge margin is used to correct the surge boundary of the nonlinear onboard model of the engine to obtain the actual surge boundary, thereby guaranteeing confidence. Finally, a nonlinear onboard adaptive model based on an improved spherical unscented Kalman filter is employed to achieve wide-range high-confidence surge margin estimation throughout the engine’s life cycle.

As a traditional probabilistic mid-term conflict detection algorithm, the Prandini algorithm plays an essential role in ensuring flight safety in the aircraft route area. For the issue of mutation error in the calculation results of the Prandini algorithm, this research provides an improved Prandini conflict detection algorithm. First, the integral of the standard Gaussian distribution is solved using randomization. The minimum prediction interval moment is then calculated, and the critical time points at which conflicts may exist before and after that moment are approximated separately using a bisection method. N moment values are selected uniformly within the time range formed by the two critical time points. The instantaneous conflict probabilities for these N moments are calculated and the maximum value is selected from them as a measure of the likelihood of conflict between the two aircraft over the entire route for an extreme case.

Taking into account the high rotor speed of the generator and the trend of high voltage in direct current microgrids in high-power aviation hybrid propulsion systems, a hybrid power system with a power of 200 kilowatts (kW), a voltage of 540 volts (V), and a rated generator speed of 10500 r/min was established. Anticipating the demands of future high-power system tests, a matching simulation model was developed. The paper discusses various aspects including model construction, test design, and result validation, proposing an overall control strategy for series hybrid aviation propulsion systems – utilizing lithium-ion batteries to stabilize grid voltage and using the turboshaft-generator unit as the primary power source to meet the main power demands of the electric propulsion system. The established model consists of four modules: turboshaft engine, power generator, voltage-stabilizing battery, and electric motor/propeller.

Electric vertical takeoff and landing (eVTOL) aircraft, which is used extensively in both military and civilian fields, has the advantages of good maneuverability, high cruising speed, and low requirements for the takeoff and landing modes. Robust and stable control is crucial to ensuring its safety because the dynamics model of an eVTOL aircraft will change significantly between fixed-wing and vertical takeoff and landing mode. In this paper, we first study the structural characteristics of the eVTOL aircraft and establish its dynamic model by considering typical flight modes and mechanical parameters. Then we design a closed-loop controller based on cascade PID technique. Finally, the effectiveness of the control algorithms is verified based on the semi-physical flight simulation platform, which can lower the development cost of control algorithms significantly.

Direct debugging of a vertical takeoff and landing (VTOL) fixed-wing aircraft’s control system can easily result in risk and personnel damage. It is effectively to employ simulation and numerical methods to validate control performance. In this paper, the attitude stabilization controller for VTOL fixed-wing aircraft is designed, and the controller performance is verified by MATLAB and visual simulation software, which significantly increases designed efficiency and safety of the controller. In detail, we first develop the VTOL fixed-wing aircraft’s six degrees of freedom kinematics and dynamics models using Simulink module, and the cascade PID control technique is applied to the VTOL aircraft’s attitude stabilization control. Then the visual simulation program records the flight data and displays the flight course and condition, which can validate the designed controller performance effectively.

This paper focuses on lane-changing trajectory planning and trajectory tracking control in autonomous vehicle technology. Aiming at the lane-changing behavior of autonomous vehicles, this paper proposes a new lane-changing trajectory planning method based on particle swarm optimization (PSO) improved third-order Bezier curve path planning and polynomial curve speed planning. The position of Bezier curve control points is optimized by the particle swarm optimization algorithm, and the lane-changing trajectory is optimized to improve the comfort of lane changing process. Under the constraints of no-collision and vehicle dynamics, the proposed method can ensure that the optimal lane-changing trajectory can be found in different lane-changing scenarios. To verify the feasibility of the above planning algorithm, this paper designs the lateral and longitudinal controllers for trajectory tracking control based on the vehicle dynamic tracking error model.

Advanced flight control system, aviation battery and motor technologies are driving the rapid development of eVTOL to offer possibilities for Urban Air Mobility. The safety and airworthiness of eVTOL aircraft and systems are the critical issues to be considered in eVTOL design process. Regarding to the flight control system, its complexity of design and interfaces with other airborne systems require detailed safety assessment through the development process. Based on SAE ARP4754A, a forward architecture design process with comprehensive safety assessment is introduced to achieve complete safety and hazard analysis. The new features of flight control system for eVTOL are described to start function capture and architecture design. Model-based system engineering method is applied to establish the functional architecture in a traceable way. SFHA and STPA methods are applied in a complementary way to identify the potential safety risk caused by failure and unsafe control action.

The main objective of platoon control is coordinated motion of autonomous vehicle platooning with small intervehicle spacing while maintaining the same speed and acceleration as the leading vehicle, which can save energy consumption and improve traffic throughput. The conventional platoon control methods are confronted with the problem of manual parameter tuning. In order to addres this isue, a novel bifold platoon control approach leveraging a deep reinforcement learning-based model is proposed, which enables the platoon adapt to the complex traffic environment, and guarantees the safety of platoon. The upper layer controller based on the TD3 tuned PID algorithm outputs the desired acceleration. This integration mitigates the inconvenience of frequent manual parameter tuning asociated with the conventional PID algorithm. The lower layer controller tracks the desired acceleration based on the inverse vehicle dynamics model and feedback control.

Quadrotor unmanned aerial vehicle (Q-UAV) is an underactuated system, which is often used in complex environments, such as maintenance tasks in pipelines. However, flying in pipelines will lead to strong ground effect, implying that the Q-UAV will face time-varying uncertainties. Since the flight space is limited, it needs to achieve agile and high-security flight to avoid collisions. This research proposes an adaptive robust tracking control method to ensure that the Q-UAV can complete the agile and high-security flight missions. Agile and high-security flight is guaranteed by achieving prescribed transient and steady-state performance (PTSSP). Our approach takes into account (potentially rapid) time-varying uncertainties with unknown bounds. We formulate the desired flight altitude and attitude trajectories as equality constraints and the PTSSP as inequality constraints, and use state transformation approach to transform the inequality constraints into equality constraints.

Electrical Vertical Takeoff and Landing (eVTOL) vehicles hold great promises for revolutionizing urban mobility. Their emergences as a transformative transportation technology has led multiple Original Equipment Manufacturers (OEM) competing for market share, with important variety of technical solutions, all necessitating to demonstrate the compliance to safety requirements and regulations. Model Based Safety Analysis (MBSA), newly introduced in ARP4761A and based on compositional and modular representation of failure propagation paths within one system, provides a unique opportunity to increase efficiency by maximizing the possible reuse of safety analyses elements across multiple architectures (“product line” philosophy). Generic library of safety models for elements of variant architectures can be efficiently constructed using MBSA techniques that can then support safety analyses on variant architectures or architectures trade-off.

The driving risk field model offers a feasible approach for assessing driving risks and planning safe trajectory in complex traffic scenarios. However, the conventional risk field fails to account for the vehicle size and acceleration, results in the same trajectories are generated when facing different vehicle types and unable to make safe decisions in emergency situations. Therefore, this paper firstly introduces the acceleration and vehicle size of surrounding vehicles for improving the driving risk model. Then, an integrated decision-making and planning model is proposed based on the combination of the novelty risk field and model predictive control (MPC), in which driving risk and vehicle dynamics constraints are taken into consideration. Finally, the multiple driving scenarios are designed and analyzed for validate the proposed model.

This overview and study article scrutinizes the evolution and challenges of electric vertical takeoff and landing aircraft (eVTOL), with a primary focus on airworthiness and safety certification. The paper discusses key issues such as high-energy-density aviation-grade batteries and the light weighting of electrical propulsion systems. Utilizing scientific models and real-world data, the study outlines the required battery technology and electrical propulsion specifications for eVTOLs with effective commercial load capabilities. For eVTOLs operating in the 300 km range, aviation-grade batteries must achieve energy densities between 300-600 wh/kg. For those covering a 600 km range, the energy density requirements exceed 600 wh/kg. Compliance with stringent safety standards, including triple certification by the FAA under 14 CFR Part 23, is imperative. This article conducted research and offered flowchart of the complicated FAA standard, which is rare in existing articles.

Unmanned Aerial Vehicles (UAVs) encounter various uncertainties, including unfamiliar environments, signal delays, limited control precision, and other disturbances during task execution. Such factors can significantly compromise flight safety in complex scenarios. In this paper, to enhance the safety of UAVs amidst these uncertainties, a control accuracy prediction model based on ensemble learning abnormal state detection is designed. By analyzing the historical state data, the trained model can be used to judge the current state and obtain the command tracking control accuracy of the UAV at that instant. Ensemble learning offers superior classification capabilities compared to weak learners, particularly for anomaly detection in flight data. The learning efficacy of support vector machine, random forest classifier is compared and achieving a peak accuracy of 95% for the prediction results using random forest combined with adaboost model .