Browse Topic: Architecture
The turbine hybrid electric propulsion system is an important form of green aviation. Unlike the single form of aviation power scheme, the hybrid energy system is flexible in architecture, uses two or more energy forms, and has diverse energy sources. Under different mission requirements, it needs to meet the requirements of mass balance, energy balance, and power demand, etc. Therefore, The control and distribution management between different energy systems have become the key to hybrid power, and power management technology is one of the key challenges in the development of aviation hybrid power control systems. This paper reviews the current structural forms of aviation turbine hybrid electric propulsion systems, analyzes the current research status of power management technology for aviation hybrid systems, and points out that the online power management method based on optimization is the best power management technology solution for turbine hybrid electric propulsion systems. Establishing a high-precision and realtime on-board power calculation model, breaking through the power management method based on the integrated flight and engine, and improving the applicability of the power management method throughout the service life are important directions for promoting the development of online power management technology.
Electrical/Electronic Architectures (EEAs) are continuously evolving to meet newly emerging demands. In recent years, major drivers of this evolution have been the increasing software-defined nature of vehicles and the push toward automated driving. Key technologies such as edge-enhanced functions, vehicle-to-vehicle communication, and service-oriented architectures are therefore the focus of current research efforts. This paper presents a vision of how these technologies can be used to enable cooperation between vehicles, illustrated by using parked vehicles as edge nodes. These are typically seen as obstructions, as they significantly increase the risk of missing or misinterpreting vulnerable road users such as pedestrians or cyclists. Our proposed approach to counteract this problem is the use of the parked vehicles themselves as edge nodes that support object detection or even trajectory planning. Current research primarily considers smart traffic infrastructure, roadside units, and other vehicles as potential edge nodes. Including parked vehicles as edge nodes means that, instead of acting solely as obstacles, we leverage their built-in sensors to contribute to cooperative awareness. While such cooperation will enhance the safety of automated vehicles in urban areas, several challenges arise. In this paper, we discuss how data traceability, decision-making in the presence of conflicting information, and incentive mechanisms for owners of parked vehicles can be addressed. Based on these challenges, the paper outlines requirements for future cooperative architecture and highlights the role of edge-enhanced functions, Vehicle-to-Vehicle (V2V) communication, and service-oriented architectures in enabling fully automated driving.
The UMV Peoplemover 2+2 is part of a modular vehicle family (Urban Modular Vehicle) that includes derivatives for passenger and cargo transport in urban environments. The platform supports automated movers as well as conventionally controlled vehicles with a human driver, ensuring high flexibility across applications. The modular platform enables the extensive use of common parts, allowing the efficient and cost-effective realization of multiple vehicle variants. The increased share of common parts also improves sustainability by reducing derivative-specific parts, material usage, and production complexity. A drivable demonstrator of the UMV Peoplemover 2+2 has already been realized. The vehicle is designed for the automated transport of up to four occupants in a 2+2 vis-à-vis seating arrangement and is targeted at demand-oriented shuttle services. While the drivable demonstrator validated the proof of concept, it lacked the core Level 4 hardware and software stack for automated driving functions. To address this limitation, we deployed a software-defined vehicle architecture to the concept. This paper introduces the novel e/e-architecture and software stack enabling the Peoplemover 2+2 to initiate its first shuttle service at the German Aerospace Center (DLR e.V.) in Stuttgart. We further detail the deployed multi-modal sensor suite, comprising modern solid-state LiDARs and a 4D imaging radar, which were carefully selected to meet the operational design domain requirements while also serving as a versatile research platform for future advanced perception studies. Finally, we analyze the SDV-based modular software stack, which facilitates rapid application development through straightforward switching between commercial, open-source, and in-house software domains, and supports parallel execution of domain-specific functions across all three software sources.
Software-defined, highly customizable vehicle architectures drastically increase the number of hardware–software constellations that must be validated, especially under safety and timing constraints. Traditional unit and integration testing, as well as current regression and combinatorial methods, cannot practically cover this configuration space or reliably capture emergent effects arising from complex interactions, such as bandwidth contention and non-linear latency behavior. This work presents a proof-of-concept for predictive, situational validation of self-describing hardware and software components within realistic automotive E/E architectures. Proposing a novel Machine Learning- (ML) based method for early systemic feasibility prediction of automotive configurations using Graph Neural Networks (GNNs). Specifically, the subclass Graph Isomorphism Networks (GINs) is applied to predict the compatibility of a randomly composed configuration of software and hardware components, assessing both structural compatibility and functional stability. The trained models achieve recall and accuracy above 90%, even when detailed behavioral metadata is hidden during training, indicating that systemic incompatibilities are learnable from topological features alone. Results were achieved from training on a realistic, synthetic dataset representing less than 10e−27% of all possible permutations without finetuning or further parameter optimization. It demonstrates the potential of GIN-based graph learning to enable early, automated feasibility assessment, substantially reducing testing time and development effort for modular, personalized, and update-capable vehicle architectures.
Precision agriculture, also known as smart farming, was once reserved for early adopters or large-scale operations, but is now an expectation within the farming industry. Across various regions and farm sizes, smart farming techniques are changing the way crops are planted as well as how they are monitored and harvested. However, farmers today are under increasing pressure to reduce labor, decrease chemical inputs, conserve water and operate in tighter windows. Couple this with factors such as narrow seasonal windows, productivity demands and safety considerations, and the need for smarter decisions becomes imperative. Going one step further, global food demands and environmental pressures are further increasing demand for precise, accurate and intelligent farming solutions.
In response to the problems of urban traffic congestion and the limited expansion of infrastructure, this paper conducts two core research focusing on the intelligent chassis system of split-type flying vehicle. Firstly, an autonomous navigation strategy for the intelligent chassis module is proposed based on chassis module Navigation 2 architecture, which fuses LIDAR and IMU positioning to plan paths using the A* global planning algorithm on a global cost map, and update the local cost map in real time with sensor data. It is orchestrated by the BT Navigator using a behavior tree, with failures handled by the Recovery Server, to achieve autonomous driving across multiple waypoints. In simulation and closed-field experiments, the system can stably reach the preset target points. The positioning accuracy and trajectory tracking performance can meet the design requirements. Secondly, a mechanical slide rail-type docking structure adapted to the split flying vehicle architecture is designed. Deformation analysis under the representative working conditions are evaluated through finite element software. The test results show that the maximum deformation of this docking structure under typical load is significantly lower than the docking tolerance and positioning repeatability requirements. The structural stiffness and stability meet the design indicators. The above work indicates that the proposed autonomous navigation strategy and the docking structure for the intelligent chassis can effectively support the modular operation of “air trunk & ground terminal” mode, providing a scientific basis for the functional integration and system reliability research of split-type flying vehicles.
This work describes the flight control system architecture of the VSDDL VT-03-s Shadow, a cost-effective subscale aircraft used as a testbed for novel flight control schemes. The highlight is the Maneuver Control System comprising the Trajectory Control System, which facilitates Simplified Vehicle Operations, and the Tactical Maneuvering System, which permits more aggressive maneuvering. The control laws permit the selection of both vertical takeoff and landing and conventional takeoff and landing modes of operation. Flight test results shown include transitions between vertical and forward flight modes performed using both Trajectory Control System and Tactical Maneuvering System, limited aerobatic maneuvering performed using the Tactical Maneuvering System, and demonstration of some of the automatic flight functions and capabilities.
Developing high-integrity software is a complex process that involves meeting strict standards across various industries. For instance, in the avionics sector, the DO-178C Design Assurance Level A (DAL-A) sets the highest level of rigor, requiring comprehensive evidence that the software will perform its intended safety functions. Modern avionics systems are made up of hardware and software from different vendors, all integrated by prime contractors. By achieving modularity in these systems, we can reduce interface complexity, manage version control, address supply chain vulnerabilities, and significantly lower recertification costs. To support a high degree of integration and software reuse in avionics systems, certain architectural elements are necessary. These include a certified Real-Time Operating System (RTOS), open standards consortia like FACE® and MOSA, multicore partitioning strategies, deterministic networking, and hypervisor-based virtualization. The role of a certified RTOS, for example, is crucial in ensuring the reliable and efficient operation of safety-critical software components. Open standards consortia, on the other hand, facilitate the development of interoperable systems, while multicore partitioning strategies enable the efficient use of system resources. The use of deterministic networking and hypervisor-based virtualization also plays a key role in enabling the integration of multiple systems and reducing the complexity of system design. By leveraging these technologies, we can create a 'certify once, deploy anywhere' paradigm, which reduces development timelines, lowers lifecycle costs, and positions safety-critical software components for reuse across heterogeneous platforms. This approach not only improves the efficiency of system development but also enhances the reliability and safety of the resulting systems. In essence, the development of high-integrity software for avionics systems requires a comprehensive approach that considers the complex interactions between hardware and software components. By adopting modular architectures and leveraging open standards, certified RTOS, and advanced networking and virtualization technologies, we can create systems that are not only safe and reliable but also efficient and cost-effective. This, in turn, can help reduce the risks associated with system development and deployment, while also improving the overall performance and safety of the resulting systems.
Enterprises that develop complex products or systems often struggle to reuse technology efficiently across their portfolios. This challenge is especially prevalent in aerospace, transportation, energy, and defense industries, where preserving freedom of action is critical. In this context, freedom of action is defined as the ability to avoid vendor lock imposed by integrators or third parties, while enabling competition within clearly defined functional boundaries that establish effective market segments for system components. This paper presents eight best practices for Enterprise Reference Architecture (ERA) development to address this challenge and applies them to aviation functionality spanning both vertical lift and fixed wing platforms. Because complex systems can be modularized in many ways, a consistent set of guiding rules is required to produce an organized set of modules that are reusable across an enterprise portfolio. The best practices presented in this paper are intended to fulfill that role.
Within the next years, it is expected that the capabilities that are demanded to the rotorcraft fleet would be enhanced with respect to the current ones. Very long range, speed above typical rotorcraft performance, hot and high HOGE capability and high payload capacity are foreseen, together with limitation on aircraft take-off weight (TOW): among these sizing cardinal requirements, speed characteristics and long-range operations drive the sizing towards innovative solution, to overcome the physical limitation of a conventional rotorcraft. The work starts with a performance-based comparison of different fast rotorcraft architectures, comparing it with respect to the conventional helicopter, used as benchmark. Once first investigation loop is completed with a preliminary sizing analysis, a detailed one is focused on tiltrotor architecture, showing the impact of hover and high-speed capability on lifting and powerplant systems, as well as the impact of sizing criteria on the overall performance. In such second step, a matrix scenario is proposed, where both requirements and sizing criteria are evaluated to show the peculiarity on tiltrotor solution. In conclusion, considerations on balanced criteria for tiltrotor sizing are reported, with focus on sizing trade-off.
This study addresses the integrated plant-controller design problem for sizing a VTOL air vehicle. An Explicit Model Following control architecture is employed, where the reference model is formulated according to selected ADS-33 handling qualities criteria, and parametrized to introduce design flexibility within the optimization framework. An iterative algorithm is developed based on the Linear Matrix Inequalities formulation of the H∞ synthesis problem, enabling sequential optimization of the controller, the parameters of the vehicle's linear parametric model, and the parameters defined for the design objectives. The proposed approach is evaluated using a simplified design scenario. The results indicate that the design objectives are improved without compromising the closed-loop system performance.
This paper presents a mission architecture framework for enabling interoperability in Next Generation Command and Control (NGC2) systems by integrating Modular Open Systems Approach (MOSA) principles with a shared mission data model. Current C2 systems are fragmented and cannot dynamically integrate capabilities to meet requirements across systems-of-systems (SoSs). This work introduces a Multi-Level MOSA-to-Mission Framework (ML-MMF), which aligns modular system interfaces, a common data model, and mission execution threads to enable composable mission capabilities. The framework supports dynamic orchestration of heterogeneous system functions and enables interoperability across domains from a common data model. The approach is demonstrated conceptually through mission-engineering constructs, such as mission threads and integrated kill chains. The results suggest that aligning MOSA with mission-level data and behaviors enables scalable, adaptive, and reconfigurable C2 architectures.
This paper presents a spatio-temporal graph neural network (STGNN) centric approach to enable heterogeneous agents to collaborate and cooperate for different types of missions. The STGNN-centric approach and corresponding autonomy are encapsulated in the Advanced Graph-enabled Network Technology for Collaborative Autonomous Agents (AGENTCA) technology. Various decentralized and distributed control architectures are reported in the literature, but in some instances these approaches do not leverage the inherent graph network which can increase scalability to larger teams and algorithmic efficiency. Specifically, in this paper advances in artificial intelligence are leveraged to parameterize and encode optimal, or nearly optimal, swarm control techniques. For this work, the team focused on developing a diffusion-based STGNN swarm controller using imitation learning. An expert, centralized swarm control law was used to guide the STGNN during the learning process. The STGNN controller enables the swarm to follow a leader while avoiding static and dynamic obstacles and maintaining a desired separation distance from neighbors and obstacles. The approach is demonstrated in simulation with hundreds of agents and in flight tests with up to thirteen test vehicles.
This study evaluates whether a statewide layered medical-drone architecture can improve time-critical EMS logistics in Florida by delivering blood products, AEDs, and critical support devices. We define Time-To-Clinical-Support (TTCS) as the interval from incident recognition to first effective therapy and use Florida EMS benchmark intervals, county-level population and centroid distance data, and p-median hub placement to model system performance. Scenario analysis compares 20-, 40-, and 60-hub deployments and estimates order-of-magnitude effects on AED TTCS and survival gains under explicit assumptions for availability, cruise speed, dispatch overhead, and bystander uptake. The results indicate that a mid-scale network may reduce delay sufficiently to produce meaningful clinical benefit, provided it is integrated with EMS dispatch, medical direction, cold-chain controls, and hurricane-resilient infrastructure. Regulatory pathway constraints, incomplete county-level OHCA data, and uncertainty in mission availability remain the primary limitations on precision and external validity.
Deep Reinforcement Learning (DRL) for quadrotor flight control typically relies on Domain Randomization (DR) for sim-to-real transfer, resulting in overly conservative policies that struggle with dynamic disturbances. To overcome this, we propose a novel adaptive control architecture that actively perceives and reacts to instantaneous perturbations. First, we train an optimal outer-loop policy, then replace its reliance on ground-truth disturbance data with a Residual Dynamics Predictor (RDP). The RDP estimates the external forces and moments acting on the aircraft in flight online using only the history of states and control actions. For seamless hardware transfer, we introduce a data-efficient linear calibration bridge and an online thrust correction mechanism that align the simulated latent space with reality using mere seconds of flight data. Real-world validations on a Crazyflie micro-quadrotor demonstrate that our adaptive controller significantly outperforms baselines, maintaining precise trajectory tracking under severe uncertainties including mass variations, asymmetric payloads, and dynamic slung loads.
The convergence of highly capable edge AI models and advanced commercial-off-the-shelf (COTS) edge AI accelerators is reshaping how computation is deployed across defense, aerospace, and commercial platforms. Mission-critical decisions increasingly must be made at the edge, onboard vehicles, satellites, and infrastructure nodes, where latency, connectivity, and power availability are constrained.
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