Browse Topic: Electrical, Electronics, and Avionics
ABSTRACT Over time, the National Institute of Standards and Technology (NIST) has refined the 4Dimension / Real-time Control System (4D/RCS) architecture for use in Unmanned Ground Vehicles (UGVs). This architecture, when applied to a fully autonomous vehicle designed for missions in urban environments, can greatly assist in the process of saving time and lives by creating a more intelligent vehicle that acts in a safer and more efficient manner. Southwest Research Institute (SwRI®) has undertaken the Southwest Safe Transport Initiative (SSTI) aimed at investigating the development and commercialization of vehicle autonomy as well as vehicle-based telemetry systems to improve active safety systems and autonomy. This paper will discuss the implementation of the 4D/RCS architecture to the SSTI autonomous vehicle, a 2006 Ford Explorer.
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Helicopter tail shake constitutes a significant limitation to both passenger comfort and aircraft stability. Under powered descent conditions, elevated Angle of Attack (AoA) cause flow separation around the rotor hub and engine cowling, leading to the development of an unsteady wake dominated by large-scale turbulent structures. To support the helicopter tail shake phenomenon investigation, a dedicated Particle Image Velocimetry (PIV) experimental setup was designed in this work, together with four aerodynamic devices aimed at mitigating tail shake. These components were then tested through a wind tunnel campaign with the PIV setup. The proposed aerodynamic components were conceived to either deflect the hub wake away from the tail empennages or to decrease the Turbulent Kinetic Energy (TKE) within the wake. To achieve these objectives, a dorsal fin, a horse-collar, and two spoiler configurations inspired by automotive applications were designed and experimentally evaluated. The devices were tested both as standalone solutions and in combined arrangements on a scaled helicopter wind tunnel model featuring a rotating hub and blade shanks. The vertical velocity component, was used as an indicator of wake deflection, and the Turbulent Kinetic Energy was used as an indicator of wake turbulence. The Horse Collar and the Large Spoiler showed a reduction in both indicators suggesting possible tail shake mitigating capabilities, and additional improvements were achieved when the two devices were deployed in combination.
The current work presents a methodology to estimate the mission and performance capabilities of a generic rotorcraft configuration, to satisfy the need of evaluating the integration of a full electric powertrain in the aircraft design. To include all the design steps, two different approaches are proposed. For the preliminary phase, the "Analytic Method" is considered, which exploits a purely resistive model. Conversely, a method based on look-up tables called "Table Method" is intended to be used in more advanced phase, when the battery pack is defined. Both approaches are tested by evaluating a reference mission and a hover chart. Finally, a verification of the presented methodology is carried out by comparing the mission results with a commercial software, specialized in the evaluation of the cell discharge when a given power spectrum is provided.
Deep learning (DL) models have attained state-of-the-art performance in numerous fields. Nevertheless, for certain real-world applications, existing models encounter diverse challenges, ranging from a lack of generability to new data to issues of scalability and overfitting. In this context, integrating information extracted from different modalities holds promise as a potential solution to alleviate these challenges. This paper introduces MAVEN, a multimodal deep-learning framework for long-range atmospheric visibility estimation. Using multimodal deep learning, MAVEN fuses various modalities to estimate long-range atmospheric visibility. These modalities include RGB imagery, Edge Map, Entropy Map, Depth Map, and Normal Surface Map. Results show that in contrast to single-modality RGB, which achieves only 87.92% accuracy, multimodal deep learning models achieve an accuracy of over 96%. This significant improvement highlights the potential of multimodal approaches to enhance the accuracy and reliability of atmospheric visibility estimation, which is crucial for improving safety in applications such as aviation, maritime navigation, and autonomous vehicles. By addressing challenges such as data variability, environmental factors, and the inherent complexity of atmospheric conditions, MAVEN contributes to more reliable and robust visibility estimation systems, thereby enhancing safety and operational efficiency in critical environments.
The certification of highly integrated electric Vertical Take-Off and Landing (eVTOL) aircraft requires a rigorous bridge between simulation and flight reality. This paper presents the Joby Disturbance Generator, a high-integrity software framework natively integrated into the aircraft flight control system. The system utilizes a deterministic state machine to inject a library of signals, ranging from standard doublets and chirps to complex waveforms, directly into internal control loops. Applications include frequency sweeps for stability margin extraction and structural mode identification, time-domain inputs for handling qualities assessment, synthetic fault injection for redundancy management verification, and precise loads model validation. The system continuously monitors vehicle health, automatically aborting test points upon detecting genuine failures. For loads validation, it coordinates temporary relaxation of flight envelope protections with precise disturbance injection. This methodology accelerates development by shifting risk from flight execution to software verification, providing deterministic, data-driven evidence for certification. Flight test results demonstrating these capabilities are presented.
This organizational process survey provides insight into the technical aspects of approved airworthy aircraft modifications applied in government organization vertical lift flight test. The publication reviews processes applied by the National Research Council of Canada's Flight Research Laboratory (NRC-FRL) and its Airworthiness Control System to enable research flight testing. Dominated by the need for integrating experimental payloads, the NRC-FRL embeds a Design and Fabrication Service organization for modification of internal and external client projects and flight test aircraft. In context of experimental flight testing, this work reviews technical information on process, facilities, and methodology for airworthy integration of flight test payloads. Information is used to synthesize recommendations in experimental vertical lift flight testing that satisfy both formal (regulated compliance) and informal (compliance intent) airworthiness requirements.
This digital standard is a requirements extract of AS50881H Wiring Aerospace Vehicle. This file contains a general requirements extraction as well as files that are optimized for use with Doors Classic, Siemens Polarian, and PTC. 
The recent discovery of glacier remains in Noctis Labyrinthus, the "Maze of the Night" near Mars' equator sheds new light on the history of water on Mars, the evolution of the planet’s climate and geology, and the possibility of life. It also opens the possibility for massive amounts of clean glacier ice to be accessed by astronauts at low latitudes on Mars, alleviating the need to operate in more frigid higher latitudes. Further reconnaissance of the site requires a robotic vehicle capable of traversing rough, salt-crusted glacier surfaces and leaping across crevasse fields. To address this need, we propose a conceptual hybrid aerial/ground vehicle, LILI (Long-term Ice-field Levitating Investigator). LILI combines episodic rotary-wing flight with ground mobility as a propeller-driven sled through an arrangement of skis/runners, wheels, and tilting proprotors. A high-level look at the Noctis Labyrinthus "relict glacier" site is presented, along with a notional LILI mission traverse concept designed to ensure critical scientific measurements are captured. The NASA Design and Analysis of Rotorcraft (NDARC) software is utilized to ensure that mission requirements and sizing constraints are met. Furthermore, future work considers guidance, navigation, and control requirements to satisfy mission objectives, and an initial construction for a simplified LILI small-scale prototype.
Fault detection in autonomous VTOL aircraft is critical because even minor degradations can quickly destabilize multirotor vehicles in safety-critical environments. However, real-flight fault detection remains challenging due to sensor noise, environmental disturbances, and the nonlinear aeromechanics of multirotor platforms. This study proposes a comprehensive machine-learning framework for rotor fault detection, isolation, and severity prediction using real flight data. A convolutional neural network (CNN) architecture is developed to learn spatio-temporal patterns from multivariate flight dynamics, enabling direct inference of both the faulty rotor and its damage level. The framework is first validated using simulated data generated by our in-house flight dynamic model. Next, to verify the framework using real flight data, a hexcopter was designed, fabricated and flight tested for both nominal and faulty cases by introducing controlled blade-tip breakage. The trained model achieves rotor-wise fault classification accuracies above 99% and sample-wise severity estimation accuracy of 96% within a ±1% tolerance in experimental data, demonstrating strong generalization and supporting real-time health monitoring for autonomous VTOL systems.
This paper presents a reinforcement learning (RL)–based outer-loop controller for quadrotor UAV trajectory tracking and its real-world experimental validation. The proposed approach integrates RL into a standard cascaded flight-control architecture by replacing the conventional PID outer loop while retaining the onboard attitude and body-rate PID controllers. This hierarchical design preserves reliable inner-loop stabilization while leveraging RL to address nonlinear dynamics, coupling effects, and modeling uncertainty in translational motion. The controller is trained entirely in a physics-based simulation using Proximal Policy Optimization (PPO) and transferred directly to a Crazyflie quadrotor without additional tuning. Performance is evaluated through real-world figure-8 trajectory tracking experiments with varying time scales to impose increasing dynamic demands. Compared to a conventional PID outer-loop controller operating under identical conditions, the RL-based controller consistently reduces effective phase delay and achieves lower position and velocity tracking errors, particularly for aggressive trajectories. The results demonstrate robust sim-to-real transfer and highlight the potential of learning-based outer-loop control as a drop-in enhancement to classical quadrotor flight controllers.
This paper presents an initial handling qualities analysis of an Electric Vertical Take-Off and Landing (eVTOL) hexacopter. The analysis uses the Distributed Electric Propulsion Simulation (DEPSim), developed by Penn State University (PSU) and the Comprehensive Hierarchical Aeromechanics Rotorcraft Model (CHARM), developed by Continuum Dynamics, Inc. (CDI). The study focuses on evaluating a generic AAM hexacopter performing Handling Qualities Task Elements (HQTE) as defined by the DOT / FAA. A trajectory controller was developed to enable simulation of prescribed flight paths, allowing automated simulation of four HQTEs: Heliport Approach, Hovering Turn and Hold, Pirouette, Lateral Reposition and Hold. Design modifications incorporating lateral mast tilt and Direct Side Force Control (DSFC) were implemented to enhance yaw control and ride qualities. Piloted simulations were conducted at the PSU rotorcraft flight simulation facility using DEPSim, employing an Attitude Command Attitude Hold (ACAH) architecture with mode switching to Translational Rate Command / Position Hold (TRC / PH) and TRC plus DSFC modes. Two of the four HQTEs were tested in piloted simulations. Though formal ratings were not collected at this time, pilot commands and performance indicated that TRC / PH and TRC plus DSFC modes enhance handling qualities over ACAH mode. The DSFC control law was found to have substantially reduced roll attitude, which could potentially enhance visual cueing, pilot comfort, and pilot-perceived handling qualities.
An experimental investigation was conducted to characterize the effects of partial-ground on the aerodynamics of a hovering rotor. A model-scale rotor was tested at a range of heights above ground and under partial-ground coverage, and rotor hub forces and moments were measured using a six-axis force/torque transducer during constant-power operation. The measurements were used to develop a semi-empirical thrust ratio model that accurately captures trends from out-of-ground effect to full-ground effect conditions. This model predicts realistic thrust behavior at low ground-coverage conditions, exhibiting high adjusted R2 and minimal root mean square error. Time-resolved particle image velocimetry was conducted for selected cases to examine induced flow features and to qualitatively assess changes in the downwash and edge-driven crossflow associated with partial-ground interactions. A geometric rotor-ground interaction area based on a circular-segment formulation was correlated to the thrust coefficient over the interacting region. Results show that thrust increases with ground coverage and decreases with increasing out-of-ground height, whereas moment coefficients increase with decreasing height. The increases in thrust and moment can be attributed to a reduction in the induced velocity above ground, as observed in the measured flow field, thereby increasing the effective ground-induced pressure beneath the rotor.
Rotor-rotor and rotor-boundary aerodynamic interactions of a quadrotor system without a fuselage in ground effect and ceiling effect for varying rotor-boundary distances and hub spacings were investigated. A GPU-accelerated Lattice-Boltzmann Method (LBM) was coupled to new unsteady actuator disk method (ADM) and actuator slice method (ASM) based rotor models for this purpose. Validation was conducted against experiments for both performance and particle image velocimetry flow field data. The trends in thrust and power were accurately predicted by both actuator methods, with high computational efficiency. Interactional flow physics were resolved, causing the consistent performance benefits very close to the ground, the performance penalties caused by the fountain flow effect between rotors occurring over a limited range of ground distances, and the persistent performance augmentation in ceiling effect. The ASM rotor model, with its individual blade representation, was found to predict rotor-rotor interactions more accurately than the ADM. However, it generally overpredicted aerodynamic loads, which was attributed to the Gaussian regularization applied along the blades. The results indicate that the GPU-accelerated LBM coupled with the introduced actuator methods is capable of computationally efficient prediction of multirotor aerodynamic performance and flow fields in ground and ceiling effects.
The paper discusses the design and high-fidelity flight dynamics modeling of a 13-lb lift-plus-cruise unmanned aerial vehicle (UAV) using Rotorcraft Comprehensive Analysis System (RCAS) in order to (1) better understand its physics of flight during a wide range of maneuvers, and (2) provide insight into the fidelity needed to achieve quantitative accuracy when compared to flight test data. Wind tunnel tests of the full aircraft were performed at a 65% scale to provide lookup tables for the flight dynamics model. Flight test data was collected while providing high control inputs to excite a variety of dynamic states in hovering and cruising modes to systematically validate the physics model. Near quantitative agreement was observed between the model predictions and test data during hover; however, the predictions began to disagree at higher forward cruising speeds. To address the discrepancy between the prediction and experiment, the flight dynamics model was improved by learning a correction from flight test data using a neural network. This hybrid physics plus data-driven approach reduced the error between the physics model and experiment by 74% and only needing 12 minutes of flight data for training. This hybrid methodology presents an alternate approach to high fidelity modeling which only needs a relatively small amount of flight test data.
This study aims to develop Control Equivalent Gust Input (CEGI) and Rotor Control Equivalent Gust Input (RCEGI) profiles that accurately reproduce vehicle response to deterministic gusts. This involves creating an inverse model using adaptive neural networks in order to map vehicle response to pilot and rotor control inputs. The accuracy of the CEGI and RCEGI models are then quantified using the Time Domain Integrated Cost Function (Ref. 1) to determine trends within the CEGI and RCEGI models for gusts of varying shape, magnitude, and duration as well as at varying flight conditions. Analysis using the cost function shows that the CEGI and RCEGI models follow similar trends. Both models are more accurate for gusts of short duration and small amplitude, and both models are more accurate for sinusoidal gusts than top hat gusts.
This paper presents a comprehensive evaluation of data-driven machine learning (ML) frameworks for the estimation of critical operational parameters, gross weight (GW), longitudinal center-of-gravity (CGx ), and airspeed (Ux ) for a UAM-scale Lift plus Cruise eVTOL aircraft. Artificial Neural Networks (ANN), Gaussian Process Regression (GPR), and Support Vector Machines (SVM) are compared for their ability to track these dynamic parameters across both low-speed rotor-borne and high-speed wing-borne flight regimes. The models are rigorously tested on steady-state clean data and stochastic atmospheric turbulence data sets to assess performance trade-offs between computational cost, noise robustness, and predictive accuracy. Results demonstrate that GPR consistently achieves the highest accuracy on clean data, particularly for GW and CGx estimation, though it exhibits the highest sensitivity to stochastic noise. Conversely, SVM demonstrates the greatest relative robustness under turbulent conditions and superior computational efficiency, identifying it as a practical candidate for resource-constrained onboard flight computers. Furthermore, a dynamic continuous-time analysis reveals a critical trade-off between responsiveness and accuracy. Instantaneous predictions are shown to suffer from severe transient error spikes during maneuvers, whereas a moving average filtering strategy effectively mitigates these errors at the cost of response latency. These analyses demonstrate the feasibility of ML-based parameter estimation for UAM operations and highlight the necessity of adaptive temporal filtering to balance agility with resilience in turbulent environments.
This paper considers the opportunities and challenges of supporting Disaster Relief and Emergency Response (DRER) missions employing new aerial vehicle and systems concepts. This paper is a broad survey of the possible aerial-vehicle-assisted approaches to aid in DRER missions. The intent of this paper is to elevate this DRER mission application domain as a critical area of investigation for rotorcraft, robotics, intelligent systems, and other research. Current work is primarily focused on assessing air space integration challenges for Commercial Off-The-Shelf (COTS) aerial platforms (typically small multirotor drones and/or small fixed-wing uncrewed aerial vehicles (UAVs)) in disasters such as earthquakes and wildfires. Though this is an important area of investigation, truly efficient and effective DRER systems and response efforts will not be possible without the development of novel aircraft, technologies, and system architectures of COTS DRER drones/UAVs. This paper seeks to address that knowledge gap and encourage government, academia, and industry to pursue DRER mission research.
A 4-rotor uninhabited air vehicle is described, with a primary mission of supporting personnel fighting wildfires. The paper demonstrates the use of technical design tools for a small Uninhabited Aircraft System (sUAS). A description of the design process is provided, including developing requirements, identifying constraints, the software tools employed, and examination of results. The vehicle is capable of delivering more than 20 kg of supplies to a delivery point 10 nm away while penetrating 30 kt winds. The sized vehicle is transportable in a medium-duty pickup truck and can be picked up and moved for ground handling by one or two individuals. The vehicle information will be publicly released for NDARC software users. Future work will examine other requirements, such as maneuvering and gust rejection.
Dimensional reduction of data can be accomplished through various methods and has applications critical to machine learning and surrogate modeling. Within the rotorcraft community, leveraging these techniques allows for improved rotor parameterization and performance prediction. Machine learning models generally perform faster and better with lower input dimensions, so long as all necessary information is retained, making appropriate dimension reduction paramount. Data can also be arranged in a one-dimensional (concatenated/stacked) or two-dimensional arrays to take advantage of function correlations, and this arrangement may allow for greater reduction at lower reconstruction costs. Principal Component Analysis with a stacked input shape proves to be the most effective reduction method considered, with reconstruction accuracy being validated though a suite of mid-fidelity aerodynamic simulations. A blade geometry defined using 204 original parameters can be fully described using just 10 component parameters with the reconstructed blade maintaining performance figures within 1% of the original blade.
Urban Air Mobility (UAM) concepts require multidisciplinary analyses across multiple modes of operation and often involve discrete architectural differences such as propulsion type, rotor configuration, and mission context. Existing optimization and workflow frameworks support continuous design variables but provide limited mechanisms for handling discrete variants, multi-modal vehicle definitions, and vehicle management for UAM vehicles. This paper presents uam4x, an open-source Python framework that addresses these challenges through a structured problem definition representation, a plugin-based execution engine, integrated version control, and a function-based branching script mechanism for constructing analysis scenarios. The framework provides integration of existing tools including Open Vehicle Sketch Pad (OpenVSP), NASA Design and Analysis of Rotorcraft (NDARC), M4 Structures Studio (M4SS), and Intelligent Cross Section Generator (IXGEN) through unified plugin interfaces. Parameter sweeps, nested analyses, and optimization via OpenMDAO are supported within the same architecture. This paper also presents demonstrations that were created to illustrate the various capabilities and integration efforts of the framework.
A comprehensive numerical study was conducted to reduce helicopter rotor hub vibratory loads and fuselage vibrations using the Higher Harmonic Control (HHC) technique. A CAMRAD II model of a medium utility helicopter was developed for aeromechanical simulation, and a linear system model representing both hub vibratory load and fuselage vibration characteristics was identified offline. Optimal control inputs were then computed to minimize vibration responses under different weightings on hub vibratory load and fuselage vibration in the objective function. The predicted performance was verified through CAMRAD II simulations. Additionally, a closed-loop HHC system incorporating actuator amplitude limitations was investigated. A control algorithm regulated actuator amplitudes while maintaining phase consistency, dynamically adjusting control inputs after each iteration. The results demonstrate that the amplitude-limited closed-loop control limits excessive pitch link loads while maintaining effective vibration suppression, thereby maintaining structural safety and ensuring practical feasibility for real-world helicopter applications.
Characterization of rotor–rotor wake interactions and their influence on flight dynamics is an important step toward advancing control system design and evaluating the performance of next-generation Mars multirotors. In this work, a Viscous Vortex Particle Method (VVPM) is utilized to generate rotor–rotor interference data for the Chopper Mars Helicopter platform, a large-scale hexacopter concept designed to be capable of carrying payload and pursuing independent science tasks. A reduced-order model compatible with finite state dynamic inflow is derived from the database. Interpolation strategies for continuous look-up are evaluated, with Gaussian Process Regression providing up to 20% improvement in prediction accuracy over linear interpolation of the interference data, although its scalability is limited by the large number of output channels. The interference model is implemented in HeliCAT, the flight dynamics analysis framework used for the Ingenuity Mars Helicopter, to assess the impact of rotor–rotor interference on trim, open-loop response and key stability derivatives. For Chopper, the first-order impact of rotor–rotor interaction on flight dynamics in hover is small, but with an observed increased pitch and roll damping. In forward flight, on-axis bare airframe responses exhibit attenuation at the lower frequencies due to interference, and increased sensitivity of the pitch response to vertical perturbations in cruise. Finally, a real-time closed-loop simulation is performed with and without the interference model to assess the impact on rotor power during a representative science mission flight trajectory on Mars, showing that total increase in mechanical shaft power due to multirotor wake interactions does not exceed 5% throughout the flight.
Vertical Take-Off and Landing (VTOL) aircraft introduce complex monitoring challenges due to distributed propulsion, lightweight structures, and variable operating conditions. This paper presents advanced Frequency and Orders domain techniques that repurpose existing flight control, propulsion, and structural sensor data to enhance observability without additional instrumentation. By transforming vibration, acoustic, and electrical signals into frequency and order domains, the approach enables detection of harmonics, resonance, and fault signatures tied to rotor dynamics, supporting adaptive control and predictive maintenance. Beyond rotor systems, these techniques are equally effective for monitoring electric motor health, gearbox wear, bearing degradation, and structural coupling effects in composite airframes. They also provide insight into power electronics and thermal management systems by identifying spectral anomalies linked to electrical imbalance or cooling inefficiencies. Aggregated fleet data strengthens prognostic capabilities, enabling early detection of systemic issues and trend analysis. Applications include mitigating ground resonance and modal instabilities, as well as improving reliability of propulsion and structural subsystems. Integration into avionics emphasizes computational efficiency, scalability, and compliance with standards such as DO-160 [1], DO-178 [2], ARP4761 [3] and ARP4764 [4]. Simulation and bench testing confirm feasibility, demonstrating potential to enhance safety, reliability, and lifecycle cost for next-generation urban air mobility platforms.
This experimental study showcases the changes to tip vortex evolution caused by unsteady plunging and pitching motions. Three cases of motion are tested on a NACA 0012 airfoil using time-resolved stereoscopic particle image velocimetry (PIV). Tests are taken while the airfoil is subjected to sinusoidal plunging and sinusoidal pitching motions. A control test is also taken for a static airfoil for the baseline comparisons. Changes to the tangential velocity, axial velocity, turbulence intensity, and Reynolds shear stress are analyzed for the duration of each cycle of motion and compared to the static results. For the plunging and pitching cases, the swirl velocity magnitude during the downstroke is significantly greater than static values, exhibiting greater levels of turbulence intensity and large Reynolds shear stress peaks about the vortex core. In contrast, data from the upstroke phase yields minimal turbulence contents, due to the loss of lift. The vortex grows weaker during the upstroke plunge and nose-up pitch from the lift decrease for the plunging and pitching cases, even dispersing before reaching the test section at z/c = 5 for a period of the oscillation. While the tangential and axial velocity magnitude are similar for the plunging and pitching cases, the local velocity maxima come at different times during the cycle, with the plunge cycle reaching its maximum first earlier in the downstroke phase. The static, plunging, and pitching cases follow a similar Richardson number trend, where the outer region of the vortex falls below the stratification threshold to become turbulent. However, the unsteady motions introduce variations in the Richardson number’s stratification radius, and relaminarization is observed outside the core radius. The turbulence intensity exhibited by the plunging and pitching cases during the downstroke is greater than for the static case, but lesser during the upstroke.
This paper presents updates to The Rotorcraft Optimization Tools (RCOTools) package to streamline iterative rotorcraft comprehensive design. The work is presented in three parts. Part I. a brief introduction to our simplified API is shown, in addition to a new mission profile dashboard. Part II. demonstrates high-throughput using the embarrassingly parallel paradigm to produce large-scale datasets structured by simple design of experiments (DOE) as shown by our discussion on urban air mobility (UAM) emission minimization. Such datasets provide a necessary component for rapid database generation and supervised machine learning. Part III. the API is used to couple rotor performance and sizing optimization. A simple technique for ultra-fast hover calibration is given, as well as possible applications for neural network modeling in comprehensive design. These enhancements accelerate design workflows and enable data-driven approaches for next-generation urban air mobility and planetary rotorcraft concepts.
As per Committee/Henry E. Harschburger recommendations
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