Navigation, Guidance, Control and Dynamics Commons^™

Aircraft Fault Detection Via Weight And Bias Analysis Of A Custom First Neural Network Layer, George Harrison Chen

Theses and Dissertations

Fault detection in aircraft is traditionally handled through redundant hardware and comparison algorithms to detect failures. Alternatives like model-based residual generation and data-driven approaches such as supervised fault classification and unsupervised anomaly detection have been explored, but they suffer from practical limitations; model-based methods require accurate system models, and data-driven methods have large constraints on the data limiting scalability and adaptability. This work presents a purely data-driven neural network architecture featuring a custom first layer designed for real-time fault detection where the weights and biases of this layer are used to detect faults. The network requires zero supervision and complements …

Adaptive Artificial Potential Field Guidance And Control For Autonomous Docking With Uncooperative And Unknown Spacecraft, Steven Holmberg

Theses and Dissertations

The increasing demand for on-orbit servicing (OOS), active debris removal (ADR), and space domain awareness (SDA) missions has increased the need for autonomous spacecraft rendezvous and proximity operations (RPO) with uncooperative and unknown targets. Traditional guidance and control methods are typically designed for cooperative systems with known geometry and state information. This work builds on previous research to develop and evaluate an artificial potential field (APF)-based control framework capable of autonomous operation with minimal prior target knowledge and applicability to both relatively static and tumbling spacecraft.

The proposed APF formulation incorporates established safety constructs from cooperative docking systems, including an …

A Machine Learning-Based Apogee Prediction Methodology For Experimental Student Rockets, Price Hamilton Drawdy

Senior Honors Theses

The ability to predict the maximum altitude of a rocket (apogee) in real-time is incredibly useful for collegiate-level competition rockets. This project creates a machine learning-based real-time apogee prediction methodology. Three model types were tested: linear regression, random forest, and a 3-layer multi-layer perceptron (MLP) neural network. These models were trained on a large dataset of simulated flights. All models performed well on simulated test flights, with the linear regression model showing most promise for use on edge compute. More development and real-world testing are necessary to determine how applicable this method is for real-time operation. Nevertheless, this methodology provides …

The Pid-Based Three Quadcopter Uavs Formation Control Under External Disturbance, Vo Van An, Trinh Luong Mien

Makara Journal of Technology

This paper presents the design and evaluation of a formation control strategy for three quadcopter UAVs, based on a PID controller in a leader–follower structure, under the influence of external disturbances. Each UAV employs a six-degree of-freedom dynamic model and utilises a cascade PID control architecture, in which the inner control loop stabilises the attitude. In contrast, the outer control loop regulates position and maintains the formation. The PID parameters are tuned using the Ziegler–Nichols method to ensure simple implementation and low computational cost. The performance of the control system is evaluated through simulations in the MATLAB environment for two …

Analysis Toolkit For Potential Lunar Landing Zones, Jacobo Matallana

Morehead State Theses and Dissertations

A thesis presented to the faculty of the College of Science and Engineering Morehead State University in partial fulfillment of the requirements for the Degree Master of Science by Jacobo Matallana on April 24, 2026.

Cooperative Unmanned Aerial System (Uas) Geolocation Of Emitters, Christopher Peters

Electrical Engineering Theses and Dissertations

A collection of unmanned aerial systems (UAS) can be networked as a cooperative wireless sensor array to geolocate an unknown-location RF emitter using time-based measurements. In operation, however, environmental multipath and hardware errors in sensor positioning and timing can degrade emitter localization accuracy and limit the practicality of single-snapshot solutions. This dissertation evaluates time-of-arrival and time-difference-of-arrival (TOA/TDOA) geolocation for cooperative UAS arrays under realistic error sources and develops geometry-control strategies that actively reduce localization uncertainty through iterative UAS repositioning.

This work studies the Location on a Conic Axis (LOCA) method for emitter localization. Using Monte Carlo simulations with hardware error …

Intelligent Flight Control Systems Using Adaptive Deep Neural Networks And Concurrent Learning-Based Design Methods, Maddox C. Morrison

Doctoral Dissertations and Master's Theses

This thesis investigates deep neural network (DNN)-based adaptive control strategies for unmanned aerial vehicles (UAVs) operating under aerodynamic uncertainty and complex actuator dynamics.

The first contribution presents a control strategy employing a concurrent learning (CL)-based DNN training algorithm, which combines online adaptive DNN weight adaptation with offline batch-like training updates using a recorded data stack. The analysis focuses on the closed-loop performance improvements resulting from the use of optimum CL data-selection algorithms, which ensure that the recorded data stack maintains sufficient data diversity to provide a statistically meaningful representation of the operating conditions using a reduced data set. Specifically, this …

Safety-Aware Trajectory Generation For Increased Autonomy In Advanced Air Mobility, Edison Alberto Martinez Samaniego

Doctoral Dissertations and Master's Theses

Advanced Air Mobility (AAM) envisions highly automated aircraft that will enable short and medium range transportation. Unlike conventional aviation, these vehicles are expected to operate closer to populated areas and with increased levels of autonomy, making safe operation under abnormal or degraded conditions a critical requirement. Failures or performance degradation can reduce the maneuvering capability of an aircraft, causing trajectories planned under nominal conditions to become dynamically unfeasible.

This thesis presents a trajectory generation and replanning framework designed to maintain safe and feasible flight under reduced flight envelope conditions for a lift+cruise eVTOL aircraft. A unified control architecture based on …

Reinforcement Learning - Driven Satellite Attitude Recovery: Unknown Faults, Simulation-To-Processor In Loop, Chinmay Mirji, Saeed A Ahmadi

Student Research Symposium (SRS)

Conventional attitude control algorithms often degrade when faced with actuator faults, sensor noise, or system uncertainties. This work presents a reinforcement-learning (RL) framework for satellite attitude recovery under unknown failures, focusing on real-time deployment through a processor-in-the-loop (PIL) setup. A continuous-control DDPG agent is trained in a high-fidelity Python/Basilisk simulation environment, where domain randomization captures variations in inertia, external torque, and actuator limitations to promote robust policy learning.

Evaluating Runtime Monitoring For Reinforcement Learning-Based Flight Control, Andrew Zubyk

Doctoral Dissertations and Master's Theses

Ensuring safety in adaptive flight controls systems is an ongoing challenge in aviation, especially as advancements in artificial intelligence and machine learning (AI/ML) trend upwards. Reinforcement learning is becoming more common in aerospace applications due to the ability to improve these models through training.  While models such as reinforcement learning enable controllers to learn complex behaviors from interaction with the environment, their unpredictability in novel or disturbed conditions raises severe concerns in safety-critical domains. This research investigates the integration of runtime monitoring, a real-time assurance technique, with reinforcement learning-based flight controllers to ensure safety and reliability during flight. By supervising …

Initial Development Of Cooperative Aerial And Ground Vehicles Experimental Testbed, Javier S. Robinson, Morad Nazari

Beyond: Undergraduate Research Journal

Heterogeneous multi-agent systems represents a growing area of research in autonomous vehicles, which involves the real-time cooperation of vehicles operating under different roles or dynamical mod- els. While there is plenty of theoretical work, the existing experimental research focuses on cooperation between physically identical vehicles. This is likely because testing heterogeneous vehicles naturally in- volves more complicated dynamics and communication frameworks to ensure compatibility. The goal of the Cooperative Aerial and Ground Vehicles Experimental (CAGE) testbed is to develop an experimen- tal testbed that involves cooperation of two distinct vehicle models: multiple Crazyflie 2.1 quadcopter drones and at least one …

Statistical Analysis Of Log Transformation Effectiveness In Air Traffic Movement Forecasting During Covid-19 In South Africa, John Lehlaka Masekoameng

Journal of Aviation Technology and Engineering

This study evaluates the effectiveness of log transformation in enhancing multiple regression models used to forecast air traffic movements (ATMs) in South Africa during the COVID-19 pandemic. Using 60 monthly observations from October 2016 to September 2021, the analysis incorporates variables such as revenue, lockdown levels, COVID-19 metrics, exchange rates, gross domestic product, and population. Two models are compared: one using raw ATMs and another with log-transformed ATMs as the dependent variable.

While the untransformed model shows stronger explanatory power (R² = 0.904, adjusted R² = 0.891) compared to the log-transformed model (R² = 0.772, adjusted R² = 0.741), the …

Spline-Based Factor-Graph Optimization With High-Grade Inertial Sensors, Kyle Leland, Clark N. Taylor, David Woodburn, Randal Beard

Faculty Publications

Inertial measurement units (IMUs) are central to global navigation satellite system-based and alternative navigation solutions. This paper combines three lines of research to explore a novel methodology for using inertial sensors: factor graphs, spline-based trajectory estimation, and high-grade inertial sensing. Spline-based factor-graph trajectory estimation is increasingly used in the literature, especially for asynchronous or high-rate sensors. However, prior models neglect the impact of the Earth’s rotation, which is significant for high-grade IMUs. We extend spline-based factor graphs to incorporate accelerometer and gyroscope models that account for the Earth’s rotation. We apply this approach to simulated data from high-grade inertial sensors …

Set-Theoretic Reachability-Informed Model Predictive Control For Mechanical And Aerospace Systems, Jinaykumar Nitinkumar Patel

Mechanical and Aerospace Engineering Dissertations

Modern mechanical and aerospace systems increasingly operate autonomously in environments characterized by nonlinear dynamics, uncertainty, and safety constraints. In these settings, estimation and control methods based on nominal models and single-point trajectory predictions are usually insufficient to ensure safe and reliable operation. This dissertation uses a set-theoretic perspective, in which the system state, uncertainty, and admissible behavior are described by sets instead of point estimates. The key question is not only what the state is, but what set of states remains consistent with the dynamics, disturbances, control limits, and available measurements. This provides bounded descriptions of uncertainty and supports control …

Students For The Exploration And Development Of Space (Seds) Air Brake Subsystem: High-Altitude Autonomous Apogee Modulator System For High-Powered Rockets (Haamshr), Camden J. Maclean, Matthew Wharton, Nick Revis, Colin Guido

Honors Theses and Capstones

The objective of this project was to research, design, fabricate, and analyze an autonomous apogee modulation air brake system for the University of New Hampshire (UNH) Students for Exploration and Development of Space (SEDS) high-powered rocket as competitors in the Friends of Amateur Rocketry – Oxidizers Uninhibited Tournament (FAR-OUT) competition.

This air brake system would be developed with considerations for full autonomy, structural reliability, and repeatable deployment. The design would also be easily integrated into the existing high-powered rocket airframe and mechanically simple to increase reliability and practical functionality. The final design would be evaluated using finite element analysis simulation …

Constrained Dynamics Of Rapid Orbit Motion Emulator (Rome) Using Udwadia-Kalaba Approach, Keanu Brayman

Honors Undergraduate Theses

The Rapid Orbit Motion Emulator (ROME) is designed to be a hardware-in-the-loop (HIL) testbed for orbital control algorithms. It consists of a four-wheeled ground vehicle and a six-degree-of-freedom robotic manipulator. This work investigates the use of optimal control to execute orbital trajectories on ROME using the Udwadia-Kalaba (UK) formulation to model the system dynamics. The UK formulation is a novel method to derive equations of motion for constrained systems. Unlike traditional approaches, the UK approach can be applied directly to any constrained dynamical system. This project utilizes the UK approach to derive dynamics with trajectory following constraints for the ROME …

Autonomous Uav Mission Planning Under Threat Using Model Predictive Control With Proportional-Navigation Pursuers, Mehmet B. Ozcelik

Mechanical and Aerospace Engineering Theses

Autonomous unmanned aerial vehicles (UAVs) operating in contested environments must

complete mission objectives while avoiding restricted regions, radar exposure, and pos-

sible interception. This thesis develops a MATLAB-based simulation framework for

two-dimensional UAV mission planning under threat using model predictive control and

proportional-navigation chasers. The mission requires the UAV to travel from a start

location to a goal while visiting required checkpoints and avoiding no-fly zones and radar

regions. A chaser attempts to intercept the UAV using either a basic pure-pursuit-style

law or a proportional-navigation guidance law.

The framework integrates environment generation, augmented visibility-graph rout-

ing, waypoint management, UAV kinematic …

Llm-Driven Closed-Loop Uav Control With Obstacle-Aware Model Predictive Control, Halimcan Yasar

Mechanical and Aerospace Engineering Theses

This thesis presents a closed-loop control architecture for uncrewed aerial vehicles (UAVs) in which a large language model (LLM) serves as a high-level decision module operating over a persistent, metric 3D world model.

Rather than generating low-level commands or open-loop plans, the LLM selects one parameterized maneuver per decision step from a small, verified library of flight primitives conditioned on a structured representation of the drone state, tracked object positions, and mission specification.

Translational motion is executed by a planar model predictive controller (MPC) with soft obstacle avoidance, using obstacle hypotheses provided by the LLM, so that safety-critical constraint handling …

Active Altitude Control For High Powered Rockets, Henry Allen, Jason Secora, Charles Williams, Donavon Sanchez, Caleb Nedoma

Williams Honors College, Honors Research Projects

The International Rocket Engineering Competition (IREC) is the premier collegiate high power rocketry competition in the world hosted by the Experimental Sounding Rocket Association (ESRA). The Akronauts Rocket Design team has been competing in IREC since 2015 and have won several awards. The main goal of IREC is to launch a rocket to a specified altitude of 10k, 30k, or 45k ft while carrying a payload and successfully recover with little to no damage.

The control of a typical high-powered rocket is purely passive; the center of pressure of the rocket must be behind the center of gravity with respect …

Gaussian Process Regression–Based Uncertainty Quantification For Unmanned Aircraft System Traffic Management And Advanced Air Mobility Applications, Aakarshan Khanal

Mechanical and Aerospace Engineering Dissertations

Uncertainty quantification has gained significant attention in recent years as a research area in dynamical systems. Mathematical representations of the physical system, combined with an understanding of model uncertainties, enable the propagation of uncertainty in temporal space, which allows us to make informed decisions. However, what if the true dynamics of the system is unknown or too complex to define explicitly? In such cases, the system’s behavior can instead be inferred or learned from observed input–output data rather than from an analytical or physics-based model. To this end, this dissertation focuses on developing a data-driven framework for nonparametric dynamics modeling, …