Navigation, Guidance, Control and Dynamics Commons^™

Artificial Intelligence-Assisted Inertial Geomagnetic Passive Navigation, Andrei Cuenca

Doctoral Dissertations and Master's Theses

In recent years, the integration of machine learning techniques into navigation systems has garnered significant interest due to their potential to improve estimation accuracy and system robustness. This doctoral dissertation investigates the use of Deep Learning combined with a Rao-Blackwellized Particle Filter for enhancing geomagnetic navigation in airborne simulated missions.

A simulation framework is developed to facilitate the evaluation of the proposed navigation system. This framework includes a detailed aircraft model, a mathematical representation of the Earth's magnetic field, and the incorporation of real-world magnetic field data obtained from online databases. The setup allows an accurate assessment of the performance …

Verification And Validation Of Robot Manipulator Adaptive Control With Actuator Deficiency, Sebastian Comeaux

Doctoral Dissertations and Master's Theses

This work addresses the joint tracking problem of robotic manipulators with uncertain dynamical parameters and actuator deficiencies, in the form of an uncertain control effectiveness matrix, through adaptive control design, simulation, and experimentation. Specifically, two novel adaptive controller formulations are implemented and tested via simulation and experimentation. The proposed adaptive control formulations are designed to compensate for uncertainties in the dynamical system parameters as well as uncertainties in the control effectiveness matrix that pre-multiplies the control input. The uncertainty compensation of the dynamical parameters is achieved via the use of the desired model compensation–based adaptation, while the uncertainties related to …

Deep-Learning Based Multiple-Model Bayesian Architecture For Spacecraft Fault Estimation, Rocio Jado Puente

Doctoral Dissertations and Master's Theses

This thesis presents recent findings regarding the performance of an intelligent architecture designed for spacecraft fault estimation. The approach incorporates a collection of systematically organized autoencoders within a Bayesian framework, enabling early detection and classification of various spacecraft faults such as reaction-wheel damage, sensor faults, and power system degradation.

To assess the effectiveness of this architecture, a range of performance metrics is employed. Through extensive numerical simulations and in-lab experimental testing utilizing a dedicated spacecraft testbed, the capabilities and accuracy of the proposed intelligent architecture are analyzed. These evaluations provide valuable insights into the architecture's ability to detect and classify …

Development Of A Constellation Simulator For A 5g/Iot Mission Planning System, Franco Criscola

Doctoral Dissertations and Master's Theses

The advancement of 5G and Internet-of-Things technologies has presented new challenges for telecommunications providers. One of the challenges is integrating these technologies with present networks. A solution has been found in low-Earth orbit satellite constellations. On one hand, this method increases coverage and reduces costs, but on the other it raises new problems like how to efficiently manage large constellations of spacecraft. This thesis introduces the Constellation Management System, developed in collaboration with i2Cat foundation. This novel tool is composed of two modules: the simulator and the scheduler. The former propagates satellite motion and computes visibility events to various targets …

Marss: Multi-Agent Reinforcement Learning For Satellite Swarms, Nicholas J. Yielding

Theses and Dissertations

Multi-agent systems and swarms in spacecraft formation flying are of ever-increasing importance in a contested space environment—use of multiple spacecraft to contribute to a cooperative mission potentially increases positive outcomes on orbit, while autonomy becomes an ever more important requirement to reduce reaction time in dynamic situations and lower the burden on space operators. This research explores difficult swarm Guidance Navigation and Control (GNC) scenarios using Deep Reinforcement Learning (DRL). DRL polices are trained to provide guidance inputs to agents in multi-agent swarm environments for completing complex, teamwork focused objectives in geosynchronous orbit. An example scenario is explored for a …

Controlled Manipulation And Transport By Microswimmers In Stokes Flows, Jake Buzhardt

All Dissertations

Remotely actuated microscale swimming robots have the potential to revolutionize many aspects of biomedicine. However, for the longterm goals of this field of research to be achievable, it is necessary to develop modelling, simulation, and control strategies which effectively and efficiently account for not only the motion of individual swimmers, but also the complex interactions of such swimmers with their environment including other nearby swimmers, boundaries, other cargo and passive particles, and the fluid medium itself. The aim of this thesis is to study these problems in simulation from the perspective of controls and dynamical systems, with a particular focus …

Safe Navigation Of Quadruped Robots Using Density Functions, Andrew Zheng

All Theses

Safe navigation of mission-critical systems is of utmost importance in many modern autonomous applications. Over the past decades, the approach to the problem has consisted of using probabilistic methods, such as sample-based planners, to generate feasible, safe solutions to the navigation problem. However, these methods use iterative safety checks to guarantee the safety of the system, which can become quite complex. The navigation problem can also be solved in feedback form using potential field methods. Navigation function, a class of potential field methods, is an analytical control design to give almost everywhere convergence properties, but under certain topological constraints and …

Testing And Implementation Of Attitude Determination & Control System For Arksat-1 Cubesatellite, Cassandra Sands

Graduate Theses and Dissertations

ARKSAT-1 is a CubeSatellite (CubeSat) developed at the University of Arkansas and launched to the International Space Station on SpaceX mission SPX-27 launching from Kennedy Space Center as part of the NASA’s 8th CubeSat Launch Initiative CSLI-8. ARKSAT-1’s payload features a high-powered LED, the Solid State Inflatable Balloon (SSIB) deorbiting system applicable to small satellites, and a series of InfraRed and Visible cameras. To point the LED or take images of desired observational targets, the spacecraft will need to be able to determine its orientation within its orbit, as well as rotate. This will be achieved through the use of …

Kwad - Ksu All Weather Autonomous Drone, Nick Farinacci, Sebastian Gomez, Stewart Baker, Ed Sheridan

Symposium of Student Scholars

"KWAD" or "KSU all-Weather Autonomous Drone" project was sponsored by Ultool, LLC to the KSU Research and Service Foundation to create a lightweight drone capable of capturing HD video during all-weather operations. The conditions of all-weather operation include rainfall of one inch per hour and wind speeds of up to twenty miles per hour. In addition, a global minimum structural safety factor of two is required to ensure the system's integrity in extreme weather conditions. Potential mission profiles include autonomous aerial delivery, topological mapping in high moisture areas, security surveillance, search and rescue operations, emergency transportation of medical supplies, and …

Six-Degree-Of-Freedom Optimal Feedback Control Of Pinpoint Landing Using Deep Neural Networks, Omkar S. Mulekar, Hancheol Cho, Riccardo Bevilacqua

Student Works

Machine learning regression techniques have shown success at feedback control to perform near-optimal pinpoint landings for low fidelity formulations (e.g. 3 degree-of-freedom). Trajectories from these low-fidelity landing formulations have been used in imitation learning techniques to train deep neural network policies to replicate these optimal landings in closed loop. This study details the development of a near-optimal, neural network feedback controller for a 6 degree-of-freedom pinpoint landing system. To model disturbances, the problem is cast as either a multi-phase optimal control problem or a triple single-phase optimal control problem to generate examples of optimal control through the presence of disturbances. …

Exploring Underwater Noise Issues : A Study Of Decentralized Approach, Takanori Uzumaki

World Maritime University Dissertations

No abstract provided.

Stability Of Deep Neural Networks For Feedback-Optimal Pinpoint Landings, Omkar S. Mulekar, Hancheol Cho, Riccardo Bevilacqua

Student Works

The ability to certify systems driven by neural networks is crucial for future rollouts of machine learning technologies in aerospace applications. In this study, the neural networks are used to represent a fuel-optimal feedback controller for two different 3-degree-of-freedom pinpoint landing problems. It is shown that the standard sum-ofsquares Lyapunov candidate is too restrictive to assess the stability of systems with fuel-optimal control profiles. Instead, a parametric Lyapunov candidate (i.e. a neural network) can be trained to sufficiently evaluate the closed-loop stability of fuel-optimal control profiles. Then, a stability-constrained imitation learning method is applied, which simultaneously trains a neural network …

Hardware-In-The-Loop Reaction Wheel Testbed With Camera Vision, Abigail Romero, Harvey Perkins, Stephen Kwok-Choon

College of Engineering Summer Undergraduate Research Program

Reaction wheels are widely used in aerospace systems as a method of attitude control. This research was focused on the design, development, and testing of a hardware-in-the-loop reaction wheel testbed that can be used for research and teaching applications related to satellite navigation and control. This project successfully utilized commercial off-the-shelf components to develop a reaction wheel capable of controlling the orientation of a freely rotating platform, as well as tracking objects using computer vision.

Online Aircraft System Identification Using A Novel Parameter Informed Reinforcement Learning Method, Nathan Schaff

Doctoral Dissertations and Master's Theses

This thesis presents the development and analysis of a novel method for training reinforcement learning neural networks for online aircraft system identification of multiple similar linear systems, such as all fixed wing aircraft. This approach, termed Parameter Informed Reinforcement Learning (PIRL), dictates that reinforcement learning neural networks should be trained using input and output trajectory/history data as is convention; however, the PIRL method also includes any known and relevant aircraft parameters, such as airspeed, altitude, center of gravity location and/or others. Through this, the PIRL Agent is better suited to identify novel/test-set aircraft.

First, the PIRL method is applied to …

Rigid Body Constrained Motion Optimization And Control On Lie Groups And Their Tangent Bundles, Brennan S. Mccann

Doctoral Dissertations and Master's Theses

Rigid body motion requires formulations where rotational and translational motion are accounted for appropriately. Two Lie groups, the special orthogonal group SO(3) and the space of quaternions H, are commonly used to represent attitude. When considering rigid body pose, that is spacecraft position and attitude, the special Euclidean group SE(3) and the space of dual quaternions DH are frequently utilized. All these groups are Lie groups and Riemannian manifolds, and these identifications have profound implications for dynamics and controls. The trajectory optimization and optimal control problem on Riemannian manifolds presents significant opportunities for theoretical development. Riemannian optimization is an attractive …

Design And Implementation Of A Launching Method For Free To Oscillate Dynamic Stability Testing, Kristen M. Carey

Mechanical & Aerospace Engineering Theses & Dissertations

Magnetic Suspension and Balance Systems (MSBS) allow for static, forced oscillation and free to oscillate dynamic stability testing in a wind tunnel without the need for a physical support. The objectives of study are to assist in the application of the free to oscillate testing method in an MSBS to determine dynamic stability characteristics for various re-entry capsule designs.

This thesis discusses the development and testing of a launching method called the grabber for use in the MSBS Subsonic Wind Tunnel at NASA Langley Research Center. Aerodynamic tests were run to support the use of this method and compare the …

Multiple Uncertain Robot Manipulator Control For A Modular System, Thitiphun Vongkunghae

Doctoral Dissertations and Master's Theses

Modular systems have gained significant attention in various domains due to their flexibility, scalability, and adaptability. These systems consist of multiple interconnected modules that can be combined and reconfigured to achieve desired functionalities. The modular approach offers numerous advantages, including ease of maintenance, reduced downtime, and cost-effectiveness. However, designing effective distributed control strategies for modular systems presents unique challenges. Specifically, modular systems that consist of robot manipulators are subject to non-linear unknown terms as well as unknown effects. This thesis focuses on developing and designing a distributed model reference adaptive controller for a robotic modular system that is subject to …

Integrated Energy-Aware Motion Planning And Charge Scheduling Of Unmanned Aerial Vehicles, Hayleyesus Alemayehu

All Theses

The growing demand for unmanned aerial vehicles (UAVs) is driven by their operational convenience, cost-effectiveness, availability, and adaptability to various scenarios. In energy-constrained environments, optimizing energy consumption and ensuring a continuous power supply for UAVs is crucial for mission success. The objective of this thesis is to address this issue by integrating energy-aware motion planning and charge scheduling for UAVs by utilizing a charger hosted on an unmanned ground vehicle (UGV), whose rendezvous locations and routes are jointly computed to minimize overall energy consumption.

This thesis proposes a hierarchical trajectory and control framework comprising local and global planners for each …

Adaptive Control Allocation For Unmanned Aerial Vehicles With Model Uncertainties, Eren Sarioglu

Doctoral Dissertations and Master's Theses

Model uncertainties and effector degradation carry risks to weaken the performance of any aerial system and endanger operation during flight. To this end, over-actuated aerial systems are used in flight operations to take precautions for possible incidents, which may result in improper control or a fall of the aerial vehicle. Adaptive control and adaptive control allocation methods are effective solutions for over-actuated uncertain aerial vehicles to overcome these phenomena because of their ability to estimate unknown effects. In this work, a literature search for control allocation methods is first revealed. An adaptive control allocation solution is introduced and implemented for …

In-Flight Testing & Verification Of An Adaptive Distributed Fault-Tolerant Control Architecture, Michael Budihartono

Doctoral Dissertations and Master's Theses

The interest in utilizing multi-agent systems (MAS) has increased in the aerospace industry. Its scalability, efficiency, robustness, fault tolerance, and cost-effectiveness make it ideal for performing real-world missions that require more than one agent. However, in completing the tasks, the multi-agent systems are still vulnerable to environmental disturbances, cyber-attacks, and hardware failures. Therefore, an adaptive distributed fault-tolerant control architecture is needed to minimize the impacts of the previously stated circumstances and ensure the mission can continue successfully.

This thesis describes the development of an experimental setup for testing and validating an adaptive consensus algorithm and a bio-inspired health management architecture. …

Accurate Covariance Estimation For Pose Data From Iterative Closest Point Algorithm, Rick H. Yuan, Clark N. Taylor, Scott L. Nykl

Faculty Publications

One of the fundamental problems of robotics and navigation is the estimation of the relative pose of an external object with respect to the observer. A common method for computing the relative pose is the iterative closest point (ICP) algorithm, where a reference point cloud of a known object is registered against a sensed point cloud to determine relative pose. To use this computed pose information in downstream processing algorithms, it is necessary to estimate the uncertainty of the ICP output, typically represented as a covariance matrix. In this paper, a novel method for estimating uncertainty from sensed data is …

Crazyflie 2.1 Quadcopter Nonlinear System Identification, Nhat V. Nguyen, Hope Storro, John Plimpton

2023 Symposium

Quadcopters (quad) are used widely in many industries with crucial applications such as infrastructure inspection or package delivery. The Crazyflie 2.1 quad from Bitcraze provides an excellent platform for research and development. In this project, our goal is to perform system identification on the Crazyflie to propose a complete model. A gray box method is explored, which includes leveraging the parameters that are already known, to develop a set of equations. Through theory, simulations, and measurements, a complete quadcopter model is developed.

Gyroless Nanosatellite Attitude Determination Using An Array Of Spatially Distributed Accelerometers, Kory J. Haydon

Master's Theses

The low size and budget of typical nanosatellite missions limit the available sensors for attitude estimation. Relatively high noise MEMS gyroscopes often must be employed when accurate knowledge of the spacecraft’s angular velocity is necessary for attitude determination and control. This thesis derived and tested in simulation the “Virtual Gyroscope” algorithm, which replaced a standard gyroscope with an array of spatially distributed accelerometers for a 1U CubeSat mission. A MEMS accelerometer model was developed and validated using Root Allan Variance, and the Virtual Gyroscope was tested both in the open loop configuration and as a replacement for a gyroscope in …

Autonomous Attitude Consensus For Nanosatellite Formations In Leo, Laird J. Mendelson

Master's Theses

Consensus strategies are examined as a possible approach to achieving attitude alignment for a large, close-proximity formation of nanosatellites in low earth orbit (LEO). An attitude-only distributed consensus approach is selected for further consideration due to its comparatively low data transmission requirements. The convergence of a connected network of satellites to the attitude agreement subspace under this control law is shown using a Lyapunov stability approach with a set of idealizing assumptions. A moderate-fidelity simulation demonstrates the performance of the control law under realistic conditions that violate those assumptions. Particular emphasis is placed on the conditions that arise from the …

Instrumented Control Column For Optionally Piloted Aircraft, Andrew J. Klein

Electrical Engineering

Natilus, an aerospace company that is rapid-prototyping optionally piloted aircraft (OPA) for the shipping industry, needs a system that retrieves control column position data in order to manipulate flight simulator parameters in software. At present, a universally compatible system for all aircraft does not exist. Typically, established aerospace companies will sink significant time and money into developing proprietary systems for control column data retrieval as every aircraft is unique in its layout and linkage design. However, as a startup developing their first aircraft, Natilus does not have the privilege of modifying an existing sensor system to work with their HIL …

Distributed Control Of Servicing Satellite Fleet Using Horizon Simulation Framework, Scott Plantenga

Master's Theses

On-orbit satellite servicing is critical to maximizing space utilization and sustainability and is of growing interest for commercial, civil, and defense applications. Reliance on astronauts or anchored robotic arms for the servicing of next-generation large, complex space structures operating beyond Low Earth Orbit is impractical. Substantial literature has investigated the mission design and analysis of robotic servicing missions that utilize a single servicing satellite to approach and service a single target satellite. This motivates the present research to investigate a fleet of servicing satellites performing several operations for a large, central space structure.

This research leverages a distributed control approach, …

Online Estimation Of Unknown Parameters For Flexible Spacecraft, Nicolo Woodward

Doctoral Dissertations and Master's Theses

Attitude controls methods of highly flexible spacecraft have seen increased interest over the last decades thanks to the technological development of flexible solar panels and deploy-ables, which improves the capabilities of small satellites. However, a high-fidelity model of the flexible mode dynamics is hard to obtain in on-ground testing because not all modes of frequencies can be observed, complicating the controller design. Furthermore, plastic deformations due to long periods of storage of stowed flexible components could result in exciting frequencies outside of the designed controller’s bandwidth, leading to an uncontrollable system. This thesis proposes a method to develop a high-fidelity …

An Online Adaptive Machine Learning Framework For Autonomous Fault Detection, Nolan Coulter

Doctoral Dissertations and Master's Theses

The increasing complexity and autonomy of modern systems, particularly in the aerospace industry, demand robust and adaptive fault detection and health management solutions. The development of a data-driven fault detection system that can adapt to varying conditions and system changes is critical to the performance, safety, and reliability of these systems. This dissertation presents a novel fault detection approach based on the integration of the artificial immune system (AIS) paradigm and Online Support Vector Machines (OSVM). Together, these algorithms create the Artificial Immune System augemented Online Support Vector Machine (AISOSVM).

The AISOSVM framework combines the strengths of the AIS and …

Solar Sailing Adaptive Control Using Integral Concurrent Learning For Solar Flux Estimation, Luis Mendoza Zambrano

Doctoral Dissertations and Master's Theses

In the interest of exploiting natural forces for propellant-less spacecraft missions, this thesis proposes an adaptive control strategy to account for unknown parameters in the dynamic modeling of a reflectivity-controlled solar sail spacecraft. A Lyapunov-based control law along with integral concurrent learning is suggested to accomplish and prove global exponential tracking of the estimated parameters and states of interest, without satisfying the common persistence of excitation condition, which in most nonlinear systems cannot be guaranteed a priori. This involves estimating the solar flux or irradiance from the Sun to account for uncertainty and variation over time in this value. To …

Statistical Approach To Quantifying Interceptability Of Interaction Scenarios For Testing Autonomous Surface Vessels, Benjamin E. Hargis, Yiannis E. Papelis

Modeling, Simulation and Visualization Student Capstone Conference

This paper presents a probabilistic approach to quantifying interceptability of an interaction scenario designed to test collision avoidance of autonomous navigation algorithms. Interceptability is one of many measures to determine the complexity or difficulty of an interaction scenario. This approach uses a combined probability model of capability and intent to create a predicted position probability map for the system under test. Then, intercept-ability is quantified by determining the overlap between the system under test probability map and the intruder’s capability model. The approach is general; however, a demonstration is provided using kinematic capability models and an odometry-based intent model.