Navigation, Guidance, Control and Dynamics Commons^™

Distributed Adaptive Control Methods For Uncertain Multiagent Systems With Coupled Dynamics, Islam Aly

Doctoral Dissertations and Master's Theses

For a multiagent system, a major challenge is achieving overall system stability and performance in the presence of not only uncertainties but also coupled dynamics. Another challenge for these systems is designing distributed adaptive controllers with user-assigned positions in this case and defining the convergence rate of the reference model for each agent using only local (i.e., agent-based) information. Discrete-time architectures have an advantage over their continuous counterparts as they can be directly executed on embedded hardware without the need for discretization. Yet, because of the difficulty of ensuring Lyapunov difference expressions, their designs, which are based on quadratic Lyapunov-based …

Implementation Of Path Planning Methods To Detect And Avoid Gps Signal Degradation In Urban Environments, Ayush Raminedi

Doctoral Dissertations and Master's Theses

In the modern world, various missions are being carried out under the assistance of autonomous flight vehicles due to their ability to operate in a wide range of flight conditions. Regardless, these autonomous vehicles are prone to GPS signal loss in urban environments due to obstructions that cause scintillation, multi-path, and shadowing. These effects that decrease the GPS functionality can deteriorate the accuracy of GPS positioning causing losses in signal tracking leading to a decrease in navigation performance. These effects are modeled into the simulation environment and are used as part of the path planning algorithm to provide better navigation …

Prediction Of Handling Qualities Deficiencies For Advanced Air Mobility Aircraft, Louis Spier

Doctoral Dissertations and Master's Theses

To date, there are hundreds of Advanced Air Mobility (AAM) vehicles under development. Most of these vehicles differ significantly from traditional airplanes and rotorcraft when it comes to configuration and handling qualities. Handling qualities for traditional airplanes and rotorcraft are often very predictable. All AAM concepts currently under development feature some sort of fly-by-wire flight control system. Regulatory agencies already have decades of experience with certifying fly-by-wire airplanes. Fly-by-wire rotorcraft have proven to be significantly more difficult to certify and several flight test accidents have occurred as a result of handling qualities deficiencies, or “cliffs”. To ensure the safe and …

State Omniscience For Cooperative Local Catalog Maintenance Of Close Proximity Satellite Systems, Chris Hays

Doctoral Dissertations and Master's Theses

Resiliency in multi-agent system navigation is reliant on the inherent ability of the system to withstand, overcome, or recover from adverse conditions and disturbances. In large part, resiliency is achieved through reducing the impact of critical failure points to the success and/or performance of the system. In this view, decentralized multi-agent architectures have become an attractive solution for multi-agent navigation, but decentralized architectures place the burden of information acquisition directly on the agents themselves. In fact, the design of distributed estimators has been a growing interest to enable complex multi-sensor/multi-agent tasks. In such scenarios, it is important that each local …

Trustable Adaptive Controllers For Multi-Agent Systems With Actuator Dynamics, Atahan Kurttisi

Doctoral Dissertations and Master's Theses

Multi-agent systems have become a powerful tool for a wide range of applications due to the effective and cheap solutions they offer. Especially during the last decade, they have impacted a wide array of civilian and military applications (e.g., surveillance, reconnaissance, and payload transportation). However, they suffer system anomalies due to the operational and material conditions, which yields degraded performance or even instability. Hence, this dissertation investigates the distributed adaptive control design process for uncertain multi-agent systems with scalar and high-order dynamics in the presence of unknown control effectiveness and actuator dynamics. First, it provides an approach for driving a …

Adaptive Control Of An Aeroelastic System For Active Flutter Suppression And Disturbance Rejection, Patrick Sterling Downs

Doctoral Dissertations and Master's Theses

The future of aircraft design strives for lighter weight, more aerodynamically efficient structures. These improvements may come with the drawback of increased structural flexibility and elevated aeroelastic effects, often resulting in a lower flutter speed. This motivates the implementation of advanced control methods to control aeroelastic systems over a range of flight conditions, suppress and delay the onset of flutter, and compensate for disturbances, actuator dynamics, and unmodeled nonlinear dynamics.

This dissertation first develops a novel method for constructing time-domain simulation models of two and three-dimensional aeroelastic systems, resulting in models that are suitable for the implementation of state-space control …

Adaptive Control For A Class Of Nonlinear, Time Varying Rotational Systems, John Zelina

Doctoral Dissertations and Master's Theses

Aerospace systems often exhibit nonlinear, time varying dynamics. Expansive mission profiles, fuel burn or transfer and payload deployments or slung loads can supply additional complexity that can excite the plant dynamics and result in undesirable performance. Because of these often unknown dynamical effects in aerospace systems, an emphasis is placed on controller robustness as significant safety risks are present. Due to the difficulty of predicting uncertainties, robust control can be achieved in two distinct ways. The first approach is to design a control law to be tolerant to a large amount of uncertainty, and the second is to design a …

Farmer Perceptions Of Land Cover Classification Of Uas Imagery Of Coffee Agroecosystems In Puerto Rico, Jose Cabrera, Blake Neal, Kevin Adkins, Ronny Schroeder, Gwendolyn Klenke, Shannon Brines, Nayethzi Hernandez, Kevin Li, Riley Glancy, Ivette Perfecto

Publications

Highly diverse agroecosystems are increasingly of interest as the realization of farms’ invaluable ecosystem services grows. Simultaneously there has been an increased use of uncrewed aerial systems (UAS) in remote sensing as drones offer a finer spatial resolution and faster revisit rate than traditional satellites. With the combined utility of UAS and the attention on agroecosystems, there exists an opportunity to assess UAS practicality in highly biodiverse settings. In this study, we utilized UAS to collect fine-resolution 10-band multispectral imagery of coffee agroecosystems in Puerto Rico. We created land cover maps through a pixel-based supervised classification of each farm and …

Cubesat Reaction Wheel Attitude Control Platform System Architecture, Justin Hartland

Beyond: Undergraduate Research Journal

In the classroom, physics behind spacecraft attitude dynamics and controls is abstract and difficult to comprehend. It is common that students struggle to develop the connection between the math they learn and how it can be applied in the real world. The goal of this project is to design and manufacture a 1U, 3U, and 6U CubeSat testbed for autonomous control systems utilizing reaction wheels. The testbed will include three separate reaction wheels each mounted on its own respective axis to control the attitude in 3 degrees of freedom. The end goal of the CubeSat Control Platform is to be …

Immersive Framework For Designing Trajectories Using Augmented Reality, Joseph Anderson, Leo Materne, Karis Cooks, Michelle Aros, Jaia Huggins, Jesika Geliga-Torres, Kamden Kuykendall, David Canales, Barbara Chaparro

Publications

The intuitive interaction capabilities of augmented reality make it ideal for solving complex 3D problems that require complex spatial representations, which is key for astrodynamics and space mission planning. By implementing common and complex orbital mechanics algorithms in augmented reality, a hands-on method for designing orbit solutions and spacecraft missions is created. This effort explores the aforementioned implementation with the Microsoft Hololens 2 as well as its applications in industry and academia. Furthermore, a human-centered design process and study are utilized to ensure the tool is user-friendly while maintaining accuracy and applicability to higher-fidelity problems.

A New Trajectory In Uav Safety: Leveraging Reinforcement Learning For Distance Maintenance Under Wind Variations, Xiaolin Xu, Jeffrey Sun

Journal of Aviation/Aerospace Education & Research

In the field of aviation, safety is a critical cornerstone, and the operation of Unmanned Aerial Vehicle (UAV) systems is deeply connected with this principle. A thorough analysis and rigorous simulation and testing of aircraft systems are essential to avoid severe safety hazards. This paper delves into the safety issue in UAV operations, specifically regarding maintaining minimum safety distances under fluctuating wind conditions. The study introduces a novel solution based on a Deep Deterministic Policy Gradient (DDPG) model, a reinforcement learning method. The DDPG model was trained using a simulated environment created through the Gazebo simulator, with values for wind …

Adaptive Control Of Vscmgs For Satellite Attitude Tracking, Kyle Vernyi

Doctoral Dissertations and Master's Theses

The democratization of space has brought about many new opportunities and challenges for industry, academia, and governments. Smaller and more affordable platforms are now regularly being launched. These platforms have higher uncertainty due to cost, engineering constraints, unmodeled dynamics, coupled effects, and the challenging space environment. In addition, both mission requirements and complexity are simultaneously being increased. In order to achieve these requirements, new satellite control algorithms are required. Adaptive control offers a strong solution to the uncertainty problem. This thesis develops several adaptive control algorithms for satellites equipped with momentum management devices. Momentum management devices such as reaction wheels …

Leveraging Artificial Intelligence To Improve Data Configuration & Accuracy In Modern Flight Management Systems, Sreeram Chittayil

International Journal of Aviation, Aeronautics, and Aerospace

AI (Artificial intelligence) can automate the process of generating optimized flight routes using real-time data, such as AIRAC (Aeronautical Information Regulation and Control), significantly reducing the time needed for flight management tasks. While AIRAC data typically takes up to 28 days to refresh, AI could condense this process to just minutes, enhancing operational efficiency and ensuring pilots have timely and accurate flight information. The research includes practical experiments, prototype code, and visual case studies to demonstrate AI's role in optimizing FMS functions while addressing issues related to data input errors and human intervention. Key findings from trials show the algorithm's …

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 …

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. …

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 …

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 …

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 …

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. …

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 …

Nonlinear Dynamics Analysis And Control Of Space Vehicles With Flexible Structures, Marco Fagetti

Doctoral Dissertations and Master's Theses

Space vehicles that implement hardware such as antennas, solar panels, and other extended appendages necessary for their respective missions must consider the nonlinear rotational and vibrational dynamics of these flexible structures. Formulation and analysis of these flexible structures must account for the rigid-flexible coupling present in the system dynamics for stability analysis and control design. The system model is represented by a flexible appendage attached to a central rigid body, where the flexible appendage is modeled as a cantilevered Euler-Bernoulli beam. Discretization techniques, such as the assumed modes method and the finite element method, are used to model the coupled …

Optical Orbit Tracking And Estimation, Matthew Gillette

Doctoral Dissertations and Master's Theses

Angles-only initial orbit determination methods are currently limited in their use as they require some prior knowledge of where the observed object will be and when it will be there. This research aims to produce a viable method to automate this process so that objects whose trajectories are not saved in a user’s catalog can be observed. A method is devised using a novel approach to satellite recognition in an image. This method is used in addition to Astrometry to determine the right ascension and declination of the object. This information is then used to either obtain the initial conditions …

Autonomous Space Surveillance For Arbitrary Domains, David Zuehlke

Doctoral Dissertations and Master's Theses

Space is becoming increasingly congested every day and the task of accurately tracking satellites is paramount for the continued safe operation of both manned and unmanned space missions. In addition to new spacecraft launches, satellite break-up events and collisions generate large amounts of orbital debris dramatically increasing the number of orbiting objects with each such event. In order to prevent collisions and protect both life and property in orbit, accurate knowledge of the position of orbiting objects is necessary. Space Domain Awareness (SDA) used interchangeably with Space Situational Awareness (SSA), are the names given to the daunting task of tracking …