This thesis presents the design of an attitude determination system for small satellites that automatically corrects for attitude drift. Existing attitude determination systems suffer from attitude drift due to the integration of noisy rate gyro sensors used to measure the change in attitude. This attitude drift leads to a gradual loss in attitude knowledge, as error between the estimated attitude and the actual attitude increases. In this thesis a Kalman filter is used to complete sensor fusion which combines sensor observations with a projected attitude based on the dynamics of the satellite. The system proposed in this thesis also utilizes a novel sensor called the stellar gyro to correct for the drift. The stellar gyro compares star field images taken at different times to determine orientation, and works in the presence of the sun and during eclipse. This device provides a relative attitude fix that can be used to update the attitude estimate provided by the Kalman filter, effectively compensating for drift. Simulink models are developed of the hardware and algorithms to model the effectiveness of the system. The Simulink models show that the attitude determination system is highly accurate, with steady state errors of less than 1 degree.

Instrumented umanned aerial vehicles represent a new way of measuring turbulence in the atmospheric boundary layer. However, autonomous measurements require control methods with disturbance-rejection and altitude command-following capabilities. Filtered dynamic inversion is a control method with desirable disturbance-rejection and command-following properties, and this controller requires limited model information. We implement filtered dynamic inversion as the pitch controller in an altitude-hold autopilot. We design and numerically simulate the continuous-time and discrete-time filtered-dynamic-inversion controllers with anti-windup on a nonlinear aircraft model. Finally, we present results from a flight experiment comparing the filtered-dynamic-inversion controller to a classical proportional-integral controller. The experimental results show that the filtered-dynamic-inversion controller performs better than a proportional-integral controller at certain values of the parameter.

The obstacle avoidance navigation problem for Unmanned Aerial Vehicles (UAVs) is a very challenging problem. It lies at the intersection of many fields such as probability, differential geometry, optimal control, and robotics. We build a mathematical framework to solve this problem for quadrotors using both a theoretical approach through a Hamiltonian system and a machine learning approach that learns from human sub-experts' multiple demonstrations in obstacle avoidance. Prior research on the machine learning approach uses an algorithm that does not incorporate geometry. We have developed tools to solve and test the obstacle avoidance problem through mathematics.

This thesis presents a decentralized electromagnetic formation flying (EMFF) control method using frequency-multiplexed sinusoidal control signals. We demonstrate the EMFF control approach in open-loop and closed-loop control experiments using a single-degree-of-freedom testbed with an electromagnetic actuation system (EAS). The EAS sense the relative position and velocity between satellites and implement a frequency-multiplexed sinusoidal control signal. We use a laser-rangefinder device to capture the relative position and an ARM-based microcontroller to implement the closed-loop control algorithm. We custom-design and build the EAS that implements the formation control in one dimension. The experimental results in this thesis demonstrate the feasibility of the decentralized formation control algorithm between two satellites.

This dissertation presents a novel approach to develop a system for real-time tracking of the position and orientation of the human hand in three-dimensional space, using MEMS inertial measurement units (IMUs) and infrared cameras. This research focuses on the study and implementation of an algorithm to correct the gyroscope drift, which is a major problem in orientation tracking using commercial-grade IMUs. An algorithm to improve the orientation estimation is proposed. It consists of: 1.) Prediction of the bias offset error while the sensor is static, 2.) Estimation of a quaternion orientation from the unbiased angular velocity, 3.) Correction of the orientation quaternion utilizing the gravity vector and the magnetic North vector, and 4.) Adaptive quaternion interpolation, which determines the final quaternion estimate based upon the current conditions of the sensor. The results verified that the implementation of the orientation correction algorithm using the gravity vector and the magnetic North vector is able to reduce the amount of drift in orientation tracking and is compatible with position tracking using infrared cameras for real-time human hand motion tracking. Thirty human subjects participated in an experiment to validate the performance of the hand motion tracking system. The statistical analysis shows that the error of position tracking is, on average, 1.7 cm in the x-axis, 1.0 cm in the y-axis, and 3.5 cm in the z-axis. The Kruskal-Wallis tests show that the orientation correction algorithm using gravity vector and magnetic North vector can significantly reduce the errors in orientation tracking in comparison to fixed offset compensation. Statistical analyses show that the orientation correction algorithm using gravity vector and magnetic North vector and the on-board Kalman-based orientation filtering produced orientation errors that were not significantly different in the Euler angles, Phi, Theta and Psi, with the p-values of 0.632, 0.262 and 0.728, respectively. The proposed orientation correction algorithm represents a contribution to the emerging approaches to obtain reliable orientation estimates from MEMS IMUs. The development of a hand motion tracking system using IMUs and infrared cameras in this dissertation enables future improvements in natural human-computer interactions within a 3D virtual environment.

The Cal Poly Spacecraft Simulator is currently being developed for future testing and verifying theoretical control applications. This paper details the effort to balance the platform and remove undesired external torque from the system using System Identification technique developed by Patrick Healy. Since the relationship between the input and output of the system is linear, the least square method is proposed to identify the mass properties and location of center of mass of the system. The tests use four sine wave generators that are out of phase with different amplitudes as the inputs to excite various structural modes of the system. The outputs, angular rates of the platform, are measured by the newly implemented LN-200 Inertial Measurement Unit that helps reducing the measurement noise. Two test cases of 90o yaw rotations with the identified inertia were performed and validated against the computer simulation model; and the result shows that the test cases trajectories followed closely with the computer simulation model.

In recent years, unmanned aerial vehicles (UAVs) have become a dominant force in the aerospace industry. Recent technological developments have moved these aircraft from remote operation roles to more active response missions. Of particular interest is the possibility of applying UAVs toward solving complex problems in long-endurance missions. Under that belief, the feasibility of utilizing UAVs for wildfire detection and response was investigated in a partnership that included NASA’s Aeronautics Research Mission Directorate and Science Mission Directorate, and the United States Forest Service. Under NASA’s Intelligent Mission Management (IMM) project, research was conducted to develop a mission architecture that would enable use of a high altitude UAV to search for reported wildfires with a separate low altitude UAV supporting ground assets. This research proposes a “straw man” concept incorporating both a High Altitude Long Endurance (HALE) UAV and a Low Altitude Short Endurance (LASE) UAV in a loosely coupled, low cost solution tailored towards wildfire response. This report identifies the communications architecture, algorithms, and required system configuration that meets the outlined goals of the IMM project by mitigating wildfires and addressing the United States Forest Service immediate needs. The end product is a defined parent-child framework capable of meeting all wildfire mission goals. The concept has been implemented in simulation, the results of which are presented in this report.

The Cal Poly CubeSat Laboratory (CPCL) is currently facing unprecedented engineering challenges—both technically and programmatically—due to the increasing cost and complexity of CubeSat flight missions. In responding to recent RFPs, the CPCL has been forced to find commercially available solutions to entire mission critical spacecraft subsystems such as propulsion and attitude determination & control, because currently no in-house options exist for consideration. The commercially available solutions for these subsystems are often extremely expensive and sometimes provide excessively good performance with respect to mission requirements. Furthermore, use of entire commercial subsystems detracts from the hands-on learning objectives of the CPCL by removing engineering responsibility from students. Therefore, if these particular subsystems can be designed, tested, and integrated in-house at Cal Poly, the result would be twofold: 1) the space of missions supportable by the CPCL under tight budget constraints will grow, and 2) students will be provided with unique, hands-on guidance, navigation, and control learning opportunities. In this thesis, the CPCL’s attitude determination and control system design and analysis toolkit is significantly improved to support in-house ADCS development. The toolkit—including the improvements presented in this work—is then used to complete the existing, partially complete CPCL ADCS design. To fill in missing gaps, particular emphasis is placed on guidance and control algorithm design and selection of attitude actuators. Simulation results show that the completed design is competitive for use in a large class of small satellite missions for which pointing accuracy requirements are on the order of a few degrees.

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, implemented using the Horizon Simulation Framework (HSF), to develop a tool capable of integrated mission modeling and task scheduling for a servicing satellite fleet. HSF is a modeling and simulation framework for verification of system level requirements with an emphasis on state representations, modularity, and event scheduling. HSF consists of two major modules: the main scheduling algorithm and the system model. The distributed control architecture allocates processing and decision making for this multi-agent cooperative control problem across multiple subsystem models and the main HSF scheduling algorithm itself. Models were implemented with a special emphasis on the dynamics, control, trajectory constraints, and trajectory optimization for the servicing satellite fleet. The integrated mission modeling and scheduling tool was applied to a sample scenario in which a fleet of 3 servicing assets is tasked with performing 12 servicing activities for a large satellite in Geostationary Orbit. The tool was able to successfully determine a schedule in which all 12 servicing activities were completed in under 32 hours, subject to the fuel and trajectory constraints of the servicing assets.

Intersection Navigation plays a significant role in autonomous vehicle operation. This paper focuses on enhancing autonomous vehicle intersection navigation through advanced computer vision and Vehicle-to-Infrastructure (V2I) communication systems. The research unfolds in two phases. In the first phase, an approach utilizing YOLOv8s is proposed for precise traffic light detection and recognition, trained on the Small-Scale Traffic Light Dataset (S2TLD). The second phase establishes seamless connectivity between autonomous vehicles and traffic lights in a simulated Mississippi State University Autonomous Vehicle Simulation (MAVS) environment resembling a small city with multiple intersections. This V2I system enables the transmission of Signal Phase and Timing (SPaT) messages to vehicles, providing information on current traffic light phases and time until the next phase change which enables the vehicles to adjust their speed and behavior in real-time. The simulation demonstrates accurate traffic light detection, with vehicles receiving SPaT messages, showcasing the system’s effectiveness in a multi-intersection scenario.