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 a Multiplicative Extended Kalman Filter. It was found that the quality of the Virtual Gyroscope’s rate measurement improved with a larger and higher quality array, but the error in the estimate was very large. The low signal-to-noise ratio and the unknown bias in the accelerometers caused the angular velocity estimate from the accelerometer array to be too poor for use in the propagation step of the Kalman filter. The Kalman filter performed better with attitude measurements alone than with the Virtual Gyroscope, even when the attitude were delivered at a low rate with added noise. Overall, the current Virtual Gyroscope algorithm that is presented in this thesis is not suitable to replace a MEMS gyroscope in a nanosatellite mission, although there is room for future improvements using bias prediction for the individual accelerometers in the array.

Legged robots have the potential to be a valuable technology that provides agile and adaptive locomotion over complex terrain. To realize legged locomotion's full abilities a control design must consider the nonlinear piecewise dynamics of the systems. This paper aims to develop a controller for the planar bounding of a quadrupedal robot. The bounding of the quadruped robot is characterized by a simplified hybrid model that consists of two subsystems for stance and flight phases and the switching laws between the two states. An additional model, the Multibody model, with fewer simplifications, is used concurrently to best approximate real-world behavior. The bounding gait (periodic orbit) of the robot is predicted by an optimization method based on the numerical integration of the differential equations of subsystems. To stabilize the gait, a switching controller is applied which can be split into two separate phases: stance-phase and swing-phase control. The stance phase implements reaction force control utilizing a body state feedback controller and a gait stabilizer, while the swing phase deploys position control in conjunction with a trajectory planning algorithm to ensure proper footfall. Numerical simulations are carried out for the system with/without control. The control strategy is further validated by simulations of the Simscape multibody model. The overall simulated controller results are promising and demonstrate stable bounding for four system cycles.

This thesis proposes a set of methods for the purpose of improving the calibration of three-axis magnetometers. Specifically, these methods aim to improve the accuracy of the bias estimation methods currently in use. The first proposed method utilizes a constrained optimization problem based on norm preserving. The second proposed method finds the same bias estimate as the first method, but in a computationally more efficient manner. The last proposed method tackles the case where the value of the local geomagnetic field is only imprecisely known. Computer simulations demonstrate the viability of the proposed methods.

In the upcoming decade, the proliferation of high-LEO constellations is expected to exceed 20,000 objects, yet comprehensive Post Mission Disposal (PMD) strategies for these constellations are currently lacking. With the inherent challenges of efficiently deorbiting satellites from High-LEO orbits, there arises an urgent need to explore innovative approaches. Building upon insights garnered from the ReDSHIFT project and anticipating the proliferation of high-LEO constellations such as OneWeb, TeleSat, and GuoWang, this thesis delves into the potential viability of the Resonance Corridor Method for PMD. The investigation encompasses key metrics, including deorbit timelines and $\Delta v$ requirements to meet regulatory standards or recommendations, with comparisons drawn against alternative methods like Perigee Decrease and Graveyard Orbit solutions. Through this analysis, scenarios emerge where the Resonance Corridor method demonstrates advantages, offering feasible delta-v values while ensuring compliance with regulatory standards and recommendations. The findings yield categorizations of high-LEO constellation shells into specific disposal feasibility groups, thereby providing valuable insights into how space sustainability practices can be added into spacecraft design to align with evolving space debris mitigation standards. Additionally, certain altitude-inclination combinations are found to naturally align with the resonance corridor method, while others necessitate minor architectural adjustments to optimize effectiveness.

As the use of small satellites for advanced space missions continues to grow, the importance of low mass and cost three-axis attitude stabilization systems increases as well, with these systems requiring high accuracy attitude knowledge. Star trackers provide the most accurate attitude knowledge of any type of attitude sensor, but the high cost, size, and weight of commercial star trackers can be prohibitive to small satellite missions. Many simple star trackers have been developed using commercial off-the-shelf camera sensors and processing hardware, but the challenge remains in testing and characterizing these devices. A common solution is night sky tests, in which the star tracker is held up to the night sky to image the star field and perform attitude determination. Commercial star trackers, on the other hand, are regularly tested with manufacturer provided star field images that attach directly to the sensor. These methods, however, severely limit the sky conditions that can be used in testing. Night sky tests depend on weather and can only image regions of the sky the user has access to, while lab-based testing uses the few provided still images. This thesis presents a hardware-in-the-loop star tracker test bed developed for comprehensive ground-based testing of both in-house and commercial star trackers. The system consists of a small screen to display a star field, a simple in-house camera star tracker, and a microprocessor. This test bed allows any star field image to be simulated. The system is set up for use on a stationary tabletop, but its small size lends itself for use with a spacecraft dynamics platform, which can facilitate testing of control algorithms using real star tracker output.

The goal of this thesis is to use the Cal Poly Spacecraft Attitude Dynamics Simulator to provide proof of concept of two adaptive control theories developed by former Cal Poly students: Nonlinear Direct Model Reference Adaptive Control and Adaptive Output Feedback Control. The Spacecraft Attitude Dynamics Simulator is a student-built air bearing spacecraft simulator controlled by four reaction wheels in a pyramidal arrangement. Tests were performed to determine the effectiveness of the two adaptive control theories under nominal operating conditions, a “plug-and-play” spacecraft scenario, and under simulated actuator damage. Proof of concept of the adaptive control theories applied to attitude control of a spacecraft is provided. The adaptive control theories are shown to attain similar or improved performance over a Full State Feedback controller. However, the measurement capabilities of the simulator need to be improved before strong comparisons between the adaptive controllers and Full State Feedback can be achieved.

Fixed-wing unmanned aerial vehicles (UAVs) with the ability to hover combine the speed and endurance of traditional fixed-wing fight with the stable hovering and vertical takeoff and landing (VTOL) capabilities of helicopters and quadrotors. This combination of abilities can provide strategic advantages for UAV operators, especially when operating in urban environments where the airspace may be crowded with obstacles. Traditionally, fixed-wing UAVs with hovering capabilities had to be custom designed for specific payloads and missions, often requiring custom autopilots and unconventional airframe configurations. With recent government spending cuts, UAV operators like the military and law enforcement agencies have been urging UAV developers to make their aircraft cheaper, more versatile, and easier to repair. This thesis discusses the use of the commercially available ArduPilot open source autopilot, to autonomously transition a fixed-wing UAV to and from hover flight. Software modifications were made to the ArduPilot firmware to add hover flight modes using both Proportional, Integral, Derivative (PID) Control and Model Reference Adaptive Control (MRAC) with the goal of making the controllers robust enough so that anyone in the ArduPilot community could use their own ArduPilot board and their own fixed-wing airframe (as long as it has enough power to maintain stable hover) to achieve autonomous hover after some simple gain tuning. Three new hover flight modes were developed and tested first in simulation and then in flight using an E-Flight Carbon Z Yak 54 RC aircraft model, which was equipped with an ArduPilot 2.5 autopilot board. Results from both the simulations and flight test experiments where the airplane transitions both to and from autonomous hover flight are presented.

In this thesis, a new guidance method for fighter aircrafts and a new guidance method for missiles are developed. Also, guidance and control systems of the aircraft and the missile used are designed to simulate the generic engagement scenarios between the missile and the aircraft. Suggested methods have been tested under excessive simulation studies. The aircraft guidance method developed here is a rule based missile evasion method. The main idea to develop this method stems from the maximization of the miss distance for an engagement scenario between a missile and an aircraft. To do this, an optimal control problem with state and input dependent inequality constraints is solved and the solution method is applied on different problems that represent generic scenarios. Then, the solutions of the optimal control problems are used to extract rules. Finally, a method that uses the interpolation of the extracted rules is given to guide the aircraft. The new guidance method developed for missiles is formulated by modifying the classical proportional navigation guidance method using the position estimates. The position estimation is obtained by utilization of a Kalman based filtering method, called interacting multiple models.

Two controller performances are assessed for generalization in the path following task of autonomously backing up a tractor-trailer. Starting from random locations and orientations, paths are generated to loading docks with arbitrary pose using Dubins Curves. The combination vehicles can be varied in wheelbase, hitch length, weight distributions, and tire cornering stiffness. The closed form calculation of the gains for the Linear Quadratic Regulator (LQR) rely heavily on having an accurate model of the plant. However, real-world applications cannot expect to have an updated model for each new trailer. Finding alternative robust controllers when the trailer model is changed was the motivation of this research. Reinforcement learning, with neural networks as their function approximators, can allow for generalized control from its learned experience that is characterized by a scalar reward value. The Linear Quadratic Regulator and the Deep Deterministic Policy Gradient (DDPG) are compared for robust control when the trailer is changed. This investigation quantifies the capabilities and limitations of both controllers in simulation using a kinematic model. The controllers are evaluated for generalization by altering the kinematic model trailer wheelbase, hitch length, and velocity from the nominal case. In order to close the gap from simulation and reality, the control methods are also assessed with sensor noise and various controller frequencies. The root mean squared and maximum errors from the path are used as metrics, including the number of times the controllers cause the vehicle to jackknife or reach the goal. Considering the runs where the LQR did not cause the trailer to jackknife, the LQR tended to have slightly better precision. DDPG, however, controlled the trailer successfully on the paths where the LQR jackknifed. Reinforcement learning was found to sacrifice a short term reward, such as precision, to maximize the future expected reward like reaching the loading dock. The reinforcement learning agent learned a policy that imposed nonlinear constraints such that it never jackknifed, even when it wasn't the trailer it trained on.

Unmanned aerial vehicles (UAV's) have recently gained popularity in military, civil service, agriculture, commercial, and hobby use. This is due in part to their affordability, which comes from advances in component technology. That technology includes microelectromechanical systems (MEMS) for inertial sensing, microprocessor technology for sequential algorithm processing, field programmable gate arrays (FPGA's) for parallel data processing, camera technology, global navigation satellite systems (GNSS's) for navigation, and battery technology such as the high energy density of lithium polymer batteries. Despite the success of the technology to date, there remains development before UAV's should be flying alongside manned aircraft or over populated areas. One concern is that UAV electronics are not as safe, reliable or robust as manned-aircraft electronics because UAV's are not certified by the FAA. Another concern for UAV operation is with control algorithms and sensors, particularly in the estimation of the aircraft state, which is the position, velocity, and orientation of the aircraft. Some problems, such as numerical stability of a control algorithm or flight in windy and turbulent conditions have only been solved for certain conditions of wind, weather, or maneuvers. Outside those conditions, the actual orientation of a flying craft can mislead to the control system, and the control system may not be able to recover without a crash. When pilots fly manned aircraft in instrument meteorological conditions, or conditions of limited visibility of the ground, terrain, and obstacles, the pilot must fly in a manner which avoids abrupt maneuvers which could disturb accuracy of the aircraft's instruments. In a UAV without a pilot, there is a need to estimate the position and orientation of a UAV in an absolute manner unambiguous relative to the Earth. The position and orientation estimate must not depend on carefully controlled flight paths, but instead the estimate must be robust in the presence of UAV flight dynamics. This thesis describes the design, implementation, and evaluation of a hardware platform for GPS based orientation sensing research. In this work, we considered a receiver with three or four RF sections, each connected to an antenna in a triangular or tetrahedral pyramid constellation. Specific requirements for the receiver hardware and functionality were created. Circuitry was designed to meet the requirements using commercial off-the-shelf (COTS) radio frequency (RF) modules, a mid-sized microcontroller, an FPGA, and other supporting components. A printed circuit board (PCB) was designed, fabricated, assembled, and tested. A GPS baseband processor was designed and coded in Verilog hardware description language. The design was synthesized and loaded to the FPGA, and the microcontroller was programmed to track satellites. With the hardware platform implemented, live satellite signals were found and tracked, and experiments were performed to explore the validity of GPS based orientation sensing using short antenna baselines. The platform successfully allows the user to develop correlator designs and explore carrier phase based orientation measurement using only software/Verilog modifications. Initial results of carrier phase based orientation sensing are promising, but the presence of multipath signal interference shows room for improvement to the baseband processing code.