The deployment of unmanned aerial vehicles (UAV) in military applications increased the research about them and the importance of them. The unmanned helicopters are the most agile and maneuverable vehicles among the unmanned aerial vehicles (UAV). The ability of hovering and low speed cruise makes them even more attractive. Such abilities supply more areas to deploy the usage of the unmanned helicopters like search &amp / rescue, mapping, surveillance. Autonomy is the key property for these vehicles. In order to provide autonomy to an unmanned vehicle, the guidance and the autopilot units are designed in the first step. Waypoints are used to track the desired trajectories. The line of sight guidance is used to reach an active waypoint. In order to realize the guidance commands controllers are designed by using LQR. In addition, position and heading controllers are designed by root-locus method. The trimming and linearization are implemented in order to extract linear models used for controller design. Keywords: Helicopter, control, guidance

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This paper presents a flying qualities built-in-test for UAS application with the scope limited to the longitudinal axis. A doublet input waveform excites the AV and both α and q are used by EUDKF to estimate the A and B matrices which are short period approximations of the system. ζ, ω, GM, PM, observability, and controllability are calculated to determine flying qualities with the results displayed to the AVO in a color-coded, easy to interpret display. While SID algorithms have been flying in vehicles with adaptive control schemes, vehicles with other schemes (such as classical feedback) lack this built-in self assessment tool. In addition, adaptive control SID results are not analyzed and displayed but instead used internally. This work intends to extend this self-assessment option to all UASs regardless of control scheme as a “plug-and-play” add-on by building a reliable and robust tool that requires little tuning.

The design process of unmanned ISR systems has typically driven in the direction of increasing system mass to increase stabilization performance and imagery quality. However, through the use of new sensor and processor technology high performance stabilization feedback is being made available for control on new small and low mass stabilized platforms that can be placed on small UAVs. This project develops and implements a LOS stabilization controller design, typically seen on larger gimbals, onto a new small stabilized gimbal, the Tigereye, and demonstrates the application on several small UAV aircraft. The Tigereye gimbal is a new 2lb, 2-axis, gimbal intended to provided high performance closed loop LOS stabilization through the utilization of inertial rate gyro, electronic video stabilization, and host platform state information. Ground and flight tests results of the LOS stabilization controller on the Tigereye gimbal have shown stabilization performance improvements over legacy systems. However, system characteristics identified in testing still limit stabilization performance, these include: host system vibration, gimbal joint friction and backlash, joint actuation compliance, payload CG asymmetry, and gyro noise and drift. The control system design has been highly modularized in anticipation of future algorithm and hardware upgrades to address the remaining issues and extend the system's capabilities.

As Unmanned Aerial Vehicles (UAVs) are integrated into the national airspace to comply with the 2012 Federal Aviation Administration Reauthorization Act, new civilian uses for robotic aircraft will come about in addition to the more obvious military applications. One particular area of interest for UAV development is the autonomous cooperative control of multiple UAVs. In this thesis, a decentralized leader-follower control strategy is designed, implemented, and tested from the follower’s perspective using vision-based localization. The tasks of localization and control were carried out with separate processing hardware dedicated to each task. First, software was written to estimate the relative state of a lead UAV in real-time from video captured by a camera on-board the following UAV. The software, written using OpenCV computer vision libraries and executed on an embedded single-board computer, uses the Efficient Perspective-n-Point algorithm to compute the 3-D pose from a set of 2-D image points. High-intensity, red, light emitting diodes (LEDs) were affixed to specific locations on the lead aircraft’s airframe to simplify the task if extracting the 2-D image points from video. Next, the following vehicle was controlled by modifying a commercially available, open source, waypoint-guided autopilot to navigate using the relative state vector provided by the vision software. A custom Hardware-In-Loop (HIL) simulation station was set up and used to derive the required localization update rate for various flight patterns and levels of atmospheric turbulence. HIL simulation showed that it should be possible to maintain formation, with a vehicle separation of 50 ± 6 feet and localization estimates updated at 10 Hz, for a range of flight conditions. Finally, the system was implemented into low-cost remote controlled aircraft and flight tested to demonstrate formation convergence to 65.5 ± 15 feet of separation.

Optimization based flight control design tools depend on automatic linearization tools, such as Simulink®’s LINMOD, to extract linear models. In order to ensure the usefulness and correctness of the generated linear model, this linearization must be accurate. So a method of independently verifying the linearized model is needed. This thesis covers the automation of a system identification tool, CIFER®, for use as a verification tool integrated with CONDUIT®, an optimization based design tool. Several test cases are built up to demonstrate the accuracy of the verification tool with respect to analytical results and matches with LINMOD. Several common nonlinearities are tested, comparing the results from CIFER and LINMOD, as well as analytical results where possible. The CIFER results show excellent agreement with analytical results. LINMOD treated most nonlinearity as a unit gain, but some nonlinearities linearized to a zero, causing the linearized model to omit that path. Although these effects are documented within Simulink, their presence may be missed by a user. The verification tool is successful in identifying these problems when present. A section is dedicated to the diagnosis of linearization errors, suggesting solutions where possible.

In October 2017, the first interstellar object, designated 1I/2017 U1 and more commonly referred to as Oumuamua, was detected passing through our solar system by the Pan-STARRS telescope, followed recently by the detection of 2I/Borisov in August 2019. These detections came much sooner than thought possible, and have redefined our understanding of the population of interstellar objects. With the construction of the next generation of powerful observatories, future detections are estimated to occur as frequently as two per year, and while there is significant scientific understanding to be gained from observing these objects remotely, a spacecraft sent to intercept one might be the only way to collect up-close, detailed information on the composition of extra solar object. The ideal mission scenario would be a combination flyby and impact as performed and proven feasible by the Deep Impact encounter with the comet Temple 1. A study has already been done showing that trajectories to interstellar objects are feasible with current chemical propulsion and a “launch on detection” paradigm, with an estimated 10 year wait time between favorable mission opportunities, assuming future detection capabilities. However, while a trajectory to one of these objects might be feasible, accurately performing a flyby and impacting an object with a hyperbolic orbit presents unprecedented navigational challenges. Spacecraft-target relative velocities can range between 10 km/s to 110 km/s with high phase angles between 90° and 180°. The goal of this thesis is to determine the required navigation hardware – an optical navigation camera and attitude determination system – which could provide high mission success probability for many potential encounter scenarios. This work is performed via a simulation program developed at the Jet Propulsion Laboratory that generates simulated images of a target during the terminal guidance phase of a mission, and feeds them into the algorithms behind autonomous navigation software (AutoNav) used for the Deep Impact mission. Observations are derived from the images and used to perform target-relative orbit determination and calculate correction maneuvers.

The Horizon Simulation Framework is a modeling and simulation framework developed to verify system level requirements. In this thesis, the framework is extended to include the Dynamic position type that existed in the early development phase of the framework. The Dynamic position type is tested through the modeling and simulation of a sounding rocket. An active control system based on linear-quadratic regulator (LQR) control theory is implemented and tested in the simulation to determine the overall effect on altitude. A first order aerodynamics and aeroprediction model are created within the framework to allow for rapid changes early in the design process of the sounding rocket. The flight dynamics are compared to two different sounding rocket flights and the aeroprediction model is validated against public wind tunnel test data.

Aircraft structures have varying stiffness levels making them flexible. Consequently, this elastic property becomes increasingly important at high speeds affecting the flight dynamics of the aircraft. In high speed aircraft such as the F/A-18, elastic structural properties must be accounted for to ensure confidence in predicted flight dynamics in order to avoid adverse aeroelastic phenomena throughout flight. Data from the F/A-18 Active Aeroelastic Wing (AAW) program was used to create aeroservoelastic (ASE) models at varying flight conditions. The discretized ASE models were integrated into the NASA Dryden F/A-18 simulator in parallel with the traditional 6-DOF (degrees-of-freedom) flight dynamics calculations to ensure minimal disruption to the existing operating framework of the simulator. An interpolation scheme was used to construct ASE models within the known flight condition models. Data was processed through the state-space ASE models to compute the elastic effects during flight. Total flight dynamics from the simulation were analyzed and showed expected behavior for the combined elastic and rigid-body components in flight.

The Cal Poly Spacecraft Dynamics Simulator, also known as the Pyramidal Reaction Wheel Platform (PRWP), is an air-bearing four reaction wheel spacecraft simulator designed to simulate the low-gravity, frictionless condition of the space environment and to test and validate spacecraft attitude control hardware and control laws through real-time motion tests. The PRWP system was modified to the new Mk.III configuration, which adopted the MATLAB xPC kernel for better real-time hardware control. Also the Litton LN-200 IMU was integrated onto the PRWP and replaced the previous attitude sensor. Through the comparison of various control laws through motion tests the Mk.III configuration was tested for robust control law verification capability. Two fixed-gain controllers, full-state feedback (FSFB) and linear quadratic regulator with set-point control(LQRSP), and two adaptive controllers, nonlinear direct model reference adaptive controller (NDMRAC) and the adaptive output feedback (AOF), were each tested in three different cases of varying plant parameters to test controller robustness through real-time motion tests. The first two test cases simulate PRWP inertia tensor variations. The third test case simulates uncertainty of the reaction wheel dynamic by slowing down the response time for one of the four reaction wheels. The Mk.III motion tests were also compared with numerical simulations as well as the older Mk.II motion tests to confirm controller validation capability. The Mk.III test results confirmed certain patterns from the numerical simulations and the Mk.II test results. The test case in which actuator dynamics uncertainty was simulated had the most effect on controller performance, as all four control laws experienced an increase in steady-state error. The Mk.III test results also confirmed that the NDMRAC outperformed the fixed-gain controllers.