Modeling, Simulation, and Flight Test for Automatic Flight Control of the Condor Hybrid-Electric Remote Piloted Aircraft

This thesis describes the modeling and verification process for the stability and control analysis of the Condor hybrid-electric Remote-Piloted Aircraft (HE-RPA). Due to the high-aspect ratio, sailplane-like geometry of the aircraft, both longitudinal and lateral/directional aerodynamic moments and effects are investigated. The aircraft is modeled using both digital DATCOM as well as the JET5 Excel-based design tool. Static model data is used to create a detailed assessment of predictive flight characteristics and PID autopilot gains that are verified with autonomous flight test. PID gain values were determined using a six degree of freedom linear simulation with the Matlab/SIMULINK software. Flight testing revealed an over-prediction of the short period poles natural frequency, and a prediction to within 0.5% error of the long-period pole frequency. Flight test results show the tuned model PID gains produced a 21.7% and 44.1% reduction in the altitude and roll angle error, respectively. This research effort was successful in providing an analytic and simulation model for the hybrid-electric RPA, supporting first-ever flight test of parallel hybrid-electric propulsion system on a small RPA

Test and Evaluation of the Piccolo Autopilot System on a One-Third Scale Yak-54

To gain a better understanding of the dynamics of the great ice sheets the National Science Foundation established the Center for Remote Sensing of Ice Sheets (CReSIS) to develop technologies that would improve data gathering of said ice sheets. CReSIS was tasked with the development of an unmanned aerial vehicle, named the Meridian, which would have the ability to make use of advanced radar systems that could be used to gather data on the ice sheets of remote Polar Regions. CReSIS decided to use commercial-off-the-shelf autopilot systems on the Meridian, selecting the Cloud Cap Technologies Piccolo II UAV autopilot system as the initial system to be tested and evaluated. A process for test and evaluation of modeling, simulation and control systems is presented. Three dynamic models for a one-third scale Yak-54 are developed. A deliberate and methodical flight test program is developed to evaluate the Piccolo II flight control system. Parameter identification flight tests are performed to evaluate the three modeling and simulation techniques. Closed loop flight testing is performed to evaluate the flight control system's ability to control an aircraft and the ability of the gains to be performance optimized. Finally flaws are found in the communication system architecture of the Piccolo II autopilot system which causes the system to go pilot-in-loop unstable and to be rejected from further consideration by the CReSIS team

Design, Development and Implementation of Intelligent Algorithms to Increase Autonomy of Quadrotor Unmanned Missions

This thesis presents the development and implementation of intelligent algorithms to increase autonomy of unmanned missions for quadrotor type UAVs. A six-degree-of freedom dynamic model of a quadrotor is developed in Matlab/Simulink in order to support the design of control algorithms previous to real-time implementation. A dynamic inversion based control architecture is developed to minimize nonlinearities and improve robustness when the system is driven outside bounds of nominal design. The design and the implementation of the control laws are described. An immunity-based architecture is introduced for monitoring quadrotor health and its capabilities for detecting abnormal conditions are successfully demonstrated through flight testing. A vision-based navigation scheme is developed to enhance the quadrotor autonomy under GPS denied environments. An optical flow sensor and a laser range finder are used within an Extended Kalman Filter for position estimation and its estimation performance is analyzed by comparing against measurements from a GPS module. Flight testing results are presented where the performances are analyzed, showing a substantial increase of controllability and tracking when the developed algorithms are used under dynamically changing environments. Healthy flights, flights with failures, flight with GPS-denied navigation and post-failure recovery are presented

Development of Autonomous Unmanned Aerial Vehicle Platform: Modeling, Simulating, and Flight Testing

The Advanced Navigation Technology (ANT) Center at the Air Force Institute of Technology (AFIT) conducts extensive research in advanced guidance, navigation, and control to exploit the full potential of autonomous Unmanned Aerial Vehicles (UAV). The research in this thesis describes a UAV research platform developed to support the ANT Center\u27s goals. This platform is now the bedrock for UAV simulation and local flight test at AFIT. The research has three major components. The first component is development of a physical, inertial, and aerodynamic model representing an existing aircraft. A systematic analysis of the airframe leads to a complete geometric, inertial, and aerodynamic representation. The airframe analysis included the use of USAF Digital Datcom, an aerodynamic modeling software tool. Second is the development and implementation of a non-linear, six degree of freedom simulation, employing the developed model. Constructed in Matlab/SIMULINK, the simulation enables control design and pre-flight analysis throughout the entire flight envelope. Detailed post-flight analysis was also performed in Matlab/SIMULINK. Additionally, Hardware in the Loop benchmark simulation was constructed and used for initial flight test plans as well as test team training. The third and final component of the research was an experimental flight test program. Both open loop and autonomous flights were conducted. Openloop flights characterized the aircraft dynamics for comparison with the Matlab simulation results. Autonomous flights tuned the autopilot controller through waypoint tracking in preparation for future advanced navigation research and provided data for Hardware in the Loop simulation validation. This report, along with other significant legacy documentation and procedures, builds the foundation from which future AFIT and ANT Center UAV simulations and flight tests are based

Design, Implementation and Testing of Advanced Control Laws for Fixed-wing UAVs

The present PhD thesis addresses the problem of the control of small fixed-wing Unmanned Aerial Vehicles (UAVs). In the scientific community much research is dedicated to the study of suitable control laws for this category of aircraft. This interest is motivated by the several applications that these platforms can perform and by their peculiarities as dynamical systems. In fact, small UAVs are characterized by highly nonlinear behavior, strong coupling between longitudinal and latero-directional planes, and high sensitivity to external disturbances and to parametric uncertainties. Furthermore, the challenge is increased by the limited space and weight available for the onboard electronics. The aim of this PhD thesis is to provide a valid confrontation among three different control techniques and to introduce an innovative autopilot configuration suitable for the unmanned aircraft field. Three advanced controllers for fixed-wing unmanned aircraft vehicles are designed and implemented: PID with H1 robust approach, L1 adaptive controller and nonlinear backstepping controller. All of them are analyzed from the theoretical point of view and validated through numerical simulations with a mathematical UAV model. One is implemented on a microcontroller board, validated through hardware simulations and tested in flight. The PID with H1 robust approach is used for the definition of the gains of a commercial autopilot. The proposed technique combines traditional PID control with an H1 loop shaping method to assess the robustness characteristics achievable with simple PID gains. It is demonstrated that this hybrid approach provides a promising solution to the problem of tuning commercial autopilots for UAVs. Nevertheless, it is clear that a tradeoff between robustness and performance is necessary when dealing with this standard control technique. The robustness problem is effectively solved by the adoption of an L1 adaptive controller for complete aircraft control. In particular, the L1 logic here adopted is based on piecewise constant adaptive laws with an adaptation rate compatible with the sampling rate of an autopilot board CPU. The control scheme includes an L1 adaptive controller for the inner loop, while PID gains take care of the outer loop. The global controller is tuned on a linear decoupled aircraft model. It is demonstrated that the achieved configuration guarantees satisfying performance also when applied to a complete nonlinear model affected by uncertainties and parametric perturbations. The third controller implemented is based on an existing nonlinear backstepping technique. A scheme for longitudinal and latero-directional control based on the combination of PID for the outer loop and backstepping for the inner loop is proposed. Satisfying results are achieved also when the nonlinear aircraft model is perturbed by parametric uncertainties. A confrontation among the three controllers shows that L1 and backstepping are comparable in terms of nominal and robust performance, with an advantage for L1, while the PID is always inferior. The backstepping controller is chosen for being implemented and tested on a real fixed-wing RC aircraft. Hardware-in-the-loop simulations validate its real-time control capability on the complete nonlinear model of the aircraft adopted for the tests, inclusive of sensors noise. An innovative microcontroller technology is employed as core of the autopilot system, it interfaces with sensors and servos in order to handle input/output operations and it performs the control law computation. Preliminary ground tests validate the suitability of the autopilot configuration. A limited number of flight tests is performed. Promising results are obtained for the control of longitudinal states, while latero-directional control still needs major improvements

An OpenEaagles Framework Extension for Hardware-in-the-Loop Swarm Simulation

Unmanned Aerial Vehicle (UAV) swarm applications, algorithms, and control strategies have experienced steady growth and development over the past 15 years. Yet, to this day, most swarm development efforts have gone untested and thus unimplemented. Cost of aircraft systems, government imposed airspace restrictions, and the lack of adequate modeling and simulation tools are some of the major inhibitors to successful swarm implementation. This thesis examines how the OpenEaagles simulation framework can be extended to bridge this gap. This research aims to utilize Hardware-in-the-Loop (HIL) simulation to provide developers a functional capability to develop and test the behaviors of scalable and modular swarms of autonomous UAVs in simulation with high confidence that these behaviors will prop- agate to real/live ight tests. Demonstrations show the framework enhances and simplifies swarm development through encapsulation, possesses high modularity, pro- vides realistic aircraft modeling, and is capable of simultaneously accommodating four hardware-piloted swarming UAVs during HIL simulation or 64 swarming UAVs during pure simulation