Frequency-Domain System Identification for Unmanned Helicopters from Flight Data

Developing accurate and realistic models for Unmanned Aerial Vehicles (UAVs) is a central task in effective controller design, autopilot design, and simulation model validation. System identification methods have been extensively used as reliable and less expensive alternatives for conventional analytical modeling for large-scale aircraft in the past. Yet, there is limited work on the identification of mathematical models for small-scale unmanned helicopters. This thesis focuses on development of a system identification tool for rotary-wing UAVs based on frequency-domain non-parametric and parametric identification methods. The tool, which is designed to be embedded in the computer simulation software available for a UAV platform, employs nonlinear parameter estimation and optimization techniques with the purpose of predicting dominant dynamics of the UAV from measured responses and controls. The real flight data acquired from the testbed have been used for testing and verifying the developed system identification tool. The testbed is a commercially available radio-controlled helicopter, Trex-700, equipped with MP2128G2Heli MicroPilot autopilot, and the flight tests are conducted by MicroPilot in hover regime to excite attitude dynamics of the vehicle. The identification results using the developed tool are validated with CIFER framework which is a highly reliable tool in aircraft system identification. The results demonstrate excellent prediction capability of the developed tool for model identification of the testing UAV platform

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

Experimental study on circularly towed aerial tethers

Understanding the behaviour of towed objects is key in many disciplines. For example, towed underwater equipment for geological survey, or space applications such as “slingshot” deployment of satellites. In the airborne case, the free end of a long, circularly towed cable will become almost stationary below the centre of the tow vehicle circle. This near-stationary state offers promise of a payload delivery and retrieval technique. Many contributions have been made of modelling and analysis methods which have provided solid understanding of the behaviour of an ideal airborne, circularly-towed system. Fewer contributions exist of experimental results. This Thesis responds to that lack by documenting a series of experiments conducted by the author, which specifically explored the behaviour of small scale systems and a single full scale system. In the course of the experimental program, several behaviours, not predicted by analytical work to date, were observed. One was further explored and also analysed, using an existing model. This demonstrated that the model used could yield results consistent with the observed behaviour. Observation of these unpredicted behaviours has identified several areas where further work is required, in order to move closer to a practical realisation of the payload delivery concept. The key contributions of this Thesis are the identification of previously unknown tether behaviours and experimental data which can be used for verification of existing models

Collision Avoidance for Unmanned Aerial Vehicles

Master'sMASTER OF ENGINEERIN

Navigation and autonomy of soaring unmanned aerial vehicles

The use of Unmanned Aerial Vehicles (UAV) has exploded over the last decade with the constant need to reduce costs while maintaining capability. Despite the relentless development of electronics and battery technology there is a sustained need to reduce the size and weight of the on-board systems to free-up payload capacity. One method of reducing the energy storage requirement of UAVs is to utilise naturally occurring sources of energy found in the atmosphere. This thesis explores the use of static and semi-dynamic soaring to extract energy from naturally occurring shallow layer cumulus convection to improve range, endurance and average speed. A simulation model of an X-Models XCalibur electric motor-glider is used in combination with a refined 4D parametric atmospheric model to simulate soaring flight. The parametric atmospheric model builds on previous successful models with refinements to more accurately describe the weather in northern Europe. The implementation of the variation of the MacCready setting is discussed. Methods for generating efficient trajectories are evaluated and recommendations are made regarding implementation. For micro to small UAVs to be able to track the desired trajectories a highly accurate Attitude Heading Reference System (AHRS) is needed. Detailed analysis of the practical implementation of advanced attitude determination is used to enable optimal execution of the trajectories generated. The new attitude determination methods are compared to existing Kalman and complimentary type filters. Analysis shows the methods developed are capable of providing accurate attitude determination with extremely low computational requirements, even during extreme manoeuvring. The new AHRS techniques reduce the need for powerful on-board microprocessors. This new AHRS technique is used as a foundation to develop a robust navigation filter capable of providing improved drift performance, over traditional filters, in the temporary absence of global navigation satellite information. All these algorithms have been verified by flight tests using a mixture of manned and unmanned aerial vehicles and avionics developed specifically for this thesis

An application of mu-synthesis for control of a small air vehicle and simulation results

This paper discusses a nonlinear robust control design procedure to micro air vehicle that combines the singular value (µ) and µ-synthesis technique, which overcomes structured uncertainty of the control plant and is valid over the entire flight envelope. The uncertainty model consists with multiplicative plug-in dynamics disturbances and parametric uncertainty. The uncertainty is conducted with the aircraft aerodynamics characteristics and parameters. These uncertainties are bounded in size based on wind tunnel experiments, flight test and analytical calculations. Furthermore, these investigations allow us to obtain the linearized model of the aircraft called here nominal model

Vision-based control of near-obstacle flight

This paper presents a novel control strategy, which we call optiPilot, for autonomous flight in the vicinity of obstacles. Most existing autopilots rely on a complete 6-degree-of-freedom state estimation using a GPS and an Inertial Measurement Unit (IMU) and are unable to detect and avoid obstacles. This is a limitation for missions such as surveillance and environment monitoring that may require near-obstacle flight in urban areas or mountainous environments. OptiPilot instead uses optic flow to estimate proximity of obstacles and avoid them. Our approach takes advantage of the fact that, for most platforms in translational flight (as opposed to near-hover flight), the translatory motion is essentially aligned with the aircraft main axis. This property allows us to directly interpret optic flow measurements as proximity indications. We take inspiration from neural and behavioural strategies of flying insects to propose a simple mapping of optic flow measurements into control signals that requires only a lightweight and power-efficient sensor suite and minimal processing power. In this paper, we first describe results obtained in simulation before presenting the implementation of optiPilot on a real flying platform equipped only with lightweight and inexpensive optic computer mouse sensors, MEMS rate gyroscopes and a pressure-based airspeed sensor. We show that the proposed control strategy not only allows collision-free flight in the vicinity of obstacles, but is also able to stabilise both attitude and altitude over flat terrain. These results shed new light on flight control by suggesting that the complex sensors and processing required for 6 degree-of-freedom state estimation may not be necessary for autonomous flight and pave the way toward the integration of autonomy into current and upcoming gram-scale flying platform

Mining Aircraft Telemetry Data With Evolutionary Algorithms

The Ganged Phased Array Radar - Risk Mitigation System (GPAR-RMS) was a mobile ground-based sense-and-avoid system for Unmanned Aircraft System (UAS) operations developed by the University of North Dakota. GPAR-RMS detected proximate aircraft with various sensor systems, including a 2D radar and an Automatic Dependent Surveillance - Broadcast (ADS-B) receiver. Information about those aircraft was then displayed to UAS operators via visualization software developed by the University of North Dakota. The Risk Mitigation (RM) subsystem for GPAR-RMS was designed to estimate the current risk of midair collision, between the Unmanned Aircraft (UA) and a General Aviation (GA) aircraft flying under Visual Flight Rules (VFR) in the surrounding airspace, for UAS operations in Class E airspace (i.e. below 18,000 feet MSL). However, accurate probabilistic models for the behavior of pilots of GA aircraft flying under VFR in Class E airspace were needed before the RM subsystem could be implemented. In this dissertation the author presents the results of data mining an aircraft telemetry data set from a consecutive nine month period in 2011. This aircraft telemetry data set consisted of Flight Data Monitoring (FDM) data obtained from Garmin G1000 devices onboard every Cessna 172 in the University of North Dakota\u27s training fleet. Data from aircraft which were potentially within the controlled airspace surrounding controlled airports were excluded. Also, GA aircraft in the FDM data flying in Class E airspace were assumed to be flying under VFR, which is usually a valid assumption. Complex subpaths were discovered from the aircraft telemetry data set using a novel application of an ant colony algorithm. Then, probabilistic models were data mined from those subpaths using extensions of the Genetic K-Means (GKA) and Expectation- Maximization (EM) algorithms. The results obtained from the subpath discovery and data mining suggest a pilot flying a GA aircraft near to an uncontrolled airport will perform different maneuvers than a pilot flying a GA aircraft far from an uncontrolled airport, irrespective of the altitude of the GA aircraft. However, since only aircraft telemetry data from the University of North Dakota\u27s training fleet were data mined, these results are not likely to be applicable to GA aircraft operating in a non-training environment