The problem of aircraft control allocation is that of finding a combination of control positions that cause the resulting aircraft moments to most closely satisfy a given desired moment vector. The problem is easily solved for the case of an aircraft having three control surfaces, each of which primarily imparts moments in each of the three aircraft axes. In this simple case, the solution to the control allocation problem is uniquely determined. However, many current and future aircraft designs employ a larger set of control effectors, resulting in a control redundancy in the sense that more than one combination of control positions can produce the same desired moment. When taking into account both the position and rate constraints of the control effectors, the problem is significantly more complex. Constrained moment-rate control allocation guarantees a control solution that can achieve every possible moment that is physically realizable by the aircraft. Addressed here is the real-time performance of moment-rate constrained control allocation as tested on a desktop simulation. Issues that were deemed interesting or potentially problematic in earlier batch simulation, such as control chattering due to restoring and apparent control wind-up, are investigated and an evaluation is made of the overall feasibility of these algorithms. The purpose of the research is to confirm that the results obtained from batch simulation testing are also valid using maneuvers representative of real-time flight and representative simulation frame sizes, and to uncover potential problems not observed in batch simulation. NOTE: An updated copy of this ETD was added on 05/29/2013. / Master of Science

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A comparison of two control allocation methods is performed utilizing the F-15 ACTIVE research vehicle. The control allocator currently implemented on the aircraft is replaced in the simulation with a control allocator that accounts for both control effector positions and rates. Validation of the performance of this Moment Rate Allocation scheme through real-time piloted simulations is desired for an aircraft with a high fidelity control law and a larger control effector suite. A more computationally efficient search algorithm that alleviates the timing concerns associated with the early work in Direct Allocation is presented. This new search algorithm, deemed the Bisecting, Edge-Search Algorithm, utilizes concepts derived from pure geometry to efficiently determine the intersection of a line with a convex faceted surface. Control restoring methods, designed to drive control effectors towards a ``desired" configuration with the control power that remains after the satisfaction of the desired moments, are discussed. Minimum-sideforce restoring is presented. In addition, the concept of variable step size restoring algorithms is introduced and shown to yield the best tradeoff between restoring convergence speed and control chatter reduction. Representative maneuvers are flown to evaluate the control allocator's ability to perform during realistic tasks. An investigation is performed into the capability of the control allocators to reconfigure the control effectors in the event of an identified control failure. More specifically, once the control allocator has been forced to reconfigure the controls, an investigation is undertaken into possible performance degradation to determine whether or not the aircraft will still demonstrate acceptable flying qualities. A direct comparison of the performance of each of the two control allocators in a reduced global position limits configuration is investigated. Due to the highly redundant control effector suite of the F-15 ACTIVE, the aircraft, utilizing Moment Rate Allocation, still exhibits satisfactory performance in this configuration. The ability of Moment Rate Allocation to utilize the full moment generating capabilities of a suite of controls is demonstrated. NOTE: (02/2011) An updated copy of this ETD was added after there were patron reports of problems with the file. / Master of Science

With the rapid proliferation of small unmanned aircraft systems (UAS), the risk of mid-air collisions is growing, as is the risk associated with the malicious use of these systems. The airborne detect-and-avoid (ABDAA) problem and the counter-UAS problem have similar sensing requirements for detecting and tracking airborne threats. In this dissertation, two image-based sensing methods are merged to mimic human vision in support of counter-UAS applications. In the proposed sensing system architecture, a ``peripheral vision'' camera (with a fisheye lens) provides a large field-of-view while a ``central vision'' camera (with a perspective lens) provides high resolution imagery of a specific object. This pair form a heterogeneous stereo vision system that can support range resolution. A novel peripheral-central vision (PCV) system to detect, localize, and classify an airborne threat is first introduced. To improve the developed PCV system's capability, three novel algorithms for the PCV system are devised: a model-based path prediction algorithm for fixed-wing unmanned aircraft, a multiple threat scheduling algorithm considering not only the risk of threats but also the time required for observation, and the heterogeneous stereo-vision optimal placement (HSOP) algorithm providing optimal locations for multiple PCV systems to minimize the localization error of threat aircraft. The performance of algorithms is assessed using an experimental data set and simulations. / Doctor of Philosophy / With the rapid proliferation of small unmanned aircraft systems (UAS), the risk of mid-air collisions is growing, as is the risk associated with the malicious use of these systems. The sensing technologies for detecting and tracking airborne threats have been developed to solve these UAS-related problems. In this dissertation, two image-based sensing methods are merged to mimic human vision in support of counter-UAS applications. In the proposed sensing system architecture, a ``peripheral vision'' camera (with a fisheye lens) provides a large field-of-view while a ``central vision'' camera (with a perspective lens) provides high resolution imagery of a specific object. This pair enables estimation of an object location using the different viewpoints of the different cameras (denoted as ``heterogeneous stereo vision.'') A novel peripheral-central vision (PCV) system to detect an airborne threat, estimate the location of the threat, and determine the threat class (e.g. aircraft, bird) is first introduced. To improve the developed PCV system's capability, three novel algorithms for the PCV system are devised: an algorithm to predict the future path of an fixed-wing unmanned aircraft, an algorithm to decide an efficient observation schedule for multiple threats, and an algorithm that provides optimal locations for multiple PCV systems to estimate the threat position better. The performance of algorithms is assessed using an experimental data set and simulations.

Small unmanned aircraft systems (UAS) must be able to detect and avoid conflicting traffic, an especially challenging task when the threat is another small UAS. Collision avoidance requires trajectory prediction and the performance of a collision avoidance system can be improved by extending the prediction horizon. In this thesis, an algorithm for predicting the trajectory of a small, fixed-wing UAS using an estimate of its orientation and for maneuvering around the threat, if necessary, is developed. A computer vision algorithm locates specific feature points of a threat aircraft in an image and the POSIT algorithm uses these feature points to estimate the pose (position and attitude) of the threat. A sequence of pose estimates is then used to predict the trajectory of the threat aircraft and to avoid colliding with it. To assess the algorithm's performance, the predictions are compared with predictions based solely on position estimates for a variety of encounter scenarios. Simulation and experimental results indicate that trajectory prediction using orientation estimates provides quicker response to a change in the threat aircraft trajectory and results in better prediction and avoidance performance. / Master of Science / Small unmanned aircraft systems (UAS) must be able to detect and avoid conflicting traffic, an especially challenging task when the threat is another small UAS. Collision avoidance requires trajectory prediction and the performance of a collision avoidance system can be improved by extending the prediction horizon. In this thesis, an algorithm for predicting the trajectory of a small, fixed-wing UAS using an estimate of its orientation and for maneuvering around the threat, if necessary, is developed. A computer vision algorithm locates specific feature points of a threat aircraft in an image and a pose (position and attitude) estimation algorithm uses these feature points to estimate the pose of the threat. A sequence of pose estimates is then used to predict the trajectory of the threat aircraft and to avoid colliding with it. To assess the algorithm’s performance, the predictions are compared with predictions based solely on position estimates for a variety of encounter scenarios. Simulation and experimental results indicate that trajectory prediction using orientation estimates provides quicker response to a change in the threat aircraft trajectory and results in better prediction and avoidance performance.

This dissertation integrates in a single mathematical formulation the disciplines pertinent to the flight of flexible aircraft, namely, analytical dynamics, structural dynamics, aerodynamics and controls. The unified formulation is based on fundamental principles and incorporates in a natural manner both rigid body motions of the aircraft as a whole and elastic deformations of the flexible components (fuselage, wing and empennage), as well as the aerodynamic, propulsion, gravity and control forces. The aircraft motion is described in terms of three translations (forward motion, sideslip and plunge) and three rotations (roll, pitch and yaw) of a reference frame attached to the undeformed fuselage, and acting as aircraft body axes, and elastic displacements of each of the flexible components relative to corresponding body axes. The mathematical formulation consists of six ordinary differential equations for the rigid body motions and one set of ordinary differential equations for each elastic displacement. A perturbation approach permits division of the problem into a nonlinear "zero-order Problem" for the rigid body motions, corresponding to flight dynamics, and a linear "first-order problem" for the elastic deformations and perturbations in the rigid body translations and rotations, corresponding to "extended aeroelasticity." Due to computational speed advantages, the aerodynamic forces are derived by means of strip theory. The control forces for the flight dynamics problem are obtained by an "inverse" process. On the other hand, the feedback control forces for the extended aeroelasticity problem are derived by means of LQG theory. A numerical example corresponding to steady level flight and steady level turn maneuver is included. / Ph. D.

abstract: The objective of this work is to develop a Stop-Rotor Multimode UAV. This UAV is capable of vertical take-off and landing like a helicopter and can convert from a helicopter mode to an airplane mode in mid-flight. Thus, this UAV can hover as a helicopter and achieve high mission range of an airplane. The stop-rotor concept implies that in mid-flight the lift generating helicopter rotor stops and rotates the blades into airplane wings. The thrust in airplane mode is then provided by a pusher propeller. The aircraft configuration presents unique challenges in flight dynamics, modeling and control. In this thesis a mathematical model along with the design and simulations of a hover control will be presented. In addition, the discussion of the performance in fixed-wing flight, and the autopilot architecture of the UAV will be presented. Also presented, are some experimental "conversion" results where the Stop-Rotor aircraft was dropped from a hot air balloon and performed a successful conversion from helicopter to airplane mode. / Dissertation/Thesis / M.S.Tech Mechanical Engineering 2011

This thesis deals with high angle of attack behaviour of a generic delta wing model aircraft. A high angle of attack wind tunnel database has been generated for this aircraft and based upon the bifurcation analysis of the data and the results of extensive simulations, it has been shown in the thesis that the post stall behaviour of this aircraft is both unstable and unpredictable. Unpredictability of aircraft behaviour arises from the fact that the aircraft response is oscillatory and divergent; the aircraft state trajectories do not settle down to any stable limit set and very often exceed valid aerodynamic database limits. This unpredictability of behaviour raises a major difficulty in the design of a procedure to recover the aircraft to normal flight regime in case the aircraft stalls and departs accidentally. A new methodology has been presented in this thesis to recover such unstable aircraft. In this methodology, a nonlinear controller is first designed at high angles of attack. This controller is connected by the pilot after the departure of the aircraft and the controller drives the aircraft to a well-defined spin condition. Thus, the controller makes the post stall aircraft behaviour predictable. Then a set of automatic recovery inputs is designed to reduce aircraft rotations and to lower the angle of attack. The present aircraft model is unstable at low angle of attack flight conditions as well and therefore to stabilize the aircraft to a low angle of attack level flight, another controller is designed. The high angle of attack controller is disconnected and the low angle of attack controller is connected automatically during the recovery process. The entire methodology is tested using extensive non-linear six degree-of-freedom simulations and the efficacy of the technique is established. The nonlinear controller that stabilizes the aircraft to a spin condition is designed using feedback linearization. The stability of a closed loop system obtained using feedback linearization is determined by the stability of the zero dynamics of the open loop plant. It has been shown in literature that the eigenvalues of the linearized zero dynamics are the same as the transmission zeros of the linearized plant at the equilibrium point. It is also well known that the location of transmission zeros of a linear system can be changed by the choice of outputs. In this thesis it is shown that if it is possible to reassign the outputs, then the feedback linearization based design for a linear system becomes very similar to a controller design for eigenvalue assignment. This thesis presents a new two-step procedure to obtain a locally stable and optimally robust closed loop system using feedback linearization. In the first step of this procedure optimal locations of the transmission zeros are found and in the second step, optimal outputs are constructed to place the system transmission zeros at these locations. The same outputs can then be used to construct nonlinear feedback for the nonlinear system and the resultant closed loop system is guaranteed to be locally robustly stable. The high angle of attack controller is designed using this procedure and its performance is presented in the thesis. The stabilized spin equilibrium point of the closed loop system is also shown to have a large domain of attraction. Having designed a locally robust stabilizing controller, the thesis addresses the problem of the evaluation of robustness of the stability of the equilibrium point in a nonlinear framework. The thesis presents a general method to construct bounds on the additive perturbations of the system vector field over a large region in the domain of attraction of a stable equilibrium point using Lyapunov functions. If the system perturbations lie within these bounds, the system is guaranteed to be stable. The thesis first proposes a method to numerically construct a Lyapunov function over a large region in the domain of attraction. In this method a sequence of Lyapunov functions are constructed such that each function in the sequence gives a larger estimate of the domain of attraction than the previous one. The seminal idea for this method is obtained from the existing literature and this idea is considerably generalized. Using this method, it is possible to numerically obtain a Lyapunov function value at each point in the domain of attraction, but the Lyapunov function does not have an analytical form. Hence, it is proposed to represent this function using neural networks. The thesis then discusses a new method to construct perturbation bounds. It is shown that the perturbation bounds obtained over a large region in the domain of attraction using a single Lyapunov function is too conservative. Using the concept of sequence of Lyapunov functions, the thesis proposes three methods to obtain the least conservative bounds for an initial local Lyapunov function. These general ideas are then applied to the aircraft example and the bounds on the perturbation of the aerodynamic database are presented.