This thesis presents one of the first applications of H∞-optimization to the design of controllers for industrial problems. The system considered was an unstable helicopter model, obtained from a large nonlinear simulation (provided by the Royal Aircraft Establishment, Bedford) configured to represent future high performance helicopters. The problem was to design a full authority flight control system, to stabilize the aircraft and decouple the controlled inputs, thus reducing pilot workload. Robustness was a primary issue because of model uncertainty, particularly due to the omission from the design model of higher order rotor dynamics. The optimization problem was based on the minimization of sensitivity (for performance) and control output (for robustness) transfer functions. Simple weighting functions were found to be useful for examining the fundamental performance-versus-robustness trade-off, and to be more effective at shaping the closed loop transfer functions than LQG/LTR techniques. A controller designed for a 4-input, 6-output, 8-state linearized plant model was successfully implemented in a non-linear simulation with rotor dynamics. This stabilized the system and enabled good control for small variations about the design operating point. The 'standard problem', consisting of the plant augmented with weights, had 20 states; the controller had 18, which was much smaller than researchers had been predicting, and it is conjectured that all H∞-optimal controllers will have at most the same number of states as the defining 'standard problem'. An important improvement to the H∞-optimization solution process was the development of a numerically reliable algorithm to perform minimal realization. This algorithm solves for a truncated balanced realization of stable state-space systems that are arbitrarily close to being either uncontrollable and/or unobservable. Depending on the choice of partitioning of the Hankel singular values, it can be used to perform minimal realization, or model reduction, with a guaranteed L∞ error bound.

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Miniature Unmanned Aerial Vehicles (UAVs) are currently being researched for a wide range of tasks, including search and rescue, surveillance, reconnaissance, traffic monitoring, fire detection, pipe and electrical line inspection, and border patrol to name only a few of the application domains. Although small / miniature UAVs, including both Vertical Takeoff and Landing (VTOL) vehicles and small helicopters, have shown great potential in both civilian and military domains, including research and development, integration, prototyping, and field testing, these unmanned systems / vehicles are limited to only a handful of university labs. For VTOL type aircraft the number is less than fifteen worldwide! This lack of development is due to both the extensive time and cost required to design, integrate and test a fully operational prototype as well as the shortcomings of published materials to fully describe how to design and build a "complete" and "operational" prototype system. This dissertation overcomes existing barriers and limitations by describing and presenting in great detail every technical aspect of designing and integrating a small UAV helicopter including the on-board navigation controller, capable of fully autonomous takeoff, waypoint navigation, and landing. The presented research goes beyond previous works by designing the system as a testbed vehicle. This design aims to provide a general framework that will not only allow researchers the ability to supplement the system with new technologies but will also allow researchers to add innovation to the vehicle itself. Examples include modification or replacement of controllers, updated filtering and fusion techniques, addition or replacement of sensors, vision algorithms, Operating Systems (OS) changes or replacements, and platform modification or replacement. This is supported by the testbed's design to not only adhere to the technology it currently utilizes but to be general enough to adhere to a multitude of technology that have yet to be tested. This research will allow labs without the proper expertise to build a safe and reliable vehicle that can provide them access to real world data thus increasing the effectiveness and validity of their research. It will also allow researchers working in simulation to quickly enter into UAV development without utilizing thousands of man hours to create an unmanned vehicle. The presented research is designed to benefit the entire UAV researching community by allowing in depth access to an area of research that has been typically classified as too expensive and too time consuming to enter.

Small Unmanned Aircraft Systems (UAS) are considered the stepping stone for the integration of civil unmanned vehicles in the National Airspace System (NAS) because of their low cost and risk. Such systems are aimed at a variety of applications including search and rescue, surveillance, communications, traffic monitoring and inspection of buildings, power lines and bridges. Amidst these systems, small helicopters play an important role because of their capability to hold a position, to maneuver in tight spaces and to take off and land from virtually anywhere. Nevertheless civil adoption of such systems is minimal, mostly because of regulatory problems that in turn are due to safety concerns. This dissertation examines the risk to safety imposed by UAS in general and small helicopters in particular, focusing on accidents resulting in a ground impact. To improve the performance of small helicopters in this area, the use of autonomous autorotation is proposed. This research goes beyond previous work in the area of autonomous autorotation by developing an on-line, model-based, real-time controller that is capable of handling constraints and different cost functions. The approach selected is based on a non-linear model-predictive controller, that is augmented by a neural network to improve the speed of the non-linear optimization. The immediate benefit of this controller is that a class of failures that would otherwise result in an uncontrolled crash and possible injuries or fatalities can now be accommodated. Furthermore besides simply landing the helicopter, the controller is also capable of minimizing the risk of serious injury to people in the area. This is accomplished by minimizing the kinetic energy during the last phase of the descent. The presented research is designed to benefit the entire UAS community as well as the public, by allowing for safer UAS operations, which in turn also allow faster and less expensive integration of UAS in the NAS.

This dissertation investigates the application of Genetic Algorithms (GA) and Simulated Annealing (SA) based search techniques to the problem of deriving an auto-pilot that can emulate a human operator or other controller flying a Small unmanned Helicopter (SH). A Helicopter is a type of Vertical Take Off and Landing Vehicle (VTOL). The maneuvers are none aggressive, mild maneuvers, that include u-turns, ascending spirals and other none extreme flight paths. The pilot of the helicopter is a Fuzzy logic Controller (FC) pilot; it is assumed that the pilot executes the maneuvers with skill and precision. The FC pilot is given set- points (points in space) that represent a path/flight maneuver and is expected to follow them as closely as possible. Input/Output data is then collected from the FC pilot executing maneuvers in real time. The collected data include control signals from the FC pilot to the SH and the resulting output signals from the SH that include time, x, y, z coordinates and yaw (the angle of the SH relative to the x, y axis). The Genetic Algorithm/Simulated Annealing based search algorithm attempts to generate a set of mathematical formulas that best map the collected data. The search algorithm presented in this dissertation was implemented in Java and has a JSP (Java Server Pages) graphical user interface. The results obtained show that the search technique developed; termed Genetic Algorithm / Simulated Annealing controller or (GA/SA) controller allows for the derivation of accurate SH control equations. The results include performance quantification of the algorithm in the derivation phase and the testing phase. Graphs are included; they demonstrate the accuracy and path data of the GA/SA controller as compared to the FC pilot and other controllers. The final results showing the formulas found are also included. A technique was also developed during this dissertation to encode the genetic strings that represent the candidate formulas during the search. This technique allowed the combination of strings to yield new formulas that are valid. The results can be used by other investigators to expand the complexity of the formulas generated during the search. The technique has advantages such as the ability to operate in open-loop conditions and is able to fly the SH without the need for set-point data and without the need for GPS or some other location determination technology. The technique may be used as a backup controller that can take over control of a helicopter in case the main controller is unable to function due to a GPS malfunction or another situation where accurate positioning data cannot be obtained.

The main characteristic attribute of the rotorcraft is the use of rotary wings to produce the thrust force necessary for motion. Therefore, rotorcraft have an advantage relative to fixed wing aircraft because they do not require any relative velocity to produce aerodynamic forces. Rotorcraft have been used in a wide range of missions of civilian and military applications. Particular interest has been concentrated in applications related to search and rescue in environments that impose restrictions to human presence and interference. The main representative of the rotorcraft family is the helicopter. Small scale helicopters retain all the flight characteristics and physical principles of their full scale counterpart. In addition, they are naturally more agile and dexterous compared to full scale helicopters. Their flight capabilities, reduced size and cost have monopolized the attention of the Unmanned Aerial Vehicles research community for the development of low cost and efficient autonomous flight platforms. Helicopters are highly nonlinear systems with significant dynamic coupling. In general, they are considered to be much more unstable than fixed wing aircraft and constant control must be sustained at all times. The goal of this dissertation is to investigate the challenging design problem of autonomous flight controllers for small scale helicopters. A typical flight control system is composed of a mathematical algorithm that produces the appropriate command signals required to perform autonomous flight. Modern control techniques are model based, since the controller architecture depends on the dynamic description of the system to be controlled. This principle applies to the helicopter as well, therefore, the flight control problem is tightly connected with the helicopter modeling. The helicopter dynamics can be represented by both linear and nonlinear models of ordinary differential equations. Theoretically, the validity of the linear models is restricted in a certain region around a specific operating point. Contrary, nonlinear models provide a global description of the helicopter dynamics. This work proposes several detailed control designs based on both dynamic representations of small scale helicopters. The controller objective is for the helicopter to autonomously track predefined position (or velocity) and heading reference trajectories. The controllers performance is evaluated using X-Plane, a realistic and commercially available flight simulator.

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