In this thesis, we describe a method for controlling the cycle-averaged velocity direction of a fixed-wing aircraft in an unpowered, helical descent. While the aircraft propulsion system is disabled, either intentionally or due to a failure, the aerodynamic control surfaces (aileron, elevator, and rudder) are assumed to be functional. Our approach involves two steps: (i) establishing a stable, steady, helical motion for which the control surfaces are not fully deflected and (ii) modulating the aircraft control surfaces about their nominal positions to ``slant'' the helical flight path in a desired direction relative to the atmosphere, whether to attain a desired impact location, to counter a steady wind, or both. The effectiveness of the control law was evaluated in numerical simulations of a general transport model (GTM). / Master of Science / When an unmanned aircraft is near an authorized airspace (a region of space where the aircraft is not authorized to fly) and experiences a failure such as loss of communication with the control tower,or failure of the GPS or propulsion system, then the aircraft is generally put into an aerodynamic flight termination. In this flight termination method, the aircraft propulsion system is switched off and the control surfaces (aileron, elevator and rudder) are fixed to induce a spin in the aircraft causing it to descend in a helical fashion. However, in the presence of external gusts the aircraft might drift into the unauthorized airspace; or once the aircraft is put into spin, one may want to be able to change the impact location to a safer place. To the best of our knowledge, there exist no control strategies to alter the impact location of the aircraft once it is put into spin and while is continues to spin. In this thesis we describe a method to do so. The aircraft impact location is altered by controlling the cycle-averaged velocity direction of a fixed-wing aircraft in an unpowered, helical descent. While the aircraft propulsion system is disabled, either intentionally or due to a failure, the aerodynamic control surfaces (aileron, elevator, and rudder) are assumed to be functional. Our approach involves two steps: (i) establishing a stable, steady, helical motion for which the control surfaces are not fully deflected and (ii) modulating the aircraft control surfaces about their nominal positions to “slant” the helical flight path in a desired direction relative to the atmosphere, whether to attain a desired impact location, to counter a steady wind, or both. The effectiveness of the control law was evaluated in numerical simulations of a general transport model (GTM).

M.Ing. / Linear control system theory is well developed and has lead to a number of control system types with well-defined design methods that can be applied to any linear system. Unfortunately, no system in nature is truly linear. As a result, such non-linear systems must be represented by a linear model that is accurate over some region of the operating states of the system. The success of linear control theory in commercial applications is testament to the fact that some types of systems can be adequately represented by a linear model. However, systems with time-varying dynamics or non-linearities such as input or operating state saturation cannot always be adequately controlled by linear control systems. For that reason, non-linear control techniques must be investigated. This project aims to investigate Non-linear Model Predictive Control theory and practical implementation in the context of developing an autopilot for an Unmanned Aerial Vehicle based on a miniature helicopter. A non-linear model of the dynamics of an X-Cell Spectra G radio-controlled helicopter was developed based on the existing literature. A number of experiments were performed to determine the parameters of this model. Significant future work exists in designing additional ground experiments since certain parameters are difficult to measure safely in the laboratory. Additional work to improve the accuracy of the model at high airspeeds, as well as incorporating a more accurate yaw dynamics model, is also required. Following this, a Non-linear Model Predictive Control autopilot was simulated using MATLAB®. The simulation tested the effects of control system parameters such as control horizon and sampling period, as well as the sensor noise susceptibility and its ability to handle wind as a random disturbance. The results determined adequate control system parameters for level flight as well as landing the helicopter under ideal conditions. Simulations in which sensor noise and wind were added showed that the control system is significantly affected by sensor noise and that it cannot hover in the presence of wind. A real-time implementation was not achieved during this work; however, several directions for future research have been discussed.