Autonomous aerobatic maneuvering of miniature helicopters

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2003.Includes bibliographical references (p. 83-86).In this thesis, I present an experimentally proven control methodology for the autonomous execution of aerobatic maneuvers with small-scale helicopters, and a low-order dynamic model which adequately describes a miniature helicopter in a wide range of flight conditions, including aerobatics. The control laws consist of steady-state trim trajectory controllers, used prior to, and upon exit from the maneuvers; and a maneuver execution logic inspired by human pilot strategies. In order to test the control laws, a miniature helicopter was outfitted with a custom digital avionics system, and a hardware-in-the-loop simulation was developed. The logic was tested with several aerobatic maneuvers and maneuver sequences, which demonstrated smooth maneuver entry, automatic recovery to a steady-state trim trajectory, and robustness of the trim-trajectory control system toward measurement and modeling errors. Based on these results, I further propose a simplified hybrid model for a helicopter under such closed loop control. The model can be utilized in the development of computationally tractable motion-planning algorithms for agile vehicles.by Vladislav Gavrilets.Ph.D

Guidance and control using model predictive control for low altitude real-time terrain following flight

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2004.Includes bibliographical references (p. 123-125).This thesis presents the design and implementation of a model predictive control based trajectory optimization method for Nap-of-the-Earth (NOE) flight. A NOE trajectory reference is generated over a subspace of the terrain. It is then inserted into the cost function and the resulting trajectory tracking error term is weighted for more precise longitudinal tracking than lateral tracking through the introduction of the TF/TA ratio. The TF/TA ratio, control effort penalties and MPC prediction horizon are tuned for this application via simulation and eigenvalue analysis for stability and performance. Steps are taken to reduce complexity in the optimization problem including perturbational linearization in the prediction model generation and the use of control basis functions which are analyzed for their trade-off between approximation of the optimal cost/solution and reduction of the optimization complexity. Obstacle avoidance including preclusion of ground collision is accomplished through the establishment of hard state constraints. These state constraints create a 'safe envelope' within which the optimal trajectory can be found. Results over a variety of sample terrains are provided to investigate the sensitivity of tracking performance to nominal velocities. The mission objective of low altitude and high speed was met satisfactorily without terrain or obstacle collision, however, methods to preclude or deal with infeasibility must be investigated as terrain severity (measured by commanded flight path angle) is increased past 30 degrees or speed is increased to and past 30 knots.by Tiffany Rae Lapp.S.M

Robust hybrid control for autonomous vehicle motion planning

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2001.Includes bibliographical references (p. 141-150).This dissertation focuses on the problem of motion planning for agile autonomous vehicles. In realistic situations, the motion planning problem must be solved in real-time, in a dynamic and uncertain environment. The fulfillment of the mission objectives might also require the exploitation of the full maneuvering capabilities of the vehicle. The main contribution of the dissertation is the development of a new computational and modelling framework (the Maneuver Automaton), and related algorithms, for steering underactuated, nonholonomic mechanical systems. The proposed approach is based on a quantization of the system's dynamics, by which the feasible nominal system trajectories are restricted to the family of curves that can be obtained by the interconnection of suitably defined primitives. This can be seen as a formalization of the concept of "maneuver", allowing for the construction of a framework amenable to mathematical programming. This motion planning framework is applicable to all time-invariant dynamical systems which admit dynamic symmetries and relative equilibria. No other assumptions are made on the dynamics, thus resulting in exact motion planning techniques of general applicability. Building on a relatively expensive off-line computation phase, we provide algorithms viable for real-time applications. A fundamental advantage of this approach is the ability to provide a mathematical foundation for generating a provably stable and consistent hierarchical system, and for developing the tools to analyze the robustness of the system in the presence of uncertainty and/or disturbances.(cont.) In the second part of the dissertation, a randomized algorithm is proposed for real-time motion planning in a dynamic environment. By employing the optimal control solution in a free space developed for the maneuver automaton (or for any other general system), we present a motion planning algorithm with probabilistic convergence and performance guarantees, and hard safety guarantees, even in the face of finite computation times. The proposed methodologies are applicable to a very large class of autonomous vehicles: throughout the dissertation, examples, simulation and experimental results are presented and discussed, involving a variety of mechanical systems, ranging from simple academic examples and laboratory setups, to detailed models of small autonomous helicopters.by Emilio Frazzoli.Ph.D