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

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. Thesis Supervisor: Dr. Leena Singh Title: Senior Member of the Technical Staff, The Charles Stark Draper Laboratory, Inc. Thesis Supervisor: Dr. Brent Appleby Title: Lecturer, Department of Aeronautics and Astronautics Massachusetts Institute of Technology Principle Member of the Technical Staff, The Charles Stark Draper Laboratory, Inc.