Optimal Control in Aircraft Trajectory Planning

Trajectory planning for autonomous and piloted aircraft must satisfy not only geometric constraints but also full six-degree-of-freedom dynamics, actuator limits, and mission- or safety-driven performance objectives. This thesis investigates optimal control as a refinement tool for trajectory-planning problems that are typically addressed using geometric or search-based methods. Two case studies are considered. The first addresses loss-of-thrust contingency landing for a fixed-wing aircraft, formulating a nonlinear optimal control problem that minimizes a population-density-weighted ground risk metric, with and without an explicit time penalty, subject to a six-degree-of-freedom Cessna 182 model and operational constraints. Initial trajectories are provided by discrete search and Dubins paths, and the refined solutions reduce risk and/or flight time while remaining dynamically feasible and trackable in closed-loop simulation. The second case study develops an energy-optimal trajectory generation framework for a Lift+Cruise QuadPlane small uncrewed aircraft system. Starting from spline-based reference paths, a six-degree-of-freedom QuadPlane model and power-based cost function are used to compute dynamically feasible trajectories that include hover--to--cruise mode transitions and multi-waypoint maneuvers. Results show consistent reductions in power and energy consumption, smoother attitude and angular-rate histories, and explicit characterization of trade-offs among energy use, maneuver aggressiveness, and operational constraints. Collectively, these case studies demonstrate how optimal control can bridge fast geometric planning and high-fidelity dynamics, supporting preflight design of risk-aware and energy-efficient trajectories for both contingency and nominal operations.