Graduation Semester and Year
Summer 2026
Language
English
Document Type
Thesis
Degree Name
Master of Science in Aerospace Engineering
Department
Mechanical and Aerospace Engineering
First Advisor
Dr. Kamesh Subbarao
Abstract
Autonomous unmanned aerial vehicles (UAVs) operating in contested environments must
complete mission objectives while avoiding restricted regions, radar exposure, and pos-
sible interception. This thesis develops a MATLAB-based simulation framework for
two-dimensional UAV mission planning under threat using model predictive control and
proportional-navigation chasers. The mission requires the UAV to travel from a start
location to a goal while visiting required checkpoints and avoiding no-fly zones and radar
regions. A chaser attempts to intercept the UAV using either a basic pure-pursuit-style
law or a proportional-navigation guidance law.
The framework integrates environment generation, augmented visibility-graph rout-
ing, waypoint management, UAV kinematic simulation, chaser guidance models, model
predictive control, plotting, animation, metric logging, and a graphical user interface for
demonstration. Four full-mission cases are evaluated: route-following guidance with a
basic chaser, route-following guidance with a proportional-navigation chaser, model pre-
dictive control guidance with a basic chaser, and model predictive control guidance with
a proportional-navigation chaser. Additional parameter sweeps examine the effects of
proportional-navigation gain, chaser-to-UAV speed ratio, and MPC prediction horizon.
The results show that proportional navigation improves chaser effectiveness compared
with the basic pursuit law. In the final full-mission comparison, the route-following
UAV avoids the basic chaser but is captured by the proportional-navigation chaser near
the end of the mission. In contrast, the model-predictive-control-guided UAV avoids
captureagainstbothchasertypes. Theresultsalsoshowthattheproportional-navigation
chaser remains more dangerous under model predictive control because it produces a
smaller miss distance than the basic chaser. The model predictive control horizon study
demonstrates that longer prediction horizons improve safety-related metrics but increase
computation time. Overall, the framework provides a modular and repeatable simulation
platform for studying UAV guidance, threat avoidance, and pursuit-evasion trade-offs.
Keywords
Unmanned Aerial Vehicles (UAVs); Model Predictive Control (MPC); Autonomous Mission Planning; Proportional Navigation; Pursuit-Evasion
Disciplines
Aeronautical Vehicles | Multi-Vehicle Systems and Air Traffic Control | Navigation, Guidance, Control and Dynamics
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Ozcelik, Mehmet B., "Autonomous UAV Mission Planning Under Threat Using Model Predictive Control with Proportional-Navigation Pursuers" (2026). Mechanical and Aerospace Engineering Theses. 7.
https://mavmatrix.uta.edu/mechaerospace_theses2/7
Included in
Aeronautical Vehicles Commons, Multi-Vehicle Systems and Air Traffic Control Commons, Navigation, Guidance, Control and Dynamics Commons