Fault Tolerant Control of Unmanned Aerial Vehicles Using Nonlinear Model Predictive Control

Abstract

Within the scope of this thesis, a fault tolerant controller is designed to achieve a safe flight of the aircraft under the failure scenarios which might occur during a flight operation. A six degree of freedom aircraft model have been developed. To overcome the different types of failures, nonlinear model predictive control (NMPC) is implemented in the 6-DoF aircraft model. In particular, the thesis focuses on modeling and simulating the UAV model performance under three distinct failure scenarios: aileron jamming, rudder jamming, and a decrease in elevator effectiveness. Statistical data and decision-making rule algorithms are applied to the model to detect failure during the simulation. NMPC is designed separately for longitudinal motion and lateral-directional motion. Extensive simulations have examined the NMPC behavior under the fault scenarios. The outcomes of the thesis exhibit the reliability and robustness of the proposed approach in handling various fault situations, contributing to the progress of unmanned aerial vehicle control systems.