Comparative Synthesis of Finite and Infinite Optimal Controllers for a Quadcopter with Input Constraints

Recently, unmanned aerial vehicles have been received a great attention in many applications, e.g., exploration, environmental monitoring, and search missions. These tasks need to implement a proper controller to follow a flight path and fulfills these missions with optimal performance, e.g., minimum energy consumption and tracking error. This work propose a comparative synthesis between finite and infinite optimization based controllers. These controllers are implemented to achieve accurate tracking of altitude and attitude commands for quadcopter platform. In the simulation results, we compare the controllers performance considering the vehicle dynamics and input constraints. The results of this study indicate that model predictive controller tracks efficiently the desired flight path with robust performance satisfying the vehicle constraints compared to other optimal controllers.