Experimentally validated model predictive controller for a hexacopter

In recent years, Unmanned Aerial Vehicles (UAVs) have become feasible solutions to various applications such as military flight missions, search and rescue operations, and geological mapping. The attitude control systems for UAVs need to be very robust to ensure safe and stable flights. To improve the stability beyond conventional control methods, a novel Model Predictive Control (MPC) formulation is proposed. In this paper, the mathematical derivation of the MPC controller is discussed. The novelty lies in the three-termed MPC cost function for attitude flight stability. The control strategy introduced in this paper is validated with experiments on a hexacopter in a custom fabricated experimental rig and with outdoor flight tests. The results show that the UAV is able to follow the operator's aggressive manoeuvring commands. Based on the data obtained, the MPC controller appears to be a promising control method to improve UAVs’ performance.