Design and Simulation of a Model Predictive Control System Navigation of a Drone in Confined Spaces

Abstract

In the context of GPS-denied confined spaces, such as underground mining, the use of drones equipped with optical sensors is proposed to inspect and detect potential hazards. The aim of this research is to develop a robust controller that can effectively mitigate external disturbances and sensor measurement errors. In this paper, a hierarchical control structure based on two Model Predictive Control (MPC) loops that enables navigation of the drone in GPS-denied confined spaces using a LiDAR sensor and an inertial measurement unit (IMU) is proposed. This controller based on the mathematical model of the drone is designed, and the trajectory control system is simulated. This paper compares the proposed controller with the classical strategy used in commercial drones, considering the operational constraints in confined spaces and the robustness against external disturbances or sensor errors. Preliminary results demonstrate that the MPC exhibits improved disturbance rejection with lower overshoot when compared to a classical controller.