Model Predictive Control for Stabilizing Quadcopter Flight and Following Trajectories

Abstract

Controlling a quadcopter is inherently challenging due to its nonlinear dynamics and susceptibility to external disturbances such as wind gusts and sensor noise. This paper explores the use of Model Predictive Control (MPC) to tackle these challenges. The goal is to create a robust control system capable of stabilizing and directing a quadcopter along a specified trajectory, even when faced with disturbances. The next step derives the Linear Quadratic Regulator (LQR), which is used to stabilize the quadcopter. The research involves creating a detailed mathematical model of the quadcopter's dynamics, followed by the design and implementation of LQR and MPC. Through extensive simulations, the effectiveness of the MPC approach is validated, demonstrating its ability to maintain stability and achieve precise control under various conditions. This study underscores the potential of MPC as a powerful control strategy for UAVs, offering significant advantages for real-world applications where traditional control methods may fall short. The LQR controller balances the performance of the drone and the energy it consumes by specifying the weighting matrix of performance cost Q and control cost R to calculate an optimized controller. MPC provides a robust and effective control strategy for quadcopters, offering improved performance and reliability. Its ability to handle nonlinear dynamics, manage constraints, and optimize control actions makes it an essential tool for advanced aerial vehicle control.