Design and Simulation Verification of Model Predictive Attitude Control Based on Feedback Linearization for Quadrotor UAV

The flight dynamics of quadrotor UAVs are characterized by significant nonlinearity and inter-axis coupling, posing challenges for the direct application of linear control theory in flight control system design. This paper proposes a feedback linearization transformation method to reduce the computational burden of MPC while addressing the nonlinear coupled flight dynamics in attitude control. The quadrotor UAV’s attitude motion is transformed via feedback linearization into a linear decoupled model, consisting of three independent second-order systems. MPC theory is then employed to design the attitude control system. By solving the constrained optimal control problem within the MPC framework, control inputs for each second-order system are derived, enabling precise attitude tracking through receding horizon control theory. Simulation verifications were subsequently conducted, with the experiment comparing FLMPC, LMPC and AMPC. The results indicate that FLMPC outperforms both the other controllers in terms of control precision, computational efficiency and robustness to disturbances, suggesting its effectiveness for real-time UAV operations where both performance and computational resource constraints are critical.