## 5 Conclusions  
In this work, a feedback linearization model predictive control (FLMPC) approach is proposed to address the nonlinear and coupled characteristics of quadrotor UAV attitude dynamics. By explicitly incorporating the nonlinear mapping between Euler angular velocities and body-frame angular velocities into the feedback linearization process, the proposed method achieves effective linearization and decoupling of roll, pitch and yaw channels. This allows the design of an MPC controller on a linearized model with appropriately transformed constraints, thereby ensuring both theoretical soundness and practical feasibility.  
Simulation results demonstrate the effectiveness of the proposed FLMPC method under various constraint and disturbance conditions. Comparative analyses with LMPC and AMPC show that, while all controllers can achieve the desired tracking performance, FLMPC consistently offers more stable and precise tracking, stronger disturbance rejection,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Performance Metrics**} & \multicolumn{3}{c}{**Response Time (s)**} & \multicolumn{3}{c}{**Overshoot (\%)**} & \multirow{2}{*}{**Mean Computation Time (ms)**} \\  & \(\boldsymbol{\phi}\) & \(\theta\) & \(\boldsymbol{\psi}\) & \(\boldsymbol{\phi}\) & \(\boldsymbol{\theta}\) & \(\boldsymbol{\psi}\) & \\ \hline FLMPC & 0.83 & 0.95 & 2.07 & 0.46 & 0.00 & 0.01 & 6.03 \\ LMPC & 0.76 & 2.7 & 2.25 & 1.07 & 2.17 & 1.88 & 5.23 \\ AMPC & 0.63 & 1.65 & 1.90 & 1.67 & 0.00 & 0.00 & 47.59 \\ \hline \end{tabular}
\end{table}
Table 2: Key performance metrics for the three controllers.  
and lower computational cost compared to AMPC. Unlike LMPC, which suffers from axis coupling, FLMPC successfully mitigates this issue through dynamic decoupling. These advantages make FLMPC a promising candidate for real-time attitude control of quadrotors in nonlinear and constrained environments.  
However, it is also acknowledged that despite its improved computational efficiency, the optimization process in FLMPC still imposes a notable computational burden for embedded processors. As such, future work will focus on the development of faster and more efficient optimization algorithms, or approximation-based solvers, to further enhance the real-time applicability of the proposed method in practical embedded systems.  
Methodology, X.Y.; Validation, X.Y.; Writing--review & editing, J.X.; Funding acquisition, J.X.; Investigation, S.L. All authors contributed extensively to the current study. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Laboratory of Rotorcraft Aeromechanics Fund Program of China (9140C400504130C4148); supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions of China.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
Author [PERSON] was employed by the company Harbin Aircraft Industry Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.