## 1 Introduction  
A quadrotor Unmanned Aerial Vehicle (UAV) is an aircraft with four rotors capable of vertical takeoff, landing and hovering. With its simple mechanical structure and excellent maneuverability, it is increasingly used in applications like military reconnaissance, cargo transport, emergency rescue and terrain mapping [1, 2, 3]. To ensure a safe flight and the completion of complex tasks, attitude control is crucial for the quadrotor UAV flight control system. A quadrotor UAV is a multi-input, multi-output (MIMO) nonlinear system with strong inter-axis coupling, making attitude control challenging, as adjustments on one axis often affect others. This challenge is further compounded by the system's under-actuated nature, with only four inputs available to control six outputs. Additionally, its lightweight design makes it highly sensitive to disturbances such as wind, requiring robust control strategies to maintain stability [4, 5]. Consequently, numerous engineers and researchers have conducted in-depth studies on these control challenges [6, 7]. Proportional-Integral-Derivative (PID) control is a classic control design method known for its simple structure and ease of implementation [8, 9, 10]. Active Disturbance Rejection Control (ADRC) offers robust control by estimating and compensating for both external and internal disturbances in real time, while its nonlinear state error feedback ensures high-quality control performance [11]. The Sliding Mode Control (SMC) method is a control technique capable of handling nonlinear problems and exhibits strong robustness against system parameter variations and external disturbances [12; 13]. However, each method has its limitations and achieving optimal control performance remains challenging. Model Predictive Control (MPC) is a receding horizon control method known for its strong disturbance rejection, and robustness. Reference [14] demonstrated that Linear MPC (LMPC) outperforms both Proportional-Derivative (PD) and Linear Quadratic Regulator (LQR) in terms of control effectiveness and disturbance rejection for quadrotor UAV trajectory tracking. Despite this, LMPC struggles to effectively address the inherent nonlinear characteristics of the quadrotor UAV. In the most MPC-based methods, the linearized dynamics of the system is employed to avoid the complexity that results from nonlinear optimization problems [15; 16]. The scholars in [17] designed an adaptive MPC method using Laguerre polynomials for quadrotor attitude stabilization and validated its feasibility. And in [18], Nonlinear MPC (NLMPC) provides better performance in trajectory tracking and system stability compared to LMPC, but this comes with a high computational cost. Although parameterized NMPC can reduce computational complexity, they may compromise control system performance to some extent [19]. In real-world flight scenarios, most MPC based methods rely on linearized system dynamics to simplify the control problem and avoid the computational complexity associated with solving nonlinear optimization problems. However, the inherently nonlinear dynamics of quadrotor UAVs often demand nonlinear control methods to achieve precise and stable flight performance. Although these nonlinear approaches can offer improved control accuracy and robustness, they are typically more challenging to design and implement, as they require advanced modeling and considerable computational resources. This trade-off highlights the ongoing need for efficient, real-time nonlinear control strategies in practical quadrotor applications. Feedback Linearization (FL) can address the problem by transforming the system with coordinate adjustments and nonlinear state feedback, effectively bypassing intrinsic nonlinearities to enable the use of linear control methods [20; 21]. Unlike conventional linearization, which only approximates system dynamics around a specific operating point and neglects higher-order dynamics, FL provides a globally valid linearization [22; 23].