## 6 Discussion  
The duration of the simulation is configured to 30 s. This timeframe is chosen as it sufficiently exhibits the convergence trend of positional control error with the meta-learning-based methods. Further, extending the simulation does not yield a significant reduction in error. We have configured the quadrotor with a diagonal length of 0.9 m, measured tip-to-tip across the opposing rotors. The meta-learning-based INDI method yields an ACE of approximately 0.05 m. The achieved accuracy is sufficient for applications such as environmental monitoring and parcel delivery. In the simulation, we compare the performance of the meta-learning-based PID and INDI methods, while both methodologies are effective in position control, the meta-learning-based INDI method outperforms in terms of ACE. This emphasizes the superior robustness and precision of the INDI control  
Figure 8: Trends of ACE values under different control methods in the mass-asymmetric scenario. Meta-learning-based methods demonstrate satisfactory performance.  
in controlling the position of quadrotors. However, this does not diminish the utility of the PID method. In fact, owing to its simplicity and ease of implementation, the PID control continues to find extensive applications in a variety of control systems.  
In addition, certain equations are simplified to enhance the computational efficiency. For instance, we have overlooked some secondary dynamic effects, presumed the quadrotor to be a rigid body with its geometric center coinciding with its center of mass, assumed that the resistance and gravity experienced by the quadrotor are not influenced by factors such as flight altitude, and taken the thrust in all directions to be directly proportional to the square of the propeller rotation speed. In order to maintain a quadratic relationship of the pitch and roll channels, we also compromise the precision of the yaw channel. However, these simplifications are made with the aim of minimizing any compromise in model accuracy. The algorithm in this paper can operate in real-time on conventional embedded processors, negating the need for specialized hardware or elevated computational resources.  
Our simulation encapsulates the principal dynamical characteristics of the quadrotor and environmental factors such as wind disturbances. Despite the fact that simulations cannot fully replicate the intricacies of the real world, we have reason to believe that the results should closely mirror those in practical implementation, given that the model captures the predominant behaviors of the system. We also compare our simulation results with [19], where experimental results were carried out with simple PD control law, and find that the error in position of the proposed method in this paper is significantly smaller than that in their results. In their flight tests where the quadrotor flies at a low speed, the discrepancy between the real experimental results and the simulation outcomes is modest, with the exception of some latency caused by the network link. Therefore, we expect that the actual flight performance of our method in hovering scenarios will also be satisfactory. In sum, we are confident in the accuracy of our simulations, and we believe the proposed approach to be viable in real-world implementations. Naturally, we are in the process of setting up the necessary experimental framework and look forward to sharing our experimental findings in future works. Our ultimate aim is to ensure that the proposed method is not just theoretically sound, but also practically feasible and effective in handling the complexities of real-world scenarios.