One potential limitation of the closed-loop training approach is its reliance on the accuracy of the initial state estimates and the performance of the MPC during the training phase. If the initial conditions are not representative of the actual operating environment, the closed-loop training may lead to suboptimal performance. To address this, the framework can incorporate a wider range of initial conditions during training, ensuring that the DNNs learn to adapt to various scenarios.
Another limitation is the computational complexity associated with real-time closed-loop training, especially in large-scale systems with multiple quadrotors. This can lead to increased latency in control commands, which may affect the system's responsiveness. To mitigate this, the framework can implement parallel processing techniques and leverage distributed computing resources to accelerate the training process. Additionally, using simplified models or approximations during the training phase can help reduce computational demands while still capturing essential dynamics.
Lastly, closed-loop training may require extensive data collection to ensure robust learning, which can be challenging in dynamic environments. To overcome this, the framework can utilize simulation-based training in conjunction with real-world data, allowing for a more comprehensive training dataset that captures a variety of operational conditions and uncertainties.