The field of quadrotor control is rapidly advancing, with a focus on developing innovative control strategies that can handle complex aerodynamic interactions and nonlinear dynamics. Recent research has explored the use of reinforcement learning, model predictive control, and sliding mode control techniques to improve the agility and robustness of quadrotor maneuvers. A key direction in this field is the development of control methods that can effectively address system nonlinearities and uncertainties, while also ensuring real-time operability and stability. Notably, the integration of local feedback gains and sensitivity analysis has been shown to significantly enhance control performance.

Some noteworthy papers in this area include:

• Quadrotor Morpho-Transition: Learning vs Model-Based Control Strategies, which demonstrates the potential of reinforcement learning for agile landing and mid-air transformation.
• Feedback-MPPI: Fast Sampling-Based MPC via Rollout Differentiation, which introduces a novel framework for rapid closed-loop corrections and robust control.
• Model Predictive Path-Following Control for a Quadrotor, which presents a promising approach for automating drone-assisted processes with explicit handling of state and input constraints.