In the evolving landscape of high-speed, agile quadrotor flight, achieving accurate tracking at the platform's operational limits is paramount. Controllers have to handle actuator constraints, demonstrate robustness to aerodynamic disturbances, and remain computationally efficient for safety-critical applications. Incremental control methods have shown robust performance to aerodynamic disturbances and model uncertainties while constrained optimization methods excel in handling actuator constraints and optimal control objectives. The tight coupling of both, model predictive control and sensor-based control unifies their strengths and addresses their individual limitations. The controller achieves accurate tracking of aggressive trajectories that surpass the feasibility of the actuators in exceedingly disturbance-prone settings.