Optimizing UAV performance in turbulent environments using cascaded model predictive control algorithm and Pixhawk hardware

Abstract The modelling and control of unmanned aerial vehicles (UAVs), especially quadrotors with significant position–orientation coupling, pose considerable challenges in practical applications, such as environmental monitoring in wind-affected mangrove forests. Traditional control methods, such as the PID controller, which are commonly used in simulations owing to their simplicity, often fail to perform optimally under real-world conditions because of their reliance on linear assumptions. This study addresses these challenges through three key innovations: (1) a hierarchical cascaded model predictive control (MPC) framework that decouples translational and rotational dynamics into computationally efficient subsystems, significantly reducing the computational load of conventional MPC implementations; (2) a control strategy explicitly optimized for turbulent mangrove ecosystems, where dense vegetation amplifies wind disturbances; and (3) experimental validation of the framework’s real-world applicability via simulations and hardware-in-the-loop flight tests using Pixhawk autopilots under realistic wind conditions. These tests demonstrated that the proposed controller significantly outperformed the conventional PID controller, particularly in terms of stability and disturbance rejection. These results highlight the potential of this control system for UAV applications in challenging environments bridging the gap between theoretical control design and field-ready robustness.