Cascade model predictive control for enhancing UAV quadcopter stability and energy efficiency in wind turbulent mangrove forest environment

• Significant Challenge Addressed: • Unmanned aerial vehicle (UAV) modelling and control, particularly for quadrotors with high position-orientation coupling, are challenging for practical applications in windy mangrove forests. • Limitations of Conventional Control Strategies: • Conventional PID controllers, commonly used in simulations, often fail to perform adequately in real-world scenarios due to their linear assumptions. • Novel Control Framework Proposed: • Introduction of a hierarchical cascaded model predictive control (MPC) framework specifically designed for altitude, attitude, and battery efficiency of quadrotors operating in mangrove environments. • The proposed framework decomposes the overall MPC strategy into two distinct schemes for translational displacements and rotational movements, addressing computational complexity and enhancing UAV resilience to wind turbulence. • Superior Performance Demonstrated: • Rigorous simulations involving complex trajectory tracking and extreme wind conditions show the proposed controller's superior performance compared to conventional PID controllers. • Key improvements include enhanced stability, better disturbance rejection, and improved battery management, highlighting the potential for UAV applications in challenging environments. Unmanned aerial vehicle (UAV) modelling and control, particularly in quadrotors with high position-orientation coupling, present significant challenges for practical applications, such as environmental monitoring missions in windy mangrove forests. Conventional control strategies like the PID controller, often employed in simulations due to their simplicity, often underperform in real-world scenarios due to their linear assumptions. This research proposes a novel hierarchical cascaded model predictive control system for altitude, attitude, and battery efficiency for quadrotor in mangrove area. This control system addresses computational complexity by decomposing the overall MPC strategy into two distinct schemes, one for translational displacements and another for rotational movements, enhancing the UAV's resilience to wind turbulence, a significant disturbance factor in mangrove environments. Rigorous simulation and experiment test flight involving complex trajectory tracking and windy conditions demonstrate the proposed controller's superior performance compared to conventional PID controller, particularly in terms of stability, disturbance rejection, underscoring its potential for UAV applications in challenging environments.