Path Following Controller and Obstacles Avoidance Using Nonlinear Model Predictive Control

Recently, unmanned aerial vehicles have received great attention in many applications, e.g., exploration, environmental monitoring, and search missions. These tasks need to implement a proper controller for flight path following with optimal performance, e.g., minimum energy consumption and tracking error. This work proposed a nonlinear model predictive control considering the vehicle’s nonlinear dynamics and constraints such as obstacle avoidance. The proposed controller is implemented using the ACADO toolkit to achieve real-time online optimization using the onboard limited computation power. During these missions, autonomous vehicles must avoid obstacles (nonconvex constraints). For this reason, we augment the barrier function on the optimization problem. The simulation results clarify robust performance, efficient trajectory tracking, and avoiding obstacles for a quadcopter under external disturbances.