Tracking Nonlinear Model Predictive Control for Obstacle Avoidance
This work proposes a single-layer nonlinear finite-horizon optimal control strategy to solve the autonomous navigation problem while providing obstacle avoidance feature in cluttered environments with unknown obstacles. Inspired by the tracking model predictive control framework, the central idea of including artificial variables into the control problem is considered. This approach allows to address the problem of combining different objectives and provide the closed- loop system with an enlarged domain of attraction and with feasibility insurances in the face of any changing reference. This idea is considered together with an avoidance cost functional to establish the basis of the obstacle avoidance feature of the strategy, while providing feasibility insurance in the presence of pop-up obstacles. Finally, numerical results for a quadrotor UAV are provided to corroborate the proposed strategy.