Abstract
This paper describes and analyzes the performance of two different formulations of model predictive control (MPC) applied to a quadrotor unmanned aerial vehicle (UAV), one that explicitly handles constraints and another that doesn’t. The objective of the MPC strategy is to compute an optimal sequence of actions within its prediction horizon to track desired states. The optimization strategies adopted in this work are based on an approximated dynamical model for prediction while imposing a quadratic cost function. One prominent vantage of MPC is its ability to handle constraints inherent to the process, based either on actuators’ limitations or security concerns. Through simulations, the impacts of imposing a convex set of constraints is analyzed, regarding the performance and computational effort involved in solving a trajectory tracking problem. Keywords: Optimal control, Predictive Control, Quadrotors, UAVs
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