Fast Nonlinear Model Predictive Control of Quadrotors

Abstract

This paper proposes a computationally fast scheme for implementing Nonlinear Model Predictive Control (NMPC) as a high-level controller for unmanned quadrotors. The NMPC-based controller is designed using a more realistic highly nonlinear control-oriented model which requires heavy computations for practical implementations. To deal with this issue, the Newton generalized minimal residual (Newton/GMRES) method is applied to solve the NMPC's real-time optimizations rapidly during the control process. The Kalman filter and Luenberger observer algorithms are used, as well as compared, to estimate unknown states. The NMPC-based controller operation is simulated and compared with a proportional controller which shows great improvements in the response of the quadrotor. Experimental results using a commercial drone, called AR.Drone, in our laboratory instrumented by a Vicon motion capture system demonstrate that our control method is sufficiently fast for practical implementations and it can solve the trajectory tracking problem properly.