Gaussian Process Model Predictive Control of unmanned quadrotors
Two issues of quadrotor control without deterministic dynamical equations are addressed in this paper by using Gaussian Process (GP) based Model Predictive Control (MPC) algorithm. Firstly, the first issue of modelling unknown dynamical motions is solved by using GP models based on sampled data. In this way, the model uncertainty can be numerically evaluated during modelling and prediction process. This is not easy when using other data driven methods, such as Artificial Neural Networks (ANN) and Fuzzy Models (FMs) Then a MPC scheme based on obtained GP models is proposed to address the second issue of designing appropriate quadrotor controllers. The proposed algorithm directly takes model uncertainty into account when planning MPC policies, and can be computationally efficiently implemented through using analytical gradients in the optimization process. The performance of quadrotor control using proposed approach is demonstrated by simulations on a trajectory tracking problem.
2016 2nd International Conference on Control, Automation and Robotics (ICCAR)