Extensions of learning-based model predictive control for real-time application to a quadrotor helicopter

A new technique called learning-based model predictive control (LBMPC) rigorously combines statistics and learning with control engineering, while providing levels of guarantees about safety, robustness, and convergence. This paper describes modifications of LBMPC that enable its realtime implementation on an ultra-low-voltage processor that is onboard a quadrotor helicopter testbed, and it also discusses the numerical algorithms used to implement the control scheme on the quadrotor. Experimental results are provided that demonstrate the improvement to dynamic response that the learning in LBMPC provides, as well as the robustness of LBMPC to mis-learning.