  Accurate path following is challenging for autonomous robots operating in
uncertain environments. Adaptive and predictive control strategies are crucial
for a nonlinear robotic system to achieve high-performance path following
control. In this paper, we propose a novel learning-based predictive control
scheme that couples a high-level model predictive path following controller
(MPFC) with a low-level learning-based feedback linearization controller
(LB-FBLC) for nonlinear systems under uncertain disturbances. The low-level
LB-FBLC utilizes Gaussian Processes to learn the uncertain environmental
disturbances online and tracks the reference state accurately with a
probabilistic stability guarantee. Meanwhile, the high-level MPFC exploits the
linearized system model augmented with a virtual linear path dynamics model to
optimize the evolution of path reference targets, and provides the reference
states and controls for the low-level LB-FBLC. Simulation results illustrate
the effectiveness of the proposed control strategy on a quadrotor path
following task under unknown wind disturbances.
