  Model predictive Path-Following Control (MPFC) is a viable option for motion
systems in many application domains. However, despite considerable progress on
tailored numerical methods for predictive control, the real-time implementation
of predictive control and MPFC on small-scale autonomous platforms with
low-cost embedded hardware remains challenging. While usual stabilizing MPC
formulations lead to static feedback laws, the MPFC feedback turns out to be
dynamic as the path parameter acts as an internal controller variable. In this
paper, we leverage deep learning to implement predictive path-following control
on microcontrollers. We show that deep neural networks can approximate the
dynamic MPFC feedback law accurately. Moreover, we illustrate and tackle the
challenges that arise if the target platform employs limited precision
arithmetic. Specifically, we draw upon a post-stabilization with an additional
feedback law to attenuate undesired quantization effects. Simulation examples
underpin the efficacy of the proposed approach.
