System-level Safety Guard: Safe Tracking Control through Uncertain
  Neural Network Dynamics Models
Xiao Li Yutong Li Anouck Girard Ilya Kolmanovsky
The Neural Network (NN), as a black-box function approximator, has been
considered in many control and robotics applications. However, difficulties in
verifying the overall system safety in the presence of uncertainties hinder the
modular deployment of NN in safety-critical systems. In this paper, we leverage
the NNs as predictive models for trajectory tracking of unknown dynamical
systems. We consider controller design in the presence of both intrinsic
uncertainty and uncertainties from other system modules. In this setting, we
formulate the constrained trajectory tracking problem and show that it can be
solved using Mixed-integer Linear Programming (MILP). The proposed MILP-based
solution enjoys a provable safety guarantee for the overall system, and the
approach is empirically demonstrated in robot navigation and obstacle avoidance
through simulations. The demonstration videos are available at
https://xiaolisean.github.io/publication/2023-11-01-L4DC2024.