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The Neural Network (NN), as a black-box function approximator, has beenconsidered in many control and robotics applications. However, difficulties inverifying the overall system safety in the presence of uncertainties hinder themodular deployment of NN in safety-critical systems. In this paper, we leveragethe NNs as predictive models for trajectory tracking of unknown dynamicalsystems. We consider controller design in the presence of both intrinsicuncertainty and uncertainties from other system modules. In this setting, weformulate the constrained trajectory tracking problem and show that it can besolved using Mixed-integer Linear Programming (MILP). The proposed MILP-basedsolution enjoys a provable safety guarantee for the overall system, and theapproach is empirically demonstrated in robot navigation and obstacle avoidancethrough simulations. The demonstration videos are available athttps://xiaolisean.github.io/publication/2023-11-01-L4DC2024.

System-level Safety Guard: Safe Tracking Control through Uncertain Neural Network Dynamics Models