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 deployment of NN modules 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 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.

翻译：神经网络（NN）作为一种黑箱函数逼近器，已被广泛应用于众多控制与机器人领域。然而，在存在不确定性的情况下验证系统整体安全性的困难，阻碍了神经网络模块在安全关键系统中的部署。本文利用神经网络作为预测模型，实现对未知动力学系统的轨迹跟踪。我们考虑了在存在内在不确定性及其他系统模块不确定性的情况下的控制器设计问题。在此设定下，我们阐述了带约束的轨迹跟踪问题，并证明该问题可通过混合整数线性规划（MILP）求解。通过机器人导航与避障仿真实验，所提出的基于MILP的方法得到了实证验证。演示视频详见 https://xiaolisean.github.io/publication/2023-11-01-L4DC2024。