Approximate Robust Control of Uncertain Dynamical Systems
This work addresses safety in autonomous driving, but it is incremental as it builds on existing robust control frameworks with approximations for tractability.
The paper tackles the problem of designing safe control policies for large-scale non-linear systems in uncertain environments by introducing two tractable methods based on sampling or conservative approximation to overcome the intractability of robust control optimization, applied to autonomous driving.
This work studies the design of safe control policies for large-scale non-linear systems operating in uncertain environments. In such a case, the robust control framework is a principled approach to safety that aims to maximize the worst-case performance of a system. However, the resulting optimization problem is generally intractable for non-linear systems with continuous states. To overcome this issue, we introduce two tractable methods that are based either on sampling or on a conservative approximation of the robust objective. The proposed approaches are applied to the problem of autonomous driving.