We present a case study applying learning-based distributionally robust model predictive
control to highway motion planning under stochastic uncertainty of the lane change behavior …

Abstract Model predictive control (MPC) is an effective approach to control multivariable
dynamic systems with constraints. Most real dynamic models are however affected by plant …

We propose an interaction-aware stochastic model predictive control (MPC) strategy for lane
merging tasks in automated driving. The MPC strategy is integrated with an online learning …

Distributionally robust control (DRC) aims to effectively manage distributional ambiguity in
stochastic systems. While most existing works address inaccurate distributional information …

Incorporating pattern-learning for prediction (PLP) in many discrete-time or discrete-event
systems allows for computation-efficient controller design by memorizing patterns to …

Stochastic control models are a class of mathematical models that have applications in
various fields. They are widely used in applications like autopilot, robotics, and financial …

The availability of reliable methods for data-driven, optimal decision making under
uncertainty is of central importance in several fields of study, including operations research …

This thesis establishes control and estimation architectures that combine both model-based
and model-free methods by theoretically characterizing several types of jump stochastic …