We propose a methodology for autonomous aerial navigation and obstacle avoidance of
micro aerial vehicles (MAVs) using non-linear model predictive control (NMPC) and we …

In this letter we introduce a novel approach to distributionally robust optimal control that
supports online learning of the ambiguity set, while guaranteeing recursive feasibility. We …

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 …

This letter studies the problem of distributionally robust model predictive control (MPC) using
total variation distance ambiguity sets. For a discrete-time linear system with additive …

We consider the problem of risk-sensitive motion planning in the presence of randomly
moving obstacles. To this end, we adopt a model predictive control (MPC) scheme and pose …

In this article, we present a data-driven learning model predictive control (MPC) scheme for
chance-constrained Markov jump systems with unknown switching probabilities. Using …

This paper studies the problem of risk-averse receding horizon motion planning for agents
with uncertain dynamics, in the presence of stochastic, dynamic obstacles. We propose a …

Risk-aware control, though with promise to tackle unexpected events, requires a known
exact dynamical model. In this work, we propose a model-free framework to learn a risk …

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 …

The behaviour of a stochastic dynamical system may be largely influenced by those low-
probability, yet extreme events. To address such occurrences, this paper proposes an …