Adaptive robust optimization (ARO) is a well-known technique to deal with the parameter
uncertainty in optimization problems. While the ARO framework can actually be borrowed to …

We introduce a general framework for robust data-enabled predictive control (DeePC) for
linear time-invariant systems, which enables us to obtain robust and optimal control in a …

We demonstrate how adjustable robust optimization (ARO) problems with fixed recourse can
be cast as static robust optimization problems via Fourier–Motzkin elimination (FME) …

This paper presents an online approach to safety critical control. The common approach for
enforcing safety of a system requires the offline computation of a viable set, which is either …

Deployment of robotic systems in the real world requires a certain level of robustness in
order to deal with uncertainty factors, such as mismatches in the dynamics model, noise in …

Increasing integration of distributed energy resources (DERs) within distribution feeders
provides unprecedented flexibility at the distribution-transmission interconnection. To exploit …

Traditional power grids are mainly based on centralized power generation and subsequent
distribution. The increasing penetration of distributed renewable energy sources and the …

Legged robots have great potential to perform complex loco-manipulation tasks, yet it is
challenging to keep the robot balanced while it interacts with the environment. In this paper …

This paper focuses on robustness to disturbance forces and uncertain payloads. We present
a novel formulation to optimize the robustness of dynamic trajectories. A straightforward …

Many real-world applications require the joint optimization of a large number of flexible
devices over time. The flexibility of, eg, multiple batteries, thermostatically controlled loads …