In this paper, we propose a distributed, first-order, feedback-based approach to solve
nonlinear optimal control problems with aggregative cost functions over networks of …

This paper presents a benchmark study of dual decomposition-based distributed
optimization algorithms applied to constraint-coupled model predictive control problems …

A novel real-time approximated MPC control policy based on deep learning is proposed to
address the high computational burden of model predictive control (MPC) for large-scale …

The industrial implementation of the model predictive control (MPC) is driven by the
necessity to design, tune, and validate the constructed control policy. We present a novel …

This paper investigates algorithms for solving distributed consensus optimization problems
that are non-convex. Since Typical ALADIN (Typical Augmented Lagrangian based …

This thesis deals with the distributed optimization of constraint-coupled systems. This
problem class is often encountered in systems consisting of multiple individual subsystems …

In recent years, the interest of the control community in large-scale systems has surged,
driven by their capacity to encompass behaviors integrating human and cyber-physical …