Decentralized optimization algorithms are of interest in different contexts, eg, optimal power
flow or distributed model predictive control, as they avoid central coordination and enable …

Distributed real-time optimization (RTO) enables optimal operation of large-scale process
systems with common resources shared across several clusters. Typically in distributed …

The modular open-source framework GRAMPC-D for model predictive control of distributed
systems is presented in this paper. The modular concept allows to solve optimal control …

In an increasingly digitized, integrated, and connected production environment, the
coordination of complex coupled systems of systems is essential to ensure a resource …

Massive device connectivity in Internet of Thing (IoT) networks with sporadic traffic poses
significant communication challenges. To overcome this challenge, the serving base station …

Alternating Directions Method of Multipliers (ADMM) is a form of decomposition-coordination
method that typically requires several iterations/communication rounds between the …

Nonlinear model predictive control (NMPC) has been widely adopted to manipulate bilinear
systems with dynamics that include products of the inputs and the states. These systems are …

Mathematical optimization techniques are among the most successful tools for controlling
technical systems optimally with feasibility guarantees. Yet, they are often centralized—all …

In this chapter, we argue that model predictive control (MPC) can be a very powerful
technique to mitigate some of the challenges that arise when designing and deploying …

This paper extends the recently introduced ALADIN algorithm to non-convex continuous-
time optimal control problems with nonlinear dynamics and linear coupling constraints. The …