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 …

The optimal design of experiments is crucial for maximizing the information content of data
across a wide-range of experimental goals. This paper presents a Bayesian approach to …

In order to estimate parameters accurately in nonlinear dynamic systems, experiments that
yield a maximum of information are invaluable. Such experiments can be obtained by …

Stochastic optimal control is a branch of control theory that deals with uncertain system
parameters. One of the requirements for an accurate description of systems is a complete …

We propose an algorithm that calculates heuristically optimal solutions for dynamic
optimization problems with path chance constraints. The solution is a feasible point in the …

The physical and virtual connectivity of systems via flows of energy, material, information,
etc., steadily increases. This paper deals with systems of sub-systems that are connected by …

Abstract Model-based experiment design for parameter estimation is aimed at obtaining
accurate parameter estimates with minimal variance. However, these experiment designs …

This letter presents an algorithm based on the alternating direction method of multipliers
(ADMM) for the distributed solution of optimal control problems of stochastic multi-agent …

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

This study is concerned with the development of a robust open-loop optimal control (ROC)
framework that distributes different generalized polynomial chaos (gPC) sub-problems from …