Robust optimization (RO) has attracted much attention from the optimization community over
the past decade. RO is dedicated to solving optimization problems subject to uncertainty …

This article explores various uncertain control co-design (UCCD) problem formulations.
While previous work offers formulations that are method-dependent and limited to only a …

Helping readers gain a solid, hands-on background in batch processing, this best-selling
text explains how to effectively design, synthesize, and make operations decisions related to …

Control technology permeates every aspect of our lives. We rely on them to perform a wide
variety of tasks without giving much thought to the origins of the technology or how it became …

Dynamic optimization or optimal control problems are omnipresent in the (bio) chemical
industry. In addition, these problems often involve multiple and conflicting objectives …

Modeling of dynamic systems of optimal control problems (OCPs) is very important issue in
applied sciences and engineering. In this analysis, by developed the reproducing kernel …

Dynamic optimization techniques for complex nonlinear systems can provide the process
industry with sustainable and efficient operating regimes. The problem with these regimes is …

We present a multistage NMPC scheme with adaptive on-line scenario-tree generation. The
scenario tree is assembled from predictions of worst-case uncertainty realizations that are …

In this paper, we propose a reinforcement learning structure for auto-tuning PID gains by
solving an optimal tracking control problem for robot manipulators. Capitalizing on the actor …

The problem of optimizing a dynamic system in the presence of uncertainty is typically
tackled using measurements. The method most widely documented in the literature is based …