Process systems are characterized by nonlinearity, uncertainty, large scales, and also the
need of pursuing both safety and economic optimality in operations. As a result they are …

We consider the application of the type-I Anderson acceleration to solving general
nonsmooth fixed-point problems. By interleaving with safeguarding steps and employing a …

The kernel of industrial advanced process control (APC) lies in the formulation and solution
of model predictive control (MPC) problems, which specify the controller moves according to …

This work explores the design of distributed model predictive control (DMPC) systems for
nonlinear processes using machine learning models to predict nonlinear dynamic behavior …

Decomposition is a fundamental principle of resolving complexity by scale, which is utilized
in a variety of decomposition-based algorithms for control and optimization. In this paper, we …

This article proposes two decentralized multiagent optimal control methods that combine the
computational efficiency and scalability of differential dynamic programming (DDP) and the …

This work regards the development of a distributed model predictive control strategy for
large-scale systems, as centralized implementations often suffer from non-scalability. The …

This paper provides a perspective on the major challenges and directions in academic
process control research over the next 5–10 years, and its industrial implementation. Large …

Marine and terrestrial biogeochemical models are key components of the Earth System
Models (ESMs) used to project future environmental changes. However, their slow …

In this work, we propose a novel safe and scalable decentralized solution for multi-agent
control in the presence of stochastic disturbances. Safety is mathematically encoded using …