Distributed optimization, based on a decomposition of the entire optimization problem, has
been applied to many complex decision making problems in process systems engineering …

We study the distributed model predictive control (DMPC) problem for a network of linear
discrete-time subsystems in the presence of stochastic noise among communication …

We consider the problem of solving a distributed optimization problem using a distributed
computing platform, where the communication in the network is limited: each node can only …

The significant progress that has been made in recent years both in hardware
implementations and in numerical computing has rendered real-time optimization-based …

Large-scale and complex process systems are essentially interconnected networks. The
automated operation of such process networks requires the solution of control and …

This paper develops an effective distributed strategy for the solution of constrained
multiagent stochastic optimization problems with coupled parameters across the agents. In …

Learning cooperative policies for multi-agent systems is often challenged by partial
observability and a lack of coordination. In some settings, the structure of a problem allows a …

We present a novel distributed primal‐dual active‐set method for model predictive control.
The primal‐dual active‐set method is used for solving model predictive control problems for …

The model predictive control (MPC) of large-scale systems should adopt a distributed
optimization approach, where controllers for the constituent subsystems optimize their …

Real-time data-driven optimization and control problems over networks may require
sensitive information of participating users to calculate solutions and decision variables …