In this paper, we provide a tutorial review of recent results in the design of distributed model
predictive control systems. Our goal is to not only conceptually review the results in this area …

Model-predictive control (MPC) is an optimization-based control technique that uses 1) a
mathematical model of a system to predict the system's behavior over a given horizon, 2) an …

Fault diagnosis and prognosis are some of the most crucial functionalities in complex and
safety-critical engineering systems, and particularly fault diagnosis, has been a subject of …

In this paper, distributed model predictive control (MPC) based energy scheduling problem
is presented for islanded multi-microgrids. The objective is to achieve supply–demand …

This paper describes nonlinear methods in model building, dynamic data reconciliation, and
dynamic optimization that are inspired by researchers and motivated by industrial …

Extensive digitization and interconnection through networks have ushered in a number of
new paradigms over the last years: Internet of Things, cyber–physical systems, Industry 4.0 …

Standard benchmarks are important repositories to establish comparisons between
competing model and control methods, especially when a new method is proposed. This …

A novel input/output feedback linearization control method by utilizing nonlinear disturbance
observer (NDOB) is proposed for a quadruple-tank liquid level (QTLL) system in this paper …

Distributed model predictive control (DMPC) schemes have become a popular choice for
networked control problems. Under this approach, local controllers use a model to predict its …

This paper proposes a generalized Hopfield network for solving general constrained convex
optimization problems. First, the existence and the uniqueness of solutions to the …