This paper is concerned with the parameter estimation problem for the multivariate system
disturbed by coloured noises. Since coloured noises will reduce the estimation accuracy, the …

Koopman-based modeling and model predictive control have been a promising alternative
for optimal control of nonlinear processes. Good Koopman modeling performance …

In this article, an event-triggered distributed state estimation mechanism is proposed for
general linear systems that comprise several subsystems. Two distributed moving horizon …

In this article, we address a partition‐based distributed state estimation problem for large‐
scale general nonlinear processes by proposing a Kalman‐based approach. First, we …

In this article, we consider a state estimation problem for large‐scale nonlinear processes in
the absence of first‐principles process models. By exploiting process operation data, both …

In this paper, a data-driven constrained state estimation method is proposed for nonlinear
processes. Within the Koopman operator framework, we propose a data-driven model …

An appropriate subsystem configuration is a prerequisite for a successful distributed
control/state estimation design. Existing subsystem decomposition methods are not …

A multi-agent integrated distributed moving horizon estimation (DMHE) and model predictive
control (DMPC) framework is developed for complex process networks. This framework …

In this work, we consider the distributed economic model predictive control (EMPC) of a
wastewater treatment plant described by Benchmark Simulation Model No. 1 and compare …

This work presents a general framework for the advanced control of a common class of
activated sludge plants (ASPs). Based on a dynamic model of the process and plant sensors …