Model transformation based distributed stochastic gradient algorithm for multivariate output-error systems
Q Liu, F Chen - International Journal of Systems Science, 2023 - Taylor & Francis
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 …
disturbed by coloured noises. Since coloured noises will reduce the estimation accuracy, the …
Event-triggered distributed moving horizon state estimation of linear systems
In this article, an event-triggered distributed state estimation mechanism is proposed for
general linear systems that comprise several subsystems. Two distributed moving horizon …
general linear systems that comprise several subsystems. Two distributed moving horizon …
An adaptive distributed architecture for multi-agent state estimation and control of complex process systems
AM Ebrahimi, DB Pourkargar - Chemical Engineering Research and …, 2024 - Elsevier
A multi-agent integrated distributed moving horizon estimation (DMHE) and model predictive
control (DMPC) framework is developed for complex process networks. This framework …
control (DMPC) framework is developed for complex process networks. This framework …
Subsystem decomposition of process networks for simultaneous distributed state estimation and control
An appropriate subsystem configuration is a prerequisite for a successful distributed
control/state estimation design. Existing subsystem decomposition methods are not …
control/state estimation design. Existing subsystem decomposition methods are not …
Data‐driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large‐scale nonlinear processes
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 …
the absence of first‐principles process models. By exploiting process operation data, both …
Distributed economic model predictive control of wastewater treatment plants
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 …
wastewater treatment plant described by Benchmark Simulation Model No. 1 and compare …
Machine learning-based input-augmented Koopman modeling and predictive control of nonlinear processes
Koopman-based modeling and model predictive control have been a promising alternative
for optimal control of nonlinear processes. Good Koopman modeling performance …
for optimal control of nonlinear processes. Good Koopman modeling performance …
Partition‐based distributed extended Kalman filter for large‐scale nonlinear processes with application to chemical and wastewater treatment processes
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 …
scale general nonlinear processes by proposing a Kalman‐based approach. First, we …
Distributed estimation and nonlinear model predictive control using community detection
DB Pourkargar, M Moharir, A Almansoori… - Industrial & …, 2019 - ACS Publications
A combined distributed moving horizon estimation and distributed model predictive control
architecture is proposed to address the distributed output–feedback control problem for …
architecture is proposed to address the distributed output–feedback control problem for …
[HTML][HTML] A model-based framework for controlling activated sludge plants
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 …
activated sludge plants (ASPs). Based on a dynamic model of the process and plant sensors …