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 …
Reduced-order Koopman modeling and predictive control of nonlinear processes
In this paper, we propose an efficient data-driven predictive control approach for general
nonlinear processes based on a reduced-order Koopman operator. A Kalman-based sparse …
nonlinear processes based on a reduced-order Koopman operator. A Kalman-based sparse …
Resolving large-scale control and optimization through network structure analysis and decomposition: A tutorial review
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 …
in a variety of decomposition-based algorithms for control and optimization. In this paper, we …
A cyber‐secure control‐detector architecture for nonlinear processes
This work presents a detector‐integrated two‐tier control architecture capable of identifying
the presence of various types of cyber‐attacks, and ensuring closed‐loop system stability …
the presence of various types of cyber‐attacks, and ensuring closed‐loop system stability …
The future of control of process systems
P Daoutidis, L Megan, W Tang - Computers & Chemical Engineering, 2023 - Elsevier
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 …
process control research over the next 5–10 years, and its industrial implementation. Large …
[LLIBRE][B] Process operational safety and cybersecurity
Z Wu, PD Christofides - 2021 - Springer
Traditionally, the operational safety of chemical processes has been addressed through
process design considerations and through a hierarchical, independent design of control …
process design considerations and through a hierarchical, independent design of control …
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 …
[HTML][HTML] Efficient data-driven predictive control of nonlinear systems: A review and perspectives
Abstract Model predictive control (MPC) has become a key tool for optimizing real-time
operations in industrial systems and processes, particularly to enhance performance, safety …
operations in industrial systems and processes, particularly to enhance performance, safety …
[HTML][HTML] Multi-agent distributed control of integrated process networks using an adaptive community detection approach
AM Ebrahimi, DB Pourkargar - Digital Chemical Engineering, 2024 - Elsevier
This paper focuses on develo** an adaptive system decomposition approach for multi-
agent distributed model predictive control (DMPC) of integrated process networks. The …
agent distributed model predictive control (DMPC) of integrated process networks. The …
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 …