4.6 Article

Data-driven moving horizon state estimation of nonlinear processes using Koopman operator

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Engineering, Chemical

Machine learning-based ethylene and carbon monoxide estimation, real-time optimization, and multivariable feedback control of an experimental electrochemical reactor

Berkay Citmaci et al.

Summary: The electrochemical reduction of CO2 gas is a new technique for mitigating the global climate crisis and storing energy from renewable sources. However, there is a lack of explicit models for CO2 reduction and limited effort in developing process modeling and control of CO2 electrochemical reactors. This study focuses on developing a control scheme for a rotating cylinder electrode (RCE) reactor using artificial and recurrent neural network modeling, nonlinear optimization, and process controller design. The experimental results demonstrate the effectiveness of the control system in regulating the production rates of ethylene and carbon monoxide.

CHEMICAL ENGINEERING RESEARCH & DESIGN (2023)

Article Engineering, Chemical

Physics-informed machine learning for MPC: Application to a batch crystallization process

Guoquan Wu et al.

Summary: This work proposes a framework for developing physics-informed recurrent neural network (PIRNN) models and PIRNN-based predictive control schemes for batch crystallization processes. The population balance model is developed to describe the formation of crystals in the aspirin crystallization process. The PIRNN modeling scheme integrates observational data and mechanistic models to develop machine learning models, and physical constraints are embedded in the models to prevent unreasonable predictions. The PIRNN model, capturing the dynamic behavior of batch crystallization process, is utilized in the design of a model predictive controller.

CHEMICAL ENGINEERING RESEARCH & DESIGN (2023)

Article Automation & Control Systems

Hybrid Koopman model predictive control of nonlinear systems using multiple EDMD models: An application to a batch pulp digester with feed fluctuation

Sang Hwan Son et al.

Summary: In this study, a hybrid Koopman model predictive control (KMPC) framework was developed for a batch pulping process to regulate the Kappa number and cell wall thickness (CWT) of fibers in the presence of feed fluctuations. Multiple local models were constructed and an extended dynamic mode decomposition (EDMD) was used to develop local controllers for each cluster, successfully predicting the local behavior and obtaining desired properties despite feed fluctuations.

CONTROL ENGINEERING PRACTICE (2022)

Article Automation & Control Systems

Development of offset-free Koopman Lyapunov-based model predictive control and mathematical analysis for zero steady-state offset condition considering influence of Lyapunov constraints on equilibrium point

Sang Hwan Son et al.

Summary: Koopman operator theory allows for a global linear representation of a nonlinear dynamical system, but it faces challenges due to plant-model mismatch. In this study, we propose a new approach called offset-free Koopman Lyapunov-based model predictive control (KLMPC) to address this issue by incorporating disturbance dynamics.

JOURNAL OF PROCESS CONTROL (2022)

Article Automation & Control Systems

Efficient low-order system identification from low-quality step response data with rank-constrained optimization

Qingyuan Liu et al.

Summary: In the presence of low-quality industrial process data, a novel rank-constrained optimization approach is proposed for identifying low-order systems accurately and robustly. This method effectively bypasses error accumulation and achieves better modeling accuracy.

CONTROL ENGINEERING PRACTICE (2021)

Article Engineering, Chemical

Application of offset-free Koopman-based model predictive control to a batch pulp digester

Sang Hwan Son et al.

Summary: This work applies a Koopman operator approach and extended dynamic mode decomposition to model and control a batch pulp digester, achieving successful regulation of Kappa number and fiber cell wall thickness while compensating for plant-model mismatch and disturbances. The numerical experiments demonstrate the effectiveness of the linear state-space model and offset-free KMPC system in predicting and controlling the behavior of the pulp digester.

AICHE JOURNAL (2021)

Article Computer Science, Interdisciplinary Applications

Nonlinear state and parameter estimation using derivative information: A Lie-Sobolev approach

Wentao Tang et al.

Summary: This paper proposes a Lie-Sobolev parameter and state estimation method based on Sobolev theory to match the model and true dynamics, demonstrating strong advantages in nonlinear processes.

COMPUTERS & CHEMICAL ENGINEERING (2021)

Article Automation & Control Systems

Data-driven robust model predictive control framework for stem water potential regulation and irrigation in water management

Wei-Han Chen et al.

Summary: This work proposes a data-driven robust model predictive control framework that utilizes stem water potential for effective irrigation control of high value-added crops. The model linearizes and discretizes a nonlinear dynamic model of water dynamics, with three compartments to describe the current water status of the system, achieving feasibility and stability.

CONTROL ENGINEERING PRACTICE (2021)

Article Robotics

Data-Driven Control of Soft Robots Using Koopman Operator Theory

Daniel Bruder et al.

Summary: Controlling soft robots with precision is challenging, but Koopman operator theory offers a data-driven approach to construct explicit dynamical models for better control. By applying the Koopman-based method, three MPC controllers for a pneumatic soft robot arm were developed and showed to be over three times more accurate than a benchmark controller based on a linear state-space model.

IEEE TRANSACTIONS ON ROBOTICS (2021)

Article Engineering, Chemical

Nonlinear observer design for two-time-scale systems

Zhaoyang Duan et al.

AICHE JOURNAL (2020)

Article Engineering, Chemical

Post cyber-attack state reconstruction for nonlinear processes using machine learning

Zhe Wu et al.

CHEMICAL ENGINEERING RESEARCH & DESIGN (2020)

Article Automation & Control Systems

Comprehensive modeling and identification of nonlinear joint dynamics for collaborative industrial robot manipulators

Emil Madsen et al.

CONTROL ENGINEERING PRACTICE (2020)

Article Mathematics, Applied

Data-Driven Model Predictive Control using Interpolated Koopman Generators

Sebastian Peitz et al.

SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS (2020)

Article Automation & Control Systems

Robust FIR State Estimation of Dynamic Processes Corrupted by Outliers

Shunyi Zhao et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)

Article Automation & Control Systems

Forming Distributed State Estimation Network From Decentralized Estimators

Xunyuan Yin et al.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2019)

Article Engineering, Chemical

Koopman Lyapunov-based model predictive control of nonlinear chemical process systems

Abhinav Narasingam et al.

AICHE JOURNAL (2019)

Article Automation & Control Systems

Multirate Sampled-Data Observer Design Based on a Continuous-Time Design

Chen Ling et al.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2019)

Article Engineering, Chemical

Subsystem decomposition and distributed moving horizon estimation of wastewater treatment plants

Xunyuan Yin et al.

CHEMICAL ENGINEERING RESEARCH & DESIGN (2018)

Article Computer Science, Interdisciplinary Applications

Integrating operations and control: A perspective and roadmap for future research

Prodromos Daoutidis et al.

COMPUTERS & CHEMICAL ENGINEERING (2018)

Article Automation & Control Systems

Triggered Communication in Distributed Adaptive High-Gain EKF

Mohammad Rashedi et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2018)

Article Engineering, Electrical & Electronic

A Robust Data-Driven Koopman Kalman Filter for Power Systems Dynamic State Estimation

Marcos Netto et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2018)

Article Mathematics, Applied

Generalizing Koopman Theory to Allow for Inputs and Control

Joshua L. Proctor et al.

SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS (2018)

Article Multidisciplinary Sciences

Deep learning for universal linear embeddings of nonlinear dynamics

Bethany Lusch et al.

NATURE COMMUNICATIONS (2018)

Article Automation & Control Systems

Distributed moving horizon state estimation of two-time-scale nonlinear systems

Xunyuan Yin et al.

AUTOMATICA (2017)

Review Automation & Control Systems

Sustainability and process control: A survey and perspective

Prodromos Daoutidis et al.

JOURNAL OF PROCESS CONTROL (2016)

Article Mathematics, Applied

Dynamic Mode Decomposition with Control

Joshua L. Proctor et al.

SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS (2016)

Article Mathematics, Applied

A Data-Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition

Matthew O. Williams et al.

JOURNAL OF NONLINEAR SCIENCE (2015)

Article Mathematics, Applied

A Computational Method to Extract Macroscopic Variables and Their Dynamics in Multiscale Systems

Gary Froyland et al.

SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS (2014)

Article Engineering, Chemical

Moving horizon state estimation for nonlinear systems with bounded uncertainties

Jinfeng Liu

CHEMICAL ENGINEERING SCIENCE (2013)

Article Computer Science, Interdisciplinary Applications

Advances and selected recent developments in state and parameter estimation

Costas Kravaris et al.

COMPUTERS & CHEMICAL ENGINEERING (2013)

Review Computer Science, Interdisciplinary Applications

Distributed model predictive control: A tutorial review and future research directions

Panagiotis D. Christofides et al.

COMPUTERS & CHEMICAL ENGINEERING (2013)

Article Automation & Control Systems

On simultaneous on-line state and parameter estimation in non-linear state-space models

Aditya Tulsyan et al.

JOURNAL OF PROCESS CONTROL (2013)

Article Mathematics, Applied

Applied Koopmanism

Marko Budisic et al.

Article Automation & Control Systems

Optimization-based state estimation: Current status and some new results

James B. Rawlings et al.

JOURNAL OF PROCESS CONTROL (2012)

Article Mechanics

Dynamic mode decomposition of numerical and experimental data

Peter J. Schmid

JOURNAL OF FLUID MECHANICS (2010)

Review Automation & Control Systems

Architectures for distributed and hierarchical Model Predictive Control - A review

Riccardo Scattolini

JOURNAL OF PROCESS CONTROL (2009)

Article Mathematics, Applied

Comparison of systems with complex behavior

I Mezic et al.

PHYSICA D-NONLINEAR PHENOMENA (2004)

Article Automation & Control Systems

A data driven subspace approach to predictive controller design

R Kadali et al.

CONTROL ENGINEERING PRACTICE (2003)

Article Automation & Control Systems

Receding-horizon estimation for discrete-time linear systems

A Alessandri et al.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2003)

Article Automation & Control Systems

Constrained state estimation for nonlinear discrete-time systems: Stability and moving horizon approximations

CV Rao et al.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2003)

Article Automation & Control Systems

Constrained linear state estimation - a moving horizon approach

CV Rao et al.

AUTOMATICA (2001)