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Tuning of Multivariable Model Predictive Control for Industrial Tasks

Robert Nebeluk, Maciej Ławryńczuk
2021 Algorithms  
This work is concerned with the tuning of the parameters of Model Predictive Control (MPC) algorithms when used for industrial tasks, i.e., compensation of disturbances that affect the process (process  ...  The effectiveness of the tuning method is demonstrated for a multivariable distillation column.  ...  Introduction There are a few advanced control methods, including model reference adaptive control [1] , fault-tolerant control [2] , stochastic control [3] , fuzzy control [4] and Model Predictive  ... 
doi:10.3390/a14010010 fatcat:v52zc3n7mzdr5e5v7kiu7e6fbi

Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers [article]

Andreas Doerr, Duy Nguyen-Tuong, Alonso Marco, Stefan Schaal, Sebastian Trimpe
2017 arXiv   pre-print
In this paper, we extend PILCO, a model-based policy search framework, to automatically tune multivariate PID controllers purely based on data observed on an otherwise unknown system.  ...  PID control architectures are widely used in industrial applications. Despite their low number of open parameters, tuning multiple, coupled PID controllers can become tedious in practice.  ...  Contribution of the paper: We propose a general framework for multivariate PID controller tuning based on PILCO.  ... 
arXiv:1703.02899v1 fatcat:tvs2sc6jhbehnjkeqnlbz26y2q

Practice of automatic production control engineering at Nikolaevskaya Processing Plant

D. A. Bashkov, V. A. Veselov, E. A. Isaev
2016 Gornyi Zhurnal  
Predict & Control State-of-the-art multivariate model-predictive process control P&C features -State space modeling -Kalman filter with explicit state estimation -Process input and output disturbance estimates  ...  ABB's advanced solutions encompass the following products: -Predict & Control -multivariable model predictive control -Inferential Modeling Platform -combines neural networks, statistical regressions,  ... 
doi:10.17580/gzh.2016.11.16 fatcat:gzitxcwkdjhvjiket7fv7b4wla

State-of-the-art in control engineering

Štefan Kozák
2014 Journal of Electrical Systems and Information Technology  
Advanced control methods and new distributed embedded control structures represent the most effective tools for realizing high performance of many technological processes.  ...  ) and new possibilities of their SW and HW realizations and successful implementation in industry.  ...  Acknowledgment The work on this paper was supported by the Scientific Grant Agency of the Ministry of Education, Science and Sports of the Slovak Republic under grant No 1/0973/14, and by the Slovak Research  ... 
doi:10.1016/j.jesit.2014.03.002 fatcat:hxcl5r2325eljcguo54p5lj53i

Robust multivariable predictive control: an application to an industrial test stand

2001 IEEE Control Systems  
The Multivariable Predictive Controller System Model Ever since the original generalized predictive controller (GPC) was introduced by Clarke et al. in [7] , studies have been done to extend such algorithms  ...  As a result of theoretical work dedicated to advanced predictive control that has been conducted at ENSICA (see [4] and [5] ), a multivariable predictive controller (MPC) has been developed to regulate  ... 
doi:10.1109/37.918265 fatcat:bme6oomzqffzri52pmmsrm2ebu

Controller Performance Analysis Technology for Industry: Implementation and Case Studies

Kwan Ho Lee, Fangwei Xu, Biao Huang, Edgar C. Tamayo
2008 IFAC Proceedings Volumes  
A plant-oriented framework for APC performance monitoring is proposed on the basis of industrial computer control systems background.  ...  The major components of PATS are discussed including process data collection, data preprocessing, process model identification, similarity clustering, control valve stiction detection, multivariate controller  ...  One of the main purposes of PATS is to perform controller performance monitoring in process industries, especially for the widely used model predictive control (MPC) applications.  ... 
doi:10.3182/20080706-5-kr-1001.02524 fatcat:mzrrho74yvgonmz6p5llrw2tiu

Page 535 of SPE Reservoir Evaluation & Engineering Vol. 8, Issue 6 [page]

2005 SPE Reservoir Evaluation & Engineering  
Multivariable Optimization in the Oil Industry.  ...  Poor performance prediction from existing models and lack of integration with continuous data acquisition are considerable in- centives for developing effective modeling strategies that incorpo- rate knowledge  ... 

The State of the Art in Advanced Chemical Process Control in Japan

Manabu Kano, Morimasa Ogawa
2009 IFAC Proceedings Volumes  
This paper surveys how the three central pillars of process control -PID control, conventional advanced control, and linear/nonlinear model predictive control -have been used and how they have contributed  ...  In this age of globalization, the realization of production innovation and highly stable operation is the chief objective of the process industry in Japan.  ...  ACKNOWLEDGEMENTS The authors express their appreciation to the process control group of MCC for permitting the disclosure of many data and to the task force members of JSPS PSE 143rd committee for their  ... 
doi:10.3182/20090712-4-tr-2008.00005 fatcat:jvridiibrranxllo7wwgjol56q

Supervisory GPC and Evolutionary PI Controller for Web Transport Systems

2015 Acta Polytechnica Hungarica  
Maintaining constant web tension in the presence of disturbance is a challenging task.  ...  Web Transport Systems (WTS) are used in material processing industries to maintain constant tension on the transported material (web), that is required for assuring material integrity and to reduce production  ...  Acknowledgement The authors would like to express their gratitude to SASTRA UNIVERSITY for their support and motivation throughout this investigation.  ... 
doi:10.12700/aph.12.5.2015.5.8 fatcat:umq26tiulnadjdernmkkbistga

LSTM-based Flow Prediction [article]

Hongzhi Wang, Yang Song, Shihan Tang
2019 arXiv   pre-print
In this paper, a method of prediction on continuous time series variables from the production or flow -- an LSTM algorithm based on multivariate tuning -- is proposed.  ...  The main innovation of this paper consists in introducing the concepts of periodic measurement and time window in the industrial prediction problem, especially considering industrial data with time series  ...  CONCLUSIONS Based on the analysis of the characteristics of industrial big data, this paper improves the existing time series prediction algorithm, proposes the LSTM based on multivariate tuning, considering  ... 
arXiv:1908.03571v1 fatcat:5vx3vmexpnaptpojndqwjjfx44

Model Predictive Control of BSM1 benchmark of wastewater treatment process: A tuning procedure

M. Francisco, P. Vega, S. Revollar
2011 IEEE Conference on Decision and Control and European Control Conference  
In this work a multivariable Model Predictive Controller (MPC) is implemented and optimally tuned.  ...  The paper presents results for different tuning situations without the use of long time consuming simulations and considering uncertainty by means of multiple linearized models.  ...  ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the Spanish Government through the MICINN project DPI2009-14410-C02-01 and AECID project A/024016/09  ... 
doi:10.1109/cdc.2011.6160378 dblp:conf/cdc/FranciscoVR11 fatcat:pah2cym4grag5blym4kxrrrwqu

Performance Assessment of Predictive Control—A Survey

Paweł D. Domański
2020 Algorithms  
The first applications were in chemical engineering, and now Model Predictive Control can be found in almost all kinds of applications, from the process industry to embedded control systems or for autonomous  ...  Model Predictive Control constitutes an important element of any modern control system. There is growing interest in this technology.  ...  Model Predictive Control Model Predictive Control [15] significantly contributes to the frequent usage of the APC in process industry.  ... 
doi:10.3390/a13040097 fatcat:bk5f3bavgvcxrchtqpjj3rbxpe


Mohammad Hossein Sobhani
2019 Zenodo  
Three-tank benchmark system is a nonlinear multivariable process that is a good prototype of chemical industrial processes.  ...  Then, maxlikelihood method is used to fit an ARMAV model to the MTS. The obtained model is used for determining MV index.  ...  Founding best model for the multivariate time series is obtained by the method (max-likelihood) explained in [10] .  ... 
doi:10.5281/zenodo.3357290 fatcat:pe6c6aoomfavbdnajj44mlmk7u

MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Series [article]

Yiwei Fu, Feng Xue
2022 arXiv   pre-print
In this paper, we introduce Masked Anomaly Detection (MAD), a general self-supervised learning task for multivariate time series anomaly detection.  ...  With the increasing availability of sensor data from industrial systems, being able to detecting anomalies from streams of multivariate time series data is of significant importance.  ...  ACKNOWLEDGMENT This material is based upon work supported by the Department of Energy, National Energy Technology Laboratory under Award Number DE-FE0031763. 3  ... 
arXiv:2205.02100v1 fatcat:ptk7d4c6dzeyhh5tsgabnhkgb4

High-speed visual servoing of a 6-d.o.f. manipulator using multivariable predictive control

Jacques A. Gangloff, Michel F. de Mathelin
2003 Advanced Robotics  
The predictive feature of the GPC is used for optimal trajectory following in Cartesian space. Experimental results on a 6 DOF industrial robot are presented that validate the proposed model.  ...  The modeling and control strategy take into account the dynamics of the velocity controlled 6 DOF manipulator as well as a simplified model of the camera and acquisition system in order to significantly  ...  A Generalized Predictive Control strategy is chosen where the controller is optimally tuned with respect to the model that we propose.  ... 
doi:10.1163/156855303322554391 fatcat:tbvsuycprzgzlgplin4uc3robi
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