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Tuning of Multivariable Model Predictive Control for Industrial Tasks
2021
Algorithms
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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]
2017
arXiv
pre-print
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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
2016
Gornyi Zhurnal
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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
2014
Journal of Electrical Systems and Information Technology
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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
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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
2008
IFAC Proceedings Volumes
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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
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The Internet Archive has digitized a microfilm copy of this work. It may be possible to borrow a copy for reading.
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
2009
IFAC Proceedings Volumes
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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
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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]
2019
arXiv
pre-print
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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
2011
IEEE Conference on Decision and Control and European Control Conference
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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
2020
Algorithms
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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
PERFORMANCE ASSESSMENT OF THE DESIGNED CONTROLLERS FOR THREE-TANK BENCHMARK SYSTEM
2019
Zenodo
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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]
2022
arXiv
pre-print
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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
2003
Advanced Robotics
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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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