Unconstrained and Constrained Model Predictive Control for a Modified Quadruple Tank System

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2019Researchpeer-review

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Unconstrained and Constrained Model Predictive Control for a Modified Quadruple Tank System. / Mohd. Azam, Sazuan Nazrah; Jørgensen, John Bagterp.

Proceedings of 2018 IEEE Conference on Systems, Process and Control. IEEE, 2018. p. 147-152.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2019Researchpeer-review

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Mohd. Azam, SN & Jørgensen, JB 2018, Unconstrained and Constrained Model Predictive Control for a Modified Quadruple Tank System. in Proceedings of 2018 IEEE Conference on Systems, Process and Control. IEEE, pp. 147-152, 2018 IEEE Conference on Systems, Process and Control, Melaka, Malaysia, 14/12/2018. https://doi.org/10.1109/SPC.2018.8704144

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Mohd. Azam, Sazuan Nazrah ; Jørgensen, John Bagterp. / Unconstrained and Constrained Model Predictive Control for a Modified Quadruple Tank System. Proceedings of 2018 IEEE Conference on Systems, Process and Control. IEEE, 2018. pp. 147-152

Bibtex

@inproceedings{3d92cb8d8e2141fdb27d97acf9352692,
title = "Unconstrained and Constrained Model Predictive Control for a Modified Quadruple Tank System",
abstract = "In this paper, an implementation of Model Predictive Control (MPC) for a Modified Quadruple Tank System (MQTS) is addressed. The MQTS system is a multi-input-multi-output (MIMO) system and has complicated variables interactions. The aim of this work is to demonstrate the implementation of MPC, including the derivations of unconstrained and constrained MPC equations for the particular system. Besides that, we want to evaluate the performance of the MPCs in terms of the behaviour of the system and to verify should the realisations are physically feasible. For the purpose of the study, a linear discrete-time state space model is employed. The model is from an existing dynamics of the system which comprises deterministic and stochastic components. As for the controller, the MPC consists of a state estimator and a constrained regulator. A Kalman filter is incorporated to estimate the current state from the filtered part while the predictions part is used by the constrained regulator, which is an Optimal Control Problem (OCP) to predict the future output trajectory. The objective of the OCP consists of a tracking error term that penalizes deviations of the predicted outputs from the setpoint and a regularization term that penalizes the changes in the inputs (manipulated variables). The resulting OCP is represented as a Quadratic Programming (QP) is solved and the performance of MPC is demonstrated through simulations using MATLAB.",
keywords = "Mathematical model, Stochastic processes, Predictive control, Regulators, Kalman filters, Linear programming",
author = "{Mohd. Azam}, {Sazuan Nazrah} and J{\o}rgensen, {John Bagterp}",
year = "2018",
doi = "10.1109/SPC.2018.8704144",
language = "English",
isbn = "9781538663271",
pages = "147--152",
booktitle = "Proceedings of 2018 IEEE Conference on Systems, Process and Control",
publisher = "IEEE",
address = "United States",

}

RIS

TY - GEN

T1 - Unconstrained and Constrained Model Predictive Control for a Modified Quadruple Tank System

AU - Mohd. Azam, Sazuan Nazrah

AU - Jørgensen, John Bagterp

PY - 2018

Y1 - 2018

N2 - In this paper, an implementation of Model Predictive Control (MPC) for a Modified Quadruple Tank System (MQTS) is addressed. The MQTS system is a multi-input-multi-output (MIMO) system and has complicated variables interactions. The aim of this work is to demonstrate the implementation of MPC, including the derivations of unconstrained and constrained MPC equations for the particular system. Besides that, we want to evaluate the performance of the MPCs in terms of the behaviour of the system and to verify should the realisations are physically feasible. For the purpose of the study, a linear discrete-time state space model is employed. The model is from an existing dynamics of the system which comprises deterministic and stochastic components. As for the controller, the MPC consists of a state estimator and a constrained regulator. A Kalman filter is incorporated to estimate the current state from the filtered part while the predictions part is used by the constrained regulator, which is an Optimal Control Problem (OCP) to predict the future output trajectory. The objective of the OCP consists of a tracking error term that penalizes deviations of the predicted outputs from the setpoint and a regularization term that penalizes the changes in the inputs (manipulated variables). The resulting OCP is represented as a Quadratic Programming (QP) is solved and the performance of MPC is demonstrated through simulations using MATLAB.

AB - In this paper, an implementation of Model Predictive Control (MPC) for a Modified Quadruple Tank System (MQTS) is addressed. The MQTS system is a multi-input-multi-output (MIMO) system and has complicated variables interactions. The aim of this work is to demonstrate the implementation of MPC, including the derivations of unconstrained and constrained MPC equations for the particular system. Besides that, we want to evaluate the performance of the MPCs in terms of the behaviour of the system and to verify should the realisations are physically feasible. For the purpose of the study, a linear discrete-time state space model is employed. The model is from an existing dynamics of the system which comprises deterministic and stochastic components. As for the controller, the MPC consists of a state estimator and a constrained regulator. A Kalman filter is incorporated to estimate the current state from the filtered part while the predictions part is used by the constrained regulator, which is an Optimal Control Problem (OCP) to predict the future output trajectory. The objective of the OCP consists of a tracking error term that penalizes deviations of the predicted outputs from the setpoint and a regularization term that penalizes the changes in the inputs (manipulated variables). The resulting OCP is represented as a Quadratic Programming (QP) is solved and the performance of MPC is demonstrated through simulations using MATLAB.

KW - Mathematical model

KW - Stochastic processes

KW - Predictive control

KW - Regulators

KW - Kalman filters

KW - Linear programming

U2 - 10.1109/SPC.2018.8704144

DO - 10.1109/SPC.2018.8704144

M3 - Article in proceedings

SN - 9781538663271

SP - 147

EP - 152

BT - Proceedings of 2018 IEEE Conference on Systems, Process and Control

PB - IEEE

ER -