Identification and Multi-Objective H∞ Control Design for a Quadruple Tank System

Harsha, Sanangala; Vinod, Dinsha; Saikrishna, P.S.

doi:10.1109/icspc47137.2019.9068067

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…Between white-box and black-box models, there exists a grey area that offers a third way to create engineering system models. This grey-box approach utilizes a priori knowledge about the process and estimates the unknown parts of the model from experimentally measured data [3]. When there is good prior knowledge of the mechanisms underlying the behavior of a process, the relevant equilibrium equations can be expressed as a set of differential equations.…”

Section: Introductionmentioning

confidence: 99%

“…In one of the first studies on QTS [19], a decentralized PI control was designed for the QTS configured with a multivariable RHP zero. Other authors have applied internal model control [22], multivariable H ∝ control [3], quantitative feedback control [23], LQG optimal control [24], predictive control [25,26], and distributed model predictive control [27]. More recent works have applied nonlinear techniques to the QTS such as sliding mode control [28,29], feedback linearization [20], fuzzy control [30,31], and neural networks [32], among others.…”

Section: Introductionmentioning

confidence: 99%

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Grey-Box Modeling and Decoupling Control of a Lab Setup of the Quadruple-Tank System

Garrido,

Garrido-Jurado,

Vázquez

2024

Actuators

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The quadruple-tank system (QTS) is a popular educational resource in universities for studying multivariable control systems. It enables the analysis of the interaction between variables and the limitations imposed by multivariable non-minimum phase zeros, as well as the evaluation of new multivariable control methodologies. The works utilizing this system present a theoretical model that may be too idealistic and based on erroneous assumptions in real-world implementations, such as the linear behavior of the actuators. In other cases, an identified linear model is directly provided. This study outlines the practical grey-box modeling procedure conducted for the QTS at the University of Cordoba and provides guidance for its implementation. A configurable nonlinear model was developed and controlled in a closed loop using different controllers. Specifically, decentralized control, static decoupling control, and simplified decoupling control were compared. The simulation designs were experimentally validated with high accuracy, demonstrating that the conclusions reached with the developed model can be extrapolated to the real system. The comparison of these three control designs illustrates the advantages and disadvantages of decoupling in certain situations, especially in the presence of non-minimum phase zeros.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Grey-Box Modeling and Decoupling Control of a Lab Setup of the Quadruple-Tank System

Garrido,

Garrido-Jurado,

Vázquez

2024

Actuators

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“…Inoescu et al 5 proposed a relay‐based auto‐tuning procedure to regulate proportional integral derivative (PID) parameters for liquid‐level control of the QT system. Harsha et al 6 modeled QT system with a gray box approach and implemented a H ∞ controller based on multi‐objective linear matrix inequality for the real‐time liquid level control of the QT system.…”

Section: Introductionmentioning

confidence: 99%

Application of recurrent neural networks for modeling and control of a quadruple‐tank system

et al. 2023

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The quadruple tank (QT) system consists of four interacting tanks and can switch between the minimum and non‐minimum phase behavior with changes in the positions of pump valves and is considered a benchmark control problem. In the present study, long‐short term memory (LSTM), a type of recurrent neural networks (RNN) is designed for the benchmark QT system based on the model‐based control framework. Random input–output sequences are generated from the white box model of the QT system to train an LSTM network model. The LSTM network is tuned by adjusting its hyperparameters such as the number of hidden layers, hidden units, and epochs to minimize the prediction error on the test data. The trained model is cross validated both during and after training to avoid overfitting. Once a reasonably reliable model is obtained, another LSTM network is trained for use as a controller. The network architecture is constantly modified till the controller is able to track the test setpoints with minimum error. This procedure is repeated with a gated recurrent unit (GRU) network and the servo and regulatory response of both the network models and controller are evaluated in terms of standard performance measure namely root mean square error (RMSE), integral square error (ISE), and control effort (CE). It is observed that the controller designed based on RNN performs better than a conventional centralized controller.

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Identification and Multi-Objective H∞ Control Design for a Quadruple Tank System

Cited by 2 publications

References 15 publications

Grey-Box Modeling and Decoupling Control of a Lab Setup of the Quadruple-Tank System

Grey-Box Modeling and Decoupling Control of a Lab Setup of the Quadruple-Tank System

Application of recurrent neural networks for modeling and control of a quadruple‐tank system

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