Evaluating the Performance of the Well-Known Controller Tuning Methods for the Flow Control Using the Process Model

Assani, Nur; Matić, Petar; Kezić, Danko

doi:10.1109/codit58514.2023.10284155

2023 9th International Conference on Control, Decision and Information Technologies (CoDIT) 2023

DOI: 10.1109/codit58514.2023.10284155

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Evaluating the Performance of the Well-Known Controller Tuning Methods for the Flow Control Using the Process Model

Nur Assani,

Petar Matić,

Danko Kezić

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2024

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Modeling Fluid Flow in Ship Systems for Controller Tuning Using an Artificial Neural Network

Assani,

Matić,

Kezić

et al. 2024

JMSE

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Flow processes onboard ships are common in order to transport fluids like oil, gas, and water. These processes are controlled by PID controllers, acting on the regulation valves as actuators. In case of a malfunction or refitting, a PID controller needs to be re-adjusted for the optimal control of the process. To avoid experimenting on operational real systems, models are convenient alternatives. When real-time information is needed, digital twin (DT) concepts become highly valuable. The aim of this paper is to analyze and determine the optimal NARX model architecture in order to achieve a higher-accuracy model of a ship’s flow process. An artificial neural network (ANN) was used to model the process in MATLAB. The experiments were performed using a multi-start approach to prevent overtraining. To prove the thesis, statistical analysis of the experimental results was performed. Models were evaluated for generalization using mean squared error (MSE), best fit, and goodness of fit (GoF) measures on two independent datasets. The results indicate the correlation between the number of input delays and the performance of the model. A permuted k-fold cross-validation analysis was used to determine the optimal number of voltage and flow delays, thus defining the number of model inputs. Permutations of training, test, and validation datasets were applied to examine bias due to the data arrangement during training.

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Modeling Fluid Flow in Ship Systems for Controller Tuning Using an Artificial Neural Network

Assani,

Matić,

Kezić

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

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Evaluating the Performance of the Well-Known Controller Tuning Methods for the Flow Control Using the Process Model

Cited by 1 publication

References 15 publications

Modeling Fluid Flow in Ship Systems for Controller Tuning Using an Artificial Neural Network

Modeling Fluid Flow in Ship Systems for Controller Tuning Using an Artificial Neural Network

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