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- W3202204263 abstract "Conical tank system (CTS) is a highly nonlinear process due to its varyingcross-sectional area and it is a challenging task to design a controller for maintaining itslevel. Model based controllers are preferred to control such systems so that it will provideimproved performance, greater robustness, better insight about system behaviour and costefficiency. For designing model-based controller, the estimated model should be moreaccurate with less error. This paper proposes an efficient modelling of a CTS bycomparing different supervised algorithms for System Identification (SI) such as NeuralNetwork (NN) modelling, Auto Regressive Integrated Moving Average model(ARIMA) modelling and Long Short-Term Memory (LSTM) modelling. The models aredeveloped using the experimental data obtained from the real time laboratory conicaltank system and modelling through simulation using MATLAB and Google colabsoftware tools. This study reveals that LSTM modelling gives the best performance wthless mean square error (MSE) when compared to other techniques and it is proposed tobe used for designing the model based predictive controller to get better performance androbustness." @default.
- W3202204263 created "2021-10-11" @default.
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- W3202204263 date "2021-06-01" @default.
- W3202204263 modified "2023-09-23" @default.
- W3202204263 title "Modelling A Nonlinear Conical Tank System Using Supervised Learning Algorithms" @default.
- W3202204263 hasPublicationYear "2021" @default.
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