Modelling A Nonlinear Conical Tank System Using Supervised Learning Algorithms

Document Type : Primary Research paper


Department of Instrumentation and Control Systems Engineerimg, PSG College of Technology,Coimbatore, Tamilnadu.


Conical tank system (CTS) is a highly nonlinear process due to its varying
cross-sectional area and it is a challenging task to design a controller for maintaining its
level. Model based controllers are preferred to control such systems so that it will provide
improved performance, greater robustness, better insight about system behaviour and cost
efficiency. For designing model-based controller, the estimated model should be more
accurate with less error. This paper proposes an efficient modelling of a CTS by
comparing different supervised algorithms for System Identification (SI) such as Neural
Network (NN) modelling, Auto Regressive Integrated Moving Average model
(ARIMA) modelling and Long Short-Term Memory (LSTM) modelling. The models are
developed using the experimental data obtained from the real time laboratory conical
tank system and modelling through simulation using MATLAB and Google colab
software tools. This study reveals that LSTM modelling gives the best performance wth
less mean square error (MSE) when compared to other techniques and it is proposed to
be used for designing the model based predictive controller to get better performance and