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Licensed Unlicensed Requires Authentication Published by De Gruyter October 22, 2019

Level Control of Coupled Tank System Based on Neural Network Techniques

B. S. Sousa, F. V. Silva and A. M. F. Fileti

Abstract

The control design of coupled tanks is not an easy task due to the nonlinear characteristic of the valves, and the interactions between the controlled variables. Those features pose a challenge in the automatic control, so that linear controllers, such as conventional PID, might not work properly for regulating this MIMO system. Some advanced control techniques (e. g. control based on neural networks) can be used since neural networks are universal approximators which can deal with nonlinearities and interactions between process variables. In the present work, an experimental investigation was performed presenting a comparison between two neural network-based techniques and testing the feasibility of these techniques in the coupled tanks system. First principles simulations helped to find suitable parameters for the controllers. The results showed that the model predictive control based on artificial neural networks presented the best performance for supervisory tests. On the other hand, the inverse neural network needed a very accurate model and small plant-model mismatches led to undesirable offsets.

Nomenclature

wk,j

Sinaptic weight of the connection between neuron “k” and neuron “j

bk

Bias of the layer “k

φ

Activation function

xj

Neuron input

yk

Neuron output

ym

Predicted output

yr

Reference trajectory

yp

Measured output

u’

Calculated future input value

u

Current input

J

Objective function

Np

Prediction Horizon

Nc

Control Horizon

w

Weight of the control action in the objective function

wy

Weight of the control error in objective function

dk

Discrepancy between the measured value of the plant and the predicted value

yc

Output corrected by the disturbance model

Δt

Sample time

ysp1

Set point of level 1

ysp2

Set point of level 2

yi

Level of the tank “i”

Pi

Power of the pump “i”

ρ

Mass Density

Aj

Cross Sectional Area of the tank “j”

Cvj

Valve coefficient

Lc

Height of the tank

T

Time to complete the volume of the vessel

Δt

Sample time

ts

Settling time

Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001.

Appendix

A

A.1 Simulink Figures

Figure 13: Simulink diagram used to collect identification data.

Figure 13:

Simulink diagram used to collect identification data.

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Received: 2019-06-13
Revised: 2019-09-10
Accepted: 2019-09-11
Published Online: 2019-10-22

© 2019 Walter de Gruyter GmbH, Berlin/Boston