Radial Basis Function Neural Network Control for Coupled Water Tank - Halim Mudia (Indonesian Journal of Artificial Intelligence and Data Mining, 2020)
The level and flow control in tanks are the heart of all chemical engineering system. The control of liquid level in tanks and flow between tanks is a basic problem in the process industries. Many times the liquids will be processed by chemical or mixing treatment in the tanks, but always the level of fluid in the tanks must be controlled and the flow between tanks must be regulated in presence of non-linearity. Therefore, in this paper will use neural network based on radial basis function (RBF) to control of  level 2 in the tank 2 with the setpoint of 10 centimeters and can follow the setpoint changes to 8 centimeters given in 225 seconds. The results show that neural netwotk based on radial basis function can follow setpoint given with steady state error is 0 cm, overshoot is 0%, rising time is 48 seconds, settling time is 52 seconds and can follow setpoint changes in 51 seconds.