Artificial Neural Network Model-Based Predictive Real-Time Control Of A Cascaded Two Tank System - Bamimore, A.; Kehinde-Abajo, T.E.; Sobowale, N.B.; Ogunba, K.S.; Osunleke, A.S.; Taiwo, O. (Ife Journal of Technology, 2019)
The development of reliable first principle models that totally describe the dynamic behaviour of nonlinear systems is a difficult and time-consuming task. This poses a major challenge in the development of nonlinear model-based controllers for industrial processes. Hence, an alternative approach which involves the use of artificial neural network (ANN) models for real-time predictive control of a cascaded two tank system housed in our laboratory is explored in this research work. To achieve this, the tank process was excited by well-designed input signals within a specified range to obtain real-time input-output data at a sampling time of 2s. The datasets obtained were used to fit recurrent neural network (RNN) and feedforward neural network (FFNN) models for the process. Thereafter, the models were used in the design of predictive controllers. The designed controllers were compiled and deployed to an Arduino microcontroller interfaced with the process to achieve real-time control. Validation results showed both models have good fits. The closed loop experimental results also showed good setpoint tracking performance for both controllers.