Application of recurrent neural networks for modeling and control of a quadruple-tank system

Abstract

The quadruple tank (QT) system consists of four interacting tanks and can switch between the minimum and non-minimum phase behavior with changes in the positions of pump valves and is considered a benchmark control problem. In the present study, long-short term memory (LSTM), a type of recurrent neural networks (RNN) is designed for the benchmark QT system based on the model-based control framework. Random input–output sequences are generated from the white box model of the QT system to train an LSTM network model. The LSTM network is tuned by adjusting its hyperparameters such as the number of hidden layers, hidden units, and epochs to minimize the prediction error on the test data. The trained model is cross validated both during and after training to avoid overfitting. Once a reasonably reliable model is obtained, another LSTM network is trained for use as a controller. The network architecture is constantly modified till the controller is able to track the test setpoints with minimum error. This procedure is repeated with a gated recurrent unit (GRU) network and the servo and regulatory response of both the network models and controller are evaluated in terms of standard performance measure namely root mean square error (RMSE), integral square error (ISE), and control effort (CE). It is observed that the controller designed based on RNN performs better than a conventional centralized controller.