A Reference-Model-Based Artificial Neural Network Approach for a Temperature Control System
Song Xu, Seiji Hashimoto, Yuqi Jiang, Katsutoshi Izaki, Takeshi Kihara, Ryota Ikeda, Wei Jiang
Abstract
Artificial neural networks (ANNs), which have excellent self-learning performance, have been applied to various applications, such as target detection and industrial control. In this paper, a reference-model-based ANN controller with integral-proportional-derivative (I-PD) compensation has been proposed for temperature control systems. To improve the ANN self-learning efficiency, a reference model is introduced for providing the teaching signal for the ANN. System simulations were carried out in the MATLAB/SIMULINK environment and experiments were carried out on a digital-signal-processor (DSP)-based experimental platform. The simulation and experimental results were compared with those for a conventional I-PD control system. The effectiveness of the proposed method was verified.