MIMO furnace control with neural networks

The development of a multilayered neural network control scheme for a multi-input multi-output (MIMO) furnace is discussed. The scheme is based on the back-error-propagation algorithm and uses neural net emulators and controllers. The neural network models are trained using only the input-output characteristics of the plant without the need for using any initial conventional controller or knowledge regarding dynamics. The scheme allows online learning of the neural net emulators and controllers whereby their performance can be further improved. The approach is applicable to a wide variety of open-loop stable systems. Experiments are conducted to see how well the neurocontrol scheme compares with two established control schemes implemented for the same process. Comparisons are made with respect to set-point changes, load disturbance rejection, parameter variations, and controller saturations. The experimental results show that the neurocontrol scheme has considerable robustness and performs better than the other two controllers