Temperature regulation with neural networks and alternative control schemes

Currently, neural networks are being used to solve problems related to control. One way to determine the reliability of the neuro-control technique is to test it on a variety of realistic problems. Another way is to compare it directly with existing traditional control techniques, to see whether it works well and where it needs further refinement. In this article, we compare the neuro-control algorithm to three other control algorithms: fuzzy logic control, generalised predictive control, and proportional-plus-integral (PI) control. Each of these four algorithms is implemented on a water bath temperature control system. The four systems are compared through experimental studies under identical conditions with respect to set-point regulation, the effect of unknown load disturbances, large parameter variation, and variable deadtime in the system. It is found that the neuro-control system compares well with the other three control systems and offers encouraging advantages. However, from the results of the experimental studies, the best characteristics of each of these different classes of control systems may be combined for realising a more efficient and intelligent control scheme