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 te chnique 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, vie compare the neuro-control algorithm to three othe r control algorithms; fuzzy logic control, generalized predictive cont rol, and proportional-plus-integral control. Each of these four algori thms is implemented on a water bath temperature control system. The fo ur systems are compared through experimental studies under identical c onditions 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 w ith the other three control systems and offers encouraging advantages. From the results of the experimental studies, however, the best chara cteristics of each of these different classes of control systems may b e combined for realizing a more efficient and intelligent control sche me.