Design of Neuro-Controller and its Application to Process Control

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Aim of the study is to design the neuro-controller and apply it to process control problems. In this study, we have designed three kinds of neuro-controllers, which are called series type, parallel type, and self-tuning type neuro-controllers. For the series type one, we have proposed the direct inverse control algorithm by using neural networks. In the parallel type one, we have introduced a feed-back error learning algorithm. As the last type, we have designed a self-tuning PID type neuro-controller where the proportional, intergral, and derivative gains are tuned in such a way that the error between plant output and desired output is minimized. After performing the neuro-controller designed here by simulations, we have determined the above PID gains by changing the structure of neural networks, Then we have applied the neuro-controller to real process control systems. First, we have applied the temperature control for water-bath and heating furnace and then applied the speed control of electric vehicle systems. From these applications, we have shown that proposed neuro-controller has worked well in real application problems.