Design of an Adaptive Neural Controller Applied to Pressure Control in Industrial Processes

In the present work, an adaptive neural controller is designed and applied to pressure control in industrial processes, implementing artificial neural networks and adding an algorithm based on adaptive interaction theory to them, with which it is possible to obtain an intelligent controller. The controller based on neural networks has adaptive properties through the new Brandt-Lin algorithm; this will allow controlling the process without a training phase and prior knowledge of the plant. Therefore, the intelligent controller can adapt online to changes in industrial processes. The challenge of this work is the implementation of this intelligent controller in a real plant (Festo MPS PA Compact Workstation), whose study will be carried out in a training kit that will simulate the industrial process to be controlled. Finally, the work will demonstrate the supremacy of the proposed controller compared with a classic PID controller, being far superior in all the simulations carried out in the control of the real plant.