A Comparison of Adaptive PID Methodologies Controlling a DC Motor With a Varying Load

This work addresses the problem of controlling un-known and time varing plants for industrial aplications. To deal with such problem several Self-Tuning Controllers with a Proportional Integral and Derivative (PID) struc-ture have been chosen. The selected controllers are based on different methodologies, and some use implicit identifi-cation techniques (Single Neuron and Support Vector Ma-chine) while the others use explicit identification (Dahlin, Pole placement, Deadbeat and Ziegler-Nichols) based in the Least Squares Method. The controllers were tested on a real DC motor with a varying load. The results have shown that all the tested methods were able to properly control an unknown plant with varying dynamics.