Download PDFOpen PDF in browser

Intelligent Settling-time based PID Tuning Algorithm for DC motor speed control

EasyChair Preprint no. 2002

10 pagesDate: November 20, 2019

Abstract

One of the lowest level control tasks, upon which other high-level controls are dependent, is the speed control of a dc motor, especially in robotics and other manufacturing indus- tries. Usually, tuning the parameters of the proportional-integral- derivative (PID) control for this task, employs the knowledge of process model parameters. These classical methods are powerful, but can the PID control algorithm achieve similar good control performance without using a parametric mathematical model information about the actual dc motor plant to be controlled? In this paper we propose an answer to this question. First, using the intuitive notion of unity loop gain, the closed loop PID control loop is analyzed. It then leads to an ideal or optimal close-loop model response that the PID control algorithm plus physical process loop dynamics will always be forced to follow. The final result is an intelligent tuning algorithm that integrates the use of the open-loop settling-time and time-delay values of the actual open-loop process behaviour using a fuzzy inference system. Simulation results illustrate the promise and effectiveness of the proposed tuning method in guaranteeing good closed-loop performance, without using the knowledge of a process model.

Keyphrases: algorithm, DC motors, dead time, fuzzy inference, intelligent control, loop gain, Model Reference Adaptive Control, PID, process models, settling time, speed control, Tuning, two degree of freedom

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:2002,
  author = {Oluwasegun Somefun and Kayode Akingbade and Folasade Dahunsi},
  title = {Intelligent Settling-time based PID Tuning Algorithm for DC motor speed control},
  howpublished = {EasyChair Preprint no. 2002},

  year = {EasyChair, 2019}}
Download PDFOpen PDF in browser