Speed Control of DC Motors: Optimal Closed PID-Loop Model Predictive Control

One of the lowest level control tasks, especially in robotics and other manufacturing industries, upon which other high-level controls are dependent, is the speed control of a dc motor. Usually, tuning the parameters of the proportional integral derivative (PID) controller for this task employs established conventional methods that expose the knowledge of nominal process model parameters to the control algorithm. These methods have found widespread use. Notwithstanding, a promising line of inquiry is: to search alternate possibilities of a PID being designed to automatically achieve comparable good control performance without using a formal mathematical process model approximation of the actual physical system, such as dc motor plant, in the frequency or time domain. In this paper, we propose an intelligent PID design method, “optimal closed PID-loop model predictive control”, that answers this question using the characteristic settling-time (including delay-time) property of dynamic processes. The performance of this proposed method is benchmarked with popular process-model based methods. Simulation results illustrate the promise and effectiveness of the proposed tuning method, in ensuring good closed-loop performance quality for the dc motor, without using formal process models.