Implementing optimization of PID controller for DC motor speed control

Abstract

The point of this paper presents an optimization technique which is flexible and quick tuning by using a genetic algorithm (GA) to obtain the optimum proportional-integral-derivative (PID) parameters for speed control of aseparately excited DC motor as a benchmark for performance analysis. The optimization method is used for searching for the proper value of PID parameters. The speed controller of DC motor using PID tuning method sincludes three types: MATALB PID tunner app., modified Ziegler-Nicholsmethod and genetic algorithm (GA). PID controller parameters (Kp, Ki and Kd) will be obtained by GA to produce optimal performance for the DC motor control system. Simulation results indicate that the tuning method of PID by using a genetic algorithm is shown to create the finest result in system performance such as settling time, rise time, percentage of overshoot and steady state error. The MATLAB/Simulink software is used to model and simulate the proposed DC motor controller system.

Developing low-cost two wheels balancing scooter using proportional derivative controller

“…Other parameters that should be put in the Kalman filter loop are the noise covariance Ro and the process noise covariance Qo. The predictor equations are as (12), (13):…”

“…The (12) calculates state estimate of the state variable, and error covariance estimate is calculated in (13).…”

“…This balancing platform is a nonlinear system. However, a linear controller such as proportional-integral-derivative (PID) can balance the system [12]- [16]. To get a better response in controlling a nonlinear system using PID, some researchers use hybrid PID [17]- [19].…”

Enhancement of motor speed identification using artificial neural networks

“…It is a powerful tool [8], [10]. This integrated approach was able to properly determine the motor's rotational speed in a simulated experiment using DTC [11], [12]. As a Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  Enhancement of motor speed identification using artificial neural networks (Arshad B. Salih) 1389 result, we may replace the DTC system's speed sensor with ant colony optimization rw-back propagation neural network-based speed identification and perform direct toque control for speed sensor less operation [13], [14].…”

Particle Swarm Optimization (PSO) Tuning of PID Control on DC Motor

“…Researchers began to study the intelligent behavior of animals to be applied in solving optimization problems such as Whale Optimizer Algorithm [39], Fish Migration Optimization Algorithm [40], Grey Wolf Optimizer [41], Artificial Bee Colony Algorithm [42], Bat Algorithm [43], Harris Hawk Optimization [44] [45]. Several optimization methods based on conventional methods and intelligent methods have been widely used to optimize PID parameters on DC motors [46][47] [48][49] [50]. In this study, one of the smart methods for tuning PID parameters on a DC motor will be used, namely the Particle Swarm Optimization (PSO) method [51].…”