Tuning of PID Controller for Speed Control of DC-Motor by using Generalized Regression Neural Network and Invasive Weed Optimization

Abstract

The Generalized Recurrent Neural Network (GRNN) and Invasive Weed Optimization (IWO) algorithms are two powerful techniques that can be used to optimize motor drive speed. GRNN is a type of artificial neural network designed to process time-series data, while IWO is a metaheuristic optimization technique inspired by the behavior of invasive weed species. To optimize motor drive speed using GRNN and IWO algorithms, data on motor performance over time must be collected and used to train a GRNN model that can predict future motor performance based on past performance. By optimizing the parameters of the GRNN model, the optimal combination of parameters can be found to maximize motor efficiency and performance while minimizing energy consumption and wear and tear on the motor. The objective of this study is to regulate the speed of a Per Magnetic DC (PMDC) motor with high precision and rapid response using a GRNN/IWO controller. The IWO-GRNN controller exhibits superior damping response and reduced overshoot in comparison to conventional GRNN controllers. Additionally, the drive current limiting mechanism ensures that the motor operates within its rated continuous current limit during continuous operation. The IWO-tuned single-loop GRNN controller outperforms the single-loop GRNN controller when tuned. The GRNN-IWO controller provides excellent damping response and minimal overshoot, enabling faster control response of the DC motor, with an accuracy of 98.85% compared to MATLAB-tuned IWO.