Comparative Performance Study of ACO and ABC Optimization based PID Controller Tuning for Speed Control of DC Motor Drives

Abstract

The aim of this thesis is to design a speed controller of a DC motor by selection of PID parameters using Ant Colony Optimization (ACO) and Artificial Bee Colony Algorithm (ABC). These algorithms are come under the category of bio-inspired optimization techniques. The model of a DC motor is considered as a second order system for speed control and third order system for position control. Here, there is a comparison between conventional tuning methods and optimization techniques of parameters for PID controller. In some cases, it was found that the proposed PID parameters adjusted by optimization technique is better than the conventional techniques like a Ziegler-Nicholls’ method. These proposed optimization methods could be applied for higher order system also to provide better system performance with minimum errors. It is decided to create an objective function which will evaluate the optimum PID gains based on the controlled systems and overall error. This tries to explore the potential of using optimization techniques in controllers and their advantages over conventional methods. PID controller is the most widely used controller in the industry applications, need efficient methods to control the different parameters of the DC motor. The conventional approach is not very efficient due to the presence of non-linearity in the system. The output of the conventional PID system has a quite high overshoot and settling time. The main aim this is to apply two ACO and ABC techniques to design and tuning of PID controller to get an output with better dynamic and static performance. The application of ACO and BA to the PID controller imparts it the ability of tuning itself automatically in an on-line process while the application of optimization algorithm to the PID controller makes it to give an optimum output by searching for the best set of solutions for the PID parameters.