Comparative Performance Study of PSO and BFO Algorithm based PID Controller Tuning for Speed Control of DC Motor Drives

Abstract

Particle Swarm Optimization (PSO) algorithm and Bacterial Foraging Optimization (BFO) algorithm are stochastic optimization techniques that come under the category of bio inspired optimization techniques. The objective of thesis is to analyze the implementation of PSO algorithm and BFO algorithm to PID Controller optimal tuning for DC machine model servo speed control. The model of a separately excited DC motor is considered as a second order system for speed control. The comparative study is based on step response, time domain analysis and frequency domain analysis. First various classical PID tuning methods are discussed and implemented on DC machine model and their results are analyzed. Next optimal tuning methods of PID tuning using various Error Performance indices like IAE, ISE, ITAE and ISTE is applied for DC machine model. Than PSO algorithm is utilized to optimize the parameters of PID controller to obtain optimal performance for DC machine model. Next BFO algorithm is implemented for DC machine model control using PID controller by obtaining optimal values of Kp, Ki, Kd parameters. The results obtained for PSO algorithm and BFO algorithm are compared mainly on basis of time domain analysis and frequency domain analysis. MATLAB is used for all the calculation and analysis necessary for the thesis. 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 of thesis is to apply two techniques namely PSO and BFO to design and tuning of PID controller to get an output with better dynamic and static performance. The application of PSO and BFO 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.