INTELLIGENT ALGORITHM BASED ADAPTIVE CONTROL OF NONLINEAR SYSTEM

Abstract

Evolutionary algorithms and Swarm Intelligent Algorithms, grouped together as nature-inspired algorithms. Evolutionary algorithms have inspired researchers all around the world for solving optimization problem. But now a day’s besides evolutionary algorithms, swarm intelligent algorithm has also gained importance in solving complex and non-linear problems. Particle Swarm Optimization was the first one to be explored in this field and showed improved results. Other Swarm based algorithms such as the Artificial Bee Colony (ABC), the Bacterial Foraging Optimization Algorithm (BFOA), Firefly Algorithm (FFA) etc. have been proposed recently to deal with different types of optimization problems. Several advancements have also been made in these algorithms. This dissertation carves out a comparative analysis of Particle Swarm Optimization (PSO), Bacterial Foraging Algorithm (BFA) and Hybrid PSO-BFA in detail along with simulation results carried out on two different types of problems. In first problem a controller is designed for buck and boost converter, different controller parameters are found using all the three algorithms and comparative analysis is made on the basis of response given by them. Second problem is liquid level control of surge tank system. In this reference input and liquid level of surge tank is tried to made track using PSO, BFA and Hybrid PSO-BFA and analysis is drawn on the basis of simulation result. In the last brief conclusion is drawn regarding all the three algorithms.