Open AccessGenetic Algorithm and Particle Swarm Optimization Techniques for Inverted Pendulum Stabilization
Author(s) -
S.Suganthi Amudhan*,
Dwivedi Vedvyas J,
Dr.Bhavin Sedani
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f3426.049620
Subject(s) - inverted pendulum , particle swarm optimization , computation , evolutionary computation , flocking (texture) , mathematical optimization , computer science , genetic algorithm , mathematics , algorithm , nonlinear system , physics , quantum mechanics
Inverted Pendulum is a popular non-linear, unstable control problem where implementation of stabilizing the pole angle deviation, along with cart positioning is done by using novel control strategies. Soft computing techniques are applied for getting optimal results. The evolutionary computation forms the key research area for adaptation and optimization. The approach of finding optimal or near optimal solutions to the problem is based on natural evolution in evolutionary computation. The genetic algorithm is a method based on biological evolution and natural selection for solving both constrained and unconstrained problems. Particle swarm optimization is a stochastic search method inspired by collective behavior of animals like flocking of birds, schooling of fishes, swarming of bees etc. that is suited to continuous variable problems. These methods are applied to the inverted pendulum problem and their performance studied.