By Topic

Hybrid Predictive Control design based on Particle Swarm Optimization and Genetic Algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Nezhad, Y.M. ; Dept. of Instrum. & Autom., Pet. Univ. of Technol., Ahwaz, Iran ; Shahbazian, M.

This paper discusses a model predictive control approach to hybrid systems with continuous and discrete inputs. The algorithm, which takes into account a model of a hybrid system, described as Hybrid Automaton. However, to avoid computational complexity and computation time, the nonlinear optimization problem is solved by evolutionary algorithms (EA) such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). We have applied both GA and PSO algorithms for nonlinear optimization in Hybrid Predictive Control (HPC) for the start-up of a Continuous Stirred-Tank Reactor (CSTR). The simulation results show the good performance of approaches and their capability to use in online application.

Published in:

Computer Research and Development (ICCRD), 2011 3rd International Conference on  (Volume:2 )

Date of Conference:

11-13 March 2011