Support vector machine based predictive controller with swarm intelligence for PEMFC

Lu, Jun, and Zahedi, Ahmad (2012) Support vector machine based predictive controller with swarm intelligence for PEMFC. In: Proceedings of the 22nd Australasian Universities Power Engineering Conference, pp. 1-6. From: Australasian Universities Power Engineering Conference 2012, 26-29 September 2012, Bali, Indonesia.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website: http://ieeexplore.ieee.org/Xplore/defden...
 
3


Abstract

The modelling and control of the proton exchange membrane fuel cell (PEMFC) possess great challenges due to PEMFC's inherent nonlinearities and time-varying properties. The objective of this paper is to propose a nonlinear model predictive control (MPC) strategy based on the support vector machine (SVM) and the particle swarm optimization (PSO). SVM is employed to establish the predictive model by mapping PEMFC performance as a function of operating conditions. PSO is then used to solve the optimization problem formulated by MPC. The SVM model and MPC strategy are implemented in the MATALB environment. Simulation results demonstrate the proposed control strategy can achieve robust control of PEMFC voltage with good performance in tracking reference trajectory.

Item ID: 25285
Item Type: Conference Item (Refereed Research Paper - E1)
Keywords: model predictive control (MPC), particle swarm optimization (PSO), proton exchange membrane fuel cell (PEMFC), support vector machine (SVM)
Related URLs:
ISBN: 978-1-4673-2933-0
Date Deposited: 07 Mar 2013 02:00
FoR Codes: 09 ENGINEERING > 0906 Electrical and Electronic Engineering > 090608 Renewable Power and Energy Systems Engineering (excl Solar Cells) @ 100%
SEO Codes: 85 ENERGY > 8505 Renewable Energy > 850599 Renewable Energy not elsewhere classified @ 100%
Downloads: Total: 3
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page