TY  - JOUR
T1  - Design of an Adaptive Neural Voltage-Tracking Controller for
Nonlinear Proton Exchange Membrane Fuel Cell System Based on
Optimization Algorithms
AU - E. Dagher, Khulood 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 15
SP  - 6188
EP  - 6198
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.6188.6198
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.6188.6198
KW  - chaotic particle swarm optimization
KW  -NARMA-L2 neural network
KW  -adaptive inverse controller
KW  -Fuel cell system
KW  -firefly algorithm
KW  -Iraq
AB  - This study proposes an enhancement for the performance of the neural voltage-tracking controller
based on different types of on-line optimization algorithms for nonlinear Proton Exchange Membrane Fuel Cell
(PEMFC) system. The goal of this research is to employ the NARMA-L2 neural model in order to identify and
control the nonlinear system. The task of the proposed nonlinear adaptive neural inverse voltage-tracking
controller is to find precisely and quickly the optimal hydrogen partial pressure action which is used to control
the (PEMFC) stack terminal voltage. Three intelligent optimization algorithms are used to learn and tune the
weights of the neural model, the first one is the FireFly Algorithm (FFA), the second one is the Chaotic Particle
Swarm Optimization (CPSO) algorithm and the third one is the Hybrid Firefly-Chaotic Particle Swarm
Optimization (HFF-CPSO) algorithm. The numerical simulation results show that the NARMA-L2 controller with
(HFF-CPSO) algorithm is more accurate than CPSO and FFA in terms of quickly obtaining the neural controller’s
parameters with high reduction for the number of function evolutions and moreover in its capability of
generating smooth partial pressure control response for the nonlinear (PEMFC) system without voltage
oscillation in the output through investigating under random load-current variations.
ER  -