TITLE:
Neural Model-Based Self-Tuning PID Strategy Applied to PEMFC
AUTHORS:
Cédric Damour, Michel Benne, Brigitte Grondin-Perez, Jean-Pierre Chabriat
KEYWORDS:
Self-Tuning PID Controller; Artificial Neural Network Model; Proton Exchange Membrane Fuel Cell; Real-Time Control Scheme; Experimental Implementation
JOURNAL NAME:
Engineering,
Vol.6 No.4,
March
14,
2014
ABSTRACT: This paper illustrates the benefits of a self-tuning PID
strategy applied to a proton exchange membrane fuel cell system. Controller parameters
are updated on-line, at each sampling time, based on an instantaneous linearization
of an artificial neural network model of the process and a General Minimum Variance
control law. The self-tuning PID scheme allows managing nonlinear behaviors of the
system while avoiding heavy computations. The applicability, efficiency and robustness of the proposed control strategy are experimentally confirmed using
varying control scenarios. In this aim, the original built-in controller is overridden
and the self-tuning PID controller is implemented externally
and executed on-line. Experimental results show good performance in setpoint tracking accuracy and robustness against plant/model mismatch.
The proposed strategy appears to be a promising alternative to heavy computation
nonlinear control strategies and not optimal linear control strategies.