Beyou, S., Grondin-Perez, B., Benne, M., Damour, C. and Chabriat, J.P. (2009) Control Improvement of a C Sugar Cane Crystallization Using an Auto-Tuning PID Controller Based on Linearization of a Neural Network. Proceeding of the World Academy of Science Engineering and Technology, Paris, June 2009, 190-195.

has been cited by the following article:

• 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.