Open AccessPredictive Nonlinear PID Neural Voltage-Tracking Controller Design for Fuel Cell based on Optimization Algorithm
Publication year - 2019
Publication title -
al-maǧallaẗ al-ʻirāqiyyaẗ li-handasaẗ al-ḥāsibāt wa-al-ittiṣālāt wa-al-sayṭaraẗ wa-al-naẓm
Language(s) - English
Resource type - Journals
eISSN - 2617-3352
pISSN - 1811-9212
DOI - 10.33103/uot.ijccce.19.4.6
Subject(s) - pid controller , control theory (sociology) , particle swarm optimization , proton exchange membrane fuel cell , nonlinear system , controller (irrigation) , model predictive control , computer science , artificial neural network , voltage , control engineering , algorithm , engineering , fuel cells , temperature control , artificial intelligence , control (management) , agronomy , physics , electrical engineering , quantum mechanics , chemical engineering , biology
This paper proposes a predictive nonlinear PID neural voltage-tracking controller design for Proton Exchange Membrane Fuel Cell (PEMFC) Model with an on-line auto-tuning intelligent algorithm. The purpose of the proposed robust feedback nonlinear PID neural predictive voltage controller is to find the optimal value of the hydrogen partial pressure action in order to control the stack terminal voltage of the (PEMFC) model for one-step-ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) is utilized as a stable and intelligent robust on-line auto-tuning algorithm to obtain the near-optimal weights for the proposed controller so as to improve the performance index of the system as well as to minimize the energy consumption. The Simulation results demonstrated the effectiveness of the proposed controller compared with the linear PID neural controller.