scholarly journals Feedback-Linearization-Based Fuel-Cell Adaptive-Control Paradigm in a Microgrid Using a Wavelet-Entrenched NeuroFuzzy Framework

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1850
Author(s):  
Muhammad Awais ◽  
Laiq Khan ◽  
Saghir Ahmad ◽  
Mohsin Jamil
Keyword(s):  
Adaptive Control ◽  
Fuel Cell ◽  
Feedback Linearization ◽  
Solid Oxide ◽  
Performance Parameters ◽  
Nonlinear Functions ◽  
Adaptive Feedback ◽  
Control Paradigm ◽  
Control Scheme ◽  
Control Schemes

The article portrays an adaptive control paradigm for the swift response of a solid-oxide fuel cell (SOFC) in a grid-connected microgrid. The control scheme is based on an adaptive feedback-linearization-embedded fully recurrent NeuroFuzzy Laguerre wavelet control (FBL-FRNF-Lag-WC) framework. The nonlinear functions of feedback linearization (FBL) are estimated using a fully recurrent NeuroFuzzy Laguerre wavelet control (FRNF-Lag-WC) architecture with a recurrent Gaussian membership function in the antecedent part and a recurrent Laguerre wavelet in the consequent part, respectively. The performance of the proposed control scheme is validated for various stability, quality, and reliability factors obtained through a simulation testbed implemented in MATLAB/Simulink. The proposed scheme is compared against adaptive NeuroFuzzy, PID, and adaptive PID (aPID) control schemes using different performance parameters for a grid-connected load over 24 h.

Adaptive Feedback Linearization Control of SISO Nonlinear Processes Using a Self-Generating Neural Network-Based Approach

2011 ◽  
Vol 6 (1) ◽  
Author(s):  
Karim Salahshoor ◽  
Amin Sabet Kamalabady
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Adaptive Control ◽  
Feedback Linearization ◽  
Learning Algorithm ◽  
Rbf Neural Networks ◽  
Adaptive Feedback ◽  
Control Scheme ◽  
Feedback Linearization Control ◽  
Adaptive Control Scheme

This paper presents a new adaptive control scheme based on feedback linearization technique for single-input, single-output (SISO) processes with nonlinear time-varying dynamic characteristics. The proposed scheme utilizes a modified growing and pruning radial basis function (MGAP-RBF) neural network (NN) to adaptively identify two self-generating RBF neural networks for online realization of a well-known affine model structure. An extended Kalman filter (EKF) learning algorithm is developed for parameter adaptation of the MGAP-RBF neural networks. The MGAP-RBF growing and pruning criteria have been endeavored to enhance its performance for online dynamic model identification purposes. A stability analysis has been provided to ensure the asymptotic convergence of the proposed adaptive control scheme using Lyapunov criterion. Capabilities of the adaptive feedback linearization control scheme is evaluated on two nonlinear CSTR benchmark processes, demonstrating good performances for both set-point tracking and disturbance rejection objectives.

Process Flow Model of Combined High Temperature Fuel Cell Operated With Mixture of Methane and Carbon Dioxide

2011 ◽  
Author(s):  
Farshid Zabihian ◽  
Alan S. Fung
Keyword(s):  
Carbon Dioxide ◽  
Fuel Cell ◽  
Flow Model ◽  
Carbon Dioxide Concentration ◽  
Solid Oxide ◽  
Specific Work ◽  
Performance Parameters ◽  
Reference Case ◽  
Pure Methane ◽  
Level Model

This paper investigates the impacts of carbon dioxide concentration in the inlet fuel on the performance of a hybrid tubular solid oxide fuel cell (SOFC) and gas turbine (GT) cycle with two configurations: system with and without anode exhaust recirculation. The reference case is introduced when the system is fueled by pure methane. Then, the performance of the hybrid SOFC-GT system is investigated when methane is partially replaced by CO2 from concentration of 0% to 90% with an increment of 5% at each step. The steady-state macro level model of the SOFC-GT hybrid system was developed in Aspen Plus® using built in and user-defined modules. The performance of the system was monitored by estimating and recording performance parameters, such as SOFC and system thermal efficiency; net and specific work of SOFC, GT, and cycle as a whole; air to fuel ratio; and mass and molar flow rate and temperature of various streams. The results demonstrate that the CO2 fraction in the inlet fuel has remarkable influences on the system’s operating parameters, such as efficiency and specific work.

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