Use this URL to cite or link to this record in EThOS:
Title: Advanced model based control for PEM fuel cell stacks
Author: Ragb, Omar B. K.
Awarding Body: Liverpool John Moores University
Current Institution: Liverpool John Moores University
Date of Award: 2012
Availability of Full Text:
Access from EThOS:
This thesis investigates the application of three advanced control strategies in oxygen ratio control of fuel cell stacks. The major objective of these control schemes is to maintain the oxygen ratio at the desired value of 2 for variable load current as disturbance and system uncertainty in order to prevent oxygen starvation. These strategies include, feed-forward (FF) plus feedback (FB) control scheme, model predictive control (MPC) scheme and multi variable control. All the developed methods have been assessed using a non-linear simulation of the fuel cell stack (FCS) model. Satisfactory control performances in terms of effective regulation and robustness to disturbance and system component change have been achieved. FF control has been developed based on neural network, fuzzy logic (5 & 9 membership functions) and look-up table. A PID controller is used in the feedback to adjust the difference between the requested and the actual oxygen ratio by compensating the FF controller output. The simulation results show that, the fuzzy logic and neural network FF controllers performed better than the traditional look-up table and proportional FF controllers. An inverse model control that is based on a radial basis function (RBF) model has been developed and is used as feed-forward approach, and is used in combination with feedback control. Furthermore, the RBF model is updated on-line to cope with rapid change of load current, significant parameters uncertainty and stack time-varying dynamics, which leads to the inverse control being adaptive. Simulations show the effectiveness of the method in rejecting the rapid change of the load current and a simulated actuator fault.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available