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|Title: ||Adaptive peak seeking control of a proton exchange membrane fuel cell|
|Authors: ||METHEKAR, RN|
|Keywords: ||predictive control|
|Issue Date: ||2010|
|Publisher: ||ELSEVIER SCI LTD|
|Citation: ||JOURNAL OF PROCESS CONTROL, 20(2), 73-82|
|Abstract: ||The primary aim of operating any fuel cell (PEMFC) system is to produce the power/electricity at maximum efficiency. The cell voltage/current manipulation appear to be the most suitable choice for controlling the power density. However, the power density exhibits a highly nonlinear and complex dynamic relationship with respect to the cell voltage. Since the process Output variable (i.e. power density) itself is the objective function for the optimization, there exists a singularity at the optimum operating condition. In addition, the location of the optimum operating point changes with time due to the occurrence of variety of disturbances and/or changes in the operating conditions. Thus, the need to operate the PFMFC at its peak power density and track the shifting Optimum turns out to be a challenging control problem. The task of on-line optimizing control of PEMFC poses difficulties in real time control due to its fast dynamics and it is impractical to employ a mechanistic model for locating the changing optimum on-line. In this context the adaptive optimizing control scheme developed by Bamberger and Isermann ( 1978) 111 appears interesting. Their scheme is based on on-line adaptation of a nonlinear black box time series models and facilitates analytical computation of changing optimum. Recently, Bedi et a]. (2007) 121 have developed a closed form multi-step predictive control law under nonlinear internal model control framework using a black-box nonlinear model and employed it for peak power control in PEMFC. From the viewpoint of PEMFC operation, this nonlinear IMC controller meets the demand on the fast computations as a closed form solution is obtained for the nonlinear control problem at each time step. In this work, we propose to develop an adaptive optimizing control scheme, which combines the attractive features of the on-line optimization approach proposed by Bamberger and Isermann (1978)  and closed form control law developed by Bedi et A. (2007) . We demonstrate the effectiveness of the proposed adaptive optimizing scheme by conducting simulation studies on the distributed an along-the-channel model of PEMFC. Analysis of the simulation results indicate that the proposed adaptive optimizing control scheme satisfactorily tracks the shifting optimum operating point in the face of changing unmeasured disturbances (C) 2009|
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