Neural Optimal Control of PEM Fuel Cells With Parametric CMAC Networks

P.E.M. Almeida, M.G. Simoes
2005 IEEE transactions on industry applications  
This work demonstrates an application of the Parametric CMAC (P-CMAC) Network --a neural structure derived from Albus's CMAC algorithm and Takagi-Sugeno-Kang parametric fuzzy inference systems. It resembles the original CMAC proposed by James Albus in the sense that it is a local network, (i.e., for a given input vector); only a few of the networks nodes (or neurons) will be active and will effectively contribute to the corresponding network output. The internal mapping structure is built in
more » ... h a way that it implements, for each CMAC memory location, one linear parametric equation of the network input strengths. First, a new approach to design Neural Optimal Control (NOC) systems is proposed. Gradient descent techniques are still used here to adjust network weights; but this approach has many differences when compared to classical error back-propagation algorithm. Then, P-CMAC is used to control output voltage of a Proton Exchange Membrane-Fuel Cell (PEM-FC), by means of NOC. The proposed control system allows the definition of an arbitrary performance/cost criterion to be maximized/minimized, resulting in an approximated optimal control strategy. Practical results of PEM-FC voltage behavior at different load conditions are shown, to demonstrate effectiveness of the NOC algorithm.
doi:10.1109/tia.2004.836135 fatcat:f2fejezuzra47evifi4kmls2se