Neural optimal control of PEM fuel cells with parametric CMAC networks

This paper demonstrates an application of the parametric cerebellar model articulation controller (P-CMAC) network-a neural structure derived from Albus' CMAC algorithm and Takagi-Sugeno-Kang parametric fuzzy inference systems. It resembles the original CMAC proposed by Albus in the sense that it is a local network, i.e., for a given input vector, only a few of the networks neurons will be active and will effectively contribute to the corresponding network output. The internal mapping structure is built in such 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 backpropagation algorithm. Then, P-CMAC is used to control the 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 the effectiveness of the NOC algorithm.