Neural optimal control of PEM-fuel cells with parametric CMAC networks

This work shows an application of the parametric CMAC (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 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 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. 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.