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Neural optimal control of PEM fuel cells with parametric CMAC networks
- Source :
- IEEE Transactions on Industry Applications. Jan-Feb, 2005, Vol. 41 Issue 1, p237, 9 p.
- Publication Year :
- 2005
-
Abstract
- 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. Index Terms--Control systems, fuel cells (FCs), neural networks, optimal control.
Details
- Language :
- English
- ISSN :
- 00939994
- Volume :
- 41
- Issue :
- 1
- Database :
- Gale General OneFile
- Journal :
- IEEE Transactions on Industry Applications
- Publication Type :
- Academic Journal
- Accession number :
- edsgcl.128671666