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Anytime Models in Fuzzy Control

In time critical applications, anytime mode of operation offers a way to ensure continuous operation and to cope with the possibly dynamically changing time and resource availability. Soft Computing, especially fuzzy model based operation proved to be very advantageous in power plant control where the high complexity, nonlinearity, and possible partial knowledge usually limit the usability of classical methods. Higher Order Singular Value Decomposition based complexity reduction makes possible to convert different classes of fuzzy models into anytime models, thus offering a way to combine the advantages, like low complexity, flexibility, and robustness of fuzzy and anytime techniques. By this, a model based anytime control methodology can be suggested which is able to keep on continuous operation using nonexact, approximate models of the plant, thus preventing critical breakdowns in the operation. In this paper, an anytime modeling method is suggested which makes possible to use complexity optimized fuzzy models in control. The technique is able to filter out the redundancy of fuzzy models and can determine the near optimal non-exact model of the plant considering the available time and resources. It also offers a way to improve the granularity (quality) of the model by building in new information without complexity explosion.