Partial, noisy and qualitative models for adaptive critic based neurocontrol

The roles of plant models in adaptive critic methods for approximate dynamic programming are considered, with primary focus given to the DHP methodology. In place of complete system identification, partial, approximate, and qualitative models of plant dynamics are considered. Such models are found to be sufficient for successful controller design. As classification is in general easier than regression, the results for qualitative models suggest an avenue for simplifying ongoing system identification in adaptive control applications.