Analysis of Model Set and Time-Varying Control based on Complexity

点击翻译按钮获取中文摘要

1 Real-Time Identification of Time-Varying SystemsWe are successful in obtaining sequential version of the 4-SID method which enables us to track the parameter variation of the system. We have clarified some fundamental structure of sequential 4-SID method from the viewpoint of realization theory. This is the most remarkable result we have obtained in this research program.2 Analysis and Quantification of Model Complexity and Its Relation to LearningWe have applied learning theory to identification of model set and obtained the lower and upper bound of the samples required for guaranteering a prescribed model accuracy. We have defined complexity of model set through this sample number bounds. We have pointed out that the prevailing stochastic analysis of model set identification connected with control has some drawback.3 Control Algorithm for Linear Parameter Varying (LPV) SystemsWe have obtained some fundamental results on this issue. H-infinity control structure and topological aspects of model set have been considered from the LPV point of view. Also, we have considered the application of the so-called just-in-time (JIT) modeling. However, we are unable to establish so far the solid control architecture and algorithm for LPV systems based on the results obtained in (1) and (2).