In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Associ...In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.展开更多
Approximate Dynamic Inversion (ADI) is basically an approximation of exact dynamic inversionor feedback linearisation, which converts a nonlinear system to an equivalent linear structure.This method can be widely appl...Approximate Dynamic Inversion (ADI) is basically an approximation of exact dynamic inversionor feedback linearisation, which converts a nonlinear system to an equivalent linear structure.This method can be widely applied for controlling minimum phase, nonaffine-in-control systems.For applying the ADI method, a fast dynamic subsystem for deriving explicit inversion of thenonaffine equation is required. With full state feedback, ADI may be expressed in the same way asa Proportional Integral (PI) controller with only knowledge of the sign of control effectiveness andalso without any approximation. The Model Reference Adaptive Controller (MRAC) augmentedwith the PI method is an adaptive control technique where the PI parameters are updated/tunedas per the control methodology based on the MRAC-Massachusetts Institute of Technology (MIT)rule so that the plant is capable to follow the reference model. The main objective of this paperis to find the relationship between ADI and MRAC augmented with a PI controller.展开更多
基金supported by the Key Program of the National Natural Science Foundation of China(Grant No.50539010)the Special Fund for Public Welfare Industry of the Ministry of Water Resources of China(Grant No.200801019)
文摘In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.
文摘Approximate Dynamic Inversion (ADI) is basically an approximation of exact dynamic inversionor feedback linearisation, which converts a nonlinear system to an equivalent linear structure.This method can be widely applied for controlling minimum phase, nonaffine-in-control systems.For applying the ADI method, a fast dynamic subsystem for deriving explicit inversion of thenonaffine equation is required. With full state feedback, ADI may be expressed in the same way asa Proportional Integral (PI) controller with only knowledge of the sign of control effectiveness andalso without any approximation. The Model Reference Adaptive Controller (MRAC) augmentedwith the PI method is an adaptive control technique where the PI parameters are updated/tunedas per the control methodology based on the MRAC-Massachusetts Institute of Technology (MIT)rule so that the plant is capable to follow the reference model. The main objective of this paperis to find the relationship between ADI and MRAC augmented with a PI controller.
