Title :
Genetic adaptive state estimation for a jet engine compressor
Author :
Gremling, James R. ; Passino, Kevin M.
Author_Institution :
Dept. of Electr. Eng., Ohio State Univ., Columbus, OH, USA
Abstract :
A genetic algorithm (GA) uses the principles of evolution, natural selection, and genetics to offer a method for parallel search of complex spaces. In this paper we develop a GA that can perform online adaptive state estimation. First, we show how to construct a genetic adaptive state estimator where a GA evolves the model in a state estimator in real time so that the state estimation error is driven to zero. Next, we show how to use a genetic adaptive state estimator for predicting when surge and stall occur in a nonlinear jet engine compressor model
Keywords :
adaptive estimation; aerospace engines; aircraft; compressors; genetic algorithms; search problems; state estimation; adaptive state estimation; genetic algorithm; jet engine compressor; parallel search; Economic forecasting; Game theory; Genetic algorithms; Genetic engineering; Jet engines; Observers; Parameter estimation; Predictive models; State estimation; Surges;
Conference_Titel :
Intelligent Control, 1997. Proceedings of the 1997 IEEE International Symposium on
Conference_Location :
Istanbul
Print_ISBN :
0-7803-4116-3
DOI :
10.1109/ISIC.1997.626431
