Title :
Genetic adaptive observers
Author :
Porter, La Moyne L, II ; 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 show how to utilize GA´s to perform online adaptive state estimation for nonlinear systems. In particular, we show how to construct a genetic adaptive observer (GAO) where a GA evolves the gains in a state observer in real time so that the state estimation error is driven to zero. A simple example is used to illustrate the operation and performance of the GAO and research directions are identified
Keywords :
adaptive control; continuous time systems; genetic algorithms; nonlinear systems; observers; search problems; adaptive state estimation; complex spaces; estimation error; genetic adaptive observers; genetic algorithm; nonlinear systems; parallel search; Computer errors; Concurrent computing; Control system synthesis; Genetic algorithms; Nonlinear systems; Observers; Performance gain; Real time systems; State estimation; System identification;
Conference_Titel :
American Control Conference, Proceedings of the 1995
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-2445-5
DOI :
10.1109/ACC.1995.531206
