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.

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