Abstract
A genetic algorithm (GA) uses the principles of evolution, natural selection, and genetics to offer a method for the parallel search of complex spaces. This paper shows how to utilize GAs to perform on-line adaptive state estimation for nonlinear systems. First, it shows 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. Next, several examples are used to illustrate the operation and performance of the GAO. The paper starts by showing how the GAO can pick the observer gains for a linear state estimation problem. Following this it demonstrates how the GAO performs in estimating the state of a nonlinear, chaotic system for various inputs, noise, and model mismatches.
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