Genetic Adaptive Observers
نویسنده
چکیده
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. First, 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. Next, we use several examples to illustrate the operation and performance of the GAO. We begin by showing how the GAO can pick the observer gains for a linear state estimation problem. Following this we show how the GAO performs in estimating the state of a nonlinear, chaotic system for various inputs, noise, and model mismatches.
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