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.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.