Abstract
SUMMARYSince particle filters can be used in non‐Gaussian and nonlinear system models, they have a wider range of applications than Kalman filters. In this paper, a construction method for a state feedback control system using a particle filter as an observer for probabilistic state estimation is described. In order to assure robustness to non‐Gaussian noise, a maximum a‐posteriori probability estimation extraction method and an method for evaluation of the effective sample size have been incorporated into the particle filter. The effectiveness of the constructed system was verified experimentally, and the effectiveness of the state observer constructed with the particle filter is demonstrated through a comparison with a Kalman filter.
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 callDisclaimer: 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.
