抄録
Since the particle filter is able to apply to non-Gausian and nonlinear system models, it is capable of wide application than the Kalman filters. In this paper, a construction method of a state feedback control system using the particle filter as an observer for a probabilistic state estimation is described. In order to be robust to non-Gaussian noise, maximum a posteriori probability estimation extraction method and evaluation method of the effective sample size have been incorporated in the particle filter. Then, effectiveness of the constructed system is verified experimentally, and the effectiveness of the state observer constructed by the particle filter is indicated by comparison with the Kalman filter.
