An Adaptive Fuzzy Neural Network Based on Progressive Gaussian Approximate Filter with Variable Step Size

The nonlinear filtering problem is a hot spot in robot navigation research. Based on this idea, I focus on how to resolve the nonlinear filtering problem in the application of tightly coupled integration under the premise of the prior uncertainty and further promote robustness high measurement accuracy. In order to improve the estimation accuracy of the progressive Gaussian approximate filter with variable step size(PGAFVS), this paper selects the optimal values in practical applications and proposed an adaptive fuzzy and neural network controller. The controller, as well as the measurement noise covariance matrix, is jointly estimated based on the PGAF, from which the PGAFVS is developed. The simulation results show that the proposed algorithm outperforms the state of the art methods.