Nonlinear State Estimation by Adaptive Embedded RBF Modules

Abstract

A modeling compensation method is introduced to enhance the performance of the extended Kalman filter (EKF) in coping with the uncertainty of estimation model. In this method, single-input single-output radial basis function (RBF) modules are embedded within the nonlinear estimation model to provide additional degrees of freedom for model adaptation. The weights of the embedded RBF modules are adapted by the EKF, concurrent with state estimation. This compensation method is tested in application to a benchmark problem. Simulation results indicate that the RBF modules provide the means to model the uncertain components of the estimation model within their range of variation.