System identification using the neural-extended Kalman filter for state-estimation modification

Abstract

The neural extended Kalman filter has been shown to be able to work and train on-line in a control loop and as a state estimator for maneuver target tracking. Often, however, an adaptive component in the feedback loop is not considered desirable by the designer of a control system. Instead, the tuning of parameters is considered to be more acceptable. The ability of the NEKF to learn dynamics in an open-loop implementation, such as with target tracking and intercept prediction, can be used to identify mismodeled dynamics. The improved system model can then be used to adapt the state estimator of the control law to provide better performance based on the actual system dynamics. This new approach to neural extended Kalman filter control operations is introduced in this work using applications to the nonlinear version of the standard cart-pendulum system.