Abstract
The neural extended Kalman filter (NEKF) is an adaptive state estimation technique. The neural network training occurs while the system is in operation then the NEKF is able to learn on-line. The NEKF identifies mismodeled dynamics of the system to improve state estimation by learning the differences between the previous model and the measurements that it observes. The prediction from the NEKF can then be used for target tracking or different kinds of interceptions. The NEKF controls and adapts the state estimator and the state feedback gains in the control law. Thus, it will provide better performance based on the actual system dynamics. Experimental results of the NEKF control system on an inverted-pendulum system are used to evaluate the method.
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