Nonlinear auto regressive Elman neural network combined with unscented Kalman filter for data-driven dynamic data reconciliation in dynamic systems

Abstract

Measurement data plays an important role in the control system, but the data collected by sensors often has measurement noise, which makes the states of the system cannot be accurately revealed. Unscented Kalman filter (UKF) is a highly accurate and robust filtering algorithm, but its limitation is the requirement of prior knowledge of the exact dynamic mathematical model, which is a critical issue to be addressed in practice. In this work, a data-driven dynamic data reconciliation scheme called nonlinear auto regressive Elman neural network (ENN) with exogenous inputs combined with UKF (NARX-ENN-UKF) is proposed, where nonlinear auto regressive ENN with exogenous inputs is used for dynamic data-driven modeling, and then UKF is applied for dynamic data reconciliation of the measurements based on the trained model. The scheme is applied to a DC/AC inverter experimental system and a self-developed sliding electrical contact experimental system to verify the effectiveness of NARX-ENN-UKF.