Active Disturbance Rejection Control Using Artificial Neural Network for Dual-Active-Bridge-Based Energy Storage System

The dual-active-bridge (DAB) converter has become a popular isolated solution to integrate energy storage systems (ESSs) and dc microgrids (MGs). However, constant power loads (CPLs) and pulsed power loads (PPLs) may reduce system damping and cause voltage oscillations in DAB converter-based ESSs (DAB-ESSs). An artificial neural network-based active disturbance rejection control (ANN-ADRC) is proposed to regulate constant output voltage quickly and accurately under different operating conditions. First, the ADRC controller is designed based on the small-signal modeling of the DAB-ESSs. Feedforward compensation and uncertainty estimations of the extended state observer (ESO) help to improve the dynamic performance and to reduce the number of current sensors. Then, after satisfying the conditions of stability analysis, the parameters of the ADRC controller are selected automatically via ANN. The ANN is trained with two inputs (ADRC controller parameters) and two outputs (performance indicators of the ADRC controller). The well-trained ANN can be used as a surrogate model to obtain the optimal solution of the objective function easily and quickly. The proposed ANN-ADRC algorithm with selected parameters is implemented and validated on the hardware experimental setup. The experimental results illustrate that the proposed controller can achieve fast dynamic performance under various operating conditions.