Artificial Neural Network Based Adaptive Control of Single Phase Dual Active Bridge With Finite Time Disturbance Compensation

Single phase Dual Active Bridge (DAB) has found numerous applications in modern energy architectures such as direct current (DC) microgrid, electrical vehicle charging and high voltage direct current (HVDC) system. Due to the model complexities of DAB, this work proposes a model free adaptive control method based on artificial neural network (AANN) which is capable of adjusting the weights online in finite time. The finite time learning property of the proposed controller makes it perfectly robust for the compensation of the disturbances due to source and load side variations. A proportional integral (PI) controller is used to stabilize the nominal dynamics of the system along with the AANN controller. The structure of the proposed controller is as simple as PID controller and as robust as any nonlinear control method. The AANN-PI controller is implemented on TI Launchpad (TMS320F28379D) with a 50 Watts laboratory scale DAB test bench. Finally, the performance of the AANN-PI method is compared experimentally with classical PI and sliding mode controllers.