Real-time online optimal control of current-fed dual active bridges based on machine learning

This paper proposes a real-time online optimal (RT-OPT) control method based on machine learning for a current-fed dual active bridge (CF-DAB). The basis of this control strategy is the linear quadratic optimal control, which designs the sliding surface and realizes power control based on sliding mode control (SMC). For the parameters of Q and R in the objective function of the linear quadratic regulator (LQR), a genetic algorithm is used to find the optimal value, and the optimal value is taken as the sample data. Through machine learning offline training, a neural network is obtained and run online to realize real-time online optimal control. The control method was verified by simulations in MATLAB/Simulink. The RT-OPT method achieves the expected functionality, and has better dynamic and steady-state performance than the PI controller.