DC microgrids encounter the challenges of constant power loads (CPLs) and pulsed power loads (PPLs), which impose the requirements of fast dynamics, large stability margin, high robustness that cannot be easily addressed by conventional linear control methods. This necessitates the implementation of advanced control technologies in order to significantly improve the robustness, dynamic performance, stability and flexibility of the system. This paper presents an overview of advanced control technologies for bidirectional dc/dc converters in DC microgrids. First, the stability issue caused by CPLs and the power balance issue caused by PPLs are discussed, which motivate the utilization of advanced control technologies for addressing these issues. Next, typical advanced control technologies including model predictive control, backstepping control, sliding mode control, passivitybased control, disturbance estimation techniques, intelligent control and nonlinear modeling approaches are reviewed. Then the applications of advanced control technologies in bidirectional DC/DC converters are presented for the stabilization of CPLs and accommodation of PPLs. Finally, advanced control techniques are explored in other high gain non-isolated (e.g., interleaved, multilevel, cascaded) and isolated converters (e.g., dual active bridge) for high power applications.

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Electric vehicles (EVs) play a significant role in different applications, such as commuter vehicles and short distance transport applications. This study presents a new structure of model-predictive control based on the Takagi-Sugeno fuzzy model, linear matrix inequalities, and a non-quadratic Lyapunov function for the speed control of EVs including time-delay states and parameter uncertainty. Experimental data, using the Federal Test Procedure (FTP-75), is applied to test the performance and robustness of the suggested controller in the presence of time-varying parameters. Besides, a comparison is made between the results of the suggested robust strategy and those obtained from some of the most recent studies on the same topic, to assess the efficiency of the suggested controller. Finally, the experimental results based on a TMS320F28335 DSP are performed on a direct current motor. Simulation and experimental results demonstrate the flawless performance of the suggested controller and the fast and accurate tracking of the EV speed to its set-point.