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This brief proposes a solution to the long-standing problem of designing an adaptive control for the dc–dc boost converter with unknown load and unavailable input current measurement. The difficulty lies in the parametric uncertainty of the output dynamics, which poses a manifold of equilibria in the classical adaptive observer design. This is known as the detectability obstacle that imposes a restrictive assumption on the system behavior to ensure convergence. To overcome this problem, a class of saturated dynamic controllers is designed to guarantee the asymptotic regulation of the output voltage. An immersion- and invariance-based adaptive observer is proposed, which preserves the convergence property in conjunction with the controller with no need for an extra persistent excitation condition. To evaluate the transient behavior of the proposed controller, realistic simulations are provided, and then the performance comparison with two other well-known output feedback controllers is presented. Moreover, experimental results are concluded on a prototype dc–dc boost converter to support and verify the results of theory and simulations. [ABSTRACT FROM AUTHOR]

Cascaded connection of power converters is a dominant connection form in DC microgrids. In such systems, despite the possible instability caused by the impedance interactions between the individually designed converters, tightly regulated load converters acting as constant power loads (CPLs) tend to destabilize the system owing to their negative resistance characteristics. Hence, this paper proposes a new virtual series RC damper in parallel with the source-side converter's capacitor without compromising the load's dynamic performance. Using this design-oriented active damping method, which utilizes a simple control structure with a more straightforward tuning of the control parameter, the stability and performance of the system are guaranteed. The feasibility and robustness of the suggested active stabilization idea against unanticipated variations in input voltage amplitude, and CPL power rating (load changes) as well as step changes in output voltage reference, are also authenticated. The control and operation principles, as well as the circuit physical meaning realized by the presented technique for three cascaded systems comprising the basic DC/DC converters feeding CPLs, are theoretically analyzed. Simulation and experimental results are provided to validate the effectiveness of the proposed active stabilizer. [ABSTRACT FROM AUTHOR]

This paper concentrates on the control of hybrid fuel-cell (FC)/energy-storage distributed generation (DG) systems under voltage sag in distribution systems. The proposed control strategy makes hybrid DG system work properly when a voltage disturbance occurs in distribution system, and it stays connected to the main grid. To distribute the power between dc power sources and stabilize the dc-link power, a Lyapunov-based neuro-fuzzy control strategy has been developed. This controller determines the supercapacitor power that should be generated according to the amount of available energy in dc-link. Also, current control strategies for the FC converter (boost) and supercapacitor converter (buck–boost converter) are designed by proportional-integral and sliding-mode control consequently. Moreover, a complementary control strategy for voltage source converter based on positive and negative symmetrical components is presented to investigate the voltage sag ride-through and voltage control capability. The hybrid system is studied under unbalance voltage sag condition. Simulation results are given to show the overall system performance including active power control and voltage sag ride-through capability of the hybrid DG system. [ABSTRACT FROM AUTHOR]

Decentralized output voltage tracking of cascaded DC–DC converters is an interesting topic to obtain a high voltage conversion ratio. The control purpose is challenging due to the load resistance changes, renewable energy supply voltage variations and interaction of the individual converters. In this paper, four novel decentralized adaptive neural network controllers are designed on the cascaded DC–DC buck and boost converters under load and DC supply voltage uncertainties. In the beginning, individual buck and boost converter average models that can operate in both continuous and discontinuous conduction modes are derived. Then, the interconnected and decentralized state-space models of cascaded buck and boost converters are extracted. These models are highly nonlinear with unknown uncertainties which can be estimated by neural networks. Further, two decentralized adaptive backstepping neural network voltage controllers are proposed on cascaded buck converters to deal with uncertainties and interactions. However, these control strategies are not applicable to a boost converter due to its non-minimum phase nature. Then, two novel decentralized adaptive neural network with a conventional proportional–integral reference current generator are developed on the cascaded boost converters. Practical stability of the overall system is guaranteed for the proposed controllers using Lyapunov stability theorem. Finally, four control strategies provide good quality of output voltage in the presence of uncertainties and interactions. Comparative simulations are carried out on cascaded buck and boost converters to validate the effectiveness and performance of the designed methods. • Control of cascaded converters is studied to deal with interactions/uncertainties. • Two decentralized ABNN controllers are proposed on cascaded buck converters. • Two decentralized PI-ANN controllers are developed on cascaded boost converters. • Converters average models that can operate in both CCM and DCM modes are derived. • Proposed strategies show good voltage tracking under the uncertainties/interactions. [ABSTRACT FROM AUTHOR]