In this paper, a full-bridge boost power converter topology is studied for power factor control, using output higher order sliding mode control. The AC/DC converters are used for charging the battery and super-capacitor in hybrid electric vehicles from the utility. The proposed control forces the input currents to track the desired values, which can control the output voltage while keeping the power factor close to one. Super-twisting sliding mode observer is employed to estimate the input currents and load resistance only from the measurement of output voltage. Lyapunov analysis shows the asymptotic convergence of the closed-loop system to zero. Multi-rate simulation illustrates the effectiveness and robustness of the proposed controller in the presence of measurement noise.

In this paper, a variable gain super-twisting algorithm based on a barrier function is proposed for a class of first order disturbed systems with uncertain control coefficient and whose disturbances derivatives are bounded but the upper bounds of those derivatives are unknown. The specific feature of this algorithm is that it can ensure the convergence of the output variable and maintain it in a predefined neighborhood of zero independent from the upper bound of the disturbances derivatives. Moreover, thanks to the structure of the barrier function, it forces the gain to decrease together with the output variable and the control signal follows the absolute value of the disturbances.

This paper proposes a novel disturbance-observer-based complementary sliding-mode (DO-CSM) speed controller for the field-oriented controlled permanent-magnet synchronous motor (FOC-PMSM) drive system. At first, the rotor speed dynamics of the PMSM drive system considering the lumped disturbance, which consists of external disturbances, parametric uncertainties and unmodeled dynamics, is presented. Then, the classic CSM speed controller using the saturation function and a typical artificial neural network (ANN)-based CSM speed controller, i.e., the Elman NN (ENN)-based intelligent CSM (ENN-ICSM) speed controller for the FOC-PMSM drive system are reviewed. Afterward, the design of the proposed DO-CSM speed controller, which combines the CSM controller using the signum function with the super-twisting sliding-mode observer (STSMO), is presented. In such a speed controller, the rotor speed tracking is accomplished by the adopted CSM controller, whereas the STSMO is employed to estimate and compensate the lumped disturbance in the rotor speed dynamics. Finally, experimental comparisons among the proposed DO-CSM speed controller, the classic CSM speed controller and three selected ENN-ICSM speed controllers are carried out. Experimental results verify the effectiveness and superiority of the proposed DO-CSM speed controller.

The degradation seriously affects the durability and cost of the proton exchange membrane fuel cell (PEMFC). This article presents a novel model-driven method based on the frequency-domain Kalman filter (FDKF) and voltage degradation model to predict the degradation of PEMFC in the frequency domain. The advantage of the proposed FDKF method is that it can process the data in groups; thus, the computation time can be greatly reduced with high accuracy. Two degradation experiments under constant and quasi-dynamic currents have been used to demonstrate its prognosis performances under different conditions and different training times. Compared with the traditional time-domain extended Kalman filter method and literature, it has been demonstrated that the proposed one has higher accuracy and requires much less calculation time.