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DC-DC converters work as one of the crucial components in DC microgrid intergraded power systems. In this brief, the robust output voltage regulation problem of DC-DC boost converter system is addressed by using a continuous nonsingular terminal sliding mode control (CTSMC) technique based on finite-time disturbance observer. By integrating the disturbance estimations into the controller design, an improved sliding mode control (SMC) approach is developed to achieve better voltage tracking performance. The proposed control method admits the properties of fast transient responses, strong suppression ability against time-varying disturbances and small steady state oscillations of output voltage. Experimental results in the presence of both load variations and supplied voltage fluctuations are provided to validate the effectiveness of the proposed algorithm.

DC–DC converter systems have drawn extensive research attentions and shown upward tendencies for industrial and military applications. In this article, a novel digital fast terminal sliding mode control (FTSMC) approach is investigated for dc–dc buck converters with mismatched disturbances. Specifically, the approximated discrete-time model of the converters with multiple disturbances is first obtained and analyzed based on the Euler's discretization method. Then, by adopting the delayed estimation technique, it is easy to obtain the accurate estimations of the lumped disturbances. Integrating disturbance compensations into the modified digital fast terminal sliding mode surface, the proposed controller is finally constructed on the basis of equivalent control method and the performance analysis is presented. Both simulation and experimental comparisons are made for the proposed digital FTSMC approach and the existing discretized linear sliding mode controllers to validate the effectiveness and feasibility of the presented controller. The proposed FTSMC approach is characterized by higher voltage tracking accuracy and better dynamic properties in different operating conditions.

In this brief, a harmonic disturbance observer (HDOB) based control approach is proposed for the robust voltage regulation design of a dc–ac inverter system. In distributed generation systems, wide range of load variations and effects of nonlinear loads result in significant degradations of control performance. Moreover, the loads, as the mismatched disturbances, impose adverse influences on the inverter system via a channel different from the control input. Toward that end, an HDOB is proposed to estimate the periodic load disturbances. By constructing new disturbance compensation gains, the effects of the mismatched periodic disturbances are removed from the output voltage channel. Rigorous stability analysis for the closed-loop system is presented. Experimental results are explored to illustrate the feasibility and capacity of the proposed control scheme.

The output voltage tracking problem with overcurrent protection for a pulse width modulation-based dc–dc buck converter is investigated in this brief. On the one hand, large transient current during the start-up phase is required for fast dynamics. On the other hand, overlarge transient current may lead to a risk of hardware damage. Conventionally, this problem is solved by choosing conservative control parameters, while the dynamic performance is sacrificed to a certain extent. Besides, the load uncertainty is inevitable in practical applications. Hence, a control design that keeps a good balance between dynamic performance, overcurrent protection, and robustness is desired. To this end, a novel and simple current-constrained controller is constructed. Both dynamic performance and current constraint are taken into account. The low complexity of the proposed current-constrained controller yields a higher reliability of dc–dc buck converter control system, and reduces cost of hardware. Moreover, it shows a nice property of robustness against load uncertainty. Control action is penalized by adjusting controller gains automatically when inductor current tends to the barrier bound. Rigorous stability analysis is given under the presented controller. Comparative simulation and experimental results between the proposed control scheme and the classic proportion–integration–differentiation control scheme on the buck converter system are illustrated to verify the feasibility and effectiveness of the proposed control scheme.

This brief proposes a continuous output feedback terminal sliding mode (TSM) control method for the output tracking problem of nonlinear systems with mismatched uncertainties. This method is developed by two consecutive steps: 1) design a sliding mode observer to estimate the derivative and high-order derivatives of system output and 2) construct a continuous TSM controller based on system output and the estimations of its high-order derivatives. The proposed controller not only guarantees system output converges to its reference in finite time but also keeps the continuity of control action. Experiments on single-phase dc–ac inverter circuits are also carried out to show the effectiveness of the proposed controller.

A new voltage regulation control algorithm is designed for the buck-type dc-dc converter system in the presence of unknown input voltage and load variations. To enhance the voltage regulation time by using the finite-time control theory, a new fast voltage regulation control algorithm is designed that can guarantee that the output voltage converges to the reference voltage in a finite time. To address the unknown input voltage and load variations, two finite-time convergent observers are designed to estimate the unknown parameters in a finite time. Finally, an adaptive finite-time control algorithm is developed. Compared with the PI control algorithm, experimental results show that the proposed algorithm has a faster regulation performance and stronger performance on load-variation rejection.

Snow cover in mountain areas is a key factor controlling regional energy balances, hydrological cycle, and water utilization. Optical remote sensing data offer an effective means of mapping snow cover, although their application is limited by solar illumination conditions, conversely, synthetic aperture radar (SAR) offers the ability to measure snow wetness changes in all weather. In this study, a novel method, which can be approached in two steps by using SAR and optical data, has been developed for dry and wet snow cover recognition in mountain areas. First, two ground-based synchronous observations were implemented, respectively, for snow-accumulation period and snow-melt period. Then, the RADARSAT-2 interferometric coherence images and the backscattering coefficient images of the two periods are analyzed, adopting snow-covered and snow-free areas obtained from GF-1 satellite observations as the “ground truth.” A dynamic thresholding algorithm was proposed to identify snow cover by taking the polarization mode, local incidence angle, and underlying surface type into consideration. Finally, 36 polarimetric parameters obtained from Pauli, H/A/α , Freeman, and Yamaguchi decomposition were analyzed; the results indicate that P vol from Pauli, λ3 from H/A/α , and Y vol from Yamaguchi are more applicable to discriminate dry and wet snow. These three factors, combined with training samples from Nagler algorithm and in situ data, were used to build a support vector machine to classify the extracted snow cover to dry and wet snow. The classification results demonstrate that the dry and wet snow cover extraction can achieve an accuracy of 90.3% compared with in situ measurements.

Snow cover in cold and arid regions is a key factor controlling regional energy balances, hydrological cycle, and water utilization. Interferometric synthetic aperture radar (InSAR) technology offers the ability to monitor snow cover in all weather. In this letter, a support vector machine (SVM) method for extracting snow cover based on SAR and optical data in rugged mountain terrain is introduced. In this method, RadarSat-2 InSAR interferometric coherence images are analyzed, adopting snow-covered and snow-free areas obtained from GF-1 satellite observations as the “ground truth.” The analysis results indicate that the coherence in copolarizations is clearly correlated with the underlying surface type and local incidence angle. These two factors, combined with training samples from GF-1 wide field viewer data, were used to build an SVM to classify coherence images in HH polarization. The classification results demonstrate that snow cover extraction using this method can achieve mean accuracies of 83.8% and 77.5% in areas with low and high vegetation coverage, respectively. These accuracies are significantly higher than those achieved by the typical thresholding algorithm (72.7% and 69.2%, respectively).