Adaptive Extended State Observer for the Dual Active Bridge Converters.

The DC-DC dual active bridge (DAB) converter has become one of the essential units for bidirectional energy distribution and connecting various renewable energy sources. When it comes to regulating the converter's output voltage, integrating an extended state observer (ESO) offers the advantage of eliminating the need for a current sensor, thereby reducing system costs. The ESO with a high observer bandwidth tends to acquire a faster system convergence and greater tracking accuracy. However, its disturbance suppression performance will become poor compared to the ESO with a low observer bandwidth. Based on this, the adaptive ESO (AESO) is proposed in this study to make a compromise between tracking performance and disturbance suppression. When the system is subjected to a high voltage error, the observer bandwidth will increase to improve the tracking performance and decrease to enhance the disturbance suppression. In order to demonstrate that the proposed method is effective, it is compared to the ESO with a fixed observer bandwidth and the improved model-based phase-shift control (MPSC). These comparisons are made through simulation and experimental results in various operation scenarios.

In silico validation revealed the role of SCN5A mutations and their genotype-phenotype correlations in Brugada syndrome.

BACKGROUND: Brugada syndrome (BrS) is a rare genetic disease that causes sudden cardiac death (SCD) and arrhythmia. SCN5A pathogenic variants (about 30% of diagnosed patients) are responsible for BrS. AIMS: Lack of knowledge regarding molecular characteristics and the correlation between genotype and phenotype interfere with the risk stratification and finding the optimal treatment in Vietnam. Therefore, we identified SCN5A variants and evaluated the genotype-phenotype correlation of BrS on 117 Vietnamese probands. MATERIALS AND METHODS: The clinical characteristics and blood samples of BrS patients were collected. To determine SCN5A variants, Sanger sequencing was conducted, and subsequently, these variants were analyzed by bioinformatic tools. RESULTS: In this cohort, the overall rate of detected variants in SCN5A was 25.6%, which could include both pathogenic and benign variants. In genetic testing, 21 SCN5A variants were identified, including eight novels and 15 published variants. Multiple bioinformatic tools were used to predict variant effect with c.551A>G, c.1890+14G>A, c.3338C>T, c.3578G>A, and c.5484C>T as benign, while other variants were predicted as disease-causing. The family history of SCD (risk ratio [RR] = 4.324, 95% CI: 2.290-8.269, p < 0.001), syncope (RR = 3.147, 95% CI: 1.668-5.982, p = 0.0004), and ventricular tachycardia/ventricular fibrillation (RR = 3.406, 95% CI: 1.722-5.400, p = 0.0035) presented a significantly higher risk in the SCN5A (+) group, consisting of individuals carrying any variant in the SCN5A gene, compared to SCN5A (-) individuals. CONCLUSION: The results contribute to clarifying the impact of SCN5A variants on these phenotypes. Further follow-up studies need to be carried out to understand the functional effects of these SCN5A variants on the severity of BrS.

Robust Adaptive Control Strategy for a Bidirectional DC-DC Converter Based on Extremum Seeking and Sliding Mode Control.

This paper presents a new control strategy that combines classical control and an optimization scheme to regulate the output voltage of the bidirectional converter under the presence of matched and mismatched disturbances. In detail, a control-oriented modeling method is presented first to capture the system dynamics in a common canonical form, allowing different disturbances to be considered. To estimate and compensate for unknown disturbances, an extended state observer (ESO)-based continuous sliding mode control is then proposed, which can guarantee high tracking precision, fast disturbance rejection, and chattering reduction. Next, an extremum seeking (ES)-based adaptive scheme is introduced to ensure system robustness as well as optimal control effort under different working scenarios. Finally, comparative simulations with classical proportional-integral-derivative (PID) control and constant switching gains are conducted to verify the effectiveness of the proposed adaptive control methodology through three case studies of load resistance variations, buck/boost mode switching, and input voltage variation.