High-gain DC-DC converter demand increases rapidly when integrating low voltage renewable energy sources (RES), but the converter’s switch and output diode experience voltage stress equal to the converter output voltage. And to improve the reliability of RES, a hybrid source combination structure is suggested in the literature. To integrate such a hybrid energy source, a single-switch dual-input high-gain capability with reduced voltage-stress (SDHRV) converter is adapted by connecting the parts of two standard boost converter and diode capacitor pair. The SDHRV steady-state converter’s operation is explained by using equivalent circuits and mathematical expressions. In addition to the steady-state performance, a state space average mathematical model is constructed to examine the dynamic performance of SDHRV converter. The advantages of the SDHRV converter are highlighted in the comparison section over other high-gain converters. Finally to validate the SDHRV converter performance simulation results are included.

Loop closure detection, a critical component of Simultaneous Localization and Mapping (SLAM) systems, can be computationally intensive, particularly in swarm robotics where coordination among multiple agents is essential. Traditional SLAM methods often involve comparing each frame with all previous frames, leading to performance bottlenecks, especially on battery-operated or resource-constrained devices. This leaves little room for other critical tasks, such as continuous coordination and information sharing among swarm robots, which require swift execution to perform effectively. This paper introduces a novel cone-based approach to streamline loop closure detection, significantly reducing the computational burden and improving system efficiency. By limiting frame comparisons to a predefined region, our method accelerates SLAM algorithms, enabling more efficient coordination and exploration among swarm robots. The approach is particularly advantageous for resource-constrained devices and battery-powered platforms operating in dynamic environments. The need for millisecond-level response times in swarm robotic tasks require the integration of 6G networks for seamless communication and coordination. Experimental results demonstrate the effectiveness of the cone-based method in enhancing loop closure accuracy while minimizing computational overhead. This makes it a valuable tool for advancing swarm robotic applications, particularly in 6G-enabled environments where real-time coordination and efficiency are paramount.

This survey presents an exhaustive view of the implementation of Active Disturbance Rejection Control (ADRC) across the renewable energy system components. ADRC stands out as a formidable control strategy, adept at navigating uncertainties and perturbations without the necessity for system modeling. This feature makes it particularly suitable for renewable energy systems, which are often subject to highly variable environmental conditions and inherent system nonlinearities. The survey covers the implementation of ADRC in wind energy systems, including turbine control for optimal power capture and grid integration for stable energy output. It also examines its application in solar photovoltaic systems with a particular emphasis on maximum power point extraction and inverter control to enhance efficiency and electricity quality. Furthermore, the use of ADRC in hydropower systems for regulating turbine and generator operations is discussed, highlighting its role in maintaining stable power output amidst fluctuating water flow. Additionally, the survey explores the integration of ADRC in hybrid systems, where it manages power distribution among different sources such as solar, battery storage, and wind to optimize overall performance. The advantages of ADRC, such as its adaptability, real-time disturbance rejection, and simplified implementation, are emphasized across these applications. In conclusion, the survey underscores the effectiveness of ADRC in enhancing the stability, reliability, and efficiency of renewable energy systems, positioning it as a crucial control strategy for advancing sustainable energy solutions.

Power generation is shifting towards renewable energy sources (RES) to reduce pollution and uncertainties regarding fossil fuels. Direct integrating to the load is not recommended due to the low voltage produced by these RES. A conventional boost converter is employed to connect the RES to the loads. The performance of these systems is subpar due to the converter’s low voltage gain, and they rely on transformers to enhance the voltage on the AC side, resulting in poor efficiency and a large size of the overall system. By enhancing the voltage gain on the DC side, high-gain power converters are employed as a DC-DC converter to solve the aforementioned issue and eliminate the need for additional transformers in the system. Also, to improve the reliability of RES, a dual-input high-gain quasi (DIHGQ) z-source power converter is recommended in this study to integrate RES. The converter’s operation is examined by using the theoretical waveforms and mathematical equations. The suggested converter’s analytical performance is verified using the MATLAB/Simulink platforms. Along with the steady-state performance, a state space average mathematical model is developed to analyze its dynamic performance. The merits of the DIHGQ converter are demonstrated by comparison with traditional high-gain topologies. The comparison demonstrates that the suggested DIHGQ is one of the options for integrating RES.

Disaster-altered environments present a unique challenge for unmanned ground vehicles (UGVs), navigating dynamic environments for rescue operations. Existing loop closure detection methods already face significant challenges due to environmental variations, such as changing lighting conditions and the presence of moving objects. Disaster-stricken areas present an additional layer of complexity, as previously static landmarks can also undergo significant changes, further hindering accurate loop closure detection. Inspired by humans that recognizes already visited places by prioritizing semantic information like landmarks and text rather than just visual features, this article introduces DisView, a novel approach that leverages this insight for loop closure detection in disaster-altered environments. DisView utilizes a mixed data feature extractor to analyze visual, semantic, and textual data. This combined approach goes beyond traditional methods that rely solely on visual IoT data, leading to more robust loop closure detection even in disaster-altered environments and with viewpoint changes, varying illumination conditions, and the presence of moving rescue infrastructure. DisView’s effectiveness is validated on six publicly available datasets, demonstrating superior performance in diverse lighting conditions and viewing angles. By enabling reliable loop closure detection, DisView significantly improves UGV navigation in disaster-altered environments, ultimately supporting faster and more efficient rescue operations.

We investigate the problem of feedback stabilization of networked control linear systems with unknown initial state . In particular, the sampling and the quantization effects of network are considered and we propose a quantized event-triggered control technique to handle this case. To cope with unknown initial state, a novel exponential dynamic quantizer is synthesized, which enables to capture the state in a finite time. Moreover, the dynamic quantization policy only depends on the sign of the quantization error and allows for one-bit transmission rather than packet-based transmission, which is desirable in practice. Then, the quantized state measurements are transmitted to the controller using an event-triggering mechanism to reduce the amount of transmissions over the network. The approach ensures global asymptotic stability property for the closed-loop system and prevents the occurrence of Zeno behavior. The entire closed-loop system is represented as a hybrid dynamical system and the closed-loop stability is assessed using suitable Lyapunov functions . The effectiveness of the proposed approach is demonstrated through numerical simulations.

An enormous volume of data is generated in the Internet of Things (IoT), which needs to be anonymized before sharing with public or third parties to minimize reidentification risk and protect sensitive information. Data anonymization techniques can remove information capable of identifying individuals. However, inappropriate data anonymization can increase the risk of reidentification. This work focuses on potential attack risks of anonymized data by evaluating the potential attack risks. Specifically, we analyzed the attack risks over anonymized data with both randomization and generalization techniques. We also analyzed the risk of reidentification for five commonly used data anonymization techniques. The experimental results demonstrate that the proposed solution can well evaluate the potential attack risks.

This article explores cutting-edge power conversion topologies:$2LC_{m}\ -\ Y$converters with diode capacitor stacking (DCS). The proposed topologies$(2LC_{m}-Y-{D}CS$configurations) achieve high voltage gain through the combination of traditional XY family converter topologies$(2LC_{m}-L, 2LC_{m}-2L,2LC_{m}-2LC, \text{and}2LC_{m}-2L{C}_{m}$) with DCS characteristics. The resulting topologies prove advantageous in applications requiring low to high-voltage conversion, making them well-suited for renewable energy systems such as photovoltaic systems, fuel-cell systems, electric vehicles, HVDC systems, and DC drives. What's more, the incorporation of a stack of capacitors stacking enhances the suitability of the proposed topologies for multilevel inverter based utility grids (MLI). The operating theory of the$2LC_{m}-2LC_{m}-DC{S}$configurations is thoroughly explained, accompanied by a comprehensive analysis of the voltage gain of converters. A comparative analysis of the voltage gain for similar topologies is conducted. Finally, simulation results validate the effectiveness of the research work, confirming its viability in practical applications.

This article presents a breakthrough contribution to the field of fuel-cell vehicular systems-an innovative hybrid switched-reactive (HSR) converter designed to improve voltage gain. The proposed configuration and operational principles are thoroughly discussed, accompanied by an analysis of its non-ideal model. A comprehensive comparison with other non-isolated converters reveals the distinct benefits. Notably, the converter achieves improved voltage gain without employing a transformer, coupled-inductor, or voltage multiplier circuit, resulting in reduced stresses on key components. The practicality of the design is validated through the simulation verification of a 500 W converter, affirming its exceptional performance. This research marks a significant step towards advancing energy efficiency in fuel-cell vehicular systems, with far-reaching implications for sustainable transportation.

This paper suggests a loss minimization (LM) for linear induction motors (LIMs) where the optimum value of the primary flux linkage is derived for this goal. The derivation of the optimum primary flux linkage is based on the concept of field-oriented control. Meanwhile, the main control method is finite-set model predictive control (FSMPC) based on the primary flux linkage in the cost function. The presented FSMPC removes the use of a weighting factor with only one proportional control in the output speed control loop. In addition, it reduces the number of calculations where it uses only four voltage vectors in the prediction stage. These four voltage vectors consist of two active voltage vectors and two null voltage vectors. Parameters of a 3-kW arc machine are adopted to confirm the validity of the suggested control method and check the overall performance of the LIM drive system. The obtained results compare the use of four and eight voltage vectors. Besides, it shows the difference between the adoption of the optimum primary flux linkage from LM and the rated value as reference values.

Renewable energy sources (RESs) have become the most emerging and cost-effective technology to generate electrical power for remote areas. Nevertheless, discontinuity and low performance of the power converters are considered challenges when RESs are used. This paper adopts a newly designed dc-dc converter, the so-called Two-Tier converter (TTC) in the photovoltaic (PV) system to supply dc-load with the required voltage values. Moreover, model predictive control (MPC) is proposed to extract the maximum power from the PV under various radiations. At the same time, a conventional PI controller is used for the dc-dc buck-boost converter to regulate the output dc voltage at the required value. Finally, the battery bank storage is employed to assist in extracting the maximum power through charging and discharging modes. To validate the integration of the system, the MATLAB/Simulink toolbox is used to simulate the dynamics of the entire system and the results confirm the capability of the proposed system with the control algorithms.

This paper proposes a new method for improving the performance of grid-connected single-stage Quadratic Boost Inverters (QBIs). Compared to the other single-stage boosting inverters, such as ZSI, qZSI, and the basic SSI, QBI has several advantages. It combines the features of a quadratic dc-dc boost converter and Split-Source Inverter (SSI) to give a high voltage gain. As a result, it offers superior benefits in reducing the overall system's complexity, size, and cost. The grid-connected QBI is traditionally controlled via two control loops. The first loop regulates the dc-link voltage using a PI controller, while the second controls the current injected into a grid using the decoupled control approach. In this paper, a sliding mode controller (SMC) is proposed for the grid-side controller to achieve accurate and quick responses instead of using the decoupled approach that is based on PI controllers. The basic principles and detailed analysis of the proposed method are introduced. Moreover, comprehensive simulation results based on the actual parameters of a prototype have verified the overall control system and the advantages of the proposed SMC compared to the PI controller.

In modern days, to achieve low emission of Green-House Gases (GHG), clean system, easy maintenance, high starting torque, and high efficiency, Internal Combustion (IC) engines are gradually being changed by electric motors. Smart vehicular systems (SV) either functioned from moderately or fully electrical energy generated from renewable sources. The energy storage, i.e., battery or ultra-capacitor system, gains energy from the IC engine. Alternative the electric grid, e.g., IC engines and electric grids are used to charge the battery in Hybrid Smart Electric Vehicle (H-SEV) and Plug-in Smart Electric Vehicle (PSEV), respectively. Power Electronics skills play an imperative role in the energy conversion between Grid-vehicle-Wheel (G-VW) and Wheel-vehicle-Grid (W-V-G). This letter discusses smart vehicular systems with their demand, curiosity, challenges, and power electronic skills.

This paper proposes an H∞robust state-feedback controller design for uncertain Takagi-Sugeno fuzzy systems using a nonmonotonic Lyapunov function. In the nonmonotonic approach, the monotonicity requirement of the Lyapunov function is relaxed by allowing it to increase locally. Based on the nonmonotonic Lyapunov function approach, sufficient conditions for the existence of a robust state-feedback H∞controller that guarantees stability and a prescribed H∞performance are given in terms of linear matrix inequalities. The proposed design technique is shown to be less conservative than the existing k-samples variations of the Lyapunov function. The effectiveness of the proposed approach is further illustrated via numerical examples.