This paper details the transformation of the velocity or position-tracking problem of a class of uncertain systems using finite time stability control for first-order uncertain systems. A new composite extended-state observer sliding mode (ESOSM) scheme is proposed, which includes an adaptive super-twisting-like ESO and an adaptive super-twisting controller. The adaptive super-twisting controller is implemented through a barrier function-based second-order sliding mode algorithm. To further reduce control chattering and improve control performance, the adaptive super-twisting-like ESO, which employs high-order terms in the super-twisting algorithm to accelerate convergence, is designed to observe the lumped uncertainty in real time. The advantages of the proposed scheme are verified by a numerical example and application with regard to floating offshore wind turbine (FOWT) pitch control. Compared with proportional integral (PI) and adaptive super-twisting sliding mode (ASTSM) schemes, better results are obtained in velocity tracking and fatigue load suppression. For the FOWT pitch control application, the platform roll, pitch, and yaw are decreased by 3%, 2%, and 4%, respectively, compared to the PI scheme at an average turbulent wind speed of 17 m/s and turbulence intensity of 17.27%.

The classic interactive image segmentation algorithm GrabCut achieves segmentation through iterative optimization. However, GrabCut requires multiple iterations, resulting in slower performance. Moreover, relying solely on a rectangular bounding box can sometimes lead to inaccuracies, especially when dealing with complex shapes or intricate object boundaries. To address these issues in GrabCut, an improvement is introduced by incorporating appearance overlap terms to optimize segmentation energy function, thereby achieving optimal segmentation results in a single iteration. This enhancement significantly reduces computational costs while improving the overall segmentation speed without compromising accuracy. Additionally, users can directly provide seed points on the image to more accurately indicate foreground and background regions, rather than relying solely on a bounding box. This interactive approach not only enhances the algorithm’s ability to accurately segment complex objects but also simplifies the user experience. We evaluate the experimental results through qualitative and quantitative analysis. In qualitative analysis, improvements in segmentation accuracy are visibly demonstrated through segmented images and residual segmentation results. In quantitative analysis, the improved algorithm outperforms GrabCut and min_cut algorithms in processing speed. When dealing with scenes where complex objects or foreground objects are very similar to the background, the improved algorithm will display more stable segmentation results.

Triboelectric nanogenerators (TENGs) have emerged as efficient mechanical-energy harvesters with advantages—simple architectures, broad material compatibility, low cost, and strong environmental tolerance—positioning them as key enablers of self-powered systems. This review synthesizes recent progress in energy-storage interfaces, power management, and system-level integration for TENGs. We analyze how intrinsic source characteristics—high output voltage, low current, large internal impedance, and pulsed waveforms—complicate efficient charge extraction and utilization. Accordingly, this work highlights a variety of power-conditioning approaches, including advanced rectification, multistage buffering, impedance transformation/matching, and voltage regulation. Moreover, recent developments in the integration of TENGs with storage elements, cover hybrid topologies and flexible architectures. Application case studies in wearable electronics, environmental monitoring, smart-home security, and human–machine interfaces illustrate the dual roles of TENGs as power sources and self-driven sensors. Finally, we outline research priorities: miniaturized and integrated power-management circuits, AI-assisted adaptive control, multimodal hybrid storage platforms, load-adaptive power delivery, and flexible, biocompatible encapsulation. Overall, this review provides a consolidated view of state-of-the-art TENG-based self-powered systems and practical guidance toward real-world deployment.

Floating offshore wind turbines (FOWTs) experience unbalanced loads and platform motion due to the coupling of variable wind and wave loads, which leads to output power fluctuation and increased fatigue loads. This paper introduces a new blade pitch control strategy for FOWTs that combines fuzzy logic with a linear quadratic regulator (LQR) and a proportional-integral (PI) controller. The fuzzy PI controller dynamically adjusts the PI control gains to regulate rotor speed and stabilize output power. Fuzzy LQR is employed for individual pitch control, utilizing fuzzy logic to adaptively update feedback gains to achieve stable power output, suppress platform motion, and reduce fatigue load. Co-simulations conducted with OpenFAST (Fatigue, Aerodynamics, Structures, and Turbulence) and MATLAB/Simulink under diverse conditions demonstrate the superiority of the proposed method over traditional PI control. The results show significant reductions in platform pitch, roll, and heave motion by 17%, 27%, and 48%, respectively; blade out-of-plane, pitch, and flapwise bending moments are reduced by 38%, 44%, and 36%; and the tower base roll and pitch bending moments are reduced by up to 29% and 22%, respectively. The proposed control scheme exhibits exceptional environmental adaptability, enhancing FOWT’s power regulation, platform stability, and reliability in complex marine environments.

This paper presents a novel adaptive cell recombination strategy for balancing lithium-ion battery packs, targeting electric vehicle (EV) applications. The proposed method dynamically adjusts the series–parallel configuration of individual cells based on instantaneous state of charge (SoC) and load demand, without relying on conventional DC-DC converters or passive components. A hardware-efficient switching topology using SPDT (Single Pole Double Throw) switches enables flexible recombination and fault isolation with minimal complexity. The control algorithm, implemented in MATLAB/Simulink, evaluates multiple cell-grouping configurations to optimize balancing speed, energy retention, and operational safety. Simulation results under charging, discharging, and resting conditions demonstrate up to 80% faster balancing compared to sequential methods, with significantly lower component count and minimal energy loss. Validation using Panasonic NCR18650PF cells confirms the model’s real-world applicability. The method offers a scalable, high-speed, and energy-efficient solution for integration into next-generation battery management systems (BMS), achieving performance gains typically reserved for more complex converter-based architectures.

Renewable energy sources such as wind and solar power exhibit strong stochasticity and volatility, resulting in decreased power supply security and sustainability. A strategically optimized hybrid renewable energy system (HRES) is crucial for maintaining stable load operations and achieving sustainable energy development. This paper introduces an energy optimization management model for an independent HRES consisting of wind turbines, photovoltaic systems, diesel generators, and energy storage units. Operational strategies focus on energy storage-led loads following diesel generator-led load prioritizations. The model aims to optimize objectives to include economic, environmental, and power supply reliability indices. A dynamic adaptive parameter approach balances the parameters of the objective function at various instances. The optimal capacity allocation of the model is solved using the improved gray wolf optimization (IGWO) algorithm. This approach incorporates the golden sine strategy, the levy flight strategy, and the dynamic inverse learning strategy into the traditional GWO algorithm. Analyzing different test functions, evaluation metrics, and actual load data indicates that the proposed algorithm excels in global optimization capabilities and search speeds. The model significantly reduces the economic and environmental costs of the HRES microgrids and improves the sustainable development of renewable energy in various scenarios.