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This study concentrates on the tracking control problem for nonlinear systems subject to actuator saturation. To improve the performance of the controller, we propose a fixed-time tracking control scheme, in which the upper bound of the convergence time is independent of the initial conditions. In the control scheme, first, a smooth nonlinear function is employed to approximate the saturation function so that the controller can be designed under the framework of backstepping. Then, the effect of input saturation is compensated by introducing an auxiliary system. Furthermore, a fixed-time adaptive neural network control method is given with the help of fixed-time control theory, in which the dynamic order of controllers is reduced to a certain extent since there is only one updating law in the entire control design. Through rigorous theoretical analysis, it is concluded that the proposed control scheme can guarantee that: 1) the output tracking error can converge to a small neighborhood near the origin in a fixed time and 2) all signals in the closed-loop system are bounded. Finally, a numerical example and a practical example based on the single-link manipulator are provided to verify the effectiveness of the proposed method.
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http://dx.doi.org/10.1109/TNNLS.2021.3105664 | DOI Listing |
Light Sci Appl
September 2025
Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, 230031, Hefei, China.
Marine vessels play a vital role in the global economy; however, their negative impact on the marine atmospheric environment is a growing concern. Quantifying marine vessel emissions is an essential prerequisite for controlling these emissions and improving the marine atmospheric environment. Optical imaging remote sensing is a vital technique for quantifying marine vessel emissions.
View Article and Find Full Text PDFJ Colloid Interface Sci
September 2025
Ministry-of-Education Key Laboratory for the Green Preparation and Application of Functional Materials, Hubei Key Laboratory of Polymer Materials, School of Materials Science & Engineering, Hubei University, Wuhan 430062, PR China. Electronic address:
Effective removal of ethylene (CH) during fruit and vegetables storage and transport remains a critical challenge for post-harvest preservation. Although S-scheme heterojunctions can improve charge separation and redox capacity for ethylene degradation, their efficiency is still restricted by limited carrier transfer and sluggish oxygen activation. Here, we rationally designed a novel 2D/2D SnNbO/BiMoO monolayer S-scheme heterojunction integrated with Pt co-catalyst to address these limitations.
View Article and Find Full Text PDFPhys Chem Chem Phys
September 2025
Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control, School of Energy and Mechanical Engineering, Jiangxi University of Science and Technology, Nanchang, 330013, Jiangxi Province, China.
The quest for sustainable and clean energy sources has led to significant research into photocatalytic water splitting, a process that converts solar energy into hydrogen fuel. This study demonstrates constructing a high-performance CdTe/CN van der Waals heterojunction for solar-driven water splitting hydrogen evolution. The proposed CdTe/CN heterojunction, investigated using first-principles calculations, integrates favorable structural stability and features a direct bandgap of 1.
View Article and Find Full Text PDFComput Biol Med
September 2025
Department of Electrical and Computer Engineering and the Institute of Biomedical Engineering, University of New Brunswick, Fredericton, E3B 5A3, NB, Canada.
Pattern recognition-based myoelectric control is traditionally trained with static or ramp contractions, but this fails to capture the dynamic nature of real-world movements. This study investigated the benefits of training classifiers with continuous dynamic data, encompassing transitions between various movement classes. We employed both conventional (LDA) and deep learning (LSTM) classifiers, comparing their performance when trained with ramp data, continuous dynamic data, and an LSTM pre-trained with a self-supervised learning technique (VICReg).
View Article and Find Full Text PDFPLoS One
September 2025
Information Technologies and Programming Faculty, ITMO University, Saint Petersburg, Russia.
In the paper we consider the well-known Influence Maximization (IM) and Target Set Selection (TSS) problems for Boolean networks under Deterministic Linear Threshold Model (DLTM). The main novelty of our paper is that we state these problems in the context of pseudo-Boolean optimization and solve them using evolutionary algorithms in combination with the known greedy heuristic. We also propose a new variant of (1 + 1)-Evolutionary Algorithm, which is designed to optimize a fitness function on the subset of the Boolean hypercube comprised of vectors of a fixed Hamming weight.
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