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36th Chinese Control and Decision Conference (CCDC)

作者： Yang, Jiaxing Yang, Xuebo Chen, Zhongbo Wang, Jiatong Zheng, Xiaolong Harbin Inst Technol Harbin Peoples R China Bohai Univ Jinzhou Peoples R China

ISBN: (纸本)9798350387780;9798350387797

This paper presents an event-triggered adaptive neural network (NN) backstepping control method for a class of uncertain nonlinear systems. The combination of NN and backstepping technique solves the complexity explosion and compensates for the system unmodeled dynamics. At the same time, the NN weight trained by the gradient descent algorithm can ensure NN approximation performance to the unknown nonlinear function. Further, a smooth switching trigger strategy is introduced to achieve a lower updating frequency of the system control signal. According to the Lyapunov criterion, the tracking error of the system is semi-globally uniformly bounded and the system controller does not behave Zeno. Finally, the proposed method is applied to the simulation model of a DC motor control system.

关键词： neural network gradient descent algorithm event-triggered control smooth switching threshold

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Accelerated gradient descent Estimation for Rational Models by Using Volterra Series: Structure Identification and Parameter Estimation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第3期69卷 1497-1501页

作者： Chen, Jing Ding, Feng Hu, Manfeng Zhu, Quanmin Jiangnan Univ Sch Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China Univ West England Dept Engn Design & Math Bristol BS16 1QY Avon England

This brief studies a rational model identification problem using a novel gradient descent algorithm. The rational model is approximated by a second-order Volterra system, whose parameters and structure are estimated through an accelerated gradient descent algorithm. This algorithm improves the convergence rates by computing an optimal step-size and introducing a preconditioning method. In addition, it can avoid the eigenvalue calculation if the preconditioning matrix is equal to the inverse of the information matrix. A numerical experiment is given to illustrate the performances of the proposed algorithm.

关键词： Convergence Mathematical models Eigenvalues and eigenfunctions Computational modeling Approximation algorithms White noise Transforms Rational model gradient descent algorithm Volterra system preconditioning method parameter estimation

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A novel filtering-based recursive identification method for a fractional-order Hammerstein state space system with piecewise nonlinearity

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2025年

作者： Lang, Hongjun Bi, Yiqun Li, Meihang Ji, Yan Qingdao Univ Sci & Technol Coll Automat & Elect Engn Songlin Rd 99 Qingdao 266061 Peoples R China

This paper investigates parameters and states estimation for a class of fractional-order state space systems with colored noises. To provide accurate parameter estimation, we suggest a novel gradient descent algorithm based on the extended Kalman filtering. The new approach features lower estimation error variances and a faster convergence rate than the conventional gradient descent algorithm. A data filtering is introduced to filter the input and output data, thereby reducing the impact of colored noises on the accuracy of the parameter estimates.

关键词： Piecewise linearity fractional-order system parameter estimation state filtering gradient descent algorithm

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Optimal Control of Nonlinear Competitive Epidemic Spreading Processes With Memory in Heterogeneous Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第1期12卷 96-109页

作者： You, Jin Li, Yan Cao, Xiangyang Zhao, Daduan Chen, Yangquan Zhang, Chenghui Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Luoyang Inst Sci & Technol Sch Elect Engn & Automat Luoyang 471000 Peoples R China Univ Calif Merced Mechatron Embedded Syst & Automat Lab Merced CA 95343 USA

Modeling and control of epidemic-like spreading processes, such as contagions, rumors or products in complex networks, holds profound significance for understanding transmission mechanisms, formulating effective prevention and optimizing resource allocation. This paper investigates the optimal control problems for competing processes modeled by fractional-order bi-virus systems with memory in complex networks for the first time. The proposed model performs exceptionally well in capturing the dynamics of virus transmission and immune. First, the key indicators: bi-basic reproduction numbers (BBRNs) are introduced for the metapopulation model with nonlinear infectious functions in heterogenous networks. Then they are utilized to classify the equilibria, including extinction, absolute dominance and coexistence ones. Second, sufficient conditions for the stability of the equilibria are derived. Leveraging this result, an optimal therapeutic protocol is presented to eradicate viruses and minimize the cost function simultaneously. The corresponding optimal control solutions are generated via gradient descent algorithm, which can converge relatively quickly by iteratively adjusting control variables. Particularly, the role of fractional-order $\alpha$ in system modeling and optimization is deeply probed into. Finally, numerical examples are also included at the end of this paper to further shed light on the effectiveness of our results.

关键词： Optimal control Epidemics Process control Predictive models Mathematical models Infectious diseases Prevention and mitigation COVID-19 Heuristic algorithms Heterogeneous networks Complex network competing process gradient descent algorithm fractional-order system optimal control

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Chirality-Free Full Decoupling of Jones Matrix Phase-Channels with a Planar Minimalist Metasurface

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NANO LETTERS 2025年第4期25卷 1322-1328页

作者： Mu, Xinyang Qin, Haoye Zhao, Wannian Han, Sanyang Liu, Ziqi Shi, Yuzhi Huang, Wei Li, Bo Song, Qinghua Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Chinese Acad Sci Suzhou Inst Nanotech & Nanob SINANO Key Lab Multifunct Nanomat & Smart Syst Suzhou 215123 Peoples R China Tongji Univ Inst Precis Opt Engn Sch Phys Sci & Engn Shanghai 200092 Peoples R China Suzhou Lab Suzhou 215000 Peoples R China

The complete manipulation of Jones matrix phase-channels using metasurfaces brings forth unparalleled possibilities across diverse wavefront modulation applications. Traditionally, achieving independent control over all four phase-channels usually involves the introduction of chirality with multilayer or three-dimensional metasurfaces. Here, we present a general chirality-free method that relies on polarization base transformation with a planar minimalist metasurface, effectively decoupling the four Jones matrix phase-channels, thereby unleashing the fundamental boundaries imposed by conventional linear or circular polarization bases. To achieve independent wavefront modulation across these four channels, we employ an inverse design methodology leveraging a gradient descent algorithm. Remarkably, our strategy enables four channel wavefront modulations with only three control degrees of freedom within the minimalist meta-structures. As a proof-of-concept, we experimentally project four far-field holographic images, each corresponding to one of the four phase-channels, holding promises for various applications such as augmented/virtual reality display, information encryption, optical communication and data storage.

关键词： Metasurface Meta-Hologram Polarization Multiplexing Chirality Free gradient descent algorithm Jones Matrix

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Dynamic gain-based neural network backstepping control with its applications to a quad-rotor hover

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2025年第4期47卷 677-687页

作者： Yang, Jiaxing Yang, Xuebo Zheng, Xiaolong Harbin Inst Technol Sch Astronaut Res Inst Intelligent Control & Syst Harbin 150001 Peoples R China

In this paper, a novel neural network (NN) backstepping control method is proposed for a class of uncertain nonlinear systems with unknown control direction functions. To solve the control problems caused by unknown control direction functions, a method called "virtual control coefficient" is presented using equivalence transformations. However, to improve the flexibility of the control method, a function named "dynamic gain" is introduced to design the feedback gains of the NN backstepping controllers. Moreover, instead of using sigma-modification, gradient descent algorithm is applied to train the weights of NNs such that the unknown nonlinearities of the system can be well approximated by NNs. With the help of Lyapunov stability criterion, it can be proved that the system tracking error is semi-global uniformly ultimately bounded. Finally, the effectiveness of the proposed control method is verified by the experimental results on a quad-rotor hover platform.

关键词： Neural network backstepping dynamic gain gradient descent algorithm unknown control direction functions quad-rotor hover

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Dual Jones Matrices Empowered Six Phase Channels Modulation with Single-Layer Monoatomic Metasurfaces

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LASER & PHOTONICS REVIEWS 2025年第7期19卷

作者： Liu, Ziqi Mu, Xinyang Song, Helun Yu, Yefeng Zhang, Ruirui Zou, Yonggang Geng, Zihan Wang, Mingdi Chen, Linghu Sun, Yunfei Tong, Xiaogang Song, Qinghua Huang, Wei Changchun Univ Sci & Technol Natl Key Lab High Power Semicond Lasers Changchun 130022 Jilin Peoples R China Chinese Acad Sci Suzhou Inst Nanotech & Nanobion Key Lab Multifunct Nanomat & Smart Syst Suzhou 215123 Jiangsu Peoples R China Chinese Acad Sci Suzhou Inst Nanotech & Nanobion Key Lab Semicond Display Mat & Chips Suzhou 215123 Jiangsu Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Guangdong Peoples R China Nanjing Univ Sci & Technol Sch Microelect Nanjing 210094 Jiangsu Peoples R China Soochow Univ Sch Mech & Elect Engn Suzhou 215021 Jiangsu Peoples R China Suzhou Yihua Biomed Technol Co Ltd Suzhou 215000 Jiangsu Peoples R China Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou 215009 Jiangsu Peoples R China Taiyuan Inst Technol Dept Elect Engn Taiyuan 030008 Shanxi Peoples R China

A comprehensive manipulation of the Jones matrix phase channels is crucial for enhancing the information capacity and security of the metasurface. However, a single-layer planar metasurface, without the incorporation of chirality, can only achieve a maximum of three independent phase channels for single Jones matrix under either linear or circular polarization bases. Here, an effective strategy is proposed that utilizes dual Jones matrices in different polarization bases with a single-layer monoatomic metasurface, thus extending the number of independent phase channels to six. To encode six holographic images into these six phase channels, a three-step gradient descent algorithm is introduced. As a proof of principle, a metasurface consisting of arrays of monoatomic dielectric structure is designed and fabricated in each unit cell, which exhibits six independent holographic images in the far field. The demonstrated metasurface platform is expected to open up new possibilities for the development of optical devices for applications in high-capacity information storage, high-security encrypted communication, and high-solution optical displays.

关键词： dual Jones matrices gradient descent algorithm holography metasurface six phase channels modulation

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Heat losses in directly buried solar heat collection networks in high-altitude regions

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SOLAR ENERGY 2025年 290卷

作者： Chen, Hongsen Wang, Baichao Song, Cong Wang, Dengjia Liu, Yanfeng Xian Univ Architecture & Technol Sch Bldg Serv Sci & Engn 13Yanta Rd Xian 710055 Peoples R China Xian Univ Architecture & Technol State Key Lab Green Bldg 13Yanta Rd Xian 710055 Peoples R China

Due to the fluctuations in the medium temperature, the solar collector system (SCS) based on temperature difference control currently lacks a medium temperature value for accurately calculating the operational heat loss of directly buried solar heat collection pipelines. This research proposed a method for calculating heat loss utilizing an equivalent medium temperature (EMT). Initially, a computational model is established for assessing the heat loss of directly buried solar collector pipelines in Xizang plateau. Based on the primary factors influencing heat loss, the study investigated the temperature drop and operational heat loss patterns of directly buried solar heat collection pipelines. The ranges of regional equivalent medium temperature (REMT) and condition equivalent medium temperature (CEMT) were optimized using the gradient descent algorithm and heat loss calculation theory. The variations of REMT and CEMT with different regions and key heat loss factors were analyzed, and a CEMT association model was obtained. The EMT is highest in Lhasa and lowest in Gar, related to the ambient temperature for heating design and collector field operation time. For REMT, Lhasa ranges from 50.4 degrees C to 52.8 degrees C, and Gar from 31.1 degrees C to 33.4 degrees C. CEMT decreases with increasing flow rate coefficient and nominal diameter, ranging in Lhasa from 77.1 degrees C to 35.5 degrees C, and in Gar from 54.9 degrees C to 18.5 degrees C. The study aims to provide fundamental data and theoretical support for heat loss calculation and designing the insulation thickness of directly buried solar heat collection pipelines in high-altitude regions.

关键词： Equivalent medium temperature Solar heat collection pipeline Directly buried pipeline gradient descent algorithm Heat loss calculation Altitude

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Neural fractional order differential equations

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 267卷

作者： Sivalingam, S. M. Govindaraj, V. Natl Inst Technol Puducherry Dept Math Karaikal 609609 India

Neural ordinary differential equations are architectures that connect the neural network theory with dynamical system, providing scope for continuous propagation of the layers. Meanwhile, fractional-order differential equations offer an approach to model systems with the scope to retain long-term memory. In this study, we use the idea of fractional derivative in the neural differential equations and develop the fractional version named the neural fractional differential equations. These networks can retain long-term memory and is trained using the ensemble Kalman inversion algorithm. The detailed derivation of the network architecture and the existence of a solution using Schauder's fixed point theorem are given in detail. The forward propagation of the system is executed utilizing the L1 predictor-corrector. The issue in obtaining the gradient is explained in detail. The ensemble Kalman inversion is used to train the network memory efficiently. Numerical results for studying the dynamics of linear and nonlinear systems are provided to demonstrate the effectiveness of the network with the proposed learning method. Also, the model's capacity to fit with real-world data is shown in the HIV, monkey-pox, Delhi temperature, and Netflix stock price datasets. The method has shown excellent performance in fewer iterations compared to neural ordinary differential equations, classical recurrent neural network, gated recurrent unit, long short-term memory networks and Bi-directional long short-term memory neural network.

关键词： Caputo derivative Neural ordinary differential equations gradient descent algorithm Ensemble Kalman inversion

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Reduced-order identification methods: Hierarchical algorithm or variable elimination algorithm

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AUTOMATICA 2025年 172卷

作者： Chen, Jing Mao, Yawen Wang, Dongqing Gan, Min Zhu, Quanmin Liu, Feng Jiangnan Univ Sch Sci Wuxi 214122 Peoples R China Qingdao Univ Coll Elect Engn Qingdao 266071 Peoples R China Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China Univ West England Dept Engn Design & Math Bristol BS16 1QY England Stevens Inst Technol Sch Syst & Enterprises Hoboken NJ 07030 USA

Reduced-order identification algorithms are usually used in machine learning and big data technologies, where the large-scale systems widely exist. For large-scale system identification, traditional least squares algorithm involves high-order matrix inverse calculation, while traditional gradient descent algorithm has slow convergence rates. The reduced-order algorithm proposed in this paper has some advantages over the previous work: (1) via sequential partitioning of the parameter vector, the calculation of the inverse of a high-order matrix can be reduced to low-order matrix inverse calculations;(2) has a better conditioned information matrix than that of the gradient descent algorithm, thus has faster convergence rates;(3) its convergence rates can be increased by using the Aitken acceleration method, therefore the reduced-order based Aitken algorithm is at least quadratic convergent and has no limitation on the step-size. The properties of the reduced-order algorithm are also given. Simulation results demonstrate the effectiveness of the proposed algorithm. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Reduced-order algorithm Least square algorithm gradient descent algorithm Condition number Hierarchical identification algorithm Variable elimination algorithm

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