陈伟重

所属学科专业：控制科学与工程、电子信息

导师简介：

   陈伟重，1993年生，男，副教授，工学博士，2023年毕业于哈尔滨工业大学控制科学与工程专业，硕士生导师。陕西省高层次人才（青年）、西安工程大学“青年拔尖人才”，yl8cc永利官网机器人与智能装备研究所控制理论及应用研究室主任，中国自动化学会会员、中国指挥与控制学会会员。

主要从事切换系统、智能控制神经网络、网络化控制及状态估计等前沿领域的教学与科研工作。作为项目负责人共主持国家自然科学基金项目、陕西省高层次人才引进项目、陕西省自然科学基金项目等高水平项目5项，作为核心成员参加国家自然科学基金、航空发动机及燃气轮机国际合作项目等高水平项目6项。以第一作者或通讯作者身份在控制领域的知名期刊发表SCI论文20余篇，其中在控制领域两大顶级期刊Automatica、IEEE TAC上发表论文4篇，IEEE Transactions系列汇刊4篇，国家授权发明专利5项。长期担任国内外控制领域权威期刊Automatica、IEEE Transactions on Automatic Control、IEEE Transactions on Industrial Electronics、IEEE Transactions on Neural Networks and Learning Systems、IEEE Transactions on Cybernetics等十余个期刊的审稿人。 此外，担任国家自然科学基金和陕西省教育厅人才项目评审专家。

主要研究方向：智能控制、故障诊断、切换系统、神经网络、状态估计、网络化控制等理论及应用

近年来主要科研项目：

1. 切换非线性正系统的多胞体集员估计及在交通流量预测中的应用，主持（国家自然科学基金项目，国家级，2025.01-2027.12）

2. 切换系统的高精度约束多胞体集员估计，主持（陕西省高层次人才计划项目，省部级，2023.06-2029.06）

3. 基于时间和切换信号的切换系统动态输出反馈控制，主持（陕西省自然科学基金项目，省部级，2024.01-2025.12）

4. 基于切换信号的切换正系统的控制，主持（西安工程大学青年拔尖人才项目，校级，2023.07-2026.07）

近年来主要科研成果（获奖及专利）：

1. 航空发动机执行机构的事件触发自适应控制方法,  CN114397819A, 2023.

2. 一种基于中心对称多面体的机动目标运动坐标区间估计方法, CN113885354A, 2023.

3. 基于事件驱动的水面无人艇网络攻击下的容错控制方法, CN110579965A, 2019.

4. 基于事件驱动与输出量化的水面无人艇系统的故障检测方法， CN110703742A, 2020.

5. 基于事件驱动的航天器交会故障诊断与滤波器设计方法, CN110414125A, 2019.

近年来指导学生获奖与项目资助：

1. 2024年第二十一届中国研究生数学建模竞赛，国家三等奖

2. 2024年第六届中国机器人技能大赛，国家三等奖

3. 2024年“兆易创新杯”第十九届中国研究生电子设计竞赛，省一等奖

4. 2024大学生创新创业训练计划项目: 基于机器视觉和混杂系统的交通信号调流控制方法研究，省级资助

发表论文：

[1] Radius analysis based control for switched positive systems. IEEE Transactions on Automatic Control, 2024, DOI: 10.1109/TAC.2024.3440345.

[2] State estimation of networked switched systems via event-triggered zonotopes, IEEE Transactions on Control of Network Systems, 2024, DOI: 10.1109/TCNS.2024.3425641.

[3] Finite-time state zonotopes design for asynchronously switched systems with application to a switched converter. IEEE Transactions on Circuits and Systems I: Regular Papers, 2023, 70(10): 4137-4145.

[4] Stabilization of switched linear neutral systems with time-scheduled control strategy. IEEE Transactions on Automatic Control，2023, 68(2): 1093-1100.

[5] Interval estimation for asynchronously switched positive systems. Automatica，2022, 143: 110427.

[6] Sampled-data asynchronous control for switched nonlinear systems with relaxed switching rules. IEEE Transactions on Cybernetics, 2022, 52(11): 11549-11560.

[7] Generic stability criteria for switched nonlinear systems with switching-signal-based Lyapunov functions using Takagi-Sugeno fuzzy model. IEEE Transactions on Fuzzy Systems, 2022, 30(10): 4239-4248.

[8] Recent advances on dynamical behaviors of coupled neural networks with and without reaction–diffusion terms, IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 12, pp. 5231-5244, Dec. 2020.

[9] Two-sided looped functional based sampled data control for semi-Markovian jump systems with complex transition rates, Automatica,

vol. 134, 2021.

[10] State estimation for delayed switched positive systems: delayed radius approach. Science China Information Sciences, 2024, DOI: 10.1007/s11432-023-3980-0.

[11] Time and switching signal scheduled bumpless transfer control for switched systems with application to aero-engines. Nonlinear Analysis: Hybrid Systems, 2023, 48: 101327.

[12] Event-triggered asynchronous control for switched T-S fuzzy systems based on looped functionals. Journal of the Franklin Institute, 2022, 359(12): 6311-6335.

[13] Extended dissipativity of semi-Markov jump neural networks with partly unknown transition rates. Neurocomputing, 2021, 423: 601-608.

[14] Finite-time control of switched systems under asynchronism based on quantized sampled-data. Journal of the Franklin Institute, 2020, 357(11): 6635-6652.

[15] Dissipativity of Markovian multiple-weighted coupled neural networks with dynamic event-triggered pinning control. IET Control Theory and Applications, 2020, 14(15): 2030-2037.

[16] Passivity and synchronization of coupled reaction-diffusion Cohen-Grossberg neural networks with state coupling and spatial diffusion coupling. Neurocomputing, 2018, 275: 1208-1218.

[17] Passivity of coupled memristive delayed neural networks with fixed and adaptive coupling weights. Neurocomputing, 2018, 313: 346-363.

[18] Passivity and robust passivity of delayed Cohen-Grossberg neural networks with and without reaction-diffusion terms. Circuits, Systems, and Signal Processing, 2018, 37(7): 2772-2804.

[19] Zonotope-based interval estimation for discrete-time Markovian jump systems with complex transition probabilities and quantization. Journal of the Franklin Institute, 2022, 359(9): 4540-4555.

[20] Finite-time passivity of delayed multi-weighted complex dynamical networks with different dimensional nodes. Neurocomputing, 2018, 312: 74-89.

[21] Analysis and pinning control for generalized synchronization of delayed coupled neural networks with different dimensional nodes. Journal of the Franklin Institute, 2018, 355(13): 5968-5997.

[22] Passivity and synchronization of coupled reaction-diffusion Cohen-Grossberg neural networks with fixed and switching topologies. Neural Processing Letters, 2019, 49(3): 1443-1457.

[23] Passivity and robust passivity of reaction-diffusion Cohen-Grossberg neural networks with multiple time-varying delays. 2017 29th Chinese Control And Decision Conference (CCDC), 2017, 40-45.

[24] Global exponential synchronization of nonlinearly coupled reaction-diffusion neural networks. 2017 29th Chinese Control And Decision Conference (CCDC), 2017, 4742-4747.

[25] Dynamic output feedback control for switched systems based on time scheduled multiple discontinuous Lyapunov functions, 2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC), 2024, 515-519.

[26] Time-scheduled control for switched positive systems with mode-dependent average dwell time, 2024 10th International Conference on Mechanical and Electronics Engineering (ICMEE), 2024.

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