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林娜(副教授)

日期:2023-03-01 点击数: 作者: 来源:

林娜,女,副教授,博士学位,硕士生导师,近年来主持国家自然科学基金项目1项,参与国家级/市厅级项目4项,已发表学术论文近30篇,其中20余篇被SCI收录或EI收录,合作发表英文专著1本,申请发明专利5项。主要研究兴趣包括:学习控制、自适应控制、数据驱动控制等。联系方式:linnaqingdao@163.com。

部分代表性论文如下:

[1]N. Lin, R. Chi, B. Huang, Event-triggered model-free adaptive control,IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(6): 3358-3369.

[2]N. Lin, R. Chi, B. Huang, Auxiliarypredictive compensation-based ILC for variable pass lengths,IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(7): 4048-4056.

[3]N. Lin, R. Chi, B. Huang, Event-triggered nonlinear iterative learning control,IEEE Transactions on Neural Networks and Learning Systems. 2021, 32(11):5118-5128.

[4]N. Lin, R. Chi, B. Huang. Event-triggered ILC for optimal consensus at specified data points of heterogeneous networked agents with switching topologies.IEEE Transactions on Cybernetics, 2022, 52(9): 8951-8961.

[5]N. Lin, H. Li, R. Chi, Z. Hou and B. Huang, Data-driven virtual reference set-point learning of PD control and applications to permanent magnet linear motors,IEEE Transactions on Systems, Man, and Cybernetics: Systems, DOI: 10.1109/TSMC.2023.3240182.

[6]N. Lin, R. Chi, B. Huang, Data-driven recursive least squares methods for non-affined nonlinear discrete-time systems,Applied Mathematical Modelling, 2020, 81:787-798.

[7]N. Lin, R. Chi, B. Huang, Data-driven set-point control for nonlinear nonaffine systems,Information Sciences, 2023, 625:237-254.

[8]N. Lin, R. Chi, B. Huang, Event-triggered learning consensus of networked heterogeneous nonlinear agents with switching topologies,Journal of the Franklin Institute, 2021, 358: 3803-3821.

[9]N. Lin, R. Chi, B. Huang, Z. Hou, Iterative dynamic linearization and identification of a nonlinear learning controller: A data-driven approach,Journal of the Franklin Institute, 2019, 356(13): 7009-7027.

[10]N. Lin, R. Chi, B. Huang, Z. Hou, Multi-lagged-input iterative dynamic linearization based data-driven adaptive iterative learning control,Journal of the Franklin Institute, 2019, 356(1): 457-473.

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