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Chengda Lu

Associate professor
Master Tutor

Gender : Male
Alma Mater : Swinburne University of Technology
Education Level : Doctoral Degree in Education
Degree : 博士学位
Status : Employed
School/Department : School of Automation
Discipline : Control Science and Engineering
Business Address : Room 702, School of Automation
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Personal Profile

      Dr. Chengda Lu received the B.S. degree in electronic information engineering from the Wuhan University of Science and Technology, Wuhan, China, in 2012, the M.S. degree in information and communication engineering from the China University of Geosciences, Wuhan, in 2015, and the Ph.D. degree in electrical and electronics engineering from the Swinburne University of Technology, Melbourne, VIC, Australia, in 2019.

      He joined the China University of Geosciences in 2019, where he is currently an Associate Professor with the School of Automation. His current research interests include robust control, time-delay systems, and process control.

Research Articles:

[1] C. Lu, M. Wu, L. Chen, and W. Cao, ``An event-triggered approach to torsional vibration control of drill-string system using measurement-while-drilling data'', Control Engineering Practice, vol. 106, pp. 104668, Jan. 2021.

[2] C. Lu, M. Wu, and Y. He, ``Stubborn state estimation for delayed neural networks using saturating output errors,'' IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 6, pp. 1982-1994, Jun. 2020.

[3] C. Lu, X.-M. Zhang, M. Wu, Q.-L. Han, and Y. He, ``Energy-to-peak state estimation for static neural networks with interval time-varying delays,'' IEEE Transactions on Cybernetics, vol. 48, no. 10, pp. 2823-2835, Oct. 2018.

[4] C. Lu, X.-M. Zhang, M. Wu, Q.-L. Han, and Y. He, ``Receding horizon synchronization of delayed neural networks using a novel inequality on quadratic polynomial functions'', IEEE Transactions on Systems, Man, and Cybernetics: Systems, to be published, doi: 10.1109/TSMC.2019.2957810.

[5] C. Lu, M. Wu, X. Chen, W. Cao, C. Gan, and J. She, ``Torsional vibration control of drill-string systems with time-varying measurement delays,'' Information Sciences, vol. 467, pp. 528-548, Oct. 2018.

[6] Y. Xiong, C. Lu, M. Wu, K. Jiang, and D. Wang, ``A low energy consumption storage method for cloud video surveillance data based on SLA classification,'' Mobile Information Systems, vol. 2016, Article ID 6270738, 13 pages, 2016.

[7] C. Lu, X.-M. Zhang, M. Wu, Q.-L. Han, and Y. He, ``Extended dissipativity analysis of delayed memristive neural networks based on parameter-dependent Lyapunov functional,'' Proceedings  of the 2018 Australian and New Zealand Control Conference (ANZCC 2018), Melbourne, VIC, pp. 194-198, 2018.

[8] C. Lu, M. Wu, X. Chen, W. Cao, C. Gan, and J. She, ``Downhole-friction-estimation-based rotary speed control for drillstring system with stick-slip vibrations,'' Proceedings of the 11th Asian Control Conference (ASCC 2017), Gold Coast, QLD, pp. 2618-2623, 2017.

[9] C. Lu, M. Wu, L. Chen, and A. Yang, ``Extended dissipative control for drill-string systems with input time-varying delays'', Proceedings of the 38th Chinese Control Conference (CCC 2019), Guangzhou, China, pp. 2383-2386, 2019.



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