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Chengda Lu

Professor (specially appointed)
Doctoral Supervisor
Master Tutor

Honors and Titles : 湖北省人才计划、地大学者青年拔尖人才、地大学者青年优秀人才
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
Date of Employment : 2019-10-01
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 China University of Geosciences, Wuhan, China, as an Associate Professor in 2019, and was promoted to Professor in 2023.  His current research interests include robust control, time-delay systems, and intelligent systems.

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, 2021. 

[2] C. Lu, Z. He, L. Chen, Q. Li, and M. Wu, ``Suppressing coupled axial-torsional vibration of drill-string system considering regenerative cutting induced delay and actuator saturation,'' IEEE Transactions on Industrial Electronics,  vol. 70, no. 11, pp. 11608-11617, 2022.

[3] 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, 2020.

[4] 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, 2018.

[5] 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, vol. 51, no. 10, pp. 6085-6095, 2021.

[6] 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, 2018.

[7] H. Fan, C. Lu*, X. Lai, S. Du, W. Yu, and M. Wu, ``Adaptive monitoring for geological drilling process using neighborhood preserving embedding and Jensen-Shannon divergence,'' Control Engineering Practice, vol. 134, pp. 105476, 2023.

[8] Y. Zhou, C. Lu*, M. Zhang, X. Chen, M. Wu, and W. Cao, ``A novel rate of penetration model based on support vector regression and modified bat algorithm,'' IEEE Transactions on Industrial Informatics, vol. 19, no. 5, pp. 6659-6668, 2023.

[9] H. Huang, C. Lu*, S. Ma, and M. Wu, ``Analysis of coupled axial-torsional drill-string vibration based on fuzzy bit-rock interaction model considering bit balling condition,'' IEEE Transactions on Industrial Informatics, vol. 19, no. 9, pp. 9833-9842, 2023.

[10] A. Yang, C. Lu*, W. Yu, J. Hu, Y. Nakanishi, and M. Wu, ``Data augmentation considering distribution discrepancy for fault diagnosis of drilling process with limited samples,'' IEEE Transactions on Industrial Electronics, vol. 70, no. 11, pp. 11774-11783, 2022.

[11] Y. Wang, T. Yan, W.-A. Zhang, M. Wu, and C. Lu*, ``Learning-enabled output-feedback-MPC-based synchronization tracking control of multi-axis motion systems,'' IEEE Transactions on Industrial Electronics, vol. 70, no. 7, pp. 7216-7224, 2023.

[12] H. Fan, X. Lai, S. Du, W. Yu, C. Lu*, and M. Wu, ``Distributed monitoring with integrated probability PCA and mRMR for drilling processes,'' IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 3516213, 2021. 


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