Dr. Chengda Lu is an energetic researcher in the areas of advanced control and intelligent automation, and he has gradually earned a good reputation among the researchers in the related areas.

Since 2019, he has been a principal investigator of two Projects of NSFC (National Natural Science Foundation of China), a Key R&D Project of Hubei Province, China, a Project of Natural Science Foundation of Hubei Province, China. As a core member, he has participated in a Key Project of NSFC (2018-2022) and a National Key Research and Development Program of China (2017-2021).

He has published 118 research papers (22 papers in IEEE Transactions). He has made his best efforts on the engineering applications of control theory and computational intelligence, and carried out solid work on the design and implementation of intelligent control and monitoring systems for drilling process, monitoring and early warning of geological hazard, etc.

He is an IEEE Senior Member. He served as session chairs in conferences like the 14th Asian Control Conference in 2024, the 42nd Chinese Control Conference in 2023, etc. He is the members of the Early Career Advisory Board of International Journal of Coal Science and Technology from 2024 and Coal Geology & Exploration from 2023. He is the reviewer of several international journals, including IEEE Transactions on Cybernetics, IEEE Transactions on Industrial Informatics, Control Engineering Practice, Journal of Process Control, etc.

Education:

2017-2019     Ph.D., Swinburne University of Technology, Melbourne, Australia

2015-2017     Partnered Ph.D. candidate, China University of Geosciences, Wuhan, China

2012-2015     M.S., China University of Geosciences, Wuhan, China

2008-2012     B.S., Wuhan University of Science and Technology, Wuhan, China

Work Experience：

2023-present   Professor, School of Automation, China University of Geosciences

2022-present   Director, Institute of Future Intelligent Technology, School of Future Technology, China University of Geosciences

2019-2023       Associate Professor, School of Automation, China University of Geosciences

Research Interest：

1. Drilling Process control: Geological drilling process control, drilling process control in coal mine, ocean drilling process control.

2. Time-delay systems control: Delayed neural networks, event-triggered control.

3. Intelligent system: Integrated intelligent modeling and optimal control technologies, complex process control system and implementation, intelligent optimization and control of complex processes.

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. 

Teaching：

1. Advanced Control Theory and Control Engineering (2024-present), PhD program

2. Pattern Recognition and Machine Learning (2021-present), Undergraduate program

3. Geological and Marine Equipment Control Technology (2024-present), Undergraduate program

4. Big Data Technology in Intelligent Manufacturing Process (2023, 2024), Undergraduate program

5. Embedded System Technology Practice (2020-present), Undergraduate program

6. Practice of Intelligent System Technology (2020-present), Undergraduate program