Publications

Publications

After Joining Texas Tech University (Last update: Sept. 2026) 

Papers under review:

[25]  M.A. Siddique and Q. Lu*. "Physics-guided conditional diffusion model for rare event synthesis and diagnosis for the water-gas shift reaction." Under review, 2026. [link]

[24] J. Alam, L. Tran, D. Agrawal, A. Bari, R. Phariss, K. Gonzalez, G. Modekwe, L. Tijani, Q. Lu, J. Gomez-Pastora. "Automated detection of sickled erythrocytes in peripheral blood smear  images using YOLO-based object fetection." Under review, 2026. [link]

[23] G. Modekwe and Q. Lu*. "Enhanced capacity prediction for Lithium-ion batteries with multi-attention spatio-temporal graph networks." Under review, 2026. 

Papers published:

[22] G. Modekwe, Q. Lu*. "Physics-informed neural network with transfer learning for state estimation in Lithium-ion batteries using the single particle model with electrolyte." FOCAPO-CPC, Tucson, AZ, January 2027. [link]

[21] B. Aryal, G. Modekwe, Q. Lu*. "Multi-level temporal graph networks with local-global fusion for industrial fault diagnosis." Measurement Science and Technology, 37 (23), 235109, 2026. [link]

[20] M. Chowdhury, Q. Lu*. "Equivalent-circuit thermal aging model with parameter identification for battery temperature prediction." Journal of Energy Storage, 138, 118569, 2025. [link]

[19] G. Modekwe, Q. Lu*. "Domain-adaptive capacity prediction for Lithium-ion batteries with pseudo-inverse gram matrix." Journal of Energy Storage, 128, 117037, 2025. [link]

[18] M. Chowdhury, G. Modekwe, and Q. Lu*. "Lithium-ion battery capacity prediction via conditional recurrent generative adversarial network-based time-series regeneration." In American Control Conference, 2025. [link]

[17] M. Chowdhury, G. Modekwe, and Q. Lu*. "Equivalent-circuit thermal model for batteries with one-shot parameter identification." In American Control Conference, 2025. [link]

[16] A. Arefin, N. Khatri, AKMA Habib, Q. Lu, A Idesman, P. Egan. "Heterogenous architected materials: enhancing mechanical performance through multi-objective optimization." Engineering with Computers, 41(2), 1241-1259, 2025. [link]

[15] B. Jiang, Y. Wang, Z. Ma, Q. Lu*. "Fast charging of lithium-ion batteries using deep Bayesian optimization with recurrent neural network." IEEE Transactions on Automation Science and Engineering 22, 13150-13160, 2025. [link]

[14] M.A. Chowdhury, S.SS Al-Wahaibi, Q. Lu*. "Adaptive safe reinforcement learning-enabled optimization of battery fast-charging protocols." AIChE Journal, e18605, 2024. [link]

[13] O. Abdelkerim, S. Leggett, Q. Lu, and W. Nelle. "High pressure gas lift optimization using nodal analysis." SPE Artificial Lift Conference and Exhibition-Americas, 2024. [link]

[12] G. Modekwe, S. Al-Wahaibi, Q. Lu*. "Transformer-based capacity prediction for lithium-ion batteries with data augmentation." 2024 Modeling, Estimation and Control Conference, 2024. [link]

[11] M.A. Chowdhury, S.SS Al-Wahaibi, Q. Lu*. "Entropy-maximizing TD3-based reinforcement learning for adaptive PID control of dynamical systems." Computers & Chemical Engineering, 178: 108393, 2023. [link]

[10] S. Al-Wahaibi, S. Abiola, M. Chowdhury, Q. Lu*. "Improving convolutional neural networks for fault diagnosis in chemical processes by incorporating global correlations." Computers & Chemical Engineering, 176: 108289, 2023. [link]

[9] K. Gardner, R. Joshi, M. Hasan Kashem, T. Pham, Q. Lu, W. Li. "Label free identification of different cancer cells using deep learning-based image analysis." APL Machine Learning, 1(2): 026110, 2023. [link]

[8] MA Chowdhury, Qiugang Lu. “A novel entropy-maximizing TD3-based reinforcement learning for automatic PID tuning.” American Control Conference, 2023. [link]

[7] SSS Al-Wahaibi and Qiugang Lu*. “Improving convolutional neural networks for fault diagnosis by assimilating global features.” American Control Conference, 2023. [link]

[6] Y. Hao, Q. Lu, X. Wang, B. Jiang. "Adaptive model-based reinforcement learning for fast charging optimization of Lithium-Ion batteries." IEEE Transactions on Industrial Informatics, 20(1): 127-137, 2024. [link]

[5] Q. Lu* and SSS Al-Wahaibi. “Enhanced CNN with global features for fault diagnosis of complex chemical processes.” FOCAPO/CPC 2023. [link]

[4] B. Jiang, Qiugang Lu*. "Fault Detection in industrial systems using maximized divergence analysis approach." IEEE Access, 10, 60674-60681, 2022. [link]

[3] Y. Li, Y. Wang, Y. Chen, K. Hua, J. Ren, G. Mozafari, Q. Lu, and Y. Cao. “Deep learning-based predictive control of battery management for frequency regulation.” 61(24): 8432-8442, 2022. [link]

[2] A. I. Lakhani, M. A. Chowdhury, Q. Lu*. “Stability-preserving automatic tuning of PID control with reinforcement learning.” Complex Engineering Systems, 2:3, 2022. [link]

[1] Q. Lu, L. Gonzalez, R. Kumar, and V.M. Zavala. “Bayesian optimization with reference models: A case study in MPC for HVAC central plants.” Computers & Chemical Engineering, vol. 154(107491), pp. 1-11, 2021. [link]

Prior to Joining Texas Tech University 

[28] Qiugang Lu, M.G. Forbes, P.D. Loewen, J.U. Backstrom, G.A. Dumont , R.B. Gopaluni. “Model-plant mismatch detection for cross-directional processes.” ISA Transactions, 117: 150-159, 2021. [link]

[27] Qiugang Lu, and V.M. Zavala. “Image-Based Model Predictive Control via Dynamic Mode Decomposition.” Journal of Process Control, vol. 104, pp. 146-157, 2021. [link]

[26] Q. Lu, R. Kumar, and V. Zavala. "MPC controller tuning using Bayesian optimization techniques." arXiv preprint arXiv:2009.14175, 2020. [link]

[25] S. Shin, Qiugang Lu, and V.M. Zavala. “Unifying Theorems for Subspace Identification and Dynamic Mode Decomposition.” arXiv preprint arXiv:2003.07410, 2020. [link]

[24] Qiugang Lu, R.B. Gopaluni, M.G. Forbes, P.D. Loewen, J.U. Backstrom, and G.A. Dumont. “Support vector machine approach for model-plant mismatch detection.” Computers & Chemical Engineering, 13(2): 106660, 2020. [link]

[23] Qiugang Lu, S. Shin, and V.M. Zavala. “Characterizing the predictive accuracy of dynamic mode decomposition for data-driven control.” In 21st IFAC World Congress, Berlin, Germany, July 12-17, 2020. [link]

[22] Qiugang Lu, B. Jiang, and E. Harinath. “Fault diagnosis in industrial processes by maximizing pairwise Kullback-Leibler divergence.” IEEE Transactions on Control Systems Technology, 29(2): 780-785, 2019. [link]

[21] Qiugang Lu, P.D. Loewen, R.B. Gopaluni, M.G. Forbes, J.U. Backstrom, G.A. Dumont, and M.S. Davies. “Identification of symmetric noncausal processes.''  Automatica, 103: 515-530, 2019. [link]

[20] L.D. Rippon, Qiugang Lu, M.G. Forbes, R.B. Gopaluni, J.U. Backstrom. “Machine direction adaptive control on a paper machine.” Industrial & Engineering Chemistry Research, 58(26): 11452-11473, 2019. [link]

[19] H. Tian, Qiugang Lu, R.B. Gopaluni, V.M. Zavala, J.A. Olson. “An economic model predictive control framework for mechanical pulping processes.” Control Engineering Practice, 85: 100-109, 2019. [link]

[18] Qiugang Lu, B. Jiang, R.B. Gopaluni, P.D. Loewen, R.D. Braatz. “Sparse canonical variate analysis approach for process monitoring.” Journal of Process Control, 70(1): 90-102, 2018. [link]

[17] Qiugang Lu, B. Jiang, R.B. Gopaluni, P.D. Loewen, R.D. Braatz. “Locality preserving discriminative canonical variate analysis for fault diagnosis.”  Computers & Chemical Engineering, 117(2): 309-319, 2018. [link]

[16] B. Jiang, Y. Luo, and Qiugang Lu*. “Maximized mutual information analysis based on stochastic representation for process monitoring.” IEEE Transactions on Industrial Informatics, 15(3): 1579-1587, 2018. [link]

[15] Y. Tsai, Qiugang Lu, L. Rippon, C.S. Lim, A. Tulsyan, and B. Gopaluni. “Pattern and Knowledge extraction using process data analytics: A tutorial.” In 10th IFAC International Symposium on Advanced Control of Chemical Processes, Shenyang, China, 2018. [link]

[14] Qiugang Lu, R.B. Gopaluni, M.G. Forbes, P.D. Loewen, J. Backstrom, and G.A. Dumont. “Model-plant mismatch detection with support vector machines.” In IFAC World Congress, Toulouse, France, 2017. [link]

[13] Qiugang Lu, L. Rippon, R.B. Gopaluni, M.G. Forbes, P.D. Loewen, J. Backstrom, and G.A. Dumont. “Noncausal modeling and closed-loop optimal input design for cross-directional processes of paper machines.” In American Control Conference, Seattle, WA, 2017. [link]

[12] H. Tian, Qiugang Lu, R.B. Gopaluni, V. Zavala. “Multiobjective economic model predictive control of mechanical pulping processes.” In IEEE 55th Conference on Decision and Control (CDC), Las Vegas, NV, 2016. [link]

[11] H. Tian, Qiugang Lu, R.B. Gopaluni, V. Zavala, J.A. Olson. “Economic nonlinear model predictive control for mechanical pulping processes.” In American Control Conference, Boston, MA, 2016. [link]

[10] Qiugang Lu, M. Forbes, R.B. Gopaluni, P.D. Loewen, J.U. Backstrom, G.A. Dumont. “Performance assessment of cross-directional control for paper machines.” IEEE Transactions on Control Systems Technology, 25(1): 208-221, 2017. [link]

[9] Qiugang Lu, L. Rippon, R.B. Gopaluni, M.G. Forbes, P.D. Loewen, J. Backstrom, and G.A. Dumont. “Cross-directional controller performance monitoring for paper machines.” In American Control Conference, Chicago, IL, 2015. [link]

[8] M. Yousefi, Qiugang Lu, R.B. Gopaluni, P.D. Loewen, M.G. Forbes, G.A. Dumont, J. Backstrom. “Detecting model-plant mismatch without external excitation.” In American Control Conference, Chicago, IL, 2015. [link]

[7] Qiugang Lu, L. Zhang, Q. Zhang, and H.R. Karimi. “Stability analysis and controller design for a class of T-S fuzzy Markov jump system with uncertain expectation of packet dropouts.” In American Control Conference, Washington, DC, 2013. [link]

[6] Qiugang Lu, L. Zhang, H.R. Karimi, and Y. Shi. “ H-infinity control for asynchronously switched linear parameter-varying systems with mode-dependent average dwell time.” IET Control Theory & Applications, 7(5): 673-683, 2013. [link]

[5] J. Qiu, H. Tian, Qiugang Lu, and H. Gao. “Nonsynchronized robust filtering design for continuous-time T–S fuzzy affine dynamic systems based on piecewise Lyapunov functions.”  IEEE Transactions on Cybernetics, 43(6): 1755-1766, 2013. [link]

[4] Qiugang Lu, L. Zhang, P. Shi, and H.R. Karimi. “Control design for a hypersonic aircraft using a switched linear parameter-varying system approach.” Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 227(5): 85-95, 2013. [link]

[3] Qiugang Lu, H.R. Karimi, and K.G. Robbersmyr. “A data-based approach for modeling and analysis of vehicle collision by LPV-ARMAX models.” Journal of Applied Mathematics, 1(1): 1-9, 2013. [link]

[2] H. Tian, J. Qiu, H. Gao, Qiugang Lu. “New results on robust filtering design for continuous-time nonlinear systems via T-S fuzzy affine dynamic models.” In 2012 12th International Conference on Control Automation Robotics & Vision, 2012. [link] 

[1] Qiugang Lu, L. Zhang, M. Basin, and H. Tian. “Analysis and synthesis for networked control systems with uncertain rate of packet losses.” Journal of the Franklin Institute, 349(7): 2500--2514, 2012. [link]

Books

[B1] L. Zhang, Y. Zhu. P. Shi, and Qiugang Lu. Time-Dependent Switched Discrete-Time Linear Systems: Control and Filtering. Springer International Publishing, 2016. [link]

Patents

[P4] Qiugang Lu, R.B. Gopaluni, M. Forbes, P.D. Loewen, J. Backstrom and G. Dumont. “Model-plant mismatch detection using model parameter data clustering for paper machines or other systems.” US20180081328A1, filed on September 16, 2016, granted on October 20, 2020.

[P3] Qiugang Lu, L.D. Rippon, R.B. Gopaluni, M. Forbes, P.D. Loewen, J. Backstrom and G. Dumont. “Closed-loop model parameter identification techniques for industrial model-based process controllers.” US20180081348A1, filed on September 16, 2016, granted on September 1, 2020.

[P2] Qiugang Lu, R.B. Gopaluni, M. Forbes, P.D. Loewen, J. Backstrom and G. Dumont. “Model-plant mismatch detection with support vector machine for cross-directional process behavior monitoring.” US20180081349A1, filed on September 16, 2016, Pending.

[P1] Qiugang Lu, R.B. Gopaluni, M. Forbes, P.D. Loewen, J. Backstrom and G. Dumont. “Optimal closed-loop input design for identification of flat-sheet process models.” US20170261963A1, filed on March 08, 2016, granted on October 29, 2019.