PUBLICATIONSBYYEAR:Publications

Books, Book Chapters, and Edited Issues

1. Jinfeng Liu and Bernard T. Agyeman. Model predictive control for irrigation scheduling. In Q. Zhang (Ed.), Encyclopedia of Smart Agriculture Technologies, pages 1-14, Springer, Cham, 2023.          

2. Benjamin Decardi-Nelson and Jinfeng Liu. Towards smart energy generation using economic model predictive control. In M. Li (Ed.), Energy Systems and Processes: Recent Advances in Design and Control, 7-1 - 7-16, AIP Publishing, 2023.     

2023

1. S. Debnath, S. R. Sahoo, B. T. Agyeman, and J. Liu*.

   Triggered adaptive model reduction and soil moisture estimation for agro-hydrological systems

   Computers and Electronics in Agriculture, submitted.

2. Bernard T. Agyeman, Mohamed Naouri, Willemijn Appels, Jinfeng Liu*, and Sirish L. Shah.

   Integrating machine learning paradigms and mixed-integer model predictive control for irrigation scheduling

   Control Engineering Practice, submitted.

        Preprint

3. Bernard T. Agyeman, Erfan Orouskhani, Mohamed Naouri, Willemijn Appels, Maik Wolleben, Jinfeng Liu*, and Sirish L. Shah.

   Maximizing soil moisture estimation accuracy through simultaneous hydraulic parameter estimation using microwave remote sensing: methodology and application

   Computers and Electronics in Agriculture, submitted.

        Preprint

4. X. Yin*, Y. Qin, J. Liu, B. Huang.

   Data-driven moving horizon state estimation of nonlinear processes using Koopman operator

   Chemical Engineering Research and Design, 200:481-492, 2023.

        Journal version

5. Zhuangyu Liu, Jinfeng Liu*, Shunyi Zhao, Xiaoli Luan, and Fei Liu.

   State estimation for one-dimensional agro-hydrological processes with model mismatch

   Canadian Journal of Chemical Engineering, accepted.

        Preprint      Data and code      Journal version

6. Sarupa Debnath, Soumya R. Sahoo, Bernard T. Agyeman, Xunyuan Yin and Jinfeng Liu.

   An error-triggered adaptive model reduction and soil moisture estimation for agro-hydrological systems

   In Proceedings of IEEE Conference on Decision and Control, accepted.

7. G. Li, J. Zeng*, and J. Liu.

   Effluent quality-aware event-triggered model predictive control for wastewater treatment plants

   Mathematics, 11:3912, 2023.

        Journal version

8. Sandra A. Obiri, Song Bo, Bernard T. Agyeman, Sarupa Debnath, Benjamin Decardi-Nelson, and Jinfeng Liu*.

   Optimizing the switching operation in monoclonal antibody production: Economic MPC and reinforcement learning

   Chemical Engineering Research and Design, 199:61-73, 2023.

        Preprint      Journal version

9. Sandra A. Obiri, Bernard T. Agyeman, Sarupa Debnath, Siyu Liu, and Jinfeng Liu*.

   Sensor selection and state estimation of continuous mAb production processes.

   Mathematics, 11:3860, 2023.

        Data and code      Journal version

10. Song Bo, Bernard T. Agyeman, Xunyuan Yin, and Jinfeng Liu*.

    Control invariant set enhanced safe reinforcement learning: improved sampling efficiency, guaranteed stability and robustness

    Computers and Chemical Engineering, 179:108413, 2023.

         Preprint      Data and code      Journal version

11. Zhiyinan Huang, Jinfeng Liu*, and Biao Huang.

    Generalized robust MPC with zone tracking

    Chemical Engineering Research and Design, 195:537-550, 2023.

         Preprint      Data and code      Journal version

12. Long Wu, Xunyuan Yin, Lei Pan, and Jinfeng Liu*.

    Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy

    Applied Energy, 349:121627, 2023.

         Preprint      Data and code      Journal version

13. Siyu Liu, Xunyuan Yin, and Jinfeng Liu*.

    Sensor network design for post-combustion CO2 capture plants: Computational efficiency and robustness

    Journal of Process Control, 129:103035, 2023.

         Preprint      Data and code      Journal version

14. Siyu Liu, Xunyuan Yin, Zhichao Pan, and Jinfeng Liu*.

    A sensitivity-based approach to optimal sensor selection for process networks

    Chemical Engineering Science, 278:118901, 2023.

         Preprint      Data and code      Journal version

15. Zhiyinan Huang, Jinfeng Liu*, and Biao Huang.

    Model predictive control of agro-hydrological systems based on a two-layer neural network modeling framework

    International Journal of Adaptive Control and Signal Processing, 37:1536-1558, 2023.

         Preprint      Data and code      Journal version

16. Sarupa Debnath, Soumya R. Sahoo, Bernard T. Agyeman, and Jinfeng Liu.

    Input-output selection for LSTM-based reduced-order state estimator design

    In Proceedings of IFAC World Congress, pages 7522-7527, Yokohama, Japan, 2023.

17. Bernard T. Agyeman, Mohamed Naouri, Willemijn Appels, and Jinfeng Liu.

    Irrigation management zone delineation and optimal irrigation scheduling for center pivot irrigation systems

    In Proceedings of IFAC World Congress, pages 10634-10639, Yokohama, Japan, 2023.

18. Long Wu and Jinfeng Liu.

    Decomposition and distributed predictive control of integrated energy systems

    In Proceedings of the American Control Conference, 3130-3135, San Diego, USA, 2023.

19. Siyu Liu, Xunyuan Yin, and Jinfeng Liu.

    Sensor placement for post-combustion CO2 capture plants

    In Proceedings of the American Control Conference, 3454-3459, San Diego, USA, 2023.

20. Jianbang Liu, Song Bo, Benjamin Decardi-Nelson, Jinfeng Liu*, Jintao Hu, and Tao Zou.

    Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise

    ISA Transactions, 139:35-48, 2023.

         Preprint      Journal version

21. Bernard T. Agyeman, Soumya R. Sahoo, Jinfeng Liu*, and Sirish L. Shah.

    LSTM-based model predictive control with discrete inputs for irrigation scheduling

    Canadian Journal of Chemical Engineering, 101:3362-3381, 2023.

         Preprint      Journal version

22. Yating Huang, Jun Xu*, Jinfeng Liu, and Yunjiang Lou.

    EMPC based on lattice trajectory piecewise linear model with application to wastewater treatment plant

    Journal of Process Control, 124:152-151, 2023.

         Journal version

23. Sarupa Debnath, Soumya R. Sahoo, Bernard T. Agyeman, and Jinfeng Liu*.

    Input-output selection for LSTM-based reduced-order state estimator design

    Mathematics, 11:400, 2023.

         Data and code      Journal version

24. Benjamin Decardi-Nelson and Jinfeng Liu*.

    Computing control invariant sets of nonlinear systems: decomposition and distributed computing

    Computers and Chemical Engineering, 171:108142, 2023.

         Preprint      Data and code      Journal version

25. Erfan Orouskhani, Soumya R. Sahoo, Bernard T. Agyeman, Song Bo, and Jinfeng Liu*.

    Impact of sensor placement in soil water estimation: A real-case study

    Irrigation Science, 41:395-411, 2023.

         Preprint      Data and code      Journal version

2022

1. X. Yin*, Y. Qin, H. Chen, W. Du, J. Liu, and B. Huang. Community detection based process decomposition and distributed monitoring for large-scale processes, AIChE Journal, 68:e17826, 2022. DOI
2. L. Wu, X. Yin, L. Pan, and J. Liu*. Economic model predictive control of integrated energy systems: A multi-time-scale framework, Applied Energy, 328:120187, 2022. Replication Data & Code, Preprint, DOI
3. B. Decardi-Nelson and J. Liu*. An efficient implementation of graph-based invariant set algorithm for constrained nonlinear dynamical systems, Computers and Chemical Engineering, 164:107906, 2022. Preprint, DOI
4. J. Bao, H. Durand, S. S. Jogwar, J. Liu, B. Young, and Q. Zhu (Eds.). Modeling, Control and Monitoring of Process Systems in the Era of Big Data, Special issue of Digital Chemical Engineering, 2022.
5. S. Zhao, X. Luan, J. Liu, and R. Tan (Eds.). Advances on Modeling and State Estimation for Industrial Processes, Special issue of Computer Modeling in Engineering & Sciences, 2022.
6. S.Y. Liu, X. Yin, J. Liu*, and F. Ding. Distributed simultaneous state and parameter estimation of nonlinear systems, Chemical Engineering Research and Design, 181:74-86, 2022. Preprint, DOI
7. B. Decardi-Nelson and J. Liu*. Robust economic MPC of the absorption column in post-combustion carbon capture through zone tracking, Energies, 15, 1140, 2022. Preprint, DOI (Modelling, Optimization and Control of Carbon Capture for Power Plants special issue)
8. S. R. Sahoo, B. T. Agyeman, S. Debnath, and J. Liu*. Knowledge-based optimal irrigation scheduling of agro-hydrological systems, Sustainability, 14:1304, 2022. Preprint, DOI (Emerging Frontiers, Processes and Technologies for Water Sustainability special issue)
9. S. R. Sahoo and J. Liu*. Adaptive model reduction and state estimation of agro-hydrological systems, Computers and Electronics in Agriculture, 195:106825, 2022. Preprint, DOI
10. S. Debnath, S. R. Sahoo, B. Decardi-Nelson, and J. Liu*. Subsystem decomposition and state estimation of nonlinear processes with implicit time-scale multiplicity, AIChE Journal, 68:e17661, 2022. Preprint, DOI
11. B. Decardi-Nelson and J. Liu*. Robust economic model predictive control with zone tracking, Chemical Engineering Research and Design, 177:502-512, 2022. Preprint, DOI
12. Z. Huang, Q. Liu, J. Liu*, and B. Huang. A comparative study of model approximation methods applied to economic MPC, Canadian Journal of Chemical Engineering, 100:1676-1702, 2022. Preprint, DOI
13. M. Zhang, X. Wang, B. Decardi-Nelson, S. Bo, A. Zhang, J. Liu, S. Tao, J. Cheng, X. Liu, D. Yu, M. Poon, A. Gary. SMPL: Simulated industrial manufacturing and process control learning environments. In Proceedings of Neural Information Processing Systems (NeruIPS), 2022. Preprint
14. E. Orouskhani, B. T. Agyeman, and J. Liu. Simultaneous estimation of soil moisture and hydraulic parameters for precision agriculture. Part A: methodology. In Proceedings of the 7th International Symposium on Advanced Control for Industrial Processes, pages 12-17, Vancouver, Canada, 2022.
15. B. T. Agyeman, E. Orouskhani, and J. Liu. Simultaneous estimation of soil moisture and hydraulic parameters for precision agriculture. Part B: Application to a real field. In Proceedings of the 7th International Symposium on Advanced Control for Industrial Processes, pages 18-23, Vancouver, Canada, 2022.
16. Z. Huang, J. Liu, and B. Huang. A two-layer NN framework for modeling water dynamics in agro-hydrological systems. In Proceedings of the 7th International Symposium on Advanced Control for Industrial Processes, pages 1-6, Vancouver, Canada, 2022.
17. S. Liu, X. Yin, and J. Liu. Sensor placement for wastewater treatment plants: a computationally efficient algorithm. In Proceedings of the 7th International Symposium on Advanced Control for Industrial Processes, pages 228-233, Vancouver, Canada, 2022.
18. X. Yin, Y. Qin, H. Chen, W. Du, J. Liu and B. Huang. Process decomposition and distributed fault detection of large-scale industrial processes. In Proceedings of the 7th International Symposium on Advanced Control for Industrial Processes, pages 176-181, Vancouver, Canada, 2022.
19. S. R. Sahoo, B. T. Agyeman, S. Debnath, and J. Liu. Knowledge-based optimal irrigation scheduling of three-dimensional agro-hydrological systems. In Proceedings of the 13th Symposium on Dynamics and Control of Process Systems(DYCOPS), pages 445-450, Busan, Korea, 2022.
20. E. Orouskhani, S. R. Sahoo, B. T. Agyeman, S. Bo, and J. Liu. Impact of sensor placement in soil water estimation: A real-case study. In Proceedings of the 13th Symposium on Dynamics and Control of Process Systems(DYCOPS), pages 344-349, Busan, Korea, 2022.
21. B. T. Agyeman, S. R. Sahoo, J. Liu, and S. L. Shah. LSTM-based model predictive control with discrete actuators for irrigation scheduling. In Proceedings of the 13th Symposium on Dynamics and Control of Process Systems(DYCOPS), pages 338-343, Busan, Korea, 2022.
22. S. Liu, X. Yin, and J. Liu. Simultaneous state and parameter estimation of not fully observable systems: a distributed approach. In Proceedings of the 13th Symposium on Dynamics and Control of Process Systems(DYCOPS), pages 2-7, Busan, Korea, 2022.
23. B. Decardi-Nelson and J. Liu. A distributed control invariant set computing algorithm for nonlinear cascade systems. In Proceedings of the American Control Conference, pages 172-177, Atlanta, Georgia, 2022.
24. S. R. Sahoo and J. Liu. Adaptive model reduction and state estimation of agro-hydrological systems. In Proceedings of the American Control Conference, pages 2559-2564, Atlanta, Georgia, 2022.

2021

1. Y. Zhang, J. Liu, T. Yang, Jianbang Liu, J. Shen*, and F. Fang*. Dynamic modeling and control of direct air-cooling condenser pressure considering couplings with adjacent systems, Energy, 236:121487, 2021. DOI
2. J. Zeng and J. Liu*. Distributed state estimation based distributed model predictive control, Mathematics, 9(12):1327, 2021. DOI
3. B. T. Agyeman, S. Bo, S. R. Sahoo, X. Yin, J. Liu*, and S. L. Shah. Soil moisture map construction using microwave remote sensors and sequential data assimilation, Journal of Hydrology, 598:126425, 2021. Preprint, DOI
4. B. Decardi-Nelson and J. Liu*. Computing robust control invariant sets of constrained nonlinear systems: A graph algorithm approach, Computers and Chemical Engineering, 145:107177, 2021. Preprint, DOI
5. Q. Guo, T. Pan, J. Liu*, and S. Chen. Explicit model predictive control of PMSM based on multi-point linearization, Transactions of the Institute of Measurement and Control, 43:2872-2881, 2021. DOI
6. Jianbang Liu, A. Gnanasekar, Y. Zhang, S. Bo, J. Liu*, J. Hu, and T. Zou. Simultaneous state and parameter estimation: the role of sensitivity, Industrial & Engineering Chemistry Research, 60:2971-2982, 2021. Preprint, DOI
7. X. Yin, S. Bo, J. Liu, and B. Huang*. A consensus-based approach for parameter and state estimation of agro-hydrological systems, AIChE Journal, 67:e17096, 2021. Preprint, DOI
8. S. L. Shah, B. R. Bakshi, J. Liu, C. Georgakis*, B. Chachuat, R. D. Braatz, and B. R. Young. Meeting the challenge of water sustainability: the role of process systems engineering, AIChE Journal, 67:e17113, 2021. DOI
9. G. Yan, J. Liu*, Y. Alipouri, and B. Huang. Performance assessment of distributed LQG control subject to communication delays. International Journal of Control, 60:2971-2982, 2021. DOI
10. X. Yin and J. Liu*. Event-triggered state estimation of linear systems using moving horizon estimation. IEEE Transactions on Control Systems Technology, 29:901-909, 2021. DOI
11. B. T. Agyeman, S. Bo, S. R. Sahoo, X. Yin, J. Liu, S. L. Shah. Soil moisture map construction by sequential data assimilation using an extended Kalman filter. In Proceedings of the American Control Conference, pages 4341-4346, virtual, 2021.
12. B. Decardi-Nelson, J. Liu. Robust economic model predictive control with zone control. In Proceedings of the 11th IFAC Symposium on Advanced Control of Chemical Processes, pages 237-242, virtual, 2021.

2020

1. X. Luo, J. Xu*, M. Zhang, and J. Liu. Batch to batch optimal control based on MIMO AHH prediction and Kalman filter correction. Optimal Control, Applications and Methods, 41:2048-2061, 2020. DOI
2. X. Yin and J. Liu*. Distributed state estimation for a class of nonlinear processes based on high-gain observers. Chemical Engineering Research and Design, 160:20-30, 2020. DOI
3. R. Nian, J. Liu*, and B. Huang. A review on reinforcement learning: introduction and applications in industrial process control. Computers and Chemical Engineering, 139:106886, 2020. DOI
4. S. Bo and J. Liu*. A decentralized framework for parameter and state estimation of infiltration processes. Mathematics, 8(5):1-21, 2020. DOI (Control and Optimization: From Complex Process to Systems Engineering Problems special issue)
5. Y. Mao, S. Liu, and J. Liu*. Robust economic MPC of nonlinear networked control systems with communication delays. International Journal of Adaptive Control and Signal Processing, 34:614-637, 2020. DOI
6. S. Bo, S. R. Sahoo, X. Yin, J. Liu*, and S. L. Shah. Parameter and state estimation of one-dimensional infiltration processes: A simultaneous approach. Mathematics, 8(1):1-22, 2020. DOI (Mathematics and Engineering special issue)
7. X. Yin, B. Decardi-Nelson, and J. Liu*. Distributed monitoring of the absorption column of a post-combustion CO2 capture plant. International Journal of Adaptive Control and Signal Processing, 34:757-776, 2020. DOI (Moving Horizon Estimation and New Application Perspectives special issue)
8. Y. Zhang, B. Decardi-Nelson, Jianbang Liu, J. Shen*, and J. Liu*. Zone economic model predictive control of a coal-fired boiler-turbine generating system. Chemical Engineering Research and Design, 153:246-256, 2020. DOI
9. S. R. Sahoo, X. Yin, J. Liu, and S. L. Shah. Dynamic model reduction and optimal sensor placement for agro-hydrological systems. In Proceedings of IFAC World Congress, pages 11669-11674, virtual, 2020.
10. S. Bo, S. R. Sahoo, X. Yin, J. Liu, and S. L. Shah. Simultaneous parameter and state estimation of agro-hydrological systems. In Proceedings of IFAC World Congress, pages 11767-11772, virtual, 2020.

2019

1. A. Zhang and J. Liu*. Economic MPC of wastewater treatment plants based on model reduction. Processes, 7, 21 pages, 2019. DOI (Design and Control of Sustainable Systems special issue)
2. S. R. Sahoo, X. Yin, and J. Liu*. Optimal sensor placement for agro-hydrological systems. AIChE Journal, 65:1-18, 2019. DOI (Futures issue)
3. L. Zhang, X. Yin, and J. Liu*. Complex system decomposition for distributed state estimation based on weighted graph. Chemical Engineering Research and Design, 151:10-22, 2019. DOI
4. S. A. Hussain*, R. K. K. Yuen, J. Liu, and J. Wang. Adaptive modeling for reliability in optimal control of complex HVAC systems. Building Simulation, 12 pages, 2019. DOI
5. J. Nahar, S. Liu, J. Liu*, and S. L. Shah. Improved storm water management through irrigation rescheduling for city parks. Control Engineering Practice, 87:111-121, 2019. DOI
6. J. Nahar, S. Liu, Y. Mao, J. Liu*, and S. L. Shah. Closed-loop scheduling and control for precision irrigation. Industrial & Engineering Chemistry Research, 58:11485-11497, 2019. DOI (Sirish Shah Festschrift special issue)
7. S. Liu, Y. Mao, and J. Liu*. Model predictive control with generalized zone tracking. IEEE Transactions on Automatic Control, 64:4698-4704, 2019. DOI
8. X. Yin, J. Zeng, and J. Liu*. Forming distributed state estimation network from decentralized estimators. IEEE Transactions on Control Systems Technology, 27:2430-2443, 2019. DOI
9. J. McAllister, Z. Li, J. Liu* and U. Simonsmeier. Erythropoietin dose optimization for anemia in chronic kidney disease using recursive zone model predictive control. IEEE Transactions on Control Systems Technology, 27:1181-1193, 2019. DOI
10. L. Zhang*, W. Xie, and J. Liu. Robust control of saturating systems with Markovian packet dropouts under distributed MPC. ISA Transactions, 85:49-59, 2019. DOI
11. J. Nahar, J. Liu*, and S. L. Shah. Parameter and state estimation of an agro-hydrological system based on system observability analysis. Computers & Chemical Engineering, 121:450-464, 2019. DOI
12. X. Yin and J. Liu*. Subsystem decomposition of process networks for simultaneous distributed state estimation and control. AIChE Journal, 65:904-914, 2019. DOI
13. R. Nian, J. Liu, B. Huang and T. Mutasa. Fault-tolerant control system: A reinforcement learning approach. In Proceedings of the SICE Annual Conference, pages 1010-1015, Hiroshima, Japan, 2019.
14. Y. Mao, S. Liu, B. Decardi-Nelson and J. Liu. Min-max economic MPC of networked control systems with transmission delays. In Proceedings of the American Control Conference, pages 1164-1169, Philadephia, PA, USA, 2019.
15. L. Zhang, J. Liu, W. Xie and X. Yin. Robust model predictive control of the cutterhead system in tunnel boring machines. In Proceedings of the 28th International Symposium on Industrial Electronics, pages 2277-2282, Vancouver, Canada, 2019.
16. G. Yan, J. Liu and B. Huang. MV benchmark for networked control systems with random communication delays. In Proceedings of the 12th IFAC Symposium on Dynamics and Control of Process Systems, pages 970-975, Florianopolis, Brazil, 2019.

2018

1. J. Liu and H. E. Durand (Eds.). New Directions on Model Predictive Control, Special issue of Mathematics, 2018.
2. J. Cui, S. Liu, J. Liu*, and X. Liu. A comparative study of MPC and economic MPC of wind energy conversion systems. Energies, 11, 23 pages, 2018. DOI
3. A. Zhang, X. Yin, S. Liu, J. Zeng, and J. Liu*. Distributed economic model predictive control of wastewater treatment plants. Chemical Engineering Research and Design, 141:144-155, 2018. DOI
4. Y. Mao, S. Liu, J. Nahar, J. Liu*, and F. Ding. Soil moisture regulation of agro-hydrological systems using zone model predictive control. Computers and Electronics in Agriculture, 154:239-247, 2018. DOI
5. B. Decardi-Nelson, S. Liu, and J. Liu*. Improving flexibility and energy efficiency of post-combustion CO2 capture plants using economic model predictive control. Processes, 6, 22 pages, 2018. DOI (Modeling and Simulation of Energy Systems special issue)
6. G. Yang, J. Liu, and B. Huang*. Limits of control performance for distributed networked control systems in presence of communication delays. International Journal of Adaptive Control and Signal Processing, 32:1282-1293, 2018. DOI
7. J. McAllister, Z. Li*, J. Liu, and U. Simonsmeier. EPO dosage optimization for anemia management: stochasitc control under uncertainty using conditional value at risk. Processes, 6, 22 pages, 2018. DOI (Modeling & Control of Disease States special issue)
8. S. Liu and J. Liu*. Economic model predictive control with zone tracking. Mathematics, 6, 19 pages, 2018. DOI (New Directions on Model Predictive Control special issue)
9. X. Yin, B. Decardi-Nelson and J. Liu*. Subsystem decomposition and distributed moving horizon estimation of wastewater treatment plants. Chemical Engineering Research and Design, 134, 405-419, 2018. DOI
10. X. Yin and J. Liu*. State estimation of wastewater treatment plants based on model approximation. Computers & Chemical Engineering, 111:79-91, 2018. DOI
11. M. Rashedi, O. Xu, S. Kwak, S. Sedghi, J. Liu and B. Huang*. An integrated first principle modeling to steam assisted gravity drainage (SAGD). Journal of Petroleum Science and Engineering, 163:501-510, 2018. DOI
12. M. Rashedi, J. Liu and B. Huang*. Triggered communication in distributed adaptive high-gain EKF. IEEE Transactions on Industrial Informatics, 14:58-68, 2018. DOI
13. B. Hassanzadeh*, J. Liu and J. F. Forbes. A bi-level optimization approach to coordination of distributed model predictive control systems. Industrial & Engineering Chemistry Research, 57:1516-1530, 2018. DOI
14. S. Liu and J. Liu. Economic model predictive control with zone tracking. In Proceedings of the 6th IFAC Conference on Nonlinear Model Predictive Control, pages 16-21, Madison, WI, USA, 2018.
15. X. Yin and J. Liu. State estimation of wastewater treatment plants based on reduced-order model. In Proceedings of the 10th International Symposium on Advanced Control of Chemical Processes, pages 566-571, Shenyang, China, 2018.
16. Y. Mao, S. Liu, J. Nahar, J. Liu and F. Ding. Regulation of soil moisture using zone model predictive control. In Proceedings of the 10th International Symposium on Advanced Control of Chemical Processes, pages 756-761, Shenyang, China, 2018.
17. J. McAllister, J. Liu, Z. Li and U. Simonsmeier. Erythropoiesis-stimulating-agent dose optimization for anemia management in chronic kidney disease using recursive constrained modeling and zone model predictive control. In Proceedings of the American Control Conference, pages 2326-2331, Milwaukee, Wisconsin, 2018.
18. B. Decardi-Nelson, S. Liu and J. Liu. A comparison of economic and tracking model predictive control of a post combustion CO2 capture process. In Proceedings of the American Control Conference, pages 3921-3926, Milwaukee, Wisconsin, 2018.

2017

1. T. An, X. Yin, J. Liu* and J. F. Forbes. Coordinated distributed moving horizon state estimation for linear systems based on prediction-driven method. Canadian Journal of Chemical Engineering, 95:1953-1967, 2017. DOI
2. X. Yin and J. Liu*. Distributed output-feedback fault detection and isolation of cascade process networks. AIChE Journal, 63:4329-4342, 2017. DOI
3. K. Arulmaran and J. Liu*. Handling model plant mismatch in state estimation using a multiple model based approach. Industrial & Engineering Chemistry Research, 56:5339-5351, 2017. DOI
4. M. Rashedi, J. Liu and B. Huang*. Distributed adaptive high-gain extended Kalman filtering for nonlinear systems. International Journal of Robust and Nonlinear Control, 27:4873-4902, 2017. DOI
5. X. Yin and J. Liu*. Distributed moving horizon state estimation of two-time-scale nonlinear systems. Automatica, 79:152-161, 2017. DOI
6. X. Yin and J. Liu*. Input-output pairing accounting for both structure and strength in coupling. AIChE Journal, 63:1226-1235, 2017. DOI (Identified as the Best Presentation in the session Advances in Process Control of the 2016 AIChE Annual Meeting)
7. J. Nahar, J. Liu and S. L. Shah. Observability analysis of an agro-hydrological system. In Proceedings of the Control Conference of Africa, 50-2:110-114, Johannesburg, South Africa, 2017.
8. S. Liu and J. Liu. A terminal cost for economic model predictive control with local optimality. In Proceedings of the American Control Conference, pages 1954-1959, Seattle, WA, 2017.
9. X. Yin, J. Zeng and J. Liu. From decentralized to distributed state estimation. In Proceedings of the American Control Conference, pages 1904-1909, Seattle, WA, 2017.
10. X. Yin and J. Liu. Distributed output feedback fault tolerant detection and isolation for cascade process networks. In Proceedings of the 6th International Symposium on Advanced Control of Industrial Processes, pages 547-552, Taipei, Taiwan, 2017 (Keynote paper).
11. J. Ren, J. McAllister, Z. Li, J. Liu and U. Simonsmeier. Modeling of hemoglobin response to erythropoietin therapy through constrained optimization. In Proceedings of the 6th International Symposium on Advanced Control of Industrial Processes, pages 245-250, Taipei, Taiwan, 2017.

2016

1. M. Ellis, J. Liu, and P. D. Christofides. Economic Model Predictive Control: Theory, Formulations and Chemical Process Applications. Advances in Industrial Control, Springer-Verlag, London, England, 2016.
2. B. Hassanzadeh*, P. Hallas, J. Liu and J. F. Forbes. Distributed model predictive control of nonlinear systems based on price-driven coordination. Industrial & Engineering Chemistry Research, 55:9711-9724, 2016. DOI
3. J. Zeng, J. Liu*, T. Zou and D. Yuan. Distributed extended Kalman filtering for wastewater treatment processes. Industrial & Engineering Chemistry Research, 55:7720-7729, 2016. DOI
4. S. Liu and J. Liu*. Economic model predictive control with extended horizon. Automatica, 73:180-192, 2016. DOI
5. M. Rashedi, J. Liu and B. Huang*. Communication delays and data losses in distributed adaptive high-gain EKF. AIChE Journal, 62:4321-4333, 2016. DOI
6. X. Yin, K. Arulmaran, J. Liu* and J. Zeng. Subsystem decomposition and configuration for distributed state estimation. AIChE Journal, 62:1995-2003, 2016. DOI
7. J. Zhang, X. Yin and J. Liu*. Economic MPC of deep cone thickeners in coal beneficiation. Canadian Journal of Chemical Engineering, 94:498-505, 2016. DOI
8. C. Zheng, J. Bao and J. Liu. Robust control of plantwide chemical processes based on parameter dependent dissipativity. In Proceedings of 2016 Australian Control Conference, pages 305-310, Newcastle, Australia, 2016.
9. T. An, J. Liu and J. F. Forbes. Coordinated distributed MHE for linear systems. In Proceedings of the 55th IEEE Conference on Decision and Control, pages 105-110, Las Vegas, USA, 2016.
10. J. Zeng, J. Liu, T. Zou and D. Yuan. State estimation of wastewater treatment processes using distributed extended Kalman filters. In Proceedings of the 55th IEEE Conference on Decision and Control, pages 6721--6726, Las Vegas, USA, 2016.
11. X. Yin, K. Arulmaran and J. Liu. Subsystem decomposition for distributed state estimation of nonlinear systems. In Proceedings of the American Control Conference, pages 5569-5574, Boston, MA, 2016.
12. S. Liu and J. Liu. Economic model predictive control for scheduled switching operations. In Proceedings of the American Control Conference, pages 1784-1789, Boston, MA, 2016.

2015

1. C. Zheng, M. J. Tippett, J. Bao* and J. Liu. Dissipativity-based distributed model predictive control with low rate communication. AIChE Journal, 61:3288-3303, 2015. DOI
2. J. Zeng and J. Liu*. Economic model predictive control of wastewater treatment processes. Industrial & Engineering Chemistry Research, 54:5710-5721, 2015. DOI
3. O. Xu*, J. Liu, Y. Fu and X. Chen. Dual updating strategy for moving-window partial least-squares based on model performance assessment. Industrial & Engineering Chemistry Research, 54:5273-5284, 2015. DOI
4. S. Liu, J. Zhang and J. Liu*. Economic MPC with terminal cost and application to an oilsand primary separation vessel. Chemical Engineering Science, 136:27-37, 2015. DOI
5. J. Zeng and J. Liu*. Distributed moving horizon state estimation: Simultaneously handling communication delays and data losses. Systems & Control Letters, 75:56-68, 2015. DOI
6. S. Liu, J. Zhang and J. Liu. Economic MPC with terminal cost and application to oilsand separation. In Proceedings of the 9th International Symposium on Advanced Control of Chemical Processes, pages 20-25, Whistler, British Columbia, Canada, 2015.
7. M. Rashedi, J. Liu and B. Huang. Distributed adaptive high-gain extended Kalman filtering for nonlinear systems. In Proceedings of the 9th International Symposium on Advanced Control of Chemical Processes, pages 158-163, Whistler, British Columbia, Canada, 2015.
8. M. J. Tippett, C. Zheng, J. Bao and J. Liu. Dissipativity-based analysis of controller networks with reduced rate communication. In Proceedings of the 9th International Symposium on Advanced Control of Chemical Processes, pages 705-710, Whistler, British Columbia, Canada, 2015.
9. J. Zeng and J. Liu. Distributed moving horizon estimation subject to communication delays and losses. In Proceedings of the American Control Conference, pages 5533-5538, Chicago, IL, 2015.
10. S. Li, J. Liu and J. F. Forbes. Convergence properties of two coordinated distributed MPC algorithms. In Proceedings of the American Control Conference, pages 5377-5383, Chicago, IL, 2015.

2014

1. J. Liu, D. Munoz de la Pena, and P. D. Christofides. Lyapunov-based DMPC Schemes: Sequential and Iterative Approaches, Chapter 30 of Distributed MPC Made Easy, pages 479-494. Springer-Verlag, Berlin, 2014.
2. S. Liu and J. Liu*. Distributed Lyapunov-based model predictive control with neighbor-to-neighbor communication. AIChE Journal, 60:4124-4133, 2014. DOI
3. J. Zhang, S. Liu and J. Liu*. Economic model predictive control with triggered evaluations: state and output feedback. Journal of Process Control, 24:1197-1206, 2014. DOI
4. J. Zhang and J. Liu*. Observer-enhanced distributed moving horizon state estimation subject to communication delays. Journal of Process Control, 24:672-686, 2014. DOI
5. M. Ellis, J. Zhang, J. Liu and P. D. Christofides*. Robust moving horizon estimation based output feedback economic model predictive control. Systems & Control Letters, 68:101-109, 2014. DOI
6. J. Zhang and J. Liu*. Two triggered information transmission algorithms for distributed moving horizon state estimation. Systems & Control Letters, 65:1-12, 2014. DOI
7. S. Liu, J. Liu*, Y. Feng, and G. Rong*. Performance assessment of decentralized control systems: An iterative approach. Control Engineering Practice, 22:252-263, 2014. DOI
8. J. Nahar, J. Liu and S. L. Shah. A systems engineering approach for automated irrigation. In Proceedings of the International Conference on Chemical Engineering 2014 (ICChE2014), pages 167-171, Dhaka, Bangladesh, 2014.
9. B. Hassanzadeh, J. Liu and J. F. Forbes. An analytic price-driven coordination scheme for distributed model predictive control systems. In Proceedings of the 53rd IEEE Conference on Decision and Control, pages 2461-2466, Los Angeles, CA, USA, 2014.
10. J. Zhang and J. Liu. Distributed moving horizon state estimation with triggered communication. In Proceedings of the American Control Conference, pages 5700-5705, Portland, OR, USA, 2014.

2013

1. P. Mhaskar, J. Liu, and P. D. Christofides. Fault-Tolerant Process Control: Methods and Applications. Springer-Verlag, London, England, 2013. (281 pages).
2. J. Zhang and J. Liu*. Distributed moving horizon state estimation for nonlinear systems with bounded uncertainties. Journal of Process Control, 23:1281-1295, 2013. DOI
3. J. Zhang and J. Liu*. Lyapunov-based MPC with robust moving horizon estimation and its triggered implementation. AIChE Journal, 59:4273-4286, 2013. DOI
4. S. Liu, J. Zhang, J. Liu*, Y. Feng, and G. Rong. Distributed model predictive control with asynchronous controller evaluations. Canadian Journal of Chemical Engineering, 91:1609-1620, 2013. DOI
5. J. Liu*. Moving horizon state estimation for nonlinear systems with bounded uncertainties. Chemical Engineering Science, 93:376-386, 2013. DOI
6. M. Heidarinejad, J. Liu, and P. D. Christofides*. Algorithms for improved fixed-time performance of Lyapunov-based economic model predictive control of nonlinear systems. Journal of Process Control, 23:404-414, 2013. DOI
7. P. D. Christofides*, R. Scattolini, D. Munoz de la Pena, and J. Liu. Distributed model predictive control: A tutorial review and future research directions. Computers & Chemical Engineering, 51:21-41, 2013. DOI
8. W. Qi, J. Liu, and P. D. Christofides*. Distributed supervisory predictive control of distributed wind and solar energy generation systems. IEEE Transactions on Control System Technology, 21:504-512, 2013.
9. M. Heidarinejad, J. Liu, and P. D. Christofides*. Distributed model predictive control of switched nonlinear systems with scheduled mode transitions. AIChE Journal, 59:860-871, 2013. DOI
10. M. Heidarinejad, J. Liu, and P. D. Christofides*. Economic model predictive control of switched nonlinear systems. Systems & Control Letters, 62:77-84, 2013. DOI
11. C. Zheng, M. J. Tippett, J. Bao and J. Liu. Multirate dissipativity-based distributed MPC. In Proceedings of the Australian Control Conference, pages 325-330, Perth, Australia, 2013.
12. J. Liu. Robust moving horizon state estimation for nonlinear systems. In Proceedings of the American Control Conference, pages 253-258, Washington DC, USA, 2013.
13. S. Liu, J. Liu, Y. Feng, and G. Rong. Achievable performance of decentralized control systems. In Proceedings of the American Control Conference, pages 5817-5822, Washington DC, USA, 2013.
14. B. Hassanzadeh, H. Pakravesh, J. Liu and J. F. Forbes. Coordinated-distributed MPC of nonlinear systems based on price-driven coordination. In Proceedings of the American Control Conference, pages 3159--3164, Washington DC, USA, 2013.
15. M. Heidarinejad, J. Liu, and P. D. Christofides. On fixed-time performance of Lyapunov-based economic model predictive control of nonlinear systems. In Proceedings of the American Control Conference, pages 3171--3176, Washington DC, USA, 2013.

2012

1. M. Heidarinejad, J. Liu, and P. D. Christofides*. State estimation-based economic model predictive control of nonlinear systems. Systems & Control Letters, 61:926-935, 2012. DOI
2. D. Chilin, J. Liu, X. Chen, and P. D. Christofides*. Fault detection and isolation and fault tolerant control of a catalytic alkylation of benzene process. Chemical Engineering Science, 78:155-166, 2012.
3. X. Chen, M. Heidarinejad, J. Liu, and P. D. Christofides*. Distributed economic MPC: Application to a nonlinear chemical process network. Journal of Process Control, 22:689-699, 2012.
4. X. Chen, M. Heidarinejad, J. Liu, and P. D. Christofides*. Composite fast-slow MPC design for nonlinear singularly perturbed systems. AIChE Journal, 58:1802-1811, 2012.
5. A. Leosirikul, D. Chilin, J. Liu, J. F. Davis, and P. D. Christofides*. Monitoring and retuning of low-level PID control loops. Chemical Engineering Science, 69:287-295, 2012.
6. M. Heidarinejad, J. Liu, and P. D. Christofides*. Economic model predictive control of nonlinear process systems using lyapunov techniques. AIChE Journal, 58:855-870, 2012.
7. D. Chilin, J. Liu, J. F. Davis, and P. D. Christofides*. Data-based monitoring and reconfiguration of a distributed model predictive control system. International Journal of Robust and Nonlinear Control, 22:68-88, 2012.
8. W. Qi, J. Liu, and P. D. Christofides*. Supervisory predictive control for long-term scheduling of an integrated wind/solar energy generation and water desalination system. IEEE Transactions on Control Systems Technology, 20:504-512, 2012.
9. J. Liu, X. Chen, D. Munoz de la Pena, and P. D. Christofides*. Iterative distributed model predictive control of nonlinear systems: Handling asynchronous, delayed measurements. IEEE Transactions on Automatic Control, 57:528-534, 2012.
10. M. J. Tippett, J. Bao, and J. Liu. Plant-wide control of chemical systems exhibiting time-scale separation. In Proceedings of CHEMECA 2012, pager 118, Wellington, New Zealand, 2012.
11. A. Leosirikul, D. Chilin, J. Liu, J. F. Davis, and P. D. Christofides. Monitoring of low-level PID control loops. In Proceedings of the American Control Conference, pages 5664-5669, Montreal, Canada, 2012.
12. M. Heidarinejad, J. Liu, and P. D. Christofides. Distributed model predictive control of switched nonlinear systems. In Proceedings of the American Control Conference, pages 3198-3203, Montreal, Canada, 2012.
13. X. Chen, M. Heidarinejad, J. Liu, and P. D. Christofides. Composite fast-slow MPC design for nonlinear singularly perturbed systems: Stability analysis. In Proceedings of the American Control Conference, pages 4136-4141, Montreal, Canada, 2012.
14. P. D. Christofides, R. Scattolini, D. Munoz de la Pena, and J. Liu. Distributed model predictive control: A tutorial review. In Proceedings of Chemical Process Control-8, 22 pages, Savannah, Georgia, 2012.

2011

1. P. D. Christofides, J. Liu, and D. Munoz de la Pena. Networked and Distributed Predictive Control: Methods and Nonlinear Process Network Applications. Advances in Industrial Control Series. Springer-Verlag, London, England, 2011. (230 pages).
2. X. Chen, M. Heidarinejad, J. Liu, D. Munoz de la Pena, and P. D. Christofides*. Model predictive control of nonlinear singularly perturbed systems: Application to a large-scale process network. Journal of Process Control, 21:1296-1305, 2011.
3. W. Qi, J. Liu, and P. D. Christofides*. A distributed control framework for smart grid development: Energy/water system optimal operation and electric grid integration. Journal of Process Control, 21:1504-1516, 2011.
4. M. Heidarinejad, J. Liu, D. Munoz de la Pena, J. F. Davis, and P. D. Christofides*. Handling communication disruptions in distributed model predictive control of nonlinear systems. Journal of Process Control, 21:173-181, 2011.
5. M. Heidarinejad, J. Liu, D. Munoz de la Pena, P. D. Christofides*, and J. F. Davis. Multirate Lyapunov-based distributed model predictive control of nonlinear uncertain systems. Journal of Process Control, 21:1231-1242, 2011.
6. W. Qi, J. Liu, X. Chen, and P. D. Christofides*. Supervisory predictive control of stand-alone wind-solar energy generation systems. IEEE Transactions on Control Systems Technology, 19:199-207, 2011.
7. X. Chen, M. Heidarinejad, J. Liu, D. Munoz de la Pena, and P. D. Christofides. Model predictive control of nonlinear singularly perturbed systems: Application to a reactor-separator process network. In Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, pages 8125-8132, Orlando, Florida, 2011.
8. M. Heidarinejad, J. Liu, and P. D. Christofides. Lyapunov-based economic model predictive control of nonlinear systems: Handling asynchronous, delayed measurements and distributed implementation. In Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, pages 4646-4653, Orlando, Florida, 2011.
9. D. Chilin, J. Liu, J. F. Davis, and P. D. Christofides. Data-based monitoring and reconfiguration of a distributed model predictive control system. In Proceedings of the American Control Conference, pages 3158-3165, San Francisco, California, 2011.
10. M. Heidarinejad, J. Liu, and P. D. Christofides. Lyapunov-based economic model predictive control of nonlinear systems. In Proceedings of the American Control Conference, pages 5195-5200, San Francisco, California, 2011.
11. M. Heidarinejad, J. Liu, D. Munoz de la Pena, J. F. Davis, and P. D. Christofides. Multirate distributed model predictive control of nonlinear systems. In Proceedings of the American Control Conference, pages 5181-5188, San Francisco, Califorina, 2011.
12. W. Qi, J. Liu, and P. D. Christofides. A two-time-scale framework for supervisory predictive control of an integrated wind/solar energy generation and water desalination system. In Proceedings of the American Control Conference, pages 2677-2682, San Francisco, California, 2011.

2010

1. D. Chilin, J. Liu, D. Munoz de la Pena, P. D. Christofides*, and J. F. Davis. Detection, isolation and handling of actuator faults in distributed model predictive control systems. Journal of Process Control, 20:1059-1075, 2010.
2. J. Liu, X. Chen, D. Munoz de la Pena, and P. D. Christofides*. Sequential and iterative architectures for distributed model predictive control of nonlinear process systems. AIChE Journal, 56:2137-2149, 2010.
3. J. Liu, B. J. Ohran, D. Munoz de la Pena, P. D. Christofides*, and J. F. Davis. Monitoring and handling of actuator faults in two-tier control systems for nonlinear processes. Chemical Engineering Science, 65:3179-3190, 2010.
4. J. Liu, D. Munoz de la Pena, and P. D. Christofides*. Distributed model predictive control of nonlinear systems subject to asynchronous and delayed measurements. Automatica, 46:52-61, 2010.
5. J. Liu, D. Munoz de la Pena, B. J. Ohran, P. D. Christofides*, and J. F. Davis. A two-tier control architecture for nonlinear process systems with continuous/asynchronous feedback. International Journal of Control, 83:257-272, 2010.
6. J. Liu, X. Chen, D. Munoz de la Pena, and P. D. Christofides. Iterative distributed model predictive control of nonlinear systems: Handling delayed measurements. In Proceedings of the 49th IEEE Conference on Decision and Control, pages 7251-7258, Atlanta, Georgia, 2010.
7. X. Chen, J. Liu, D. Munoz de la Pena, and P. D. Christofides. Sequential and iterative distributed model predictive control of nonlinear process systems subject to asynchronous measurements. In Proceedings of the 9th IFAC Symposium on Dynamics and Control of Process Systems, pages 611-616, Leuven, Belgium, 2010.
8. M. Heidarinejad, J. Liu, D. Munoz de la Pena, and P. D. Christofides. Handling communication disruptions in distributed model predictive control of nonlinear systems. In Proceedings of the 9th IFAC Symposium on Dynamics and Control of Process Systems, pages 282-287, Leuven, Belgium, 2010.
9. W. Qi, J. Liu, and P. D. Christofides. Supervisory predictive control of an integrated wind/solar energy generation and water desalination system. In Proceedings of the 9th IFAC Symposium on Dynamics and Control of Process Systems, pages 821-826, Leuven, Belgium, 2010.
10. D. Chilin, J. Liu, D. Munoz de la Pena, P. D. Christofides, and J. F. Davis. Monitoring and handling of actuator faults in a distributed model predictive control system. In Proceedings of the American Control Conference, pages 2847-2854, Baltimore, Maryland, 2010.
11. J. Liu, X. Chen, D. Munoz de la Pena, and P. D. Christofides. Sequential and iterative architectures for distributed model predictive control of nonlinear process systems. Part I: Theory. In Proceedings of the American Control Conference, pages 3148-3155, Baltimore, Maryland, 2010.
12. J. Liu, X. Chen, D. Munoz de la Pena, and P. D. Christofides. Sequential and iterative architectures for distributed model predictive control of nonlinear process systems. Part II: Application to a catalytic alkylation of benzene process. In Proceedings of the American Control Conference, pages 3156-3161, Baltimore, Maryland, 2010.

2009

1. J. Liu, D. Munoz de la Pena, and P. D. Christofides. Distributed Model Predictive Control System Design Using Lyapunov Techniques, volume 384 of Lecture Notes in Control and Information Science, pages 181-194. Springer-Verlag, Berlin, 2009.
2. B. Ohran, J. Liu, D. Munoz de la Pena, P. D. Christofides*, and J. F. Davis. Data-based fault detection and isolation using feedback control: Output feedback and optimality. Chemical Engineering Science, 64:2370-2383, 2009.
3. J. Liu, D. Munoz de la Pena, P. D. Christofides*, and J. F. Davis. Lyapunov-based model predictive control of nonlinear systems subject to time-varying measurement delays. International Journal of Adaptive Control and Signal Processing, 23:788-807, 2009.
4. J. Liu, D. Munoz de la Pena, and P. D. Christofides*. Distributed model predictive control of nonlinear process systems. AIChE Journal, 55:1171-1184, 2009.
5. J. Liu, D. Munoz de la Pena, and P. D. Christofides. Distributed model predictive control of nonlinear systems subject to delayed measurements. In Proceedings of the 48th IEEE Conference on Decision and Control, pages 7105-7112, Shanghai, China, December 2009.
6. B. Ohran, J. Liu, P. D. Christofides, D. Munoz de la Pena, and J. F. Davis. Networked monitoring and fault-tolerant control of nonlinear process systems. In Proceedings of the 48th IEEE Conference on Decision and Control, pages 4117-4124, Shanghai, China, December 2009.
7. B. Ohran, J. Liu, P. D. Christofides, D. Munoz de la Pena, and J. F. Davis. Data-based fault detection and isolation using output feedback control. In Proceedings of IFAC International Symposium on Advanced Control of Chemical Processes, paper 107, 6 pages, Instabul, Turkey, 2009.
8. J. Liu, D. Munoz de la Pena, and P. D. Christofides. Distributed model predictive control of nonlinear process systems using asynchronous measurements. In Proceedings of IFAC International Symposium on Advanced Control of Chemical Processes, paper 111, 6 pages, Istanbul, Turkey, 2009.
9. J. Liu, D. Munoz de la Pena, and P. D. Christofides. Distributed model predictive control of nonlinear systems with input constraints. In Proceedings of the American Control Conference, pages 2319-2326, St. Louis, Missouri, 2009.
10. J. Liu, D. Munoz de la Pena, B. J. Ohran, P. D. Christofides, and J. F. Davis. A two-tier control architecture for nonlinear process systems with continuous/asynchronous feedback. In Proceedings of the American Control Conference, pages 133-140, St. Louis, Missouri, 2009.

2008

1. J. Liu, D. Munoz de la Pena, P. D. Christofides*, and J. F. Davis. Lyapunov-based model predictive control of particulate processes subject to asynchronous measurements. Particle and Particle Systems Characterization, 25:360-375, 2008.
2. J. Liu, D. Munoz de la Pena, B. J. Ohran, P. D. Christofides*, and J. F. Davis. A two-tier architecture for networked process control. Chemical Engineering Science, 63:5394-5409, 2008.
3. J. Liu, D. Munoz de la Pena, and P. D. Christofides. Distributed control system design using Lyapunov-based model predictive control. In Proceedings of International Workshop on Assessment and Future Directions of Nonlinear Model Predictive Control, 12 pages, Pavia, Italy, 2008.
4. J. Liu, D. Munoz de la Pena, P. D. Christofides, and J. F. Davis. Lyapunov-based predictive control of particulate processes subject to asynchronous measurements. In Proceedings of the American Control Conference, pages 2233-2240, Seattle, Washington, 2008.
5. J. Liu, D. Munoz de la Pena, P. D. Christofides, and J. F. Davis. Lyapunov-based model predictive control of nonlinear systems subject to time-varying measurement delays. In Proceedings of the 47th IEEE Conference on Decision and Control, pages 4632-4639, Cancun, Mexico, 2008.

Prior to 2008

1. G. Rong*, J. Liu, and H. Gu. Mining dynamic association rules in databases. Control Theory & Applications, 24:127-131, 2007.
2. J. Liu and G. Rong*. Application of web text mining in study assistance. Information Science, 24:400-404, 2006.
3. J. Liu and G. Rong. Mining Dynamic Association Rules in Databases, volume 3801 of Lecture Notes in Computer Science, pages 688-695. Springer-Verlag, Berlin, 2005.