Triple-Mode Model Predictive Control Using Future Target Information
Abstract
:1. Introduction
2. Preliminary
Dynamic Matrix Control Algorithm
3. Main Results
3.1. Illustrations of Poor Future Trajectory Horizon
3.2. Future Trajectory Horizon Optimization Algorithm
Algorithm 1: The optimization of Future trajectory horizon based on ISE value |
|
3.3. Triple-Mode MPC Algorithm Using Future Target Information
Algorithm 2: Triple-mode MPC algorithm using future target information |
|
4. Applications
4.1. System I: A Heavy Oil Fractionator
4.2. System II: A Power Plant Coordinated Control System
4.2.1. Constant Pressure Operation
4.2.2. Sliding Pressure Operation
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A. Proof of Theorem 1
References
- Qin, S.J.; Badgwell, T.A. A survey of industrial model predictive control technology. Control Eng. Pract. 2003, 11, 733–764. [Google Scholar] [CrossRef]
- Garcia, C.E.; Morshedi, A. Quadratic programming solution of dynamic matrix control (QDMC). Chem. Eng. Commun. 1986, 46, 73–87. [Google Scholar] [CrossRef]
- Rawlings, J.B. Tutorial overview of model predictive control. IEEE Control Syst. Mag. 2000, 20, 38–52. [Google Scholar]
- Rawlings, J.B.; Mayne, D.Q. Model Predictive Control: Theory and Design; Nob Hill Pub.: San Francisco, CA, USA, 2009. [Google Scholar]
- Maciejowski, J.M. Predictive Control: With Constraints; Pearson Education: London, UK, 2002. [Google Scholar]
- Goodwin, G.; Seron, M.M.; De Doná, J.A. Constrained Control and Estimation: An Optimisation Approach; Springer Science & Business Media: Berlin, Germany, 2006. [Google Scholar]
- Zhang, K.; Zhao, J.; Zhu, Y. MPC case study on a selective catalytic reduction in a power plant. J. Process Control 2018, 62, 1–10. [Google Scholar] [CrossRef]
- Di Carlo, J.; Wensing, P.M.; Katz, B.; Bledt, G.; Kim, S. Dynamic locomotion in the mit cheetah 3 through convex model-predictive control. In Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 1–5 October 2018; pp. 1–9. [Google Scholar]
- Limón, D.; Alvarado, I.; Alamo, T.; Camacho, E.F. MPC for tracking piecewise constant references for constrained linear systems. Automatica 2008, 44, 2382–2387. [Google Scholar] [CrossRef]
- Clarke, D.W.; Mohtadi, C. Properties of generalized predictive control. Automatica 1989, 25, 859–875. [Google Scholar] [CrossRef]
- Middleton, R.H.; Chen, J.; Freudenberg, J.S. Tracking sensitivity and achievable performance in preview control. Automatica 2004, 40, 1297–1306. [Google Scholar] [CrossRef]
- Carrasco, D.S.; Goodwin, G.C. Feedforward model predictive control. Annu. Rev. Control 2011, 35, 199–206. [Google Scholar] [CrossRef]
- Rossiter, J.; Grinnell, B. Improving the tracking of generalized predictive control controllers. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 1996, 210, 169–182. [Google Scholar] [CrossRef]
- Dughman, S.; Rossiter, J. Systematic and effective embedding of feedforward of target information into MPC. Int. J. Control 2017, 1–15. [Google Scholar] [CrossRef] [Green Version]
- Valencia-Palomo, G.; Rossiter, J.; López-Estrada, F. Improving the feed-forward compensator in predictive control for setpoint tracking. ISA Trans. 2014, 53, 755–766. [Google Scholar] [CrossRef] [PubMed]
- Qin, S.J.; Badgwell, T.A. An Overview of Industrial Model Predictive Control Technology; AIChE Symposium Series; American Institute of Chemical Engineers: New York, NY, USA, 1997; Volume 93, pp. 232–256. [Google Scholar]
- Kiefer, J. Sequential minimax search for a maximum. Proc. Am. Math. Soc. 1953, 4, 502–506. [Google Scholar] [CrossRef]
- Boyd, S.; Vandenberghe, L. Convex Optimization; Cambridge University Press: Cambridge, UK, 2004. [Google Scholar]
- Prett, D.M.; Morari, M. The Shell Process Control Workshop; Elsevier: Amsterdam, The Netherlands, 2013. [Google Scholar]
- Moon, U.C.; Lee, Y.; Lee, K.Y. Practical dynamic matrix control for thermal power plant coordinated control. Control Eng. Pract. 2018, 71, 154–163. [Google Scholar] [CrossRef]
- Jizhen, L.; Liang, T.; Deliang, Z.; Xinping, L. Analysis on the Nonlinearity of Load-Pressure Characteristics of a 660MW Unit. Power Eng. 2005, 25, 533–536. [Google Scholar]
- Wood, A.J.; Wollenberg, B.F.; Sheblé, G.B. Power Generation, Operation, and Control; John Wiley & Sons: Hoboken, NJ, USA, 2013. [Google Scholar]
- Flynn, D. Thermal Power Plant Simulation and Control; Number 43; IET: London, UK, 2003. [Google Scholar]
- Green, J.; Quest, J. A short history of the European Transonic Wind Tunnel ETW. Prog. Aerosp. Sci. 2011, 47, 319–368. [Google Scholar] [CrossRef]
- Mayne, D.Q.; Rawlings, J.B.; Rao, C.V.; Scokaert, P.O. Constrained model predictive control: Stability and optimality. Automatica 2000, 36, 789–814. [Google Scholar] [CrossRef]
ISE | 0.8055 | 0.1797 | 0.3192 |
DMC | Triple-Mode MPC | DMC-FF | |
---|---|---|---|
ISE | 6.3771 | 0.7088 | 0.9515 |
Error of (MW) | Error of (MPa) | |
---|---|---|
DMC | 431.7057 | 5.6357 |
Triple-mode MPC | 104.3930 | 0.0331 |
DMC-FF | 877.6251 | 32.3255 |
Error of (MW) | Error of (MPa) | |
---|---|---|
DMC | 501.7570 | 10.7935 |
Triple-mode MPC | 124.5650 | 0.1009 |
DMC-FF | 753.2611 | 22.1475 |
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Chen, M.; Xu, Z.; Zhao, J. Triple-Mode Model Predictive Control Using Future Target Information. Processes 2020, 8, 54. https://doi.org/10.3390/pr8010054
Chen M, Xu Z, Zhao J. Triple-Mode Model Predictive Control Using Future Target Information. Processes. 2020; 8(1):54. https://doi.org/10.3390/pr8010054
Chicago/Turabian StyleChen, Minghao, Zuhua Xu, and Jun Zhao. 2020. "Triple-Mode Model Predictive Control Using Future Target Information" Processes 8, no. 1: 54. https://doi.org/10.3390/pr8010054
APA StyleChen, M., Xu, Z., & Zhao, J. (2020). Triple-Mode Model Predictive Control Using Future Target Information. Processes, 8(1), 54. https://doi.org/10.3390/pr8010054