logo

SCIENTIA SINICA Informationis, Volume 46 , Issue 8 : 1003-1015(2016) https://doi.org/10.1360/N112016-00062

Industrial process control systems: research status and \\development direction

Tianyou CHAI 1,2,3,*
More info
  • ReceivedMar 25, 2016
  • AcceptedJun 13, 2016

Abstract


Funded by

中国工程院国家自然科学基金委员会``2014年度中国工程科技中长期发展战略研究''项目(2014-zcq-03)

中国工程院国家自然科学基金委员会``2014年度中国工程科技中长期发展战略研究''项目(L1422028)

国家自然科学基金应急管理项目(61550002)


References

[1] 工业4.0工作组(德) 编; 刘晓龙, 郗振宇, 高金金, 等译. 德国工业 4.0战略计划实施建议. 2013.09. Google Scholar

[2] 乌尔里希$\cdot $森德勒(德) 编; 邓敏, 李现民 译. 工业4.0. 北京: 机械工业出版社, 2014. Google Scholar

[3] Impulse für Wachstum, Beschäftigung und Innovation. Industrie 4.0 und Digitale Wirtschaft. https://www. bmwi.de/BMWi/Redaktion/PDF/I/industrie-4-0-und-digitale-wirtschaft,property=pdf,bereich=bmwi2012,sprache= de,rwb=true.pdf. 2015. Google Scholar

[4] Cyber-physical systems. Program Announcements {&} Information. National Science Foundation. 4201 Wilson Boulevard, Arlington, Virginia 22230, USA. 2008-09-30. Retrieved 2009-07-21. http://www.nsf.gov/publications/ pub\_summ.jsp?ods\_key=nsf08611. Google Scholar

[5] Nie Y, Biegler L T, Wassick J M. Integrated scheduling and dynamic optimization of batch processes using state equipment networks. Aiche J, 2012, 58: 3416-3432 CrossRef Google Scholar

[6] Chai T Y. Challenges of optimal control for plant-wide production processes in terms of control and optimization theories. Acta Autom Sin, 2009, 35: 641-649 [柴天佑. 生产制造全流程优化控制对控制与优化理论方法的挑战. 自动化学报, 2009, 35: 641-649]. Google Scholar

[7] Chai T Y. Operational optimization and feedback control for complex industrial processes. Acta Autom Sin, 2013, 39: 1744-1757 [柴天佑. 复杂工业过程运行优化与反馈控制. 自动化学报, 2013, 39: 1744-1757]. Google Scholar

[8] Chai T Y, Qin S J, Wang H. Optimal operational control for complex industrial processes. Ann Rev Control, 2014, 38: 81-92 CrossRef Google Scholar

[9] Friedland B. Advanced Control System Design. New Jersey: Prentice Hall, 1996. Google Scholar

[10] O'Dwyer A. Handbook of PI and PID Controller Tuning Rules. London: Imperial College Press, 2006. Google Scholar

[11] Han Z G, Wang G Q. Cascade scheme of model free control law and its application. Acta Autom Sin, 2006, 32: 345-352 [韩志刚, 汪国强. 无模型控制律串级形式及其应用. 自动化学报, 2006, 32: 345-352]. Google Scholar

[12] Sugie T, Ono T. An iterative learning control law for dynamical systems. Automatica, 1991, 27: 729-732 CrossRef Google Scholar

[13] Moore K L, Johnson M, Grimble M J. Iterative Learning Control for Deterministic Systems. New York: SpringerVerlag, 1993. Google Scholar

[14] Yager R R, Zadeh L A. An Introduction to Fuzzy Logic Applications in Intelligent Systems. Norwell: Kluwer Academic Publisher, 1992. Google Scholar

[15] Wang L X. Stable adaptive fuzzy control of nonlinear systems. IEEE Trans Fuzzy Syst, 1993, 1: 146-155 CrossRef Google Scholar

[16] Astrom K J, Anton J J, Arzen K E. Expert control. Automatica, 1986, 22: 277-286 CrossRef Google Scholar

[17] Psaltis D, Sideris A, Yamamura A A. A multilayered neural network controller. IEEE Control Syst Mag, 1988, 8: 17-21 CrossRef Google Scholar

[18] Li Z S, Xu M, Zhou Q J. Acta Autom Sin, 1990, 16: 503-509 [李祖枢, 徐鸣, 周其鉴. 一种新型的仿人智能控制器. 自动化学报, 1990, 16: 503-509]. Google Scholar

[19] 吴宏鑫, 王迎春, 邢琰. 基于智能特征模型的智能控制及应用. 中国科学E辑: 技术科学, 2002, 32: 805-816. Google Scholar

[20] Wu H X. Intelligent characteristic model and intelligent control. Acta Autom Sin, 2002, 28: 30-37 [吴宏鑫. 智能特征模型和智能控制. 自动化学报, 2002, 28: 30-37]. Google Scholar

[21] Fu Y, Chai T Y. Nonlinear multivariable adaptive control using multiple models and neural networks. Automatica, 2007, 43: 1101-1110 CrossRef Google Scholar

[22] Fu Y, Chai T Y. Neural-network-based nonlinear adaptive dynamical decoupling control. IEEE Trans Neural Netw, 2007, 18: 921-925 CrossRef Google Scholar

[23] Zhang Y J, Chai T Y, Wang H, et al. An improved estimation method for unmodeled dynamics based on ANFIS and its application to controller design. IEEE Trans Fuzzy Syst, 2013, 21: 989-1005 CrossRef Google Scholar

[24] Chai T Y, Zhai L F, Yue H. Multiple models and neural networks based decoupling control of ball mill coal-pulverizing systems. J Process Control, 2011, 21: 351-366 CrossRef Google Scholar

[25] Chai T Y, Zhang Y J, Wang H, et al. Data based Virtual Un-modeled Dynamics Driven Multivariable Nonlinear Adaptive Switching Control. IEEE Trans Neural Netw, 2011, 12: 2154-2171. Google Scholar

[26] Zhao D Y, Chai T Y, Wang H, et al. Hybrid intelligent control for regrinding process in hematite beneficiation. Control Eng Pract, 2014, 22: 217-230 CrossRef Google Scholar

[27] Chai T Y, Li H B, Wang H. An intelligent switching control for the intervals of concentration and flow-rate of underflow slurry in a mixed separation thickener. In: Proceedings of the 19th IFAC World Congress, Cape Town, 2014. 47: 338-345. Google Scholar

[28] Basak K, Abhilash K S, Ganguly S, et al. On-line optimization of a crude distillation unit with constraints on product properties. Industrial Eng Chem Res, 2002, 41: 1557-1568 CrossRef Google Scholar

[29] Marchetti A, Chachuat B, Bonvin D. Real-time operations optimization of continuous processes. In: Proceedings of the 5th International Conference on Chemical Process Control. Lake Tahoe: American Institute of Chemical Engineering, 1996. 156-164. Google Scholar

[30] Chai T Y, Ding J L, Wu F H. Hybrid intelligent control for optimal operation of shaft furnace roasting process. Control Eng Pract, 2011, 3: 264-275. Google Scholar

[31] Engell S. Feedback control for optimal process operation. J Process Control, 2007, 17: 203-219 CrossRef Google Scholar

[32] Henson M A. Nonlinear model predictive control: current status and future directions. Comput Chem Eng, 1998, 23: 187-202 CrossRef Google Scholar

[33] Cannon M, Kouvaritakis B, Deshmukh V. Enlargement of polytopic terminal region in NMPC by interpolation and partial invariance. Automatica, 2004, 40: 311-317 CrossRef Google Scholar

[34] Chai T Y, Ding J L, Wang H, et al. Hybrid intelligent optimal control method for operation of complex industrial processes. Acta Autom Sin, 2008, 34: 505-515 [柴天佑, 丁进良, 王宏, 等. 复杂工业过程运行的混合智能优化控制方法. 自动化学报, 2008, 34: 505-515]. Google Scholar

[35] Chai T Y, Ding J L, Wu F H. Hybrid intelligent control for optimal operation of shaft furnace roasting process. Control Eng Pract, 2011, 3: 264-275. Google Scholar

[36] Zhou P, Chai T Y, Sun J. Intelligence-based supervisory control for optimal operation of a DCS-controlled grinding system. IEEE Trans Control Syst Tech, 2013, 21: 162-175 CrossRef Google Scholar

[37] Zhou P, Chai T Y, Wang H. Intelligent optimal-setting control for grinding circuits of mineral processing process. IEEE Trans Autom Sci Eng, 2009, 6: 730-743 CrossRef Google Scholar

[38] Chai T Y, Zhao L, Qiu J B, et al. Integrated network based model predictive control for setpoints compensation in industrial processes. IEEE Trans Ind Inf, 2013, 9: 417-426 CrossRef Google Scholar

[39] Liu F Z, Gao H J, Qiu J B, et al. Networked multirate output feedback control for setpoints compensation and its application to rougher flotation process. IEEE Trans Ind Electron, 2014, 61: 460-468. Google Scholar

[40] Isermann R, Balle P. Trends in the application of model based fault detection and diagnosis of technical processes. Control Eng Pract, 1997, 5: 709-719 CrossRef Google Scholar

[41] Capisani L M, Ferrara A, Ferreira L A, et al. Manipulator fault diagnosis via higher order sliding-mode observers. IEEE Trans Ind Electron, 2012, 59: 2979-3986 CrossRef Google Scholar

[42] Qin S J. Survey on data-driven industrial process monitoring and diagnosis. Ann Rev Control, 2012, 36: 220-234 CrossRef Google Scholar

[43] Wu Z W, Wu Y J, Chai T Y, et al. Data-driven abnormal condition identification and self-healing control system for fused magnesium furnace. IEEE Trans Ind Electron, 2015, 62: 1703-1715 CrossRef Google Scholar

[44] Liu Q, Qin S J, Chai T Y. Decentralized fault diagnosis of continuous annealing processes based on multilevel PCA. IEEE Trans Autom Sci Eng, 2013, 10: 687-698 CrossRef Google Scholar

[45] Liu Q, Qin S J, Chai T Y. Multi-block concurrent PLS for decentralized monitoring of continuous annealing processes. IEEE Trans Ind Electron, 2014, 61: 6429-6437 CrossRef Google Scholar

[46] Dai W, Zhou P, Zhao D Y, et al. Hardware-in-the-loop simulation platform for supervisory control of mineral grinding process. Powder Tech, 2016, 288: 422-434 CrossRef Google Scholar

[47] Dai W, Chai T Y, Yang S. Data-driven optimization control for safety operation of hematite grinding process. IEEE Trans Ind Electron, 2015, 62: 2930-2941 CrossRef Google Scholar

[48] Martin O, Daniele R, Cecilia M. Low cost and high speed embedded two-rail code checker. IEEE Trans Comput, 2005, 54: 153-164 CrossRef Google Scholar

[49] QianY , Liu J, Johnson T M. Efficient embedded speech recognition for very large vocabulary mandarin car-navigation systems. IEEE Trans Consum Electron, 2009, 55: 1496-1500 CrossRef Google Scholar

[50] Munir A, AGordon-Ross A, Ranka S. Multi-core embedded wireless sensor networks: architecture and applications. IEEE Trans Parall Distrib Syst, 2014, 25: 1553-1562 CrossRef Google Scholar

[51] Kim M, Song J H, Kim D, et al. Hybrid partitioned H.264 full high definition decoder on embedded quad-core. IEEE Trans Consum Electron, 2012, 58: 1038-1044. Google Scholar