Incremental multivariable predictive functional control and its application in a gas fractionation unit

The control of gas fractionation unit (GFU) in petroleum industry is very difficult due to multivariable characteristics and a large time delay. PID controllers are still applied in most industry processes. However, the traditional PID control has been proven not sufficient and capable for this particular petro-chemical process. In this work, an incremental multivariable predictive functional control (IMPFC) algorithm was proposed with less online computation, great precision and fast response. An incremental transfer function matrix model was set up through the step-response data, and predictive outputs were deduced with the theory of single-value optimization. The results show that the method can optimize the incremental control variable and reject the constraint of the incremental control variable with the positional predictive functional control algorithm, and thereby making the control variable smoother. The predictive output error and future set-point were approximated by a polynomial, which can overcome the problem under the model mismatch and make the predictive outputs track the reference trajectory. Then, the design of incremental multivariable predictive functional control was studied. Simulation and application results show that the proposed control strategy is effective and feasible to improve control performance and robustness of process.

[1]  Rolf Isermann.  Towards intelligent control of mechanical processes , 1993 .

[2]  Ajith Abraham,et al.  Hybrid Intelligent Predictive Control System for High Speed BLDC Motor in Aerospace Application , 2010, 2010 3rd International Conference on Emerging Trends in Engineering and Technology.

[3]  Jerzy Z. Sasiadek,et al.  Experimental evaluation of energy optimization algorithm for mobile robots in three-dimension motion using predictive control , 2013, 21st Mediterranean Conference on Control and Automation.

[4]  James R. Slagle,et al.  An explanation facility for today's expert systems , 1989, IEEE Expert.

[5]  Zhongjun Xiao,et al.  Paper breaking prediction control in papermaking process based on analysis of 1.5 dimension cepstrum , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[6]  S. Al-Assadi,et al.  Optimal gain for proportional-integral-derivative feedback , 1987, IEEE Control Systems Magazine.

[7]  Sašo Blažič,et al.  Multivariable Predictive Functional Control of an Autoclave , 2013 .

[8]  Ch. Arber,et al.  On the predictive functional control of an elastic industrial robot , 1986, 1986 25th IEEE Conference on Decision and Control.

[9]  C. De Prada,et al.  Identification and constrained multivariable predictive control of chemical reactors , 1995, Proceedings of International Conference on Control Applications.

[10]  Andreas Jacubasch,et al.  Predictive Functional Control - Application to Fast and Accurate Robots , 1987 .

[11]  Carlos E. Garcia,et al.  Internal model control. A unifying review and some new results , 1982 .

[12]  B. Loop,et al.  An optimization approach to estimating stability regions using genetic algorithms , 2005, Proceedings of the 2005, American Control Conference, 2005..

[13]  Bing Bu,et al.  Predictive Function Control for Communication-Based Train Control (CBTC) Systems , 2013 .

[14]  K. Zalis.  Application of expert systems in diagnostics of high voltage insulating systems , 2004, Proceedings of the 2004 IEEE International Conference on Solid Dielectrics, 2004. ICSD 2004..

[15]  Zhihuan Zhang.  Predictive function cascade control scheme and its application to hydraulic robot systems , 2009, 2009 IEEE International Conference on Automation and Logistics.

[16]  E. Camacho,et al.  Generalized Predictive Control , 2007 .

[17]  K. Zalis.  Evaluation of partial discharge measurement by expert systems , 2000, Proceedings of the 6th International Conference on Properties and Applications of Dielectric Materials (Cat. No.00CH36347).

[18]  Wang Mengxiao,et al.  Time-delay process Multivariable model predictive function control for basis weight & moisture content control system , 2009, 2009 Chinese Control and Decision Conference.

[19]  Hisao Ishibuchi,et al.  Performance evaluation of genetic algorithms for flowshop scheduling problems , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[20]  Douglas J. Cooper,et al.  A novel tuning strategy for multivariable model predictive control , 1997 .

[21]  K. Parker.  Design of proportional-integral derivative controllers by the use of optimal-linear-regulator theory , 1972 .

[22]  Esko Juuso,et al.  Intelligent control of a rotary kiln fired with producer gas generated from biomass , 2001 .

[23]  S. H. Huang,et al.  Artificial neural networks in manufacturing: concepts, applications, and perspectives , 1994 .

[24]  C. R. Cutler,et al.  Dynamic matrix control¿A computer control algorithm , 1979 .

[25]  Furong Gao,et al.  Multivariable decoupling predictive functional control with non-zero-pole cancellation and state weighting: Application on chamber pressure in a coke furnace , 2013 .

[26]  Zhihua Xiong,et al.  Integrated Framework of Probabilistic Signed Digraph Based Fault Diagnosis Approach to a Gas Fractionation Unit , 2011 .

[27]  J. Tan,et al.  Predictive control development for non-minimum phase processes with application in food manufacturing , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[28]  Yuhong Li,et al.  Applications of Artificial Neural Networks in Financial Economics: A Survey , 2010, 2010 International Symposium on Computational Intelligence and Design.

[29]  Jingjiao Li,et al.  Intelligent control research based on the smart car , 2010, 2010 2nd International Conference on Advanced Computer Control.

[30]  Victor M. Zavala,et al.  A game-theoretical model predictive control framework for electricity markets , 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[31]  Rizauddin Ramli,et al.  A genetic algorithm for optimizing defective goods supply chain costs using JIT logistics and each-cycle lengths , 2014 .

[32]  Marek J. Patyra,et al.  Synthesis of current mode building blocks for fuzzy logic control circuits , 1994, Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94.

[33]  David W. Clarke,et al.  Generalized predictive control - Part I. The basic algorithm , 1987, Autom..

[34]  Mahdi Mahfouf,et al.  Generalised predictive control (GPC): a powerful control tool in medicine , 1997 .

[35]  Ta-Wen Kuan,et al.  A new hybrid and dynamic fusion of multiple experts for intelligent porch system , 2012, Expert Syst. Appl..

[36]  F. Allgower,et al.  Nonlinear model predictive control: From chemical industry to microelectronics , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[37]  Anke Xue,et al.  Modeling and nonlinear predictive functional control of liquid level in a coke fractionation tower , 2011 .

[38]  J Richalet,et al.  An approach to predictive control of multivariable time-delayed plant: stability and design issues. , 2004, ISA transactions.

[39]  J. Richalet,et al.  Model predictive heuristic control: Applications to industrial processes , 1978, Autom..