DOI: https://doi.org/10.15587/2312-8372.2017.100466

Control system synthesis of formation of carbon products

Oleksii Zhuchenko

Abstract


The object of research is the process of formation of carbon products.

The production of carbon products is characterized by considerable resource and energy intensity, therefore the actual task is to increase the efficiency of this production by introducing the optimum operating modes of its component technological processes.

One of the main technological processes in the production of carbon products is the process of their formation by squeezing of the electrode mass through a mouthpiece of the corresponding shape in a hydraulic press. All inherited properties that determine the quality of finished products are laid at the stage of pressing the electrode blanks

The analysis of the existing control systems for the process of formation of carbon products shows that the formation process is a typical cyclic process. For such processes, the control task is, as a rule, to implement such controls that would ensure that one or more output variables are tracked by a predetermined path of motion, which is repeated from cycle to cycle.

A new control system is proposed that provides for iterative learning control in formation of the electrode mass by squeezing through a mouthpiece of the corresponding shape in a hydraulic press. The corresponding ILC algorithm is proposed. Thanks to this solution, a high quality control is provided in the absence of initial uncertainties and external disturbances.


Keywords


carbon products; iterative learning control (ILC); control system; ILC algorithm

References


Chalyh, E. F. (1972). Tehnologiia i oborudovanie elektrodnyh i elektrougol'nyh predpriiatii. Moscow: Metallurgiia, 432.

Sannikov, A. K., Somov, A. B., Kliuchnikov, V. V. et al. (1985). Proizvodstv oelektrodnoi produktsii. Moscow: Metallurgiia, 129.

Yao, K., Gao, F. (2007). Optimal start-up control of injection molding barrel temperature. Polymer Engineering & Science, 47 (3), 254–261. doi:10.1002/pen.20701

Lu, C.-H., Tsai, C.-C. (2001). Adaptive decoupling predictive temperature control for an extrusion barrel in a plastic injection molding process. IEEE Transactions on Industrial Electronics, 48 (5), 968–975. doi:10.1109/41.954561

Chia, T. L. (2002). Model predictive control helps to regulate slow processes-robust barrel temperature control. ISA Transactions, 41 (4), 501–509. doi:10.1016/s0019-0578(07)60105-0

Huang, S., Tan, K., Lee, T. (1999). Adaptive GPC control of melt temperature in injection moulding. ISA Transactions, 38 (4), 361–373. doi:10.1016/s0019-0578(99)00029-4

Moon, U.-C. (2007). A Practical Multiloop Controller Design for Temperature Control of a TV Glass Furnace. IEEE Transactions on Control Systems Technology, 15 (6), 1137–1142. doi:10.1109/tcst.2007.899717

Kaymak, D. B., Luyben, W. L. (2005). Comparison of Two Types of Two-Temperature Control Structures for Reactive Distillation Columns. Industrial & Engineering Chemistry Research, 44 (13), 4625–4640. doi:10.1021/ie058012m

Wolff, E. A., Skogestad, S. (1996). Temperature Cascade Control of Distillation Columns. Industrial & Engineering Chemistry Research, 35 (2), 475–484. doi:10.1021/ie940758p

Uchiyama, M. (1978). Formation of High-Speed Motion Pattern of a Mechanical Arm by Trial. Transactions of the Society of Instrument and Control Engineers, 14 (6), 706–712. doi:10.9746/sicetr1965.14.706

Arimoto, S., Kawamura, S., Miyazaki, F. (1984). Bettering operation of Robots by learning. Journal of Robotic Systems, 1 (2), 123–140. doi:10.1002/rob.4620010203

Lee, K. S., Bang, S. H., Yi, S., Son, J. S., Yoon, S. C. (1996). Iterative learning control of heat-up phase for a batch polymerization reactor. Journal of Process Control, 6 (4), 255–262. doi:10.1016/0959-1524(96)00048-0

Böttcher, A., Grudsky, S. M. (2000). Toeplitz Matrices, Asymptotic Linear Algebra, and Functional Analysis. Birkhäuser Basel, 112. doi:10.1007/978-3-0348-8395-5

Amann, N., Owens, D. H., Rogers, E. (1996). Iterative learning control for discrete-time systems with exponential rate of convergence. IEE Proceedings – Control Theory and Applications, 143 (2), 217–224. doi:10.1049/ip-cta:19960244

Li, X., Xu, J.-X., Huang, D. (2014). An Iterative Learning Control Approach for Linear Systems With Randomly Varying Trial Lengths. IEEE Transactions on Automatic Control, 59 (7), 1954–1960. doi:10.1109/tac.2013.2294827

Xu, J.-X. (2011). A survey on iterative learning control for nonlinear systems. International Journal of Control, 84 (7), 1275–1294. doi:10.1080/00207179.2011.574236

Gantmacher, F. R. (2005). Applications of the Theory of Matrices. Dover Publications, 336.

Owens, D. H., Hatonen, J. (2005). Iterative learning control – An optimization paradigm. Annual Reviews in Control, 29 (1), 57–70. doi:10.1016/j.arcontrol.2005.01.003

Quarteroni, A., Saleri, F. (2006). Scientific Computing with MATLAB and Octave. Texts in Computational Science and Engineering. Ed. 2. Springer Berlin Heidelberg, 324. doi:10.1007/3-540-32613-8


GOST Style Citations


Chalyh, E. F. Tehnologiia i oborudovanie elektrodnyh i elektrougol'nyh predpriiatii [Text] / E. F. Chalyh. – Moscow: Metallurgiia, 1972. – 432 p.

Sannikov, A. K. Proizvodstv oelektrodnoi produktsii [Text] / A. K. Sannikov, A. B. Somov, V. V. Kliuchnikov et al. – Moscow: Metallurgiia, 1985. – 129 p.

Yao, K. Optimal start-up control of injection molding barrel temperature [Text] / K. Yao, F. Gao // Polymer Engineering & Science. – 2007. – Vol. 47, № 3. – P. 254–261. doi:10.1002/pen.20701

Lu, C.-H. Adaptive decoupling predictive temperature control for an extrusion barrel in a plastic injection molding process [Text] / C.-H. Lu, C.-C. Tsai // IEEE Transactions on Industrial Electronics. – 2001. – Vol. 48, № 5. – P. 968–975. doi:10.1109/41.954561

Chia, T. L. Model predictive control helps to regulate slow processes-robust barrel temperature control [Text] / T. L. Chia // ISA Transactions. – 2002. – Vol. 41, № 4. – P. 501–509. doi:10.1016/s0019-0578(07)60105-0

Huang, S. Adaptive GPC control of melt temperature in injection moulding [Text] / S. Huang, K. Tan, T. Lee // ISA Transactions. – 1999. – Vol. 38, № 4. – P. 361–373. doi:10.1016/s0019-0578(99)00029-4

Moon, U.-C. A Practical Multiloop Controller Design for Temperature Control of a TV Glass Furnace [Text] / U.-C. Moon // IEEE Transactions on Control Systems Technology. – 2007. – Vol. 15, № 6. – P. 1137–1142. doi:10.1109/tcst.2007.899717

Kaymak, D. B. Comparison of Two Types of Two-Temperature Control Structures for Reactive Distillation Columns [Text] / D. B. Kaymak, W. L. Luyben // Industrial & Engineering Chemistry Research. – 2005. – Vol. 44, № 13. – P. 4625–4640. doi:10.1021/ie058012m

Wolff, E. A. Temperature Cascade Control of Distillation Columns [Text] / E. A. Wolff, S. Skogestad // Industrial & Engineering Chemistry Research. – 1996. – Vol. 35, № 2. – P. 475–484. doi:10.1021/ie940758p

Uchiyama, M. Formation of High-Speed Motion Pattern of a Mechanical Arm by Trial [Text] / M. Uchiyama // Transactions of the Society of Instrument and Control Engineers. – 1978. – Vol. 14, № 6. – P. 706–712.  doi:10.9746/sicetr1965.14.706

Arimoto, S. Bettering operation of Robots by learning [Text] / S. Arimoto, S. Kawamura, F. Miyazaki // Journal of Robotic Systems. – 1984. – Vol. 1, № 2. – P. 123–140. doi:10.1002/rob.4620010203

Lee, K. S. Iterative learning control of heat-up phase for a batch polymerization reactor [Text] / K. S. Lee, S. H. Bang, S. Yi, J. S. Son, S. C. Yoon // Journal of Process Control. – 1996. – Vol. 6, № 4. – P. 255–262. doi:10.1016/0959-1524(96)00048-0

Bottcher, A. Toeplitz Matrices, Asymptotic Linear Algebra, and Functional Analysis [Text] / A. Bottcher, S. M. Grudsky. – Birkhäuser Basel, 2000. – 112 p. doi:10.1007/978-3-0348-8395-5

Amann, N. Iterative learning control for discrete-time systems with exponential rate of convergence [Text] / N. Amann, D. H. Owens, E. Rogers // IEE Proceedings – Control Theory and Applications. – 1996. – Vol. 143, № 2. – P. 217–224. doi:10.1049/ip-cta:19960244

Li, X. An Iterative Learning Control Approach for Linear Systems With Randomly Varying Trial Lengths [Text] / X. Li, J.-X. Xu, D. Huang // IEEE Transactions on Automatic Control. – 2014. – Vol. 59, № 7. – P. 1954–1960. doi:10.1109/tac.2013.2294827

Xu, J.-X. A survey on iterative learning control for nonlinear systems[Text] / J.-X. Xu // International Journal of Control. – 2011. – Vol. 84, № 7. – P. 1275–1294. doi:10.1080/00207179.2011.574236

Gantmacher, F. R. Applications of the Theory of Matrices [Text] / F. R. Gantmacher. – Dover Publications, 2005. – 336 p.

Owens, D. H. Iterative learning control – An optimization paradigm [Text] / D. H. Owens, J. Hatonen // Annual Reviews in Control. – 2005. – Vol. 29, № 1. – P. 57–70. doi:10.1016/j.arcontrol.2005.01.003

Quarteroni, A. Scientific Computing with MATLAB and Octave [Text] / A. Quarteroni, F. Saleri // Texts in Computational Science and Engineering. – Ed. 2. – Springer Berlin Heidelberg, 2006. – 324 p. doi:10.1007/3-540-32613-8







Copyright (c) 2017 Oleksii Zhuchenko

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ISSN (print) 2664-9969, ISSN (on-line) 2706-5448