Abstract
Efficient control of industrial delay processes is a challenging problem in the field of process control. Time delays are generally experienced in industrial processes from distance velocity lags, composition analysis loops, recycle time, mass, and energy transportation time. A high time delay adds a large phase lag to the system, thereby affecting the closed-loop control system stability and thus not easily controlled with PID approach. Smith predictor (SP) is a prominent technique based on process model for processes with high time delay. Unfortunately, the performance of SP deteriorates when the plant model is inaccurate. To overcome the problems related to conventional SP, various modifications have been suggested over the years in terms of structure alterations and controller parameters tuning improvements. This paper focuses on a comparative study of various Smith predictor configurations available in the literature for controlling inverse, integrating, stable and unstable industrial processes with time delay.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Åström, KJ, Hägglund, T. The future of PID control. Contr Eng Pract 2001;9:1163–75. https://doi.org/10.1016/s0967-0661(01)00062-4.Suche in Google Scholar
2. Palmor, ZJ. The control handbook, chap. Time delay compensation: Smith predictor and its modifications. USA: CRC Press and IEEE Press; 1996.Suche in Google Scholar
3. Åström, KJ, Hägglund, T, Astrom, KJ. Advanced PID control. Research Triangle Park: ISA-The Instrumentation, Systems, and Automation Society; 2006.Suche in Google Scholar
4. Smith, OJ. Closer control of loops with dead time. Chem Eng Progr 1957;53:217–9.Suche in Google Scholar
5. Smith, OJM. A controller to overcome dead time. ISA J 1959;6:28–33.Suche in Google Scholar
6. Tore, H. A predictive PI controller for processes with long dead times. IEEE Contr Syst Mag 1992;12:57–60. https://doi.org/10.1109/37.120455.Suche in Google Scholar
7. Prakash, P, Verma, NK, Behera, L. Eigenvalue assignment via the Smith predictor based IMC-PID & the matrix lambert W function for control of time-delayed process systems. IFAC Proc Vol 2014;47:997–1002. https://doi.org/10.3182/20140313-3-IN-3024.00125.Suche in Google Scholar
8. Zhang, HY, Sun, J, Zhang, D, zong, CS, Zhang, X. Improved Smith prediction monitoring AGC system based on feedback-assisted iterative learning control. J Cent South Univ 2014;21:3492–7. https://doi.org/10.1007/s11771-014-2327-3.Suche in Google Scholar
9. Vu, TNL, Lee, M. Smith predictor based fractional-order PI control for time-delay processes. Kor J Chem Eng 2014;31:1321–9. https://doi.org/10.1007/s11814-014-0076-5.Suche in Google Scholar
10. Deniz, FN, Tan, N. A model identification method for tuning of PID controller in a Smith predictor structure. IFAC-PapersOnLine [Internet] 2016;49:13–8. https://doi.org/10.1016/j.ifacol.2016.07.465.Suche in Google Scholar
11. Hajdu, D, Insperger, T. Demonstration of the sensitivity of the Smith predictor to parameter uncertainties using stability diagrams. Int J Dyn Contr [Internet] 2016;4:384–92. https://doi.org/10.1007/s40435-014-0142-1.Suche in Google Scholar
12. Gao, F, Wu, M, She, J, He, Y. Delay-dependent guaranteed-cost control based on combination of Smith predictor and equivalent-input-disturbance approach. ISA Trans 2016;62:215–21. https://doi.org/10.1016/j.isatra.2016.02.008.Suche in Google Scholar PubMed
13. Vunder, NA, Ushakov, AV. Sensitivity analysis of systems with a cascade compensator embedded in a Smith predictor to dead-time variation. Optoelectron Instrum Data Process 2016;52:274–9. https://doi.org/10.3103/s8756699016030092.Suche in Google Scholar
14. González, A, Aranda, M, López-Nicolás, G, Sagüés, C. Time delay compensation based on Smith Predictor in multiagent formation control. IFAC-PapersOnLine 2017;50:11645–51. https://doi.org/10.1016/j.ifacol.2017.08.1667.Suche in Google Scholar
15. Mohammadzaheri, M, Tafreshi, R. An enhanced Smith predictor based control system using feedback- feedforward structure for time- delay processes. J Eng Res 2017;14:156–65. https://doi.org/10.24200/tjer.vol14iss2pp156-165.Suche in Google Scholar
16. Safaei, M, Tavakoli, S. Smith predictor based fractional-order control design for time-delay integer-order systems. Int J Dyn Contr 2018;6:179–87. https://doi.org/10.1007/s40435-017-0312-z.Suche in Google Scholar
17. Pashaei, S, Bagheri, P. Parallel cascade control of dead time processes via fractional order controllers based on Smith predictor. ISA Trans 2020;98:186–97. https://doi.org/10.1016/j.isatra.2019.08.047.Suche in Google Scholar PubMed
18. Devan, PAM, Hussin, FAB, Ibrahim, R, Bingi, K, Abdulrab, HQA. Fractional-order predictive PI controller for dead-time processes with set-point and noise filtering. IEEE Access 2020;8:183759–73. https://doi.org/10.1109/access.2020.3029068.Suche in Google Scholar
19. Padhan, DG, Majhi, S. Modified Smith predictor and controller for time delay processes. Electron Lett 2011;47:959–61. https://doi.org/10.1049/el.2011.0378.Suche in Google Scholar
20. Padhan, DG, Majhi, S. Modified Smith predictor based cascade control of unstable time delay processes. ISA Trans [Internet] 2012;51:95–104. https://doi.org/10.1016/j.isatra.2011.08.002.Suche in Google Scholar PubMed
21. Mataušek, MR, Ribić, AI. Control of stable, integrating and unstable processes by the Modified Smith Predictor. J Process Contr 2012;22:338–43.10.1016/j.jprocont.2011.08.006Suche in Google Scholar
22. Rivas-Perez, R, Feliu-Batlle, V, Castillo-Garcia, FJ, Benitez-Gonzalez, I. Temperature control of a crude oil preheating furnace using a modified Smith predictor improved with a disturbance rejection term. IFAC Proc Vol 2014;47:5760–5. https://doi.org/10.3182/20140824-6-ZA-1003.01999.Suche in Google Scholar
23. Bowthorpe, M, Tavakoli, M, Becher, H, Howe, R. Smith predictor-based robot control for ultrasound-guided teleoperated beating-heart surgery. IEEE J Biomed Heal Inf 2014;18:157–66. https://doi.org/10.1109/jbhi.2013.2267494.Suche in Google Scholar
24. Benitez, IO, Rivas, R, Feliu, V, Castillo, FJ. Temperature control based on a modified Smith predictor for injectable drug formulations. IEEE Lat Am Trans 2015;13:1041–7. https://doi.org/10.1109/tla.2015.7106355.Suche in Google Scholar
25. de Oliveira, FSS, Souza, FO, Palhares, RM. PID tuning for time-varying delay systems based on modified Smith predictor. IFAC-PapersOnLine [Internet] 2017;50:1269–74. https://doi.org/10.1016/j.ifacol.2017.08.130.Suche in Google Scholar
26. Lloyds Raja, G, Ali, A. Smith predictor based parallel cascade control strategy for unstable and integrating processes with large time delay. J Process Contr [Internet] 2017;52:57–65. https://doi.org/10.1016/j.jprocont.2017.01.007.Suche in Google Scholar
27. Tan, F, Han, X, Li, PS. Smith predictor-based multiple periodic disturbance compensation for long dead-time processes. Int J Contr 2018;91:999–1010. https://doi.org/10.1080/00207179.2017.1303748.Suche in Google Scholar
28. Praveen Kumar, M, Venkata Lakshmi Narayana, K. Multi control scheme with modified Smith predictor for unstable first order plus time delay system. Ain Shams Eng J [Internet] 2018;9:2859–69. https://doi.org/10.1016/j.asej.2017.10.005.Suche in Google Scholar
29. Araújo, JM, Santos, TLM. Control of a class of second-order linear vibrating systems with time-delay: Smith predictor approach. Mech Syst Signal Process 2018;108:173–87. https://doi.org/10.1016/j.ymssp.2018.02.013.Suche in Google Scholar
30. Saków, M, Marchelek, K. Model-free and time-constant prediction for closed-loop systems with time delay. Contr Eng Pract 2018;81:1–8. https://doi.org/10.1016/j.conengprac.2018.08.021.Suche in Google Scholar
31. Qiang, YC, Wang, HT, Sun, Q, Zhao, L. Improved cascade control system for a class of unstable processes with time delay. Int J Contr Autom Syst 2019;17:126–35.10.1007/s12555-018-0096-8Suche in Google Scholar
32. Karan, S, Dey, C. Set point weighted modified Smith predictor for delay dominated integrating processes. In: 2019 devices for integrated circuit (DevIC). IEEE; 2019:172–6 pp.10.1109/DEVIC.2019.8783297Suche in Google Scholar
33. Karan, S, Dey, C, Mukherjee, S. Simple internal model control based modified Smith predictor for integrating time delayed processes with real-time verification. ISA Trans 2021 [Internet], in press. https://doi.org/10.1016/j.isatra.2021.04.008.Suche in Google Scholar PubMed
34. Normey-Rico, JE, Garcia, P, Gonzalez, A. Robust stability analysis of Filtered Smith Predictor for time-varying delay processes. J Process Contr [Internet] 2012;22:1975–84. https://doi.org/10.1016/j.jprocont.2012.08.012.Suche in Google Scholar
35. Torrico, BC, Cavalcante, MU, Braga, APS, Normey-Rico, JE, Albuquerque, AAM. Simple tuning rules for dead-time compensation of stable, integrative, and unstable first-order dead-time processes. Ind Eng Chem Res 2013;52:11646–54. https://doi.org/10.1021/ie401395x.Suche in Google Scholar
36. Normey-Rico, JE, Guzmán, JL. Unified PID tuning approach for stable, integrative, and unstable dead-time processes. Ind Eng Chem Res 2013;52:16811–9. https://doi.org/10.1021/ie401722y.Suche in Google Scholar
37. Santos, TLM, Flesch, RCC, Normey-Rico, JE. On the filtered Smith predictor for MIMO processes with multiple time delays. J Process Contr 2014;24:383–400. https://doi.org/10.1016/j.jprocont.2014.02.011.Suche in Google Scholar
38. Rodríguez, C, Normey-Rico, JE, Guzmán, JL, Berenguel, M. On the filtered Smith predictor with feedforward compensation. J Process Contr 2016;41:35–46. https://doi.org/10.1016/j.jprocont.2016.02.005.Suche in Google Scholar
39. Santos, TLM, Torrico, BC, Normey-Rico, JE. Simplified filtered Smith predictor for MIMO processes with multiple time delays. ISA Trans 2016;65:339–49. https://doi.org/10.1016/j.isatra.2016.08.023.Suche in Google Scholar PubMed
40. Giraldo, SAC, Flesch, RCC, Normey-Rico, JE. Multivariable greenhouse control using the filtered Smith predictor. J Contr Autom Electr Syst 2016;27:349–58. https://doi.org/10.1007/s40313-016-0250-6.Suche in Google Scholar
41. Liu, T, Hao, S, Li, D, Chen, WH, Wang, QG. Predictor-Based disturbance rejection control for sampled systems with input delay. IEEE Trans Contr Syst Technol 2019;27:772–80. https://doi.org/10.1109/tcst.2017.2781651.Suche in Google Scholar
42. Giraldo, SAC, Flesch, RCC, Normey-Rico, JE, Sejas, MZP. A method for designing decoupled filtered Smith predictor for square MIMO systems with multiple time delays. IEEE Trans Ind Appl 2018;54:6439–49. https://doi.org/10.1109/tia.2018.2849365.Suche in Google Scholar
43. Franklin, TS, Santos, TLM. Robust filtered Smith predictor for processes with time-varying delay: a simplified stability approach. Eur J Contr 2020;56:38–50. https://doi.org/10.1016/j.ejcon.2020.01.005.Suche in Google Scholar
44. Torrico, BC, Pereira, RDO, Sombra, AKR, Nogueira, FG. Simplified filtered Smith predictor for high-order dead-time processes. ISA Trans 2021;109:11–21. https://doi.org/10.1016/j.isatra.2020.10.007.Suche in Google Scholar PubMed
45. Azarmi, R, Tavakoli-Kakhki, M, Fatehi, A, Sedigh, AK. Frequency domain tuning of a filtered Smith predictor based PI λ controller and its application to pressure plant. In: 7th international conference on control, mechatronics and automation (ICCMA). IEEE; 2019:49–55 pp.10.1109/ICCMA46720.2019.8988625Suche in Google Scholar
46. Zhang, B, Tan, W, Li, J. Tuning of Smith predictor based generalized ADRC for time-delayed processes via IMC. ISA Trans [Internet] 2020;99:159–66. https://doi.org/10.1016/j.isatra.2019.11.002.Suche in Google Scholar PubMed
47. Morato, MM, Normey-Rico, JE. A novel unified method for time-varying dead-time compensation. ISA Trans [Internet] 2021;108:78–95. https://doi.org/10.1016/j.isatra.2020.08.018.Suche in Google Scholar PubMed
48. Sanz, R, García, P, Albertos, P. A generalized Smith predictor for unstable time-delay SISO systems. ISA Trans [Internet] 2018;72:197–204. https://doi.org/10.1016/j.isatra.2017.09.020.Suche in Google Scholar PubMed
49. Liu, T, García, P, Chen, Y, Ren, X, Albertos, P, Sanz, R. New predictor and 2DOF control scheme for industrial processes with long time delay. IEEE Trans Ind Electron 2018;65:4247–56. https://doi.org/10.1109/tie.2017.2760839.Suche in Google Scholar
50. García, P, Albertos, P. Robust tuning of a generalized predictor-based controller for integrating and unstable systems with long time-delay. J Process Contr 2013;23:1205–16.10.1016/j.jprocont.2013.07.008Suche in Google Scholar
51. Wei, QY, Wang, WQ. Research on fuzzy self-adaptive PI-Smith control in long time-delay system. J China Univ Posts Telecommun [Internet] 2011;18. 114–7, 128. https://doi.org/10.1016/s1005-8885(10)60112-4.Suche in Google Scholar
52. Chen, H, Zouaoui, Z, Chen, Z. A modified Smith predictive scheme based back-propagation neural network approach for FOPDT processes control. J Process Contr 2013;23:1261–9. https://doi.org/10.1016/j.jprocont.2013.07.003.Suche in Google Scholar
53. Benitez, IO, Rivas, R, Feliu, V, Sánchez, LP, Sánchez, LA. Fuzzy gain scheduled Smith predictor for temperature control in an industrial steel slab reheating furnace. IEEE Lat Am Trans 2016;14:4439–47. https://doi.org/10.1109/tla.2016.7795812.Suche in Google Scholar
54. Pamela, D, Godwin Premi, MS. Wireless control and automation of hot air temperature in oven for sterilization using fuzzy PID controller and adaptive Smith predictor. Wireless Pers Commun 2017;94:2055–64. https://doi.org/10.1007/s11277-016-3358-x.Suche in Google Scholar
55. Huang, H, Zhang, S, Yang, Z, Tian, Y, Zhao, X, Yuan, Z, et al.. Modified Smith fuzzy PID temperature control in an oil-replenishing device for deep-sea hydraulic system. Ocean Eng [Internet] 2018;149:14–22. https://doi.org/10.1016/j.oceaneng.2017.11.052.Suche in Google Scholar
56. Wu, Y, Wu, Y. A novel predictive control scheme with an enhanced Smith predictor for networked control system. Automat Contr Comput Sci 2018;52:126–34. https://doi.org/10.3103/s0146411618020098.Suche in Google Scholar
57. Tang, Y, Du, F, Cui, Y, Zhang, Y. New Smith predictive fuzzy immune PID control algorithm for MIMO networked control systems. EURASIP J Wireless Commun Netw 2018;2018. https://doi.org/10.1186/s13638-018-1229-8.Suche in Google Scholar
58. Batista, AP, Jota, FG. Performance improvement of an NCS closed over the internet with an adaptive Smith predictor. Contr Eng Pract [Internet] 2018;71:34–43. https://doi.org/10.1016/j.conengprac.2017.10.006.Suche in Google Scholar
59. Patel, B, Patel, H, Vachhrajani, P, Shah, D, Sarvaia, A. Adaptive Smith predictor controller for total intravenous anesthesia automation. Biomed Eng Lett [Internet] 2019;9:127–44. https://doi.org/10.1007/s13534-018-0090-3.Suche in Google Scholar PubMed PubMed Central
60. Özbek, NS, Eker, I. A fractional fuzzy PI-PD based modified Smith predictor for controlling of FOPDT process. In: 2016 5th international conference on electronic devices, systems and applications (ICEDSA). Ras Al Khaimah, UAE: IEEE; 2016:1–4 pp.10.1109/ICEDSA.2016.7818488Suche in Google Scholar
61. Özbek, NS, Eker, İ. Design of an optimal fractional fuzzy gain-scheduled Smith Predictor for a time-delay process with experimental application. ISA Trans 2020;97:14–35. https://doi.org/10.1016/j.isatra.2019.08.009.Suche in Google Scholar PubMed
62. Abu-Rmileh, A, Garcia-Gabin, W. Smith predictor sliding mode closed-loop glucose controller in type 1 diabetes. IFAC Proc Vol 2011;44:1733–8.10.3182/20110828-6-IT-1002.01213Suche in Google Scholar
63. Dong, C, Lu, J, Meng, Q. Position control of an electro-hydraulic servo system based on improved Smith predictor. In: Proceedings of 2011 international conference on electronic & mechanical engineering and information technology. Harbin, China: IEEE; 2011, vol 6:2818–21 pp.10.1109/EMEIT.2011.6023688Suche in Google Scholar
64. De Oliveira, V, Karimi, A. Robust Smith predictor design for time-delay systems with H ∞ performance. IFAC Proc Vol 2013;46:102–7.10.1007/978-3-319-26369-4_15Suche in Google Scholar
65. Chen, H, Zouaoui, Z, Chen, Z. Neuro-fuzzy modified Smith predictor for IPDT and FOPDT processes control. IFAC Proc Vol 2013;46:839–44.10.3182/20130904-3-FR-2041.00093Suche in Google Scholar
66. Feliu-Batlle, V, Rivas-Perez, R, Castillo-García, FJ. Simple fractional order controller combined with a Smith predictor for temperature control in a steel slab reheating furnace. Int J Contr Autom Syst 2013;11:533–44. https://doi.org/10.1007/s12555-012-0355-z.Suche in Google Scholar
67. Zhu, Q, Xiong, L, Liu, H. A robust speed controller with Smith predictor for a PMSM drive system with time delay. Int J Contr Autom Syst 2017;15:2448–54. https://doi.org/10.1007/s12555-015-0198-5.Suche in Google Scholar
68. Lee, DH, Jung, JH, Yoon, HN, Park, YS, Lee, JM. Simulation of time delay compensation controller for a mobile robot using the SMC and Smith predictor. Adv Intell Syst Comput 2017;531:687–94. https://doi.org/10.1007/978-3-319-48036-7_50.Suche in Google Scholar
69. Huang, C, Gui, W, Xie, Y. Decoupling Smith control for multivariable system with time delays. IFAC Proc Vol 2011;44:5765–70.10.3182/20110828-6-IT-1002.02899Suche in Google Scholar
70. Jabri, K, Dumur, D, Godoy, E, Mouchette, A, Bèle, B. Particle swarm optimization based tuning of a modified Smith predictor for mould level control in continuous casting. J Process Contr [Internet] 2011;21:263–70. https://doi.org/10.1016/j.jprocont.2010.10.019.Suche in Google Scholar
71. Bobál, V, Chalupa, P, Dostál, P, Kubalčik, M. Digital Smith predictor for control of unstable and integrating time-delay processes. In: Proceedings of the 2014 international conference on mechatronics and robotics, structural analysis (MEROSTA 2014). Santorini Island, Greece; 2014:105–10 pp.Suche in Google Scholar
72. Jesus, IS, Barbosa, RS. Smith-fuzzy fractional control of systems with time delay. AEU - Int J Electron Commun [Internet] 2017;78:54–63. https://doi.org/10.1016/j.aeue.2017.05.014.Suche in Google Scholar
73. Qi, C, Gao, F, Zhao, X, Wang, Q, Ren, A. Hybrid Smith predictor and phase lead based divergence compensation for hardware-in-the-loop contact simulation with measurement delay. Acta Astronaut [Internet] 2018;147:175–82. https://doi.org/10.1016/j.actaastro.2018.04.010.Suche in Google Scholar
74. Chuong, VL, Vu, TNL, Truong, NTN, Jung, JH. An analytical design of simplified decoupling Smith predictors for multivariable processes. Appl Sci 2019;9. https://doi.org/10.3390/app9122487.Suche in Google Scholar
75. Lloyds Raja, G, Ali, A. New PI-PD controller design strategy for industrial unstable and integrating processes with dead time and inverse response. J Contr Autom Electr Syst [Internet] 2021;32:266–80. https://doi.org/10.1007/s40313-020-00679-5.Suche in Google Scholar
76. Mukherjee, D, Raja, GL, Kundu, P. Optimal fractional order IMC-based series cascade control strategy with dead-time compensator for unstable processes. J Contr Autom Electr Syst [Internet] 2021;32:30–41. https://doi.org/10.1007/s40313-020-00644-2.Suche in Google Scholar
77. Kaya, I. Optimal PI–PD controller design for pure integrating processes with time delay. J Contr Autom Electr Syst [Internet] 2021;32:563–72. https://doi.org/10.1007/s40313-021-00692-2.Suche in Google Scholar
78. Kirtania, K, Choudhury, MS. A two-degree-of-freedom dead time compensator for stable processes with dead time. In: 2011 international symposium on advanced control of industrial processes (ADCONIP). IEEE; 2011;385–90 pp.10.1109/ICELCE.2010.5700677Suche in Google Scholar
79. Kaya, I. IMC based automatic tuning method for PID controllers in a Smith predictor configuration. Comput Chem Eng 2004;28:281–90. https://doi.org/10.1016/j.compchemeng.2003.01.001.Suche in Google Scholar
80. Kuphaldt, TR. Lessons in industrial instrumentation. Samurai Media Limited; 2008.Suche in Google Scholar
81. Bequette, BW. Process control: modeling, design, and simulation. Prentice Hall Professional; 2003.Suche in Google Scholar
82. Astrom, KJ, Hang, CC, Lim, BC. A new Smith predictor for controlling a process with an integrator and long dead-time. IEEE Trans Automat Contr 1994;39:343–5. https://doi.org/10.1109/9.272329.Suche in Google Scholar
83. Normey-Rico, JE. Control of dead-time processes. Springer Science & Business Media; 2007.Suche in Google Scholar
84. Zile, M. Intelligent and adaptive control. In: Microgrid architectures, control and protection methods power systems. Cham: Springer; 2020.10.1007/978-3-030-23723-3_17Suche in Google Scholar
85. Béla, GL. Instrument engineers’ handbook. Volume two: Process control and optimization. United Kingdom: CRC Press; 2018.Suche in Google Scholar
86. Li, T, Zhang, B, Feng, Z, Zheng, B. Robust control with engineering applications. Math Probl Eng 2014;2014:2–4. https://doi.org/10.1155/2014/567672.Suche in Google Scholar
87. Sikora, J, Wagnerova, R. Overview of reinforcement learning and its application in control theory. In: 21st international carpathian control conference (ICCC). IEEE; 2020:1–4 pp.10.1109/ICCC49264.2020.9257272Suche in Google Scholar
88. Moerland, TM, Broekens, J, Jonker, CM. Model-based reinforcement learning: a survey. arXiv preprint arXiv:2006.16712; 2020.Suche in Google Scholar
89. Kravets, AG. Robotics: industry 4.0 issues & new intelligent control paradigms. Springer; 2020.10.1007/978-3-030-37841-7Suche in Google Scholar
90. Shimkin, N. Nonlinear control systems. In: Binder, MD, Hirokawa, N, Windhorst, U, editors. Encyclopedia of neuroscience. Heidelberg: Springer Berlin; 2009.10.1007/978-3-540-29678-2_4021Suche in Google Scholar
91. Mo, H, Farid, G. Nonlinear and adaptive intelligent control techniques for Quadrotor UAV – a survey. Asian J Contr 2019;21. https://doi.org/10.1002/asjc.1758.Suche in Google Scholar
92. Wilamowski, BM, Irwin, JD, editors. The industrial electronics handbook - five volume set, 2nd ed. CRC Press; 2011.10.1201/NOE1439802892Suche in Google Scholar
93. Kozák, Š. State-of-the-art in control engineering. J Electr Syst Inf Technol 2014;1:1–9. https://doi.org/10.1016/j.jesit.2014.03.002.Suche in Google Scholar
94. Albertos, P, Antonio, S. Multivariable control systems: An engineering approach. Springer Science & Business Media; 2006.Suche in Google Scholar
95. Grimble, MJ, Majecki, P. Nonlinear industrial process and power control applications. In: Nonlinear industrial control systems; 2020. https://doi.org/10.1007/978-1-4471-7457-8_14.Suche in Google Scholar
96. Kuo, CY, Yang, CL, Margolin, C. Optimal controller design for nonlinear systems. IEE Proc Contr Theor Appl [Internet] 1998;145:97–105. https://doi.org/10.1049/ip-cta:19981647.10.1049/ip-cta:19981647Suche in Google Scholar
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Artikel in diesem Heft
- Frontmatter
- Research Articles
- CFD simulation of the ethylbenzene dehydrogenation reaction in the fixed bed reactor with a cylindrical catalyst of various sizes
- Energy and exergy optimization of oxidative steam reforming of acetone–butanol–ethanol–water mixture as a renewable source for H2 production via thermodynamic modeling
- A novel LSSVM-L Hammerstein model structure for system identification and nonlinear model predictive control of CSTR servo and regulatory control
- Environmental and thermodynamic performance assessment of biomass gasification process for hydrogen production in a downdraft gasifier
- Modeling of lime production process using artificial neural network
- Control of TITO processes using sliding mode controller tuned by ITAE minimizing criterion based Nelder-Mead algorithm
- To the problem of forming the equation system for pressure swing adsorption mathematical model
- Review
- A comparative study of various Smith predictor configurations for industrial delay processes
Artikel in diesem Heft
- Frontmatter
- Research Articles
- CFD simulation of the ethylbenzene dehydrogenation reaction in the fixed bed reactor with a cylindrical catalyst of various sizes
- Energy and exergy optimization of oxidative steam reforming of acetone–butanol–ethanol–water mixture as a renewable source for H2 production via thermodynamic modeling
- A novel LSSVM-L Hammerstein model structure for system identification and nonlinear model predictive control of CSTR servo and regulatory control
- Environmental and thermodynamic performance assessment of biomass gasification process for hydrogen production in a downdraft gasifier
- Modeling of lime production process using artificial neural network
- Control of TITO processes using sliding mode controller tuned by ITAE minimizing criterion based Nelder-Mead algorithm
- To the problem of forming the equation system for pressure swing adsorption mathematical model
- Review
- A comparative study of various Smith predictor configurations for industrial delay processes