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Smart process measurement and automation: challenges, solution and future direction

  • S.I.: Visvesvaraya
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Abstract

Industrial processes are multi input multi output, non-linear, and constrained in nature. The sensors, data acquisition systems, controller and the actuators of the above said processes are all networked to form as networked control system with various network constraints. Other issues in the smart measurement sensor non-linearity, uncertainty and noises. The PID and MPC are the famous industrial controller for controlling the above processes. PID controller is popular due to its simplicity in tuning parameter and the MPC is preferred for its superior control performance. However, in process industries, the MPC based controller is used in the supervisory level which gives the command signal to the regulatory level PID controller. This PID controller is used for controlling the industrial process which makes limited MPC performance. It makes challenges to the researcher to develop next generation controller which can able to address the above stated problems. This enhances the research scope in the domain of SMART process measurement and automation. There arises a necessity to come up with a solution for the above problems which has industrial acceptability, simplicity, flexibility and scalability. This research paper is aimed to bring into sight the various research challenges involved in smart process measurement and automation and solution to some of the same.

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References

  1. Galán O, Romagnoli JA, Palazoglu A (2004) Real-time implementation of multi-linear model-based control strategies—an application to a bench-scale pH neutralization reactor. J Process Control 14(5):571–579. https://doi.org/10.1016/j.jprocont.2003.10.003

    Article  Google Scholar 

  2. de Oliveira Nuno MC, Biegler LT (1994) Constraint handling and stability properties of model-predictive control. Am Inst Chem Eng J 40(7):1138–1155

    Article  MathSciNet  Google Scholar 

  3. Zhang L, Gao H, Kaynak O (2013) Network-induced constraints in networked control systems-a survey. IEEE Trans Ind Inform 9:403–416. https://doi.org/10.1109/TII.2012.2219540

    Article  Google Scholar 

  4. Zahmati AS, Fernando X, Kojori H (2011) Transmission delay in wireless sensing, command and control applications for aircraft. In: 4th Annual Caneus fly-by-wireless work. FBW vol 11, pp 91–94. https://doi.org/10.1109/fbw.2011.5965563

  5. Zhang D, Shi P, Wang QG, Yu L (2017) Analysis and synthesis of networked control systems: a survey of recent advances and challenges. ISA Trans 66:376–392. https://doi.org/10.1016/j.isatra.2016.09.026

    Article  Google Scholar 

  6. Ge M, Chiu M-S, Wang Q-G (2002) Robust PID controller design via LMI approach. J Process Control 2(1):3–13. https://doi.org/10.1016/S0959-1524(00)00057-3

    Article  Google Scholar 

  7. Huang HP, Jeng JC, Chiang CH, Pan W (2003) A direct method for multi-loop PI/PID controller design. J Process Control 13(8):769–786. https://doi.org/10.1016/0959-1524(03)00009-X

    Article  Google Scholar 

  8. Morley A, Derrick J, Mainland P, Lee BB, Short TG (2000) Closed loop control of anaesthesia: an assessment of the bispectral index as the target of control. Anaesthesia 55(10):953–959

    Article  Google Scholar 

  9. Cao N, Lynch AF (2016) Inner-outer loop control for quadrotor UAVs with input and state constraints. IEEE Trans Control Syst Technol 24(5):1797–1804. https://doi.org/10.1109/TCST.2015.2505642

    Article  Google Scholar 

  10. Qin SJ, Badgwell TA (2003) A survey of industrial model predictive control technology. Control Eng Pract 11(7):733–764. https://doi.org/10.1016/S0967-0661

    Article  Google Scholar 

  11. Ferramosca A, Limon D, Alvarado I, Camachoa EF (2013) Cooperative distributed MPC for tracking. J Autom 49(4):906–914. https://doi.org/10.1016/j.automatica.2013.01.019

    Article  MathSciNet  MATH  Google Scholar 

  12. Ding T, Bo R, Li F, Gu Y, Guo Q, Sun H (2015) Exact penalty function based constraint relaxation method for optimal power flow considering wind generation uncertainty. IEEE Trans Power Syst 30(3):1546–1547. https://doi.org/10.1109/TPWRS.2014.2341177

    Article  Google Scholar 

  13. Ogunnaike BA, Mukati K (2006) An alternative structure for next generation regulatory controllers. Part i: basic theory for design, development and implementation. J Process Control 6(5):499–509. https://doi.org/10.1016/j.jprocont.2005.08.001

    Article  Google Scholar 

  14. Yelneedi S, Woon YT, Rangaiah GP, Samavedham L (2009) A comprehensive evaluation of pid, cascade, model-predictive, and RTDA controllers for regulation of hypnosis. IndEngChem Res 48(2009):5719–5730. https://doi.org/10.1021/ie800927u

    Google Scholar 

  15. Srinivasan Kand Anbarasan K (2013) Fuzzy scheduled RTDA controller design. ISA Trans 52(2):252–267. https://doi.org/10.1016/j.isatra.2012.11.008

    Article  Google Scholar 

  16. Sendjaja AY, Ng ZF, How SS, Kariwala V (2011) Analysis and tuning of RTD-A controllers. Ind Eng Chem Res 50(6):3415–3425. https://doi.org/10.1021/ie102154y

    Article  Google Scholar 

  17. Mukati K, Rasch M, Ogunnaike BA (2009) An alternative structure for next generation regulatory controllers. Part ii: stability analysis, tuning rules and experimental validation. J Process Control 19(2):272–287. https://doi.org/10.1016/j.jprocont.2008.03.004

    Article  Google Scholar 

  18. Anbarasan K, Srinivasan K (2015) Design of RTDA controller for industrial process using SOPDT model with minimum or non-minimum zero. ISA Trans 57:231–244. https://doi.org/10.1016/j.isatra.2015.02.016

    Article  Google Scholar 

  19. Diamond JM (1970) Linearization of resistance thermometers and other transducers. Rev Sci Instrum 41(1):53–60. https://doi.org/10.1063/1.1684279

    Article  Google Scholar 

  20. Patra J, Kot AC, Panda G (2000) An intelligent pressure sensor using neural networks. IEEE Trans Instrum Meas 49(4):829–834. https://doi.org/10.1109/19.863933

    Article  Google Scholar 

  21. Patra JC, Ang EL, Meher PK (2006) A Novel neural network-based linearization and auto-compensation technique for sensors. In: IEEE-ISCAS pp 1167–1170. https://doi.org/10.1109/ISCAS.2006.1692798

  22. Sun S, Xie L, Xiao W, Soh YK (2008) Optimal linear estimation for systems with multiple packet dropouts. Automatica 44:1333–1342. https://doi.org/10.1016/j.automatica.2007.09.023

    Article  MathSciNet  MATH  Google Scholar 

  23. Haseena BA, Srinivasan K (2018) Development of mixed constrained RTDA controller for industrial applications. ISA Trans 81:197–209. https://doi.org/10.1016/j.isatra.2018.07.005

    Article  Google Scholar 

  24. Sarawade PD, Srinivasan K (2018) T-S fuzzy-based multi-LAE approach for sensor linearization. IET Sci Meas Technol 12(8):1015–1022. https://doi.org/10.1049/iet-smt.2018.5228

    Article  Google Scholar 

  25. Roy AK, Srinivasan K (2017) Stochastic output feedback controller for two tank interacting nonlinear system. In: International conference IEEE INDICON, IIT Roorkee, pp 1–6. https://doi.org/10.1109/INDICON.2017.8487242

  26. Sarawade PD, Srinivasan K (2018) Thermocouple linearization using data driven approach. In: 4th national conference on recent trends in instrumentation and control RTIC, pp 1015–1022. https://doi.org/10.1049/iet-smt.2018.5228

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Acknowledgements

This publication is an outcome of the R&D work undertaken in the project under the Visvesvaraya Ph.D. scheme of Ministry of Electronics and Information Technology, Government of India, being implemented by the Digital India Corporation (formerly Media Lab Asia).

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Correspondence to Kannan Srinivasan.

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Kannan, S. Smart process measurement and automation: challenges, solution and future direction. CSIT 7, 93–98 (2019). https://doi.org/10.1007/s40012-019-00238-7

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