Normalized Normal Constraint Algorithm Based Multi-objective Optimal Tuning of Decentralised PI Controller of Nonlinear Multivariable Process – Coal Gasifier

  • Rangasamy Kotteeswaran
  • Lingappan Sivakumar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8297)


Almost all the industrial processes are multivariable in nature and are very difficult to control, since it involves many variables, strong interactions and nonlinearities. Conventional controllers are most widely used with its optimal parameters for such processes because of its simplicity, reliability and stability. Coal gasifier is a highly nonlinear multivariable process with strong interactions among the loop and it is difficult to control at 0% operating point with sinusoidal pressure disturbance. The present work uses Normalized Normal Constraint (NNC) algorithm to tune the parameters of decentralised PI controller of coal gasifier. Maximum absolute error (AE) and Integral of Absolute Error (IAE) are objective function while the controller parameters of decentralised PI controller are the decision variables for the NNC algorithm. With the optimal controller the coal gasifier provides better response at 0%, 50% and 100% operating points and also the performance tests shows good results.


Coal gasifier Multi-Objective Optimization Multivariable process Normalized Normal Constraint Algorithm PID Controller tuning 


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  1. 1.
    Dixon, R., Pike, A.W.: Alstom Benchmark Challenge II on Gasifier Control. IEE Proceedings - Control Theory and Applications 153(3), 254–261 (2006)CrossRefGoogle Scholar
  2. 2.
    Chin, C.S., Munro, N.: Control of the ALSTOM gasifier benchmark problem using H2 methodology. Journal of Process Control. 13(8), 759–768 (2003)CrossRefGoogle Scholar
  3. 3.
    Al Seyab, R.K., Cao, Y., Yang, S.H.: Predictive control for the ALSTOM gasifier problem. IEE Proceedings - Control Theory and Application 153(3), 293–301 (2006)CrossRefGoogle Scholar
  4. 4.
    Al Seyab, R.K., Cao, Y.: Nonlinear model predictive control for the ALSTOM gasifier. Journal of Process Control 16(8), 795–808 (2006)CrossRefGoogle Scholar
  5. 5.
    Agustriyanto, R., Zhang, J.: Control structure selection for the ALSTOM gasifier benchmark process using GRDG analysis. International Journal of Modelling, Identification and Control 6(2), 126–135 (2009)CrossRefGoogle Scholar
  6. 6.
    Tan, W., Lou, G., Liang, L.: Partially decentralized control for ALSTOM gasifier. ISA Transactions 50(3), 397–408 (2011)CrossRefGoogle Scholar
  7. 7.
    Sivakumar, L., Anitha Mary, X.: A Reduced Order Transfer Function Models for Alstom Gasifier using Genetic Algorithm. Int. J. of Computer Applications 46(5), 31–38 (2012)CrossRefGoogle Scholar
  8. 8.
    Kotteeswaran, R., Sivakumar, L.: Lower Order Transfer Function Identification of Nonlinear MIMO System-Alstom Gasifier. International Journal of Engineering Research and Applications 2(4), 1220–1226 (2012)Google Scholar
  9. 9.
    Huang, C., Li, D., Xue, Y.: Active disturbance rejection control for the ALSTOM gasifier benchmark problem. Control Engineering Practice 21(4), 556–564 (2013)CrossRefGoogle Scholar
  10. 10.
    Simm, A., Liu, G.P.: Improving the performance of the ALSTOM baseline controller using multiobjective optimization. IEE-Control Theory and Applications 153(3), 286–292 (2006)CrossRefGoogle Scholar
  11. 11.
    Nobakhti, A., Wang, H.: A simple self-adaptive Differential Evolution algorithm with application on the ALSTOM gasifier. Applied Soft Computing 8(1), 350–370 (2008)CrossRefGoogle Scholar
  12. 12.
    Xue, Y., Li, D., Gao, F.: Multi-objective optimization and selection for the PI control of ALSTOM gasifier problem. Control Engineering Practice 18(1), 67–76 (2010)CrossRefGoogle Scholar
  13. 13.
    Zhou, A., Qu, B.Y., Li, H., Zhao, S.-Z., Suganthan, P.N., Zhang, Q.: Multiobjective Evolutionary Algorithms: A Survey of the State-of-the-art. Swarm and Evolutionary Computation 1(1), 32–49 (2011)CrossRefGoogle Scholar
  14. 14.
    Zhao, S.Z., Willjuice Iruthayarajan, M., Baskar, S., Suganathan, P.N.: Multi-objective robust PID controller tuning using two lbests multi-objective particle swarm optimization. Information Sciences 181(16), 3323–3335 (2011)CrossRefGoogle Scholar
  15. 15.
    Messac, A., Ismail-Yahaya, A., Mattson, C.A.: The Normalized Normal Constraint Method for Generating the Pareto Frontier. Structural and Multidisciplinary Optimization 25(2), 86–98 (2003)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Rangasamy Kotteeswaran
    • 1
  • Lingappan Sivakumar
    • 2
    • 3
  1. 1.Department of Instrumentation and Control EngineeringSt.Joseph’s College of EngineeringChennaiIndia
  2. 2.Formerly General Manager(Corporate R&D)BHELHyderabadIndia
  3. 3.Sri Krishna college of Engineering and TechnologyCoimbatoreIndia

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