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Reduced order modelling and predictive control of multivariable nonlinear process

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Abstract

In this paper, an efficient model-predictive control strategy that can be applied to complex multivariable process is presented. A reduced order generalized predictive algorithm is proposed for online applications with reduction in complexity and time elapsed. The complex multivariable process considered in this work is a binary distillation column. The reduced order model is developed with a recently proposed hybrid algorithm known as Clustering Dominant Pole Algorithm and is able to compute the full set of dominant poles and their cluster centre efficiently. The controller calculates the optimal control action based on the future reference signals, current state and constraints on manipulated and controlled variables for a high-order dynamic simulated model of nonlinear multivariable binary distillation column process. The predictive control algorithm uses controlled auto-regressive integrated moving average model. The performance of constraint generalized predictive control scheme is found to be superior to that of the conventional PID controller in terms of overshoot, settling time and performance indices, mainly ISE, IAE and MSE.

Keywords

Predictive control distillation column reduced order model dominant pole clustering 

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Copyright information

© Indian Academy of Sciences 2018

Authors and Affiliations

  • Anuj Abraham
    • 1
  • N Pappa
    • 2
  • Daniel Honc
    • 3
  • Rahul Sharma
    • 4
  1. 1.Department of Applied Electronics & Instrumentation, Rajagiri School of Engineering & TechnologyAPJ Abdul Kalam Technological UniversityKeralaIndia
  2. 2.Department of Instrumentation Engineering, Madras Institute of Technology CampusAnna UniversityChennaiIndia
  3. 3.Department of Process Control, Faculty of Electrical Engineering and InformaticsUniversity of PardubicePardubiceCzech Republic
  4. 4.Department of Electrical and Electronics Engineering, Amrita School of EngineeringAmrita UniversityCoimbatoreIndia

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