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Nonlinear Dynamics

, Volume 92, Issue 2, pp 127–138 | Cite as

Nonlinear model predictive control based on Nelder Mead optimization method

  • Wassila Chagra
  • Hajer Degachi
  • Moufida Ksouri
Article

Abstract

In this paper, a model predictive control (MPC) scheme based on Hammerstein model is carried on. The use of such nonlinear models complicates the implementation of the MPC in terms of computational time and burden since a nonlinear and so a nonconvex optimization problem will result. The Nelder Mead (NM) algorithm, as a free derivative method, is used to solve the resulting optimization problem. NM algorithm proves its efficiency in terms of computation time and global optimum seeking that can be successfully exploited especially with fast dynamic systems. A comparative study between the NM algorithm and the gradient-based method (GBM) based on computation time is established. The efficiency of the NM algorithm is illustrated with SISO and MIMO examples compared to GBM algorithm.

Keywords

Model predictive control Hammerstein model Optimization Nelder Mead algorithm Gradient method Computation time 

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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Wassila Chagra
    • 1
  • Hajer Degachi
    • 2
  • Moufida Ksouri
    • 2
    • 3
  1. 1.Tunis El Manar University El Manar Preparatory Institute for Engineering Studies LR11ES20, Analysis, Conception and Control of Systems LaboratoryTunisTunisia
  2. 2.Tunis El Manar University National Engineering School of Tunis LR11ES20, Analysis, Conception and Control of Systems LaboratoryTunisTunisia
  3. 3.TunisTunisia

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