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Multi-criteria optimization in nonlinear predictive control

Author

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  • Laabidi, Kaouther
  • Bouani, Faouzi
  • Ksouri, Mekki

Abstract

The multi-criteria predictive control of nonlinear dynamical systems based on Artificial Neural Networks (ANNs) and genetic algorithms (GAs) are considered. The (ANNs) are used to determine process models at each operating level; the control action is provided by minimizing a set of control objective which is function of the future prediction output and the future control actions in tacking in account constraints in input signal. An aggregative method based on the Non-dominated Sorting Genetic Algorithm (NSGA) is applied to solve the multi-criteria optimization problem. The results obtained with the proposed control scheme are compared in simulation to those obtained with the multi-model control approach.

Suggested Citation

  • Laabidi, Kaouther & Bouani, Faouzi & Ksouri, Mekki, 2008. "Multi-criteria optimization in nonlinear predictive control," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 76(5), pages 363-374.
    • Handle: RePEc:eee:matcom:v:76:y:2008:i:5:p:363-374
    DOI: 10.1016/j.matcom.2007.04.002
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    Cited by:

    1. Qin, Rui & Liu, Yan-Kui, 2010. "Modeling data envelopment analysis by chance method in hybrid uncertain environments," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(5), pages 922-950.
    2. Yi, Chenfu & Zhang, Yunong & Guo, Dongsheng, 2013. "A new type of recurrent neural networks for real-time solution of Lyapunov equation with time-varying coefficient matrices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 92(C), pages 40-52.
    3. Stefan Banholzer & Giulia Fabrini & Lars Grüne & Stefan Volkwein, 2020. "Multiobjective Model Predictive Control of a Parabolic Advection-Diffusion-Reaction Equation," Mathematics, MDPI, vol. 8(5), pages 1-19, May.

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