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Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

Author

Listed:
  • Adel Taieb
  • Moêz Soltani
  • Abdelkader Chaari

Abstract

This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO) Fuzzy Optimal Model Predictive Control (FOMPC) using the Adaptive Particle Swarm Optimization (APSO) algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS) fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR) and Tank system, where the proposed approach provides better performances compared with other methods.

Suggested Citation

  • Adel Taieb & Moêz Soltani & Abdelkader Chaari, 2017. "Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO," Complexity, Hindawi, vol. 2017, pages 1-11, October.
    • Handle: RePEc:hin:complx:5813192
    DOI: 10.1155/2017/5813192
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    References listed on IDEAS

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    1. Bououden, S. & Chadli, M. & Filali, S. & El Hajjaji, A., 2012. "Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach," Renewable Energy, Elsevier, vol. 37(1), pages 434-439.
    2. Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2009. "A novel chaotic particle swarm optimization approach using Hénon map and implicit filtering local search for economic load dispatch," Chaos, Solitons & Fractals, Elsevier, vol. 39(2), pages 510-518.
    3. Liu, Bo & Wang, Ling & Jin, Yi-Hui & Tang, Fang & Huang, De-Xian, 2005. "Improved particle swarm optimization combined with chaos," Chaos, Solitons & Fractals, Elsevier, vol. 25(5), pages 1261-1271.
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    Cited by:

    1. Qu, Jingguo & Zhang, Zilong & Zhang, Huiqi, 2019. "An improved predictive control model for stochastic max-plus-linear systems," Chaos, Solitons & Fractals, Elsevier, vol. 128(C), pages 210-218.
    2. Xiaomeng Yin & Xing Wei & Lei Liu & Yongji Wang, 2018. "Improved Hybrid Fireworks Algorithm-Based Parameter Optimization in High-Order Sliding Mode Control of Hypersonic Vehicles," Complexity, Hindawi, vol. 2018, pages 1-16, March.

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