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An improved predictive control model for stochastic max-plus-linear systems

Author

Listed:
  • Qu, Jingguo
  • Zhang, Zilong
  • Zhang, Huiqi

Abstract

In order to improve the robustness and stability of the model predictive control system, this paper research the problem by combination of stochastic predictive control and max-plus theory. Based on the analysis of the stochastic predictive control model, the maximum plus stochastic predictive control model is constructed, which is improved by the max-plus algebraic theory. The superiority of the maximum plus stochastic predictive control model is verified by simulation and experiment. The max-plus algebra is an algorithm which is suitable for noise processing of input signal, which can stabilize the input of the control system. The disadvantage of stochastic predictive control model is that the input signal is subjected to random disturbance in the external environment, max-plus algebraic theory can better compensate for the defect. The simulation results show that the stochastic predictive control model has significant advantages in accuracy, stability and robustness.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:chsofr:v:128:y:2019:i:c:p:210-218
    DOI: 10.1016/j.chaos.2019.07.009
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    References listed on IDEAS

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    1. Glenn Merlet, 2010. "Memory Loss Property for Products of Random Matrices in the Max-Plus Algebra," Mathematics of Operations Research, INFORMS, vol. 35(1), pages 160-172, February.
    2. 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.
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