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Machine-Learning-Based Improved Smith Predictive Control for MIMO Processes

Author

Listed:
  • Xinlan Guo

    (College of Rail Transportation, Nanjing Vocational Institute of Transport Technology, Nanjing 211188, China)

  • Mohammadamin Shirkhani

    (Department of Electrical Engineering, Ilam University, Ilam 69315-516, Iran)

  • Emad M. Ahmed

    (Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
    Department of Electrical Engineering, College of Engineering, Aswan University, Aswan 81542, Egypt)

Abstract

Controlling time-delayed processes is one of the challenges in today’s process industries. If the multi-input/multi-output system is dynamically coupled, the delay problem becomes more critical. In this paper, a new method based on Smith’s predictive method, with the help of a type-2 fuzzy system to control the system with the mentioned features, is presented. The variability in the time delay, the existence of disturbances and the existence of structural and parametric uncertainty lead to the poor performance of the traditional Smith predictor. Even if the control system is set up correctly at the beginning of the setup, it will eventually wear out, and the above problems will appear. Therefore, computational intelligence is used here, and by updating the parameters of the control system at the same time as the system changes, the control system adapts itself to achieve the best performance. To evaluate the proposed control system, a complex process system is simulated, the results of which show the good performance of Smith’s prediction method based on a type-2 fuzzy system.

Suggested Citation

  • Xinlan Guo & Mohammadamin Shirkhani & Emad M. Ahmed, 2022. "Machine-Learning-Based Improved Smith Predictive Control for MIMO Processes," Mathematics, MDPI, vol. 10(19), pages 1-19, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3696-:d:937141
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    References listed on IDEAS

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    1. Haifeng Huang & Mohammadamin Shirkhani & Jafar Tavoosi & Omar Mahmoud, 2022. "A New Intelligent Dynamic Control Method for a Class of Stochastic Nonlinear Systems," Mathematics, MDPI, vol. 10(9), pages 1-15, April.
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    3. Rahmat Aazami & Omid Heydari & Jafar Tavoosi & Mohammadamin Shirkhani & Ardashir Mohammadzadeh & Amir Mosavi, 2022. "Optimal Control of an Energy-Storage System in a Microgrid for Reducing Wind-Power Fluctuations," Sustainability, MDPI, vol. 14(10), pages 1-14, May.
    4. Xikui Liu & Wencong Li & Chenxin Yao & Yan Li, 2022. "Finite-Time Guaranteed Cost Control for Markovian Jump Systems with Time-Varying Delays," Mathematics, MDPI, vol. 10(12), pages 1-12, June.
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    Cited by:

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    2. Ayad Ghany Ismaeel & Krishnadas Janardhanan & Manishankar Sankar & Yuvaraj Natarajan & Sarmad Nozad Mahmood & Sameer Alani & Akram H. Shather, 2023. "Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network," Sustainability, MDPI, vol. 15(19), pages 1-17, October.
    3. Chan Gu & Encheng Chi & Chujia Guo & Mostafa M. Salah & Ahmed Shaker, 2023. "A New Self-Tuning Deep Neuro-Sliding Mode Control for Multi-Machine Power System Stabilizer," Mathematics, MDPI, vol. 11(7), pages 1-18, March.
    4. Ye Wang & Zhaiaibai Ma & Mostafa M. Salah & Ahmed Shaker, 2023. "An Evolutionarily Based Type-2 Fuzzy-PID for Multi-Machine Power System Stabilization," Mathematics, MDPI, vol. 11(11), pages 1-18, May.
    5. Weijun Hu & Jiale Quan & Xianlong Ma & Mostafa M. Salah & Ahmed Shaker, 2023. "Analytical Design of Optimal Model Predictive Control and Its Application in Small-Scale Helicopters," Mathematics, MDPI, vol. 11(8), pages 1-15, April.

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