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A New Intelligent Dynamic Control Method for a Class of Stochastic Nonlinear Systems

Author

Listed:
  • Haifeng Huang

    (School of Electrical and Information Technology, Zhenjiang College, Zhenjiang 212028, China)

  • Mohammadamin Shirkhani

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

  • Jafar Tavoosi

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

  • Omar Mahmoud

    (Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt)

Abstract

This paper presents a new method for a comprehensive stabilization and backstepping control system design for a class of stochastic nonlinear systems. These types of systems are so abundant in practice that the control system designer must assume that random noise with a definite probability distribution affects the dynamics and observations of state variables. Stochastic control is intended to determine the time course of control variables so that the control target is achievable even with minimal cost. Since the mathematical equations of stochastic nonlinear systems are not always constant, not every model-based controller can be accurate. Therefore, in this paper, a type-3 fuzzy neural network is used to estimate the parameters of the backstepping control method. In the simulation, the proposed method is compared with the Type-1 fuzzy and RBFN methods. Results clearly show that the proposed method has a very good performance and can be used for any system in this class.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1406-:d:799886
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    References listed on IDEAS

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    1. Andrey Borisov & Alexey Bosov & Gregory Miller, 2022. "Optimal Stabilization of Linear Stochastic System with Statistically Uncertain Piecewise Constant Drift," Mathematics, MDPI, vol. 10(2), pages 1-16, January.
    2. Hu, Jun & Zhang, Panpan & Kao, Yonggui & Liu, Hongjian & Chen, Dongyan, 2019. "Sliding mode control for Markovian jump repeated scalar nonlinear systems with packet dropouts: The uncertain occurrence probabilities case," Applied Mathematics and Computation, Elsevier, vol. 362(C), pages 1-1.
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    Cited by:

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    2. Wei Xu & Dillip Kumar Das & Željko Stević & Marko Subotić & Adel F. Alrasheedi & Shiru Sun, 2023. "Trapezoidal Interval Type-2 Fuzzy PIPRECIA-MARCOS Model for Management Efficiency of Traffic Flow on Observed Road Sections," Mathematics, MDPI, vol. 11(12), pages 1-22, June.
    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. Khanh Hieu Nguyen & Sung Hyun Kim, 2022. "Event-Triggered Non-PDC Filter Design of Fuzzy Markovian Jump Systems under Mismatch Phenomena," Mathematics, MDPI, vol. 10(16), pages 1-25, August.
    5. Xin Xu & Ahmed Shaker & Marwa S. Salem, 2022. "Automatic Control of a Mobile Manipulator Robot Based on Type-2 Fuzzy Sliding Mode Technique," Mathematics, MDPI, vol. 10(20), pages 1-18, October.
    6. Jiabao Gu & Hui Wang & Wuquan Li & Ben Niu, 2022. "Adaptive State-Feedback Stabilization for Stochastic Nonlinear Systems with Time-Varying Powers and Unknown Covariance," Mathematics, MDPI, vol. 10(16), pages 1-16, August.
    7. 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.
    8. Ruitao Wang & Hui Wang & Wuquan Li & Ben Niu, 2022. "Output Tracking Control of Random Nonlinear Time-Varying Systems," Mathematics, MDPI, vol. 10(14), pages 1-13, July.
    9. 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.
    10. Yaqiong Ding & Hanguang Jia & Yunong Zhang & Binbin Qiu, 2023. "High-Order Modeling, Zeroing Dynamics Control, and Perturbations Rejection for Non-Linear Double-Holding Water Tank," Mathematics, MDPI, vol. 11(13), pages 1-18, July.
    11. Libo Yang & Mei Guo & Ardashir Mohammadzadeh & Amir Mosavi, 2022. "Taylor Series-Based Fuzzy Model Predictive Control for Wheeled Robots," Mathematics, MDPI, vol. 10(14), pages 1-13, July.
    12. 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.
    13. Mohammad Soleimani Amiri & Rizauddin Ramli, 2022. "Utilisation of Initialised Observation Scheme for Multi-Joint Robotic Arm in Lyapunov-Based Adaptive Control Strategy," Mathematics, MDPI, vol. 10(17), pages 1-14, August.
    14. 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.
    15. Dong Zhao & Shuyan Sun & Ardashir Mohammadzadeh & Amir Mosavi, 2022. "Adaptive Intelligent Model Predictive Control for Microgrid Load Frequency," Sustainability, MDPI, vol. 14(18), pages 1-14, September.

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