IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i19p5069-d420484.html
   My bibliography  Save this article

On Stability of Perturbed Nonlinear Switched Systems with Adaptive Reinforcement Learning

Author

Listed:
  • Phuong Nam Dao

    (Department of Automatic Control, Hanoi University of Science and Technology, 1 Dai Co Viet Road, Hanoi 100000, Vietnam)

  • Hong Quang Nguyen

    (Department of Automation, Thai Nguyen University of Technology, 666, 3/2 Street, Tich Luong Ward, Thai Nguyen City 251750, Vietnam)

  • Minh-Duc Ngo

    (Department of Automation, Thai Nguyen University of Technology, 666, 3/2 Street, Tich Luong Ward, Thai Nguyen City 251750, Vietnam)

  • Seon-Ju Ahn

    (Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Korea)

Abstract

In this paper, a tracking control approach is developed based on an adaptive reinforcement learning algorithm with a bounded cost function for perturbed nonlinear switched systems, which represent a useful framework for modelling these converters, such as DC–DC converter, multi-level converter, etc. An optimal control method is derived for nominal systems to solve the tracking control problem, which results in solving a Hamilton–Jacobi–Bellman (HJB) equation. It is shown that the optimal controller obtained by solving the HJB equation can stabilize the perturbed nonlinear switched systems. To develop a solution to the translated HJB equation, the proposed neural networks consider the training technique obtaining the minimization of square of Bellman residual error in critic term due to the description of Hamilton function. Theoretical analysis shows that all the closed-loop system signals are uniformly ultimately bounded (UUB) and the proposed controller converges to optimal control law. The simulation results of two situations demonstrate the effectiveness of the proposed controller.

Suggested Citation

  • Phuong Nam Dao & Hong Quang Nguyen & Minh-Duc Ngo & Seon-Ju Ahn, 2020. "On Stability of Perturbed Nonlinear Switched Systems with Adaptive Reinforcement Learning," Energies, MDPI, vol. 13(19), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5069-:d:420484
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/19/5069/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/19/5069/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Abualkasim Bakeer & Andrii Chub & Dmitri Vinnikov, 2020. "Step-Up Series Resonant DC–DC Converter with Bidirectional-Switch-Based Boost Rectifier for Wide Input Voltage Range Photovoltaic Applications," Energies, MDPI, vol. 13(14), pages 1-14, July.
    2. Manoharan Premkumar & Umashankar Subramaniam & Hassan Haes Alhelou & Pierluigi Siano, 2020. "Design and Development of Non-Isolated Modified SEPIC DC-DC Converter Topology for High-Step-Up Applications: Investigation and Hardware Implementation," Energies, MDPI, vol. 13(15), pages 1-27, August.
    3. Hermes Loschi & Robert Smolenski & Piotr Lezynski & Douglas Nascimento & Galina Demidova, 2020. "Aggregated Conducted Electromagnetic Interference Generated by DC/DC Converters with Deterministic and Random Modulation," Energies, MDPI, vol. 13(14), pages 1-9, July.
    4. Oleksandr Korkh & Andrei Blinov & Dmitri Vinnikov & Andrii Chub, 2020. "Review of Isolated Matrix Inverters: Topologies, Modulation Methods and Applications," Energies, MDPI, vol. 13(9), pages 1-30, May.
    5. Bi-Ying Chen & Xing-Chen Shangguan & Li Jin & Dan-Yun Li, 2020. "An Improved Stability Criterion for Load Frequency Control of Power Systems with Time-Varying Delays," Energies, MDPI, vol. 13(8), pages 1-14, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sadeq D. Al-Majidi & Hisham Dawood Salman Altai & Mohammed H. Lazim & Mohammed Kh. Al-Nussairi & Maysam F. Abbod & Hamed S. Al-Raweshidy, 2023. "Bacterial Foraging Algorithm for a Neural Network Learning Improvement in an Automatic Generation Controller," Energies, MDPI, vol. 16(6), pages 1-19, March.
    2. Sadeq D. Al-Majidi & Mohammed Kh. AL-Nussairi & Ali Jasim Mohammed & Adel Manaa Dakhil & Maysam F. Abbod & Hamed S. Al-Raweshidy, 2022. "Design of a Load Frequency Controller Based on an Optimal Neural Network," Energies, MDPI, vol. 15(17), pages 1-28, August.
    3. Mohamed Derbeli & Cristian Napole & Oscar Barambones & Jesus Sanchez & Isidro Calvo & Pablo Fernández-Bustamante, 2021. "Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications," Energies, MDPI, vol. 14(22), pages 1-31, November.
    4. Wenxi Feng & Yanshan Xie & Fei Luo & Xianyong Zhang & Wenyong Duan, 2021. "Enhanced Stability Criteria of Network-Based Load Frequency Control of Power Systems with Time-Varying Delays," Energies, MDPI, vol. 14(18), pages 1-22, September.
    5. Giordano Luigi Schiavon & Eloi Agostini & Claudinor Bitencourt Nascimento, 2023. "Quasi-Resonant Single-Switch High-Voltage-Gain DC-DC Converter with Coupled Inductor and Voltage Multiplier Cell," Energies, MDPI, vol. 16(9), pages 1-14, May.
    6. Pranta Das & Shuvra Prokash Biswas & Sudipto Mondal & Md Rabiul Islam, 2023. "Frequency Fluctuation Mitigation in a Single-Area Power System Using LQR-Based Proportional Damping Compensator," Energies, MDPI, vol. 16(12), pages 1-18, June.
    7. Abualkasim Bakeer & Andrii Chub & Andrei Blinov & Jih-Sheng Lai, 2021. "Wide Range Series Resonant DC-DC Converter with a Reduced Component Count and Capacitor Voltage Stress for Distributed Generation," Energies, MDPI, vol. 14(8), pages 1-20, April.
    8. Ilya A. Galkin & Andrei Blinov & Maxim Vorobyov & Alexander Bubovich & Rodions Saltanovs & Dimosthenis Peftitsis, 2021. "Interface Converters for Residential Battery Energy Storage Systems: Practices, Difficulties and Prospects," Energies, MDPI, vol. 14(12), pages 1-32, June.
    9. Ashraf Khalil & Dina Shona Laila, 2022. "An Accurate Method for Computing the Delay Margin in Load Frequency Control System with Gain and Phase Margins," Energies, MDPI, vol. 15(9), pages 1-21, May.
    10. Debanjan, Mukherjee & Karuna, Kalita, 2022. "An Overview of Renewable Energy Scenario in India and its Impact on Grid Inertia and Frequency Response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    11. Wang Hu & Yunxiang Xie & Zhiping Wang & Zhi Zhang, 2020. "A Novel Three-Phase Current Source Rectifier Based on an Asymmetrical Structure to Reduce Stress on Semiconductor Devices," Energies, MDPI, vol. 13(13), pages 1-16, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5069-:d:420484. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.