IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i11p1262-d566290.html
   My bibliography  Save this article

An Optimized Triggering Algorithm for Event-Triggered Control of Networked Control Systems

Author

Listed:
  • Sunil Kumar Mishra

    (School of Electrical Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India)

  • Amitkumar V. Jha

    (School of Electrical Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India)

  • Vijay Kumar Verma

    (Control and Digital Electronics Group, U R Rao (ISRO) Satellite Centre Department of Space, Government of India, Bengaluru 560017, India)

  • Bhargav Appasani

    (School of Electrical Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India)

  • Almoataz Y. Abdelaziz

    (Faculty of Engineering and Technology, Future University in Egypt, 90th St, First New Cairo, Cairo Governorate, Cairo 11835, Egypt)

  • Nicu Bizon

    (Faculty of Electronics, Communication and Computers, University of Pitesti, 110040 Pitesti, Romania
    Doctoral School, Polytechnic University of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania
    ICSI Energy, National Research and Development Institute for Cryogenic and Isotopic Technologies, 240050 Ramnicu Valcea, Romania)

Abstract

This paper presents an optimized algorithm for event-triggered control (ETC) of networked control systems (NCS). Initially, the traditional backstepping controller is designed for a generalized nonlinear plant in strict-feedback form that is subsequently extended to the ETC. In the NCS, the controller and the plant communicate with each other using a communication network. In order to minimize the bandwidth required, the number of samples to be sent over the communication channel should be reduced. This can be achieved using the non-uniform sampling of data. However, the implementation of non-uniform sampling without a proper event triggering rule might lead the closed-loop system towards instability. Therefore, an optimized event triggering algorithm has been designed such that the system states are always forced to remain in stable trajectory. Additionally, the effect of ETC on the stability of backstepping control has been analyzed using the Lyapunov stability theory. Two case studies on an inverted pendulum system and single-link robot system have been carried out to demonstrate the effectiveness of the proposed ETC in terms of system states, control effort and inter-event execution time.

Suggested Citation

  • Sunil Kumar Mishra & Amitkumar V. Jha & Vijay Kumar Verma & Bhargav Appasani & Almoataz Y. Abdelaziz & Nicu Bizon, 2021. "An Optimized Triggering Algorithm for Event-Triggered Control of Networked Control Systems," Mathematics, MDPI, vol. 9(11), pages 1-22, May.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:11:p:1262-:d:566290
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/11/1262/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/11/1262/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sunil Kumar Mishra & Bhargav Appasani & Amitkumar Vidyakant Jha & Izaskun Garrido & Aitor J. Garrido, 2020. "Centralized Airflow Control to Reduce Output Power Variation in a Complex OWC Ocean Energy Network," Complexity, Hindawi, vol. 2020, pages 1-16, August.
    2. Maurice Clerc, 2010. "Beyond Standard Particle Swarm Optimisation," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 1(4), pages 46-61, October.
    3. Cui, Lili & Zhang, Yong & Wang, Xiaowei & Xie, Xiangpeng, 2021. "Event-triggered distributed self-learning robust tracking control for uncertain nonlinear interconnected systems," Applied Mathematics and Computation, Elsevier, vol. 395(C).
    4. Mayank Kumar Gautam & Avadh Pati & Sunil Kumar Mishra & Bhargav Appasani & Ersan Kabalci & Nicu Bizon & Phatiphat Thounthong, 2021. "A Comprehensive Review of the Evolution of Networked Control System Technology and Its Future Potentials," Sustainability, MDPI, vol. 13(5), pages 1-39, March.
    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. Sunil Kumar Mishra & Amitkumar V. Jha & Bhargav Appasani & Nicu Bizon & Phatiphat Thounthong & Pongsiri Mungporn, 2023. "Ocean Wave Energy Control Using Aquila Optimization Technique," Energies, MDPI, vol. 16(11), pages 1-21, June.
    2. Minjeong Sim & Dongjun Suh & Marc-Oliver Otto, 2021. "Multi-Objective Particle Swarm Optimization-Based Decision Support Model for Integrating Renewable Energy Systems in a Korean Campus Building," Sustainability, MDPI, vol. 13(15), pages 1-18, August.
    3. Zhai, Ganghui & Tian, Engang & Luo, Yuqiang & Liang, Dong, 2024. "Data-driven optimal output regulation for unknown linear discrete-time systems based on parameterization approach," Applied Mathematics and Computation, Elsevier, vol. 461(C).
    4. Kazem Shahverdi & Hossein Talebmorad, 2023. "Automating HEC-RAS and Linking with Particle Swarm Optimizer to Calibrate Manning’s Roughness Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 975-993, January.
    5. Ascher, Simon & Watson, Ian & You, Siming, 2022. "Machine learning methods for modelling the gasification and pyrolysis of biomass and waste," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    6. Blom, Evelin & Söder, Lennart, 2022. "Accurate model reduction of large hydropower systems with associated adaptive inflow," Renewable Energy, Elsevier, vol. 200(C), pages 1059-1067.
    7. Qiang Yang & Yu-Wei Bian & Xu-Dong Gao & Dong-Dong Xu & Zhen-Yu Lu & Sang-Woon Jeon & Jun Zhang, 2022. "Stochastic Triad Topology Based Particle Swarm Optimization for Global Numerical Optimization," Mathematics, MDPI, vol. 10(7), pages 1-39, March.
    8. Emre Yakut & Ezel Özkan, 2020. "Modeling of Energy Consumption Forecast with Economic Indicators Using Particle Swarm Optimization and Genetic Algorithm: An Application in Turkey between 1979 and 2050," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(1), pages 59-78, June.
    9. Zhen, Lu & Wu, Yiwei & Wang, Shuaian & Laporte, Gilbert, 2020. "Green technology adoption for fleet deployment in a shipping network," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 388-410.
    10. Takele Ferede Agajie & Armand Fopah-Lele & Isaac Amoussou & Ahmed Ali & Baseem Khan & Emmanuel Tanyi, 2023. "Optimal Design and Mathematical Modeling of Hybrid Solar PV–Biogas Generator with Energy Storage Power Generation System in Multi-Objective Function Cases," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    11. Arnaud Flori & Hamouche Oulhadj & Patrick Siarry, 2022. "QUAntum Particle Swarm Optimization: an auto-adaptive PSO for local and global optimization," Computational Optimization and Applications, Springer, vol. 82(2), pages 525-559, June.
    12. Wu, Taocheng & Wu, Jiajing & You, Wei, 2018. "Optimizing robustness of complex networks with heterogeneous node functions based on the Memetic Algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 143-153.
    13. Zhao, Yanwei & Wang, Huanqing & Xu, Ning & Zong, Guangdeng & Zhao, Xudong, 2023. "Reinforcement learning-based decentralized fault tolerant control for constrained interconnected nonlinear systems," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    14. Gilani, Seyyed-Omid & Sattarvand, Javad & Hajihassani, Mohsen & Abdullah, Shahrum Shah, 2020. "A stochastic particle swarm based model for long term production planning of open pit mines considering the geological uncertainty," Resources Policy, Elsevier, vol. 68(C).
    15. Cui, Lili & Xie, Xiangpeng & Guo, Hongyan & Luo, Yanhong, 2022. "Dynamic event-triggered distributed guaranteed cost FTC scheme for nonlinear interconnected systems via ADP approach," Applied Mathematics and Computation, Elsevier, vol. 425(C).
    16. Da Xue & Nael H. El-Farra, 2022. "Supervisory Event-Triggered Control of Uncertain Process Networks: Balancing Stability and Performance," Mathematics, MDPI, vol. 10(12), pages 1-24, June.
    17. Piyush Dhawankar & Prashant Agrawal & Bilal Abderezzak & Omprakash Kaiwartya & Krishna Busawon & Maria Simona Raboacă, 2021. "Design and Numerical Implementation of V2X Control Architecture for Autonomous Driving Vehicles," Mathematics, MDPI, vol. 9(14), pages 1-24, July.
    18. Deepak Kumar Gupta & Ankit Kumar Soni & Amitkumar V. Jha & Sunil Kumar Mishra & Bhargav Appasani & Avireni Srinivasulu & Nicu Bizon & Phatiphat Thounthong, 2021. "Hybrid Gravitational–Firefly Algorithm-Based Load Frequency Control for Hydrothermal Two-Area System," Mathematics, MDPI, vol. 9(7), pages 1-15, March.
    19. Deepak Kumar Gupta & Amitkumar V. Jha & Bhargav Appasani & Avireni Srinivasulu & Nicu Bizon & Phatiphat Thounthong, 2021. "Load Frequency Control Using Hybrid Intelligent Optimization Technique for Multi-Source Power Systems," Energies, MDPI, vol. 14(6), pages 1-16, March.
    20. Nirbheram, Joshi Sukhdev & Mahesh, Aeidapu & Bhimaraju, Ambati, 2023. "Techno-economic analysis of grid-connected hybrid renewable energy system adapting hybrid demand response program and novel energy management strategy," Renewable Energy, Elsevier, vol. 212(C), pages 1-16.

    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:jmathe:v:9:y:2021:i:11:p:1262-:d:566290. 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.