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Advanced Control Design and Fault Diagnosis

Author

Listed:
  • Silvio Simani

    (Engineering Department, University of Ferrara, Via Saragat 1E, 44123 Ferrara, Italy)

  • Elena Zattoni

    (Dipartimento di Ingegneria dell’Energia Elettrica e dell’Informazione “G. Marconi”, Alma Mater Studiorum Università di Bologna, Viale Risorgimento 2, 40136 Bologna, Italy)

Abstract

This document provides the motivations and a brief introduction to the Special Issue entitled “Advanced Control Design and Fault Diagnosis”, which aims at presenting several solutions to the advanced control design and fault diagnosis systems. These methodologies can be considered in the general framework of advanced control, fault diagnosis and fault tolerant control systems, which are also able to improve the safety of the system under monitoring. The focuses of the current research in this field addressed in this Special Issue are also presented with emphasis on the practical application to simulated and realistic examples, which should provide an overall picture of current and future developments in this area. The works of this Special Issue represent suitably extended contributions selected by the proponents from the ACD2019—the 15th European Workshop on Advanced Control and Diagnosis, which was organised in Bologna, Italy on 21st–22nd November.

Suggested Citation

  • Silvio Simani & Elena Zattoni, 2021. "Advanced Control Design and Fault Diagnosis," Energies, MDPI, vol. 14(18), pages 1-6, September.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5699-:d:632830
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    References listed on IDEAS

    as
    1. Dumitru Popescu & Catalin Dimon & Pierre Borne & Severus Constantin Olteanu & Mihaela Ancuta Mone, 2020. "Advanced Control for Hydrogen Pyrolysis Installations," Energies, MDPI, vol. 13(12), pages 1-15, June.
    2. Nan Jin & Chao Pan & Yanyan Li & Shiyang Hu & Jie Fang, 2020. "Model Predictive Control for Virtual Synchronous Generator with Improved Vector Selection and Reconstructed Current," Energies, MDPI, vol. 13(20), pages 1-16, October.
    3. Di Wang & Xiao Wu & Jiong Shen, 2020. "An Efficient Robust Predictive Control of Main Steam Temperature of Coal-Fired Power Plant," Energies, MDPI, vol. 13(15), pages 1-24, July.
    4. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Fang, Xiaolei & Cai, Xiao & Yan, Tao, 2021. "Remaining useful life prediction based on a multi-sensor data fusion model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    5. Marcin Witczak & Marcin Mrugalski & Bogdan Lipiec, 2021. "Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework," Energies, MDPI, vol. 14(8), pages 1-23, April.
    6. Giovanni Bucci & Fabrizio Ciancetta & Andrea Fioravanti & Edoardo Fiorucci & Simone Mari & Alberto Prudenzi, 2020. "Testing System for the On-Site Checking of Magneto-Thermal Switches with Arc Fault Detection," Energies, MDPI, vol. 13(18), pages 1-18, September.
    7. Faiçal Hamidi & Severus Constantin Olteanu & Dumitru Popescu & Houssem Jerbi & Ingrid Dincă & Sondess Ben Aoun & Rabeh Abbassi, 2020. "Model Based Optimisation Algorithm for Maximum Power Point Tracking in Photovoltaic Panels," Energies, MDPI, vol. 13(18), pages 1-20, September.
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