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H ∞ Optimal Performance Design of an Unstable Plant under Bode Integral Constraint

Author

Listed:
  • Fanwei Meng
  • Aiping Pang
  • Xuefei Dong
  • Chang Han
  • Xiaopeng Sha

Abstract

This paper proposed the H ∞ state feedback and H ∞ output feedback design methods for unstable plants, which improved the original H ∞ state feedback and H ∞ output feedback. For the H ∞ state feedback design of unstable plants, it presents the complete robustness constraint which is based on solving Riccati equation and Bode integral. For the H ∞ output feedback design of unstable plants, the medium-frequency band should be considered in particular. Besides, this paper presents the method to select weight function or coefficients in the H ∞ design, which employs Bode integral to optimize the H ∞ design. It takes a magnetic levitation system as an example. The simulation results demonstrate that the optimal performance of perturbation suppression is obtained with the design of robustness constraint. The presented method is of benefit to the general H ∞ design.

Suggested Citation

  • Fanwei Meng & Aiping Pang & Xuefei Dong & Chang Han & Xiaopeng Sha, 2018. "H ∞ Optimal Performance Design of an Unstable Plant under Bode Integral Constraint," Complexity, Hindawi, vol. 2018, pages 1-10, August.
  • Handle: RePEc:hin:complx:4942906
    DOI: 10.1155/2018/4942906
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    Cited by:

    1. Mohammed Balubaid & Osman Taylan & Mustafa Tahsin Yilmaz & Ehsan Eftekhari-Zadeh & Ehsan Nazemi & Mohammed Alamoudi, 2022. "Central Nervous System: Overall Considerations Based on Hardware Realization of Digital Spiking Silicon Neurons (DSSNs) and Synaptic Coupling," Mathematics, MDPI, vol. 10(6), pages 1-20, March.
    2. Guoxin Hua & Fei Wang & Jianhui Zhang & Khalid A. Alattas & Ardashir Mohammadzadeh & Mai The Vu, 2022. "A New Type-3 Fuzzy Predictive Approach for Mobile Robots," Mathematics, MDPI, vol. 10(17), pages 1-16, September.
    3. Abdulaziz S. Alkabaa & Osman Taylan & Mustafa Tahsin Yilmaz & Ehsan Nazemi & El Mostafa Kalmoun, 2022. "An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach," Mathematics, MDPI, vol. 10(4), pages 1-21, February.
    4. Fenghua Chen & Xinguo Qiu & Khalid A. Alattas & Ardashir Mohammadzadeh & Ebrahim Ghaderpour, 2022. "A New Fuzzy Robust Control for Linear Parameter-Varying Systems," Mathematics, MDPI, vol. 10(18), pages 1-22, September.

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