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Auto-Tuning Process of State Feedback Speed Controller Applied for Two-Mass System

Author

Listed:
  • Rafal Szczepanski

    (Institute of Engineering and Technology, Nicolaus Copernicus University, Grudziadzka 5/7, 87-100 Toruń, Poland)

  • Marcin Kaminski

    (Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, Smoluchowskiego 19, 50-372 Wroclaw, Poland)

  • Tomasz Tarczewski

    (Institute of Engineering and Technology, Nicolaus Copernicus University, Grudziadzka 5/7, 87-100 Toruń, Poland)

Abstract

The state feedback controller is increasingly applied in electrical drive systems due to robustness and good disturbance compensation, however its main drawback is related to complex and time consuming tuning process. It is particularly troublesome for designer, if the plant is compound, nonlinear elements are taken into account, measurement noise is considered, etc. In this paper the application of nature-inspired optimization algorithm to automatic tuning of state feedback speed controller (SFC) for two-mass system (TMS) is proposed. In order to obtain optimal coefficients of SFC, the Artificial Bee Colony algorithm (ABC) is used. The objective function is described and discussed in details. Comparison with analytical tuning method of SFC is also included. Additionally, the stability analysis for the control system, optimized using the ABC algorithm, is presented. Synthesis procedure of the controller is utilized in Matlab/Simulink from MathWorks. Next, obtained coefficients of the controller are examined on the laboratory stand, also with variable moment of inertia values, to indicate robustness of the controller with optimal coefficients.

Suggested Citation

  • Rafal Szczepanski & Marcin Kaminski & Tomasz Tarczewski, 2020. "Auto-Tuning Process of State Feedback Speed Controller Applied for Two-Mass System," Energies, MDPI, vol. 13(12), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3067-:d:371061
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    References listed on IDEAS

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    1. Ahmed G. Abo-Khalil & Ali S. Alghamdi & Ali M. Eltamaly & M. S. Al-Saud & Praveen R. P. & Khairy Sayed & G. R. Bindu & Iskander Tlili, 2019. "Design of State Feedback Current Controller for Fast Synchronization of DFIG in Wind Power Generation Systems," Energies, MDPI, vol. 12(12), pages 1-26, June.
    2. Rizka Bimarta & Thuy Vi Tran & Kyeong-Hwa Kim, 2018. "Frequency-Adaptive Current Controller Design Based on LQR State Feedback Control for a Grid-Connected Inverter under Distorted Grid," Energies, MDPI, vol. 11(10), pages 1-29, October.
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    Cited by:

    1. Mateusz Malarczyk & Mateusz Zychlewicz & Radoslaw Stanislawski & Marcin Kaminski, 2023. "Electric Drive with an Adaptive Controller and Wireless Communication System," Future Internet, MDPI, vol. 15(2), pages 1-20, January.
    2. Radoslaw Stanislawski & Jules-Raymond Tapamo & Marcin Kaminski, 2023. "Virtual Signal Calculation Using Radial Neural Model Applied in a State Controller of a Two-Mass System," Energies, MDPI, vol. 16(15), pages 1-23, July.
    3. Krzysztof Szabat & Tomasz Pajchrowski & Tomasz Tarczewski, 2021. "Modern Electrical Drives: Trends, Problems, and Challenges," Energies, MDPI, vol. 15(1), pages 1-4, December.
    4. Marcin Kamiński & Krzysztof Szabat, 2021. "Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft," Energies, MDPI, vol. 14(12), pages 1-26, June.
    5. Mengnan Chen & Yongquan Zhou & Qifang Luo, 2022. "An Improved Arithmetic Optimization Algorithm for Numerical Optimization Problems," Mathematics, MDPI, vol. 10(12), pages 1-27, June.

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