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Study of the Angular Positioning of a Rotating Object Based on Some Computational Intelligence Methods

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  • Constantin Volosencu

    (Department of Automation and Applied Informatics, Faculty of Automation and Computers, “Politehnica” University Timisoara, 300223 Timisoara, Romania)

Abstract

The paper presents the result of a study that can be included in the broader field of research aimed at increasing the performance of automatic motion control systems. The main contribution of the article is the comparative study of three control methods from the domain of computational intelligence—state feedback fuzzy control, neural predictive control, and neural model reference control—and three linear control methods—error feedback control, digital control, and state feedback control, in the case of positioning a rotating object around a central axis. The developed control structures were modeled and simulated using MATLAB/Simulink. The paper presents the chosen control structures; how to dimension them; the parameters of the linear, fuzzy, and neural regulators; the training parameters of the neural networks; and the characteristics of the variables of the control systems in the transient regime and the steady-state regime. Transient characteristics obtained for the six control structures are compared from the point of view of their control efficiency criteria. The differences in performance criteria between the control methods studied are small. All these studied methods make the regulated system to be carried on various state trajectories, short response times are obtained with aperiodic and asymptotic behavior, and the differences between the values of the efficiency indicators are small.

Suggested Citation

  • Constantin Volosencu, 2022. "Study of the Angular Positioning of a Rotating Object Based on Some Computational Intelligence Methods," Mathematics, MDPI, vol. 10(7), pages 1-46, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1157-:d:786349
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    References listed on IDEAS

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    1. Sanaz Sabzevari & Rasool Heydari & Maryam Mohiti & Mehdi Savaghebi & Jose Rodriguez, 2021. "Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters," Energies, MDPI, vol. 14(8), pages 1-12, April.
    2. Valery Vodovozov & Andrei Aksjonov & Eduard Petlenkov & Zoja Raud, 2021. "Neural Network-Based Model Reference Control of Braking Electric Vehicles," Energies, MDPI, vol. 14(9), pages 1-22, April.
    3. Zhe Wu & David Rincon & Quanquan Gu & Panagiotis D. Christofides, 2021. "Statistical Machine Learning in Model Predictive Control of Nonlinear Processes," Mathematics, MDPI, vol. 9(16), pages 1-37, August.
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