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Study and Application of Intelligent Sliding Mode Control for Voltage Source Inverters

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  • En-Chih Chang

    (Department of Electrical Engineering, I-Shou University, No.1, Sec. 1, Syuecheng Rd., Dashu District, Kaohsiung City 84001, Taiwan)

Abstract

In this paper, an intelligent sliding mode controlled voltage source inverter (VSI) is developed to achieve not only quick transient behavior, but satisfactory steady-state response. The presented approach combines the respective merits of a nonsingular fast terminal attractor (NFTA) as well as an adaptive neuro-fuzzy inference system (ANFIS). The NFTA allows no singularity and error states to be converged to the equilibrium within a finite time, while conventional sliding mode control (SMC) leads to long-term (infinite) convergent behavior. However, there is the likelihood of chattering or steady-state error occurring in NFTA due to the overestimation or underestimation of system uncertainty bound. The ANFIS with accurate estimation and the ease of implementation is employed in NFTA for suppressing the chatter or steady-state error so as to improve the system’s robustness against uncertain disturbances. Simulation results display that this described approach yields low distorted output wave shapes and quick transience in the presence of capacitor input rectifier loading as well as abrupt connection of linear loads. Experimental results conducted on a 1 kW VSI prototype with control algorithm implementation in Texas Instruments DSP (digital signal processor) support the theoretic analysis and reaffirm the robust performance of the developed VSI. Because the proposed VSI yields remarkable benefits over conventional terminal attractor VSIs on the basis of computational quickness and unsophisticated realization, the presented approach is a noteworthy referral to the designers of correlated VSI applications in future, such as DC (direct current) microgrids and AC (alternating current) microgrids, or even hybrid AC/DC microgrids.

Suggested Citation

  • En-Chih Chang, 2018. "Study and Application of Intelligent Sliding Mode Control for Voltage Source Inverters," Energies, MDPI, vol. 11(10), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2544-:d:171694
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    References listed on IDEAS

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    1. Ali Hadi Abdulwahid & Shaorong Wang, 2016. "A Novel Approach for Microgrid Protection Based upon Combined ANFIS and Hilbert Space-Based Power Setting," Energies, MDPI, vol. 9(12), pages 1-25, December.
    2. Xinghuo Yu & Bin Wang & Batsukh Batbayar & Liuping Wang & Zhihong Man, 2011. "An improved training algorithm for feedforward neural network learning based on terminal attractors," Journal of Global Optimization, Springer, vol. 51(2), pages 271-284, October.
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    Cited by:

    1. Grzegorz Dec & Grzegorz Drałus & Damian Mazur & Bogdan Kwiatkowski, 2021. "Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic," Energies, MDPI, vol. 14(6), pages 1-16, March.
    2. Óscar Gonzales-Zurita & Jean-Michel Clairand & Elisa Peñalvo-López & Guillermo Escrivá-Escrivá, 2020. "Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids," Energies, MDPI, vol. 13(13), pages 1-29, July.

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