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Nonsingular Terminal Sliding Mode Control Based on Binary Particle Swarm Optimization for DC–AC Converters

Author

Listed:
  • En-Chih Chang

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

  • Chun-An Cheng

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

  • Lung-Sheng Yang

    (Department of Electrical Engineering, Far East University, No.49, Zhonghua Rd., Xinshi Dist., Tainan City 74448, Taiwan)

Abstract

This paper proposes an improved feedback algorithm by binary particle swarm optimization (BPSO)-based nonsingular terminal sliding mode control (NTSMC) for DC–AC converters. The NTSMC can create limited system state convergence time and allow singularity avoidance. The BPSO is capable of finding the global best solution in real-world application, thus optimizing NTSMC parameters during digital implementation. The association of NTSMC and BPSO extends the design of classical terminal sliding mode to converge to non-singular points more quickly and introduce optimal methodology to avoid falling into local extremum and low convergence precision. Simulation results show that the improved technique can achieve low total harmonic distortion (THD) and fast transients with both plant parameter variations and sudden step load changes. Experimental results of a DC–AC converter prototype controlled by an algorithm based on digital signal processing have been shown to confirm mathematical analysis and enhanced performance under transient and steady-state load conditions. Since the improved DC–AC converter system has significant advantages in tracking accuracy and solution quality over classical terminal sliding mode DC–AC converter systems, this paper will be applicable to designers of relevant robust control and optimal control technique.

Suggested Citation

  • En-Chih Chang & Chun-An Cheng & Lung-Sheng Yang, 2019. "Nonsingular Terminal Sliding Mode Control Based on Binary Particle Swarm Optimization for DC–AC Converters," Energies, MDPI, vol. 12(11), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2099-:d:236320
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    References listed on IDEAS

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    1. Wei Jin & Yongli Li & Guangyu Sun & Lizhi Bu, 2017. "H∞ Repetitive Control Based on Active Damping with Reduced Computation Delay for LCL-Type Grid-Connected Inverters," Energies, MDPI, vol. 10(5), pages 1-19, April.
    2. Jaehong Kim & Jitae Hong & Hongju Kim, 2016. "Improved Direct Deadbeat Voltage Control with an Actively Damped Inductor-Capacitor Plant Model in an Islanded AC Microgrid," Energies, MDPI, vol. 9(11), pages 1-15, November.
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    Cited by:

    1. Zheng Zhao & Ziyu Zhou & Ye Lu & Zhuoge Li & Qiang Wei & Hongbin Xu, 2023. "Predictions of the Key Operating Parameters in Waste Incineration Using Big Data and a Multiverse Optimizer Deep Learning Model," Sustainability, MDPI, vol. 15(19), pages 1-22, October.

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