Disturbance-Resilient Voltage/Power-Quality Management for Islanded Inverters Using Self-Tuning Type-3 Fuzzy Controller

Tian M.-W. Mao J.-B. Zhakiyev N. Yedilkhan D. Omirgaliyev R. Changdong D. Sarvenoee A.K. Mohammadzadeh A.
2026 John Wiley and Sons Ltd

Energy Science and Engineering
2026

This paper proposes a disturbance-resilient adaptive fuzzy sliding-mode control (AFSMC) strategy for off-grid (islanded) inverters using an interval type-3 (IT3) fuzzy logic system (FLS). An integral sliding surface is formulated to eliminate the reaching phase and ensure robust output-voltage regulation under lumped uncertainties. A model-based inductor-current reference with an embedded limiter is incorporated to provide overcurrent protection during abnormal operating conditions. To mitigate the chattering associated with conventional sliding-mode switching and to avoid relying on tight uncertainty bounds, the discontinuous term is replaced by a continuous IT3-FLS compensator. The rule weights, membership-function (MF) parameters of the IT3-FLS, and translation width are tuned online via Lyapunov-based adaptation laws. Comparative simulations and real-time co-simulation under load variations, DC-link fluctuations, filter-parameter perturbations, and master–slave inverter connection/disconnection events demonstrate improved tracking and power quality. The proposed controller reduces output-voltage THD to 0.81% (vs. 1.59% for SMC and 4.85% for PID) and lowers the control-input chattering index from (Formula presented.) to (Formula presented.), while achieving up to 76.4% improvement in voltage MSE at the disturbance instant under parameter variations. The resulting IT3-FLS-based controller provides a practical and extensible framework for uncertain nonlinear power-electronic systems, with potential applicability to motor drives and related converter-based applications.

adaptive fuzzy sliding-mode control , chattering phenomenon reduction , islanded inverter , lyapunov stability , type-3 fuzzy logic

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School of Public Administration, Xiangtan University, Xiangtan, China
Davis Center for Russian and Eurasian Studies, Harvard University, Cambridge, MA, United States
Smart City Research Center, Astana IT University, Astana, Kazakhstan
School of Computer Engineering, Astana IT University, Astana, Kazakhstan
Department of Electrical and Electronics Engineering, Faculty of Engineering, Sakarya University, Sakarya, Turkey
School of Artificial Intelligence and Data Science, Astana IT University, Astana, Kazakhstan

School of Public Administration
Davis Center for Russian and Eurasian Studies
Smart City Research Center
School of Computer Engineering
Department of Electrical and Electronics Engineering
School of Artificial Intelligence and Data Science

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