Reachable set estimation for wind energy conversion system via nonfragile memory sampled-data control

Raghul Venkateswaran; Woosuk Choi; Jae Hoon Jeong; Joo Woo; Raghul Venkateswaran; Woosuk Choi; Jae Hoon Jeong; Joo Woo

doi:10.3934/math.2026341

AIMS Mathematics

2026, Volume 11, Issue 3: 8308-8331. doi: 10.3934/math.2026341

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Reachable set estimation for wind energy conversion system via nonfragile memory sampled-data control

1.
Department of Artificial Intelligence and Data Science, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea
2.
College of Computer and Software, Kunsan National University, 588 Daehak-ro, Gunsan-si, Jeonbuk 54150, Republic of Korea

Received: 30 January 2026 Revised: 22 March 2026 Accepted: 24 March 2026 Published: 27 March 2026
MSC : 93C10, 93D05, 93D09, 93B52, 93C42

This paper investigated the problem of reachable set estimation (RSE) and stabilization for nonlinear permanent magnet vernier generator (PMVG)-based wind energy conversion systems (WECSs) under external disturbances and control gain uncertainties. The inherent nonlinearity of the system, together with transmission delays and sampling effects, poses significant challenges for robust control design. To address these issues, a Takagi–Sugeno fuzzy modeling approach was employed to represent the nonlinear dynamics through a set of linear subsystems. A nonfragile memory-based sampled-data control (NFMSDC) scheme was developed to effectively address control gain perturbations, sampling constraints, and constant transmission delays. Stability conditions and RSE bounds were derived using Lyapunov–Krasovskii functionals and formulated as linear matrix inequalities, ensuring that system trajectories remain within prescribed ellipsoidal regions under bounded disturbances. The effectiveness of the proposed method was validated through numerical simulations, including wind disturbance scenarios and parameter variation analysis. The results show that the system states converge smoothly, the control inputs remain within practical limits, and the reachable sets are confined within the derived ellipsoidal bounds. Comparative analysis further demonstrated that the proposed approach achieves improved robustness and larger admissible sampling intervals compared to existing methods. These results confirm the practical applicability of the proposed NFMSDC scheme for PMVG-based WECSs under uncertain operating conditions.
- PMVG,
- eachable set estimation,
- T-S fuzzy control,
- memory sampled-data control,
- LMIs
Citation: Raghul Venkateswaran, Woosuk Choi, Jae Hoon Jeong, Joo Woo. Reachable set estimation for wind energy conversion system via nonfragile memory sampled-data control[J]. AIMS Mathematics, 2026, 11(3): 8308-8331. doi: 10.3934/math.2026341

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Abstract

This paper investigated the problem of reachable set estimation (RSE) and stabilization for nonlinear permanent magnet vernier generator (PMVG)-based wind energy conversion systems (WECSs) under external disturbances and control gain uncertainties. The inherent nonlinearity of the system, together with transmission delays and sampling effects, poses significant challenges for robust control design. To address these issues, a Takagi–Sugeno fuzzy modeling approach was employed to represent the nonlinear dynamics through a set of linear subsystems. A nonfragile memory-based sampled-data control (NFMSDC) scheme was developed to effectively address control gain perturbations, sampling constraints, and constant transmission delays. Stability conditions and RSE bounds were derived using Lyapunov–Krasovskii functionals and formulated as linear matrix inequalities, ensuring that system trajectories remain within prescribed ellipsoidal regions under bounded disturbances. The effectiveness of the proposed method was validated through numerical simulations, including wind disturbance scenarios and parameter variation analysis. The results show that the system states converge smoothly, the control inputs remain within practical limits, and the reachable sets are confined within the derived ellipsoidal bounds. Comparative analysis further demonstrated that the proposed approach achieves improved robustness and larger admissible sampling intervals compared to existing methods. These results confirm the practical applicability of the proposed NFMSDC scheme for PMVG-based WECSs under uncertain operating conditions.

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