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<p>To address the issue of inaccurate motor control in Doubly-fed Induction Generator (DFIG) due to parameter mismatch, we propose a Model-free Predictive Current Control (MFPCC) strategy that incorporates an Extended State Observer (ESO) and a Sliding Mode Compensator (SMC). Initially, a hyperlocal model of the current loop is developed based on the mathematical framework that accounts for DFIG parameter mismatches. Subsequently, the ESO is employed to estimate both the current and the unknown total perturbation within the hyperlocal model, effectively mitigating the one-beat delay inherent in digital systems and confirming stability in both the s- and z-domains. Furthermore, in light of the output errors present in the hyperlocal model, an SMC is designed to rectify these errors. An improved exponential convergence rate with respect to current states is introduced to minimize system jitter, with stability being validated through the Lyapunov function. Finally, simulation experiments demonstrate that the proposed control strategy exhibits superior steady-state performance and parameter robustness when compared to model predictive control under parameter identification and conventional ESO-based MFPCC.</p>

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Model-Free Predictive Current Control of Doubly-Fed Induction Generators Based on ESO and Sliding Mode Compensator

  • 期刊论文

  • 发表日期：

    2025年11月3日

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  • Shenyuan Yu,
  • Shuxi Liu,
  • Siyuan Huang,
  • Bo Tang,
  • Zhen Wang

摘要

To address the issue of inaccurate motor control in Doubly-fed Induction Generator (DFIG) due to parameter mismatch, we propose a Model-free Predictive Current Control (MFPCC) strategy that incorporates an Extended State Observer (ESO) and a Sliding Mode Compensator (SMC). Initially, a hyperlocal model of the current loop is developed based on the mathematical framework that accounts for DFIG parameter mismatches. Subsequently, the ESO is employed to estimate both the current and the unknown total perturbation within the hyperlocal model, effectively mitigating the one-beat delay inherent in digital systems and confirming stability in both the s- and z-domains. Furthermore, in light of the output errors present in the hyperlocal model, an SMC is designed to rectify these errors. An improved exponential convergence rate with respect to current states is introduced to minimize system jitter, with stability being validated through the Lyapunov function. Finally, simulation experiments demonstrate that the proposed control strategy exhibits superior steady-state performance and parameter robustness when compared to model predictive control under parameter identification and conventional ESO-based MFPCC.