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<p>Permanent magnet synchronous motor (PMSM) drives typically operate under state constraints and load disturbances. In practice, parameter variations and unknown control gains further increase the control difficulty. To address these issues, this paper develops an adaptive control scheme based on asymmetric barrier Lyapunov functions (BLFs) and Nussbaum-type gains. First, asymmetric BLFs are introduced to keep all states within prescribed bounds. Then, a Nussbaum-based adaptive law is designed to handle parameter uncertainties and unknown control gains. A disturbance compensation term is further incorporated to attenuate the effect of load changes. Moreover, an adaptive event-triggered mechanism is developed with separate triggering conditions for each control input and dynamically decaying thresholds. As a result, asynchronous control updates are achieved, the computational burden is reduced, and asymptotic tracking performance is guaranteed. Finally, comparative simulation results verify accurate tracking and effective constraint satisfaction under time-varying load torque and plant parameter perturbation, while avoiding Zeno behavior.</p>

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Asymptotic Tracking Control of PMSM with Full-State Constraints via Adaptive Event-Triggered Nussbaum-Based Design

  • 期刊论文

  • 发表日期：

    2026年8月5日

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  • Zhenzhuo Shan,
  • Yongliang Yang,
  • Chunyu Zheng,
  • Hu Zhang,
  • Guofeng Yuan

摘要

Permanent magnet synchronous motor (PMSM) drives typically operate under state constraints and load disturbances. In practice, parameter variations and unknown control gains further increase the control difficulty. To address these issues, this paper develops an adaptive control scheme based on asymmetric barrier Lyapunov functions (BLFs) and Nussbaum-type gains. First, asymmetric BLFs are introduced to keep all states within prescribed bounds. Then, a Nussbaum-based adaptive law is designed to handle parameter uncertainties and unknown control gains. A disturbance compensation term is further incorporated to attenuate the effect of load changes. Moreover, an adaptive event-triggered mechanism is developed with separate triggering conditions for each control input and dynamically decaying thresholds. As a result, asynchronous control updates are achieved, the computational burden is reduced, and asymptotic tracking performance is guaranteed. Finally, comparative simulation results verify accurate tracking and effective constraint satisfaction under time-varying load torque and plant parameter perturbation, while avoiding Zeno behavior.