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Practical Predefined-Time Sliding-Mode Adaptive Resilient Control for PMSM Cyber-Physical Systems.

Zhenzhong Wang1, Shu Zhang1, Yun Jiang2

  • 1Advanced Institute of Information Technology, Peking University, Hangzhou 311200, China.

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|December 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a resilient control algorithm for permanent magnet synchronous motors (PMSMs) in cyber-physical systems (CPSs) to ensure speed control under cyber-attacks. The novel approach guarantees predefined-time convergence, enhancing system security and performance.

Keywords:
PMSMcyber physical systemscyber-attackspredefined time controlresilient control

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Area of Science:

  • Control Systems Engineering
  • Cyber-Physical Systems Security
  • Robotics and Automation

Background:

  • Permanent magnet synchronous motors (PMSMs) are crucial in cyber-physical systems (CPSs).
  • Networked control systems face vulnerabilities to malicious cyber-attacks impacting PMSM performance.
  • Existing control algorithms may have limitations in convergence time and resilience to uncertainties.

Purpose of the Study:

  • To develop a novel resilient control algorithm for uncertain PMSMs under cyber threats.
  • To ensure predefined-time convergence of trajectory tracking errors (TTEors) despite malicious attacks.
  • To enhance the robustness and security of PMSM-based CPSs.

Main Methods:

  • Introduction of a new Lyapunov stability criterion with adjustable gain reaching law and predefined-time convergence (PreTC).
  • Design of a sliding-mode (SMS) controller incorporating PreTC.
  • Utilizing an extreme learning machine (ELM) for real-time identification of physical layer models and cyber-attacks.
  • Development of a sliding-mode adaptive resilient controller without explicit physical model information.

Main Results:

  • The proposed algorithm guarantees predefined-time (PDT) stability for PMSMs in CPSs.
  • Demonstrated superior resilience against cyber-attacks, parameter perturbations, and external disturbances.
  • Achieved a motor speed tracking error accuracy of 0.008 in comparative simulations.
  • Validated effectiveness through comparisons with four distinct resilient control algorithms.

Conclusions:

  • The developed resilient control algorithm effectively ensures PMSM speed control within a specified timeframe under cyber-attacks.
  • The algorithm provides enhanced robustness and security for PMSM-driven CPSs against various uncertainties.
  • This work offers a significant advancement in securing networked power drive systems.