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Artificial Intelligence Control Methodologies for Shape Memory Alloy Actuators: A Systematic Review and Performance

Stefano Rodinò1, Giuseppe Rota1, Matteo Chiodo1

  • 1Dipartimento di Ingegneria Meccanica, Energetica e Gestionale (DIMEG), University of Calabria, 87036 Rende, CS, Italy.

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Summary

Artificial intelligence (AI) enhances control for Shape Memory Alloy (SMA) and Magnetic SMA (MSMA) actuators, mitigating non-linearities and hysteresis. AI offers improved precision and adaptability for advanced engineering applications.

Keywords:
Shape Memory Alloys (SMAs)artificial intelligence controlnon-linear hysteresis compensationsmart actuatorssystematic review

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Area of Science:

  • * Engineering and Materials Science: Focuses on advanced actuator technologies and control systems.
  • * Artificial Intelligence: Explores AI's role in sophisticated control methodologies.
  • * Robotics and Mechatronics: Addresses applications in aerospace, biomedical devices, and soft robotics.

Background:

  • * Shape Memory Alloy (SMA) and Magnetic SMA (MSMA) actuators offer unique thermomechanical properties but present control challenges due to non-linearities, hysteresis, and temperature sensitivity.
  • * Existing control methods struggle to fully address these inherent complexities, limiting actuator performance and reliability.
  • * Developing advanced control strategies is crucial for unlocking the full potential of SMA and MSMA actuators in demanding applications.

Purpose of the Study:

  • * To systematically review and evaluate Artificial Intelligence (AI)-based control methodologies for SMA and MSMA actuators.
  • * To analyze the efficacy of different AI control architectures in enhancing precision, adaptability, and reliability.
  • * To identify optimal AI strategies for specific actuator types (SMA vs. MSMA) and application requirements.

Main Methods:

  • * A PRISMA-guided systematic literature review (2003-2025) of 24 studies on AI control for SMA and MSMA.
  • * Categorization of control architectures: hybrid AI-linear, pure AI, adaptive, and Model Predictive Control (MPC).
  • * Quantitative evaluation using Root Mean Square Error (RMSE%) and a weighted scoring system for experimental rigor.

Main Results:

  • * Hybrid AI-linear controllers were most common (36%); online-trained neural networks showed superior accuracy (+2.4%).
  • * Feedforward neural networks outperformed recurrent networks (+3.1%); MPC excelled for SMA (+5.8%) but not MSMA (-7.7%).
  • * Sensorless strategies benefited MSMA systems (+5.0%), utilizing electrical resistance for state estimation.

Conclusions:

  • * AI effectively mitigates hysteresis and non-linear dynamics in SMA and MSMA actuators.
  • * Material-specific optimization is critical: SMA favors dynamic control/MPC; MSMA benefits from sensorless AI/pure neural networks.
  • * Future research should focus on adaptive algorithms for fatigue, lightweight AI for embedded systems, and standardized benchmarking.