Artificial Intelligence Control Methodologies for Shape Memory Alloy Actuators: A Systematic Review and Performance Analysis
Shape Memory Alloy (SMA) actuators are pivotal in modern engineering due to their unique thermomechanical properties, but their inherent non-linearities, hysteresis, and temperature sensitivity pose significant control challenges. This systematic review evaluates artificial intelligence (AI)-based c...
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MDPI AG
2025-06-01
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| author | Stefano Rodinò Giuseppe Rota Matteo Chiodo Antonio Corigliano Carmine Maletta |
| author_facet | Stefano Rodinò Giuseppe Rota Matteo Chiodo Antonio Corigliano Carmine Maletta |
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| description | Shape Memory Alloy (SMA) actuators are pivotal in modern engineering due to their unique thermomechanical properties, but their inherent non-linearities, hysteresis, and temperature sensitivity pose significant control challenges. This systematic review evaluates artificial intelligence (AI)-based control methodologies to address these limitations, analyzing their efficacy in enhancing precision, adaptability, and reliability for SMA and Magnetic SMA (MSMA) systems. A PRISMA-guided literature review (2003–2025) identified 24 studies, which were categorized by control architectures (hybrid AI-linear, pure AI, adaptive, and model predictive control) and evaluated through quantitative metrics, including Root Mean Square Error (RMSE%) and a weighted scoring system for experimental rigor. Results revealed hybrid AI-linear controllers as the dominant approach (36%), with online-trained neural networks achieving superior accuracy (+2.4%) over offline methods. Feedforward neural networks outperformed recurrent architectures (+3.1%), while Model Predictive Control (MPC) excelled for SMA actuators (+5.8% accuracy) but underperformed for MSMAs (−7.7%). Sensorless strategies proved advantageous for MSMAs (+5.0%), leveraging intrinsic material properties like electrical resistance for state estimation. The analysis underscores AI’s capacity to mitigate hysteresis and non-linear dynamics, though material-specific optimization is critical: SMA systems favor dynamic control and MPC, whereas MSMAs benefit from sensorless AI and pure neural networks. Challenges persist in computational demands for online training and reinforcement learning’s exploration–exploitation trade-offs. Future research should prioritize adaptive algorithms for fatigue compensation, lightweight AI models for embedded deployment, and standardized benchmarking to bridge material-specific performance gaps. This synthesis establishes AI as a transformative paradigm for SMA actuation, enabling precise control in aerospace, biomedical, and soft robotics applications. |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-06-01 |
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| series | Micromachines |
| spelling | doaj-art-c1a2ff6985d04f42bf2f8351557cc2b42025-08-20T03:58:26ZengMDPI AGMicromachines2072-666X2025-06-0116778010.3390/mi16070780Artificial Intelligence Control Methodologies for Shape Memory Alloy Actuators: A Systematic Review and Performance AnalysisStefano Rodinò0Giuseppe Rota1Matteo Chiodo2Antonio Corigliano3Carmine Maletta4Dipartimento di Ingegneria Meccanica, Energetica e Gestionale (DIMEG), University of Calabria, 87036 Rende, CS, ItalyDipartimento di Ingegneria Meccanica, Energetica e Gestionale (DIMEG), University of Calabria, 87036 Rende, CS, ItalyDipartimento di Ingegneria Meccanica, Energetica e Gestionale (DIMEG), University of Calabria, 87036 Rende, CS, ItalyDipartimento di Ingegneria Meccanica, Energetica e Gestionale (DIMEG), University of Calabria, 87036 Rende, CS, ItalyDipartimento di Ingegneria Meccanica, Energetica e Gestionale (DIMEG), University of Calabria, 87036 Rende, CS, ItalyShape Memory Alloy (SMA) actuators are pivotal in modern engineering due to their unique thermomechanical properties, but their inherent non-linearities, hysteresis, and temperature sensitivity pose significant control challenges. This systematic review evaluates artificial intelligence (AI)-based control methodologies to address these limitations, analyzing their efficacy in enhancing precision, adaptability, and reliability for SMA and Magnetic SMA (MSMA) systems. A PRISMA-guided literature review (2003–2025) identified 24 studies, which were categorized by control architectures (hybrid AI-linear, pure AI, adaptive, and model predictive control) and evaluated through quantitative metrics, including Root Mean Square Error (RMSE%) and a weighted scoring system for experimental rigor. Results revealed hybrid AI-linear controllers as the dominant approach (36%), with online-trained neural networks achieving superior accuracy (+2.4%) over offline methods. Feedforward neural networks outperformed recurrent architectures (+3.1%), while Model Predictive Control (MPC) excelled for SMA actuators (+5.8% accuracy) but underperformed for MSMAs (−7.7%). Sensorless strategies proved advantageous for MSMAs (+5.0%), leveraging intrinsic material properties like electrical resistance for state estimation. The analysis underscores AI’s capacity to mitigate hysteresis and non-linear dynamics, though material-specific optimization is critical: SMA systems favor dynamic control and MPC, whereas MSMAs benefit from sensorless AI and pure neural networks. Challenges persist in computational demands for online training and reinforcement learning’s exploration–exploitation trade-offs. Future research should prioritize adaptive algorithms for fatigue compensation, lightweight AI models for embedded deployment, and standardized benchmarking to bridge material-specific performance gaps. This synthesis establishes AI as a transformative paradigm for SMA actuation, enabling precise control in aerospace, biomedical, and soft robotics applications.https://www.mdpi.com/2072-666X/16/7/780Shape Memory Alloys (SMAs)artificial intelligence controlsmart actuatorssystematic reviewnon-linear hysteresis compensation |
| spellingShingle | Stefano Rodinò Giuseppe Rota Matteo Chiodo Antonio Corigliano Carmine Maletta Artificial Intelligence Control Methodologies for Shape Memory Alloy Actuators: A Systematic Review and Performance Analysis Micromachines Shape Memory Alloys (SMAs) artificial intelligence control smart actuators systematic review non-linear hysteresis compensation |
| title | Artificial Intelligence Control Methodologies for Shape Memory Alloy Actuators: A Systematic Review and Performance Analysis |
| title_full | Artificial Intelligence Control Methodologies for Shape Memory Alloy Actuators: A Systematic Review and Performance Analysis |
| title_fullStr | Artificial Intelligence Control Methodologies for Shape Memory Alloy Actuators: A Systematic Review and Performance Analysis |
| title_full_unstemmed | Artificial Intelligence Control Methodologies for Shape Memory Alloy Actuators: A Systematic Review and Performance Analysis |
| title_short | Artificial Intelligence Control Methodologies for Shape Memory Alloy Actuators: A Systematic Review and Performance Analysis |
| title_sort | artificial intelligence control methodologies for shape memory alloy actuators a systematic review and performance analysis |
| topic | Shape Memory Alloys (SMAs) artificial intelligence control smart actuators systematic review non-linear hysteresis compensation |
| url | https://www.mdpi.com/2072-666X/16/7/780 |
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