Machine learning-based self-sensing of the stiffness of shape memory coil actuator
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geometry and Topology,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s00500-022-06818-1.pdf
Reference22 articles.
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2. Awan AU, Park J, Kim HJ, Ryu J, Cho M (2016) Adaptive control of a shape memory alloy actuator using neural-network feedforward and RISE feedback. Int J Precis Eng Manuf 17:409–418. https://doi.org/10.1007/s12541-016-0051-7
3. Bachiller P, Rodriguez-Criado D, Jorvekar RR et al (2021) A graph neural network to model disruption in human-aware robot navigation. Multimed Tools Appl. https://doi.org/10.1007/s11042-021-11113-6
4. Bhagoji BS, Dhanalakshmi K. (2018) Self-sensing characteristics and analysis of the stiffness of the SMA spring actuator. In: 8th IEEE India international conference on power electronics (IICPE), https://doi.org/10.1109/IICPE.2018.8709334
5. Bhagoji BS, Dhanalakshmi K (2019) Modified sigmoid based model and experimental analysis of SMA spring as variable stiffness actuator. Smart Struct Syst 24(3):361–377. https://doi.org/10.12989/sss.2019.24.3.361
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