DOAJ Open Access 2022

Force Control of a Shape Memory Alloy Spring Actuator Based on Internal Electric Resistance Feedback and Artificial Neural Networks

Nathan L.D. Sarmento José Marques Basílio Maxsuel F. Cunha Cícero R. Souto Andreas Ries

Lihat Sumber DOI

Abstrak

This paper presents a study of the resistive behavior of a Shape Memory Alloy spring, with a focus on the application of electrical resistance feedback in control systems. Artificial Neural Networks of different topologies were designed to learn the relation between spring electrical resistance and the force exerted. The feedback between layers in Neural Networks is demonstrated to be a key parameter in learning the non-linear and hysteretic behavior of Shape Memory Alloys. Experiments with closed-loop systems showed that shape memory alloy springs generated forces that converged satisfactorily to the desired reference values. The scientific contribution of this work is the use of electrical resistance variation as feedback for controlling the spring force, eliminating the use of an external force sensor. Neural networks were used for both, the sensing process and the system control; in that way the nonlinear and hysterical behavior of the shape memory alloy actuator was well considered.

Topik & Kata Kunci

Electronic computers. Computer science Cybernetics

Penulis (5)

Nathan L.D. Sarmento

José Marques Basílio

Maxsuel F. Cunha

Cícero R. Souto

Andreas Ries

Format Sitasi

APA MLA BibTeX

Sarmento, N.L., Basílio, J.M., Cunha, M.F., Souto, C.R., Ries, A. (2022). Force Control of a Shape Memory Alloy Spring Actuator Based on Internal Electric Resistance Feedback and Artificial Neural Networks. https://doi.org/10.1080/08839514.2021.2015106

Akses Cepat

Lihat di Sumber doi.org/10.1080/08839514.2021.2015106

Informasi Jurnal

Tahun Terbit: 2022
Sumber Database: DOAJ
DOI: 10.1080/08839514.2021.2015106
Akses: Open Access ✓