Sensorimotor Control Using Adaptive Neuro-Fuzzy Inference for Human-Like Arm Movement
Küçük Resim Yok
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Mdpi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In this study, a sensorimotor controller is designed to characterize the required muscle force to enable a robotics system to perform a human-like circular movement. When the appropriate muscle internal forces are chosen, the arm end-point tracks the desired path via joint-space feedback. An objective function of the least-change rate of muscle forces is determined to find suitable feedback gains. The parameter defining the muscle force is then treated as a learning parameter through an adaptive neuro-fuzzy inference system, incorporating the rate of change of muscle forces. In experimental section, the arm motion of healthy subjects is captured using the inertial measurement unit sensors, and then the image of the drawn path is processed. The inertial measurement unit sensors detect each segment motion's orientation using quaternions, and the image is employed to identify the exact end-point position. Experimental data on arm movement are then utilized in the control parameter computation. The proposed brain-motor control mechanism enhances motion performance, resulting in a more human-like movement.
Açıklama
Anahtar Kelimeler
sensorimotor control, adaptive neuro-fuzzy inference system, human-like movement
Kaynak
Applied Sciences-Basel
WoS Q Değeri
N/A
Scopus Q Değeri
Q2
Cilt
14
Sayı
7
