Combined Feedback Feedforward Control of a 3-Link Musculoskeletal System Based on the Iterative Training Method.
The investigation and study of the limbs, especially the human arm, have inspired a wide range of humanoid robots, such as movement and muscle redundancy, as a human motor system. One of the main issues related to musculoskeletal systems is the joint redundancy that causes no unique answer for each...
| Publicado en: | BioMed Research International pp. 1 - 9 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
11/8/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153458640&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153458640 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 11/8/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 153458640 153458640 153458640 10.1155/2021/8701869 153458640 ppf: 1 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Combined Feedback Feedforward Control of a 3-Link Musculoskeletal System Based on the Iterative Training Method. aug: au: Valizadeh, Amin Akbari, Ali Akbar affil: Department of Mechanical Engineering, Ferdowsi University of Mashhad, Iran sug: subj: Feedback Musculoskeletal System Human ab: The investigation and study of the limbs, especially the human arm, have inspired a wide range of humanoid robots, such as movement and muscle redundancy, as a human motor system. One of the main issues related to musculoskeletal systems is the joint redundancy that causes no unique answer for each angle in return for an arm's end effector's arbitrary trajectory. As a result, there are many architectures like the torques applied to the joints. In this study, an iterative learning controller was applied to control the 3-link musculoskeletal system's motion with 6 muscles. In this controller, the robot's task space was assumed as the feedforward of the controller and muscle space as the controller feedback. In both task and muscle spaces, some noises cause the system to be unstable, so a forgetting factor was used to a convergence task space output in the neighborhood of the desired trajectories. The results show that the controller performance has improved gradually by iterating the learning steps, and the error rate has decreased so that the trajectory passed by the end effector has practically matched the desired trajectory after 1000 iterations. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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