{"title":"神经回路驱动的关节摆,机电臂模型方法","authors":"Yitong Guo, Chunni Wang, Jun Ma","doi":"10.1016/j.chaos.2024.115739","DOIUrl":null,"url":null,"abstract":"The mechanical characteristic of an arm can be investigated in a two-stage cascade pendulum, which two jointed pendulums rotate to a jointed point and move forward for keeping stable gaits. The arm gaits and stability are controlled by the electrical signal interacted with the muscle. In this paper, two short beams are jointed to mimic the motion and stability of an arm driven by electromechanical force, which is generated from the gear or friction interaction between a beam and electromotor activated by electric signals from a neural circuit. On end of the artificial arm is jointed to a fixed point, another end is connected to a moving beam along horizontal direction. An electrical motor is driven by the output signals from a neural circuit, and it generates effective horizontal force to control the stability and gaits in the coupled pendulums via a gear interaction. When the electrical motor (EM) is activated, it has a feedback on the driving neural circuit by changing the firing activities because the load circuit of the EM generates induced electromotive force as an additive branch circuit of the neural circuit, and this interaction is similar to the processing that athletic training can modify the mentality by training the neural activities. External physical signal is applied and changed to control the neural circuit, and then the moving beam can impose time-varying force to control the stability of the jointed pendulums. In presence of noisy excitation, similar nonlinear resonance can be induced in the neural circuit. The dynamics in the neural circuit-coupled pendulums is explored in detail. That is, the neural circuit regulates the EM for generating electromechanical force and then the jointed pendulums are controlled in the arm gaits. This mechanical process is similar to the rehabilitation training for disabled arms with movement disorders. The results provide helpful clues to design artificial electromechanical arm and application of arm rehabilitation for muscular injuries.","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"232 1","pages":""},"PeriodicalIF":5.3000,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Jointed pendulums driven by a neural circuit, electromechanical arm model approach\",\"authors\":\"Yitong Guo, Chunni Wang, Jun Ma\",\"doi\":\"10.1016/j.chaos.2024.115739\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The mechanical characteristic of an arm can be investigated in a two-stage cascade pendulum, which two jointed pendulums rotate to a jointed point and move forward for keeping stable gaits. The arm gaits and stability are controlled by the electrical signal interacted with the muscle. In this paper, two short beams are jointed to mimic the motion and stability of an arm driven by electromechanical force, which is generated from the gear or friction interaction between a beam and electromotor activated by electric signals from a neural circuit. On end of the artificial arm is jointed to a fixed point, another end is connected to a moving beam along horizontal direction. An electrical motor is driven by the output signals from a neural circuit, and it generates effective horizontal force to control the stability and gaits in the coupled pendulums via a gear interaction. When the electrical motor (EM) is activated, it has a feedback on the driving neural circuit by changing the firing activities because the load circuit of the EM generates induced electromotive force as an additive branch circuit of the neural circuit, and this interaction is similar to the processing that athletic training can modify the mentality by training the neural activities. External physical signal is applied and changed to control the neural circuit, and then the moving beam can impose time-varying force to control the stability of the jointed pendulums. In presence of noisy excitation, similar nonlinear resonance can be induced in the neural circuit. The dynamics in the neural circuit-coupled pendulums is explored in detail. That is, the neural circuit regulates the EM for generating electromechanical force and then the jointed pendulums are controlled in the arm gaits. This mechanical process is similar to the rehabilitation training for disabled arms with movement disorders. The results provide helpful clues to design artificial electromechanical arm and application of arm rehabilitation for muscular injuries.\",\"PeriodicalId\":9764,\"journal\":{\"name\":\"Chaos Solitons & Fractals\",\"volume\":\"232 1\",\"pages\":\"\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chaos Solitons & Fractals\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1016/j.chaos.2024.115739\",\"RegionNum\":1,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chaos Solitons & Fractals","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1016/j.chaos.2024.115739","RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Jointed pendulums driven by a neural circuit, electromechanical arm model approach
The mechanical characteristic of an arm can be investigated in a two-stage cascade pendulum, which two jointed pendulums rotate to a jointed point and move forward for keeping stable gaits. The arm gaits and stability are controlled by the electrical signal interacted with the muscle. In this paper, two short beams are jointed to mimic the motion and stability of an arm driven by electromechanical force, which is generated from the gear or friction interaction between a beam and electromotor activated by electric signals from a neural circuit. On end of the artificial arm is jointed to a fixed point, another end is connected to a moving beam along horizontal direction. An electrical motor is driven by the output signals from a neural circuit, and it generates effective horizontal force to control the stability and gaits in the coupled pendulums via a gear interaction. When the electrical motor (EM) is activated, it has a feedback on the driving neural circuit by changing the firing activities because the load circuit of the EM generates induced electromotive force as an additive branch circuit of the neural circuit, and this interaction is similar to the processing that athletic training can modify the mentality by training the neural activities. External physical signal is applied and changed to control the neural circuit, and then the moving beam can impose time-varying force to control the stability of the jointed pendulums. In presence of noisy excitation, similar nonlinear resonance can be induced in the neural circuit. The dynamics in the neural circuit-coupled pendulums is explored in detail. That is, the neural circuit regulates the EM for generating electromechanical force and then the jointed pendulums are controlled in the arm gaits. This mechanical process is similar to the rehabilitation training for disabled arms with movement disorders. The results provide helpful clues to design artificial electromechanical arm and application of arm rehabilitation for muscular injuries.
期刊介绍:
Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.