Jointed pendulums driven by a neural circuit, electromechanical arm model approach

IF 5.3 1区 数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Yitong Guo , Chunni Wang , Jun Ma
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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.
神经回路驱动的关节摆,机电臂模型方法
手臂的机械特性可以通过两级级联摆来研究,即两个关节摆旋转到一个关节点并向前移动,以保持稳定的步态。手臂的步态和稳定性由与肌肉相互作用的电信号控制。在本文中,两根短横梁被连接起来,以模拟由机电力驱动的手臂的运动和稳定性。机电力是由神经回路的电信号激活横梁和电动马达之间的齿轮或摩擦相互作用产生的。人工手臂的一端与一个固定点连接,另一端与沿水平方向移动的横梁连接。电动机由神经回路的输出信号驱动,通过齿轮相互作用产生有效的水平力,以控制耦合摆的稳定性和步态。当电动马达(EM)启动时,会通过改变发射活动对驱动神经回路产生反馈,因为电动马达的负载电路会产生感应电动势,作为神经回路的加法支路,这种相互作用类似于运动训练通过训练神经活动来改变心态的处理过程。通过施加和改变外部物理信号来控制神经回路,然后通过移动横梁施加时变力来控制关节摆的稳定性。在有噪声激励的情况下,神经回路也会产生类似的非线性共振。本文详细探讨了神经回路耦合摆的动力学特性。也就是说,神经回路调节电磁产生机电力,然后在手臂步态中控制关节摆。这一机械过程类似于运动障碍残疾手臂的康复训练。这些结果为设计人工机电手臂和应用于肌肉损伤的手臂康复训练提供了有益的线索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Chaos Solitons & Fractals
Chaos Solitons & Fractals 物理-数学跨学科应用
CiteScore
13.20
自引率
10.30%
发文量
1087
审稿时长
9 months
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.
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