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Memristive FHN neuron controls electromechanical arm by shunting current
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DOI：10.1007/s11071-026-12850-9.png)
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Abstract
En 中文
Some electromechanical systems driven by electrical signals from neural circuits can mimic the limb movement of human and animals, and effective control of the neural circuits is crucial for maintaining the gait stability. Physical stimsuli and excitations account for ion-channel energy redistribution in a neuron and neural circuit, and then the output electrical signals are affected by the intrinsic energy level. When neural circuits are used to drive electromagnetic arms, conversion of electrical and mechanical energy occurs for further control of the stability and moving state in the electromechanical devices. In this work, a memristive neuron derived from a memristor-coupled FitzHugh–Nagumo (FHN) circuit is used to drive an electromechanical arm for mimicking the linear reciprocating motion of arms, in which a diversion element (DE) is intervened into one branch circuit of the neural circuit and the neural signals are regulated under ionic current redistribution. A dimensionless model and corresponding Hamilton energy framework are established for further dynamical analysis, and it indicates that the movement of robot arm is dependent on the electrical signals shunted from the neural circuit. The results reveal that different neuronal firing regimes (bursting, periodic, and chaotic) can maintain distinct motion patterns of the electromechanical arm, with an observable phase lag between neural activity and mechanical response. Energy analysis shows regime-dependent energy distributions and stochastic resonance (SR) can be achieved under relatively low noise intensities. Furthermore, an energy-based adaptive control strategy is developed to tune the parameter c2 for DE, demonstrating that the energy threshold ε governs the trade-off between convergence speed and control effort. The proposed scheme and obtained results confirm that energy shunting in hybrid ion channel of a neuron is effective to control some electromagnetic devices.
Keywords:
Neuron energy
Neural circuit
Memristor
Memristive neuron
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6
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1.4W
Citations:
4.1W
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