This study investigates chaotic synchronization via field-coupled nonlinear circuits, achieving both electrical synchronization and energy balance. The driving mechanism biomimetically parallels neuromuscular signal t...This study investigates chaotic synchronization via field-coupled nonlinear circuits, achieving both electrical synchronization and energy balance. The driving mechanism biomimetically parallels neuromuscular signal transduction, where synchronized neuronal firing induces coordinated muscle contractions that produce macroscopic movement. We implement a Chua circuit-driven robotic arm with tunable periodic/chaotic oscillations through parameter modulation and external current injection. Bifurcation analysis maps oscillation modes under varying external stimuli. Inductive coupling between two systems with distinct initial conditions facilitates magnetic energy transfer, optimized by an energy balance criterion. A bio-inspired exponential gain method dynamically regulates the coupling strength to optimize the energy transfer efficiency.The effects of ambient electromagnetic noise on synchronization are systematically quantified. The results indicate electrically modulatable robotic arm dynamics, with the coupled systems achieving autonomous rapid synchronization. Despite noise-induced desynchronization, inter-system errors rapidly decay and stabilize within bounded limits, confirming robust stability.展开更多
基金Project supported by the National Key R&D Program of China (Grant No. 2023YFD2000601-02)。
文摘This study investigates chaotic synchronization via field-coupled nonlinear circuits, achieving both electrical synchronization and energy balance. The driving mechanism biomimetically parallels neuromuscular signal transduction, where synchronized neuronal firing induces coordinated muscle contractions that produce macroscopic movement. We implement a Chua circuit-driven robotic arm with tunable periodic/chaotic oscillations through parameter modulation and external current injection. Bifurcation analysis maps oscillation modes under varying external stimuli. Inductive coupling between two systems with distinct initial conditions facilitates magnetic energy transfer, optimized by an energy balance criterion. A bio-inspired exponential gain method dynamically regulates the coupling strength to optimize the energy transfer efficiency.The effects of ambient electromagnetic noise on synchronization are systematically quantified. The results indicate electrically modulatable robotic arm dynamics, with the coupled systems achieving autonomous rapid synchronization. Despite noise-induced desynchronization, inter-system errors rapidly decay and stabilize within bounded limits, confirming robust stability.
