Photosensitive neuron driven electromechanical coupling system, synchronous regulation method and application thereof

Abstract

The application discloses a photosensitive neuron driving electromechanical coupling system, a synchronous control method and application thereof, and electromechanical coupling bionic control technology field, and relates to the technical field of establishing a new type of neural circuit for interacting with external light energy; the neural circuit is used as a driving mechanical arm device of an electrical system to establish an electromechanical coupling circuit system; the response dynamics of the electromechanical coupling system is explored, and it is found that there are periodic bifurcations similar to "arc-shaped curves" and "folding shapes" in a multi-parameter space; capacitor is used as a carrier to study the Hamilton energy balance and complete synchronization of the electromechanical system under the electric field coupling; and resistance is used as a carrier to study the energy evolution of the mechanical arm coupling array when the functional parameters such as light intensity and coupling strength change.

Description

Technical Field

[0001] This invention relates to the field of electromechanical coupling bionic control technology, specifically to photosensitive neuron-driven electromechanical coupling systems, synchronization control methods, and their applications. Background Technology

[0002] Various functional elements are embedded in neuronal circuits to form different functional neurons. These functional neurons can construct functional neuronal networks with different topologies through synaptic or field coupling. Using these neurons or neuronal networks as sensors capable of providing electrical energy and connecting them to robotic arms or legs can provide guidance on the mechanisms of human arm or leg movements; different neuronal circuits will produce different firing behaviors. The dynamic simulation of various functional neurons has always been a hot research topic in related fields.

[0003] Wang et al. discussed the influence of memristor on neuronal firing patterns and explored the synchronization mechanism of discretely coupled neurons; Gu et al. expanded the connotation of nonlinear dynamics, which helps to understand the complex neurodynamic behavior and physiological functions under multi-factor regulation; Bao et al. conducted design and application research on memristor discrete modeling and chaotic complexity enhancement; Sun et al. analyzed the influence of various influencing factors such as noise, time delay, external stimulation, and coupling mode on the firing dynamic characteristics of complex neuronal networks, and provided an in-depth explanation of the intrinsic mechanisms of neurodegenerative diseases and cognitive behaviors; Allehiany et al. found that the fractional-order model of Hopfield neural networks (HNN) showed high sensitivity to external stimuli, and the neural function improved when exposed to appropriate amounts of electromagnetic radiation; Panahi et al. proposed a new neuronal model based on Maxwell's electromagnetic induction theorem, which considers internal magnetic fluctuations and external electromagnetic radiation to be indispensable parts of neural activity; Mbeunga et al. designed an electromechanical system array driven by Fitzhugh-Nagumo neuronal electric lines through capacitor coupling, which can be regarded as a model of multi-cycle driving process or a leg model of a millipede system, and can also be used in various information processing systems. With the continuous development and improvement of intelligent manufacturing technology, intelligent electromechanical coupling devices, mainly composed of robotic arms, are increasingly favored by various industrial sectors. Maintaining a continuous energy supply, especially during operation in complex and changing environments, to ensure sustained operation and motion output has become a key scientific problem in the field of robotic arms. Some neural circuits with special functions, as important pathways to achieving biological intelligence and artificial intelligence, have great potential in the energy supply and application of electromechanical coupling systems.

[0004] However, traditional template matching methods have the following problems: ① lack of discussion on energy transfer when the electromechanical system is fully synchronized under different discharge states; ② lack of accurate description of nonlinear dynamic behavior, especially the reasonable matching of dynamic performance of neuron-mechanical coupling system with electromechanical system parameters; ③ the limitations of a single robotic arm in information acquisition, processing and control capabilities and the instability generated during movement have not been resolved. Summary of the Invention

[0005] In view of this, the purpose of this invention is to embed a phototube structure into a Fitzhugh-Nagumo neuron circuit to construct a photosensitive neuron that senses external light, and then use it as an electrical system in an electromechanical coupling device to drive a type of robotic arm device. This investigation explores the matching law of neuronal firing patterns under multiple parameters, and uses a capacitor-coupled electromechanical coupling device as a load test for the functional neuron circuit to examine the energy transfer problem under the completely synchronized behavior of two isomorphic electromechanical devices. Finally, this is extended to a robotic arm coupling array to explore the response dynamics of coupling coefficients and functional parameters, solving the limitations of a single robotic arm in information acquisition, processing, and manipulation capabilities. The invention also utilizes the cooperation between multiple robotic arms to increase the stability of robotic arm motion. Furthermore, it achieves light energy harvesting based on biomimicry and constructs a multi-source environmental energy capture sensor, applying it to small and medium-sized robotic arm devices.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] This disclosure provides a method for synchronously controlling a photosensitive neuron-driven electromechanical coupling system, including the following steps:

[0008] S1: Construct a photosensitive neuron circuit by embedding a phototube into the Fitzhugh-Nagumo neuron circuit to simulate the effect of light stimulation on biological neurons;

[0009] S2: Construct an electromechanical coupling device, including an electrical part of a photosensitive neuron circuit and a mechanical moving arm of a mechanical part, which are connected by an inductor coil. The mechanical device includes a coil, a moving arm and a spring, and the moving arm moves under the force of the coil and the spring.

[0010] S3: Provide at least one of the electromechanical coupling systems, including two isomorphic electromechanical coupling devices coupled by capacitors, or multiple electromechanical coupling systems forming a robotic arm coupling array by resistive coupling;

[0011] S4: Calculate the Hamiltonian energy of the system according to Helmholtz's theorem;

[0012] S5: Obtain the energy difference between the systems based on the Hamiltonian energy;

[0013] S6: The system is fully synchronized by controlling the energy difference. When the energy difference approaches 0, the system is determined to be fully synchronized.

[0014] S7: Perform numerical simulations of the fully synchronized behavior of the system under different discharge states, plot time series diagrams and energy difference diagrams, and judge the synchronization status based on the degree of overlap of the time series and the degree to which the energy difference approaches 0, or plot the Hamiltonian energy evolution diagram of the electromechanical coupling array, and judge the synchronization status of the coupled system by the disorder and order of the energy evolution diagram.

[0015] Furthermore, the numerical simulation of the fully synchronized behavior of the system under different discharge states includes:

[0016] We will discuss chaotic discharge state and periodic discharge state, analyze the synchronization of the system under different coupling strengths when the same discharge state is coupled, and the minimum coupling strength required for different discharge states to achieve complete synchronization.

[0017] Among them, the minimum coupling strength required for a chaotic electromechanical system to achieve complete synchronization can meet the coupling strength requirement for a periodic electromechanical system to achieve complete synchronization. Under the same coupling strength, it is easier to achieve complete synchronization with a lower period than with a higher period.

[0018] Furthermore, for a robotic arm coupling array formed by resistive coupling of multiple electromechanical coupling systems, the method further includes:

[0019] S1: Based on the mean field theory, a synchronization factor is defined. When the synchronization factor is close to 1, the entire electromechanical coupling array is considered to be completely synchronized.

[0020] S2: Compare the coupling situations of different discharge states under the same coupling strength, and discuss the relationship between chaotic state and low and high periodic states and coupling strength.

[0021] S3: Calculate the synchronization factor and verify it through numerical simulation.

[0022] Furthermore, when the coupling strength is large, the synchronization factor is close to 1, indicating that electromechanical coupling arrays can achieve complete synchronization.

[0023] When the coupling strength is low, the synchronization factor of the electromechanical coupling array in the high-period state is greater than that in the low-period state.

[0024] Furthermore, as the coupling strength increases, the membrane potential of the electromechanical system array changes from disorder to order, and the orderliness of the entire electromechanical coupling array is further strengthened, tending to be completely synchronized;

[0025] When the coupling strength is insufficient, the membrane potential and energy evolution diagrams show disorder, indicating that the electromechanical coupling array has failed to achieve complete synchronization.

[0026] A photosensitive neuron-driven electromechanical coupling system for implementing the aforementioned synchronization control method, the system comprising:

[0027] A photosensitive neuron circuit, the photosensitive neuron circuit including a phototube embedded with a Fitzhugh-Nagumo neuron circuit;

[0028] Coupling devices;

[0029] A mechanical device, comprising a coil, a movable arm, and a spring, wherein the movable arm moves under the force of the coil and the spring;

[0030] The electrical part is a photosensitive neuron circuit, and the mechanical part is a mechanical moving arm.

[0031] Furthermore, the system is a dual-system coupling device formed by two isomorphic systems coupled by a capacitor, used for load testing of functional neuron circuits.

[0032] Furthermore, the system is a robotic arm coupling array formed by resistive coupling of multiple systems.

[0033] Furthermore, the application of the synchronous control method in the stable motion control of the robotic arm utilizes Hamiltonian energy to regulate the energy balance of the electromechanical coupling system, thereby achieving complete synchronization and realizing the stable motion of the robotic arm.

[0034] Furthermore, the synchronous control method is applied to the construction of a multi-source environmental energy harvesting sensor. Based on biomimetic technology to achieve light energy harvesting, a multi-source environmental energy harvesting sensor is constructed and applied to a small and medium-sized robotic arm device.

[0035] The present invention provides a model of functional neurons, dynamic analysis, and motion simulation of a robotic arm driven by a neuron array. It presents a photosensitive neuron that senses external light and uses it as a robotic arm device driven by an electrical system in an electromechanical coupling device. The matching law of neuron firing modes under multiple parameters is analyzed.

[0036] The study examines the energy transfer problem under the completely synchronized behavior of two isomorphic electromechanical devices when using a capacitor-coupled electromechanical coupling device. This problem is then extended to a robotic arm coupling array. Through energy evolution diagrams, various response states and related conditions for the stable movement of the robotic arm are discovered. This solves the problem of the limitations of a single robotic arm in terms of information acquisition, processing, and control capabilities. The study also utilizes the cooperation between multiple robotic arms to increase the stability of the robotic arm's movement. Attached Figure Description

[0037] Figure 1 A schematic diagram of a photosensitive neuron-driven mechanical device provided in an embodiment of this disclosure;

[0038] Figure 2 The embodiments provided in this disclosure are about and A two-parameter bifurcation diagram;

[0039] Figure 3 Phase diagrams and time series diagrams of the systems provided in embodiments of this disclosure;

[0040] Figure 4 This is a schematic diagram of a capacitor-coupled electromechanical system provided in an embodiment of this disclosure;

[0041] Figure 5 Time series diagrams and energy difference diagrams of the coupled system provided in embodiments of this disclosure;

[0042] Figure 6 Time series diagrams and energy difference diagrams of the coupled system provided in embodiments of this disclosure;

[0043] Figure 7 This is a schematic diagram of a resistive coupling electromechanical system array provided in an embodiment of this disclosure;

[0044] Figure 8 The electromechanical system array provided in the embodiments of this disclosure , Spatial evolution diagram of membrane potential and energy at time;

[0045] Figure 9 The electromechanical coupling array provided in the embodiments of this disclosure is when , Spatial evolution diagram of membrane potential and energy at time;

[0046] Figure 10 The electromechanical coupling array provided in the embodiments of this disclosure is when , Spatial evolution diagram of membrane potential and energy at time;

[0047] Figure 11 This is an evolution diagram of the synchronization factor of the electromechanical coupling array under different discharge states provided in the embodiments of this disclosure. Detailed Implementation

[0048] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0049] The synchronization control method for photosensitive neuron-driven electromechanical coupling systems can be generally divided into three parts. The first part firstly uses... Figure 1 Using Kirchhoff's law, the mathematical equations for the electromechanical coupling system of a photosensitive neuron-driven mechanical device can be obtained:

[0050]

[0051] The symbols in the formula refer to the following references (Ma Jun. Several issues on functional neuron modeling and dynamics. Journal of Guangxi Normal University, 2022, 40(5):307-327; Zhang Xiufang. Synchronization of phototube coupled FitzHugh-Nagumo neurons. Acta Physica Sinica, 2021, 70(9):090502; Ma Jun. Biological neurons to neural circuit, review from physical perspective. Nonlinear Dynamics, 2025, 113:25365-25387. Li Siben, An Xinlei. Dynamic analysis and control of piezoelectric neuron driven mechanical device. Complex Systems and Complexity Science, First published online: https: / / link.cnki.net / urlid / 37.1402.n.20250626.1118.002.).

[0052] The following dimensionless transformation is adopted:

[0053]

[0054] The dynamic equations of the dimensionless electromechanical coupled system are obtained as follows:

[0055]

[0056] The Hamiltonian energy function of the system was calculated using Helmholtz's theorem and used for subsequent synchronization control of the coupled system. Two-parameter plots, phase diagrams, and time series plots were plotted to study the neuronal firing modes. For ease of numerical simulation, the signal generated by the phototube can be selected as a periodic excitation. The parameters were discussed. and The effect of simultaneous changes on the system's discharge behavior was investigated, and then the amplitude was analyzed. and system parameters The bifurcation behavior. The result is as follows: Figure 2 , 3 It can be seen that as the parameters and Simultaneous changes lead to complex dynamic behaviors in the system, such as periodic bifurcation (boxes 1, 2, and 3) and multistable states (box 4), as can be seen from the phase trajectory and time series diagram. and When different values ​​are taken, the system exhibits multiple discharge states, such as period one, period three, period five, and chaotic discharge.

[0057] The second part examines the energy transfer problem between two isomorphic electromechanical devices, considering the use of capacitor-coupled electromechanical coupling devices as load tests for functional neural circuits. Figure 4 Its mathematical model of motion satisfies

[0058]

[0059] Using dimensionless transformation, the dynamic equations of the system are obtained:

[0060]

[0061] Then, the Hamiltonian energy calculated in the first part is used to obtain the energy difference of the coupled system, and this energy difference is used to regulate the complete synchronization of the system. Four discharge scenarios of the system are selected to discuss the complete synchronization behavior of the electromechanical coupling system.

[0062] First, the coupling scenarios of two chaotic discharge states with different initial conditions were discussed, and time series plots and energy difference plots of the coupled systems were plotted under different coupling strengths. Figure 5 It was found that as the coupling strength increases, the energy difference between the two systems gradually decreases, and the energy difference tends to be 0 when the coupling strength is 0.006. This indicates that the two systems meet the requirement of complete synchronization under this coupling strength.

[0063] Finally, the fully synchronous behavior of an electromechanical system undergoing periodic discharge is discussed, with its time series diagram and energy difference as follows: Figure 6 It is known that as the coupling strength increases to a certain value, all periodically discharging electromechanical systems can achieve complete synchronization, and under the same coupling strength, lower periods are more likely to achieve complete synchronization than higher periods. In summary, the minimum coupling strength required for complete synchronization of a chaotic electromechanical system can satisfy the coupling strength requirement for complete synchronization of a periodic electromechanical system.

[0064] In the third part, because electromechanical system arrays have stronger information acquisition, processing, and manipulation capabilities than a single robotic arm, photosensitive neurons driving mechanical devices are used as array elements. Multiple electromechanical systems are coupled through resistors to form an array, as shown in the circuit diagram below. Figure 7 As shown, its dimensionless circuit equations can be obtained using Kirchhoff's law and dimensionless transformation:

[0065]

[0066] Based on mean-field theory, a synchronization factor is defined. This is used to determine the synchronization status of the entire electromechanical coupling array. Numerical simulation is used to solve for the electromechanical coupling array, investigating three different firing states when the neuron array couples three photosensitive neurons to drive the mechanical device, and studying the coupling strength between nodes. The spatial evolution of the membrane potential and Hamiltonian energy of the entire electromechanical system array under changing conditions.

[0067] First, for different coupling strengths Research on chaotic electromechanical coupling arrays, such as Figure 8 This indicates that, with increasing coupling strength As the potential increases, the membrane potential of the electromechanical system array shifts from disordered to ordered, further enhancing the orderliness of the entire electromechanical coupling array and approaching complete synchronization. Secondly, research on electromechanical coupling arrays under periodic five-wave oscillations is presented, such as... Figure 9 This indicates that the coupling strength When the synchronization is insufficient, the membrane potential and energy evolution diagrams exhibit disorder, indicating that the electromechanical coupling array has failed to achieve complete synchronization. With the increase of coupling strength, the orderliness of the membrane potential and energy evolution diagram is significantly enhanced, showing stronger orderliness compared to the chaotic state under the same coupling strength. Ultimately, the electromechanical coupling array is in a completely synchronized state, indicating that a larger coupling strength... This ensures stable movement of the robotic arm; then, the synchronization of the electromechanical coupling array under periodic three oscillations is demonstrated, with results as follows: Figure 10 As shown, the electromechanical coupling array exhibits lower orderliness (i.e., weaker complete synchronization) compared to the periodic five-wave oscillation under the same coupling strength. This indicates that reducing the period of the electromechanical system can make it easier for the electromechanical coupling array to achieve complete synchronization, but this is not a decisive factor. Finally, the evolution of the synchronization factor of the electromechanical coupling array under different discharge states was observed. Figure 11 This indicates that when the coupling strength reaches a saturation value, the synchronization factor... Both are close to 1, which indicates that the electromechanical coupling arrays can achieve complete synchronization. Furthermore, when the coupling strength is low, the synchronization factor of the electromechanical coupling array with period five is slightly greater than that of the electromechanical coupling array with period three.

[0068] A photosensitive neuron-driven electromechanical coupling system, used to implement the aforementioned synchronization control method, includes the following:

[0069] A photosensitive neuron circuit, the photosensitive neuron circuit including a phototube embedded with a Fitzhugh-Nagumo neuron circuit;

[0070] Coupling devices;

[0071] A mechanical device, comprising a coil, a movable arm, and a spring, wherein the movable arm moves under the force of the coil and the spring;

[0072] The electrical part is a photosensitive neuron circuit, and the mechanical part is a mechanical moving arm.

[0073] The photosensitive neuron-driven electromechanical coupling system is a dual-system coupling device formed by two isomorphic systems coupled by a capacitor, used for load testing of functional neuron circuits.

[0074] The photosensitive neuron-driven electromechanical coupling system is a robotic arm coupling array formed by resistive coupling of multiple systems.

[0075] The above-mentioned synchronization control method for photosensitive neuron-driven electromechanical coupling systems can be applied to the following areas:

[0076] The application of synchronous control method in the stable motion control of robotic arms utilizes Hamiltonian energy to regulate the energy balance of the electromechanical coupling system, thereby achieving complete synchronization and realizing the stable motion of the robotic arm. This addresses the limitations of a single robotic arm in terms of information acquisition, processing, and manipulation capabilities.

[0077] The application of synchronous control method in the construction of multi-source environmental energy harvesting sensor: Based on biomimetic realization of light energy harvesting, a multi-source environmental energy harvesting sensor is constructed and applied to small and medium-sized robotic arm device.

[0078] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0079] In this disclosure, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The block diagrams of devices, apparatuses, devices, and systems involved in this disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as "comprising," "including," "having," etc., are open-ended terms meaning "including but not limited to," and are used interchangeably with them. The terms "or" and "and" as used herein refer to the terms "and / or," and are used interchangeably with them unless the context clearly indicates otherwise. The term "such as" as used herein refers to the phrase "such as but not limited to," and is used interchangeably with it.

[0080] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0081] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0082] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0083] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0084] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for synchronously controlling a photosensitive neuron-driven electromechanical coupling system, characterized in that, Includes the following steps: S1: Construct a photosensitive neuron circuit by embedding a phototube into the Fitzhugh-Nagumo neuron circuit to simulate the effect of light stimulation on biological neurons; S2: Construct an electromechanical coupling device, including an electrical part of a photosensitive neuron circuit and a mechanical moving arm of a mechanical part, which are connected by an inductor coil. The mechanical device includes a coil, a moving arm and a spring, and the moving arm moves under the force of the coil and the spring. S3: Provide at least one of the electromechanical coupling systems, including two isomorphic electromechanical coupling devices coupled by capacitors, or multiple electromechanical coupling systems forming a robotic arm coupling array by resistive coupling; S4: Calculate the Hamiltonian energy of the system according to Helmholtz's theorem; S5: Obtain the energy difference between the systems based on the Hamiltonian energy; S6: The system is fully synchronized by controlling the energy difference. When the energy difference approaches 0, the system is determined to be fully synchronized. S7: Perform numerical simulations of the fully synchronized behavior of the system under different discharge states, plot time series diagrams and energy difference diagrams, and judge the synchronization status based on the degree of overlap of the time series and the degree to which the energy difference approaches 0, or plot the Hamiltonian energy evolution diagram of the electromechanical coupling array, and judge the synchronization status of the coupled system by the disorder and order of the energy evolution diagram.

2. The synchronous control method for a photosensitive neuron-driven electromechanical coupling system according to claim 1, characterized in that, The numerical simulation of the fully synchronized behavior of the system under different discharge states includes: We will discuss chaotic discharge state and periodic discharge state, analyze the synchronization of the system under different coupling strengths when the same discharge state is coupled, and the minimum coupling strength required for different discharge states to achieve complete synchronization. Among them, the minimum coupling strength required for a chaotic electromechanical system to achieve complete synchronization can meet the coupling strength requirement for a periodic electromechanical system to achieve complete synchronization. Under the same coupling strength, it is easier to achieve complete synchronization with a lower period than with a higher period.

3. The synchronous control method for a photosensitive neuron-driven electromechanical coupling system according to claim 1, characterized in that, For a robotic arm coupling array formed by resistive coupling of multiple electromechanical coupling systems, the method further includes: S1: Based on the mean field theory, a synchronization factor is defined. When the synchronization factor is close to 1, the entire electromechanical coupling array is considered to be completely synchronized. S2: Compare the coupling situations of different discharge states under the same coupling strength, and discuss the relationship between chaotic state and low and high periodic states and coupling strength. S3: Calculate the synchronization factor and verify it through numerical simulation.

4. The synchronous control method for a photosensitive neuron-driven electromechanical coupling system according to claim 3, characterized in that, When the coupling strength is large, the synchronization factor is close to 1, indicating that the electromechanical coupling array can achieve complete synchronization. When the coupling strength is low, the synchronization factor of the electromechanical coupling array in the high-period state is greater than that in the low-period state.

5. The synchronous control method for a photosensitive neuron-driven electromechanical coupling system according to claim 1, characterized in that, As the coupling strength increases, the membrane potential of the electromechanical system array changes from disorder to order, and the order of the entire electromechanical coupling array is further strengthened, tending to be completely synchronized; When the coupling strength is insufficient, the membrane potential and energy evolution diagrams show disorder, indicating that the electromechanical coupling array has failed to achieve complete synchronization.

6. A photosensitive neuron-driven electromechanical coupling system, characterized in that, The system for implementing the synchronization control method according to any one of claims 1-5, the system comprising: A photosensitive neuron circuit, the photosensitive neuron circuit including a phototube embedded with a Fitzhugh-Nagumo neuron circuit; Coupling devices; A mechanical device, comprising a coil, a movable arm, and a spring, wherein the movable arm moves under the force of the coil and the spring; The electrical part is a photosensitive neuron circuit, and the mechanical part is a mechanical moving arm.

7. The photosensitive neuron-driven electromechanical coupling system according to claim 6, characterized in that, The system is a dual-system coupling device formed by two isomorphic systems coupled by a capacitor, used for load testing of functional neuron circuits.

8. The photosensitive neuron-driven electromechanical coupling system according to claim 6, characterized in that, The system is a robotic arm coupling array formed by resistive coupling of multiple systems.

9. The application of the synchronization control method as described in any one of claims 1-5 in the stable motion control of a robotic arm, characterized in that, Hamiltonian energy is used to regulate the energy balance of the electromechanical coupling system, thereby achieving complete synchronization and enabling stable movement of the robotic arm.

10. The application of the synchronization control method as described in any one of claims 1-5 in the construction of a multi-source environmental energy harvesting sensor, characterized in that, Based on biomimetic technology to harvest light energy, a multi-source environmental energy capture sensor was constructed and applied to small and medium-sized robotic arm devices.

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