A bionic finger device and method based on distributed control

Abstract

This invention provides a bionic finger device and method based on distributed control, relating to the field of robotics. The device includes a main control unit, a serial communication bus, and multiple electromagnet units arranged axially. Each electromagnet unit independently includes an electromagnet coil, a local microcontroller, a drive circuit, a current sensor, and a position sensor, enabling local closed-loop control of relative displacement and relative angle based on commands from the main control unit and real-time feedback from the local sensors. The method includes: the main control unit performing global path planning to generate control commands; issuing commands via the bus; each electromagnet unit executing local closed-loop control; and each unit reporting its status for global adjustment by the main control unit. This application solves the problems of low control accuracy, slow response, and complex wiring in existing technologies, achieving high-precision, fast, modular, and highly reliable control of bionic finger posture through a distributed dual-closed-loop control architecture.

Description

Technical Field

[0001] This invention relates to the field of robotics, and more specifically, to a bionic finger device and method based on distributed control. Background Technology

[0002] Precise, rapid, and flexible control of bionic robot fingers is an important research direction in the field of robotics. Existing technologies mainly employ various drive control methods.

[0003] For example, using a miniature servo motor with a complex linkage or planetary gear system to drive finger joints can provide significant driving force, but its complex structure, large size, and weight result in low system integration, and mechanical wear and clearances can affect long-term control accuracy. Another approach uses pneumatic artificial muscles, employing compressed air to drive the contraction of flexible airbags to simulate muscle movement. This method offers good compliance, but requires a complex air circuit system, leading to a slower overall system response, limited pressure control precision, and difficulty in achieving rapid and precise posture adjustments.

[0004] Furthermore, in the field of electro-actuation, for example, Chinese patent CN2026177388.5 discloses a bionic finger and hand motion control device, which employs multiple flexible electro-actuators and a passive constraint structure. It achieves active deformation through electric field excitation and directional conversion and force transmission through a guiding structure, combined with a hand-level collaborative controller to achieve precise motion control. While this approach has made progress in flexible control and directional force transmission, it still faces the challenge of balancing the response characteristics of the actuator material with complex collaborative algorithms for scenarios requiring ultra-high dynamic response or large-scale array-based actuation.

[0005] Meanwhile, some solutions use electromagnets for driving, but these are usually centralized controls, where a central controller directly drives one or a few electromagnets. In scenarios requiring multi-point, multi-degree-of-freedom coordinated motion, control bottlenecks arise, making it difficult to achieve high-speed independent adjustment of each driving point. Furthermore, complex wiring increases the system's failure rate and maintenance difficulty. While there are solutions applying distributed control concepts to the magnetic levitation control of single objects, these primarily focus on stable levitation at a single point and do not disclose how to apply this concept to a linear array composed of multiple driving units to achieve coordinated control of the overall bending attitude of a continuous flexible structure. They also lack integrated designs for precise dual-closed-loop control of the position and current of each unit.

[0006] Therefore, existing technologies generally suffer from one or more problems such as insufficient control precision, slow response speed, complex system structure, and difficulty in modular expansion, which cannot well meet the needs of high-precision, high-dynamic, and high-integration control of bionic fingers. Summary of the Invention

[0007] In view of the deficiencies in the prior art, the purpose of this invention is to provide a bionic finger device and method based on distributed control.

[0008] According to one aspect of the present invention, a bionic finger device based on distributed control includes:

[0009] The main control unit is used to calculate and generate control commands based on the preset bending posture of the bionic finger target; A serial communication bus is connected to the main control unit; Multiple electromagnet units are arranged linearly along the axis of the bionic finger device, and all are connected to the main control unit through the serial communication bus; Each of the electromagnet units independently includes: Electromagnet coil; A local microcontroller is used to receive and parse the control commands; A driving circuit is used to adjust the current applied to the electromagnet coil according to the control signal of the local microcontroller; A current sensor is used to monitor the actual current flowing through the electromagnet coil in real time and feed the monitoring data back to the local microcontroller; and A position sensor is used to monitor in real time the relative displacement and relative angle changes of the electromagnet unit relative to the preceding electromagnet unit in the axial arrangement, and to feed back the monitoring data to the local microcontroller. The local microcontroller adjusts the current of the electromagnet coil based on the control commands and real-time feedback data from the current sensor and the position sensor, thereby achieving closed-loop control of the relative displacement and relative angle between the electromagnet unit and the preceding electromagnet unit in the axial arrangement.

[0010] Preferably, the serial communication bus is an I2C bus, a CAN bus, or an SPI bus.

[0011] Preferably, the driving circuit is an H-bridge driving circuit, and the position sensor is a Hall displacement sensor or a magnetoresistive sensor.

[0012] Preferably, the number of the plurality of electromagnet units is 5 to 20.

[0013] Preferably, the main control unit is further configured to: When a fault is detected in any of the electromagnet units, the faulty unit is disabled and the control commands of the remaining non-faulty electromagnet units are dynamically adjusted.

[0014] According to another aspect of the present invention, a control method for a bionic finger based on distributed control, applied to the above-mentioned device, includes the following steps: Global path planning steps: The main control unit obtains the desired bionic finger bending curve and calculates the target state value of each electromagnet unit accordingly. The target state value includes the target relative angle and the target relative displacement, and generates control commands. Command issuance steps: The main control unit sends the control command to the corresponding electromagnet unit through the serial communication bus; Local closed-loop execution steps: The local microcontroller of each electromagnet unit receives the control command and runs the local closed-loop control algorithm based on the deviation between the target state value in the command and the real-time feedback value from the current sensor and the position sensor. By adjusting the current applied to the electromagnet coil, the relative displacement and relative angle between the electromagnet unit and the previous electromagnet unit in the axial arrangement are controlled. Status feedback and global adjustment steps: Each electromagnet unit periodically reports its current working status to the main control unit. The current working status includes the actual current value and the actual position value. The main control unit determines whether there is a deviation between the overall attitude and the preset target based on the reported working status. If there is a deviation, it generates and sends an adjusted control command.

[0015] Preferably, the global path planning step specifically includes: The desired bionic finger bending curve is discretized using cubic spline interpolation to obtain the target relative angles of each electromagnet unit. The inverse kinematics model based on the Jacobian matrix pseudo-inverse method is used to calculate the target relative displacement of each electromagnet unit according to the target relative angle.

[0016] Preferably, in the local closed-loop execution step, the local closed-loop control algorithm is a PID algorithm.

[0017] Preferably, the control period of the PID algorithm is 1 to 10 milliseconds.

[0018] Preferably, in the state feedback and global adjustment step, when generating the adjusted control command, the main control unit also considers at least one of the following compensations: Cross-coupling compensation between adjacent electromagnet units; Temperature compensation is achieved by estimating the coil temperature through a current-temperature rise model.

[0019] Compared with the prior art, the present invention has the following beneficial effects: By integrating dual current and position sensors into each electromagnet unit and implementing high-speed closed-loop control via a local microcontroller, direct and precise adjustment of driving force and displacement is achieved. Combined with the global compensation algorithm of the main control unit, high-precision displacement control and bending angle resolution can be realized, thus achieving high-precision control. A distributed control architecture is adopted, delegating complex real-time control tasks to each local microcontroller. The main control unit is only responsible for high-level planning and low-frequency adjustments, eliminating the computational bottleneck of centralized control and significantly shortening the overall system response time. This meets the demands of high-dynamic motion and achieves rapid response. All electromagnet units are connected via a serial bus, greatly simplifying system wiring, reducing hardware complexity and potential failure points. Each unit can be flexibly added or removed according to the length and precision requirements of the bionic finger, thus exhibiting high modularity and scalability. The dual closed-loop control system effectively resists external disturbances and internal parameter changes, ensuring robust control. Simultaneously, the distributed architecture possesses fault isolation capabilities; when a single unit fails, the main control unit can identify and dynamically adjust the control strategies of the remaining units, ensuring the continuity of overall system function and improving system reliability. Attached Figure Description

[0020] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the overall system architecture of a bionic finger device based on distributed control, provided for an embodiment of this application.

[0021] Figure 2 The overall system workflow diagram of the bionic finger control method provided in the embodiments of this application is shown.

[0022] Figure 3 This is a flowchart of the PID closed-loop control within a single electromagnet unit in an embodiment of this application.

[0023] Figure 4 This is a timing diagram of the signaling interaction between the main control unit and the electromagnet unit in an embodiment of this application. Detailed Implementation

[0024] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0025] Example 1 This embodiment provides a basic implementation scheme for a bionic finger device and its control method based on distributed control. The scheme aims to achieve high-precision, high-dynamic, and high-reliability control of the bending posture of the bionic finger through a distributed, two-layer closed-loop control architecture.

[0026] Please see Figure 1 The figure is a schematic diagram of the overall system architecture of a bionic finger device based on distributed control provided in an embodiment of this application. In one embodiment of this application, the device includes a host computer as a main control unit, an I2C bus as a serial communication bus, and multiple electromagnet units 1 to N arranged linearly along the axis of the bionic finger device.

[0027] The host computer, as the decision-making core of the entire system, is responsible for high-level task planning and coordination. Its function is to perform global kinematics and dynamics calculations based on the user input or a preset program-given bionic finger bending posture (e.g., a specific bending angle or grasping shape), and generate specific control instructions for each electromagnet unit 1 to N accordingly. As an optional implementation, the host computer can be a personal computer, an embedded system motherboard, or a dedicated digital signal processor.

[0028] The I2C bus forms the communication link between the host computer and all electromagnet units 1 to N. This bus requires only two signal lines (SDA data line and SCL clock line) to achieve bidirectional communication, greatly simplifying the physical wiring of the system—crucial for a compact and flexible bionic finger. The host computer sends control commands to designated electromagnet units via this bus, while each electromagnet unit reports its operating status to the host computer via the same bus.

[0029] Electromagnetic units 1 to N are the core actuators of this device, arranged linearly along the flexible skeleton axis of the bionic finger in a modular form. In this embodiment, N=10 electromagnetic units are provided. It should be noted that each electromagnetic unit constitutes a fully functional, independently operating micro-control system. Taking electromagnetic unit 1 as an example, its internal structure includes: The electromagnet coil serves as the final mechanical actuator. When current flows through the coil, the magnetic field it generates produces a driving force through interaction (attraction or repulsion) with adjacent units or other magnetic components, thereby causing local segments of the bionic finger to bend.

[0030] The local microcontroller, specifically the MCU controller, serves as the local processing core of this unit, responsible for executing local closed-loop control tasks. In this embodiment, a high-performance 12-bit microcontroller chip, such as the ARM Cortex-M0+ series, can be selected. The main responsibilities of the MCU controller are: receiving and parsing control commands (such as target displacement, target angle, or target current) from the host computer via the I2C bus; acquiring local sensor data in real time; running local closed-loop control algorithms (such as PID algorithms); generating control signals to drive downstream circuits; and packaging its own status data and feeding it back to the host computer via the I2C bus.

[0031] The H-bridge driver circuit serves as a power amplifier stage connecting the MCU controller and the electromagnet coil. Since the control signal output by the MCU controller (typically a pulse-width modulation signal) is insufficient to directly drive the electromagnet coil, the H-bridge driver circuit receives this signal and uses it to control a higher-power supply to power the electromagnet coil. A key advantage of the H-bridge structure is its ability to easily change the direction of the current flowing through the coil, thereby switching the electromagnet between attractive and repulsive forces, essential for bidirectional bending control. This circuit typically consists of four power switching transistors (such as MOSFETs).

[0032] A current sensor is used to monitor the actual current flowing through an electromagnet coil in real time and with high accuracy. This sensor is connected in series or otherwise coupled in the coil circuit, converting the measured current value into a voltage or digital signal and feeding it back to the MCU controller. In this embodiment, a Hall effect current sensor, which offers advantages such as non-contact measurement, fast response, and high accuracy, can be selected. Accurate current feedback is fundamental to achieving closed-loop control of force (i.e., electromagnetic force).

[0033] A position sensor is used to monitor in real time the relative displacement and relative angle changes of this electromagnet unit compared to the preceding electromagnet unit in its axial arrangement. For example, the position sensor configured on electromagnet unit 2 is responsible for measuring the displacement and rotation angle of unit 2 relative to unit 1. Such relative position measurements are crucial for accurately reconstructing and controlling the bending shape of the entire finger. In this embodiment, a Hall displacement sensor or a magnetoresistive sensor capable of sensitively detecting minute displacements or angular changes caused by variations in the magnetic field can be selected. Accurate position feedback is the foundation for achieving closed-loop attitude control.

[0034] In summary, each electromagnet unit forms two tightly coupled local closed loops: a current loop based on a current sensor for precise control of the electromagnetic force, and a position loop based on a position sensor for precise control of the joint attitude. This dual-sensor, dual-closed-loop local control architecture constitutes the core of this application's high-precision, fast-response control.

[0035] The following will combine Figure 2 , Figure 3 and Figure 4 The control method of the bionic finger in this embodiment will be described in detail.

[0036] Figure 2 The overall workflow of the system is demonstrated. The entire control method mainly covers core steps such as global path planning, command issuance, local closed-loop execution, and status feedback and global adjustment.

[0037] After system startup, the system initialization step S1 is executed first. During this stage, the host computer and the MCU controllers of all electromagnet units 1-10 perform power-on self-tests. The host computer scans the I2C bus to identify all online electromagnet units and establishes an address mapping table based on their physical arrangement order.

[0038] After initialization, the system enters the global path planning step. When the host computer receives the desired bionic finger bending curve (e.g., an instruction describing that the finger should bend into an arc of radius R), it needs to transform this continuous geometric objective into a series of discrete executable control objectives. Specifically, the host computer can use cubic spline interpolation to discretize the desired curve into N (10 in this example) control points, thereby obtaining the target relative angles assigned to each electromagnet unit. .

[0039] Subsequently, the host computer invokes a pre-defined inverse kinematics model based on the pseudo-inverse of the Jacobian matrix. This model solves the equation Δθ=J + ×ΔP is used to establish the relationship between joint angle changes and end-effector pose changes, where Δθ is the variable increment vector in joint space; J + ΔP is the Moore-Penrose pseudoinverse of the Jacobian matrix; ΔP is the pose increment vector in the operation space.

[0040] Based on this model, and using the calculated target relative angles, the required target relative displacement for each element to achieve these angles can be solved in reverse. At this point, the target state values ​​(i.e., the target relative angle and the target relative displacement) of each unit have been determined.

[0041] After determining the target state value, the host computer needs to calculate the driving energy required to achieve the target displacement, i.e., the target current. This is based on a pre-calibrated or theoretically calculated magnetic force-current-displacement relationship model, such as the electromagnetic force formula. (in The force generated by the i-th unit, Where is the free permeability, N is the number of turns of the coil, and A is the cross-sectional area. For current, (The air gap length is related to the displacement). The host computer can calculate the electromagnetic force to be applied based on the required target displacement, and then calculate the target current value. And the polarity of the current (which determines attraction or repulsion). Finally, the host computer packages information including the unit address, the target's relative angle, the target's relative displacement, and the target's current into control commands.

[0042] Then proceed to step S3, which involves issuing instructions. Figure 4 As shown, the host computer M sends the generated control command C1 to one or more electromagnet units at the corresponding address via the I2C bus.

[0043] Next is the local closed-loop execution step S4. Upon receiving its dedicated control command, the MCU controller of each electromagnet unit immediately begins executing its local high-speed closed-loop control task. (See also...) Figure 3 This diagram details the PID closed-loop control process operating within a single electromagnet unit. The MCU controller takes the target state values ​​(such as target relative displacement and target current) from the instructions as setpoints and performs the following operations cyclically at an extremely high frequency (e.g., a control cycle set to 5 milliseconds, or 200 Hz): 1. Read the real-time feedback values ​​from the current sensor and position sensor, i.e., the actual current value, using an analog-to-digital converter. and actual relative displacement value .

[0044] 2. Calculate the deviation, such as displacement deviation. .

[0045] 3. Input the deviation into the local closed-loop control algorithm.

[0046] In this embodiment, an incremental PID algorithm is used, and its calculation formula is as follows:

[0047] in, These are the proportional, integral, and differential coefficients, respectively. This represents the current or displacement deviation at the current moment.

[0048] The algorithm calculates a control increment based on this. .

[0049] 4. The MCU controller updates the duty cycle of its internal PWM generator based on the calculated control quantity, thereby adjusting the control signal output to the H-bridge drive circuit.

[0050] 5. The H-bridge drive circuit, based on the new PWM duty cycle, precisely adjusts the voltage applied to the electromagnet coil, thereby changing the actual current in the coil to quickly and stably approach the target current value, ultimately driving the mechanical structure to achieve the target relative displacement. This local closed-loop process (such as...) can be understood. Figure 4 (As shown by the cyclic arrows on the U1 lifeline) It operates independently at an extremely high speed (1-10 millisecond cycles), ensuring rapid response and precise suppression to external disturbances and changes in internal parameters.

[0051] Finally, there is the state feedback and global adjustment step S5. Although the local closed-loop speed is fast, a higher-level global closed loop is still needed to ensure the coordination and consistency of the entire bionic finger posture. The MCU controller of each electromagnet unit will package its current operating state, including the actual current value and the actual position value, into state feedback information F1 at a relatively slow cycle (e.g., 10 milliseconds), and report it to the host computer M via the I2C bus.

[0052] The host computer M collects and summarizes the status information reported by all units to reconstruct the current overall posture of the bionic finger. Accordingly, it compares this actual posture with the initial desired posture to determine if there is a global deviation. If a deviation exists (possibly due to load changes, model inaccuracies, etc.), the host computer M performs global compensation calculations, generates adjusted control instructions C2, and returns to the instruction issuance step S3 to send the new fine-tuning instructions to the relevant units. This cycle of "global planning - local execution - global feedback adjustment" continues until the overall posture of the bionic finger accurately reaches the preset target.

[0053] The solution presented in this embodiment enables high-precision control of the bending posture of a bionic finger. Experimental data shows that the displacement control accuracy can reach the millimeter level, and the bending angle resolution can reach the order of 0.1°. Furthermore, thanks to the distributed processing of the control task, the overall system response time can be controlled within 10 milliseconds, meeting the requirements of high-dynamic motion.

[0054] Example 2 This embodiment modifies the specific implementation of the serial communication bus based on Embodiment 1, aiming to illustrate the universality of the proposed solution and its adaptability to different communication environments. In some application scenarios, such as industrial environments with strong electromagnetic interference or situations requiring longer communication distances, the I2C bus may not be the optimal choice.

[0055] In this embodiment, the overall hardware architecture and core control concept of the device are exactly the same as in Embodiment 1, that is, it still adopts a structure of one main control unit (host computer) and multiple distributed electromagnet units (1-N). The main difference is that the I2C bus in Embodiment 1 is replaced with a CAN bus.

[0056] Specifically, the hardware modifications are as follows: the host computer needs to be equipped with a CAN communication interface card or a USB-to-CAN device; simultaneously, on the circuit board of each electromagnet unit 1-N, its MCU controller needs to be connected to an external CAN transceiver chip via a serial peripheral interface. The CAN bus itself consists of two differential signal lines (usually labeled as...). and It consists of a bus, and the CAN transceivers of all units are connected in parallel to this bus.

[0057] Accordingly, the software and communication protocols were also adjusted. During system initialization, each electromagnet unit was assigned a unique CAN identifier. When the host computer issues control commands, instead of using the I2C slave address, the command data is packaged into a CAN data frame, and the ID field of the frame is set to the CANID of the target unit. All units on the bus can receive the data frame, but only the MCU controller of the unit whose ID matches the frame ID will receive and process the data content of the frame. Similarly, when an electromagnet unit needs to report its status to the host computer, it sends a data frame identified by its own CANID.

[0058] Its workflow is basically the same as that of Example 1: The host computer performs global path planning and generates control instructions containing target state values; the instructions are packaged into CAN messages and sent through the CAN bus; the target electromagnet unit receives and parses the messages and executes local high-speed PID closed-loop control; then the actual state is periodically packaged into CAN messages and fed back to the host computer; the host computer then performs global dynamic adjustment based on the feedback.

[0059] Compared with the I2C bus, the CAN bus has the following advantages: First, it uses differential signal transmission, which has a strong ability to resist common-mode interference and is very suitable for complex electromagnetic environments; Second, the protocol itself includes powerful error detection, notification and recovery mechanisms (such as CRC check, bit error detection, etc.), making data communication extremely reliable; Third, it has a high communication rate and long communication distance, which can support longer-distance bionic limb designs.

[0060] As can be seen from this embodiment, the distributed control architecture proposed in this application is not limited to a specific serial communication bus. Whether it is an I2C bus, a CAN bus, or an SPI bus, or other serial buses, as long as it can realize the instruction issuance and data feedback between the master controller and multiple slave controllers, it can be applied to the technical solution of this application. This fully demonstrates the good technical compatibility and scalability of the solution of this application.

[0061] Example 3 This embodiment, based on Embodiment 1, focuses on deepening and improving the control algorithm. By introducing a more advanced compensation algorithm, it aims to further enhance the trajectory tracking accuracy and posture stability of the bionic finger during rapid, large-amplitude continuous movements. Its hardware structure is basically the same as Embodiment 1, but to achieve temperature compensation, an additional patch digital temperature sensor is added to the circuit board of each electromagnet unit near the electromagnet coil and connected to the MCU controller.

[0062] In this embodiment, the software algorithms of both the host computer and the local MCU controller have been upgraded.

[0063] First, cross-coupling compensation is introduced into the global adjustment algorithm of the host computer. In Example 1, we assume that the motion of each electromagnet unit is relatively independent. However, in physical reality, the magnetic field generated by an electromagnet unit not only acts on its target object but also exerts a certain interference force on adjacent electromagnet units. This phenomenon is called cross-coupling. When all units move at high speed and high torque simultaneously, this coupling effect becomes significant, thereby affecting the overall control accuracy.

[0064] To address this issue, the host computer in this embodiment pre-stores a cross-coupling coefficient matrix C. This matrix can be obtained through offline calibration experiments: when controlling the movement of the i-th unit, the interference force or displacement it generates on the adjacent (i-1)-th and (i+1)-th units is simultaneously measured, thereby obtaining the coupling coefficients C(i,i-1) and C(i,i+1). During online global adjustment calculations, when the host computer calculates the compensation amount for the i-th unit, in addition to considering its own deviation, a cross-coupling compensation term is added. For example, a term can be added for compensation of the target current. This is used to actively cancel known interference from adjacent units, where, This represents the current generated by the (i-1)th adjacent unit; This represents the current generated by the (i+1)th adjacent unit.

[0065] Secondly, temperature compensation is incorporated into the closed-loop control algorithm of the local MCU controller for each electromagnet unit. When the electromagnet coil operates for extended periods or is driven by high current, it heats up due to the resistive effect, causing the coil temperature to rise. For common conductors such as copper wire, their resistance increases with temperature (i.e., positive temperature coefficient). According to Ohm's law, with a constant driving voltage, increased resistance leads to a decrease in actual current, resulting in a lower-than-expected electromagnetic force and affecting control accuracy.

[0066] To address this issue, the MCU controller in this embodiment periodically reads the real-time temperature T near the coil from a newly added temperature sensor while executing the PID control loop. The MCU internally stores the temperature coefficient of resistance α of the coil conductor and the resistance value at a reference temperature T0 (e.g., room temperature 25°C). When calculating the drive current, the MCU uses the formula... (where α is the temperature coefficient of resistance and T0 is the reference temperature;) The target current; To compensate for the current, the target current value is dynamically compensated, or the parameters of the PID controller are adjusted. Through this compensation, even if the coil temperature changes, the MCU can dynamically adjust the applied voltage to ensure that the actual current flowing through the coil can always accurately track the compensated target current value, thereby eliminating the influence of temperature drift on electromagnetic force and final displacement control.

[0067] In addition to the two compensation methods mentioned above, the global adjustment algorithm of the host computer can further integrate velocity feedforward and acceleration feedforward compensation. Specifically, based on the dynamic characteristics of the target trajectory, the force required to overcome inertia and viscous friction is calculated in advance and added as a feedforward term to the control command to reduce the tracking error of the PID controller during the dynamic process.

[0068] By introducing advanced algorithms such as cross-coupling compensation, temperature compensation, and feedforward control in this embodiment, the system's control model is closer to the physical reality. It can anticipate and actively counteract the interference caused by various non-ideal factors, so that the bionic finger can still maintain extremely high trajectory tracking accuracy and final posture stability when performing complex and highly dynamic tasks.

[0069] Example 4 This embodiment aims to demonstrate the high degree of modularity and design flexibility of the distributed control architecture proposed in this application. In actual bionic finger design, different parts may need to perform different functions. For example, the fingertip requires higher flexibility and fine manipulation capabilities, while the base of the finger needs to provide the main bending force and support. An array composed of identical electromagnet units may not achieve optimal performance and energy efficiency.

[0070] As an optional implementation, this embodiment constructs a bionic finger device employing a heterogeneous unit array. Assuming the bionic finger consists of a total of 7 electromagnet units, its overall hardware architecture remains as follows... Figure 1 As shown, but electromagnet units 1-N are not exactly the same.

[0071] Specifically, these seven units can be divided into two groups: The first group, the fingertip units, includes three units located at the tip of the bionic finger (e.g., units 1, 2, and 3). These units use smaller, lighter, and lower-power electromagnet coils and corresponding drive circuits, and their physical spacing is also smaller. This design makes the fingertip part lighter and more flexible, enabling rapid and precise posture adjustments with less energy, suitable for tasks such as touching, sensing, and fine grasping. The second group, the root units, includes four units located near the root of the bionic finger (e.g., units 4, 5, 6, and 7). These units use larger electromagnet coils with more turns, generating stronger magnetic force and greater driving torque, and their spacing can also be appropriately increased. These units are mainly responsible for providing the main force and structural support required for the bending of the entire finger.

[0072] This heterogeneous design places certain demands on the control system, which the distributed architecture of this application can adapt well. At the software level, the control algorithm of the host computer needs to be adjusted accordingly. Specifically, the host computer no longer stores a single unit parameter model, but a parameter library, which contains the physical parameters of each specification of electromagnet unit (such as coil inductance, resistance, force-current-displacement characteristic curves, etc.).

[0073] During operation, when the host computer performs global path planning, it retrieves the corresponding model from the parameter library based on the ID of each unit. For example, when performing kinematic and dynamic calculations, it considers the differences in mass, inertia, and force output capabilities of different units. When generating control commands, it may assign more precise and smaller displacement commands to the small units at the fingertips, while assigning commands requiring larger current outputs to the larger units at the base of the fingers to provide sufficient force.

[0074] The local control of each electromagnet unit remains unaffected; its MCU controller faithfully executes local PID closed-loop control based on the received target value and its own sensor feedback. This precisely demonstrates the advantage of distributed control: the complexity and differences at the lower levels are encapsulated in each independent module, while the upper-level controller only needs to allocate tasks differently according to the characteristics of different modules through a unified interface and protocol.

[0075] Furthermore, this embodiment effectively demonstrates the high reliability of the proposed solution. In a distributed architecture, the host computer can perform fault diagnosis and isolation. For example, during operation, if the host computer does not receive status feedback from a certain electromagnet unit (such as unit 5) within a predetermined time, or if the received feedback data remains abnormal, it can determine that the unit has failed. In this case, the host computer can execute a fault-tolerant strategy: first, it masks the faulty unit in the control network and stops sending control commands to it; then, it immediately re-performs kinematic planning, dynamically redistributing the tasks originally allocated to the seven units to the remaining six healthy units for collaborative completion. Although this may lead to a slight decrease in overall performance (e.g., a reduction in maximum bending force), it ensures that the bionic finger as a whole can continue to perform its tasks, thus preventing the entire system from paralyzing due to a single point of failure.

[0076] In summary, this embodiment, through the use of a heterogeneous unit array design, fully demonstrates the significant advantages of the technical solution of this application in terms of modularity, flexibility, and reliability. It can be highly customized according to actual application needs, in order to complete complex biomimetic operation tasks with the lowest energy consumption and optimal performance.

[0077] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0078] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A bionic finger device based on distributed control, characterized in that, include: The main control unit is used to calculate and generate control commands based on the preset bending posture of the bionic finger target; A serial communication bus is connected to the main control unit; Multiple electromagnet units are arranged linearly along the axis of the bionic finger device, and all are connected to the main control unit through the serial communication bus; Each of the electromagnet units independently includes: Electromagnet coil; A local microcontroller is used to receive and parse the control commands; A driving circuit is used to adjust the current applied to the electromagnet coil according to the control signal of the local microcontroller; A current sensor is used to monitor the actual current flowing through the electromagnet coil in real time and feed the monitoring data back to the local microcontroller; and A position sensor is used to monitor in real time the relative displacement and relative angle changes of the electromagnet unit relative to the preceding electromagnet unit in the axial arrangement, and to feed back the monitoring data to the local microcontroller. The local microcontroller adjusts the current of the electromagnet coil based on the control commands and real-time feedback data from the current sensor and the position sensor, thereby achieving closed-loop control of the relative displacement and relative angle between the electromagnet unit and the preceding electromagnet unit in the axial arrangement.

2. The apparatus according to claim 1, characterized in that, The serial communication bus is an I2C bus, a CAN bus, or an SPI bus.

3. The apparatus according to claim 1, characterized in that, The driving circuit is an H-bridge driving circuit, and the position sensor is a Hall displacement sensor or a magnetoresistive sensor.

4. The apparatus according to claim 1, characterized in that, The number of the plurality of electromagnet units is 5 to 20.

5. The apparatus according to claim 1, characterized in that, The main control unit is also used for: When a fault is detected in any of the electromagnet units, the faulty unit is disabled and the control commands of the remaining non-faulty electromagnet units are dynamically adjusted.

6. A control method for a bionic finger based on distributed control, applied to the device described in claim 1, characterized in that, Includes the following steps: Global path planning steps: The main control unit obtains the desired bionic finger bending curve and calculates the target state value of each electromagnet unit accordingly. The target state value includes the target relative angle and the target relative displacement, and generates control commands. Command issuance steps: The main control unit sends the control command to the corresponding electromagnet unit through the serial communication bus; Local closed-loop execution steps: The local microcontroller of each electromagnet unit receives the control command and runs the local closed-loop control algorithm based on the deviation between the target state value in the command and the real-time feedback value from the current sensor and the position sensor. By adjusting the current applied to the electromagnet coil, the relative displacement and relative angle between the electromagnet unit and the previous electromagnet unit in the axial arrangement are controlled. Status feedback and global adjustment steps: Each electromagnet unit periodically reports its current working status to the main control unit. The current working status includes the actual current value and the actual position value. The main control unit determines whether there is a deviation between the overall attitude and the preset target based on the reported working status. If there is a deviation, it generates and sends an adjusted control command.

7. The method according to claim 6, characterized in that, The global path planning steps specifically include: The desired bionic finger bending curve is discretized using cubic spline interpolation to obtain the target relative angles of each electromagnet unit. The inverse kinematics model based on the Jacobian matrix pseudo-inverse method is used to calculate the target relative displacement of each electromagnet unit according to the target relative angle.

8. The method according to claim 6, characterized in that, In the local closed-loop execution step, the local closed-loop control algorithm is a PID algorithm.

9. The method according to claim 8, characterized in that, The control period of the PID algorithm is 1 to 10 milliseconds.

10. The method according to claim 6, characterized in that, In the state feedback and global adjustment steps, when generating the adjusted control commands, the main control unit also considers at least one of the following compensations: Cross-coupling compensation between adjacent electromagnet units; Temperature compensation is achieved by estimating the coil temperature through a current-temperature rise model.

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