Real-time adjusting method and system for shear thickening fluid in robot joint damping

Abstract

The invention discloses a real-time adjusting method and system for shear thickening fluid in robot joint damping, and relates to the technical field of robot technology.The method comprises the steps that intrinsic sensing signals from the shear thickening fluid in a joint cavity are obtained; on the basis of a pre-established sensing signal-damping relation model, the intrinsic sensing signal is analyzed, and a real-time damping value of the joint is obtained through calculation; based on a pre-generated target task damping curve as a feedforward target, an adjusting instruction is generated through calculation of the controller; and a field variable mechanism in the joint is driven based on the adjusting instruction, so that real-time damping tracks a target task damping curve. According to the method, a digital twinborn model is combined to carry out prospective simulation on a task so as to generate an optimal damping curve as a feedforward target, a model prediction control algorithm is utilized to carry out collaborative optimization on the feedforward target and damping feedback sensed in real time, and finally real-time, accurate, smooth and adaptive adjustment of robot joint damping is achieved.

Description

Technical Field

[0001] This invention relates to the field of robotics technology, and in particular to a method and system for real-time adjustment of shear-thickening fluid in robot joint damping. Background Technology

[0002] The adaptive damping capability of robot joints is crucial to their motion accuracy and interactive safety. While adjustable damping technology based on intelligent fluids such as magnetorheology / electrorheology can be adjusted through external fields, its dynamic range, response speed, and energy consumption remain limited. In recent years, shear-thickening fluids (STFs) have attracted attention due to their unique rheological properties, but their application to robot joints still faces key bottlenecks: existing solutions rely on motor current or torque sensors to indirectly sense damping, failing to obtain in-situ information on the internal state of the STF, resulting in sensing lag; control often employs simple feedback to adjust the physical field, lacking foresight for the task and exhibiting slow response; furthermore, the various modules of the system lack deep coupling, failing to form a closed loop centered on the intrinsic state of the STF, resulting in low intelligence levels. Therefore, there is an urgent need for a solution that can directly sense the STF state and achieve precise control through feedforward and feedback coordination.

[0003] However, current common solutions have many drawbacks, including: existing technologies rely on external signals to indirectly estimate damping, resulting in information lag and inaccuracy; control uses reactive feedback, which lacks task predictability and has a slow response; the system architecture is fragmented and lacks high-fidelity prediction models, resulting in a low level of intelligence. These defects together limit the dynamic performance and adaptive ability of robot joints in high-speed and high-precision tasks. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the problems existing in the above-mentioned methods and systems for real-time adjustment of shear-thickening fluid in robot joint damping, this invention is proposed.

[0006] Therefore, the purpose of this invention is to provide a method and system for real-time adjustment of shear-thickening fluid in robot joint damping. It is applicable to solving the problems of existing technologies that rely on external signals to indirectly estimate damping, resulting in information lag and inaccuracy; use reactive feedback in control, which lacks task predictability and has a slow response; and have a fragmented system architecture with a lack of high-fidelity prediction models and a low level of intelligence.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a method for real-time adjustment of shear-thickening fluid in robot joint damping, comprising: acquiring intrinsic sensing signals from shear-thickening fluid within the joint cavity; analyzing the intrinsic sensing signals based on a pre-established sensing signal-damping relationship model to calculate the real-time damping value of the joint; using a pre-generated target task damping curve as a feedforward target and the real-time damping value as feedback, generating an adjustment command through a controller; and driving a field-induced mechanism within the joint based on the adjustment command to change the physical field acting on the shear-thickening fluid, thereby enabling the real-time damping to track the target task damping curve.

[0008] As a preferred embodiment of the real-time adjustment method for shear-thickening fluid in robot joint damping according to the present invention, wherein: the intrinsic sensing signal is an electrical or optical parameter that changes with the shear viscosity of the fluid; the intrinsic sensing signal is obtained by detecting impedance changes through an embedded microelectrode pair, or by detecting fluorescence intensity / wavelength changes through an optical fiber sensor; the target task damping curve is obtained by simulation using a digital twin model that includes joint dynamics and the intrinsic properties of the shear-thickening fluid.

[0009] As a preferred embodiment of the real-time adjustment method of shear-thickening fluid in robot joint damping described in this invention, the sensing signal-damping relationship model specifically comprises the following: the intrinsic sensing signal acquired in real time is input into the sensing signal-damping relationship model; the sensing signal-damping relationship model maps the input sensing signal value to the corresponding damping force or damping torque value based on a pre-calibrated mapping relationship; and the output damping force or damping torque value is used as the real-time damping value.

[0010] As a preferred embodiment of the real-time adjustment method of shear-thickening fluid in robot joint damping described in this invention, the step of generating adjustment commands through a controller specifically includes the following: using the value of the target task damping curve in the current and several future control cycles as a reference trajectory, and using the real-time damping value as the current state feedback; employing a model predictive control algorithm to minimize the tracking error between the real-time damping value and the reference trajectory as the optimization objective, and considering the physical constraints of the field-induced mechanism, to solve an optimal control problem in the finite-time domain online; and outputting the first element in the solved optimal control sequence, i.e., the target intensity setpoint of the physical field in the current control cycle, as the adjustment command.

[0011] As a preferred embodiment of the real-time adjustment method for shear-thickening fluid in robot joint damping described in this invention, the method involves driving a field-induced mechanism within the joint based on adjustment commands to change the physical field acting on the shear-thickening fluid, thereby enabling real-time damping to track the target task damping curve. Specifically, the adjustment command, i.e., the target field strength setting value, is converted into a specific voltage, current, or power signal driving the field-induced mechanism. This specific signal is applied to the field-induced mechanism via a power amplifier or drive circuit, thereby generating or changing the corresponding physical field within the joint cavity. The physical field directly acts on the shear-thickening fluid, altering the interaction barrier or arrangement structure of its dispersed phase particles, thus continuously and in real-time adjusting its apparent viscosity and shear-thickening response characteristics. Through rheological control, the actual damping force / torque output by the joint dynamically approaches the target value corresponding to the target task damping curve under the closed-loop action of the controller, achieving precise damping tracking.

[0012] As a preferred embodiment of the real-time adjustment method of shear-thickening fluid in robot joint damping described in this invention, the construction of the digital twin model includes: establishing a rigid-flexible coupled multibody dynamics model of the robot joint; calibrating and establishing a rheological constitutive model of the shear-thickening fluid under various physical field intensities through rheological experiments, wherein the constitutive model describes the relationship between its viscosity and the shear rate and the physical field intensity; and coupling the dynamics model and the rheological constitutive model to form a digital twin model.

[0013] As a preferred embodiment of the real-time adjustment method of shear-thickening fluid in robot joint damping described in this invention, the sensing signal-damping relationship model is pre-established through the following steps: under various steady-state shear conditions and physical field strengths covering the expected working range, the standard values ​​of the intrinsic sensing signal and the corresponding standard values ​​of the joint output damping force are simultaneously collected to form a calibration dataset; using the calibration dataset, parameter identification or machine learning training is performed on the preset model structure to obtain the pre-calibrated mapping relationship.

[0014] Secondly, to further address the aforementioned technical problems, the present invention provides a real-time adjustment system for shear-thickening fluid in robot joint damping, comprising: a signal acquisition module for acquiring intrinsic sensing signals from the shear-thickening fluid within the joint cavity; a signal analysis module for analyzing the intrinsic sensing signals based on a pre-established sensing signal-damping relationship model to calculate the real-time damping value of the joint; a command generation module for generating adjustment commands based on a pre-generated target task damping curve as a feedforward target and the real-time damping value as feedback, through a controller; and a command driving module for driving a field-induced mechanism within the joint based on the adjustment commands to change the physical field acting on the shear-thickening fluid, thereby enabling the real-time damping to track the target task damping curve.

[0015] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the real-time adjustment method of shear-thickening fluid in robot joint damping as described in the first aspect of the present invention.

[0016] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the real-time adjustment method for shear-thickening fluid in robot joint damping as described in the first aspect of the present invention.

[0017] The beneficial effects of this invention are as follows: This invention achieves in-situ, direct measurement of damping force by using a composite STF material with intrinsic sensing function, combines a digital twin model to perform forward simulation of the task to generate the optimal damping curve as a feedforward target, and uses a model predictive control algorithm to coordinate and optimize the feedforward target and the real-time sensed damping feedback, thereby constructing a deeply coupled intelligent closed-loop system. Ultimately, it realizes real-time, accurate, smooth and adaptive adjustment of robot joint damping, systematically improving dynamic response speed, trajectory tracking accuracy, anti-disturbance capability and human-computer interaction safety. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart illustrating the implementation of the present invention in Example 1.

[0019] Figure 2 This is a dynamic adjustment diagram of the real-time adjustment of robot joint damping in Example 1. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] Example 1 Reference Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a method for real-time adjustment of shear-thickening fluid in robot joint damping, including the following steps: S1: Acquire intrinsic sensing signals from the shear-thickening fluid within the joint cavity.

[0024] Preferably, the intrinsic sensing signal is an electrical or optical parameter that varies with the fluid shear viscosity.

[0025] Furthermore, the intrinsic sensing signal is obtained by detecting impedance changes through embedded microelectrode pairs or fluorescence intensity / wavelength changes through fiber optic sensors. The microelectrode pairs can be interdigitated gold electrodes, integrated into the inner wall of the joint cavity using microelectromechanical systems (MEMS) technology. The fiber optic sensor can include excitation and receiving fibers, with their ends embedded in the joint cavity wall through sealed windows to measure the fluorescence properties of the fluid in a non-contact manner.

[0026] Furthermore, the shear-thickening fluid is a composite fluid in which the surface of the dispersed phase particles is modified with functional materials.

[0027] Specifically, functional materials make their electrical impedance or optical fluorescence properties sensitive to changes in shear field.

[0028] Preferably, by designing a composite STF material with self-sensing function and integrating the micro-sensing unit in situ into the joint cavity, in-situ, direct, and real-time measurement of the micro-rheological state (viscosity) inside the STF is achieved. This completely abandons the traditional method of indirectly estimating damping through external signals such as motor current and joint torque, fundamentally eliminating sensing lag and model distortion, and providing a unique and reliable real damping state feedback for subsequent high-precision closed-loop control. It is the cornerstone for the entire system to achieve precise control.

[0029] For example, the shear-thickening fluid is composed of nano-silica particles with polyaniline-modified surfaces dispersed in polyethylene glycol. The inner wall of the joint cavity is integrated with interdigitated gold electrodes manufactured by microelectromechanical systems (MEMS) technology. When the joint moves, fluid shearing causes changes in the particle spacing, resulting in changes in the impedance of the conductive network. This impedance signal is acquired in real time as an intrinsic sensing signal.

[0030] S2: Based on the pre-established sensing signal-damping relationship model, the intrinsic sensing signal is analyzed, and the real-time damping value of the joint is calculated.

[0031] The preferred sensing signal-damping relationship model is as follows: The intrinsic sensing signals acquired in real time are input into the sensing signal-damping relationship model.

[0032] The sensing signal-damping relationship model is based on a pre-calibrated mapping relationship, which maps the input sensing signal value to the corresponding damping force or damping torque value.

[0033] Output the damping force or damping torque value as the real-time damping value.

[0034] Specifically, the sensing signal-damping relationship model is pre-established through the following steps: Under various steady-state shear conditions and physical field strengths covering the expected working range, the standard values ​​of intrinsic sensing signals and the corresponding standard values ​​of joint output damping forces are simultaneously collected to form a calibration dataset.

[0035] Using a calibrated dataset, parameter identification or machine learning training is performed on a pre-defined model structure to obtain a pre-calibrated mapping relationship.

[0036] Furthermore, the model is stored in the storage unit of the signal analysis module in the form of data files or embedded code for real-time access.

[0037] Preferably, through experimental calibration and data-driven modeling, a quantitative mapping model from the intrinsic sensing signal of the STF to the output damping force / torque of the joint is pre-established. This model transforms the complex non-Newtonian fluid dynamics calculation into a fast lookup table or forward calculation process, thereby enabling the current damping value to be "calculated" online, efficiently, and accurately based on the real-time acquired electrical / optical signals, rather than "estimated". This meets the stringent requirements of the control system for both real-time performance and accuracy, and provides robustness to adapt to the nonlinear and time-varying characteristics of the STF.

[0038] For example, during the calibration phase, the joint assembling the fluid is run at various constant speeds and set electric field strengths, and the electrode impedance value and the damping torque measured by the high-precision torque sensor are recorded simultaneously to form a dataset. Based on this dataset, the least squares method is used to fit a quadratic polynomial mapping relationship model between the damping torque and the impedance value, and the model is embedded in the signal processing chip for online real-time calculation.

[0039] S3: Based on the pre-generated target task damping curve as the feedforward target, and with the real-time damping value as feedback, the controller calculates and generates adjustment commands.

[0040] Preferably, the target task damping curve is obtained by simulation using a digital twin model that incorporates joint dynamics and the intrinsic properties of shear-thickening fluid.

[0041] Specifically, the controller calculates and generates adjustment commands, the details of which are as follows: The value of the target task damping curve in the current and several future control cycles is used as the reference trajectory, and the real-time damping value is used as the current state feedback.

[0042] A model predictive control algorithm is adopted to minimize the tracking error between the real-time damping value and the reference trajectory as the optimization objective, and the physical constraints of the field-induced mechanism are considered to solve an optimal control problem in the finite time domain online.

[0043] The first element in the optimal control sequence obtained by solving, that is, the target intensity setpoint of the physical field in the current control cycle, is used as the adjustment command output.

[0044] Furthermore, the construction of digital twin models includes: Establish a rigid-flexible coupled multibody dynamics model for robot joints.

[0045] Through rheological experiments, a rheological constitutive model of the shear-thickening fluid under various physical field intensities was calibrated and established. The constitutive model describes the relationship between its viscosity and the shear rate and physical field intensity.

[0046] The dynamic model is coupled with the rheological constitutive model to form a digital twin model.

[0047] Specifically, the digital twin model runs offline. For a typical task (such as "quickly locate and gently grasp"), the target trajectory is input into the digital twin model, and the optimal damping force curve for the entire process, i.e. the target task damping curve, is solved by the trajectory optimization algorithm with the goal of minimizing joint vibration and energy consumption.

[0048] Preferably, this step innovatively uses the "target task damping curve" generated offline based on a high-fidelity digital twin model as the feedforward target, and combines it with a model predictive control algorithm based on real-time damping feedback. The feedforward gives the system the ability to predict and plan the overall task, making the damping adjustment change from a passive response to an active adaptation. The model predictive control uses the feedforward as the setpoint, performs online rolling optimization and handles constraints and disturbances, ensuring that the actual damping accurately and robustly tracks the ideal trajectory. The two work together to achieve a unity of optimal global performance and local dynamic robustness.

[0049] For example, for the "fast and accurate pick-and-place" task, in the simulation environment, a digital twin is constructed by combining the rigid-flexible coupling dynamic model of the joint with the electric field-viscosity constitutive model of the STF. With the goal of minimizing the end-point trajectory tracking error and joint vibration, the optimal damping torque curve is obtained offline. During actual control, the model predictive controller uses this curve as a reference, combines real-time damping feedback, and continuously solves and outputs the optimal electric field strength command.

[0050] S4: Based on the adjustment command, the field-induced mechanism in the joint is driven to change the physical field acting on the shear-thickening fluid, so that the real-time damping tracks the target task damping curve.

[0051] Preferably, the field-induced mechanism within the joint is driven by adjustment commands to change the physical field acting on the shear-thickening fluid, enabling real-time damping to track the target task's damping curve. The specific details are as follows: The adjustment command, i.e. the target field strength setpoint, is converted into a specific voltage, current or power signal that drives the field-actuating mechanism (such as an electrode, coil or transducer).

[0052] A specific signal is applied to the field-induced mechanism by a power amplifier or drive circuit, thereby generating or changing the corresponding (electric, magnetic or acoustic) physical field in the joint cavity.

[0053] Physical fields act directly on shear-thickening fluids, altering the interaction barriers or arrangement of dispersed phase particles, thereby continuously and in real time adjusting their apparent viscosity and shear-thickening response characteristics.

[0054] Through rheological control, the actual damping force / torque output by the joint (i.e., the real-time damping value) is dynamically approximated to the target value on the target task damping curve under the closed-loop action of the controller, thus achieving precise tracking of damping.

[0055] Specifically, the field-induced change mechanism, sensing unit, and joint cavity need to be designed as an integrated sealed unit to ensure that the STF does not leak and that the electrical / optical connections are reliable.

[0056] Preferably, by precisely converting adjustment commands into physical fields (electric, magnetic, acoustic) and applying them to the STF, the microscopic interactions and arrangement of its dispersed phase particles are directly controlled, thereby achieving a wide range, continuous, and rapid adjustment of the material's apparent viscosity and shear thickening characteristics. At the same time, the integrated sealed design of the field-induced damping mechanism, sensing unit, and cavity is emphasized, which not only ensures the sealing reliability of the STF damping medium during long-term service, but also guarantees the stability of the sensing and control signal chain. This is the key to the entire intelligent damping system moving from theoretical schemes to engineering applications.

[0057] For example, the voltage command output by the controller drives the electrodes inside the chamber via a high-voltage amplifier to generate a regulating electric field of 0-3kV / mm. This electric field changes the interaction and arrangement of particles in the STF, thereby regulating its viscosity in real time. At the same time, the chamber is sealed in an integrated manner with a fluororubber sealing ring and potting compound to ensure fluid sealing and electrical insulation, so that the damping output can stably track the target curve.

[0058] In summary, this invention achieves in-situ, direct measurement of damping force by employing a composite STF material with intrinsic sensing capabilities. It combines a digital twin model to perform forward simulation of the task to generate an optimal damping curve as a feedforward target. Furthermore, it utilizes a model predictive control algorithm to collaboratively optimize the feedforward target and the real-time sensed damping feedback, thereby constructing a deeply coupled intelligent closed-loop system. Ultimately, this system achieves real-time, precise, smooth, and adaptive adjustment of robot joint damping, systematically improving dynamic response speed, trajectory tracking accuracy, anti-disturbance capability, and human-machine interaction safety.

[0059] Example 2, an embodiment of the present invention, provides a real-time adjustment system for shear-thickening fluid in robot joint damping, comprising: a signal acquisition module for acquiring intrinsic sensing signals from the shear-thickening fluid within the joint cavity; a signal analysis module for analyzing the intrinsic sensing signals based on a pre-established sensing signal-damping relationship model to calculate the real-time damping value of the joint; a command generation module for generating adjustment commands based on a pre-generated target task damping curve as a feedforward target and the real-time damping value as feedback, through a controller; and a command driving module for driving a field-induced mechanism within the joint based on the adjustment commands to change the physical field acting on the shear-thickening fluid, thereby enabling the real-time damping to track the target task damping curve.

[0060] Example 3 is an embodiment of the present invention, which differs from the previous embodiment in that: If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0061] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0062] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0063] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0064] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for real-time adjustment of shear-thickening fluid in robot joint damping, characterized in that: include: Acquire intrinsic sensing signals from the shear-thickened fluid within the joint cavity; Based on the pre-established sensing signal-damping relationship model, the intrinsic sensing signal is analyzed, and the real-time damping value of the joint is calculated. Based on the pre-generated target task damping curve as the feedforward target, and with the real-time damping value as feedback, the controller calculates and generates adjustment commands. Based on the adjustment command, the field-induced mechanism inside the joint is driven to change the physical field acting on the shear-thickening fluid, so that the real-time damping tracks the target task damping curve.

2. The method for real-time adjustment of shear-thickening fluid in robot joint damping as described in claim 1, characterized in that: The intrinsic sensing signal is an electrical or optical parameter that varies with the fluid shear viscosity. The intrinsic sensing signal is obtained by detecting impedance changes through an embedded microelectrode or by detecting fluorescence intensity / wavelength changes through an optical fiber sensor. The target task damping curve is obtained by simulation using a digital twin model that incorporates joint dynamics and the intrinsic properties of the shear-thickening fluid.

3. The method for real-time adjustment of shear-thickening fluid in robot joint damping as described in claim 1, characterized in that: The specific details of the sensing signal-damping relationship model are as follows: The intrinsic sensing signals acquired in real time are input into the sensing signal-damping relationship model; The sensing signal-damping relationship model is based on a pre-calibrated mapping relationship, which maps the input sensing signal value to the corresponding damping force or damping torque value. Output the damping force or damping torque value as the real-time damping value.

4. The method for real-time adjustment of shear-thickening fluid in robot joint damping as described in claim 1, characterized in that: The adjustment command is generated by the controller, and the specific content is as follows: The value of the target task damping curve in the current and several future control cycles is used as the reference trajectory, and the real-time damping value is used as the current state feedback. A model predictive control algorithm is adopted to minimize the tracking error between the real-time damping value and the reference trajectory as the optimization objective, and the physical constraints of the field-induced mechanism are considered to solve an optimal control problem in the finite time domain online. The first element in the optimal control sequence obtained by solving, that is, the target intensity setpoint of the physical field in the current control cycle, is used as the adjustment command output.

5. The method for real-time adjustment of shear-thickening fluid in robot joint damping as described in claim 1, characterized in that: The field-induced mechanism within the joint, driven by adjustment commands, alters the physical field acting on the shear-thickening fluid, enabling real-time damping to track the target task's damping curve. Details are as follows: The adjustment command, i.e. the target field strength set value, is converted into a specific voltage, current or power signal to drive the field-actuated mechanism; A specific signal is applied to the field-induced mechanism by a power amplifier or drive circuit, thereby generating or changing the corresponding physical field in the joint cavity. The physical field acts directly on the shear-thickening fluid, changing the interaction barrier or arrangement structure of its dispersed phase particles, thereby adjusting its apparent viscosity and shear-thickening response characteristics in real time and continuously. Through rheological control, the actual damping force / torque output by the joint is dynamically approximated to the target value on the target task damping curve under the closed-loop action of the controller, thus achieving precise tracking of damping.

6. The method for real-time adjustment of shear-thickening fluid in robot joint damping as described in claim 2, characterized in that: The construction of the digital twin model includes: Establish a rigid-flexible coupled multibody dynamics model for robot joints; Through rheological experiments, a rheological constitutive model of shear-thickening fluid under various physical field intensities was calibrated and established. The constitutive model describes the relationship between its viscosity and the shear rate and physical field intensity. The dynamic model is coupled with the rheological constitutive model to form a digital twin model.

7. The method for real-time adjustment of shear-thickening fluid in robot joint damping as described in claim 3, characterized in that: The sensing signal-damping relationship model is pre-established through the following steps: Under various steady-state shear conditions and physical field strengths covering the expected working range, the standard values ​​of intrinsic sensing signals and the corresponding standard values ​​of joint output damping forces are simultaneously collected to form a calibration dataset. Using a calibrated dataset, parameter identification or machine learning training is performed on a pre-defined model structure to obtain a pre-calibrated mapping relationship.

8. A real-time adjustment system for shear-thickening fluid in robot joint damping, based on the real-time adjustment method for shear-thickening fluid in robot joint damping according to any one of claims 1 to 7, characterized in that: include, The signal acquisition module is used to acquire intrinsic sensing signals from the shear-thickening fluid within the joint cavity; The signal analysis module is used to analyze the intrinsic sensing signal based on the pre-established sensing signal-damping relationship model and calculate the real-time damping value of the joint. The instruction generation module is used to generate adjustment instructions by the controller based on the pre-generated target task damping curve as the feedforward target and the real-time damping value as feedback. The command-driven module is used to drive the field-induced mechanism within the joint based on adjustment commands, thereby changing the physical field acting on the shear-thickening fluid and enabling real-time damping to track the target task's damping curve.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for real-time adjustment of shear-thickening fluid in robot joint damping as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the real-time adjustment method of the shear-thickening fluid in robot joint damping as described in any one of claims 1 to 7.

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