Intelligent clamping jaw dynamic control system based on multi-sensing feedback

Abstract

According to the intelligent clamping jaw dynamic control system based on multi-sensing feedback, through the technical scheme that the driving module, the detection module and the control module work cooperatively, the intelligent clamping jaw can be controlled on the basis that the driving module provides opening or tightening power for the intelligent clamping jaw; a weight sensor, a temperature sensor, a torque sensor and a micro pressure sensor array in the detection module collect key data of the intelligent clamping jaw in real time from different aspects respectively, the control module analyzes the data, and the voltage and / or current of the intelligent clamping jaw is accurately adjusted according to the analysis result. According to the intelligent clamping jaw, the stability and accuracy of object grabbing of the intelligent clamping jaw can be effectively improved, the problem that grabbing fails due to the fact that a traditional clamping jaw cannot sense and adapt to working state changes in real time in the grabbing process is solved, the adaptability of the clamping jaw to different working scenes is enhanced, and the working efficiency and reliability of the whole system are improved.

Description

Technical Field

[0001] The invention relates to the technical field of electromechanical control, and in particular to an intelligent gripper dynamic control system based on multi-sensor feedback. Background Art

[0002] In today's entertainment and business fields, gift machines (such as claw machines) are a popular entertainment device. The performance of the core component, the gripper drive system, directly affects the user experience and the operating efficiency of the device. Traditional gripper drive systems usually use a constant voltage to drive the electromagnetic coil to control the opening and closing of the gripper through electromagnetic force. However, this design has many technical defects, which seriously limits the performance and intelligence level of the device.

[0003] Traditional systems rely on a constant electromagnetic coil voltage (such as 48VDC), and their output force will significantly decay as the temperature rises. According to the resistance formula R = ρL / SR = ρL / S, the coil resistance value RR will increase with the temperature, resulting in a decrease in electromagnetic force. Actual measured data shows that after one hour of continuous operation, the grasping success rate of the traditional gripper system drops by as much as 37%. This temperature drift phenomenon not only affects the stability of the equipment, but also increases the operating and maintenance costs.

[0004] The existing gripper system cannot automatically adjust the gripping force according to the weight of the gift, and usually requires manual preset thresholds. This design performs poorly when facing objects of different weights, especially for lightweight objects (such as gifts less than 100g), the slip rate exceeds 60%. This lack of adaptive system not only reduces the user experience, but also increases the failure rate of the device.

[0005] It can be seen that the conventional gripper drive system has at least the following problems: (1) defective fixed gripper force; (2) poor load adaptability. Summary of the invention

[0006] The purpose of the present invention is to provide a dynamic control system of an intelligent gripper based on multi-sensor feedback, which can effectively improve the stability and accuracy of the intelligent gripper in grasping objects, overcome the problems of grasping failure caused by the inability of traditional grippers to perceive and adapt to changes in working conditions in real time during the grasping process, enhance the adaptability of the gripper to different working scenarios, and improve the working efficiency and reliability of the entire system.

[0007] To achieve the above-mentioned purpose, the present invention provides an intelligent gripper dynamic control system based on multi-sensor feedback, comprising:

[0008] A driving module for driving the intelligent gripper to open or tighten;

[0009] The detection module at least comprises a weight sensor for monitoring the weight of the object grasped by the gripper, a temperature sensor, a torque sensor and a micro pressure sensor array, wherein the temperature sensor is arranged on the surface of the electromagnetic coil of the smart gripper, the torque sensor for verifying the consistency of the support arm sensor data is installed on the rotary joint transmission shaft of any one or more grippers, and the micro pressure sensor array is distributed on the fingertip contact surface of the smart gripper;

[0010] Built-in control module with dynamic force field model or PWM compensation algorithm and temperature-resistance curve;

[0011] The control module adjusts the voltage and / or current of the smart gripper based on the data from the detection module.

[0012] According to a technical solution of the present invention, the weight sensor is an extrusion type gravity sensor, and the weight sensor is arranged at the root of the support arm of the intelligent gripper or the support roller of the overhead travelling vehicle or the swing plate of the overhead travelling vehicle or the support roller of the overhead travelling vehicle plate;

[0013] The weight sensor is a displacement weight sensor, which includes a magnetic scale arranged in the intelligent gripper and a positioning mark point arranged on the magnetic flux axis of the coil.

[0014] According to a technical solution of the present invention, the weight sensor has a range of 0-5kg and an accuracy of ±1%, and outputs a 0-5mV / g signal through a Wheatstone bridge.

[0015] The temperature sensor has a temperature measurement range of -20°C to 150°C and uses an RS485 interface output;

[0016] The torque sensor has a measuring range of 0-10 N·m / ±0.1% FS;

[0017] The measuring range of the micro pressure sensor array is 0-50N / cm 2 / ±1%.

[0018] According to a technical solution of the present invention, the intelligent gripper dynamic control system further includes:

[0019] Communication module for uploading device ID, timestamp, weight, and temperature to the cloud;

[0020] The cloud is configured with an edge computing unit for realizing temperature mutation warning and regional parameter synchronization, and the edge computing unit runs the LSTM model accelerated by TensorRT.

[0021] According to a technical solution of the present invention, the weight sensor and the torque sensor constitute a data verification mechanism.

[0022] The torque value T measured by the torque sensor and the weight value W measured by the weight sensor satisfy |TW×r|>5%, triggering an abnormal alarm, wherein r is the radius of any clamping jaw of the intelligent clamping jaw.

[0023] According to a technical solution of the present invention, the built-in PWM compensation algorithm of the control module adopts a polynomial fitting formula:

[0024] V comp =V base ×[1+α1(TT ref )+α2(TT ref )2]

[0025] Where T is the current temperature, T ref is the reference temperature, V base It represents the theoretical voltage value required for the smart gripper to grasp the target at the reference temperature, V comp The actual output voltage after correction, α1 represents the first compensation coefficient, and α2 represents the second compensation coefficient.

[0026] According to a technical solution of the present invention, the micro pressure sensor array is composed of a 4×4 pressure-sensitive unit matrix.

[0027] According to a technical solution of the present invention, an aluminum nitride ceramic layer with a thickness of 0.3 to 0.8 mm is provided between the electromagnetic coil and the temperature sensor.

[0028] According to a technical solution of the present invention, the weight sensor and the torque sensor perform data fusion:

[0029] The state equation is expressed as:

[0030] x k =x k-1 +w k

[0031] Among them, w k is the process noise;

[0032] The observation equation is expressed as:

[0033] z k =Hx k +v k

[0034] Where H = [1, 0.98] T , v k is the observation noise;

[0035] The Kalman gain is expressed as:

[0036] K k =P k|k-1 HT (HP k|k-1 H T +R) -1

[0037] Final output:

[0038] W used =(W1+0.98×W2) / 1.98

[0039] Among them, the coefficient 0.98 comes from the measured value of mechanical transmission efficiency.

[0040] According to a technical solution of the present invention, the intelligent gripper dynamic control system further includes:

[0041] A power management module supporting a 24VDC main power supply and a super capacitor group redundant power supply, wherein the overcurrent protection response time of the power management module is less than 1μs.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] According to one concept of the present invention, a dynamic control system of an intelligent gripper based on multi-sensor feedback is proposed. By setting a technical solution in which a driving module, a detection module and a control module work together, on the basis of the driving module providing opening or tightening power for the intelligent gripper, the weight sensor, temperature sensor, torque sensor and micro pressure sensor array in the detection module respectively collect key data of the intelligent gripper when working in real time from different aspects. The control module analyzes the above data and accurately adjusts the voltage and / or current of the intelligent gripper according to the analysis results. This can effectively improve the stability and accuracy of the intelligent gripper in grasping objects, overcome the problems of grasping failure caused by the inability of traditional grippers to perceive and adapt to changes in working conditions in real time during the grasping process, enhance the adaptability of the gripper to different working scenarios, and improve the working efficiency and reliability of the entire system.

[0044] The present invention adopts a PWM compensation algorithm and a dynamic force field model, which greatly suppresses the influence of temperature on the output force of the gripper, ensures that the gripper can stably grasp objects in different temperature environments, and significantly improves the success rate and stability of grasping.

[0045] The present invention has the function of automatically adjusting the clamping force according to the weight of the object, which solves the problem that the traditional clamping claws have poor adaptability to grasping objects of different weights, enhances the versatility of the equipment, and brings a better use experience to users.

[0046] The present invention uses 4G communication modules and edge computing units to achieve network analysis of equipment operation data and coordinated optimization of equipment groups. It can not only adjust equipment parameters according to data analysis to increase revenue at popular locations, but also detect and solve potential faults in advance through predictive maintenance, reduce equipment downtime, effectively reduce operating costs, and comprehensively improve operating efficiency.

[0047] The present invention provides a data verification mechanism for the weight sensor and the torque sensor, and combines it with safety protection measures such as a double emergency stop design and data integrity verification to comprehensively ensure the safety and reliability of the system operation, avoid safety accidents caused by data errors or equipment failures, and effectively protect the safety of equipment and personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 A schematic diagram schematically shows the composition of an intelligent gripper dynamic control system based on multi-sensor feedback according to an embodiment of the present invention;

[0050] Figure 2 A schematic diagram schematically showing the installation positions of a weight sensor and a torque sensor according to an embodiment of the present invention;

[0051] Figure 3 A schematic diagram schematically shows an installation position of a weight sensor according to another embodiment of the present invention;

[0052] Figure 4 A schematic diagram schematically shows an installation position of a weight sensor according to another embodiment of the present invention;

[0053] Figure 5 A schematic diagram schematically shows an installation position of a weight sensor according to another embodiment of the present invention;

[0054] Figure 6 The figure schematically shows the installation position of a weight sensor according to another embodiment of the present invention. DETAILED DESCRIPTION

[0055] The description of the embodiments of this specification should be combined with the corresponding drawings, which should be considered as part of the complete specification. In the drawings, the shape or thickness of the embodiments may be enlarged and indicated for simplification or convenience. Furthermore, the parts of each structure in the drawings will be described separately. It is worth noting that the elements not shown in the drawings or not described in words are in a form known to ordinary technicians in the relevant technical field.

[0056] The description of the embodiments herein and any reference to directions and orientations are only for the convenience of description and are not to be construed as any limitation on the scope of protection of the present invention. The following description of the preferred embodiments may involve combinations of features, which may exist independently or in combination, and the present invention is not particularly limited to the preferred embodiments. The scope of the present invention is defined by the claims.

[0057] When describing the embodiments of the present invention, the orientation or positional relationship expressed by the terms "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" are based on the orientation or positional relationship shown in the relevant drawings and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the above terms should not be understood as limiting the present invention.

[0058] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments cannot be described one by one here, but the embodiments of the present invention are not therefore limited to the following embodiments.

[0059] like Figure 1 As shown, according to one embodiment of the present invention, the present invention provides an intelligent gripper dynamic control system based on multi-sensor feedback, comprising:

[0060] A drive module for driving the intelligent gripper to open or tighten; as the power source for the intelligent gripper to open or tighten, it adopts advanced electromagnetic drive technology combined with a double-winding electromagnetic coil design. The main winding wire diameter is 0.5mm, the number of turns is 200±5, and it can provide the main clamping force; the compensation winding wire diameter is 0.3mm, the number of turns is 50±2, and it is used to compensate when the main winding is affected by temperature and other factors. By accurately controlling the current and voltage of the electromagnetic coil, the precise driving of the gripper action is achieved, ensuring that the gripper can stably grasp objects of different weights and shapes.

[0061] The detection module at least includes a weight sensor, a temperature sensor, a torque sensor and a micro pressure sensor array. The temperature sensor is arranged on the surface of the electromagnetic coil of the smart gripper. The torque sensor used to verify the consistency of the support arm sensor data is installed on the rotary joint transmission shaft of any one or more grippers. Usually, at least three grippers are arranged on a smart gripper. When multiple grippers are arranged, the accuracy can be improved by using the average value. The micro pressure sensor array is distributed on the fingertip contact surface of the smart gripper.

[0062] Built-in control module with dynamic force field model or PWM compensation algorithm and temperature-resistance curve;

[0063] The control module adjusts the voltage and / or current of the smart gripper based on the data from the detection module.

[0064] Among them, the control module adopts the STM32F407 main control chip, with built-in dynamic force field model and PWM compensation algorithm.

[0065] The dynamic force field model is expressed as: F = k*(W / ΔT) + b; where W represents weight, and ΔT is the difference between the current temperature and the reference temperature of 25°C; the dynamic force field model comprehensively considers the influence of the weight W of the clamped object and the temperature rise difference ΔT of the electromagnetic coil on the clamping force, and determines the model parameters k and b through a large number of experiments and data analysis to achieve accurate prediction and control of the clamping force. ΔT is usually set to 5°C, and the temperature sensor uses PT100 to monitor the support arm temperature in real time. When ΔT>5°C, the compensation calculation is started to ensure that the clamp can maintain a stable and appropriate clamping force under different temperature conditions.

[0066] The PWM compensation algorithm can be used to correct the duty cycle in real time according to the temperature-resistance curve, with a compensation range of 5%-15%. The PWM compensation algorithm uses a polynomial fitting formula:

[0067] V comp =V base ×[1+α1(TT ref )+α2(TT ref )2]

[0068] Where T is the current temperature, T ref is the reference temperature, V base It represents the theoretical voltage value required by the intelligent gripper to grasp the target at the reference temperature, which can be found through the weight-voltage mapping table (Lookup Table). comp The actual output voltage after correction, α1 represents the first compensation coefficient, and α2 represents the second compensation coefficient.

[0069] The α1 and α2 coefficients are determined by least squares fitting of the thermal cycle test data. The test temperature range covers -20℃ to 120℃, α1∈[0.0048,0.0055] / ℃, α2∈[-0.00025,-0.00015] / ℃2.

[0070] For example, the compensation coefficient is obtained through thermal cycle testing, and the specific data is shown in Table 1 below:

[0071]

[0072] Table 1

[0073] The least squares method is used to fit:

[0074] α1=0.00512 / ℃

[0075] α2=-0.00019 / ℃2.

[0076] In some embodiments of the present invention, the detection module is composed of a variety of high-precision sensors. The weight sensor has a range of 0-5kg and an accuracy of ±1%. The bending moment of the support arm is converted into an electrical signal with the help of a Wheatstone bridge. The weight of the clamped object is calculated based on the material mechanics formula, and a 0-5mV / g signal is output to accurately measure the weight of the object in real time. The PT100 temperature sensor fits tightly to the surface of the electromagnetic coil, with a temperature measurement range of -20°C to 150°C. It uses an RS485 interface output and can quickly and accurately monitor coil temperature changes to provide key data for subsequent control. The torque sensor is installed on the rotary joint drive shaft, with a range of 0-10N·m / ±0.1%FS. The accuracy of the support arm weight sensor data is verified through the relationship T=W×r (r is the radius of the gripper) to ensure the reliability of the measurement data. The micro pressure sensor array is distributed on the contact surface of the gripper fingertips, with a range of 0-50N / cm 2 / ±1%, composed of a 4×4 pressure-sensitive unit matrix, can accurately detect the contact pressure distribution, effectively prevent the object from slipping during the grasping process, and ensure the grasping stability.

[0077] When installing the detection module, the accuracy should be guaranteed as much as possible. When installing the weight sensor, ensure that the installation position is stable to avoid shaking that affects the measurement accuracy; the PT100 temperature sensor is attached to the surface of the electromagnetic coil, the installation distance is no more than 2mm, and it is filled with thermal conductive silicone to ensure good heat conduction; the torque sensor is installed on the transmission shaft of the rotating joint to ensure installation concentricity and reduce mechanical errors; the micro pressure sensor array is evenly distributed on the contact surface of the gripper fingertips to ensure accurate detection of contact pressure. At the same time, the connection lines of each sensor use shielded wires to reduce signal interference.

[0078] The working principle of the weight sensor is based on the strain gauge principle. When the object to be measured is placed on the sensor, its gravity causes the elastic body inside the sensor to deform. This deformation is sensed by the strain gauge fixed on the elastic body, which in turn causes the resistance value of the strain gauge to change. Through a specific circuit, this change in resistance value is converted into an electrical signal output that is proportional to the weight of the object.

[0079] When an object is caught on the sensor, the weight of the object and the elastic body inside the sensor are deformed, and this deformation is sensed by the strain gauge fixed on the elastic body, which in turn causes the resistance value of the strain gauge to change. Through a specific circuit, this change in resistance value is converted into an electrical signal output that is proportional to the weight of the object.

[0080] The weight sensor is a compression type gravity sensor, which is actually a piezoresistor. The weight sensor can be arranged inside the intelligent gripper, such as Figure 2 As shown, the squeeze gravity sensor can be set Figure 2 The one or more positions shown are preferably arranged at the root of the support arm of the smart gripper, and the target weight is obtained by squeezing between the support arms connected to the gripper.

[0081] like Figure 4 and Figure 5 As shown, the extrusion type gravity sensor can be arranged on the supporting roller of the overhead travelling vehicle, the swing plate of the overhead travelling vehicle or the supporting roller of the overhead travelling vehicle plate; Figure 6 As shown, the extrusion type gravity sensor can also be arranged between the movable paddle and the micro switch of the overhead travelling vehicle plate.

[0082] The weight sensor is a displacement weight sensor, including a magnetic scale arranged in the intelligent gripper and a positioning mark point arranged on the magnetic flux axis of the coil, such as Figure 3 shown.

[0083] The intelligent gripper dynamic control system also includes:

[0084] Communication module for uploading device ID, timestamp, weight, and temperature to the cloud;

[0085] The cloud is configured with an edge computing unit for realizing temperature mutation warning and regional parameter synchronization, and the edge computing unit runs the LSTM model accelerated by TensorRT.

[0086] The communication module uses a 4G communication module, such as SIM7600CE, which supports the LTE Cat1 standard and has a downlink speed of 10Mbps. It uploads key parameters such as device ID, timestamp, weight, temperature, grabbing stage, and award status to the cloud through the TCP / UDP / MQTT protocol. The cloud is equipped with an edge computing unit, using NVIDIA Jetson Nano to run the LSTM model accelerated by TensorRT. The model can analyze the equipment operation data in real time, realize temperature mutation warning (triggered when dT / dt>5℃ / min), and discover potential fault hazards in advance; at the same time, it generates a regional heat map and synchronizes relevant parameters to similar equipment within a radius of 500m, so as to achieve collaborative optimization of the equipment group and improve overall operational efficiency. The edge computing unit locally caches 72 hours of operation data to ensure that data is not lost and can continue to be analyzed and processed in the event of network failure.

[0087] It can analyze the equipment operation data in real time, realize temperature mutation warning (trigger when dT / dt>5℃ / min), and discover potential fault hazards in advance.

[0088] In some embodiments of the present invention, the weight sensor and the torque sensor constitute a data verification mechanism.

[0089] The torque value T measured by the torque sensor and the weight value W measured by the weight sensor satisfy |TW×r|>5%, triggering an abnormal alarm, wherein r is the radius of any clamping jaw of the intelligent clamping jaw.

[0090] The weight sensor and torque sensor have established a data verification mechanism. When the torque value T measured by the torque sensor and the weight value W measured by the weight sensor satisfy |TW×r|>5% (r is the radius of any gripper of the smart gripper), the system immediately triggers an abnormal alarm. Based on this mechanism, sensor failures or measurement anomalies can be discovered in a timely manner, ensuring the accuracy and reliability of system data, avoiding gripper control errors caused by erroneous data, and ensuring a stable and reliable grasping process.

[0091] In some embodiments of the present invention, an aluminum nitride ceramic layer with a thickness of 0.3-0.8 mm is provided between the electromagnetic coil and the temperature sensor.

[0092] In some embodiments of the present invention, the weight sensor and the torque sensor perform data fusion:

[0093] The state equation is expressed as:

[0094] x k =x k-1 +w k

[0095] Among them, w kis the process noise;

[0096] The observation equation is expressed as:

[0097] z k =Hx k +v k

[0098] Where H = [1, 0.98] T , v k is the observation noise;

[0099] The Kalman gain is expressed as:

[0100] K k =P k|k-1 H T (HP k|k-1 H T +R) -1

[0101] Final output:

[0102] W used =(W1+0.98×W2) / 1.98

[0103] Among them, the coefficient 0.98 comes from the measured value of mechanical transmission efficiency.

[0104] Automatically enhance data fusion when grasping irregular objects. The Kalman filter activation conditions are shown in Table 2 below:

[0105]

[0106] Table 2

[0107] In some embodiments of the present invention, the intelligent gripper control system further comprises:

[0108] A power management module supporting a 24VDC main power supply and a super capacitor group redundant power supply, wherein the overcurrent protection response time of the power management module is less than 1μs.

[0109] A dual redundant power supply system is built, with the main power supply being 24VDC. When the main power supply fails or the power is cut off instantly, the backup supercapacitor group can complete the switch in less than 10ms to ensure uninterrupted operation of the equipment. The integrated overvoltage / overcurrent protection circuit uses high-performance MOSFET and protection chips, with a response time of less than 1μs. When the current exceeds 15A, the hardware emergency stop circuit quickly cuts off the power supply to protect the equipment. At the same time, the temperature sensor is used to monitor the coil temperature and dynamically adjust the coil supply current to avoid overheating of the coil, ensuring that the electromagnetic coil works stably under various working conditions.

[0110] Cyclic shock at ambient temperature The control system of the present application was tested under the vibration condition of 5-500 Hz random spectrum, as shown in Table 1 below:

[0111] index Single sensor solution Multi-sensor fusion solution Improvement Weight detection accuracy ±15g ±3g 80％ Temperature drift suppression rate 45％ 92％ 104％ Clamping success rate accuracy 68％ 89％ 31％

[0112] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent gripper dynamic control system based on multi-sensor feedback, characterized in that: include: A driving module for driving the intelligent gripper to open or tighten; The detection module at least comprises a weight sensor for monitoring the weight of the object grasped by the gripper, a temperature sensor, a torque sensor and a micro pressure sensor array, wherein the temperature sensor is arranged on the surface of the electromagnetic coil of the smart gripper, the torque sensor for verifying the consistency of the support arm sensor data is installed on the rotary joint transmission shaft of any one or more grippers, and the micro pressure sensor array is distributed on the fingertip contact surface of the smart gripper; Built-in control module with dynamic force field model or PWM compensation algorithm and temperature-resistance curve; The control module adjusts the voltage and / or current of the smart gripper based on the data from the detection module.

2. The intelligent gripper dynamic control system based on multi-sensor feedback according to claim 1 is characterized in that: The weight sensor is an extrusion type gravity sensor, and the weight sensor is arranged at the root of the support arm of the intelligent gripper or the support roller of the overhead travelling vehicle or the swing plate of the overhead travelling vehicle or the support roller of the overhead travelling vehicle plate; The weight sensor is a displacement weight sensor, which includes a magnetic scale arranged in the intelligent gripper and a positioning mark point arranged on the magnetic flux axis of the coil.

3. The intelligent gripper dynamic control system based on multi-sensor feedback according to claim 1 is characterized in that: The weight sensor has a range of 0-5kg and an accuracy of ±1%. It outputs a 0-5mV / g signal through a Wheatstone bridge. The temperature sensor has a temperature measurement range of -20°C to 150°C and uses an RS485 interface output; The torque sensor has a measuring range of 0-10 N·m / ±0.1% FS; The micro pressure sensor array has a measuring range of 0-50N / cm 2 / ±1%.

4. The intelligent gripper dynamic control system based on multi-sensor feedback according to claim 1 is characterized in that: Also includes: Communication module for uploading device ID, timestamp, weight, and temperature to the cloud; The cloud is configured with an edge computing unit for realizing temperature mutation warning and regional parameter synchronization, and the edge computing unit runs the LSTM model accelerated by TensorRT.

5. The intelligent gripper dynamic control system based on multi-sensor feedback according to claim 1 is characterized in that: The weight sensor and torque sensor constitute a data verification mechanism. The torque value T measured by the torque sensor and the weight value W measured by the weight sensor satisfy |TW×r|>5%, triggering an abnormal alarm, wherein r is the radius of any clamping jaw of the intelligent clamping jaw.

6. The intelligent gripper dynamic control system based on multi-sensor feedback according to claim 1 is characterized in that: The built-in PWM compensation algorithm of the control module adopts a polynomial fitting formula: V comp =V base ×[1+α1(T-T ref )+α2(T-T ref )2] Where T is the current temperature, T ref is the reference temperature, V base It represents the theoretical voltage value required for the smart gripper to grasp the target at the reference temperature, V comp The actual output voltage after correction, α1 represents the first compensation coefficient, and α2 represents the second compensation coefficient.

7. The intelligent gripper dynamic control system based on multi-sensor feedback according to claim 6 is characterized in that: The micro pressure sensor array is composed of a 4×4 pressure-sensitive unit matrix.

8. The intelligent gripper dynamic control system based on multi-sensor feedback according to claim 1 is characterized in that: An aluminum nitride ceramic layer with a thickness of 0.3-0.8 mm is arranged between the electromagnetic coil and the temperature sensor.

9. The intelligent gripper dynamic control system based on multi-sensor feedback according to claim 1 is characterized in that: The weight sensor and the torque sensor perform data fusion: The state equation is expressed as: x k =x k-1 +w k Among them, w k is the process noise; The observation equation is expressed as: z k =Hx k +v k Where H = [1, 0.98] T , v k is the observation noise; The Kalman gain is expressed as: K k =P k|k-1 H T (HP k|k-1 H T +R) -1 Final output: Wf used =(W1+0.98×W2) / 1.98 Among them, the coefficient 0.98 comes from the measured value of mechanical transmission efficiency.

10. The intelligent gripper dynamic control system based on multi-sensor feedback according to claim 1 is characterized in that: Also includes: A power management module supporting a 24VDC main power supply and a super capacitor group redundant power supply, wherein the overcurrent protection response time of the power management module is less than 1μs.

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