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, on the basis that the driving module provides opening or tightening power for the intelligent clamping jaw, the intelligent clamping jaw is controlled to be opened or closed; 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] This utility model relates to the field of electromechanical control technology, specifically to an intelligent gripper dynamic control system based on multi-sensor feedback. Background Technology

[0002] In today's entertainment and commercial sectors, prize machines (such as claw machines) are a popular form of entertainment equipment, and the performance of their core component—the gripper drive system—directly impacts user experience and operational efficiency. Traditional gripper drive systems typically use a constant voltage to drive electromagnetic coils, controlling the opening and closing of the grippers through electromagnetic force. However, this design suffers from numerous technical flaws, severely limiting the device's performance and level of intelligence.

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

[0004] Existing gripper systems cannot automatically adjust the gripping force according to the weight of the gift, usually requiring manual preset of the threshold. This design performs poorly when dealing with items of varying weights, especially lightweight items (such as gifts weighing less than 100g), with a slippage rate exceeding 60%. This lack of adaptability not only degrades the user experience but also increases the device's failure rate.

[0005] It can be seen that traditional gripper drive systems have at least the following problems: (1) fixed gripper force deficiency; (2) poor load adaptability. Utility Model Content

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

[0007] To achieve the above-mentioned objectives, this utility model provides an intelligent gripper dynamic control system based on multi-sensor feedback, comprising:

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

[0009] The detection module comprises at least a weight sensor, a temperature sensor, a torque sensor, and a miniature pressure sensor array for monitoring the weight of the object gripped by the gripper. The temperature sensor is disposed 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 mounted on the drive shaft of the rotary joint of any one or more grippers. The miniature pressure sensor array is distributed on the fingertip contact surface of the smart gripper.

[0010] It has a built-in control module with a 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 one technical solution of this utility model, the weight sensor is a compression type gravity sensor, and the weight sensor is disposed at the root of the support arm of the intelligent gripper or on the support roller of the crane or the swing plate of the crane or the support roller of the crane plate.

[0013] The weight sensor is a displacement type weight sensor, which includes a magnetic scale set inside the smart gripper and a positioning marker set on the magnetic flux axis of the coil.

[0014] According to one technical solution of this utility model, the weight sensor has a measuring range of 0-5kg, 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℃ to 150℃ and uses an RS485 interface for output.

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

[0017] The range of the miniature pressure sensor array is 0-50 N / cm² / ±1%.

[0018] According to one technical solution of this utility model, the intelligent gripper dynamic control system further includes:

[0019] A communication module used to upload device ID, timestamp, weight, and temperature to the cloud;

[0020] The cloud is equipped with edge computing units for implementing temperature change warning and regional parameter synchronization. The edge computing units run LSTM models accelerated by TensorRT.

[0021] According to one technical solution of this utility model, the weight sensor and the torque sensor constitute a data verification mechanism.

[0022] If the torque value T measured by the torque sensor and the weight value W measured by the weight sensor satisfy |TW×r|>5%, an abnormal alarm is triggered, where r is the radius of any gripper of the intelligent gripper.

[0023] According to one technical solution of this utility model, the PWM compensation algorithm built into the control module adopts a polynomial fitting formula:

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

[0025] Where T is the current temperature, T ref V is the reference temperature. base This represents the theoretical voltage required for the intelligent gripper to grasp the target at the reference temperature, in V. comp The corrected actual output voltage, where α1 represents the first compensation coefficient and α2 represents the second compensation coefficient.

[0026] According to one technical solution of this utility model, the miniature pressure sensor array is composed of a 4×4 pressure-sensitive unit matrix.

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

[0028] According to one technical solution of this utility model, 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 This is 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 To observe noise;

[0035] Kalman gain is expressed as:

[0036] Kk =P k|k-1 H T HP k|k-1 H T +R) -1

[0037] Final output:

[0038] Wf used =(W1+0.98×W2) / 1.98

[0039] The coefficient 0.98 comes from the measured value of mechanical transmission efficiency.

[0040] According to one technical solution of this utility model, the intelligent gripper dynamic control system further includes:

[0041] A power management module that supports redundant power supply from a 24VDC main power supply and a supercapacitor bank, wherein the overcurrent protection response time of the power management module is <1μs.

[0042] Compared with the prior art, this utility model has the following advantages:

[0043] Based on one concept of this utility model, a dynamic control system for an intelligent gripper based on multi-sensor feedback is proposed. By setting up a technical solution where a drive module, a detection module, and a control module work collaboratively, the drive module provides opening or clamping power to the intelligent gripper. Meanwhile, the detection module, consisting of a weight sensor, a temperature sensor, a torque sensor, and a miniature pressure sensor array, collects key data from different aspects during the intelligent gripper's operation in real time. The control module analyzes this data and precisely adjusts the voltage and / or current of the intelligent gripper based on the analysis results. This effectively improves the stability and accuracy of the intelligent gripper in grasping objects, overcoming the problems of traditional grippers failing to grasp due to their inability to perceive and adapt to changes in working conditions in real time. It enhances the gripper's adaptability to different working scenarios and improves the overall system's efficiency and reliability.

[0044] This invention employs a PWM compensation algorithm and a dynamic force field model, which greatly suppresses the influence of temperature on the output force of the gripper, ensuring that the gripper can stably grasp items under different temperature environments, and significantly improving the success rate and stability of grasping.

[0045] This invention features an automatic adjustment function for gripping force based on the weight of the item, solving the problem of poor adaptability of traditional grippers to gripping items of different weights, enhancing the versatility of the device, and providing users with a better user experience.

[0046] This invention utilizes a 4G communication module and an edge computing unit to achieve networked analysis of equipment operation data and collaborative optimization of equipment clusters. It can not only adjust equipment parameters based on data analysis to increase revenue at popular locations, but also proactively identify and resolve potential faults through predictive maintenance, reducing equipment downtime, effectively lowering operating costs, and comprehensively improving operational efficiency.

[0047] This utility model incorporates a data verification mechanism for weight and torque sensors, along with a dual emergency stop design and data integrity verification and other safety protection measures. This comprehensively ensures the safety and reliability of the system operation, prevents safety accidents caused by data errors or equipment failures, and effectively protects the safety of equipment and personnel. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of this utility model or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this utility model. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This diagram illustrates the composition of an intelligent gripper dynamic control system based on multi-sensor feedback according to one embodiment of the present invention.

[0050] Figure 2 This diagram illustrates the installation positions of a weight sensor and a torque sensor according to one embodiment of the present invention.

[0051] Figure 3 This diagram illustrates the installation position of a weight sensor according to another embodiment of the present invention.

[0052] Figure 4 This diagram illustrates the installation position of a weight sensor according to another embodiment of the present invention.

[0053] Figure 5 This diagram illustrates the installation position of a weight sensor according to another embodiment of the present invention.

[0054] Figure 6 This diagram illustrates the installation position of a weight sensor according to another embodiment of the present invention.

[0055] Figure label:

[0056] 1. Temperature sensor; 2. Compression-type gravity sensor; 3. Torque sensor; 4. Micro switch; 5. Movable lever; 6. Electromagnetic coil; 7. Magnetic scale; 8. Coil flux shaft; 9. Positioning marker; 10. Thermally conductive silicone. Detailed Implementation

[0057] The description of the embodiments in this specification should be taken in conjunction with the accompanying drawings, which should form part of the complete specification. In the drawings, the shape or thickness of the embodiments may be exaggerated and may be indicated in a simplified or convenient manner. Furthermore, parts of the various structures in the drawings will be described separately; it is worth noting that elements not shown in the figures or not described in words are in a form known to those skilled in the art.

[0058] The description of the embodiments herein, including any references to direction and orientation, is for ease of description only and should not be construed as limiting the scope of protection of this utility model. The following description of preferred embodiments involves combinations of features, which may exist independently or in combination; this utility model is not particularly limited to the preferred embodiments. The scope of this utility model is defined by the claims.

[0059] In describing embodiments of this utility model, the terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer" express orientations or positional relationships based on the orientations or positional relationships shown in the relevant drawings. They are only for the convenience of describing this utility model and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the above terms should not be construed as limitations on this utility model.

[0060] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The embodiments cannot be described in detail here, but the embodiments of the present invention are not limited to the following embodiments.

[0061] 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:

[0062] A drive module for opening or clamping the intelligent gripper; as the power source for opening or clamping the intelligent gripper, it adopts advanced electromagnetic drive technology combined with a dual-winding electromagnetic coil 6 design. The main winding has a wire diameter of 0.5mm and 200±5 turns, providing the main clamping force; the compensation winding has a wire diameter of 0.3mm and 50±2 turns, used to compensate for performance changes in the main winding due to factors such as temperature. By precisely controlling the current and voltage of the electromagnetic coil 6, accurate drive of the gripper's movement is achieved, ensuring that the gripper can stably grasp items of different weights and shapes.

[0063] The detection module comprises at least a weight sensor, a temperature sensor 1, a torque sensor 3, and a miniature pressure sensor array. The temperature sensor 1 is disposed on the surface of the electromagnetic coil 6 of the intelligent gripper. The torque sensor 3, used to verify the consistency of the support arm sensor data, is mounted on the drive shaft of the rotary joint of any one or more grippers. Typically, an intelligent gripper has at least three grippers. When multiple grippers are disposed, the accuracy can be improved by averaging. The miniature pressure sensor array is distributed on the fingertip contact surface of the intelligent gripper.

[0064] It has a built-in control module with a dynamic force field model or PWM compensation algorithm and temperature-resistance curve;

[0065] The control module adjusts the voltage and / or current of the smart gripper based on the data from the detection module.

[0066] The control module uses an STM32F407 main control chip, which has a built-in dynamic force field model and PWM compensation algorithm.

[0067] The dynamic force field model is expressed as: F = k*(W / ΔT) + b; where W represents the weight, and ΔT is the difference between the current temperature and the reference temperature of 25℃. 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 6 on the clamping force. Through extensive experiments and data analysis, the model parameters k and b are determined to achieve accurate prediction and control of the clamping force. ΔT is typically set to 5℃. Temperature sensor 1 uses a PT100 to monitor the support arm temperature in real time. When ΔT > 5℃, compensation calculation is initiated to ensure that the gripper maintains a stable and appropriate clamping force under different temperature conditions.

[0068] The PWM compensation algorithm can be used to correct the duty cycle in real time based on the temperature-resistance curve, with a compensation range of 5%-15%. The PWM compensation algorithm uses a polynomial fitting formula:

[0069] V comp = V base × [1 + α1(T - T ref ) + α2(T - T ref )²]

[0070] Where T is the current temperature, T ref V is the reference temperature. base This represents the theoretical voltage required for the intelligent gripper to grasp the target at the reference temperature. It can be obtained from the weight-voltage lookup table. (V) comp The corrected actual output voltage, where α1 represents the first compensation coefficient and α2 represents the second compensation coefficient.

[0071] The coefficients α1 and α2 were determined by least-squares fitting of thermal cycling test data, with the test temperature range covering -20℃ to 120℃, α1∈[0.0048,0.0055] / ℃, α2∈[-0.00025,-0.00015] / ℃².

[0072] For example, the compensation coefficient is obtained through thermal cycling tests, and the specific data is shown in Table 1 below:

[0073]

[0074] Table 1

[0075] The least squares method was used to fit the data, and the following results were obtained:

[0076] α1 = 0.00512 / ℃

[0077] α1 = -0.00019 / ℃².

[0078] In some embodiments of this invention, the detection module consists of multiple high-precision sensors. The weight sensor has a range of 0-5kg and an accuracy of ±1%. It uses a Wheatstone bridge to convert the bending moment on the support arm into an electrical signal, calculates the weight of the clamped object based on material mechanics formulas, and outputs a 0-5mV / g signal for real-time and accurate weight measurement. The PT100 temperature sensor 1 is tightly fitted to the surface of the electromagnetic coil 6, with a temperature range of -20℃ to 150℃. It uses an RS485 interface for output and can quickly and accurately monitor coil temperature changes, providing crucial data for subsequent control. The torque sensor 3 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 using the relationship T=W×r (where r is the gripper radius), ensuring reliable measurement data. A miniature pressure sensor array is distributed on the contact surface of the gripper fingertips, with a range of 0-50N / cm² / ±1%. Composed of a 4×4 pressure-sensitive unit matrix, it can accurately detect the contact pressure distribution, effectively preventing slippage of the object during gripping and ensuring gripping stability.

[0079] During installation, the detection module should be designed to ensure maximum accuracy. For the weight sensor, ensure a stable mounting position to prevent movement that could affect measurement accuracy. The PT100 temperature sensor 1 should be mounted against the surface of the electromagnetic coil 6 at a distance not exceeding 2mm, and filled with thermally conductive silicone to ensure good heat conduction. The torque sensor 3 should be mounted on the rotary joint drive shaft, ensuring concentricity and minimizing mechanical errors. The miniature pressure sensor array should be evenly distributed on the contact surface of the gripper fingertips to ensure accurate detection of contact pressure. Furthermore, all sensor connection lines should use shielded cables to reduce signal interference.

[0080] The working principle of a weight sensor is based on the strain gauge principle. When the object being measured is placed on the sensor, its gravity causes the elastic body inside the sensor to deform. This deformation is sensed by a strain gauge fixed to the elastic body, which in turn causes a change in the resistance value of the strain gauge. 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.

[0081] When an object is gripped by the sensor, the object's weight and the elastic body inside the sensor deform. This deformation is sensed by a strain gauge fixed to the elastic body, causing a change in the strain gauge's resistance. Through a specific circuit, this change in resistance is converted into an electrical signal output that is proportional to the object's weight.

[0082] The weight sensor is a compression-type gravity sensor 2, which is essentially a pressure-sensitive resistor. The weight sensor can be installed inside the intelligent gripper, such as... Figure 2 As shown, the compression-type gravity sensor 2 can be set in... Figure 2 One or more positions are shown, preferably located 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.

[0083] like Figure 4 and Figure 5 As shown, the compression-type gravity sensor 2 can be installed on the crane support rollers, the crane's swing plate, or the crane plate's support rollers; as... Figure 6 As shown, the compression-type gravity sensor 2 can also be installed between the movable lever 5 and the micro switch 4 on the crane plate.

[0084] The weight sensor is a displacement-type weight sensor, including a magnetic scale disposed within the intelligent gripper and positioning markers disposed on the magnetic flux axis of the coil, such as... Figure 3 As shown.

[0085] The intelligent gripper dynamic control system also includes:

[0086] A communication module used to upload device ID, timestamp, weight, and temperature to the cloud;

[0087] The cloud is equipped with edge computing units for implementing temperature change warning and regional parameter synchronization. The edge computing units run LSTM models accelerated by TensorRT.

[0088] The communication module uses a 4G communication module, such as the SIM7600CE, supporting the LTE Cat1 standard with a downlink speed of up to 10Mbps. Key parameters such as device ID, timestamp, weight, temperature, capture stage, and prize-giving status are uploaded to the cloud via TCP / UDP / MQTT protocols. An edge computing unit is configured in the cloud, using NVIDIA Jetson Nano and running a TensorRT-accelerated LSTM model. This model can perform real-time analysis of device operating data, enabling temperature surge warnings (triggered when dT / dt > 5℃ / min) to detect potential faults early. Simultaneously, it generates regional heat maps, synchronizing relevant parameters to similar devices within a 500m radius, achieving collaborative optimization among device groups and improving overall operational efficiency. The edge computing unit locally caches 72 hours of operating data to ensure data integrity and continued analysis even in the event of network failures.

[0089] It can perform real-time analysis of equipment operating data and realize early warning of sudden temperature changes (triggered when dT / dt>5℃ / min), thus detecting potential faults in advance.

[0090] In some embodiments of this utility model, the weight sensor and the torque sensor 3 constitute a data verification mechanism.

[0091] The torque value T measured by the torque sensor 3 and the weight value W measured by the weight sensor satisfy |TW×r|>5%, triggering an abnormal alarm, where r is the radius of any gripper of the intelligent gripper.

[0092] A data verification mechanism is established between the weight sensor and the torque sensor 3. When the torque value T measured by the torque sensor 3 and the weight value W measured by the weight sensor satisfy |TW×r|>5% (where r is the radius of any gripper of the intelligent gripper), the system immediately triggers an abnormal alarm. Based on this mechanism, sensor faults or measurement anomalies can be detected in a timely manner, ensuring the accuracy and reliability of system data, avoiding gripper control errors due to erroneous data, and ensuring a stable and reliable gripping process.

[0093] In some embodiments of this utility model, an aluminum nitride ceramic layer with a thickness of 0.3~0.8mm is provided between the electromagnetic coil 6 and the temperature sensor 1.

[0094] In some embodiments of this utility model, the weight sensor and the torque sensor 3 perform data fusion:

[0095] The state equation is expressed as:

[0096] x k =x k-1 +w k

[0097] Among them, w k This is process noise;

[0098] The observation equation is expressed as:

[0099] z k =Hx k +v k

[0100] Where H=[1,0.98] T v k To observe noise;

[0101] Kalman gain is expressed as:

[0102] K k =P k|k-1 H T HP k|k-1 H T +R) -1

[0103] Final output:

[0104] Wf used =(W1+0.98×W2) / 1.98

[0105] The coefficient 0.98 comes from the measured value of mechanical transmission efficiency.

[0106] When grasping irregular objects, the Kalman filter activation conditions are shown in Table 2 below for automatically enhancing data fusion:

[0107]

[0108] Table 2

[0109] In some embodiments of this utility model, the intelligent gripper control system further includes:

[0110] A power management module that supports redundant power supply from a 24VDC main power supply and a supercapacitor bank, wherein the overcurrent protection response time of the power management module is <1μs.

[0111] A dual-redundant power supply system is established, with a main power supply of 24VDC and a backup supercapacitor bank capable of switching over in less than 10ms in the event of a main power failure or momentary power outage, ensuring uninterrupted equipment operation. An integrated overvoltage / overcurrent protection circuit is implemented, utilizing high-performance MOSFETs and protection chips with a response time of less than 1μs. When the current exceeds 15A, a hardware emergency stop circuit rapidly cuts off the power supply to protect the equipment. Simultaneously, temperature sensor 1 monitors the coil temperature and dynamically adjusts the coil supply current to prevent overheating, ensuring stable operation of the electromagnetic coil 6 under various working conditions.

[0112] The control system of this application was tested under ambient temperature cyclic shock (-10℃ to 60℃) and vibration conditions with a random spectrum of 5-500Hz, as shown in Table 3 below:

[0113]

[0114] Table 3

[0115] The above are merely preferred embodiments of the present utility model and are not intended to limit the present utility model. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present utility model shall be included within the protection scope of the present utility model.

Claims

1. A multi-sensor feedback based intelligent gripper dynamic control system, characterized in that, Comprise: a driving module for driving the smart gripper to open or tighten; a detection module comprising at least a weight sensor for monitoring the weight of the gripper, 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 is installed on the rotating joint transmission shaft of any one or more grippers for verifying the consistency of the support arm sensor data, and the micro pressure sensor array is distributed on the fingertip contact surface of the smart gripper; a control module with built-in 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 of the detection module.

2. The multi-sensor feedback based intelligent gripper dynamic control system of claim 1, wherein, The weight sensor is a squeeze type gravity sensor, which is arranged at the root of the support arm of the smart gripper or the support roller of the crown support or the swing plate of the crown or the support roller of the crown plate. The weight sensor is a displacement type weight sensor, which comprises a magnetic grating ruler arranged in the smart gripper and a positioning mark point arranged on the coil magnetic flux shaft.

3. The multi-sensor feedback based intelligent gripper dynamic control system of claim 1, wherein, The weight sensor has a range of 0-5kg, an accuracy of ±1%, and outputs a 0-5mV / g signal through a Wheatstone bridge, The temperature sensor has a temperature measurement range of -20℃ to 150℃ and outputs through an RS485 interface; The torque sensor has a range of 0-10N·m / ±0.1%FS; The micro pressure sensor array has a range of 0-50 N / cm 2 / ±1%.

4. The multi-sensor feedback based intelligent gripper dynamic control system of claim 1, wherein, Further comprising: a 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 a TensorRT accelerated LSTM model.

5. The multi-sensor feedback based intelligent gripper dynamic control system of claim 1, wherein, The micro pressure sensor array is composed of a 4×4 pressure sensitive unit matrix.

6. The multi-sensor feedback based intelligent gripper dynamic control system of claim 1, wherein, An aluminum nitride ceramic layer with a thickness of 0.3-0.8mm is arranged between the electromagnetic coil and the temperature sensor.

7. The multi-sensor feedback based intelligent gripper dynamic control system of claim 1, wherein, Further comprising: A power management module supporting 24VDC main power supply and super capacitor group redundant power supply, the overcurrent protection response time of the power management module is less than 1μs.

Images