Motor fuzzy control industrial robot

Abstract

The invention relates to the technical field of industrial robots, and discloses a motor fuzzy control industrial robot which comprises the following steps: collecting current motor state information data through a sensor group, and preprocessing the collected motor state information data; and converting the preprocessed data into fuzzy set membership, and determining the degree of each input variable belonging to each linguistic variable by using a membership function. According to the motor fuzzy control industrial robot, the current motor state information data are collected through the sensor set, the collected motor state information data are preprocessed, the influence of noise interference and abnormal values can be remarkably reduced, the reliability and stability of the data are improved, the performance of a control system is improved, and the control precision is improved. And the service life of the equipment can be prolonged, the system can better process the uncertainty and nonlinearity problems through a Gaussian membership function and fuzzy reasoning, and the robustness and adaptability of control are improved.

Description

Technical Field

[0001] The invention relates to the technical field of industrial robots, in particular to an industrial robot with fuzzy motor control. Background Art

[0002] Industrial robots are a type of automated mechanical equipment that is widely used in manufacturing, logistics, medical care, agriculture and other fields. Industrial robots are usually composed of robotic arms, end effectors, controllers and sensors, and can perform various complex tasks such as assembly, handling, welding, spraying, packaging, etc. However, traditional motor control systems are easily affected by noise and outliers when collecting motor status information, resulting in reduced control accuracy. Traditional fuzzy control systems often use simple membership functions when converting input variables into fuzzy set membership, resulting in inaccurate fuzzy processing. At the same time, traditional control systems use fixed gain values ​​when adjusting the control signal gain, and cannot be dynamically adjusted according to the actual response situation, resulting in poor system performance. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] To achieve the above object, the present invention provides the following technical solutions: An industrial robot with motor fuzzy control comprises: collecting current motor state information data through a sensor group, preprocessing the collected motor state information data; converting the preprocessed data into fuzzy set membership, using a membership function to determine the degree to which each input variable belongs to each language variable, and performing fuzzy reasoning according to the current motor input state and an existing fuzzy rule base to obtain a corresponding fuzzy output; using a maximum membership method to convert the fuzzy output into a specific control signal; a controller sends the generated control signal to an actuator and adjusts the running state of the motor accordingly; implementing a fault detection algorithm to identify abnormal conditions in the system, and when an abnormality is detected, enabling a redundancy mechanism to ensure stable operation of the system; monitoring the overall performance indicators of the system, and regularly generating performance reports for reference by maintenance personnel.

[0005] As a further solution of the present invention: the current motor status information data is collected through a sensor group, and the collected motor status information data is preprocessed. The specific steps are: collecting the motor status information through a sensor group composed of a high-precision position sensor, a speed sensor and a temperature sensor; the status information includes the motor position, speed and temperature data; using a Kalman filter to filter the collected data to remove noise; using an exponentially weighted moving average method to further smooth the data, and using a statistical method to identify and eliminate outliers; the processed data is normalized and subsequently fuzzy processed.

[0006] As a further solution of the present invention: the pre-processed data is converted into fuzzy set membership, the membership function is used to determine the degree to which each input variable belongs to each language variable, and fuzzy reasoning is performed according to the current input state of the motor and the existing fuzzy rule library to obtain corresponding fuzzy output. The specific steps are: for each input variable, a Gaussian membership function is defined; using the Gaussian membership function, the membership of each input variable relative to each language variable is calculated; the calculated membership is used as the fuzzification result of the input variable; according to the established fuzzy rule library, fuzzy reasoning is performed; multiple fuzzy outputs are obtained through fuzzy reasoning, and then the maximum membership method is used to synthesize the final fuzzy output.

[0007] As a further solution of the present invention: the maximum membership method is used to convert the fuzzy output into a specific control signal, and the specific steps are: obtaining the fuzzy output through fuzzy reasoning; selecting the value with the largest membership from all fuzzy outputs; converting the selected language variable into a specific control signal, the expression is: ; in: is a specific control signal, is the slope parameter, is the membership value, is the center point.

[0008] As a further solution of the present invention: the controller sends the generated control signal to the actuator and adjusts the running state of the motor accordingly. The specific steps are: the controller sends the generated control signal to the actuator through a high-speed communication interface; after the actuator receives the control signal, the control signal is smoothed; the smoothed control signal is converted into a voltage or current signal suitable for the motor; the actuator adjusts the running state of the motor according to the converted voltage or current signal; an adaptive gain adjustment mechanism is introduced, and the mechanism dynamically adjusts the gain of the control signal according to the actual response of the motor, and the expression is: ; in: is the adaptive gain, is the initial gain, is the error feedback coefficient, is the control error at the current moment, is the error change rate feedback coefficient, The rate of change of error.

[0009] As a further solution of the present invention: the fault detection algorithm is implemented to identify abnormal conditions in the system. When an abnormality is detected, the redundancy mechanism is enabled to ensure stable operation of the system. The specific steps are: using historical data to establish a mathematical model that describes the normal operation state of the motor; inputting the preprocessed data into the normal behavior model to calculate the residual between the actual output and the model predicted output; introducing an adaptive threshold setting method, dynamically adjusting according to historical data to adapt to changes under different working conditions, the expression is: ; in: is the adaptive threshold at the current moment, is the mean of the historical residuals, is the adjustment coefficient, is the standard deviation of the historical residuals at the current moment; the calculated residual is compared with the adaptive threshold, and the expression is: ; in: is the residual at the current moment, is the adaptive threshold at the current moment, It is a fault flag, 1 indicates a fault is detected, and 0 indicates no fault. When the residual exceeds the threshold, it is considered that the system is abnormal and the redundancy mechanism is immediately enabled when a fault is detected.

[0010] As a further solution of the present invention: the redundancy mechanism includes switching to a backup control system, starting a backup motor, or taking other emergency measures.

[0011] As a further solution of the present invention: the overall performance indicators of the monitoring system are monitored, and performance reports are regularly generated for reference by maintenance personnel. The specific steps are: Use a comprehensive performance index-based method to evaluate system performance, and combine adaptive weighting factors to dynamically adjust the importance of each performance indicator; generate performance reports regularly based on the calculated comprehensive performance index; distribute the generated performance reports to maintenance personnel and collect feedback.

[0012] The present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of an industrial robot with fuzzy motor control as described in the first aspect of the present invention is implemented.

[0013] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of an industrial robot with motor fuzzy control as described in the first aspect of the present invention is implemented.

[0014] Compared with the prior art, the present invention has the following beneficial effects: By collecting the current motor status information data through the sensor group and preprocessing the collected motor status information data, the influence of noise interference and outliers can be significantly reduced, and the reliability and stability of the data can be improved, which not only helps to improve the performance of the control system, but also can extend the service life of the equipment. Through Gaussian membership function and fuzzy reasoning, the system can better handle uncertainty and nonlinear problems, improve the robustness and adaptability of control, and at the same time, the maximum membership method ensures that the selection of control signals is clear and consistent, thereby improving the response speed and accuracy of the system. Through the maximum membership method, the system can determine the optimal control signal in a short time, reduce delays, and improve control efficiency. At the same time, the introduction of the Sigmoid function makes the transition of the control signal smoother, reduces the impact of mutations, and improves the stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The figure is a flow chart of an industrial robot with motor fuzzy control. DETAILED DESCRIPTION

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, the specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings.

[0017] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0018] 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 term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0019] Example 1 See also Figure 1 , which is the first embodiment of the present invention, and which provides an industrial robot with motor fuzzy control, comprising: S1, collecting current motor state information data through a sensor group, and preprocessing the collected motor state information data; Specifically, the current motor status information data is collected through a sensor group, and the collected motor status information data is preprocessed. The specific steps are: the motor status information is collected through a sensor group composed of a high-precision position sensor, a speed sensor and a temperature sensor; the status information includes the motor position, speed and temperature data; the collected data is filtered using a Kalman filter to remove noise; the data is further smoothed using an exponentially weighted moving average method, and statistical methods are used to identify and eliminate outliers; the processed data is normalized and subsequently fuzzy processed.

[0020] It should be noted that the sensor group consisting of high-precision position sensors, speed sensors and temperature sensors can accurately obtain the state information of the motor in real time. The Kalman filter is used to filter the collected data, which effectively removes noise and improves the accuracy of the data. The exponentially weighted moving average method is used to further smooth the data, reduce the impact of short-term fluctuations, and make the data more stable. Statistical methods are used to identify and eliminate outliers to ensure the reliability of the data. The processed data is normalized, making the data easier to handle in subsequent fuzzy processing and simplifying the calculation process.

[0021] S2, converting the preprocessed data into fuzzy set membership, using membership functions to determine the degree to which each input variable belongs to each linguistic variable, and performing fuzzy reasoning according to the current motor input state and the existing fuzzy rule base to obtain the corresponding fuzzy output; Specifically, the preprocessed data is converted into fuzzy set membership, and the membership function is used to determine the degree to which each input variable belongs to each language variable, and fuzzy reasoning is performed according to the current input state of the motor and the existing fuzzy rule library to obtain the corresponding fuzzy output. The specific steps are: for each input variable, a Gaussian membership function is defined; using the Gaussian membership function, the membership of each input variable relative to each language variable is calculated; the calculated membership is used as the fuzzification result of the input variable; according to the established fuzzy rule library, fuzzy reasoning is performed; multiple fuzzy outputs are obtained through fuzzy reasoning, and then the maximum membership method is used to synthesize the final fuzzy output.

[0022] It should be noted that defining a Gaussian membership function can smoothly map the input value to a membership within the interval [0, 1], thereby enhancing the flexibility and adaptability of the system. Using the Gaussian membership function to calculate the membership of each input variable relative to each linguistic variable can more accurately describe the relationship between the input variable and the linguistic variable. By performing fuzzy reasoning based on the established fuzzy rule base, the system can better handle uncertainty and nonlinear problems, improve the robustness and adaptability of the control, and synthesize the final fuzzy output through the maximum membership method, ensuring that the selection of the control signal is clear and consistent, thereby improving the response speed and accuracy of the system.

[0023] S3, using the maximum membership method to convert the fuzzy output into a specific control signal; Specifically, the maximum membership method is used to convert the fuzzy output into a specific control signal. The specific steps are: obtain the fuzzy output through fuzzy reasoning; select the value with the largest membership from all fuzzy outputs; convert the selected language variable into a specific control signal. The expression is: ; in: is a specific control signal, is the slope parameter, is the membership value, is the center point.

[0024] It should be noted that fuzzy outputs are obtained through fuzzy reasoning, and the value with the largest membership degree is selected from all fuzzy outputs, which ensures that the selection of control signals is clear and consistent, and converts the selected linguistic variables into specific control signals.

[0025] S4, the controller sends the generated control signal to the actuator and adjusts the running state of the motor accordingly; Specifically, the controller sends the generated control signal to the actuator and adjusts the running state of the motor accordingly. The specific steps are: the controller sends the generated control signal to the actuator through a high-speed communication interface; after the actuator receives the control signal, the control signal is smoothed; the smoothed control signal is converted into a voltage or current signal suitable for the motor; the actuator adjusts the running state of the motor according to the converted voltage or current signal; an adaptive gain adjustment mechanism is introduced, and the mechanism dynamically adjusts the gain of the control signal according to the actual response of the motor. The expression is: ; in: is the adaptive gain, is the initial gain, is the error feedback coefficient, is the control error at the current moment, is the error change rate feedback coefficient, The rate of change of error.

[0026] It should be noted that the controller sends the generated control signal to the actuator through a high-speed communication interface, ensuring the rapid transmission of the control signal. After the actuator receives the control signal, the control signal is smoothed to reduce the impact of sudden changes and improve the stability of the system. The smoothed control signal is converted into a voltage or current signal suitable for the motor to ensure the effective transmission of the control signal. An adaptive gain adjustment mechanism is introduced, which dynamically adjusts the gain of the control signal according to the actual response of the motor. The mechanism can dynamically adjust the gain according to the actual response, thereby improving the dynamic performance and stability of the system.

[0027] S5. Implement fault detection algorithms to identify abnormal conditions in the system. When an abnormality is detected, enable redundancy mechanisms to ensure stable operation of the system. Specifically, a fault detection algorithm is implemented to identify abnormal conditions in the system. When an abnormality is detected, a redundancy mechanism is enabled to ensure stable operation of the system. The specific steps are: using historical data to establish a mathematical model that describes the normal operation of the motor; inputting the preprocessed data into the normal behavior model to calculate the residual between the actual output and the model predicted output; introducing an adaptive threshold setting method to dynamically adjust according to historical data to adapt to changes under different working conditions. The expression is: ; in: is the adaptive threshold at the current moment, is the mean of the historical residuals, is the adjustment coefficient, is the standard deviation of the historical residuals at the current moment; the calculated residual is compared with the adaptive threshold, and the expression is: ; in: is the residual at the current moment, is the adaptive threshold at the current moment, It is a fault flag, 1 indicates a fault is detected, and 0 indicates no fault. When the residual exceeds the threshold, the system is considered to be abnormal, and the redundancy mechanism is immediately enabled when a fault is detected. The redundancy mechanism includes switching to a backup control system, starting a backup motor, or taking other emergency measures.

[0028] It should be noted that the use of historical data to establish a mathematical model that describes the normal operating status of the motor provides a benchmark reference to facilitate subsequent residual calculations. The preprocessed data is input into the normal behavior model to calculate the residual between the actual output and the model predicted output, which can promptly detect abnormal conditions in the system.

[0029] S6. Monitor the overall performance indicators of the system and generate performance reports regularly for reference by maintenance personnel; Specifically, the overall performance indicators of the monitoring system are monitored, and performance reports are generated regularly for reference by maintenance personnel. The specific steps are: use a method based on a comprehensive performance index to evaluate system performance, and combine adaptive weighting factors to dynamically adjust the importance of each performance indicator; generate performance reports regularly based on the calculated comprehensive performance index; distribute the generated performance reports to maintenance personnel, and collect feedback.

[0030] It should be noted that the system performance is evaluated based on a comprehensive performance index method, and the importance of each performance indicator is dynamically adjusted in combination with an adaptive weighting factor, ensuring the comprehensiveness and accuracy of the performance evaluation. Performance reports are generated regularly based on the calculated comprehensive performance index, providing maintenance personnel with detailed system operation status information. The generated performance reports are distributed to maintenance personnel, and feedback is collected, which helps to promptly identify potential problems and take preventive measures, thereby improving the maintainability and long-term stability of the system. Through these steps, the system can continuously monitor its own performance, promptly identify and resolve problems, and ensure long-term efficient and stable operation.

[0031] This embodiment also provides a computer device, which is suitable for the case of an industrial robot with fuzzy motor control, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the industrial robot with fuzzy motor control as proposed in the above embodiment.

[0032] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0033] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, an industrial robot that implements fuzzy control of a motor as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0034] In summary, by collecting the current motor state information data through the sensor group and preprocessing the collected motor state information data, the influence of noise interference and outliers can be significantly reduced, and the reliability and stability of the data can be improved, which not only helps to improve the performance of the control system, but also can extend the service life of the equipment. Through Gaussian membership function and fuzzy reasoning, the system can better handle uncertainty and nonlinear problems, improve the robustness and adaptability of control, and at the same time, the maximum membership method ensures that the selection of control signals is clear and consistent, thereby improving the response speed and accuracy of the system. Through the maximum membership method, the system can determine the optimal control signal in a short time, reduce delays, and improve control efficiency. At the same time, the introduction of the Sigmoid function makes the transition of the control signal smoother, reduces the impact of mutations, and improves the stability and reliability of the system.

[0035] Example 2 Please refer to Table 1, which is the second embodiment of the present invention. In order to further verify the technical solution of the present invention, experimental simulation data of an industrial robot with motor fuzzy control is provided.

[0036] In order to verify the performance of the motor fuzzy control industrial robot proposed in the present invention, a series of experiments were carried out. The experimental object was an industrial robot equipped with a sensor group consisting of a high-precision position sensor, a speed sensor and a temperature sensor. During the experiment, the state information of the motor was first collected through these sensors, including position, speed and temperature data. The collected data was filtered by a Kalman filter to remove noise. Then, the exponentially weighted moving average method was used to further smooth the data, and statistical methods were used to identify and eliminate outliers. The processed data was normalized for subsequent fuzzy processing.

[0037] After the preprocessing is completed, the data is converted into fuzzy set membership. For each input variable (such as position error, speed error), a Gaussian membership function is defined. The membership of each input variable relative to each linguistic variable (such as "large", "medium", and "small") is calculated using the Gaussian membership function. The calculated membership is used as the fuzzification result of the input variable. According to the established fuzzy rule base, fuzzy reasoning is performed to obtain multiple fuzzy outputs. The maximum membership method is used to select the fuzzy output with the largest membership and convert it into a specific control signal.

[0038] The controller sends the generated control signal to the actuator through a high-speed communication interface. After the actuator receives the control signal, it smoothes the control signal and then converts the smoothed control signal into a voltage or current signal suitable for the motor. The actuator adjusts the operating state of the motor according to the converted voltage or current signal. At the same time, an adaptive gain adjustment mechanism is introduced to dynamically adjust the gain of the control signal according to the actual response of the motor.

[0039] In addition, a fault detection algorithm is implemented, and a mathematical model describing the normal operating status of the motor is established using historical data. The preprocessed data is input into the normal behavior model, and the residual between the actual output and the model predicted output is calculated. An adaptive threshold setting method is introduced to dynamically adjust the threshold according to historical data to adapt to changes under different operating conditions.

[0040] The calculated residual is compared with the adaptive threshold. If the residual exceeds the threshold, the system is considered to be abnormal and the redundancy mechanism is immediately enabled to ensure stable operation of the system. The redundancy mechanism includes switching to a backup control system, starting a backup motor or other emergency measures.

[0041] Finally, the overall performance indicators of the system are monitored, and performance reports are generated regularly for reference by maintenance personnel. The system performance is evaluated using a method based on the comprehensive performance index, and the importance of each performance indicator is dynamically adjusted in combination with an adaptive weighting factor. Based on the calculated comprehensive performance index, performance reports are generated regularly and distributed to maintenance personnel to collect feedback.

[0042] The table data is as follows: Table 1: Comparison table between prior art and the present invention parameter unit Prior art The present invention Position error mm 0.54 0.23 Speed ​​error m / s 0.08 0.03 Response time ms 120 50 Steady-state error % 2.5 1.2 Energy consumption W 1200 900 Failure rate Times / month 3 0.5 Performance Index No unit 0.85 0.96 By comparing the data in the above table, it can be seen that the present invention is significantly superior to the prior art in multiple key performance indicators, and the specific analysis is as follows: The position error of the prior art is 0.54 mm, while that of the present invention is only 0.23 mm, indicating that the present invention has higher precision in position control and can more accurately control the position of the motor, thereby improving the positioning accuracy of the entire system.

[0043] The speed error of the prior art is 0.08 m / s, while that of the present invention is 0.03 m / s, indicating that the present invention also has higher precision in speed control and can more accurately control the speed of the motor, thereby improving the response speed and stability of the system.

[0044] The response time of the prior art is 120 ms, while that of the present invention is only 50 ms, indicating that the present invention has a significant improvement in response speed, can respond to control signals more quickly, and improve the real-time and dynamic performance of the system.

[0045] The steady-state error of the prior art is 2.5%, while that of the present invention is 1.2%, indicating that the present invention has higher accuracy in steady-state control and can maintain more stable performance during long-term operation.

[0046] It can be seen from the above data analysis that the present invention has significant advantages in terms of position control accuracy, speed control accuracy, response time, steady-state error, energy consumption, failure rate and comprehensive performance. These advantages not only improve the control accuracy and response speed of the system, but also enhance the reliability and stability of the system, making the present invention have higher practical value and competitiveness in practical applications, especially in the field of industrial robots. The improvement of these performances can significantly improve production efficiency and product quality, reduce maintenance costs, and thus bring significant economic benefits.

[0047] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An industrial robot with motor fuzzy control, characterized in that: include, The current motor status information data is collected through the sensor group, and the collected motor status information data is pre-processed; The preprocessed data is converted into fuzzy set membership, and the membership function is used to determine the degree to which each input variable belongs to each linguistic variable, and fuzzy reasoning is performed according to the current motor input state and the existing fuzzy rule base to obtain the corresponding fuzzy output; Use the maximum membership method to convert the fuzzy output into a specific control signal; The controller sends the generated control signal to the actuator and adjusts the operating state of the motor accordingly; Implement fault detection algorithms to identify abnormal conditions in the system. When an abnormality is detected, enable redundancy mechanisms to ensure stable operation of the system. Monitor the overall performance indicators of the system and generate performance reports regularly for reference by maintenance personnel.

2. The industrial robot with motor fuzzy control according to claim 1, characterized in that: The current motor state information data is collected through the sensor group, and the collected motor state information data is preprocessed. The specific steps are: The state information of the motor is collected through a sensor group consisting of a high-precision position sensor, a speed sensor and a temperature sensor; Status information includes motor position, speed, and temperature data; The collected data is filtered using a Kalman filter to remove noise; The exponentially weighted moving average method was used to further smooth the data, and statistical methods were used to identify and remove outliers; The processed data are normalized and then fuzzified.

3. The industrial robot with motor fuzzy control according to claim 1, characterized in that: The preprocessed data is converted into fuzzy set membership, and the membership function is used to determine the degree to which each input variable belongs to each language variable, and fuzzy reasoning is performed according to the current motor input state and the existing fuzzy rule base to obtain the corresponding fuzzy output. The specific steps are: For each input variable, define a Gaussian membership function; Using Gaussian membership functions, the membership of each input variable relative to each linguistic variable is calculated; The calculated membership degree is used as the fuzzification result of the input variable; Perform fuzzy reasoning based on the established fuzzy rule base; Multiple fuzzy outputs are obtained through fuzzy reasoning, and then the maximum membership method is used to synthesize the final fuzzy output.

4. The industrial robot with motor fuzzy control according to claim 1, characterized in that: The maximum membership method is used to convert the fuzzy output into a specific control signal, and the specific steps are as follows: Obtain fuzzy output through fuzzy reasoning; Select the value with the largest membership from all fuzzy outputs; Convert the selected language variable into a specific control signal, the expression is: ; in: is a specific control signal, is the slope parameter, is the membership value, is the center point.

5. The industrial robot with motor fuzzy control according to claim 1, characterized in that: The controller sends the generated control signal to the actuator and adjusts the running state of the motor accordingly. The specific steps are: The controller sends the generated control signal to the actuator via a high-speed communication interface; After the actuator receives the control signal, the control signal is smoothed; Convert the smoothed control signal into a voltage or current signal suitable for the motor; The actuator adjusts the operating state of the motor according to the converted voltage or current signal; An adaptive gain adjustment mechanism is introduced to dynamically adjust the gain of the control signal according to the actual response of the motor. The expression is: ; in: is the adaptive gain, is the initial gain, is the error feedback coefficient, is the control error at the current moment, is the error change rate feedback coefficient, The rate of change of error.

6. The industrial robot with motor fuzzy control according to claim 1, characterized in that: The implementation of the fault detection algorithm identifies the abnormal conditions in the system. When an abnormality is detected, the redundancy mechanism is enabled to ensure the stable operation of the system. The specific steps are: Using historical data, a mathematical model is established to describe the normal operation status of the motor; Input the preprocessed data into the normal behavior model and calculate the residual between the actual output and the model predicted output; An adaptive threshold setting method is introduced to dynamically adjust according to historical data to adapt to changes under different working conditions. The expression is: ； in: is the adaptive threshold at the current moment, is the mean of the historical residuals, is the adjustment coefficient, is the standard deviation of the historical residuals at the current moment; The calculated residual is compared with the adaptive threshold, expressed as: ; in: is the residual at the current moment, is the adaptive threshold at the current moment, It is a fault flag, 1 means a fault is detected, 0 means no fault; When the residual exceeds the threshold, the system is considered to be abnormal and the redundancy mechanism is immediately enabled when a fault is detected.

7. The industrial robot with motor fuzzy control according to claim 6, characterized in that: The redundancy mechanism includes switching to a backup control system, starting a backup motor, or taking other emergency measures.

8. The industrial robot with motor fuzzy control according to claim 1, characterized in that: The overall performance indicators of the monitoring system are monitored, and performance reports are generated regularly for reference by maintenance personnel. The specific steps are: Use a comprehensive performance index-based approach to evaluate system performance, and combine adaptive weighting factors to dynamically adjust the importance of each performance indicator; Generate performance reports regularly based on the calculated comprehensive performance index; Distribute the generated performance reports to maintainers and collect feedback.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of an industrial robot with fuzzy motor control as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of an industrial robot with fuzzy motor control as described in any one of claims 1 to 8 are implemented.

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