Self-adaptive fuzzy control system for inverter type arc welding power supply

Abstract

The invention provides a self-adaptive fuzzy control system for an inverter type arc welding power supply, and belongs to the field of welding process control and equipment automation, and the self-adaptive fuzzy control system specifically comprises the steps that a sensor module collects working numerical values corresponding to key parameters in the welding process in real time; the data processing unit converts the working values into a fuzzy set; the fuzzy controller executes fuzzy reasoning on the fuzzy set based on a preset fuzzy rule base so as to output a fuzzy control signal; the data processing unit performs defuzzification on the fuzzy control signal and converts the fuzzy control signal into an accurate control signal; the inverter power supply module receives the defuzzified accurate control signal to adjust the output power of the welding power supply; the feedback system monitors real-time parameters of welding seam quality in the welding process and transmits the real-time parameters to the self-adaptive module so as to realize closed-loop control; and the adaptive module dynamically adjusts the fuzzy rule base and the membership function according to the welding quality information provided by the feedback system. According to the processing scheme, the control precision and adaptability of the system are improved.

Description

Technical Field

[0001] The invention relates to the field of welding process control and equipment automation, and in particular to an adaptive fuzzy control system for an inverter arc welding power source. Background Art

[0002] Welding technology is widely used in the field of metal structure manufacturing, especially in the automotive manufacturing, shipbuilding, construction, aerospace and other industries. Arc welding technology is widely used. Inverter arc welding power source plays a vital role in the modern welding industry. It effectively converts direct current into adjustable alternating current through inverter technology, thereby achieving precise control of welding current and voltage to adapt to different materials and complex welding conditions.

[0003] Most traditional welding power supply control systems rely on the classic PID control strategy, which has good performance when dealing with linear systems, but its performance is relatively limited when faced with complex nonlinearities and external interference problems in the actual welding process. The characteristics of the arc during welding are affected by many factors, including material properties, changes in ambient temperature, gas flow, and the shape and position of the weld, which lead to a nonlinear relationship between welding current and voltage. Traditional PID controllers have difficulty coping with these complex situations and often require frequent adjustments to control parameters, increasing the complexity and instability of the system. In addition, during the welding process, external interference (such as changes in gas flow, changes in weld shape, etc.) will affect the stability of the arc. The PID controller responds slowly to these disturbances and cannot adjust the output of the welding power supply in time, resulting in unstable weld quality.

[0004] In modern manufacturing, the diversification of welding materials and the complexity of welding processes require the control system to have strong adaptive capabilities and be able to automatically adjust the control strategy according to different welding materials, welding processes and environmental conditions to ensure the consistency of welding quality. Existing technical solutions are not adaptable enough to different welding conditions and cannot meet their needs. Summary of the invention

[0005] Therefore, in order to overcome the shortcomings of the above-mentioned prior art, the present invention provides an adaptive fuzzy control system for an inverter arc welding power supply. The system has significant technical advantages and effects compared with the prior art in terms of control accuracy, adaptability, intelligence level, system modular design and wide application, and has brought significant technological improvements to the field of welding technology and industrial control.

[0006] In order to achieve the above-mentioned object, the present invention provides an adaptive fuzzy control system for an inverter arc welding power supply, comprising a sensor module, a data processing unit, a fuzzy controller, an adaptive module, an inverter power supply module and a feedback system, wherein the sensor module collects working values ​​corresponding to key parameters in a welding process in real time, wherein the key parameters at least include arc current, voltage, welding speed and ambient temperature; the data processing unit converts the working values ​​collected in real time into a fuzzy set; the fuzzy controller performs fuzzy reasoning operations on the fuzzy set based on a preset fuzzy rule base, and performs comprehensive judgment on a plurality of the key parameters to output a fuzzy control signal ... The data processing unit defuzzifies the fuzzy control signal and converts it into a precise control signal; the inverter power supply module receives the defuzzified precise control signal and adjusts the output power of the welding power supply through pulse width modulation to ensure the stability of the welding arc; the feedback system monitors the real-time parameters of the weld quality during the welding process, and the real-time parameters at least include the flatness and surface quality of the weld, and transmits the real-time parameters to the adaptive module to achieve closed-loop control; the adaptive module dynamically adjusts the fuzzy rule base and the shape of the membership function according to the welding quality information provided by the feedback system to optimize the control performance and enhance the adaptability and robustness of the system.

[0007] In one of the embodiments, the data processing unit uses at least one membership function to perform fuzzy processing, and the membership function is at least one of a triangular membership function, a trapezoidal membership function, a Gaussian function and a bell-shaped function.

[0008] In one of the embodiments, the fuzzy inference method adopted by the fuzzy controller is an inference method selected according to the characteristics of multiple input variables in the welding process, and the inference method is at least one of the maximum-minimum synthesis method, the weighted average method and the maximum membership method.

[0009] In one of the embodiments, the adaptive module adopts an adaptive adjustment strategy, and the adaptive adjustment strategy is at least one of a neural network, a genetic algorithm or a fuzzy neural network, a rule base for dynamically optimizing a fuzzy controller, and a membership function parameter.

[0010] In one embodiment, the feedback system includes a data fusion unit, which is used to fuse feedback data from multiple sensors to eliminate the influence of noise and uncertainty on the control effect. The data fusion unit uses Kalman filtering or Bayesian estimation for fusion.

[0011] In one embodiment, the inverter power supply module automatically adjusts the pulse width modulation parameters using a dynamic response regulation mechanism according to the arc characteristics that change in real time during the welding process, so as to improve the dynamic response characteristics and control accuracy of the power supply.

[0012] In one of the embodiments, the fuzzy rule base includes "if-then" rules designed based on expert experience and experimental data, and the construction steps of the fuzzy rule base include fuzzy rule extraction, rule optimization and redundant rule deletion.

[0013] In one of the embodiments, it also includes: a fault diagnosis unit working in cooperation with the inverter power module, the fault diagnosis unit is used to monitor the operating status of the power module and perform fault prediction and self-repair to ensure the long-term stable operation of the welding system.

[0014] In one of the embodiments, the real-time parameters include at least one of welding speed, weld position and weld angle.

[0015] In one of the embodiments, the adaptive module adopts a multi-objective optimization algorithm to comprehensively consider welding quality, control energy consumption and system response time to achieve global optimization of the control strategy.

[0016] Compared with the prior art, the advantages of the present invention are: combining the advantages of adaptive control technology and fuzzy logic control method, developing an intelligent control system suitable for inverter arc welding power supply. The system can effectively deal with complex nonlinear and dynamic change problems in the welding process, and improve welding quality and production efficiency. This concept not only provides a new idea for the development of welding technology, but also can be expanded to other industrial processes that require high-precision control, and has broad application prospects and research value. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 is a schematic diagram of the overall architecture of an adaptive fuzzy control system for an inverter arc welding power source in an embodiment of the present invention;

[0019] Figure 2 is a circuit block diagram of an inverter arc welding power supply in an embodiment of the present invention;

[0020] Figure 3 Schematic diagram of the working process of the adaptive fuzzy control system in the embodiment of the present invention;

[0021] Figure 4 It is a structural diagram of the control fuzzifier in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0023] The following describes the implementation of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The present application can also be implemented or applied through other different specific implementations, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the features in the following embodiments and embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the art without making creative work belong to the scope of protection of the present application.

[0024] It should be noted that various aspects of the embodiments within the scope of protection of the present invention are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present application, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number and aspect described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0025] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. The drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0026] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0027] The application background of adaptive fuzzy control in inverter arc welding power supply can be traced back to the widespread use of inverter arc welding power supply in the field of industrial welding. Since inverter technology can efficiently convert input AC power into high-frequency DC power, thereby achieving precise control of welding current and voltage, this equipment has become the first choice for many welding processes due to its excellent energy conversion efficiency and arc control capabilities. By combining fuzzy control with adaptive regulation, the control parameters can be adjusted dynamically, so that the system can automatically adapt to changes in welding conditions, thereby maintaining arc stability and welding quality.

[0028] Adaptive fuzzy control technology has made significant progress. By combining modern intelligent algorithms, such as machine learning and deep learning, the system has the ability to self-learn and self-optimize, and can automatically adjust control parameters and fuzzy rule bases to cope with uncertainties and nonlinear changes in the welding process. With the improvement of hardware computing power, the real-time performance of adaptive fuzzy control has been greatly improved, and dynamic adjustment of the arc during welding has been achieved, thereby ensuring the stability and high quality of welding. In addition, adaptive fuzzy control is widely used in automated welding systems, especially in welding robot path tracking and arc length adjustment. Through fuzzy processing and adjustment of sensor data, precise control of the welding trajectory is achieved.

[0029] like Figure 1 As shown, the present invention proposes an adaptive fuzzy control system for inverter arc welding power supply, which aims to achieve effective control of dynamic changes and complex working conditions in the welding process through the combination of fuzzy logic and adaptive regulation. The adaptive fuzzy control system consists of six main modules: sensor module, data processing unit, fuzzy controller, adaptive module, inverter power supply module and feedback system. The modules are interconnected through data transmission and feedback mechanism to achieve closed-loop control of the welding process.

[0030] The sensor module is used to collect the working values ​​corresponding to the key parameters in the welding process in real time. The key parameters include at least arc current, voltage, welding speed and ambient temperature.

[0031] The data processing unit is used to convert the real-time collected working values ​​into fuzzy sets. The data processing unit also defuzzifies the fuzzy control signal and converts it into a precise control signal.

[0032] The fuzzy controller performs fuzzy reasoning operations on fuzzy sets based on a preset fuzzy rule base, and makes comprehensive judgments on multiple key parameters to output fuzzy control signals.

[0033] The inverter power module receives the precise control signal after defuzzification and adjusts the output power of the welding power supply through pulse width modulation to ensure the stability of the welding arc.

[0034] The feedback system monitors the real-time parameters of the weld quality during the welding process, the real-time parameters at least including the flatness and surface quality of the weld, and transmits the real-time parameters to the adaptive module to achieve closed-loop control.

[0035] The adaptive module dynamically adjusts the fuzzy rule base and the shape of the membership function according to the welding quality information provided by the feedback system to optimize the control performance and enhance the adaptability and robustness of the system.

[0036] The working process of the adaptive fuzzy control system is as follows:

[0037] The sensor module collects the working values ​​corresponding to the key parameters in the welding process in real time. The key parameters include at least arc current, voltage, welding speed and ambient temperature;

[0038] The data processing unit converts the working values ​​collected in real time into fuzzy sets;

[0039] The fuzzy controller performs fuzzy reasoning operations on fuzzy sets based on a preset fuzzy rule base, and makes comprehensive judgments on a variety of key parameters to output fuzzy control signals;

[0040] The data processing unit defuzzifies the fuzzy control signal and converts it into a precise control signal;

[0041] The inverter power module receives the precise control signal after defuzzification and adjusts the output power of the welding power supply through pulse width modulation to ensure the stability of the welding arc;

[0042] The feedback system monitors the real-time parameters of the weld quality during the welding process, the real-time parameters at least including the flatness and surface quality of the weld, and transmits the real-time parameters to the adaptive module to achieve closed-loop control;

[0043] The adaptive module dynamically adjusts the fuzzy rule base and the shape of the membership function according to the welding quality information provided by the feedback system to optimize the control performance and enhance the adaptability and robustness of the system.

[0044] This embodiment is integrated in the adaptive fuzzy control system of the inverter arc welding power source, covering the system design, control strategy, modular structure, dynamic adjustment, self-learning mechanism and other aspects, and is optimized from the following aspects:

[0045] 1. Application of adaptive fuzzy control strategy: By introducing the adaptive fuzzy control method, the precise control of the nonlinear system in the welding process is realized. Adaptive fuzzy control combines the advantages of fuzzy logic in dealing with nonlinear and uncertain problems with the online optimization characteristics of adaptive control, thereby enhancing the adaptive ability of the system under dynamic changes in welding conditions.

[0046] 2. Real-time self-learning and dynamic adjustment mechanism: The self-learning algorithm is used to train the control system online and update it in real time to achieve adaptive adjustment of control parameters. Through machine learning methods, the system can continuously "learn" the characteristics of the welding arc state during the welding process, and adjust the control strategy according to these characteristics to gradually improve the accuracy and response speed of the control system.

[0047] 3. Modular system structure design: The modular design of the system enables each submodule to have independent functions and clear interfaces, ensuring the flexibility and scalability of the system. The system consists of multiple functional units such as sensor module, data processing unit, fuzzy controller, adaptive module, inverter power module and feedback system. Each module works together through a standard data interface to form a tightly coupled closed-loop control system. This modular architecture not only improves the maintainability of the system, but also facilitates independent optimization of each module, thereby improving the performance of the overall system.

[0048] 4. Enhance the anti-interference ability of the system: The interference in the arc welding process mainly comes from the characteristics of the welding material, environmental noise and arc instability. The anti-interference performance of the system is enhanced through fuzzy processing and adaptive adjustment. The fuzzy controller uses the membership function to fuzzy the influence of noise and interference on the input signal, thereby reducing the accuracy requirements of the system. The adaptive module adjusts the fuzzy rules in real time according to the changes in the feedback data, so that the system can still maintain accurate control of the welding process in the presence of large noise or interference.

[0049] In one of the embodiments, the data processing unit uses at least one membership function to perform fuzzy processing, and the membership function is at least one of a triangular membership function, a trapezoidal membership function, a Gaussian function, and a bell-shaped function.

[0050] In one embodiment, the fuzzy inference method used by the fuzzy controller is an inference method selected according to the characteristics of multiple input variables in the welding process, and the inference method is at least one of a maximum-minimum synthesis method, a weighted average method, and a maximum membership method.

[0051] In one embodiment, the adaptive module adopts an adaptive adjustment strategy, which is at least one of a neural network, a genetic algorithm or a fuzzy neural network, a rule base for dynamically optimizing a fuzzy controller, and a membership function parameter.

[0052] In one embodiment, the feedback system includes a data fusion unit, which is used to fuse feedback data from multiple sensors to eliminate the influence of noise and uncertainty on the control effect. The data fusion unit uses Kalman filtering or Bayesian estimation for fusion.

[0053] In one embodiment, the inverter power supply module automatically adjusts the pulse width modulation parameters using a dynamic response regulation mechanism according to the arc characteristics that change in real time during the welding process, so as to improve the dynamic response characteristics and control accuracy of the power supply.

[0054] In one embodiment, the fuzzy rule base includes "if-then" rules designed based on expert experience and experimental data, and the steps of constructing the fuzzy rule base include fuzzy rule extraction, rule optimization and redundant rule deletion.

[0055] In one of the embodiments, it also includes: a fault diagnosis unit working in cooperation with the inverter power module, the fault diagnosis unit is used to monitor the operating status of the power module and perform fault prediction and self-repair to ensure the long-term stable operation of the welding system.

[0056] In one of the embodiments, the real-time parameters include at least one of welding speed, weld position and weld angle.

[0057] In one of the embodiments, the adaptive module uses a multi-objective optimization algorithm to comprehensively consider welding quality, control energy consumption and system response time to achieve global optimization of the control strategy.

[0058] Embodiment 1

[0059] The sensor module collects the working values ​​corresponding to the key parameters in the welding process in real time. The design of the sensor module can adopt a multi-sensor fusion strategy to achieve real-time monitoring of multiple key parameters in the welding process. Specifically, the module includes current sensors, voltage sensors, temperature sensors, speed sensors, etc. By collecting data such as arc current, voltage, welding speed, and ambient temperature with high precision, the system can respond quickly when the welding conditions change. In order to improve the data collection accuracy, the sampling frequency of the sensor is set to above 1kHz, which can capture the parameter information that changes rapidly during the welding process. The results of multi-sensor fusion are pre-processed by a data fusion algorithm to reduce the impact of noise and enhance the anti-interference ability of the system.

[0060] The data processing unit is responsible for fuzzifying and defuzzifying the working values ​​collected by the sensor module, and is one of the core links for the system to implement fuzzy logic control. The fuzzification process converts the precise input data into a fuzzy set based on a predefined membership function. The design of the membership function adopts triangular membership function, trapezoidal membership function or Gaussian function to adapt to the data fluctuation range under different working conditions. The defuzzification step converts the output signal of the fuzzy controller into a specific numerical control signal, so that the numerical change of the control signal is smooth and adjustable, thereby ensuring the response characteristics of the welding power supply and the quality of the weld.

[0061] The fuzzy controller performs fuzzy reasoning operations on fuzzy sets based on a preset fuzzy rule base, and makes comprehensive judgments on multiple key parameters to output fuzzy control signals. The fuzzy controller realizes logical reasoning and decision-making of the welding process through a preset fuzzy rule base. The rule base consists of several "if-then" rules, which can cover the control requirements of various working conditions in the welding process. These rules are based on the fuzzy numerical values ​​of input variables (such as current and voltage) to generate suitable control signals. It is generally implemented through membership functions, which can be in the form of triangular membership functions, trapezoidal membership functions or Gaussian functions.

[0062] The triangular membership function is one of the most common fuzzy membership functions and is suitable for most applications. Its basic form is:

[0063]

[0064] Among them, a, b, c, and d are the four parameters that define the membership function, which represent the reference points on the left and right sides of the triangle and the location of the peak respectively.

[0065] The trapezoidal membership function is an extended triangular membership function that has flat areas at both ends and can accommodate a wider range of inputs. Its basic form is:

[0066]

[0067] Among them, a, b, c, and d are the four key points of the trapezoid, defining the left and right slopes of the trapezoid and the flat part in the middle.

[0068] The Gaussian membership function can better simulate the fuzziness of continuous changes. The formula of the Gaussian membership function is as follows:

[0069]

[0070] Where c is the center point of the Gaussian function and σ is the standard deviation of the voltage, which is used to control the width of the blurring.

[0071] The inference engine is used to deduce fuzzy rules and obtain fuzzy output results. The commonly used inference method is based on the minimum-maximum operation method. The fuzzy inference result of each rule can be expressed as:

[0072]

[0073] in, and is the membership degree of the input variable in rule j.

[0074] Different reasoning methods may have different ways of activating and combining rules. The specific reasoning process will select the most appropriate method based on the control requirements and application scenarios, so that the system has good adaptability and robustness when dealing with complex welding conditions.

[0075] The adaptive module dynamically adjusts the parameters of the fuzzy rule base and the membership function according to the welding quality information provided by the feedback system to optimize the control performance and enhance the adaptability and robustness of the system. The adaptive module adopts a parameter optimization method based on a machine learning algorithm to achieve dynamic adjustment of the fuzzy rule base and the membership function. The adaptive algorithm can use evolutionary computing methods such as neural networks or genetic algorithms to adjust the parameters of the fuzzy controller according to the real-time data of the feedback system so that the system maintains optimal performance when the welding conditions change. The core of the adaptive adjustment is to adjust the control strategy in real time according to the welding quality parameters (such as weld flatness, melt depth and spatter) provided by the feedback system.

[0076] The adaptive module usually calculates the system error and adjusts the parameters of the fuzzy control system according to the error. The error formula can be expressed as:

[0077] e(t)=y desired (t)-y(t)

[0078] Among them, y desired (t) is the desired output, y(t) is the actual output, and these error values ​​are used as inputs to the fuzzy control rules.

[0079] Adaptive parameter updating adjusts the parameters of the fuzzy controller (such as the shape of the membership function or the rule weights) to minimize the error, which is expressed as:

[0080]

[0081] Among them, θ i is the fuzzy controller parameter, E(t) is the error function, α is the learning rate, It is the gradient of the error function with respect to the control parameter. Through the gradient descent method, the controller parameters (such as the parameters of the membership function) are adjusted according to the real-time error to optimize the control effect.

[0082] This adaptability makes the system highly robust and anti-interference, and it can maintain high-quality welding results under complex and nonlinear welding conditions.

[0083] The inverter power supply module serves as the execution unit of the control signal, receives the precise control signal after defuzzification, and adjusts the output power of the inverter power supply through pulse width modulation. The design of this module needs to take into account the response speed and stability of the control signal to achieve precise control of the welding arc. The pulse width modulation control strategy adjusts the output current or voltage of the power supply by adjusting the change of the pulse width, so that the parameter fluctuations in the welding process can be effectively compensated and adjusted, thereby optimizing the formation and quality of the weld. The circuit diagram of the complete inverter arc welding power supply, including the rectifier circuit, DC-Link capacitor, inverter, fuzzy control, drive circuit, etc., is shown in the circuit block diagram. Figure 2 .

[0084] The main function of the feedback system is to monitor the real-time parameters of the weld quality during the welding process and transmit this information back to the adaptive module. The feedback data can include key indicators such as the weld formation state and surface quality, which are used to evaluate the current control effect and adjust the control strategy. The feedback system forms a continuous cycle of data acquisition-processing-feedback through a closed-loop control mechanism, allowing the system to self-optimize and adjust according to the actual welding situation.

[0085] As attached Figure 3 As shown, the operation flow of the adaptive fuzzy control system of this embodiment includes the following steps:

[0086] 1. Data collection and fuzzy processing: After the system is started, the sensor module collects welding current, voltage, speed and temperature data in real time. The data processing unit fuzzifies the collected precise data and converts it into corresponding fuzzy sets for fuzzy reasoning.

[0087] 2. Fuzzy control reasoning and adaptive adjustment: The fuzzy controller performs reasoning based on fuzzified data and a preset rule base and outputs a fuzzy control signal. The reasoning process comprehensively considers the interaction between multiple input variables and generates a fuzzy output that meets welding requirements. The adaptive module dynamically adjusts the rule base and membership function based on the real-time data provided by the feedback system to optimize the control effect.

[0088] 3. Defuzzification and inverter power supply control: The defuzzification process converts the fuzzy signal output by the fuzzy controller into a specific numerical control instruction, and sends it to the inverter power supply module to adjust the output current or voltage of the power supply so that the state of the welding arc meets the desired welding process parameters.

[0089] 4. Feedback monitoring and closed-loop regulation: The feedback system monitors the actual weld quality during the welding process and transmits the monitoring data to the adaptive module. The adaptive module continuously optimizes the control strategy based on the feedback data to form a data-driven closed-loop control to ensure the stability of the welding process and the quality of the weld.

[0090] The adaptive fuzzy control system of this embodiment is applicable to a variety of welding scenarios and working conditions, and specific application areas include but are not limited to the following situations:

[0091] Dynamic changes in welding parameters: During the welding process, the current and voltage will fluctuate as the arc state changes. The system can dynamically adjust according to the parameter data collected in real time to adapt to the rapid changes in welding conditions. When the system detects changes in weld quality, it automatically adds new control rules or modifies existing rules, reducing the complexity of the rule base and improving control efficiency.

[0092] A variety of workpiece materials and qualities meet the requirements; workpieces of different materials and thicknesses have different arc currents and voltages during welding. The parameters of the welding power supply are automatically adjusted according to the material characteristics to ensure that the formation and quality of the weld meet the requirements.

[0093] Complex welding trajectories and multi-axis linkage welding; suitable for robot welding or complex trajectory welding scenarios on CNC machine tools. By integrating with the motion control system, this system can accurately control the speed, direction and other parameters of the welding path to achieve high-quality automatic welding.

[0094] As attached Figure 4 As shown, the control fuzzy structure diagram of this embodiment includes the following parts:

[0095] Fuzzy processing: Mapping precise input signals (such as current, voltage, temperature, etc.) into membership of fuzzy sets for subsequent fuzzy reasoning and decision-making processes, such as "low", "medium", "high", etc.

[0096] Fuzzy Reasoning: Reasoning on fuzzy sets by applying a preset fuzzy rule base, such as "if voltage is high and current is low, then output decreases."

[0097] Defuzzification processing: converting the results of fuzzy reasoning into specific control signals, such as output current or voltage regulation values.

[0098] This embodiment is optimized from the following aspects:

[0099] 1. Improve the accuracy and stability of welding control: This embodiment effectively solves this problem with the help of adaptive fuzzy control technology. The fuzzy controller can handle complex nonlinear input-output relationships and generate control signals through fuzzy rule reasoning. Combined with the adaptive module, the system can adjust the fuzzy rules and membership functions in real time, so that the control strategy is automatically optimized as the arc state changes, thereby achieving a more accurate control effect.

[0100] 2. Strong dynamic adaptability to changing welding conditions: Traditional welding control methods usually require manual adjustment of control parameters when dealing with changes in welding conditions. The adjustment process is cumbersome and prone to errors. The present invention introduces an adaptive module to achieve automatic optimization and real-time adjustment of control parameters. The system can dynamically adjust the control strategy according to the fluctuations in voltage and current during the welding process, and can maintain excellent control performance even when the welding material, ambient temperature or welding speed changes. This dynamic adaptability enhances the practicality of the system in complex welding tasks.

[0101] 3. Significantly improve the anti-interference ability and robustness of the system; the arc welding process is easily disturbed by external factors, such as environmental noise, changes in workpiece material, etc. The linear control method in the prior art is often unstable in the face of such interference. The present invention combines fuzzy control with adaptive algorithms to enable the system to have stronger anti-interference ability. The fuzzy controller can fuzzify the input variables and alleviate the control deviation caused by external disturbances, while the adaptive module can quickly adjust the control parameters to further enhance the robustness and stability of the system.

[0102] 4. Reduce manual intervention and improve system intelligence: Compared with the prior art that requires frequent manual adjustments and empirical parameter settings, the control system of the present invention has a high degree of intelligence. Through the self-learning mechanism, the adaptive module can automatically optimize the control strategy according to the feedback data during the welding process, reducing the need for operator intervention. This not only improves the autonomy and intelligence of the system, but also reduces the dependence on the operator's skill level, making the system easier to operate and maintain.

[0103] 5. Modular design, easy to expand and maintain: The present invention adopts modular system design, which divides the control system into independent parts such as sensor module, data processing unit, fuzzy controller, adaptive module, inverter power module and feedback system. Compared with the existing integrated control system design, this modular structure allows each module to be upgraded and maintained separately, enhancing the flexibility and scalability of the system. Users can adjust or replace specific modules according to specific industrial application requirements, which is convenient for system upgrades and technical updates.

[0104] 6. Wide applicability, suitable for a variety of industrial application scenarios; existing welding control technologies are mostly designed for specific welding processes or equipment, and their scope of application is relatively limited. The adaptive fuzzy control system of the present invention has wide applicability. In addition to being applied to inverter arc welding power supplies, it can also be extended to other nonlinear industrial control processes, such as cutting, heat treatment, and control of power electronic equipment. Its multi-purpose characteristics make the system have a wider application prospect in the field of industrial automation.

[0105] Compared with the prior art, this embodiment has significant technical advantages and effects in terms of control accuracy, adaptability, intelligence level, system modular design and wide application, and has brought significant technical improvements to the field of welding technology and industrial control.

[0106] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed in the present application should be included in the protection scope of the present application.

Claims

1. An adaptive fuzzy control system for an inverter arc welding power source, characterized in that: It includes sensor module, data processing unit, fuzzy controller, adaptive module, inverter power module and feedback system. The sensor module collects working values ​​corresponding to key parameters in the welding process in real time, and the key parameters include at least arc current, voltage, welding speed and ambient temperature; The data processing unit converts the working values ​​collected in real time into fuzzy sets; The fuzzy controller performs fuzzy reasoning operations on the fuzzy set based on a preset fuzzy rule base, and makes comprehensive judgments on a plurality of the key parameters to output a fuzzy control signal; The data processing unit defuzzifies the fuzzy control signal and converts it into a precise control signal; The inverter power supply module receives the defuzzified precise control signal and adjusts the output power of the welding power supply by pulse width modulation to ensure the stability of the welding arc; The feedback system monitors the real-time parameters of the weld quality during the welding process, the real-time parameters at least including the flatness and surface quality of the weld, and transmits the real-time parameters to the adaptive module to achieve closed-loop control; The adaptive module dynamically adjusts the parameters of the fuzzy rule base and the membership function according to the welding quality information provided by the feedback system to optimize the control performance and enhance the adaptability and robustness of the system.

2. The adaptive fuzzy control system according to claim 1, characterized in that: The data processing unit uses at least one membership function to perform fuzzy processing, and the membership function is at least one of a triangle membership function, a trapezoidal membership function, a Gaussian function and a bell-shaped function.

3. The adaptive fuzzy control system according to claim 1, characterized in that: The fuzzy inference method adopted by the fuzzy controller is an inference method selected according to the characteristics of multiple input variables in the welding process, and the inference method is at least one of the maximum-minimum synthesis method, the weighted average method and the maximum membership method.

4. The adaptive fuzzy control system according to claim 1, characterized in that: The adaptive module adopts an adaptive adjustment strategy, which is at least one of a neural network, a genetic algorithm or a fuzzy neural network, a rule base for dynamically optimizing a fuzzy controller, and a membership function parameter.

5. The adaptive fuzzy control system according to claim 1, characterized in that: The feedback system includes a data fusion unit, which is used to perform fusion processing on feedback data from multiple sensors to eliminate the influence of noise and uncertainty on the control effect. The data fusion unit adopts Kalman filtering or Bayesian estimation for fusion.

6. The adaptive fuzzy control system according to claim 1, characterized in that: The inverter power supply module automatically adjusts the pulse width modulation parameters using a dynamic response regulation mechanism according to the arc characteristics that change in real time during the welding process, so as to improve the dynamic response characteristics and control accuracy of the power supply.

7. The adaptive fuzzy control system according to claim 1, characterized in that: The fuzzy rule base includes "if-then" rules designed based on expert experience and experimental data, and the construction steps of the fuzzy rule base include fuzzy rule extraction, rule optimization and redundant rule deletion.

8. The adaptive fuzzy control system according to claim 1, characterized in that: Also includes: A fault diagnosis unit cooperates with the inverter power module, and is used to monitor the operating state of the power module and perform fault prediction and self-repair to ensure the long-term stable operation of the welding system.

9. The adaptive fuzzy control system according to claim 1, characterized in that: The real-time parameters include at least one of welding speed, weld position and weld angle.

10. The adaptive fuzzy control system according to claim 1, characterized in that: The adaptive module adopts a multi-objective optimization algorithm to comprehensively consider welding quality, control energy consumption and system response time to achieve global optimization of the control strategy.

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