Composite control method and system based on fuzzy control algorithm and PID (Proportion Integration Differentiation)

Abstract

The invention provides a composite control method and system based on a fuzzy control algorithm and PID, and the method comprises the steps: obtaining an output error and an error change rate of a system in real time, and obtaining a state variable of the system; taking the output error, the error change rate and the state variable as input, and outputting a proportionality coefficient, an integral coefficient and a differential coefficient at the same time through a fuzzy inference engine; forming a parameter self-tuning PID (Proportion Integration Differentiation) controller by utilizing a proportionality coefficient, an integral coefficient and a differential coefficient, and calculating a control signal; and the control signal is output to a servo driver of the servo load simulator system to control an execution mechanism to act. All working conditions and nonlinear self-adaption of controller parameters are achieved through the fuzzy-PID fusion controller, and compared with a traditional PID controller, response can be made stably through self-adaption adjustment of the parameters in the face of load sudden change and other complex working conditions, overshoot is effectively reduced, the adjustment time is shortened, and the inherent high precision of the PID controller can be kept.

Description

Technical Field

[0001] This invention relates to the field of valve core control technology, and in particular to a composite control method and system based on fuzzy control algorithm and PID. Background Technology

[0002] Load simulators are critical equipment in fields such as aerospace, weapon systems, and automotive component testing. They are used to reproduce the loads experienced by the tested object under actual working conditions in a laboratory environment, thereby enabling performance verification and fatigue testing. Based on their core power source, mainstream load simulators can be divided into two main categories: electro-hydraulic servo load simulators and electric servo load simulators.

[0003] For electro-hydraulic servo load simulator systems, loads are applied by controlling hydraulic actuators through electro-hydraulic servo valves. The flow characteristics of the core component of such systems, the electro-hydraulic servo valve, are a complex nonlinear function relating the oil source pressure, load pressure, and valve spool displacement. This inherent nonlinear relationship makes it extremely difficult to establish an accurate and applicable global mathematical model.

[0004] For electric servo load simulator systems, a servo motor is controlled by a motor driver, and the load is applied via a transmission mechanism. These systems also face significant nonlinear factors, such as the control dead zone and saturation characteristics of the servo driver, and the backlash and friction (including static friction and Coulomb friction) present in the mechanical transmission chain.

[0005] Traditional PID controllers are widely used in industrial control due to their simple structure and high steady-state accuracy. However, their control parameters are usually fixed. When faced with the strong nonlinearity, time-varying parameters, and external load disturbances in the electro-hydraulic or electric servo load simulator system mentioned above, traditional fixed-parameter PID controllers struggle to achieve a balance between dynamic performance and steady-state accuracy. Summary of the Invention

[0006] This invention provides a composite control method and system based on fuzzy control algorithm and PID, which solves the shortcomings of the traditional PID in the prior art, where the fixed control parameters are difficult to adapt to strong nonlinear and time-varying characteristics, and cannot take into account dynamic performance and steady-state accuracy when facing working conditions such as sudden load changes.

[0007] In a first aspect, the present invention provides a composite control method based on fuzzy control algorithm and PID, applied to a servo load simulator system. The system controls an actuator via a servo driver, and the dynamic characteristics of the actuator are affected by the system's nonlinear and time-varying characteristics. The method includes: The system's output error and error rate of change are acquired in real time, and the system's state variables are also acquired, including physical quantities related to the system load. The output error, the rate of change of error, and the state variable are taken as inputs, and the fuzzy inference engine simultaneously outputs the proportional coefficient, integral coefficient, and derivative coefficient; wherein, the rule base of the fuzzy inference engine is configured to nonlinearly adjust the proportional coefficient, integral coefficient, and derivative coefficient according to the changes of the output error, the rate of change of error, and the state variable, so as to compensate for the nonlinearity and time-varying characteristics of the system. The proportional coefficient, the integral coefficient, and the derivative coefficient are used to construct a parameter-self-tuning PID controller to calculate the control signal; The control signal is output to the servo driver of the servo load simulator system to control the action of the actuator.

[0008] According to the composite control method based on fuzzy control algorithm and PID provided by the present invention, the rule base of the fuzzy inference engine is specifically configured as follows: When the output error increases, the proportional coefficient is increased to improve the dynamic response speed of the system; When the output error decreases, the proportional coefficient is reduced to decrease the system overshoot. When the rate of change of error increases, the integral coefficient is reduced to suppress integral saturation. When the rate of change of error decreases, the integral coefficient is increased to improve the system's response speed to load commands; Based on the combination of the output error and the rate of change of error, the differential coefficients are nonlinearly adjusted to enhance the damping characteristics of the system under different load conditions.

[0009] According to the present invention, a composite control method based on fuzzy control algorithm and PID is provided, wherein when the output error increases, the proportional coefficient is increased, including: Obtain the real-time value of the output error; Based on the real-time value of the output error, query the predefined proportional coefficient adjustment table to determine the corresponding proportional coefficient adjustment amount; The adjustment amount of the proportional coefficient is positively correlated with the real-time value of the output error.

[0010] According to the composite control method based on fuzzy control algorithm and PID provided by the present invention, the predefined proportional coefficient adjustment table is constructed based on the dynamic response characteristics of the servo load simulator system; The dynamic response characteristics include the overshoot and settling time of the system under typical load commands. The proportional coefficient adjustment is configured to: reduce the system's settling time by increasing the proportional coefficient within a specific range of the output error; and suppress the system's overshoot by decreasing the proportional coefficient within another specific range of the output error.

[0011] According to the present invention, a composite control method based on fuzzy control algorithm and PID is provided, wherein when the error rate of change increases, the integral coefficient is reduced, comprising: Obtain the real-time value of the error change rate; Based on the real-time value of the error change rate, query the predefined integral coefficient adjustment table to determine the corresponding integral coefficient adjustment amount; The adjustment amount of the integral coefficient is negatively correlated with the real-time value of the error change rate.

[0012] According to the composite control method based on fuzzy control algorithm and PID provided by the present invention, the predefined integral coefficient adjustment table is constructed based on the steady-state characteristics and response speed of the servo load simulator system; The integral coefficient adjustment is configured to: suppress integral saturation by decreasing the integral coefficient within a specific range of the error change rate; and improve the system's response speed to load commands by increasing the integral coefficient within another specific range of the error change rate.

[0013] According to the present invention, a composite control method based on fuzzy control algorithm and PID is provided, wherein the differential coefficient is nonlinearly adjusted according to the combination of the output error and the error change rate, comprising: Obtain the real-time value of the output error and the real-time value of the error change rate; Using the real-time value of the output error and the real-time value of the error change rate as joint inputs, a predefined differential coefficient adjustment table is queried to determine the corresponding differential coefficient adjustment amount; The differential coefficient adjustment scale is configured to output the differential coefficient adjustment amount based on a combination of the output error and the error change rate, using a nonlinear mapping relationship.

[0014] According to the composite control method based on fuzzy control algorithm and PID provided by the present invention, the predefined differential coefficient adjustment table is constructed based on the damping characteristics of the servo load simulator system; The differential coefficient adjustment is configured to: enhance system damping and suppress oscillations caused by sudden load changes by increasing the differential coefficient within a specific combination range of the output error and the rate of change of error; and reduce the system's sensitivity to high-frequency measurement noise by decreasing the differential coefficient within another specific combination range of the output error and the rate of change of error.

[0015] According to the present invention, a composite control method based on fuzzy control algorithm and PID is provided, wherein the state variables include system output load or actuator displacement.

[0016] Secondly, the present invention provides a composite control system based on fuzzy control algorithm and PID, wherein the system controls an actuator through a servo driver, and the dynamic characteristics of the actuator are affected by the nonlinear and time-varying characteristics of the system, including: The acquisition module is used to acquire the output error and error change rate of the system in real time, and to acquire the system's state variables, which include physical quantities related to the system load. The inference module is used to take the output error, the error change rate, and the state variable as inputs, and simultaneously output the proportional coefficient, integral coefficient, and derivative coefficient through the fuzzy inference engine; wherein, the rule base of the fuzzy inference engine is configured to nonlinearly adjust the proportional coefficient, integral coefficient, and derivative coefficient according to the changes in the output error, the error change rate, and the state variable to compensate for the nonlinearity and time-varying characteristics of the system; The control module is used to construct a parameter self-tuning PID controller using the proportional coefficient, the integral coefficient, and the derivative coefficient, calculate the control signal, and output the control signal to the servo driver of the servo load simulator system to control the action of the actuator.

[0017] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the composite control method based on fuzzy control algorithm and PID as described above.

[0018] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the composite control method based on fuzzy control algorithm and PID as described above.

[0019] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the composite control method based on fuzzy control algorithm and PID as described above.

[0020] The present invention provides a composite control method based on fuzzy control algorithm and PID, which achieves full-condition, nonlinear adaptive control of controller parameters by constructing a fuzzy-PID fusion controller with output error, error rate of change, and state variables. Compared with traditional PID controllers, when facing complex conditions such as sudden load changes, the system can respond quickly and smoothly through adaptive parameter adjustment, effectively reducing overshoot and shortening settling time. At the same time, it can maintain the inherent high precision of the PID controller in the steady-state phase, achieving a balance between dynamic performance and steady-state accuracy. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating the composite control method based on fuzzy control algorithm and PID provided in this embodiment; Figure 2 This is a schematic diagram of the electro-hydraulic servo load simulator system provided in this embodiment; Figure 3 This is a schematic diagram of the electric servo load simulator system provided in this embodiment; Figure 4 This is a schematic diagram of the structure of the composite control system based on fuzzy control algorithm and PID provided in this embodiment; Figure 5 This is a schematic diagram of the structure of the electronic device provided in this embodiment. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0024] Figure 1 This is a flowchart illustrating the composite control method based on fuzzy control algorithm and PID provided in this embodiment.

[0025] like Figure 1 As shown in the embodiments of the present invention, the composite control method based on fuzzy control algorithm and PID is provided. The servo load simulator system may include an electro-hydraulic servo load simulator system and an electric servo load simulator system, etc. The combination of fuzzy inference engine and PID controller is called fuzzy-PID fusion controller.

[0026] like Figure 2The diagram shows the schematic of an electro-hydraulic servo load simulator system. It mainly consists of a command generation module, a feedback controller, a servo valve, a servo hydraulic cylinder, a sensor, and a load / displacement output module. The command generation module generates a load loading reference command in real time based on the displacement of the test product. The feedback controller superimposes this command with the load acquisition signal fed back by the sensor, calculates the real-time load error, and sends it to the servo valve. The servo valve adjusts the flow rate of hydraulic oil in the two chambers of the servo hydraulic cylinder according to the output signal of the feedback controller, and the servo hydraulic cylinder provides the specified load to the test product based on this signal. The sensor acquires the load output signal in real time, and superimposes it with the sensor's own accuracy error and the interference signal formed by interference coupling of the acquisition system, transmitting this as a feedback signal to the feedback controller. The entire system achieves closed-loop control through a load feedback control loop, ensuring that the test product can complete the loading task according to the given load loading reference command, while simultaneously adjusting the control signal in real time through the feedback mechanism to meet the system's accuracy requirements.

[0027] like Figure 3 The diagram shows the schematic of an electric servo load simulator system. It mainly consists of an instruction generation module, a feedback controller, a motor driver, a servo motor, sensors, and a load / displacement output module. The instruction generation module generates a load loading reference instruction in real time based on the displacement of the test product. The feedback controller superimposes this instruction with the load acquisition signal fed back from the sensor to calculate the real-time load error and sends the error signal to the motor driver. The motor driver adjusts the operating state of the servo motor in real time according to the output signal of the feedback controller. The servo motor drives the loading actuator according to the control signal of the driver, providing the specified load to the test product. The sensor acquires the load output signal in real time and superimposes it with the sensor's own accuracy error and the interference signal formed by interference coupling of the acquisition system, transmitting this as a feedback signal to the feedback controller. The entire system achieves closed-loop control through a load feedback control loop, ensuring that the test product can complete the loading task according to the given load loading reference instruction, while simultaneously adjusting the control signal in real time through the feedback mechanism to meet the system's accuracy requirements.

[0028] The specific composite control method is implemented through the process of "real-time signal acquisition → fuzzy inference parameter adjustment → PID control signal calculation → actuator drive → closed-loop feedback correction". The implementation steps of this method are explained in detail below: 101. Real-time acquisition of the system's output error and error change rate, and acquisition of the system's state variables, including physical quantities related to the system load.

[0029] Specifically, the output error is defined as the difference between the load reference command value output by the command generation module and the actual output load value of the system acquired by the force sensor. The force sensor needs to be installed at the connection between the actuator and the object under test to directly acquire the real load acting on the object. During the acquisition process, the high-frequency noise of the sensor itself needs to be filtered out. This can be done through a first-order low-pass filter. The filter cutoff frequency is set according to the highest dynamic response frequency of the system to ensure coverage of the main frequency components of the load command.

[0030] The rate of change of error reflects the real-time dynamic trend of the error and can be calculated in two ways: First, the differential calculation method, which involves numerically differentiating the error signal (e.g., using first-order backward difference, calculating the rate of change by combining the difference between the error of the current sampling period and the error of the previous sampling period); second, the trend judgment method, where when the sampling period is much smaller than the system's dynamic response time, the error difference between adjacent sampling periods is directly taken as the rate of change of error. The sampling period must match the control period of the servo driver to ensure the real-time performance of signal acquisition and avoid increased control deviation due to delay.

[0031] State variables include system output load and actuator displacement: the system output load and the actual load used in error calculation are from the same source and do not require additional acquisition; the actuator displacement needs to be acquired by displacement sensor (electro-hydraulic system) or photoelectric encoder (electric system). Its function is to help judge the degree of nonlinearity of the system. For example, a sudden change in displacement in an electric system can reflect the transmission clearance, and a mismatch between displacement and load in an electro-hydraulic system can reflect the change in oil viscosity.

[0032] The acquired errors, error rate of change, and state variables need to be preprocessed to avoid interference signals affecting control accuracy. Preprocessing includes outlier removal and signal smoothing: Outlier removal requires determining whether the acquired signal exceeds the reasonable boundary of the system's rated operating range. If it does, it is determined to be an outlier, and the average of the previous few sampling periods is used to replace it to prevent the controller from malfunctioning due to abnormal signals. For signal smoothing, a moving average filter is used for displacement signals, and a Kalman filter is used for load signals to balance noise suppression while ensuring dynamic response speed.

[0033] 102. Taking the output error, error rate of change, and state variables as inputs, the fuzzy inference engine simultaneously outputs the proportional coefficient, integral coefficient, and derivative coefficient; wherein, the rule base of the fuzzy inference engine is configured to nonlinearly adjust the proportional coefficient, integral coefficient, and derivative coefficient according to the changes in the output error, error rate of change, and state variables, so as to compensate for the nonlinearity and time-varying characteristics of the system.

[0034] Specifically, the fuzzy inference engine is the key unit for achieving parameter self-tuning. Its structure must meet the requirement of "taking output error, error rate of change, and state variables as inputs, and proportional coefficient, integral coefficient, and differential coefficient as outputs," and specifically includes four parts: The fuzzification module converts continuous output errors, error rates of change, and state variables into fuzzy linguistic variables, providing a foundation for rule base matching. The rule base is the core for realizing the nonlinear and time-varying characteristics of the compensation system, covering three core logic categories: adjustment of the proportional coefficient by the output error, adjustment of the integral coefficient by the error rate of change, and adjustment of the derivative coefficient by the combination of output error and error rate of change. The fuzzy inference module matches the input fuzzy linguistic variables with corresponding rules in the rule base, generating a fuzzy output set of proportional, integral, and derivative coefficients. The declarative module converts the fuzzy output set into specific proportional, integral, and derivative coefficient values, ensuring that the output parameters can be directly used to construct a PID controller.

[0035] For the electro-hydraulic servo load simulator system, the state variables are the output load of the hydraulic actuator and the displacement of the hydraulic cylinder piston; for the electric servo load simulator system, the state variables are the output load of the mechanical actuator and the actuator displacement converted from the motor shaft rotation angle.

[0036] The purpose of fuzzification is to convert three types of continuous input quantities—output error, error rate of change, and state variables—into fuzzy linguistic variables that can be matched with the rule base. Specifically: Parameter definitions: Output error is defined as the difference between the load command value and the actual output load value of the servo load simulator system. It needs to be acquired in real time and high-frequency noise filtered out. Error change rate is defined as the change in output error over a sampling period, calculated by the difference in output error between adjacent sampling periods. State variables are taken as the system output load or actuator displacement.

[0037] Fuzzy set partitioning: The three types of input quantities are divided into seven fuzzy sets: "negative large, negative medium, negative small, zero, positive small, positive medium, and positive large". The partitioning logic needs to be adapted to the nonlinear characteristics of the system, such as the fuzzy set interval of the output error. The system steady-state error allowable range is used as the benchmark to ensure coverage of the two core operating conditions of error increase and error decrease.

[0038] Membership function selection: The triangular membership function is adopted. By calculating the membership degree of the input quantity to each fuzzy set, a quantitative basis is provided for subsequent rule matching.

[0039] The specific rules and matching methods of the rule base are as follows: Proportional coefficient adjustment rules: When the output error increases (e.g., the fuzzy set of output error is "positive" or "positive large"), the proportional coefficient is increased to improve the system's dynamic response speed and cope with nonlinear conditions such as sudden load changes. When the output error decreases (e.g., the fuzzy set of output error is "positive small" or "zero"), the proportional coefficient is decreased to reduce system overshoot (avoiding fluctuations in the steady-state phase). This is implemented using a predefined proportional coefficient adjustment table, which is built based on the dynamic response characteristics (overshoot, settling time) of the servo load simulator system. By querying the table, the proportional coefficient adjustment amount corresponding to the real-time value of the output error can be determined, and the adjustment amount is positively correlated with the real-time value of the output error.

[0040] Integral coefficient adjustment rules: When the error rate of change increases (e.g., the fuzzy set of the error rate of change is "positive middle" or "positive large"), the integral coefficient is decreased to suppress integral saturation and prevent excessive accumulation of the integral term due to rapid error changes. When the error rate of change decreases (e.g., the fuzzy set of the error rate of change is "positive small" or "zero"), the integral coefficient is increased to improve the system's response speed to load commands and strengthen steady-state error compensation. This is implemented using a predefined integral coefficient adjustment table, which is built based on the steady-state characteristics and response speed of the servo load simulator system. By querying the table, the adjustment amount of the integral coefficient corresponding to the real-time value of the error rate of change can be determined, and the adjustment amount is negatively correlated with the real-time value of the error rate of change.

[0041] Differential coefficient adjustment rules: Based on the combination of output error and error change rate, such as "small output error + large error change rate" or "large output error + small error change rate," the differential coefficients are adjusted non-linearly to enhance the damping characteristics of the system under different load conditions. A predefined differential coefficient adjustment table, constructed based on the damping characteristics of the servo load simulator system, is used as a joint input to query the table, yielding the corresponding differential coefficient adjustment amount. The adjustment amount exhibits a non-linear mapping relationship with the input combination.

[0042] Rule matching is performed using the principle of maximum membership. For output error, error rate of change, and state variable, the fuzzy set with the largest membership is selected to form an "input fuzzy vector" (e.g., "output error = positive large, error rate of change = positive small, state variable = zero"). Then, the corresponding "proportional coefficient, integral coefficient, and derivative coefficient adjustment logic" are matched from the rule base to ensure that the matching result fits the current system operating conditions, such as sudden load changes or steady-state operation.

[0043] Through fuzzy inference and declarative analysis, the fuzzy output set obtained from rule base matching is converted into specific proportional coefficients, integral coefficients, and differential coefficients. Specifically, the input fuzzy vector is ANDed with the corresponding adjustment rule in the rule base to generate a fuzzy output set of proportional coefficients, integral coefficients, and differential coefficients. For example, "large positive output error → large positive proportional coefficient adjustment" and "large positive error rate of change → small negative integral coefficient adjustment" are used. The fuzzy output set must reflect the characteristics of "non-linear adjustment" to avoid linear and monotonic changes in parameter adjustment.

[0044] Convert the fuzzy output set into specific numerical values. Scale factor calculation: The scale factor must satisfy the following conditions: within a specific range of output error values, increasing the scale factor reduces the settling time; within another range, decreasing the scale factor suppresses overshoot, ensuring that the scale factor adjustment closely matches the system's dynamic response requirements.

[0045] Integral coefficient calculation: The integral coefficient needs to satisfy the following conditions: within a specific range of error change rate, the integral coefficient can be reduced to suppress integral saturation; within another range, the integral coefficient can be increased to improve the response speed, ensuring that the integral coefficient adjustment balances steady-state accuracy and dynamic performance.

[0046] Differential coefficient calculation: The differential coefficient needs to meet the following requirements: within a specific combination range of output error and error change rate, increasing the differential coefficient enhances damping and suppresses oscillation; within another combination range, decreasing the differential coefficient reduces high-frequency noise sensitivity, ensuring that the differential coefficient adjustment adapts to different load conditions.

[0047] The specified proportional coefficient, integral coefficient, and derivative coefficient need to be directly used to construct a parameter self-tuning PID controller, providing a basis for subsequent calculation of control signals, while ensuring that parameter adjustments can effectively compensate for the nonlinear and time-varying characteristics of the servo load simulator system.

[0048] 103. Construct a PID controller with self-tuning parameters using proportional coefficient, integral coefficient, and derivative coefficient, and calculate the control signal.

[0049] Specifically, before adjusting the fuzzy parameters, the initial PID parameters (initial proportional coefficient, initial integral coefficient, initial derivative coefficient) must be determined to ensure stable initial system operation. The determination of the initial parameters must be considered in conjunction with the system type. For electro-hydraulic servo load simulator systems, the approximate transfer function of the system (usually a second-order inertial element) is first obtained through the "system identification method," and then the initial parameters are calculated using classical tuning methods to ensure that the system has no continuous oscillations and that the steady-state error is within an acceptable range. For electric servo load simulator systems, the rise time and overshoot of the system are obtained through the "step response method," and the initial parameters are set in conjunction with the system gain, also with the goal of ensuring the initial stability of the system.

[0050] Initial parameters need to be verified through no-load test runs. If the system exhibits continuous oscillations during the test run, the initial proportional coefficient should be reduced; if the steady-state error is too large, the initial integral coefficient should be increased; if the response lag is significant, the initial derivative coefficient should be increased until the system reaches a basically stable state.

[0051] PID parameter self-tuning involves superimposing the parameter adjustment output from the fuzzy inference engine with the initial PID parameters to obtain real-time PID parameters. To prevent parameters from exceeding reasonable ranges, constraints must be set on the real-time PID parameters: the proportional coefficient is constrained within a certain range of the initial proportional coefficient to prevent excessively strong or weak responses; the integral coefficient is constrained within the non-negative range and does not exceed a certain multiple of the initial integral coefficient to prevent negative integral coefficients from increasing steady-state deviation or excessive integral saturation; and the derivative coefficient is constrained within the non-negative range and does not exceed a certain multiple of the initial derivative coefficient to prevent excessively large derivative coefficients from amplifying noise.

[0052] The calculation of control signals requires selecting an appropriate PID algorithm based on the type of system actuator: For electro-hydraulic servo load simulator systems, the hydraulic actuators are position-type actuators, so a position-type PID algorithm is used. The control signal (servo valve control current) is calculated by a weighted sum of the error, error integral, and error derivative. For electric servo load simulator systems, the servo motors are incremental actuators, so an incremental PID algorithm is used. The control signal is calculated by a weighted sum of the error difference between adjacent cycles, the error integral of the current cycle, and the second-order error difference, avoiding parameter drift caused by the accumulation of integral terms. The calculated control signal needs to be converted to a standard signal format supported by the servo driver.

[0053] 104. Output control signals to the servo driver of the servo load simulator system to control the action of the actuator.

[0054] Specifically, the control signal is sent to the servo drive through the controller's output interface: For an electro-hydraulic servo drive, after receiving the control signal, the valve core displacement of the servo valve is adjusted, thereby controlling the oil flow and pressure difference between the two chambers of the hydraulic actuator, driving the actuator to output the specified load. For an electric servo drive, after receiving the control signal, the armature current or magnetic field strength of the servo motor is adjusted, and the motor torque is converted into the actuator's loading force through a mechanical transmission mechanism, outputting the specified load.

[0055] When the load collected by the sensor exceeds the safe range of the system's rated load, or the displacement exceeds the safe range of the stroke, the controller must immediately output an emergency stop signal to cut off the power to the servo drive and prevent system components from being damaged due to overload or overtravel.

[0056] After the control signal is output, the signal acquisition module continuously acquires the actual output load and actuator displacement of the system, repeating steps 101 to 103 to achieve closed-loop iterative correction. If the error between the actual load and the commanded load still exceeds the steady-state allowable range, the fuzzy inference engine further adjusts the PID parameters, such as increasing the integral coefficient to enhance integral compensation. If a sudden load change occurs, the fuzzy inference engine rapidly increases the proportional and derivative coefficients to improve the system's anti-disturbance capability. If the system enters a steady state, the fuzzy inference engine decreases the derivative coefficient to reduce high-frequency noise interference and maintains the integral coefficient to maintain steady-state accuracy.

[0057] The method described in this embodiment constructs a fuzzy-PID fusion controller based on output error, error change rate, and state variables, achieving full-condition, nonlinear adaptive control of controller parameters. Compared to traditional PID controllers, when faced with complex conditions such as sudden load changes, the system can respond quickly and smoothly through adaptive parameter adjustment, effectively reducing overshoot and shortening settling time. Simultaneously, it maintains the inherent high precision of the PID controller in the steady-state phase, achieving a balance between dynamic performance and steady-state accuracy.

[0058] Figure 4 This is a schematic diagram of the structure of the composite control system based on fuzzy control algorithm and PID provided in this embodiment.

[0059] like Figure 4 As shown, the composite control system based on fuzzy control algorithm and PID provided in this embodiment controls the actuator through a servo driver. The dynamic characteristics of the actuator are affected by the nonlinearity and time-varying characteristics of the system, including: The acquisition module 401 is used to acquire the system's output error and error change rate in real time, and to acquire the system's state variables, which include physical quantities related to the system load. The inference module 402 is used to take the output error, the rate of change of error, and the state variable as inputs, and simultaneously output the proportional coefficient, integral coefficient, and derivative coefficient through the fuzzy inference engine. The rule base of the fuzzy inference engine is configured to nonlinearly adjust the proportional coefficient, integral coefficient, and derivative coefficient according to the changes in the output error, the rate of change of error, and the state variable to compensate for the nonlinearity and time-varying characteristics of the system. The control module 403 is used to construct a parameter self-tuning PID controller using proportional coefficient, integral coefficient and derivative coefficient, calculate the control signal, and output the control signal to the servo driver of the servo load simulator system to control the action of the actuator.

[0060] Figure 5 This is a schematic diagram of the structure of the electronic device provided in this embodiment.

[0061] like Figure 5As shown, the electronic device may include a processor 501, a communication interface 502, a memory 503, and a communication bus 504. The processor 501, communication interface 502, and memory 503 communicate with each other via the communication bus 504. The processor 501 can call logic instructions from the memory 503 to execute a composite control method based on fuzzy control algorithms and PID control.

[0062] Furthermore, the logical instructions in the aforementioned memory 503 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0063] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the composite control method based on fuzzy control algorithm and PID provided by the above methods.

[0064] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the composite control method based on fuzzy control algorithm and PID provided by the above methods.

[0065] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0066] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A compound control method based on fuzzy control algorithm and PID, characterized in that, The application is applied to a servo load simulator system, the system controls an actuator through a servo driver, dynamic characteristics of the actuator are affected by system nonlinearities and time-varying characteristics, and the method comprises the following steps: Real-time output error and error change rate of the system are acquired, and state variables of the system are acquired, the state variables comprising physical quantities related to system load; The output error, the error change rate and the state variables are taken as inputs, and proportional coefficient, integral coefficient and differential coefficient are outputted simultaneously through a fuzzy inference machine; wherein, a rule base of the fuzzy inference machine is configured to nonlinearly adjust the proportional coefficient, the integral coefficient and the differential coefficient according to changes of the output error, the error change rate and the state variables, and compensate for system nonlinearities and time-varying characteristics; A PID controller with parameter self-tuning is constructed by using the proportional coefficient, the integral coefficient and the differential coefficient, and a control signal is calculated; The control signal is outputted to the servo driver of the servo load simulator system, and the actuator is controlled to act.

2. The method of claim 1, wherein the fuzzy control algorithm and PID-based compound control method is characterized by, The rule base of the fuzzy inference machine is specifically configured as: When the output error increases, the proportional coefficient is increased to improve system dynamic response speed; When the output error decreases, the proportional coefficient is decreased to reduce system overshoot; When the error change rate increases, the integral coefficient is decreased to suppress integral saturation phenomenon; When the error change rate decreases, the integral coefficient is increased to improve system response speed to load instruction; According to combinations of the output error and the error change rate, the differential coefficient is nonlinearly adjusted to enhance system damping characteristics under different load working conditions.

3. The method of claim 2, wherein the fuzzy control algorithm and PID-based compound control method is characterized by, When the output error increases, the proportional coefficient is increased, which comprises the following steps: A real-time value of the output error is acquired; According to the real-time value of the output error, a corresponding proportional coefficient adjustment amount is determined by querying a predefined proportional coefficient adjustment amount table; Wherein, the proportional coefficient adjustment amount is in positive correlation with the real-time value of the output error.

4. The method of claim 3, wherein the fuzzy control algorithm and PID-based compound control method is characterized by, The predefined proportional coefficient adjustment amount table is constructed based on dynamic response characteristics of the servo load simulator system; Wherein, the dynamic response characteristics comprise overshoot and regulation time of the system under a typical load instruction, and the proportional coefficient adjustment amount is configured to: in a certain value interval of the output error, the regulation time of the system is reduced by increasing the proportional coefficient; and in another certain value interval of the output error, the overshoot of the system is suppressed by decreasing the proportional coefficient.

5. The method of claim 2, wherein the fuzzy control algorithm and PID-based compound control method is characterized by, When the error change rate increases, the integral coefficient is decreased, which comprises the following steps: A real-time value of the error change rate is acquired; According to the real-time value of the error change rate, a corresponding integral coefficient adjustment amount is determined by querying a predefined integral coefficient adjustment amount table; Wherein, the integral coefficient adjustment amount is in negative correlation with the real-time value of the error change rate.

6. The method of claim 5, wherein the fuzzy control algorithm and PID-based compound control method is characterized by, The predefined integral coefficient adjustment amount table is constructed based on steady-state characteristics and response speed of the servo load simulator system; The integral coefficient adjustment amount is configured to: in a certain value range of the error change rate, reduce the integral coefficient to suppress integral saturation phenomenon; and in another certain value range of the error change rate, increase the integral coefficient to improve the response speed of the system to the load instruction.

7. The method of claim 2, wherein the fuzzy control algorithm and PID-based compound control method is characterized by, The differential coefficient is adjusted non-linearly according to the combination of the output error and the error change rate, including: The real-time value of the output error and the real-time value of the error change rate are obtained. The real-time value of the output error and the real-time value of the error change rate are taken as joint inputs to query a predefined differential coefficient adjustment amount table to determine the corresponding differential coefficient adjustment amount. The differential coefficient adjustment amount table is configured to output the differential coefficient adjustment amount in a non-linear mapping relationship based on the combination of the output error and the error change rate.

8. The compound control method based on fuzzy control algorithm and PID according to claim 7, characterized in that, The predefined differential coefficient adjustment amount table is constructed based on the damping characteristics of the servo load simulator system. The differential coefficient adjustment amount is configured to: in a certain combination range of the output error and the error change rate, increase the differential coefficient to enhance system damping and suppress oscillation caused by load mutation; and in another certain combination range of the output error and the error change rate, reduce the differential coefficient to reduce the sensitivity of the system to high-frequency measurement noise.

9. The fuzzy control algorithm and PID based compound control method according to any one of claims 1-8, characterized in that, The state variable includes system output load or actuator displacement.

10. A compound control system based on fuzzy control algorithm and PID, characterized in that, The system controls an actuator through a servo driver, and the dynamic characteristics of the actuator are affected by system nonlinearity and time-varying characteristics, including: An acquisition module is configured to obtain the output error and the error change rate of the system in real time, and obtain a state variable of the system, the state variable including a physical quantity related to system load. An inference module is configured to take the output error, the error change rate, and the state variable as inputs, and output a proportional coefficient, an integral coefficient, and a differential coefficient simultaneously through a fuzzy inference machine; wherein the rule base of the fuzzy inference machine is configured to non-linearly adjust the proportional coefficient, the integral coefficient, and the differential coefficient according to the changes of the output error, the error change rate, and the state variable, to compensate for system nonlinearity and time-varying characteristics. A control module is configured to use the proportional coefficient, the integral coefficient, and the differential coefficient to constitute a parameter self-tuning PID controller to calculate a control signal, and output the control signal to a servo driver of the servo load simulator system to control the actuator to act.

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