Pressure control method, system, device and storage medium for vacuum butterfly valve

Abstract

The present application relates to pressure control technical field, disclose a kind of pressure control method, system, equipment and storage medium of vacuum butterfly valve, according to the pressure control error signal and error change rate obtained from inside pressure and preset target pressure of cavity;The calculation result of composite controller is determined based on the pressure control error signal and error change rate;The calculation result of composite controller is input into smooth switching function, the mixed output quantity is calculated, and the optimal smoothing factor and error switching threshold under the current environment of smooth switching function are found by particle swarm optimization algorithm;The mixed output quantity of composite controller is nonlinearly compensated using compensation function, the control quantity after nonlinear compensation is converted into the rotation step number of stepper motor by motor drive, and the rotation angle of butterfly valve is controlled using stepper motor;The present application improves control precision and dynamic performance.

Description

Technical Field

[0001] This invention relates to the field of pressure control technology, specifically to a pressure control method, system, device, and storage medium for a vacuum butterfly valve. Background Technology

[0002] Electric vacuum butterfly valves are commonly used instruments for controlling the internal pressure of vacuum equipment. They are driven by a motor to adjust the pumping flow rate in the downstream pipeline, thereby controlling the internal pressure of the vacuum equipment. Currently, PID control algorithms and point-to-point mapping methods between pressure values ​​and valve openings are widely used in pressure control systems based on butterfly valves. Although PID control algorithms perform well in many application scenarios, in vacuum pressure control systems, due to the strong nonlinearity and pure time delay characteristics of the system, and the variable operating environment, their controllability is greatly reduced. Therefore, traditional PID control algorithms are not suitable for high-precision vacuum pressure control in complex environments. Summary of the Invention

[0003] The purpose of this invention is to solve the above-mentioned problems by designing a pressure control method, system, device, and storage medium for a vacuum butterfly valve.

[0004] The first aspect of this invention provides a pressure control method for a vacuum butterfly valve, the method comprising the following steps:

[0005] The pressure control error signal and error rate of change are obtained based on the internal pressure of the cavity and the preset target pressure.

[0006] The calculation result of the composite controller is determined based on the pressure control error signal and the error change rate, wherein a fuzzy controller and a PID controller are used to form the composite controller;

[0007] The calculation results of the composite controller are input into the smooth switching function to calculate the mixed output, and the optimal smoothing factor and error switching threshold of the smooth switching function under the current environment are found by the particle swarm optimization algorithm.

[0008] The composite controller's mixed output is nonlinearly compensated using a compensation function. The nonlinearly compensated control quantity is then converted into the number of rotation steps of a stepper motor via a motor drive. The stepper motor is then used to control the rotation angle of the butterfly valve.

[0009] Optionally, in a first implementation of the first aspect of the present invention, the pressure control error signal and the error change rate are calculated using the following formulas:

[0010]

[0011]

[0012] in, represents the pressure inside the cavity, represents the target pressure, represents the pressure control error signal, represents the error change rate.

[0013] Optionally, in the second implementation form of the first aspect of the present application, the calculation result of the compound controller based on the pressure control error signal and the error change rate comprises:

[0014] inputting the pressure control error signal and the error change rate into a fuzzy controller in the compound controller;

[0015] the fuzzy controller determining membership values of the pressure control error signal and the error change rate belonging to each fuzzy subset through a membership function;

[0016] performing fuzzy reasoning on the fuzzy subsets according to fuzzy rules, and performing defuzzification on the result of the fuzzy reasoning by using a gravity method to obtain quantized values of the fuzzy output, and the calculation formula is as follows:

[0017]

[0018] wherein, represents the fuzzy output of the fuzzy controller, represents the value after the first fuzzy reasoning, represents the membership value of the first fuzzy reasoning, represents the value after the second fuzzy reasoning, represents the membership value of the second fuzzy reasoning, represents the total number of the fuzzy rules.

[0019] Optionally, in the third implementation form of the first aspect of the present application, the calculation result of the compound controller based on the pressure control error signal and the error change rate comprises:

[0020] inputting the pressure control error signal and the error change rate into a PID controller in the compound controller;

[0021] adjusting by a discrete PID control algorithm in the PID controller to generate a control output, and the calculation process is as follows:

[0022]

[0023] wherein, PID represents the control output of the PID controller, represents the pressure control error signal at the current time, represents the pressure control error signal at the previous time, represents a proportional gain coefficient, represents an integral gain coefficient, represents a differential gain coefficient, All error accumulations from the initial moment to the current moment, Indicates the current moment.

[0024] Optionally, in the fourth implementation manner of the first aspect of the present application, the step of inputting the calculation result of the compound controller into the smooth switching function and calculating the mixed output quantity comprises:

[0025] The mixed weight of the PID controller and the fuzzy controller at the current moment is calculated based on the smooth switching function , and the calculation process is as follows:

[0026]

[0027] Wherein, x The pressure control error signal is represented by e, f The smooth factor of the smooth switching function is represented by s, Eth The set error switching threshold is represented by e0, The smooth switching function is represented by S(e, e0);

[0028] According to the calculated mixed weight , the mixed output quantity of the compound controller is calculated , and the calculation process is as follows:

[0029]

[0030] Wherein, PID The control output quantity of the PID controller is represented by u, The fuzzy output quantity of the fuzzy controller is represented by f.

[0031] Optionally, in the fifth implementation manner of the first aspect of the present application, the step of finding the optimal smooth factor and error switching threshold of the smooth switching function under the current environment by the particle swarm optimization algorithm comprises:

[0032] The initial parameters of particles are set, and iteration is performed in 3-dimensional space;

[0033] The i th particle is at the th iteration, and its position in the whole space is represented by , and its speed is represented by When the current optimal position is found, the current global optimal position is determined, and through iteration, each particle updates its speed in each dimension and position according to the following formula: :

[0034] ​

[0035]

[0036] wherein, represents the particle in the first dimension, = (1, 2, 3), represents a local learning factor, represents a global learning factor, and represents a random number between (0, 1), represents an inertia weight;

[0037] The fitness value is calculated according to the overshoot of the pressure control system σ , a pressure control error signal and a regulation time The fitness calculation function is as follows:

[0038]

[0039] wherein, represents a weighting coefficient of the pressure error, represents a weighting coefficient of the overshoot, represents a weighting coefficient of the regulation time, represents a pressure control error signal at the current time, represents a time variable from to , represents an error square term, represents a time upper limit; When the maximum number of evolutionary iterations is reached, the optimization result is output, and the optimal smoothing factor and error switching threshold of the smoothing switching function in the current environment are obtained.

[0040] Optionally, in the sixth implementation manner of the first aspect of the present application, the method further comprises:

[0041] The compensation function is introduced to perform nonlinear compensation on the mixed output of the composite controller, and the formula is as follows:

[0042]

[0043]

[0044] wherein, represents the mixed output of the composite controller, represents the internal pressure of the cavity, represents the control quantity after nonlinear compensation; ​

[0045] The control quantity after the nonlinear compensation is converted into the rotation step number of the stepping motor by the motor drive. The rotation angle of the vacuum butterfly valve driven by the stepping motor is calculated according to the following formula:

[0046]

[0047] Wherein, The maximum step number corresponding to the full opening of the vacuum butterfly valve is represented by Nmax, The maximum opening and closing angle of the vacuum butterfly valve is represented by θmax, The rotation angle of the butterfly valve driven by the stepping motor is represented by θ.

[0048] The second aspect of the present application provides a pressure control system of a vacuum butterfly valve, which comprises an air source, a vacuum cavity, a pressure sensor, a composite controller, an actuating mechanism and a valve.

[0049] The air source is configured to provide gas to the vacuum cavity.

[0050] The pressure sensor is configured to monitor the pressure inside the vacuum cavity.

[0051] The composite controller is composed of a fuzzy controller and a PID controller, and is configured to calculate a control output and a fuzzy output, obtain a mixed output, and find an optimal smoothing factor and an error switching threshold in the current environment through a particle swarm optimization algorithm.

[0052] The actuating mechanism is configured to receive the control quantity after the nonlinear compensation and convert it into the rotation step number of the stepping motor.

[0053] The valve is configured to be rotated by the actuating mechanism and adjust the gas flow into the cavity according to the rotation angle of the vacuum butterfly valve.

[0054] The third aspect of the present application provides a pressure control device of a vacuum butterfly valve, which comprises a memory and at least one processor, and the memory stores instructions; the at least one processor invokes the instructions in the memory to enable the pressure control device of the vacuum butterfly valve to perform the steps of the pressure control method of the vacuum butterfly valve according to any one of the above.

[0055] The fourth aspect of the present application provides a computer readable storage medium, which stores instructions; the instructions are executed by a processor to implement the steps of the pressure control method of the vacuum butterfly valve according to any one of the above.

[0056] Compared with the prior art, the technical scheme provided by the embodiments of the present disclosure has the following advantages:

[0057] The embodiment of the present disclosure provides a pressure control method, system, device and storage medium of a vacuum butterfly valve, the method comprises the following steps: obtaining a pressure control error signal and an error change rate according to an internal pressure of a cavity and a preset target pressure; determining a calculation result of a compound controller based on the pressure control error signal and the error change rate; inputting the calculation result of the compound controller into a smooth switching function, calculating a mixed output quantity, and finding an optimal smooth factor and an error switching threshold of the smooth switching function in the current environment through a particle swarm optimization algorithm; adopting a compensation function to perform nonlinear compensation on the mixed output quantity of the compound controller, converting the control quantity after the nonlinear compensation into a rotation step number of a stepper motor through a motor drive, and adopting the stepper motor to control a rotation angle of the butterfly valve; the compound controller composed of a fuzzy controller and a PID controller can effectively eliminate the influence of hysteresis, time-varying and other characteristics in the vacuum pressure control system, and eliminate the overshoot in the vacuum pressure control system; meanwhile, the smooth switching function is introduced, the output quantity caused by the mode switching of the controller is avoided, the control precision and dynamic performance of the system are further improved, a new idea and solution are provided for the application of the butterfly valve in high-precision pressure control; the particle swarm optimization program is used, the parameters of the smooth switching function do not need to be calculated manually, the parameters can be automatically found and optimized in different use environments, and the parameter searching efficiency and accuracy are improved; the nonlinear compensation function is designed based on the strong nonlinearity of the vacuum pressure control system, and the influence of the nonlinearity of the system on the controller is eliminated. BRIEF DESCRIPTION OF DRAWINGS

[0058] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a description of preferred embodiments and are not meant to limit the present application.

[0059] Figure 1 A flowchart of a pressure control method of a vacuum butterfly valve provided by the embodiment of the present application is provided.

[0060] Figure 2 A pressure error membership function diagram provided by the embodiment of the present application is provided.

[0061] Figure 3 A pressure error change rate membership function diagram provided by the embodiment of the present application is provided.

[0062] Figure 4 A particle swarm optimization process diagram provided by the embodiment of the present application is provided.

[0063] Figure 5 A structure block diagram of a parameter optimization unit provided by the embodiment of the present application is provided.

[0064] Figure 6 A structure diagram of a pressure control system of a vacuum butterfly valve provided by the embodiment of the present application is provided.

[0065] Figure 7 The structural schematic diagram of the pressure control device of the vacuum butterfly valve provided by the embodiment of the present application is shown in the figure.

[0066] Figure 8 The structural schematic diagram of the pressure control device of the vacuum butterfly valve provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0067] The terms "first", "second", "third", "fourth" and the like in the description and claims of the present application and in the above DETAILED DESCRIPTION (if any) are used for distinguishing between similar objects talking about the application and do not necessarily have to appear in the application in this order, or in sequence or in any order. The terms "comprise", "comprising", "including", "including", "have", "having" or any other variation thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, article, or apparatus that comprises a list of steps or units not only comprises those steps or units but can include other not expressly listed steps or units. The terms "exemplary" and "example" are used herein to mean "serving as an example, instance, or illustration". Any implementation described as exemplary or example is not necessarily to be construed as preferred or advantageous over other implementations.

[0068] For the convenience of understanding, the specific flow of the embodiment of the present application is described below, please refer to Figure 1 The flow chart of the pressure control method of the vacuum butterfly valve provided by the embodiment of the present application is shown in the figure, and the method specifically comprises the following steps:

[0069] Step 101, obtaining a pressure control error signal and an error change rate according to the internal pressure of the cavity and the preset target pressure;

[0070] In this embodiment, the calculation formula of the pressure control error signal and the error change rate is as follows:

[0071]

[0072]

[0073] Wherein, P represents the internal pressure of the cavity, P represents the target pressure, P represents the pressure control error signal, P represents the error change rate.

[0074] In this embodiment, the internal pressure of the cavity refers to the actual existing gas pressure in the vacuum cavity, which is collected by a high-precision pressure sensor in real time and used as a feedback signal; the target pressure is a desired pressure value set by the user according to the demand and input by the upper computer; the pressure control error signal is defined as the difference between the target pressure and the internal pressure of the cavity, which reflects the degree of deviation of the current system from the ideal state, and the size and sign of the error directly determine the direction and strength of the controller output; the error change rate refers to the change speed of the pressure control error signal with time, which characterizes the trend of the dynamic response of the system. If the error decreases rapidly, it means that the system is approaching the target; if the error increases rapidly, it may be disturbed by external disturbance or insufficient control. The error change rate as the input of the differential term can effectively predict the system behavior, suppress overshoot, and improve the response speed and stability of the control system.

[0075] Step 102, determining the calculation result of the composite controller based on the pressure control error signal and the error change rate, wherein the composite controller is composed of a fuzzy controller and a PID controller;

[0076] In this embodiment, the pressure control error signal and the error change rate are input to the fuzzy controller in the composite controller;

[0077] The fuzzy controller determines the membership values of the pressure control error signal and the error change rate belonging to each fuzzy subset through the membership function;

[0078] According to the fuzzy rules, the fuzzy subsets are subjected to fuzzy reasoning, and the results of the fuzzy reasoning are clarified by the barycentric method to obtain the quantization value of the fuzzy output, and the calculation formula is as follows:

[0079]

[0080] wherein, represents the fuzzy output of the fuzzy controller, represents the value after the first fuzzy reasoning, represents the membership value of represents the value after the second fuzzy reasoning, represents the membership value of represents the total number of fuzzy rules.

[0081] In this embodiment, a number of linguistic fuzzy subsets are predefined inside the fuzzy controller, and corresponding membership functions are configured for the pressure control error signal and error rate respectively. When the actual error and error rate are input, the fuzzy controller calculates the membership degree of each fuzzy subset according to the membership functions, and obtains a series of membership values between 0 and 1, which quantifies the degree of compliance of the current system state under different linguistic rules. After obtaining the membership of each input variable, the fuzzy controller performs fuzzy reasoning according to the pre-constructed fuzzy rules, activates the corresponding output fuzzy subsets, and synthesizes the conclusions of all matching rules to form a total fuzzy output set. The fuzzy rule table is shown in Table 1:

[0082]

[0083] Table 1 Fuzzy rule table

[0084] In Table 1, NB represents that the pressure deviation rate is in a negative large state, NM represents that the pressure deviation rate is in a negative medium state, NS represents that the pressure deviation rate is in a negative small state, ZO represents that the pressure deviation rate is in a zero state, PS represents that the pressure deviation rate is in a positive small state, PM represents that the pressure deviation rate is in a positive medium state, and PB represents that the pressure deviation rate is in a positive large state, represents the error, represents the error rate.

[0085] In this embodiment, the membership function used for pressure error fuzzification is shown in Figure 2 , and the membership function used for pressure error rate fuzzification is shown in Figure 3 In the fuzzy control process, the output obtained through fuzzy reasoning is usually a fuzzy set, which is represented as a plurality of linguistic values and their corresponding membership distributions, and cannot be directly used to drive the actuator. It must be converted into a certain, executable numerical output through defuzzification. In this embodiment, the barycenter method, also known as the area barycenter method, is used as the defuzzification strategy. The basic principle of the barycenter method is to regard the fuzzy output set as a continuous or discrete mass distribution, and the barycenter position is the final clear output value.

[0086] In this embodiment, the pressure control error signal and error rate are input to the PID controller in the compound controller;

[0087] The PID controller adjusts through a discrete PID control algorithm to generate a control output, and the calculation process is as follows:

[0088]

[0089] wherein, PID represents the control output of the PID controller, a pressure control error signal representing a current time, a pressure control error signal representing a previous time, a proportional gain coefficient, an integral gain coefficient, a differential gain coefficient, all error accumulations from an initial time to a current time, a current time.

[0090] In this embodiment, in the composite controller architecture, the PID controller mainly undertakes the high-precision adjustment task of the system in the small error interval, that is, fine adjustment of the pressure control process; when the internal pressure of the cavity approaches the target pressure, the adjustment effect of the fuzzy controller gradually weakens, and the PID controller is given a higher weight due to its good steady-state performance and anti-interference ability; the proportional term quickly responds to the current deviation, the integral term eliminates the steady-state error, and the differential term suppresses overshoot and improves dynamic response; the PID controller outputs an accurate numerical control quantity, which is then mapped into a driving signal of the stepper motor, such as the number of pulses or direction-step number instructions, for fine adjustment of the opening of the butterfly valve. Since the stepper motor has high resolution and good position holding ability, this fine adjustment mechanism can achieve sub-Torr level accurate control of the pressure of the vacuum cavity, significantly improving the control accuracy and anti-interference performance of the system in the steady-state stage; the PID controller focuses on fine closed-loop adjustment in the composite controller, complementing the coarse adjustment function of the fuzzy controller, and together ensuring that the system has rapidity, stability and high precision in the entire pressure range.

[0091] Step 103, input the calculation result of the composite controller into the smooth switching function, calculate the mixed output quantity, and find the optimal smooth factor and error switching threshold of the smooth switching function under the current environment through the particle swarm optimization algorithm;

[0092] In this embodiment, the smooth switching function is based on a continuous function of the pressure control error signal to realize smooth transfer of the weight between different control modes, and the mixed weight of the PID controller and the fuzzy controller at the current time is calculated based on the Sigmoid function , and the calculation process is as follows:

[0093]

[0094] wherein, x a pressure control error signal, f a smooth factor of the smooth switching function, Eth a set error switching threshold, a Sigmoid function.

[0095] According to the calculated mixed weight , calculate the hybrid output quantity of the compound controller , the calculation process is as follows:

[0096]

[0097] wherein, PID represents the control output quantity of the PID controller, represents the fuzzy output quantity of the fuzzy controller.

[0098] In this embodiment, in the compound controller architecture, the fuzzy controller is good at processing large deviations, nonlinearities, and model uncertainties in the coarse adjustment stage, while the PID controller has high precision and good steady-state performance in a small error range and is suitable for fine adjustment. In order to fully utilize the advantages of both and avoid control signal jumps or system oscillations caused by hard switching, a smooth switching function is introduced to realize continuous and adaptive fusion between the two control strategies. This ensures seamless connection of the control strategy in the full working condition range, retains the robustness of fuzzy control to large disturbances and nonlinearities, and inherits the accuracy of PID control in the steady state, while avoiding the chattering or response lag caused by traditional switching methods. The entire process is completely driven by a continuous function, without the need for logic judgment or state machine, improving the real-time performance and reliability of the system.

[0099] In this embodiment, the optimization program finds the optimal values of the constants in the smooth switching function , the smoothing factor f , and the error switching threshold Eth through the particle swarm algorithm. The optimization process is shown in Figure 4 , the population is initialized and the parameters are set, the initial parameters of particles are set, and iteration is performed in 3-dimensional space, with a maximum evolution iteration number of , the individual fitness value is calculated, the individual optimal and global optimal are initialized, and the position and speed of each particle are updated.

[0100] The i (1 i st particle at the (1 st iteration is represented as , and the speed is represented as , when the current optimal position is found, the current global optimal position is determined, and through iteration, each particle updates its speed in each dimension and position according to the following formula:

[0101]

[0102]

[0103] in, Indicates the particle in the first... dimension, = (1, 2, 3), Represents the local learning factor. Represents the global learning factor. and Represents a random number between (0, 1). Inertia weights are used to avoid the algorithm getting trapped in local optima and to improve the performance of particle swarm optimization. Using a strategy that linearly decreases from 0.9 to 0.1, for The value is dynamically adjusted.

[0104] To compare the current fitness value of each particle in the swarm with its own historical best value and the swarm's historical best value, real-time pressure error is read, individual fitness values ​​are calculated, and the overshoot of the pressure control system is considered. σ Pressure control error signal and adjustment time Calculate fitness value, fitness calculation function as follows:

[0105]

[0106] in, The weighting coefficients representing pressure error. This represents the weighting coefficient for overshoot. This represents the weighting factor for the adjustment time. This indicates the pressure control error signal at the current moment. Indicates from arrive The time variable, Represents the squared error term. Indicates the upper limit of time;

[0107] Based on the calculated individual fitness value, update the individual optimum and global optimum, determine whether the generation requirement has been met, if not, update the iteration count, then update the inertia weight and learning factor, and iterate again; when the maximum number of evolution iterations is reached, output the optimization result, and obtain the optimal smoothing factor and error switching threshold of the smooth switching function in the current environment.

[0108] In this embodiment, in the particle swarm optimization algorithm, each particle represents a set of controller parameters to be optimized, such as those of a PID controller. , , or the membership function parameters of the fuzzy rule. To guide the evolution of the particles to better solutions, the control performance of each set of parameters needs to be quantitatively evaluated, which is achieved by the fitness function. The designed fitness function comprehensively considers three key dynamic performance indicators of the vacuum pressure control system in the step response. The overshoot of the pressure control system reflects the maximum deviation percentage of the pressure exceeding the target value in the system response process, which directly affects the process stability. The adjustment time indicates the time required for the system to enter and remain within the target value error band after the disturbance or setting change, which reflects the response speed. The greater the fitness value, the better the control performance of the corresponding set of parameters. In each iteration, the PSO algorithm compares the fitness value of the current particle with its individual historical best fitness and the group historical best fitness: if the current fitness is higher, the individual historical best fitness is updated; if it is better than the group historical best fitness of all particles, the global optimal solution is updated. The particle swarm algorithm gradually converges to a set of controller parameters that achieve a good balance between rapidity, accuracy and stability. Combined with the actual needs of vacuum pressure control, the problem of excessive overshoot or slow response caused by relying on a single indicator is avoided, and automatic parameter searching and optimization under different use environments can be achieved, improving the parameter searching efficiency and accuracy.

[0109] In this embodiment, the optimal smoothing factor and error switching threshold of the smoothing switching function found by the particle swarm optimization program in the current environment are stored in the storage unit of the microcontroller. The optimization structure block diagram is shown in Figure 5 When calculating the hybrid output quantity of the composite controller, the smoothing factor and error switching threshold are read from the storage unit.

[0110] In this embodiment, the microcontroller refers to the core processing unit embedded in the pressure control system, which is responsible for executing the entire control algorithm, coordinating the work of each module, and outputting the driving signal to realize accurate closed-loop control of the cavity pressure. The functions of the microcontroller include but are not limited to: collecting sensor data, receiving analog or digital signals from the pressure sensor, obtaining real-time pressure inside the cavity; implementing the composite control logic internally, converting the final control quantity into a pulse signal recognizable by the stepper motor, receiving the target pressure set by the upper computer, and possibly uploading the state information through the UART, SPI, CAN or USB interface, or cooperating with the upper computer to complete online adjustment of the controller parameters.

[0111] Step 104, the hybrid output quantity of the composite controller is nonlinearly compensated by using the compensation function, and the control quantity after nonlinear compensation is converted into the rotation steps of the stepper motor by the motor drive, and the rotation angle of the butterfly valve is controlled by using the stepper motor.

[0112] In this embodiment, due to the strong nonlinear relationship between the valve opening degree of the butterfly valve and the pressure, the sensitivity of the pressure to the change of the valve opening degree is different in different vacuum degree intervals. In order to make the butterfly valve maintain good control performance in the working pressure range [0.1~1000]Torr, eliminate the influence of strong nonlinearity in the vacuum pressure control system, and based on the working characteristics of the butterfly valve, a compensation function is introduced to compensate the mixed output of the composite controller, and the formula is as follows:

[0113]

[0114] Wherein, represents the mixed output of the composite controller, represents the pressure inside the cavity, represents the control quantity after nonlinear compensation;

[0115] The control quantity after nonlinear compensation is converted into the rotation step number of the stepper motor by the motor drive, and the rotation angle of the vacuum butterfly valve driven by the stepper motor is calculated by the following formula:

[0116]

[0117] Wherein, represents the maximum step number corresponding to the full opening of the vacuum butterfly valve, represents the maximum opening and closing angle of the vacuum butterfly valve, represents the rotation angle of the vacuum butterfly valve driven by the stepper motor.

[0118] In this embodiment, the control quantity after nonlinear compensation comprehensively reflects the required valve adjustment intensity under the current vacuum cavity pressure, and since the control quantity after nonlinear compensation is a continuous numerical signal, the stepper motor can only accept discrete pulse instructions, so the microcontroller needs to quantize and map the signal. The microcontroller will calibrate the control- angle mapping relationship based on the mechanical characteristics and flow characteristics of the butterfly valve, and the control quantity after nonlinear compensation The conversion is corresponding to the rotation step number of the stepping motor; the microcontroller sends the step number and the rotation direction to the motor drive circuit. The drive circuit generates corresponding number and timing pulse signals according to the above, and drives the stepping motor to rotate accurately; the stepping motor is connected with the valve stem of the butterfly valve through the shaft coupling or the transmission mechanism, and the rotation movement of the stepping motor is directly converted into the rotation angle of the valve plate of the butterfly valve; since the stepping motor has good open-loop positioning accuracy and repeatability, each step corresponds to a fixed mechanical angle, so that the high-resolution, repeatable and cumulative error-free control of the valve opening is realized; the change of the opening of the butterfly valve adjusts the gas flow into the vacuum cavity, so that the pressure in the cavity converges to the target value, and even in the highly nonlinear vacuum environment of the butterfly valve response, stable, reliable and high-precision pressure control can be realized through accurate electromechanical execution.

[0119] Please refer to Figure 6 The pressure control system of the vacuum butterfly valve provided by the embodiment of the application has the advantages that the system comprises a gas source, a vacuum cavity, a pressure sensor, a composite controller, an actuating mechanism and a valve.

[0120] The gas source is used for providing gas to the vacuum cavity.

[0121] The pressure sensor is used for monitoring the pressure inside the vacuum cavity.

[0122] The composite controller is composed of a fuzzy controller and a PID controller, is used for calculating a control output and a fuzzy output, obtaining a mixed output, and finding an optimal smoothing factor and an error switching threshold of a smooth switching function in the current environment through a particle swarm optimization algorithm.

[0123] The actuating mechanism is used for receiving the control quantity after the nonlinear compensation, and converting the control quantity into the rotation step number of the stepping motor.

[0124] The valve is used for being driven to rotate by the actuating mechanism, and adjusting the gas flow into the cavity according to the rotation angle of the vacuum butterfly valve.

[0125] In the embodiment, the host computer sets pressure for the pressure control system to obtain a target pressure, and a pressure sensor collects the internal pressure of the vacuum cavity in real time, which is digitized by an analog-to-digital converter of the butterfly valve, transmitted by an SPI data transmission interface, and calculated to obtain a current pressure control error signal and an error change rate, and then sent to a fuzzy controller and a PID controller for parallel processing. The fuzzy controller performs coarse adjustment, finds the membership values of the pressure control error signal and the error change rate belonging to each fuzzy subset through a membership function, performs fuzzy reasoning on the fuzzy subsets according to fuzzy rules, and finally quantizes the fuzzy output to obtain a quantitative value. The PID controller performs fine adjustment through a discrete PID control algorithm to generate a control output. The calculation results of the PID controller and the fuzzy controller in the composite controller are input into a smooth switching function. The smooth switching function is introduced to realize smooth transition between different control modes of the composite controller, avoid sudden changes in the output caused by controller mode switching, and thus improve the stability of the system. The particle swarm optimization algorithm is introduced to solve the problem of finding and optimizing the parameters of the smooth switching function in different use environments. A nonlinear compensation function is designed based on the strong nonlinearity of the pressure control system to eliminate the influence of system nonlinearity on the controller. The control quantity after nonlinear compensation is used to drive the actuator such as a stepper motor to adjust the valve opening degree, so as to accurately control the gas flow into the cavity and make the cavity pressure stable and close to the set value. The whole process realizes adaptive, high-precision and no overshoot closed-loop control in the whole pressure range.

[0126] Figure 7 FIG. 1 is a structural schematic diagram of a pressure control device for a vacuum butterfly valve according to an embodiment of the present disclosure, as shown in the figure, the pressure control device for the vacuum butterfly valve provided by the embodiment of the present disclosure mainly comprises a pressure acquisition module, a composite calculation module, a smooth transition module and a nonlinear compensation and control module. Figure 7

[0127] The pressure acquisition module is configured to obtain a pressure control error signal and an error change rate according to the internal pressure of the cavity and a preset target pressure.

[0128] The composite calculation module is configured to determine the calculation result of the composite controller based on the pressure control error signal and the error change rate, wherein the composite controller is composed of a fuzzy controller and a PID controller.

[0129] The smooth transition module is configured to input the calculation result of the composite controller into a smooth switching function, calculate a mixed output, and find the optimal smooth factor and error switching threshold of the smooth switching function in the current environment through a particle swarm optimization algorithm.

[0130] ​The nonlinear compensation and control module is configured to compensate the mixed output of the composite controller by using a compensation function, convert the control quantity after the nonlinear compensation into the rotation steps of the stepper motor by using the motor drive, and control the rotation angle of the butterfly valve by using the stepper motor.

[0131] Figure 8 The vacuum butterfly valve pressure control device 300 can have a large difference due to different configurations or performances, and can include one or more central processing units (CPUs) 310 (for example, one or more processors) and a memory 320, one or more storage media 330 (for example, one or more mass storage devices) storing application programs 333 or data 332. The memory 320 and the storage media 330 can be temporary storage or persistent storage. The programs stored in the storage media 330 can include one or more modules (not shown in the figure), and each module can include a series of instruction operations for the vacuum butterfly valve pressure control device 300. Further, the processor 310 can be configured to communicate with the storage media 330 and execute the series of instruction operations in the storage media 330 on the vacuum butterfly valve pressure control device 300 to implement the method provided in the above embodiments.

[0132] The vacuum butterfly valve pressure control device 300 can also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art can understand that the vacuum butterfly valve pressure control device 300 can include more or fewer components than those shown, or some components can be combined, or different components can be arranged. Figure 8 The vacuum butterfly valve pressure control device structure shown does not constitute a limitation on the computer device provided by the present application, and can include more or fewer components than those shown, or some components can be combined, or different components can be arranged.

[0133] The present application also provides a computer readable storage medium, which can be a non-volatile computer readable storage medium or a volatile computer readable storage medium, and the computer readable storage medium stores instructions, which, when executed on a computer, cause the computer to perform the steps of the vacuum butterfly valve pressure control method provided in the above embodiments.

[0134] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the device or apparatus, unit described above can refer to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0135] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0136] The basic principles, main features and advantages of the present application are shown and described above. Those skilled in the art should understand that the present application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A pressure control method of a vacuum butterfly valve, characterized by, The method comprises the following steps: According to the pressure inside the cavity and the preset target pressure, a pressure control error signal and an error change rate are obtained; Determine the calculation result of the composite controller based on the pressure control error signal and the error change rate, wherein the composite controller is composed of a fuzzy controller and a PID controller; The calculation result of the composite controller is input into the smooth switching function, the mixed output quantity is calculated, and the optimal smooth factor and error switching threshold of the smooth switching function under the current environment are found through the particle swarm optimization algorithm; The mixed output quantity of the composite controller is nonlinearly compensated by using a compensation function, the control quantity after nonlinear compensation is converted into the rotation steps of the stepper motor through the motor drive, and the rotation angle of the butterfly valve is controlled by using the stepper motor; The mixed output quantity of the composite controller is nonlinearly compensated by using a compensation function, the control quantity after nonlinear compensation is converted into the rotation steps of the stepper motor through the motor drive, and the rotation angle of the butterfly valve is controlled by using the stepper motor; The mixed output quantity of the composite controller is nonlinearly compensated by using a compensation function, the control quantity after nonlinear compensation is converted into the rotation steps of the stepper motor through the motor drive, and the rotation angle of the butterfly valve is controlled by using the stepper motor; wherein, represents a mixed output quantity of the compound controller, represents the pressure inside the cavity, represents the control quantity after the non-linear compensation; The control quantity after the nonlinear compensation is converted into a control quantity for the vacuum butterfly valve The rotation angle of the vacuum butterfly valve driven by the stepping motor is calculated by the following formula: wherein, represents the maximum number of steps corresponding to the full opening of the vacuum butterfly valve, represents the maximum opening and closing angle of the vacuum butterfly valve, represents the rotation angle of the butterfly valve driven by the stepper motor.

2. The method of claim 1, wherein the pressure control method of a vacuum butterfly valve is characterized by, The mixed output quantity of the composite controller is nonlinearly compensated by using a compensation function, the control quantity after nonlinear compensation is converted into the rotation steps of the stepper motor through the motor drive, and the rotation angle of the butterfly valve is controlled by using the stepper motor; wherein, represents the pressure inside the cavity, represents the target pressure, represents the pressure control error signal, represents the error rate of change.

3. The method of claim 1, wherein the pressure control method of a vacuum butterfly valve is characterized by, The pressure control error signal and the error change rate are calculated according to the following formula: The calculation result of the composite controller based on the pressure control error signal and the error change rate comprises: The pressure control error signal and the error change rate are input into the fuzzy controller in the composite controller; The fuzzy controller determines the membership values of the pressure control error signal and the error change rate belonging to each fuzzy subset through the membership function; wherein, denotes a fuzzy output quantity of the fuzzy controller, denotes a value after the first fuzzy inference, denotes a membership value of the denotes a total number of fuzzy rules.

4. The method of claim 1, wherein the pressure control method of a vacuum butterfly valve is characterized by, According to the fuzzy rule, the fuzzy inference is performed on the fuzzy subset, the result of the fuzzy inference is clarified by using the barycentric method, and the quantized value of the fuzzy output quantity is obtained, and the calculation formula is as follows: The calculation result of the composite controller based on the pressure control error signal and the error change rate comprises: The pressure control error signal and the error change rate are input into the PID controller in the composite controller; wherein, The PID controller adjusts through the discrete PID control algorithm to generate the control output quantity, and the calculation process is as follows: represents a control output quantity of the PID controller, represents a pressure control error signal at the current time point, represents a pressure control error signal at the previous time point, represents a proportional gain coefficient, represents an integral gain coefficient, represents a differential gain coefficient, represents all errors accumulated from the initial time point to the current time point, represents the current time point.

5. The method of claim 1, wherein the pressure control method of a vacuum butterfly valve is characterized by, PID A hybrid weight of a PID controller and a fuzzy controller at a current time is calculated based on a smooth switching function The calculation process is as follows: wherein x denotes a pressure control error signal, f denotes a smoothing factor of the smooth switching function, The calculation result of the composite controller is input into the smooth switching function, and the mixed output quantity is calculated, comprising: denotes a set error switching threshold value, denotes a smooth switching function; According to the calculated mixing weight , the mixed output quantity of the compound controller is calculated , the calculation process is as follows: wherein, Eth represents a control output quantity of the PID controller, represents a fuzzy output quantity of the fuzzy controller.

6. The method of claim 1, wherein the pressure control method of a vacuum butterfly valve is characterized by, PID Setting Initial parameters of the particles are set and iterations are performed in 3-dimensional space; The first i particle at the first iteration, the position of the entire space is represented as , the speed is represented as , when the current optimal position is found , the current global optimal position is determined , through iteration, each particle updates its own speed in each dimension according to the following formula and position : wherein, represents the particle in the i-th dimension, = (1, 2, 3), represents a local learning factor, represents a global learning factor, and represents a random number between (0, 1), represents an inertia weight; According to the overshoot of the pressure control system The optimal smooth factor and error switching threshold of the smooth switching function under the current environment are found through the particle swarm optimization algorithm, comprising: , a pressure control error signal and a regulation time a fitness value is calculated, the fitness calculation function is as follows: wherein represents a weighting factor for the pressure error, represents a weighting factor for the overshoot, represents a weighting factor for the regulation time, represents the pressure control error signal at the current time instant, represents a time variable from to represents the error square term, represents the upper time limit;​ σ 7. A pressure control system for a vacuum butterfly valve, characterized by When the maximum evolution iteration number is reached, the optimization result is output, and the optimal smooth factor and error switching threshold of the smooth switching function under the current environment are obtained. The system comprises a gas source, a vacuum cavity, a pressure sensor, a composite controller, an actuator, and a valve; The gas source is used to provide gas to the vacuum cavity; The pressure sensor is used to monitor the pressure inside the vacuum cavity; The composite controller is composed of a fuzzy controller and a PID controller, and is used to calculate the control output quantity and the fuzzy output quantity, obtain the mixed output quantity, and find the optimal smooth factor and error switching threshold of the smooth switching function under the current environment through the particle swarm optimization algorithm; The actuator is used for receiving the control quantity after nonlinear compensation and converting into the rotation step number of the stepping motor; the compensation function is introduced to compensate the mixed output quantity of the compound controller, and the formula is shown as follows: wherein, represents a mixed output quantity of the compound controller, represents a pressure inside the cavity, represents a control quantity after non-linear compensation; The control quantity after the nonlinear compensation is converted into a control quantity for the vacuum butterfly valve The rotation angle of the vacuum butterfly valve driven by the stepping motor is converted into the rotation steps of the stepping motor, and the calculation formula is as follows: wherein, represents the maximum number of steps corresponding to the full opening of the vacuum butterfly valve, represents the maximum opening and closing angle of the vacuum butterfly valve, represents the rotation angle of the butterfly valve driven by the stepper motor; The valve is used for being driven to rotate by the actuator and adjusting the gas flow entering the cavity according to the rotation angle of the vacuum butterfly valve.

8. A pressure control apparatus for a vacuum butterfly valve, characterized by The pressure control device of the vacuum butterfly valve comprises a memory and at least one processor, the memory stores instructions; the at least one processor calls the instructions in the memory to make the pressure control device of the vacuum butterfly valve execute the steps of the pressure control method of the vacuum butterfly valve in any one of claims 1-6.

9. A computer-readable storage medium having stored thereon instructions, the computer-readable storage medium comprising: The instructions are executed by the processor to realize the steps of the pressure control method of the vacuum butterfly valve in any one of claims 1-6.

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