Intelligent constant volume bomb system pressure fluctuation optimization control method based on dynamic damping cavity design

Abstract

The invention discloses an intelligent constant volume bomb system pressure fluctuation optimization control method based on dynamic damping cavity design, and belongs to the technical field of power and energy. Aiming at the problem of pressure oscillation caused by combustion fluctuation of the constant volume bomb in a high-temperature and high-pressure environment and insufficient adaptability of traditional PID control in a nonlinear system, a damping cavity with a piston is formed in the bottom of the constant volume bomb, and cavity segmentation is achieved through a spring; a pressure sensor and an optical fiber displacement sensor are used for monitoring projectile body pressure and piston displacement; the error and the change rate are calculated by comparing the real-time displacement and the initial displacement of the piston; dynamically adjusting proportional, integral and differential gain coefficients based on a fuzzy rule; a valve control signal is generated through defuzzification by adopting a gravity center method; the gas flow is controlled by adjusting the opening of the damping cavity intake and exhaust electromagnetic valve; closed-loop feedback is formed to maintain the piston position stable, thereby absorbing pressure fluctuations. Broadband pressure suppression is achieved, and steady-state errors are eliminated.

Description

Technical Field

[0001] This invention belongs to the field of power and energy technology, and in particular relates to a method for optimizing and controlling pressure fluctuations in an intelligent constant-volume spring system based on dynamic damping cavity design. Background Technology

[0002] Constant-volume projectile systems are important experimental platforms for studying fuel spray and combustion characteristics, and are widely used in the field of optical measurement of internal combustion engines. Under high-pressure and high-temperature experimental environments, constant-volume projectile systems often face the challenge of pressure fluctuations. These fluctuations originate from factors such as combustion inhomogeneity, gas flow instability, and system structural vibration. Pressure fluctuations within the projectile can disrupt the consistency of the internal environment during experiments, affect the accuracy of combustion characteristic data, and may even lead to structural fatigue or safety accidents. Therefore, effectively controlling and optimizing pressure fluctuations is a key issue in improving the performance of constant-volume projectile systems.

[0003] Current pressure fluctuation control mainly employs passive and active control methods. Passive control absorbs fluctuations through fixed damping structures or sound-absorbing materials, but its absorption effect is limited to a specific frequency range, making it unable to adapt to the wide-frequency nonlinear fluctuations generated during combustion, and it lacks real-time adjustment capabilities. Active control utilizes sensors and actuators for dynamic adjustment, but in extreme environments of high temperature and high pressure, the measurement accuracy of traditional sensors is easily affected by thermal noise, and the response speed of actuators is insufficient to meet the demands of rapid pressure changes, leading to control lag or failure.

[0004] At the control algorithm level, constant-volume projectile research typically employs traditional PID control methods, adjusting the intake valve opening through proportional, integral, and derivative control to achieve the target pressure. However, PID control has limited effectiveness in dealing with nonlinear pressure fluctuations caused by the combustion process: due to the system's nonlinear response, PID controllers struggle to accurately regulate pressure fluctuations, easily exhibiting overshoot or continuous oscillations. This not only reduces control accuracy but may also lead to safety hazards due to sudden pressure changes. These limitations indicate that existing technologies struggle to achieve wide-bandwidth, adaptive pressure fluctuation suppression under extreme operating conditions.

[0005] The root cause of these problems lies in the fact that the static characteristics of passive control cannot adapt to dynamic fluctuations, the hardware reliability of active control is limited by extreme environments, and traditional PID algorithms lack adaptability to nonlinear systems. Therefore, there is an urgent need for a solution that combines structural adaptability with intelligent control algorithms to achieve real-time suppression of wideband pressure fluctuations while ensuring reliability. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention proposes an optimized control method for pressure fluctuations in an intelligent constant-volume projectile system based on dynamic damping cavity design, thereby resolving the issues present in the prior art.

[0007] In a first aspect, to achieve the above objectives, the present invention provides a method for optimizing and controlling pressure fluctuations in an intelligent constant-volume spring system based on dynamic damping cavity design, comprising the following steps:

[0008] A dynamic damping cavity is provided at the bottom of the constant volume projectile and connected to the inside of the projectile. The dynamic damping cavity is divided by a piston into a first part that is connected to the constant volume projectile and a sealed second part. The piston is connected to the wall of the damping cavity by a spring.

[0009] Gas is introduced into the constant volume projectile and the dynamic damping chamber until the target pressure is reached, and the initial position of the piston is recorded at this time.

[0010] The pressure inside the constant volume bullet and the dynamic damping cavity is monitored by a pressure sensor, and the displacement of the piston is monitored by a fiber optic displacement sensor.

[0011] Compare the real-time displacement of the piston with its initial position, and calculate the displacement error and the rate of change of the displacement error.

[0012] The displacement error and the rate of change of displacement error are input into the fuzzy PID controller. The fuzzy PID controller dynamically adjusts the proportional gain coefficient, integral gain coefficient and derivative gain coefficient according to the preset fuzzy rules, and outputs the opening control signals of the damping chamber intake solenoid valve and exhaust solenoid valve.

[0013] The opening degree of the intake solenoid valve and the exhaust solenoid valve are adjusted according to the opening degree control signal to control the gas flow rate entering or exiting the damping chamber in order to maintain the stability of the piston position.

[0014] Optionally, the piston position of the dynamic damping cavity is monitored by an optical fiber displacement sensor; the inlet and outlet channels of the dynamic damping cavity are respectively equipped with an inlet solenoid valve and an outlet solenoid valve to control the gas flow rate.

[0015] Optionally, the displacement error is the difference between the real-time position and the initial position of the piston; the displacement error change rate is the rate of change of the displacement error over time.

[0016] Optionally, the fuzzy rules include:

[0017] When the product of displacement error and the rate of change of displacement error is greater than zero and the displacement error is greater than zero, decrease the proportional gain coefficient, increase the integral gain coefficient, and keep the differential gain coefficient moderate.

[0018] When the product of displacement error and the rate of change of displacement error is greater than zero and the displacement error is less than zero, increase the proportional gain coefficient, keep the integral gain coefficient moderate, and set the derivative gain coefficient to a small value.

[0019] When the product of displacement error and displacement error rate of change is less than zero, the changes in proportional gain coefficient and integral gain coefficient should be kept small.

[0020] When the product of displacement error and displacement error rate of change is zero and displacement error is zero, if displacement error rate of change is not zero, set the changes in proportional gain coefficient, integral gain coefficient and differential gain coefficient to a smaller value.

[0021] When the product of displacement error and the rate of change of displacement error is zero and the displacement error is not zero, increase the proportional gain coefficient while keeping the integral gain coefficient moderate.

[0022] Optionally, after the fuzzy PID controller outputs the correction values ​​of the proportional gain coefficient, integral gain coefficient, and derivative gain coefficient, it obtains a precise valve opening control signal through defuzzification processing; the defuzzification processing adopts the centroid method.

[0023] Optionally, after adjusting the opening of the intake solenoid valve and the exhaust solenoid valve, the process returns to the step of monitoring the piston displacement, thus forming a closed-loop control.

[0024] Secondly, the present invention also provides a pressure fluctuation optimization control system for an intelligent constant-volume projectile system based on dynamic damping cavity design, used to implement a pressure fluctuation optimization control method for an intelligent constant-volume projectile system based on dynamic damping cavity design, the system comprising:

[0025] The dynamic damping cavity module is located at the bottom of the constant volume projectile and communicates with the inside of the constant volume projectile through a connecting hole. The module includes a piston and a spring mechanism. The piston divides the cavity into a connected area and a sealed area, and the spring connects the piston to the cavity wall to achieve displacement self-adaptation.

[0026] The multi-source sensing module includes a constant-volume bullet pressure sensor, a damping cavity pressure sensor, and a fiber optic displacement sensor, which are used to monitor the internal pressure of the constant-volume bullet, the internal pressure of the damping cavity, and the piston displacement, respectively.

[0027] The fuzzy PID control module receives the piston displacement signal at the input end, calculates the displacement error and the rate of change of displacement error by comparing the real-time piston displacement with the initial displacement, dynamically adjusts the proportional gain coefficient, integral gain coefficient and derivative gain coefficient based on preset fuzzy rules, and outputs the valve opening control signal.

[0028] The gas flow control module includes an intake solenoid valve and an exhaust solenoid valve, which are connected to the intake and exhaust channels of the damping chamber, respectively. The gas flow rate entering or exiting the damping chamber is adjusted according to the valve opening control signal.

[0029] Thirdly, the present invention also provides a computer terminal device, comprising:

[0030] One or more processors;

[0031] A memory, coupled to the processor, for storing one or more programs;

[0032] When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the intelligent constant volume spring system pressure fluctuation optimization control method based on dynamic damping cavity design in the first aspect above.

[0033] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of the method for optimizing the pressure fluctuation control of an intelligent constant-volume projectile system based on dynamic damping cavity design in the first aspect described above.

[0034] Fifthly, the present invention also provides a computer program product, including a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of the pressure fluctuation optimization control method for the intelligent constant volume spring system based on dynamic damping cavity design in the first aspect described above.

[0035] Compared with the prior art, the present invention has the following advantages and technical effects:

[0036] This invention provides a pressure fluctuation optimization control method for an intelligent constant-volume projectile system based on a dynamic damping cavity design. The invention adaptively absorbs pressure fluctuations through the piston-spring structure of the dynamic damping cavity; it utilizes a fuzzy PID controller to adjust the proportional, integral, and derivative gain coefficients in real time, and combines multi-source sensor data to precisely control the opening of the solenoid valve, effectively suppressing pressure oscillations in the nonlinear system; the closed-loop control mechanism continuously maintains piston position stability, eliminating steady-state errors; the hardware structure is compatible with existing constant-volume projectile systems, requiring only the addition of a damping cavity and sensor module to achieve wideband pressure fluctuation suppression. Attached Figure Description

[0037] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0038] Figure 1 This is a diagram of the experimental setup according to an embodiment of the present invention;

[0039] Figure 2 This is a block diagram of fuzzy PID control according to an embodiment of the present invention.

[0040] Explanation of reference numerals in the attached figures:

[0041] 1. Constant volume bullet; 2. Constant volume bullet pressure sensor; 3. Damping chamber pressure sensor; 4. Fiber optic displacement sensor; 5. Dynamic damping chamber; 6. Damping chamber air inlet; 7. Damping chamber exhaust port; 8. Constant volume bullet air inlet; 9. Constant volume bullet exhaust port; 10. Piston; 11. Connecting hole; 12. Exhaust solenoid valve; 13. Intake solenoid valve; 14. Booster pump; 15. Gas cylinder; 16. Environmental pressure controller; 17. Cloud platform. Detailed Implementation

[0042] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0043] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0044] Example 1

[0045] This embodiment provides a method for optimizing and controlling pressure fluctuations in an intelligent constant-volume projectile system based on dynamic damping cavity design, including:

[0046] A dynamic damping cavity 5 is provided at the bottom of the constant volume bullet 1 and is connected to the interior of the constant volume bullet 1. The dynamic damping cavity 5 is divided by the piston 10 into a first part that is connected to the constant volume bullet 1 and a sealed second part. The piston 10 is connected to the wall of the damping cavity 5 by a spring.

[0047] Gas is introduced into the constant volume bullet 1 and the dynamic damping chamber 5 until the target pressure is reached, and the initial position of the piston 10 is recorded at this time.

[0048] The pressure inside the constant volume bullet 1 and the dynamic damping cavity 5 is monitored by the constant volume bullet pressure sensor 2, and the displacement of the piston 10 is monitored by the fiber optic displacement sensor 4.

[0049] Compare the real-time displacement of piston 10 with its initial position, and calculate the displacement error and the rate of change of displacement error.

[0050] The displacement error and the rate of change of displacement error are input into the fuzzy PID controller. The fuzzy PID controller dynamically adjusts the proportional gain coefficient, integral gain coefficient and derivative gain coefficient according to the preset fuzzy rules, and outputs the opening control signals of the intake solenoid valve 13 and the exhaust solenoid valve 12 of the damping chamber 5.

[0051] The opening degree of the intake solenoid valve 13 and the exhaust solenoid valve 12 are adjusted according to the opening degree control signal to control the gas flow rate entering or exiting the damping chamber 5, so as to maintain the stability of the piston position.

[0052] As one implementation method in this embodiment, the piston position of the dynamic damping cavity 5 is monitored by an optical fiber displacement sensor 4; the intake channel and exhaust channel of the dynamic damping cavity 5 are respectively equipped with an intake solenoid valve 13 and an exhaust solenoid valve 12 to control the gas flow rate.

[0053] As one implementation method in this embodiment, the displacement error is the difference between the real-time position and the initial position of the piston; the displacement error change rate is the rate of change of the displacement error over time.

[0054] As one implementation method in this embodiment, the fuzzy rules include:

[0055] When the product of displacement error and the rate of change of displacement error is greater than zero and the displacement error is greater than zero, decrease the proportional gain coefficient, increase the integral gain coefficient, and keep the differential gain coefficient moderate.

[0056] When the product of displacement error and the rate of change of displacement error is greater than zero and the displacement error is less than zero, increase the proportional gain coefficient, keep the integral gain coefficient moderate, and set the derivative gain coefficient to a small value.

[0057] When the product of displacement error and displacement error rate of change is less than zero, the changes in proportional gain coefficient and integral gain coefficient should be kept small.

[0058] When the product of displacement error and displacement error rate of change is zero and displacement error is zero, if displacement error rate of change is not zero, set the changes in proportional gain coefficient, integral gain coefficient and differential gain coefficient to a smaller value.

[0059] When the product of displacement error and the rate of change of displacement error is zero and the displacement error is not zero, increase the proportional gain coefficient while keeping the integral gain coefficient moderate.

[0060] As one implementation method in this embodiment, after the fuzzy PID controller outputs the correction values ​​of the proportional gain coefficient, integral gain coefficient, and derivative gain coefficient, it obtains an accurate valve opening control signal through defuzzification processing; the defuzzification processing adopts the centroid method.

[0061] As one implementation method in this embodiment, after adjusting the opening of the intake solenoid valve 13 and the exhaust solenoid valve 12, the step of monitoring the piston displacement is returned to form a closed-loop control.

[0062] This invention provides a constant-volume bullet 1 with a dynamic damping cavity 5 based on a fuzzy PID control algorithm, which enables stable control of the internal pressure of the constant-volume bullet 1 during experiments. By real-time detection of the position of the piston 10 inside the dynamic damping cavity 5 when the internal pressure of the constant-volume bullet 1 is stable, the opening degree of the inlet and outlet valves of the damping cavity 5 is adjusted through the fuzzy PID control algorithm to maintain the stability of the piston position and reduce pressure fluctuations of the constant-volume bullet 1 during experiments.

[0063] The experimental apparatus diagram of the present invention is shown below. Figure 1 As shown, the fuzzy PID control block diagram of the present invention is as follows: Figure 2 As shown. This invention achieves the above functions through the following technical approach, and the identification and control process is as follows:

[0064] (1) An intelligent constant volume spring 1 pressure control system based on dynamic damping cavity 5, the system includes constant volume spring 1, constant volume spring pressure sensor 2, fiber optic displacement sensor 4, damping cavity 5, environmental pressure controller 16, etc.

[0065] (2) The bottom of the constant-volume bullet 1 is designed with a container connected to the constant-volume bullet 1, which is divided into two parts by a piston 10. One part is connected to the constant-volume bullet 1, and the other part is a sealed container, named the damping cavity 5. The piston of the sealed container can be moved by a spring to flexibly adjust the volume of the damping cavity 5. The constant-volume bullet pressure sensor 2 and the damping cavity pressure sensor 3 are used to detect the pressure inside the constant-volume bullet 1 and the damping cavity 5, respectively. In addition, an optical fiber displacement sensor 4 is installed on the wall of the damping cavity 5 to measure the change in piston position.

[0066] (3) Initially, gas is introduced into both the constant-volume projectile 1 and the damping cavity 5 through the air intake pipe. After the target operating condition is reached, the position of the piston 10 in the dynamic damping cavity 5 is recorded by the fiber optic displacement sensor 4. The air intake and exhaust pipes of the damping cavity 5 are equipped with air intake and exhaust solenoid valves 12 to control the valve opening. The fiber optic displacement sensor 4 transmits the measured displacement signal to the environmental pressure controller 16. The environmental pressure controller 16 adjusts the valve opening of the air intake and exhaust solenoid valves 12 according to the piston displacement, the pressure inside the constant-volume projectile 1 and the dynamic damping cavity 5, thereby controlling the pressure and piston position inside the dynamic damping cavity 5, so that the pressure inside the piston 10 and the projectile in the damping cavity remains stable. To further improve the accuracy of control, the environmental pressure controller 16 adopts a fuzzy PID control algorithm.

[0067] (4) The environmental pressure controller is a standardized fuzzy controller based on fuzzy PID control. Its construction process is as follows:

[0068] ① Tuning: First, the initial parameters of the PID controller are tuned. The piston position obtained from the fiber optic displacement sensor is used as the input signal for the initial PID algorithm, and the opening degree of the damping chamber intake and exhaust solenoid valves is used as the output signal. The system is identified by comparing the input and output signals, and the initial parameters k of the PID controller are obtained. p k i k d The basic formula is as follows:

[0069]

[0070] In the formula, u(t) is the output of the PID controller (i.e., the piston adjustment signal), and k p k i k d These are the initial values ​​of the proportional, integral, and derivative gain coefficients of the PID controller, respectively, and e(t) is the current error, representing the difference between the expected value and the current value. This is the integral term of the error, used to eliminate steady-state error. The derivative term of the error is used to predict the error change trend and adjust the system response speed. By adjusting the PID parameters, the regulation of piston 10 can be optimized according to the characteristics of the system response.

[0071] The position of piston 10 is compared with the position of the reference piston to obtain the error e(t) and de / dt. These two values ​​are used as inputs to the fuzzy controller. The fuzzy PID controller continuously detects e(t) and de / dt through real-time sampling and outputs the correction amount Δk of the three parameters of the PID controller using fuzzy control theory. p Δk i Δk d The correction algorithm for fuzzy PID control is as follows:

[0072]

[0073] ② Blurring:

[0074] In this invention, the fuzzy PID controller is a two-input, three-output system. The piston position is used as the input signal of the fuzzy PID algorithm. This position is compared with the initial position to obtain the error e(t) and de / dt. These two are used as the input of the fuzzy controller. The fuzzy controller is then fuzzified according to the fuzzification function and the fuzzy rule of area center is adopted to obtain the fuzzy controller, as shown below:

[0075]

[0076] In the formula, e and u represent the fuzzified control error and control quantity (valve opening), respectively, μ(e) and μ(u) represent the fuzzification functions of the control error and control quantity, respectively, and K pij K iijK dij Let represent the fuzzification functions for the proportional, integral, and derivative coefficients, respectively. Assume the upper and lower limits of the fuzzification control range are b and a, respectively.

[0077] ③ Establishment of fuzzy control rules:

[0078] (1) When e·(de / dt)>0 and e>0, (de / dt)>0, k p It should be reduced, k i It should increase, therefore Δk p If negative, Δk i If positive, Δk d For moderate; when e·(de / dt)>0 and e<0, (de / dt)<0, k p It should increase, therefore Δk p If positive, Δk i Moderate, Δk d Smaller.

[0079] (2) When e·(de / dt) < 0, it indicates that the system error is decreasing, and we should try to maintain the original input k. p k i Therefore, Δk p Δk i It also maintains a relatively small amount of change.

[0080] (3) When e·(de / dt)=0 and e=0, if (de / dt)=0, it means that the system has reached the target opening and is stable, and the original input k is maintained. p k i If (de / dt) ≠ 0, it means the system only reaches the target opening for a short time and is likely still in the oscillation stage. Therefore, Δk p Δk i Δk d It is a relatively small value.

[0081] (4) When e·(de / dt)=0 and e≠0, it indicates that the system control process has entered a steady state, but there is a certain steady-state error. Therefore, Δk p Larger, Δk i Moderate.

[0082] ④ Deblurring:

[0083] Defuzzification converts the fuzzified PID gain into a specific numerical value. This paper uses the centroid method for defuzzification, which calculates the centroid of the fuzzy output membership degree to obtain the specific value of the control gain, thereby obtaining the final control quantity. The defuzzification formula is:

[0084]

[0085] In the formula, u is the output clarity quantity, u j This refers to the actual control quantity.

[0086] The following implementation method based on the experimental setup is provided:

[0087] (1) Figure 1 A detailed experimental setup diagram of the present invention is provided. The constant-volume bullet pressure sensor 2 and the damping cavity pressure sensor 3 are respectively installed on the cylinder walls of the constant-volume bullet 1 and the damping cavity 5 to detect the environmental pressure within them. The fiber optic displacement sensor 4 is used to monitor the piston's position change in real time under high temperature and high pressure conditions and transmits the position signal to the environmental pressure controller 16. The controller controls the piston's displacement by adjusting the valve openings of the inlet and outlet passages of the damping cavity 5.

[0088] (2) After the gas is introduced into the constant-volume projectile 1 to reach the target pressure, the fiber optic displacement sensor 4 records the position of the piston at this time. After the experiment begins, the injection and combustion processes have a certain impact on the pressure inside the projectile, which in turn causes the piston position to change. The piston position at this time is recorded and compared with the initial position to calculate the difference and the rate of change of the difference.

[0089] (3) An environmental pressure controller 16 is designed using a fuzzy PID control algorithm. The piston position is used as the input, and the difference between the piston position and the initial position is used as a feedback signal for correction. The valve opening of the damping chamber 5 for intake and exhaust is set as the output to maintain the stability of the piston position and thus reduce the pressure fluctuation in the cylinder.

[0090] (4) Repeat steps (1) to (3) to achieve online closed-loop feedback control of the pressure inside cylinder 1 of the constant volume bomb.

[0091] Based on this, the present invention provides a method for optimizing and controlling pressure fluctuations in an intelligent constant-volume projectile system based on dynamic damping cavity design, the technical effects of which include:

[0092] (1) The present invention enhances the ability of the constant volume bullet to absorb pressure fluctuations by designing a dynamic damping cavity inside the constant volume bullet and by precisely controlling the position of the piston inside the damping cavity.

[0093] (2) The present invention proposes an environmental pressure controller design based on fuzzy PID control algorithm, which can achieve precise control of the internal pressure of the projectile by controlling the valve opening of the intake and exhaust channels, and realize real-time closed-loop control of environmental pressure.

[0094] (3) This invention does not have special requirements for experimental equipment. It only requires the addition of the necessary equipment to the existing equipment, making it highly practical and cost-effective.

[0095] Example 2

[0096] In this embodiment, a computer terminal device is provided, including:

[0097] One or more processors;

[0098] A memory, coupled to the processor, for storing one or more programs;

[0099] When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the above-described method for optimizing the pressure fluctuation control of an intelligent constant-volume projectile system based on a dynamic damping cavity design.

[0100] In this embodiment, a computer-readable storage medium is also provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the above-described method for optimizing and controlling pressure fluctuations in an intelligent constant-volume spring system based on a dynamic damping cavity design.

[0101] In this embodiment, an electronic device is also provided, including a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to execute the steps of the above-described method for optimizing the pressure fluctuation control of an intelligent constant-volume spring system based on a dynamic damping cavity design.

[0102] In this embodiment, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the above-described method for optimizing and controlling pressure fluctuations in an intelligent constant-volume spring system based on a dynamically damped cavity design.

[0103] The aforementioned program can run on a processor or be stored in memory (or a computer-readable medium). Computer-readable media include both permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0104] These computer programs may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes can be implemented using different modules, and different steps can be implemented using different modules.

[0105] This embodiment provides such a device or system. The system, referred to as a smart constant-volume spring system pressure fluctuation optimization control system based on dynamic damping cavity design, includes:

[0106] The dynamic damping cavity module 5 is located at the bottom of the constant volume projectile and communicates with the interior of the constant volume projectile through the connecting hole 11. The module includes a piston 10 and a spring mechanism. The piston 10 divides the cavity into a connecting area and a sealed area. The spring connects the piston 10 to the cavity wall to achieve displacement self-adaptation.

[0107] The multi-source sensing module includes a constant volume bullet pressure sensor 2, a damping cavity pressure sensor 3, and an optical fiber displacement sensor 4, which are used to monitor the internal pressure of the constant volume bullet, the internal pressure of the damping cavity, and the displacement of the piston 10, respectively.

[0108] The fuzzy PID control module receives the displacement signal of piston 10 at the input end, calculates the displacement error and the rate of change of displacement error by comparing the real-time displacement of piston 10 with the initial displacement, dynamically adjusts the proportional gain coefficient, integral gain coefficient and derivative gain coefficient based on preset fuzzy rules, and outputs the valve opening control signal.

[0109] The gas flow control module includes an intake solenoid valve 13 and an exhaust solenoid valve 12, which are respectively connected to the intake and exhaust channels of the damping chamber. The module adjusts the gas flow rate entering or exiting the damping chamber according to the valve opening control signal.

[0110] As one implementation method in this embodiment, the fiber optic displacement sensor 4 of the multi-source sensing module is installed on the wall of the damping cavity to monitor the piston displacement in real time; the inlet solenoid valve 13 and the exhaust solenoid valve 12 of the gas flow execution module are respectively disposed in the inlet channel and the exhaust channel of the damping cavity.

[0111] As one implementation method in this embodiment, the fuzzy PID control module includes a displacement error calculation unit. This unit generates a displacement error signal by the difference between the real-time displacement of the piston 10 and its initial position, and generates a displacement error change rate signal by the time derivative of the displacement error.

[0112] As one implementation method in this embodiment, the fuzzy PID control module has a built-in fuzzy rule base, which executes the following logic:

[0113] When the product of displacement error and the rate of change of displacement error is greater than zero and the displacement error is greater than zero, output correction instructions to decrease the proportional gain coefficient, increase the integral gain coefficient, and keep the differential gain coefficient moderate.

[0114] When the product of displacement error and displacement error change rate is greater than zero and displacement error is less than zero, output correction instructions to increase proportional gain coefficient, keep integral gain coefficient moderate, and set derivative gain coefficient to a smaller value.

[0115] When the product of displacement error and displacement error rate of change is less than zero, output a correction instruction to keep the changes in proportional gain coefficient and integral gain coefficient small.

[0116] When the product of displacement error and displacement error rate of change is zero and displacement error is zero, if displacement error rate of change is not zero, output a correction instruction to set the changes of proportional gain coefficient, integral gain coefficient and derivative gain coefficient to smaller values.

[0117] When the product of displacement error and the rate of change of displacement error is zero and the displacement error is not zero, a correction instruction is output to increase the proportional gain coefficient and keep the integral gain coefficient moderate.

[0118] As one implementation method in this embodiment, the fuzzy PID control module includes a defuzzification processing unit, which uses the centroid method to convert the gain coefficient correction amount output by the fuzzy rule base into a precise valve opening control signal.

[0119] As one implementation method in this embodiment, the system includes a closed-loop triggering module. After the gas flow execution module completes the valve opening adjustment, the module triggers the multi-source sensing module to re-acquire the piston displacement signal, forming a closed-loop control link.

[0120] The system or apparatus is used to implement the functions of the methods in the above embodiments. Each module in the system or apparatus corresponds to each step in the method, as has been described in the method and will not be repeated here.

[0121] The above implementation method solves the problem of pressure fluctuation optimization control in intelligent constant volume spring systems based on dynamic damping cavity design in related technologies, thereby ensuring that the problems existing in the prior art are resolved.

[0122] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing and controlling pressure fluctuations in an intelligent constant-volume spring system based on dynamic damping cavity design, characterized in that, Includes the following steps: A dynamic damping cavity is provided at the bottom of the constant volume projectile and connected to the inside of the projectile. The dynamic damping cavity is divided by a piston into a first part that is connected to the constant volume projectile and a sealed second part. The piston is connected to the wall of the damping cavity by a spring. Gas is introduced into the constant volume projectile and the dynamic damping chamber until the target pressure is reached, and the initial position of the piston is recorded at this time. The pressure inside the constant volume bullet and the dynamic damping cavity is monitored by a pressure sensor, and the displacement of the piston is monitored by a fiber optic displacement sensor. Compare the real-time displacement of the piston with its initial position, and calculate the displacement error and the rate of change of the displacement error. The displacement error and the rate of change of displacement error are input into the fuzzy PID controller. The fuzzy PID controller dynamically adjusts the proportional gain coefficient, integral gain coefficient and derivative gain coefficient according to the preset fuzzy rules, and outputs the opening control signals of the damping chamber intake solenoid valve and exhaust solenoid valve. The opening degree of the intake solenoid valve and the exhaust solenoid valve are adjusted according to the opening degree control signal to control the gas flow rate entering or exiting the damping chamber in order to maintain the stability of the piston position.

2. The method according to claim 1, characterized in that, The piston position of the dynamic damping cavity is monitored by an optical fiber displacement sensor; the inlet and outlet channels of the dynamic damping cavity are respectively equipped with an inlet solenoid valve and an outlet solenoid valve to control the gas flow rate.

3. The method according to claim 1, characterized in that, The displacement error is the difference between the real-time position and the initial position of the piston; the displacement error change rate is the rate of change of the displacement error over time.

4. The method according to claim 1, characterized in that, The fuzzy rules include: When the product of displacement error and the rate of change of displacement error is greater than zero and the displacement error is greater than zero, decrease the proportional gain coefficient, increase the integral gain coefficient, and keep the differential gain coefficient moderate. When the product of displacement error and the rate of change of displacement error is greater than zero and the displacement error is less than zero, increase the proportional gain coefficient, keep the integral gain coefficient moderate, and set the derivative gain coefficient to a small value. When the product of displacement error and displacement error rate of change is less than zero, the changes in proportional gain coefficient and integral gain coefficient should be kept small. When the product of displacement error and displacement error rate of change is zero and displacement error is zero, if displacement error rate of change is not zero, set the changes in proportional gain coefficient, integral gain coefficient and differential gain coefficient to a smaller value. When the product of displacement error and the rate of change of displacement error is zero and the displacement error is not zero, increase the proportional gain coefficient while keeping the integral gain coefficient moderate.

5. The method according to claim 1, characterized in that, After the fuzzy PID controller outputs the correction values ​​of the proportional gain coefficient, integral gain coefficient, and derivative gain coefficient, it obtains a precise valve opening control signal through defuzzification processing; the defuzzification processing adopts the centroid method.

6. The method according to claim 1, characterized in that, After adjusting the opening of the intake and exhaust solenoid valves, the process returns to the step of monitoring the piston displacement, thus forming a closed-loop control.

7. A pressure fluctuation optimization control system for an intelligent constant-volume spring system based on dynamic damping cavity design, characterized in that, The system includes: The dynamic damping cavity module is located at the bottom of the constant volume projectile and communicates with the interior of the constant volume projectile through a connecting hole. The module includes a piston and a spring mechanism. The piston divides the cavity into a connected area and a sealed area, and the spring connects the piston to the cavity wall to achieve displacement self-adaptation. The multi-source sensing module includes a constant-volume bullet pressure sensor, a damping cavity pressure sensor, and a fiber optic displacement sensor, which are used to monitor the internal pressure of the constant-volume bullet, the internal pressure of the damping cavity, and the piston displacement, respectively. The fuzzy PID control module receives the piston displacement signal at the input end, calculates the displacement error and the rate of change of displacement error by comparing the real-time piston displacement with the initial displacement, dynamically adjusts the proportional gain coefficient, integral gain coefficient and derivative gain coefficient based on preset fuzzy rules, and outputs the valve opening control signal. The gas flow control module includes an intake solenoid valve and an exhaust solenoid valve, which are respectively connected to the intake and exhaust channels of the damping chamber. The gas flow rate entering or exiting the damping chamber is adjusted according to the valve opening control signal.

8. A computer terminal device, characterized in that, include: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors perform the steps of the method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

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