Hydraulic vibrator control method based on adaptive fuzzy PID

Abstract

The present application relates to a kind of hydraulic controllable seismic source control method based on adaptive fuzzy PID, using input module to set parameter and calculate theoretical output;Using execution module to drive exciter and floor coupling, to ground radiate seismic wave signal;Using multiple acceleration sensors placed on the bottom plate and exciter to collect signal, substitute into formula to calculate actual output;The difference between theoretical output and actual output and the change rate of difference are selected as the input parameters of fuzzy controller, reasoning is carried out through fuzzy rule and defuzzification, then the correction value obtained after defuzzification is superimposed with the original value to obtain new PID control parameter, so as to realize the adaptive control of hydraulic servo seismic source system.The present application can effectively reduce the influence of nonlinear factors of seismic source system, shorten the system response time, reduce the overshoot of system, has good robustness and control accuracy, and further improves the seismic exploration precision and efficiency.

Description

Technical Field

[0001] This invention belongs to the field of geophysical seismic exploration and relates to hydraulic servo control technology. It is applicable to lightweight hydraulic servo controllable seismic source systems, specifically involving a hydraulic controllable seismic source control method based on adaptive fuzzy PID. Background Technology

[0002] With the continuous advancement of science and technology, energy shortages are hindering the development of human society. Currently, one of the most effective methods to address this problem is to improve energy exploration technology, and seismic exploration technology is one of the most mature and practical exploration techniques. A seismic exploration system consists of a seismograph, a source, and a detector. The source, as the input source of the exploration system, generates a signal according to exploration requirements and is highly correlated with the quality of the final seismic imaging effect. Controllable source control technology is a key means to improve the quality of the source scanning signal and the exploration signal-to-noise ratio, requiring real-time and rapid correction of the source system's errors.

[0003] Portable hydraulic seismic sources refer to a type of hydraulically controllable seismic source with a maximum nominal output of less than 116 kN (SY / T5249-2019). Their excellent mobility makes them mainly used in shallow and mid-level seismic exploration in urban streets, mountainous areas, and forest areas. Due to the short stroke and low output of the hammer, portable seismic sources require a control system that can achieve simple, fast, and real-time control, while also possessing good dynamic performance.

[0004] Current lightweight hydraulically controllable seismic source systems face the following problems:

[0005] First, factors such as hydraulic oil, machinery, and base plate materials in current controllable source exploration systems introduce various nonlinear errors into the source system. On the one hand, this causes significant distortion in the scanning signal generated by the source during the source generation process. On the other hand, it makes it difficult to build an accurate mathematical model for the controllable source system.

[0006] Secondly, the mainstream approach to controlling controllable source systems currently uses Kalman filtering combined with an optimal controller, which has achieved good results on large seismic sources. However, this method relies on complex models, requires massive computational resources and advanced hardware support, and cannot adapt to complex geological conditions.

[0007] Chinese patent CN112711073A discloses a multi-sensor fusion ground force estimation method based on neural networks. This method fuses displacement, velocity, and acceleration signals as the input layer and ground force signals as the output layer. A supervised neural network is used iteratively to obtain the optimal weight allocation strategy for the input signals, ultimately achieving ground force estimation using multi-parameter fusion and improving estimation accuracy. However, this method requires first acquiring seismic source operating signals at different frequencies, and then determining the weights through multiple neural network learning iterations. The process is cumbersome and computationally complex, making it difficult to implement in actual industrial settings. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides a hydraulic controllable vibration source control method based on adaptive fuzzy PID. For large-scale nonlinear system production processes and industrial control processes where precise mathematical models are difficult to establish, adaptive fuzzy control technology is employed. The controller is built based on the experience of the control personnel, achieving effective system control. To address the problem of complex control systems, traditional PID control is used to achieve simple and ideal control of lightweight vibration source systems. Adaptive fuzzy controllers have good robustness and excellent dynamic characteristics, but static errors are difficult to eliminate. The integral element in a PID controller can effectively eliminate steady-state error, but its dynamic response is poor. Therefore, combining adaptive fuzzy control and PID control algorithms, leveraging the strengths of both to form an adaptive fuzzy PID control algorithm, achieves better control performance.

[0009] This invention is achieved through the following technical solution:

[0010] A hydraulically controllable vibration source control method based on adaptive fuzzy PID is characterized by the following steps:

[0011] Step 1: First, the system automatically performs initialization settings. Then, according to the exploration requirements, the parameters of the seismic source scanning signal are set through the display screen. The control chip controls the input signal generator to generate the initial vibration signal according to the parameters.

[0012] Step 2: Place acceleration sensors on the vibrator of the controllable source vibrator and on the back plate respectively. After the acceleration of the vibrator and the back plate is collected in real time by the multi-channel analog-to-digital converter on the acquisition circuit, the collected signals are filtered by the filtering circuit, and the control chip calculates the estimated force value of the base plate according to the formula.

[0013] Step 3: Design a fuzzy controller. First, fuzzify the output error and the rate of change of error. Then, perform fuzzy inference according to the fuzzification rules to obtain the fuzzy values ​​of the correction parameters. Finally, defuzzify the fuzzy values ​​of the correction parameters to obtain the correction values ​​of the PID controller parameters. After superimposing them with the initial values, adjust the control parameters of the PID controller.

[0014] Step 4: The voltage signal processed by the PID controller is converted into a corresponding electro-hydraulic servo valve control signal through the drive circuit. The servo valve will output corresponding hydraulic oil according to the control signal to drive the vibration source exciter to vibrate. The exciter then couples with the ground through the base plate to generate ground force and radiates scanning signals underground.

[0015] Furthermore, in step 1, the system will automatically load the corresponding model and interface according to the program pre-downloaded in the control chip; in addition, the system can initialize the model and parameters according to the command. If the default initialization is selected, the initial values ​​set by the program will be used for initialization.

[0016] Furthermore, the control screen in step 1 communicates with the control chip in full-duplex mode via a serial port, allowing the parameters of the scanning signal to be set on the screen according to exploration requirements. The input signal generator in the control chip contains a transfer function established based on an ideal seismic source model, which can calculate the theoretical base plate output force G at time k based on the input parameters and the parameters of the seismic source. T (k); In addition, the signal generator is also preset with commonly used scanning signals such as linear and e-exponential signals, which can quickly generate corresponding source signals as needed.

[0017] Furthermore, in step 2, the exciter acceleration A acquired by the accelerometer is... m (k) and backplate acceleration A b (k) The actual estimated ground force G at time k is calculated using the weighted sum of ground forces formula (1). F (k):

[0018] -G F (k)=M m ·A m (k)+M b ·A b (k) (1)

[0019] M in the formula m For the exciter mass, M b Let be the mass of the backplate, and both are constants.

[0020] Based on the theoretical output value and the actual output value, the error value e and the error change rate ec are obtained:

[0021] e = G T (k)-G F (k) (2)

[0022] ec = d[G T -G F ] / dt (3)

[0023] Furthermore, in step 3, the fuzzy control uses the aforementioned output error value e and error transformation rate ec as input variables. Based on fuzzy rules, the relationship between the PID control parameters and these two variables is established, and multiple iterative calculations are performed to ultimately complete the controllable vibration source. The specific steps of one iteration are as follows:

[0024] Step 1: Given the initial number K of control parameters P0 K I0 K D0 Determine the upper limit of the error value e, the error change rate ec, and the control parameter K. P ,K I ,K D The upper limit values ​​are e max , ec max K p max ,K I max ,K D max ;;

[0025] Step 2: Map the input quantities e and ec to the universe of discourse to obtain the fuzzy value e. f With EC f ;

[0026] Step 3: Based on the fuzzy value e f With EC f Calculate its membership degree across each linguistic variable;

[0027] Step 4: Based on the rule table, find the control parameter K that maps to the corresponding fuzzy linguistic variable. c ;

[0028] Step 5: Defuzzify the fuzzy values ​​to obtain the PID controller control parameter correction values;

[0029] Step 6: Superimpose the correction value and the initial value to obtain new control parameters, and then correct the output signal;

[0030] Furthermore, the fuzzification involves dividing the domains of e and ec into multiple regions, thereby mapping the values ​​within each region to fuzzy linguistic variables.

[0031] Furthermore, the fuzzy linguistic variables of the membership functions of the error value e and the error change rate ec are divided into seven levels, namely {NB, NM, NS, ZO, PS, PM, PB}, representing negative large, negative medium, negative small, zero, positive small, positive medium, and positive large. When e is at fuzzy value a and ec is at fuzzy value b, the membership degree calculation formula is as follows:

[0032] μ(k)=μ e (a)*μec (b) (4)

[0033] Furthermore, the defuzzification process involves first normalizing the fuzzy values ​​of the PID controller's control parameters, and then converting them into numerical values. For the control parameters, the normalization is expressed as a fuzzy value K. f (k):

[0034]

[0035] Then map it to the actual value:

[0036]

[0037] In the formula, N is the upper limit of the universe of discourse. ΔK is the upper limit of the control coefficient. p / I / D (k) represents the control coefficient correction value at time k.

[0038] Furthermore, the specific formulas for the superposition of initial values ​​and correction values ​​are as follows:

[0039] K p / I / D (k)=K P0 / I0 / D0 +ΔK p / I / D (k) (7)

[0040] K in the formula P0 K I0 K D0 K is the initial value of the coefficient. p / I / D (k) represents the updated control coefficient.

[0041] Furthermore, the drive circuit in step 4 converts the input voltage signal into a specific servo valve drive current signal. The servo valve outputs a corresponding amount of hydraulic oil according to the current magnitude of the signal. The hydraulic oil flows into the piston through a pipe, driving the piston to move. The piston end is connected to the vibrator. When the hydraulic pressure in the upper chamber of the piston is greater than that in the lower chamber, the vibrator moves downward; when the hydraulic pressure in the upper chamber is less than that in the lower chamber, the vibrator moves upward, and so on. There is a relatively rigid backplate on the ground. The vibrator is coupled to the backplate through vibration isolation springs and transmits the vibration force to the backplate. The backplate is then directly coupled to the ground, thereby generating downward seismic waves. At the same time, the acceleration signals collected by the acceleration sensors placed on the vibrator and the backplate are filtered and transmitted to the control chip. The control chip calculates and estimates the actual ground output force according to the weighted sum and ground force estimation formula, thus forming a closed-loop real-time control.

[0042] Compared with existing methods, the control method provided by this invention offers the following advantages: A hydraulically controllable seismic source control method based on adaptive fuzzy PID can effectively control the system by establishing a fuzzy controller based on the experience of control personnel, taking into account the complexity and nonlinearity of controllable seismic source devices. Compared to traditional PID, this invention offers better dynamic performance and smaller overshoot. Compared to Kalman control, this invention provides faster response and a simpler control system. Real-time and effective control of the seismic source system can improve the accuracy and efficiency of seismic exploration. Attached Figure Description

[0043] Figure 1 This is a structural diagram of the controllable vibration source system and fuzzy controller of the present invention;

[0044] Figure 2 This is a flowchart of the fuzzy control algorithm of the present invention;

[0045] Figure 3 This is a mechanical structure diagram of the seismic source system of the present invention;

[0046] Figure 4 This is a Simulink model diagram of the PID and fuzzy PID of this invention;

[0047] Figure 5 This is an iterative flowchart of the adaptive fuzzy PID controller of the present invention;

[0048] Figure 6 These are Simulink simulation results of the PID and fuzzy PID of this invention; Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0050] See Figure 1 The present invention provides a control system for a controllable vibration source, including an input module, a fuzzy controller module, an execution module and a feedback module, wherein the signal generator and the fuzzy PID controller of the input module are both implemented in the control chip.

[0051] The input screen is a capacitive touchscreen, allowing users to select and set parameters for the required scanning signal, including but not limited to the signal type, rise time, fall time, start frequency, cutoff frequency, and signal length. After selection, information 'a' is transmitted to the control chip via a serial port in a specific format. The control chip, a microcontroller, is the core of the seismic source control system. Upon receiving control signal 'a', it parses the information according to the format and generates a specific signal. The input generation model, located within the control chip, is based on the theoretical seismic source model and can automatically calculate the theoretical output force based on input signal 'a'. Another function of the input generation model is to transmit the initial input signal 'b' to the fuzzy controller.

[0052] The process of the fuzzy controller is as follows Figure 2 As shown, in this specific example: the difference between the theoretical output force and the estimated actual output force, along with the interpolated rate of change, are used as input variables. After fuzzifying the input and control variables and establishing a fuzzy rule, the triangular membership formula is used to calculate the membership degree for different fuzzy linguistic variables to match them. Then, based on the fuzzy rule table, the corresponding parameter fuzzy values ​​are found, and after defuzzification, the corrected values ​​for the PID proportional control coefficient (c), derivative control coefficient (d), and integral control coefficient (f) are output. Superimposing these corrected values ​​with the initial values ​​updates the PID controller's control coefficients.

[0053] The voltage signal g processed by the controller is converted into a corresponding servo valve drive current signal h by the drive circuit. The servo valve will output corresponding hydraulic oil i according to the magnitude of the drive current. However, due to the characteristics of the liquid and the zero-point dead zone characteristics of the servo valve, this process is not nonlinear. Therefore, a nonlinear control method is required for control.

[0054] like Figure 4 As shown, hydraulic oil i flows into the piston through the pipe, driving the piston to move. The piston end is connected to the vibrator. When the hydraulic pressure in the upper chamber of the piston is greater than that in the lower chamber, a downward pressure j is formed, and the vibrator moves downward; when the hydraulic pressure in the upper chamber of the piston is less than that in the lower chamber, an upward pressure j is formed, and the vibrator moves upward, and so on. There is a relatively rigid back plate on the ground. The vibrator is coupled to the back plate through the vibration isolation spring and transmits the vibration force k to the back plate. The back plate is then directly coupled to the ground, thereby generating downward seismic waves. At the same time, the acceleration signal m collected by the acceleration sensor placed on the vibrator and the back plate through the signal acquisition circuit is processed by the filtering circuit and transmitted to the control chip. The control chip calculates and estimates the actual ground output according to formula (1), thereby forming a closed-loop real-time control.

[0055] See Figure 5Simulink models of PID control and fuzzy PID control were built in Matlab software and simulations were performed. The upper part of the figure shows the traditional PID control model, and the lower part shows the fuzzy PID control model. The simulated transfer function is:

[0056]

[0057] In this example, the theoretical output value and the actual output value are used as input parameters to obtain the error value e and the error change rate ec, which are obtained from formulas (2) and (3), in order to obtain K. P ,K I ,K D The correction value is the output parameter.

[0058] Specific steps are as follows Figure 5 As shown:

[0059] Step 1: Given the initial number K of control parameters P0 K I0 K D0 Determine the upper limit of the error value e, the error change rate ec, and the control parameter K. P ,K I ,K D The upper limit values ​​are e max , ec max K p max ,K I max ,K D max ;;

[0060] Step 2: Map the input quantities e and ec to the universe of discourse to obtain the fuzzy value e. f With EC f ;

[0061] Step 3: Based on the fuzzy value e f With EC f Calculate its membership degree across each linguistic variable;

[0062] Step 4: Based on the rule table, find the control parameter K that maps to the corresponding fuzzy linguistic variable. c ;

[0063] Step 5: Defuzzify the fuzzy values ​​to obtain the PID controller control parameter correction values;

[0064] Step 6: Superimpose the correction value and the initial value to obtain new control parameters, and then correct the output signal;

[0065] The initial values ​​of the PID control parameters can be obtained by tuning using the stability boundary method. The universe of discourse for e is [-6, 6], the universe of discourse for ec is [-0.8, 0.8], and K... P The domain of discourse is [0,5], K I The universe of discourse is [-0.6, 0.6], K D The domain of discourse is [0, 2.5].

[0066] The so-called rule table is established based on expert experience and can select the fuzzy linguistic values ​​of control parameters according to the fuzzy linguistic values ​​of the input parameters. K can be obtained by consulting Tables 1, 2, and 3 respectively. P ,K I ,K D The fuzzy values ​​of the three output variables.

[0067] Table 1: K P Fuzzy rule table

[0068]

[0069]

[0070] Table 2: K I Fuzzy rule table

[0071]

[0072] Table 3: K D Fuzzy rule table

[0073]

[0074] Defuzzification involves normalizing the fuzzy values ​​of the PID controller's control parameters and then converting them into numerical values. For the control parameters, they are normalized to fuzzy values ​​K according to formula (5). f (k), and then substitute it into formula (6) to get the control coefficient correction value at time k after mapping it to the actual value. After obtaining the correction value, the correction value can be superimposed with the initial value to achieve the purpose of correcting the PID.

[0075] The simulation results obtained in Simulink are as follows: Figure 6 As shown, the fuzzy PID used in this invention has a faster response speed and smaller overshoot compared to the traditional PID, achieving a rapid response to the system. This means that the algorithm can quickly correct the nonlinear error of the seismic source during seismic exploration, further improving the accuracy and efficiency of seismic exploration.

[0076] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should be considered within the scope of protection of the present invention.

Claims

1. A hydraulically controllable vibration source control method based on adaptive fuzzy PID, characterized in that... The steps are as follows: Step 1: First, the system automatically performs initialization settings. Then, according to the exploration requirements, the parameters of the seismic source scanning signal are set through the display screen. The control chip controls the input signal generator to generate the initial vibration signal according to the parameters. Step 2: Place acceleration sensors on the vibrator of the controllable source vibrator and on the back plate respectively. After the multi-channel analog-to-digital converter on the acquisition circuit acquires the acceleration of the vibrator and the back plate in real time, the acquisition signal is filtered by the filtering circuit. The control chip calculates the estimated output force value of the base plate according to the formula. Step 3: Design a fuzzy controller. First, fuzzify the output error and the rate of change of error. Then, perform fuzzy inference according to the fuzzification rules to obtain the fuzzy values ​​of the correction parameters. Finally, defuzzify the fuzzy values ​​of the correction parameters to obtain the correction values ​​of the PID controller parameters. After superimposing them with the initial values, adjust the new control parameters of the PID controller. Step 4: The voltage signal processed by the PID controller is converted into the corresponding electro-hydraulic servo valve control current signal through the drive circuit. The servo valve will output hydraulic oil to drive the vibration source exciter to vibrate according to the control signal. The exciter then couples with the ground through the base plate to generate ground force and radiates scanning signals underground.

2. The hydraulic controllable vibration source control method based on adaptive fuzzy PID according to claim 1, characterized in that: In step 1, the control screen communicates with the control chip via a serial port in full-duplex mode, allowing the parameters of the scanning signal to be set on the screen according to exploration requirements. The input signal generator in the control chip contains a transfer function established based on an ideal seismic source model, which can calculate the theoretical base plate output force G at time k based on the input parameters and the parameters of the seismic source. T (k); In addition, the signal generator is also preset with commonly used scanning signals such as linear and e-exponential signals, which can quickly generate source signals with different parameters as needed.

3. The hydraulic controllable vibration source control method based on adaptive fuzzy PID according to claim 1, characterized in that: In step 2, the control chip acquires the exciter acceleration A at time k via an acceleration sensor. m (k) and backplate acceleration A b (k) Substitute into the weighted sum of ground forces formula to calculate the actual estimated ground force G at that moment. F (k); Based on the theoretical output value and the actual output value, the error value e and the error change rate ec are obtained.

4. The hydraulic controllable vibration source control method based on adaptive fuzzy PID according to claim 1, characterized in that: The fuzzy controller in step 3 uses the above-mentioned output error value e and error transformation rate ec as input variables. It establishes the relationship between the PID control parameters and the two according to the fuzzy rules, performs multiple iterative calculations, and realizes real-time and rapid control of the controllable source system. The specific steps of one iteration are as follows: Step 1: Given the initial number K of control parameters P0 K I0 K D0 Determine the error value e, the rate of change of error ec, and the control parameter K. P ,K I ,K D The upper limit values ​​are e max , ec max K p max ,K I max ,K D max ; Step 2: Map the input quantities e and ec to the universe of discourse to obtain the fuzzy value e. f With EC f ; Step 3: Based on the fuzzy value e f With EC f Calculate the membership degree of each linguistic variable, and determine the fuzzy linguistic variable with fuzzy value based on the membership degree; Step 4: Based on the rule table, find the control parameter K that maps to the corresponding fuzzy linguistic variable. c ; Step 5: Defuzzify the fuzzy values ​​to obtain the PID controller control parameter correction values; Step 6: Superimpose the correction value and the initial value to obtain the new control parameters, and then correct the output signal.

5. The hydraulic controllable vibration source control method based on adaptive fuzzy PID according to claim 1, characterized in that: The drive circuit in step 4 can convert the input voltage signal into a specific servo valve drive current signal; the servo valve outputs a corresponding amount of hydraulic oil according to the current magnitude of the signal, and the hydraulic oil flows into the piston through the pipeline, driving the piston to move. The piston end is connected to the vibrator. When the hydraulic pressure in the upper chamber of the piston is greater than that in the lower chamber, the vibrator moves downward; when the hydraulic pressure in the upper chamber of the piston is less than that in the lower chamber, the vibrator moves upward, and so on. There is a relatively rigid back plate on the ground. The vibrator is coupled to the back plate through the vibration isolation spring and transmits the vibration force to the back plate. The back plate is then directly coupled to the ground, thereby generating downward seismic waves. Meanwhile, the acceleration signals collected by the acceleration sensors placed on the vibrator and backplate are filtered and transmitted to the control chip. The control chip calculates the actual ground output force according to the weighted sum and ground force estimation formula, thereby forming a closed-loop real-time control.

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