A method and apparatus for adaptive control of a piezoelectric resonator system

Abstract

This invention relates to a method and apparatus for adaptively controlling a piezoelectric vibrator system, belonging to the field of ultrasonic vibration system design technology. It constructs a method for controlling an ultrasonic vibration system (controlling a piezoelectric vibrator system), effectively improving the performance prediction accuracy and adaptability of the ultrasonic vibration system (controlling the piezoelectric vibrator system). It can deduce the most suitable set of piezoelectric ceramic constitutive model parameters under the current working condition online and in real time. This process avoids destructive offline material testing, realizing the adaptive control of the load vibration of the piezoelectric vibrator system according to the current working condition, achieving optimal control to meet working performance requirements, and improving the working accuracy and reliability of ultrasonic tools.

Description

Technical Field

[0001] This invention relates to the field of ultrasonic vibration system design technology, and in particular to a method and apparatus for adaptive control of a piezoelectric vibrator system. Background Technology

[0002] An ultrasonic vibration system refers to a device that mechanically amplifies the microscopic stretching vibrations generated by the inverse piezoelectric effect of piezoelectric ceramics through a longitudinal resonant piezoelectric transducer and an amplitude transformer, ultimately resulting in high-speed reciprocating motion at the end of a tool head. Such ultrasonic vibration systems are widely used in ultrasonic medical equipment (such as ultrasonic scalpels and ultrasound probes), precision industrial machining (ultrasonic welding and cleaning), aerospace active vibration control, and microelectromechanical systems (MEMS).

[0003] Traditional ultrasonic vibration systems are primarily designed for specific, single working conditions. When the contact state between the end-effector and the workpiece changes (e.g., increased load, wear, or replacement with a different tool size), the ambient temperature fluctuates, or the drive power parameters drift, key performance indicators such as resonant frequency and amplitude easily deviate from their optimal values. This leads to unstable output power, decreased energy conversion efficiency, and even problems such as system overheating and shortened tool life. For example, in ultrasonic welding, if the thickness or hardness of the welding material changes, traditional systems struggle to adjust vibration parameters in real time, potentially resulting in incomplete welds or over-weld defects. In precision machining, even small changes in the end-effector load can significantly affect machining accuracy, failing to meet the demands of high-precision manufacturing. This dependence on a single working condition severely limits the adaptability and reliability of ultrasonic vibration systems in complex and variable working environments.

[0004] Therefore, there is an urgent need for an adaptive control system that can dynamically adjust its own parameters according to changes in external operating conditions, thereby solving the above problems. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the fact that the ultrasonic vibration system in the prior art is mainly used for a single working condition and cannot be applied to complex and variable working scenarios.

[0006] To solve the above-mentioned technical problems, the present invention provides a method for adaptively controlling a piezoelectric oscillator system, comprising:

[0007] Step S1: Measure the result parameter Y1 of the ultrasonic vibration system at the current working resonant frequency, wherein the ultrasonic vibration system includes a piezoelectric vibrator system and a load, and the piezoelectric vibrator system is used to control the vibration of the load;

[0008] Step S2: Obtain a set of piezoelectric ceramic constitutive model parameters under independent states, input the piezoelectric ceramic constitutive model parameters under independent states into the simulation model, and simulate the ultrasonic vibration system at the current working resonant frequency to obtain the result parameter Y2;

[0009] Step S3: Select variables A, B, and C from the piezoelectric ceramic constitutive model parameters that have the greatest impact on the result parameter Y2, and determine the value range of variables A, B, and C. The purpose of selecting variables A, B, and C is to make the result parameter Y2 closer to Y1.

[0010] Step S4: Generate several experimental groups related to variables A, B, and C based on variables A, B, and C and their value ranges. Input the values ​​of variables A, B, and C in each experimental group into the simulation model and simulate the ultrasonic vibration system at the current working resonant frequency to obtain the result parameter Y3 consistent with the number of experimental groups.

[0011] Step S5: Input all result parameters Y3 into the BBD response curve research software to obtain the software output results. Based on the software output results, construct a second-order function relationship about variables A, B, and C, and construct a fitted second-order function relationship based on the second-order function relationship.

[0012] Step S6: Substitute the result parameter Y1 into the fitted second-order function relationship to obtain multiple sets of experimental variables. Substitute the multiple sets of experimental variables into the simulation model to obtain the corresponding result parameter Y4. Compare the result parameters Y1 and Y4.

[0013] Step S7: Based on the comparison results of result parameters Y1 and Y4, take the data of variables A, B, and C of the experimental group whose result parameters Y1 and Y4 are closest as the parameters of the piezoelectric ceramic constitutive model at the current working resonant frequency;

[0014] Step S8: Control the piezoelectric oscillator system to drive the load vibration by using the piezoelectric ceramic constitutive model parameters at the current operating resonant frequency.

[0015] In one embodiment of the present invention, step S1 involves measuring the result parameter Y1 of the ultrasonic vibration system at the current operating resonant frequency, wherein the result parameter Y1 includes: the series resonant frequency f. s Parallel resonant frequency f p Series resonant impedance Z s Parallel resonant impedance Z P .

[0016] In one embodiment of the present invention, the method for selecting variables A, B, and C, which have the greatest impact on the result parameter Y2 among the parameters of the piezoelectric ceramic constitutive model, in step S3 includes:

[0017] Among the parameters of the piezoelectric ceramic constitutive model, variables A, B, and C that have the greatest impact on the result parameter Y2 are selected, where A represents the elastic stiffness coefficient c in the polarization direction. 33 B represents the piezoelectric stress coupling coefficient in the polarization direction. 33C represents the damping ratio tanδ.

[0018] In one embodiment of the present invention, step S4 generates several experimental groups related to variables A, B, and C based on the variables A, B, and C and their value ranges. The experimental groups include 15 experimental groups, namely:

[0019] Experimental group 1: a1, b1, c2;

[0020] Experimental group 2: a1, b3, c2;

[0021] Experimental group 3: a3, b1, c2;

[0022] Experimental group 4: a3, b3, c2;

[0023] Experimental group 5: a1, b2, c1;

[0024] Experimental group 6: a1, b2, c3;

[0025] Experimental group 7: a3, b2, c1;

[0026] Experimental group 8: a3, b2, c3;

[0027] Experimental group 9: a2, b1, c1;

[0028] Experimental group 10: a2, b1, c3;

[0029] Experimental group 11: a2, b3, c1;

[0030] Experimental group 12: a2, b3, c3;

[0031] Experimental group 13: a2, b2, c2;

[0032] Experimental group 14: a2, b2, c2;

[0033] Experimental group 15: a2, b2, c2;

[0034] Where a1, a2, and a3 are different values ​​of variable A, b1, b2, and b3 are different values ​​of variable B, and c1, c2, and c3 are different values ​​of variable C, satisfying a1 <a2<a3,b1<b2<b3,c1<c2<c3。

[0035] In one embodiment of the present invention, the second-order functional relationship between variables A, B, and C constructed based on the software output in step S5 is expressed as follows:

[0036] (1);

[0037] in, The output is a second-order function relation. For constant terms, for The linear effect coefficient, for The secondary effect coefficient, for The interaction effect coefficient, In order to be in The variables A, B, and C are respectively 1, 2, and 3; In order to be in When the values ​​are 2 and 3, the corresponding variables are B and C; For error;

[0038] The second-order function relationship corresponding to formula (1) can be written in matrix form as follows:

[0039] (2);

[0040] in, For the output matrix, The corresponding content includes .

[0041] In one embodiment of the present invention, step S5, the method for constructing a fitted second-order function relationship based on the second-order function relationship, includes:

[0042] The predicted coefficients are obtained by calculating formula (2) using the least squares method. , represented as:

[0043] (3);

[0044] For formula (3) Taking the derivative and setting the reciprocal to 0, we obtain the normal equation:

[0045] (4);

[0046] Applying the least squares estimate to equation (4), we obtain:

[0047] (5);

[0048] Based on the obtained formula (5), formula (2) can be written as the fitting second-order function relationship. , represented as:

[0049] (6).

[0050] To solve the above-mentioned technical problems, the present invention provides a device for adaptively controlling a piezoelectric oscillator system, comprising:

[0051] Measurement module: used to measure the result parameter Y1 of the ultrasonic vibration system at the current working resonant frequency, wherein the ultrasonic vibration system includes a piezoelectric vibrator system and a load, and the piezoelectric vibrator system is used to control the vibration of the load;

[0052] The first simulation parameter acquisition module is used to acquire a set of piezoelectric ceramic constitutive model parameters under independent states, input the piezoelectric ceramic constitutive model parameters under independent states into the simulation model, and simulate the ultrasonic vibration system at the current working resonant frequency to obtain the result parameter Y2.

[0053] Selection module: Used to select the variables A, B, and C that have the greatest impact on the result parameter Y2 among the parameters of the piezoelectric ceramic constitutive model, and to determine the value range of the variables A, B, and C. The purpose of selecting the variables A, B, and C is to make the result parameter Y2 closer to Y1.

[0054] The second simulation parameter acquisition module is used to generate several experimental groups related to variables A, B, and C based on the variables A, B, and C and their value ranges, input the values ​​of variables A, B, and C in each experimental group into the simulation model, and simulate the ultrasonic vibration system at the current working resonant frequency to obtain the result parameter Y3 consistent with the number of experimental groups.

[0055] The construction module is used to input all result parameters Y3 into the BBD response curve research software, obtain the software output results, construct a second-order function relationship about variables A, B, and C based on the software output results, and construct a fitted second-order function relationship based on the second-order function relationship.

[0056] The third simulation parameter acquisition module is used to substitute the result parameter Y1 into the fitted second-order function relationship to obtain multiple sets of experimental variable groups, substitute the multiple sets of experimental variable groups into the simulation model to obtain the corresponding result parameter Y4, and compare the result parameter Y1 with Y4.

[0057] Comparison module: Based on the comparison results of result parameters Y1 and Y4, the variable A, B, and C data of the experimental group whose result parameters Y1 and Y4 are closest are used as the parameters of the piezoelectric ceramic constitutive model at the current working resonant frequency;

[0058] Control module: Used to control the piezoelectric oscillator system to drive the load vibration by using the piezoelectric ceramic constitutive model parameters at the current operating resonant frequency.

[0059] To address the aforementioned technical problems, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for adaptively controlling a piezoelectric oscillator system as described above.

[0060] To address the aforementioned technical problems, the present invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method for adaptively controlling a piezoelectric resonator system as described above.

[0061] To address the aforementioned technical problems, the present invention provides a computer program product, comprising a computer program that, when executed by a processor, implements the steps of the method for adaptively controlling a piezoelectric oscillator system as described above.

[0062] Compared with the prior art, the above-described technical solution of the present invention has the following advantages:

[0063] The adaptive control method for piezoelectric oscillator systems described in this invention can effectively improve the performance prediction accuracy and operating condition adaptability of ultrasonic vibration systems (controlled piezoelectric oscillator systems). It can deduce the most suitable set of piezoelectric ceramic constitutive model parameters under the current operating conditions online and in real time. This process avoids destructive offline material testing and realizes the adaptive control of load vibration of the controlled piezoelectric oscillator system according to the current operating conditions. Attached Figure Description

[0064] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0065] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0066] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0067] Example 1

[0068] Reference Figure 1 As shown, to improve the performance prediction accuracy and operating condition adaptability of an ultrasonic vibration system (controlled piezoelectric vibrator system), this invention relates to a method for adaptively controlling a piezoelectric vibrator system, comprising:

[0069] Step S1: Measure the result parameter Y1 of the ultrasonic vibration system at the current working resonant frequency, wherein the ultrasonic vibration system includes a piezoelectric vibrator system and a load, and the piezoelectric vibrator system is used to control the vibration of the load;

[0070] Step S2: Obtain a set of piezoelectric ceramic constitutive model parameters under independent states, input the piezoelectric ceramic constitutive model parameters under independent states into the simulation model, and simulate the ultrasonic vibration system at the current working resonant frequency to obtain the result parameter Y2;

[0071] Step S3: Select variables A, B, and C from the piezoelectric ceramic constitutive model parameters that have the greatest impact on the result parameter Y2, and determine the value range of variables A, B, and C. The purpose of selecting variables A, B, and C is to make the result parameter Y2 closer to Y1.

[0072] Step S4: Generate several experimental groups related to variables A, B, and C based on variables A, B, and C and their value ranges. Input the values ​​of variables A, B, and C in each experimental group into the simulation model and simulate the ultrasonic vibration system at the current working resonant frequency to obtain the result parameter Y3 consistent with the number of experimental groups.

[0073] Step S5: Input all result parameters Y3 into the BBD response curve research software to obtain the software output results. Based on the software output results, construct a second-order function relationship about variables A, B, and C, and construct a fitted second-order function relationship based on the second-order function relationship.

[0074] Step S6: Substitute the result parameter Y1 into the fitted second-order function relationship to obtain multiple sets of experimental variables. Substitute the multiple sets of experimental variables into the simulation model to obtain the corresponding result parameter Y4. Compare the result parameters Y1 and Y4.

[0075] Step S7: Based on the comparison results of result parameters Y1 and Y4, take the data of variables A, B, and C of the experimental group whose result parameters Y1 and Y4 are closest as the parameters of the piezoelectric ceramic constitutive model at the current working resonant frequency;

[0076] Step S8: Control the piezoelectric oscillator system to drive the load vibration by using the piezoelectric ceramic constitutive model parameters at the current operating resonant frequency.

[0077] This embodiment can deduce the most suitable set of piezoelectric ceramic constitutive model parameters online and in real time under the current working conditions. This process avoids destructive offline material testing and realizes the control of the piezoelectric oscillator system to adaptively control the load vibration according to the current working conditions.

[0078] The following is a detailed description of this embodiment:

[0079] The working conditions in this embodiment include, but are not limited to: 1. Mechanical load (such as installing different models of amplitude transformers and tool heads); 2. Object characteristics (such as contact with biological tissues or metal workpieces of different densities and hardness); 3. Driving conditions (such as different driving voltage amplitudes and frequencies); 4. Environmental conditions (such as temperature changes) will correspond to an actual effective and equivalent set of piezoelectric ceramic constitutive model parameters.

[0080] Step S1: Measure the result parameter Y1 at the working resonant frequency of the ultrasonic vibration system. This mainly consists of four parameters: f s -Series resonant frequency, fp - Parallel resonance frequency, Z s - Series resonance impedance, Z P - Parallel resonance impedance.

[0081] Step S2: Initially set the constitutive model parameters of the piezoelectric ceramic to the values in its free state (independent state), which are generally provided by the piezoelectric ceramic manufacturer (i.e., the constitutive model parameters of the piezoelectric ceramic are the parameters available at the factory). Input all the constitutive model parameters of the piezoelectric ceramic in the independent state into the simulation model (i.e., a set of mathematical models used to describe the coupling relationship between its mechanical behavior (such as stress, strain, etc.) and electrical behavior (such as electric field, electric displacement, etc.), that is, the piezoelectric ceramic constitutive model), and simulate the result parameter Y2 obtained by the ultrasonic vibration system at the current working resonance frequency. Generally, there is a large difference between Y2 (simulated value) and Y1 (measured value).

[0082] Step S3: Select 3 variables A, B, and C that have the greatest impact on the result parameter Y2 from the constitutive model parameters of the piezoelectric ceramic, and determine the value ranges of the variables A, B, and C. In this embodiment, the purpose of selecting variables A, B, and C is to make the result parameter Y2 closer to Y1. In this embodiment, 3 variables are selected from the constitutive model parameters of the piezoelectric ceramic: c 33 - Elastic stiffness coefficient in the polarization direction (corresponding to variable A), e 33 - Piezoelectric stress coupling coefficient in the polarization direction (corresponding to variable B), tanδ - damping ratio (corresponding to variable C). Research shows that these 3 variables have the greatest impact on the result parameter Y2.

[0083] Step S4: Input the 3 variables A, B, and C and their value ranges in Step S3 into the BBD response curve research software. The input variables A, B, and C are data of three factors and three levels, that is, a1, a2, a3 are different values of variable A, b1, b2, b3 are different values of variable B, c1, c2, c3 are different values of variable C, and a1 < a2 < a3, b1 < b2 < b3, c1 < c2 < c3 are satisfied. The software can automatically generate 15 experimental groups. Input the corresponding assigned values of A, B, and C in each experimental group into the simulation model to obtain the values of the corresponding 4 result parameters Y3. It should be noted that each experimental group (3 variables A, B, and C) corresponds to a simulation result, and finally there are 15 Y3s in total. The purpose of this embodiment is to use the values obtained by simulation to find the value closest to Y1.

[0084] The following are the specific 15 experimental groups:

[0085] Experimental group 1: a1, b1, c2;

[0086] Experimental group 2: a1, b3, c2;

[0087] Experimental group 3: a3, b1, c2;

[0088] Experimental group 4: a3, b3, c2;

[0089] Experimental group 5: a1, b2, c1;

[0090] Experimental group 6: a1, b2, c3;

[0091] Experimental group 7: a3, b2, c1;

[0092] Experimental group 8: a3, b2, c3;

[0093] Experimental group 9: a2, b1, c1;

[0094] Experimental group 10: a2, b1, c3;

[0095] Experimental group 11: a2, b3, c1;

[0096] Experimental group 12: a2, b3, c3;

[0097] Experimental group 13: a2, b2, c2;

[0098] Experimental group 14: a2, b2, c2;

[0099] Experimental group 15: a2, b2, c2.

[0100] It should be noted that the purpose of the above experimental groups 13, 14, and 15 is the same: to measure random error, test the accuracy of the model, and reveal the second-order effect of the response surface, so as to provide a reliable basis for BBD statistical inference.

[0101] Step S5: Input 15 Y3 values ​​into the BBD response curve analysis software and obtain the software output results. Based on software output results Construct a second-order functional relationship between variables A, B, and C, expressed as:

[0102] (1);

[0103] in, The output is a second-order function relation. For constant terms, for The linear effect coefficient, for The secondary effect coefficient, for The interaction effect coefficient, In order to be in The variables A, B, and C are respectively 1, 2, and 3; In order to be in When the values ​​are 2 and 3, the corresponding variables are B and C; This is the error.

[0104] The second-order function relationship corresponding to formula (1) can be written in matrix form as follows:

[0105] (2);

[0106] in, The corresponding content includes ( It is a 15×10 matrix, with 15 rows representing 15 experimental groups and 10 columns representing 1. , , , , , , , , Taking experimental group 1 as an example, the values ​​of the 10 columns are as follows: ).

[0107] Step S5 is based on the second-order function relation Methods for constructing fitted second-order function relationships include:

[0108] The predicted coefficients are obtained by calculating formula (2) using the least squares method. , represented as:

[0109] (3);

[0110] For formula (3) Taking the derivative and setting the reciprocal to 0, we obtain the normal equation:

[0111] (4);

[0112] Applying the least squares estimate to equation (4), we obtain:

[0113] (5);

[0114] Based on the obtained formula (5), formula (2) can be written as the fitting second-order function relationship. , represented as:

[0115] (6).

[0116] This embodiment can also determine each item using F-value and P-value (i.e., The corresponding content includes The influence and confidence level of the F-value on the model: The F-value represents the degree of influence of the corresponding term on the simulated target quantity (i.e., the degree of influence of each X on Y). When the F-value of the term is large, it indicates that the influence on the model is large. When the F-value is greater than the critical value (8.812), it indicates that the model is significant at the current level (0.05). The P-value represents the confidence level of the influence of the independent variable parameter noise on the F-value (i.e., the confidence level of X causing F). The smaller the P-value, the higher the confidence level of the influence value of the parameter. A P-value less than 0.01 indicates a high confidence level. When the P-value is greater than 0.05, it indicates that the influence of the parameter F-value is not meaningful.

[0117] Step S6: Substitute the resulting parameter Y1 into the second-order function relationship estimate. In the middle, we obtain For each experimental group consisting of multiple variables A, B, and C, the corresponding result parameter Y4 is obtained by substituting each experimental group into the simulation model, and the result parameters Y1 and Y4 are compared.

[0118] Step S7: Based on the comparison results of parameters Y1 and Y4, select the experimental group in Y4 that is closest to Y1. Within this experimental group, the three specific variables A (corresponding to c) 33 B (corresponding to e) 33 C (corresponding to tanδ) is selected as the parameters of the piezoelectric ceramic constitutive model under the current operating conditions.

[0119] Example 2

[0120] This embodiment provides a device for adaptively controlling a piezoelectric oscillator system, comprising:

[0121] Measurement module: used to measure the result parameter Y1 of the ultrasonic vibration system at the current working resonant frequency, wherein the ultrasonic vibration system includes a piezoelectric vibrator system and a load, and the piezoelectric vibrator system is used to control the vibration of the load;

[0122] The first simulation parameter acquisition module is used to acquire a set of piezoelectric ceramic constitutive model parameters under independent states, input the piezoelectric ceramic constitutive model parameters under independent states into the simulation model, and simulate the ultrasonic vibration system at the current working resonant frequency to obtain the result parameter Y2.

[0123] Selection module: Used to select the variables A, B, and C that have the greatest impact on the result parameter Y2 among the parameters of the piezoelectric ceramic constitutive model, and to determine the value range of the variables A, B, and C. The purpose of selecting the variables A, B, and C is to make the result parameter Y2 closer to Y1.

[0124] The second simulation parameter acquisition module is used to generate several experimental groups related to variables A, B, and C based on the variables A, B, and C and their value ranges, input the values ​​of variables A, B, and C in each experimental group into the simulation model, and simulate the ultrasonic vibration system at the current working resonant frequency to obtain the result parameter Y3 consistent with the number of experimental groups.

[0125] The construction module is used to input all result parameters Y3 into the BBD response curve research software, obtain the software output results, construct a second-order function relationship about variables A, B, and C based on the software output results, and construct a fitted second-order function relationship based on the second-order function relationship.

[0126] The third simulation parameter acquisition module is used to substitute the result parameter Y1 into the fitted second-order function relationship to obtain multiple sets of experimental variable groups, substitute the multiple sets of experimental variable groups into the simulation model to obtain the corresponding result parameter Y4, and compare the result parameter Y1 with Y4.

[0127] Comparison module: Based on the comparison results of result parameters Y1 and Y4, the variable A, B, and C data of the experimental group whose result parameters Y1 and Y4 are closest are used as the parameters of the piezoelectric ceramic constitutive model at the current working resonant frequency;

[0128] Control module: Used to control the piezoelectric oscillator system to drive the load vibration by using the piezoelectric ceramic constitutive model parameters at the current operating resonant frequency.

[0129] Example 3

[0130] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for adaptive control of the piezoelectric resonator system described in Embodiment 1.

[0131] Example 4

[0132] This embodiment provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the steps of the method for adaptive control of the piezoelectric oscillator system described in Embodiment 1.

[0133] Example 5

[0134] This embodiment provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method for adaptive control of the piezoelectric oscillator system described in Embodiment 1.

[0135] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0136] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0137] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0138] These computer program instructions 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 of the function specified in one or more boxes.

[0139] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for adaptively controlling a piezoelectric oscillator system, characterized in that, include: Step S1: Measure the result parameter Y1 of the ultrasonic vibration system at the current working resonant frequency, wherein the ultrasonic vibration system includes a piezoelectric vibrator system and a load, and the piezoelectric vibrator system is used to control the vibration of the load; Step S2: Obtain a set of piezoelectric ceramic constitutive model parameters under independent states, input the piezoelectric ceramic constitutive model parameters under independent states into the simulation model, and simulate the ultrasonic vibration system at the current working resonant frequency to obtain the result parameter Y2; Step S3: Select variables A, B, and C from the piezoelectric ceramic constitutive model parameters that have the greatest impact on the result parameter Y2, and determine the value range of variables A, B, and C. The purpose of selecting variables A, B, and C is to make the result parameter Y2 closer to Y1. Step S4: Generate several experimental groups related to variables A, B, and C based on variables A, B, and C and their value ranges. Input the values ​​of variables A, B, and C in each experimental group into the simulation model and simulate the ultrasonic vibration system at the current working resonant frequency to obtain the result parameter Y3 consistent with the number of experimental groups. Step S5: Input all result parameters Y3 into the BBD response curve research software to obtain the software output results. Based on the software output results, construct a second-order function relationship about variables A, B, and C, and construct a fitted second-order function relationship based on the second-order function relationship. Step S6: Substitute the resulting parameter Y1 into the fitted second-order function relationship. In the middle, we obtain For each experimental group consisting of multiple A, B, and C variables, substitute each experimental group into the simulation model to obtain the corresponding result parameter Y4, and compare the result parameters Y1 and Y4. Step S7: Based on the comparison results of result parameters Y1 and Y4, take the data of variables A, B, and C of the experimental group whose result parameters Y1 and Y4 are closest as the parameters of the piezoelectric ceramic constitutive model at the current working resonant frequency; Step S8: Control the piezoelectric oscillator system to drive the load vibration by using the piezoelectric ceramic constitutive model parameters at the current operating resonant frequency.

2. The method for adaptive control of a piezoelectric oscillator system according to claim 1, characterized in that: The result parameter Y1 of the ultrasonic vibration system measured in step S1 at the current operating resonant frequency includes: the series resonant frequency f. s Parallel resonant frequency f p Series resonant impedance Z s Parallel resonant impedance Z P .

3. The method for adaptive control of a piezoelectric oscillator system according to claim 1, characterized in that: The method for selecting the variables A, B, and C that have the greatest impact on the result parameter Y2 from the piezoelectric ceramic constitutive model parameters in step S3 includes: Among the parameters of the piezoelectric ceramic constitutive model, variables A, B, and C that have the greatest impact on the result parameter Y2 are selected, where A represents the elastic stiffness coefficient c in the polarization direction. 33 B represents the piezoelectric stress coupling coefficient in the polarization direction. 33 C represents the damping ratio tanδ.

4. The method for adaptive control of a piezoelectric oscillator system according to claim 1, characterized in that: Step S4 generates several experimental groups related to variables A, B, and C based on variables A, B, and C and their value ranges. These experimental groups include 15 groups, as follows: Experimental group 1: a1, b1, c2; Experimental group 2: a1, b3, c2; Experimental group 3: a3, b1, c2; Experimental group 4: a3, b3, c2; Experimental group 5: a1, b2, c1; Experimental group 6: a1, b2, c3; Experimental group 7: a3, b2, c1; Experimental group 8: a3, b2, c3; Experimental group 9: a2, b1, c1; Experimental group 10: a2, b1, c3; Experimental group 11: a2, b3, c1; Experimental group 12: a2, b3, c3; Experimental group 13: a2, b2, c2; Experimental group 14: a2, b2, c2; Experimental group 15: a2, b2, c2; Where a1, a2, and a3 are different values ​​of variable A, b1, b2, and b3 are different values ​​of variable B, and c1, c2, and c3 are different values ​​of variable C, and satisfy a1 <a2<a3,b1<b2<b3,c1<c2<c3。 5. The method for adaptively controlling a piezoelectric oscillator system according to claim 1, characterized in that: In step S5, a second-order functional relationship is constructed for variables A, B, and C based on the software output, expressed as: （1）； in, The output is a second-order function relation. For constant terms, for The linear effect coefficient, for The secondary effect coefficient, for The interaction effect coefficient, In order to be in The variables A, B, and C are respectively 1, 2, and 3; In order to be in The corresponding variables are B and C when they are 2 and 3 respectively; For error; The second-order function relationship corresponding to formula (1) can be written in matrix form as follows: （2）； in, For the output matrix, The corresponding content includes .

6. The method for adaptive control of a piezoelectric oscillator system according to claim 5, characterized in that: The method for constructing a fitted second-order function relationship based on the second-order function relationship in step S5 includes: The predicted coefficients are obtained by calculating formula (2) using the least squares method. , is represented as: （3）； For formula (3) Taking the derivative and setting the reciprocal to 0, we obtain the normal equation: （4）； Applying the least squares estimate to equation (4), we obtain: （5）； Based on the obtained formula (5), formula (2) can be written as the fitting second-order function relationship. , is represented as: （6）。 7. A device for adaptively controlling a piezoelectric oscillator system, characterized in that, include: Measurement module: used to measure the result parameter Y1 of the ultrasonic vibration system at the current working resonant frequency, wherein the ultrasonic vibration system includes a piezoelectric vibrator system and a load, and the piezoelectric vibrator system is used to control the vibration of the load; The first simulation parameter acquisition module is used to acquire a set of piezoelectric ceramic constitutive model parameters under independent states, input the piezoelectric ceramic constitutive model parameters under independent states into the simulation model, and simulate the ultrasonic vibration system at the current working resonant frequency to obtain the result parameter Y2. Selection module: Used to select the variables A, B, and C that have the greatest impact on the result parameter Y2 among the parameters of the piezoelectric ceramic constitutive model, and to determine the value range of the variables A, B, and C. The purpose of selecting the variables A, B, and C is to make the result parameter Y2 closer to Y1. The second simulation parameter acquisition module is used to generate several experimental groups related to variables A, B, and C based on the variables A, B, and C and their value ranges, input the values ​​of variables A, B, and C in each experimental group into the simulation model, and simulate the ultrasonic vibration system at the current working resonant frequency to obtain the result parameter Y3 consistent with the number of experimental groups. The construction module is used to input all result parameters Y3 into the BBD response curve research software, obtain the software output results, construct a second-order function relationship about variables A, B, and C based on the software output results, and construct a fitted second-order function relationship based on the second-order function relationship. The third simulation parameter acquisition module is used to substitute the result parameter Y1 into the fitted second-order function relationship to obtain multiple sets of experimental variable groups, substitute the multiple sets of experimental variable groups into the simulation model to obtain the corresponding result parameter Y4, and compare the result parameter Y1 with Y4. Comparison module: Based on the comparison results of result parameters Y1 and Y4, the variable A, B, and C data of the experimental group whose result parameters Y1 and Y4 are closest are used as the parameters of the piezoelectric ceramic constitutive model at the current working resonant frequency; Control module: Used to control the piezoelectric oscillator system to drive the load vibration by using the piezoelectric ceramic constitutive model parameters at the current operating resonant frequency.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the steps of the method for adaptively controlling a piezoelectric vibrator system as described in any one of claims 1 to 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 for adaptively controlling a piezoelectric resonator system as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method for adaptively controlling a piezoelectric vibrator system as described in any one of claims 1 to 6.

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