Vibration intelligent control parameter optimization method and system for piezoelectric laminated beam

Abstract

The invention provides a vibration intelligent control parameter optimization method and system for a piezoelectric laminated beam, relates to the technical field of piezoelectric control, and aims to optimize the array layout of piezoelectric devices by using a multi-target genetic algorithm so as to improve the vibration suppression effect and the energy collection efficiency. And a self-adaptive PID closed-loop controller is adopted to dynamically adjust system parameters. Running state data, including stress, electric field intensity and the like, of the piezoelectric laminated beam are collected, correlation analysis is conducted through a Bouc-Wen model, output force is predicted in real time, and a control signal is generated to adjust a piezoelectric actuator. According to the method, the response speed and the control precision of the system are improved, the problems of fixed layout and inflexible response in the prior art are solved, and intelligent control of the piezoelectric laminated beam system is realized.

Description

Technical Field

[0001] This invention relates to the field of piezoelectric control technology, specifically to a method and system for optimizing the intelligent vibration control parameters of a piezoelectric laminated beam. Background Technology

[0002] Nowadays, with the rapid development of high-tech such as optical communication technology and aerospace technology, the requirements for the precision and stability of mechanical systems are becoming increasingly stringent. The control of micro-vibrations has become one of the important bottleneck technologies restricting the accuracy and stability of systems. Traditional vibration control methods mainly use vibration isolation devices, damping dampers, or filtering modules to suppress and control vibration signals. However, this method has high requirements for vibration velocity and installation space volume, and is only suitable for vibration signals with large amplitude and concentrated frequency band. Precision mechanical systems are difficult to install these devices due to limited space. Moreover, micro-vibrations have the characteristics of small amplitude, wide frequency domain and multiple degrees of freedom. Traditional vibration detection technology and control methods are difficult to meet the high precision and high stability performance requirements of precision mechanical systems. Therefore, there is an urgent need for a vibration control method that can meet higher precision and variable working conditions.

[0003] In the prior art, CN114741934 discloses a method and system for optimizing the vibration intelligent control parameters of a piezoelectric layer composite beam. The method involves uniformly pasting piezoelectric ceramic sheets onto the upper and lower surfaces of the piezoelectric layer composite beam plate, analyzing the element mass matrix and stiffness matrix of the composite beam plate using the finite element method, establishing an electromechanical coupling model of the composite beam plate, analyzing the dynamic characteristics of the piezoelectric layer composite beam plate based on the electromechanical coupling model, optimizing the piezoelectric actuator using an improved particle swarm optimization algorithm to control the corresponding vibration parameters of the composite beam plate, and establishing a hysteresis model based on the optimized parameters of the piezoelectric actuator using a hysteresis compensation algorithm to optimize the vibration parameters of the piezoelectric actuator.

[0004] The main problems with the above scheme are: when establishing the electromechanical coupling model, the nonlinear properties of the material are easily ignored, which affects the accuracy of the model and makes it impossible for the model to accurately capture the dynamic characteristics of the system. Especially in the high-frequency range and complex vibration environment, the particle swarm algorithm is prone to getting stuck in local optima and the convergence accuracy decreases when optimizing vibration parameters. When establishing the hysteresis model, the error of the hysteresis model is not sufficiently corrected, resulting in inaccurate model prediction.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for optimizing the intelligent vibration control parameters of piezoelectric laminated beams, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for optimizing vibration intelligent control parameters of a piezoelectric laminated beam, comprising the following steps:

[0009] Step 1: Randomly generate a layout scheme for piezoelectric devices, with the optimization objectives of maximizing vibration suppression and energy harvesting efficiency. Optimize the array layout of the piezoelectric devices using a multi-objective genetic algorithm.

[0010] Step 2: Based on the piezoelectric device array layout determined by the genetic algorithm, obtain the geometric and material properties of the piezoelectric laminate beam composed of piezoelectric devices, manufacture piezoelectric devices based on the geometric and material properties, and establish an adaptive PID closed-loop controller.

[0011] Step 3: Collect the operating status data of the piezoelectric laminated beam system in the piezoelectric device. The operating status data includes the stress magnitude, electric field strength, electric displacement and output force of the piezoelectric laminated beam system. During the operation of the piezoelectric laminated beam system, the output force is used as the evaluation target to optimize the parameters of the adaptive PID closed-loop controller.

[0012] Step 4: Perform correlation analysis on the operating status data of the piezoelectric laminated beam system, and establish a Bouc-Wen model with stress magnitude and electric field strength as inputs and output force as output.

[0013] Step 5: Input the stress magnitude and electric field strength of the piezoelectric laminated beam system at the current moment into the Bouc-Wen model to obtain the output force at the current moment, and input the output force into the adaptive PID closed-loop controller to obtain the control signal. Based on the control signal, control and adjust the piezoelectric actuator of the piezoelectric device.

[0014] Furthermore, the principle underlying the optimization of the piezoelectric device array layout using a multi-objective genetic algorithm is as follows:

[0015] Each piezoelectric device layout is considered as an individual, and a fitness function is generated for each individual. The formula for generating the fitness function is as follows:

[0016] F(a) = w1·f1(a) + w2·f2(a)

[0017] Where F(a) represents the fitness function of the a-th individual, a represents the index of the individual, f1(a) represents the vibration suppression effect of the a-th individual, f2(a) represents the energy harvesting efficiency of the a-th individual, w1 and w2 represent the weights of vibration suppression effect and energy harvesting efficiency, respectively, w1+w2=1, and w1>w2;

[0018] Individuals are selected using a roulette wheel algorithm and arranged in descending order of fitness. Adjacent pairs of individuals form parents, and genes are exchanged between parents to generate offspring. Genes are randomly selected from the offspring to be modified, and the fitness function of the individuals is recalculated. This genetic operation is repeated until a preset number of iterations is reached, and the layout scheme with the highest fitness is selected as the array layout of the piezoelectric devices.

[0019] The gene represents the control parameters that need to be optimized.

[0020] Furthermore, the geometric properties include the length, width, and thickness of the piezoelectric laminate beam, and the material properties include the Young's modulus, density, piezoelectric constant, and dielectric constant of the piezoelectric material.

[0021] Furthermore, the expression for the Bouc-Wen model is as follows:

[0022] D(T)=δ1·d·σ(t)+δ2·ε·E(t)

[0023] G(t) = α·D(t) + β·z(t)

[0024]

[0025] Where t is the time variable, D(t) represents the electric displacement at time t, d represents the piezoelectric constant of the piezoelectric material, σ(t) represents the stress magnitude at time t, ε represents the dielectric constant of the piezoelectric material, E(t) represents the electric field strength at time t, and δ1 and δ2 are the first weighting coefficient and the second weighting coefficient, respectively.

[0026] Where G(t) represents the output force of the piezoelectric laminated beam system at time t, z(t) represents the nonlinear hysteresis variable inside the system at time t, α represents the linear coefficient, and β represents the nonlinear coefficient. Let A represent the time derivative of the electric displacement at time t, which is the velocity at time t. Let B represent the velocity-related proportionality coefficient, B represent the interaction coefficient between velocity and internal variables, and C represent the degree of nonlinearity of the hysteresis response.

[0027] Among them, δ1, δ2, α, β, A, B, and C are all parameters to be optimized in the Bouc-Wen model.

[0028] Furthermore, the process of optimizing the parameters of the adaptive PID closed-loop controller is as follows:

[0029] e(t) = r(t) - G(t)

[0030]

[0031] e(t+1) = r(t+1) - G(t+1)

[0032]

[0033] Where e(t) represents the output force error at time t, and r(t) represents the expected output force at time t;

[0034] Where u(t) represents the control signal sent by the adaptive PID closed-loop controller to the piezoelectric actuator at time t, and K p (t) represents the proportional gain at time t, K i (t) represents the integral gain at time t, K d (t) represents the differential gain at time t;

[0035] Where r(t+1) represents the expected output force at time t+1, G(t+1) represents the output force generated by the piezoelectric laminated beam system under the influence of the control signal u(t), and e(t) represents the output force error at time t+1.

[0036] Among them, K p (t+1) represents the optimized proportional gain at time t+1, and K i (t+1) represents the optimized integral gain at time t+1, K d (t+1) represents the differential gain at time t+1 after optimization, μ1 represents the weight of the output force error, μ2 represents the weight of the rate of change of the output force error, and μ1+μ2=1 and μ1>μ2;

[0037] Wherein, ε1, ε2, and ε3 are the first optimization weight, the second optimization weight, and the third optimization weight, respectively, and ε1 > ε2 > ε3.

[0038] This invention also provides a vibration intelligent control parameter optimization system for piezoelectric laminated beams. The system is used to implement the aforementioned vibration intelligent control parameter optimization method for piezoelectric laminated beams, specifically including:

[0039] The array optimization module is used to randomly generate layout schemes for piezoelectric devices, with the optimization objectives of maximizing vibration suppression and energy harvesting efficiency. The array layout of the piezoelectric devices is optimized through a multi-objective genetic algorithm.

[0040] The PID control module is used to obtain the geometric and material properties of the piezoelectric laminate beam composed of piezoelectric devices based on the piezoelectric device array layout determined by the genetic algorithm, manufacture piezoelectric devices based on the geometric and material properties, and establish an adaptive PID closed-loop controller.

[0041] The parameter optimization module is used to collect the operating status data of the piezoelectric laminated beam system in the piezoelectric device. The operating status data includes the stress magnitude, electric field strength, electric displacement and output force of the piezoelectric laminated beam system. During the operation of the piezoelectric laminated beam system, the output force is used as the evaluation target to optimize the parameters of the adaptive PID closed-loop controller.

[0042] The model building module is used to perform correlation analysis on the operating status data of the piezoelectric laminated beam system and establish a Bouc-Wen model with stress magnitude and electric field strength as inputs and output force as output.

[0043] The output control module is used to input the stress magnitude and electric field strength of the piezoelectric laminated beam system at the current moment into the Bouc-Wen model to obtain the output force at the current moment, and input the output force into the adaptive PID closed-loop controller to obtain the control signal, and control and adjust the piezoelectric actuator of the piezoelectric device based on the control signal.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] This invention first optimizes the layout of piezoelectric devices using a multi-objective genetic algorithm, while considering vibration suppression and energy harvesting efficiency. Before establishing the model, the layout is flexibly adjusted according to specific application requirements to improve the overall performance of the system and enable it to adapt to complex and variable vibration environments. Based on this array layout, an adaptive PID closed-loop controller is established. According to the real-time operating state of the piezoelectric laminated beam, the parameters of the adaptive PID closed-loop controller are optimized with output force as the evaluation target, so that the system can dynamically adjust according to the real-time state, improving control accuracy and response speed.

[0046] This invention also establishes a Bouc-Wen model to more effectively predict the system's output force, optimize the generation of control signals, thereby enhancing the overall response speed and stability of the solution, providing a more accurate input-output relationship, inputting the output force into the adaptive PID closed-loop controller to obtain control signals, adjusting the piezoelectric actuator, achieving precise and timely vibration suppression, and improving the overall control efficiency of the solution. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the method flow of an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of the system modules in an embodiment of the present 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 specific embodiments.

[0050] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0051] Example:

[0052] Please see Figure 1 The present invention provides a technical solution:

[0053] A method for optimizing vibration intelligent control parameters of a piezoelectric laminated beam, comprising the following steps:

[0054] Step 1: Randomly generate a layout scheme for piezoelectric devices, with the optimization objectives of maximizing vibration suppression and energy harvesting efficiency. Optimize the array layout of the piezoelectric devices using a multi-objective genetic algorithm.

[0055] In this embodiment, the principle underlying the optimization of the piezoelectric device array layout using a multi-objective genetic algorithm is as follows:

[0056] Each piezoelectric device layout is considered as an individual, and a fitness function is generated for each individual. The formula for generating the fitness function is as follows:

[0057] F(a) = w1·f1(a) + w2·f2(a)

[0058] Where F(a) represents the fitness function of the a-th individual, a represents the index of the individual, f1(a) represents the vibration suppression effect of the a-th individual, f2(a) represents the energy harvesting efficiency of the a-th individual, w1 and w2 represent the weights of vibration suppression effect and energy harvesting efficiency, respectively, w1+w2=1, and w1>w2;

[0059] The fitness function reflects the comprehensive performance of the piezoelectric device array layout in terms of both vibration suppression and energy harvesting efficiency. Vibration suppression and energy harvesting can often be complementary. Improving energy harvesting efficiency while suppressing vibration can better utilize materials and space and maximize resource utilization. In this scheme, vibration control is the main objective, so the weight of vibration suppression is higher than that of energy harvesting efficiency. Specifically, the values ​​are w1 = 0.65 and w2 = 0.35.

[0060] The vibration suppression effect can be determined by measuring the amplitude change of the piezoelectric device system before and after adopting the layout scheme. The initial state is the vibration amplitude of the piezoelectric device when no piezoelectric device layout is adopted. The vibration suppression effect is determined by calculating the percentage reduction in vibration amplitude after adopting the layout scheme relative to the initial state.

[0061] Energy harvesting efficiency represents the ratio of electrical energy successfully converted and harvested from vibration to the input vibration energy. In other words, it is the degree to which a piezoelectric device effectively converts mechanical vibration energy into electrical energy. It is obtained by the ratio of the output electrical energy to the input vibration energy of the piezoelectric device.

[0062] Individuals are selected using a roulette wheel algorithm and arranged in descending order of fitness. Adjacent pairs of individuals form parents, and genes are exchanged between parents to generate offspring. Genes are randomly selected from the offspring to be modified, and the fitness function of the individuals is recalculated. This genetic operation is repeated until a preset number of iterations is reached, and the layout scheme with the highest fitness is selected as the array layout of the piezoelectric devices.

[0063] The steps for selecting individuals with high fitness using the roulette wheel algorithm are as follows:

[0064] The total fitness of all individuals is calculated. For each individual, its fitness value is divided by the total fitness of all individuals to generate the probability of selection of the corresponding individual. A cumulative probability table is constructed based on the selection probability of each individual, and a random number between 0 and 1 is generated. The individual corresponding to the cumulative probability table is selected based on the random number. This can maintain the diversity of the population while selecting individuals with higher fitness.

[0065] The gene represents the control parameters that need to be optimized, which are the arrangement and layout of piezoelectric devices, including the geometric layout, electrode configuration, packaging, and electrical connections of the piezoelectric devices.

[0066] Step 2: Based on the piezoelectric device array layout determined by the genetic algorithm, obtain the geometric and material properties of the piezoelectric laminate beam composed of piezoelectric devices, manufacture piezoelectric devices based on the geometric and material properties, and establish an adaptive PID closed-loop controller.

[0067] In this embodiment, the geometric properties include the length, width, and thickness of the piezoelectric laminate beam, and the material properties include the Young's modulus, density, piezoelectric constant, and dielectric constant of the piezoelectric material.

[0068] Step 3: Collect the operating status data of the piezoelectric laminated beam system in the piezoelectric device. The operating status data includes the stress magnitude, electric field strength, electric displacement and output force of the piezoelectric laminated beam system. During the operation of the piezoelectric laminated beam system, the output force is used as the evaluation target to optimize the parameters of the adaptive PID closed-loop controller.

[0069] The process of optimizing the parameters of the adaptive PID closed-loop controller is as follows:

[0070] e(t) = r(t) - G(t)

[0071]

[0072] e(t+1) = r(t+1) - G(t+1)

[0073]

[0074] Where e(t) represents the output force error at time t, and r(t) represents the expected output force at time t;

[0075] Where u(t) represents the control signal sent by the adaptive PID closed-loop controller to the piezoelectric actuator at time t, and K p (t) represents the proportional gain at time t, K i (t) represents the integral gain at time t, K d (t) represents the differential gain at time t;

[0076] Where r(t+1) represents the expected output force at time t+1, G(t+1) represents the output force generated by the piezoelectric laminated beam system under the influence of the control signal u(t), and e(t) represents the output force error at time t+1.

[0077] Among them, K p (t+1) represents the optimized proportional gain at time t+1, and K i (t+1) represents the optimized integral gain at time t+1, K d (t+1) represents the differential gain at time t+1 after optimization, μ1 represents the weight of the output force error, μ2 represents the weight of the rate of change of the output force error, and μ1+μ2=1 and μ1>μ2;

[0078] Output force error directly reflects the deviation between the system output force and the expected output force. In the process of optimizing vibration control parameters, the accuracy requirement is high. Therefore, the weight of output force error is greater. In the optimization, a set of weight values ​​is μ1 = 0.6 and μ2 = 0.4.

[0079] Wherein, ε1, ε2, and ε3 are the first optimization weight, the second optimization weight, and the third optimization weight, respectively, and ε1 > ε2 > ε3.

[0080] In PID control, the proportional gain directly affects the response speed and steady-state error, so it needs to be adjusted first and has the highest optimization weight. The integral gain is used to eliminate steady-state error, but if it is too high, it may cause oscillation and instability. Therefore, its optimization weight is lower than that of the proportional gain. The derivative gain mainly reduces overshoot and oscillation by responding to the rate of change of error, thereby improving system stability. Therefore, its optimization weight is the lowest. Preferably, a set of optimization weights is: ε1 = 0.5, ε2 = 0.3, ε3 = 0.2.

[0081] Step 4: Perform correlation analysis on the operating status data of the piezoelectric laminated beam system, and establish a Bouc-Wen model with stress magnitude and electric field strength as inputs and output force as output.

[0082] In this embodiment, the expression of the Bouc-Wen model is as follows:

[0083] D(t)=δ1·d·σ(t)+δ2·ε·E(t)

[0084] G(t) = α·D(t) + β·z(t)

[0085]

[0086] Where t is the time variable, D(t) represents the electric displacement at time t, d represents the piezoelectric constant of the piezoelectric material, σ(t) represents the stress magnitude at time t, ε represents the dielectric constant of the piezoelectric material, E(t) represents the electric field strength at time t, and δ1 and δ2 are the first weighting coefficient and the second weighting coefficient, respectively.

[0087] Where G(t) represents the output force of the piezoelectric laminated beam system at time t, z(t) represents the nonlinear hysteresis variable inside the system at time t, α represents the linear coefficient, and β represents the nonlinear coefficient. Let A represent the time derivative of the electric displacement at time t, which is the velocity at time t. Let B represent the velocity-related proportionality coefficient, B represent the interaction coefficient between velocity and internal variables, and C represent the degree of nonlinearity of the hysteresis response.

[0088] Among them, δ1, δ2, α, β, A, B, and C are all parameters to be optimized in the Bouc-Wen model;

[0089] The parameters to be optimized are substituted into the model for training to achieve optimization. Taking the linear coefficient α as an example, the steps to optimize the linear coefficient α are as follows:

[0090] Establish the mean squared error loss function:

[0091]

[0092] Where L represents the mean squared error, N represents the total number of samples, i.e., the total number of data collection times, and G(t) represents the predictive output force of the model. act (t) represents the actual output force;

[0093] The formula used to calculate the gradient of the loss function for the linear coefficient α is:

[0094]

[0095] The optimized parameters are:

[0096]

[0097] Where, α new η represents the linear coefficients after optimization, α represents the linear coefficients before optimization, and η represents the linear coefficients before optimization. α The learning rate represents α;

[0098] Starting from 0.001 and gradually increasing to 0.1, the learning rate is selected based on expert experience to ensure that the model convergence speed is not too slow and that the loss function does not fluctuate drastically or diverge.

[0099] For each parameter that needs optimization, calculate the gradient of the loss function using the same mean squared error loss function, following the steps described above, and then perform optimization.

[0100] Step 5: Input the stress magnitude and electric field strength of the piezoelectric laminated beam system at the current moment into the Bouc-Wen model to obtain the output force at the current moment, and input the output force into the adaptive PID closed-loop controller to obtain the control signal. Based on the control signal, control and adjust the piezoelectric actuator of the piezoelectric device.

[0101] Please see Figure 2 The present invention also provides a vibration intelligent control parameter optimization system for piezoelectric laminated beams. The system is used to implement the aforementioned vibration intelligent control parameter optimization method for piezoelectric laminated beams, specifically including:

[0102] The array optimization module is used to randomly generate layout schemes for piezoelectric devices, with the optimization objectives of maximizing vibration suppression and energy harvesting efficiency. The array layout of the piezoelectric devices is optimized through a multi-objective genetic algorithm.

[0103] The PID control module is used to obtain the geometric and material properties of the piezoelectric laminate beam composed of piezoelectric devices based on the piezoelectric device array layout determined by the genetic algorithm, manufacture piezoelectric devices based on the geometric and material properties, and establish an adaptive PID closed-loop controller.

[0104] The parameter optimization module is used to collect the operating status data of the piezoelectric laminated beam system in the piezoelectric device. The operating status data includes the stress magnitude, electric field strength, electric displacement and output force of the piezoelectric laminated beam system. During the operation of the piezoelectric laminated beam system, the output force is used as the evaluation target to optimize the parameters of the adaptive PID closed-loop controller.

[0105] The model building module is used to perform correlation analysis on the operating status data of the piezoelectric laminated beam system and establish a Bouc-Wen model with stress magnitude and electric field strength as inputs and output force as output.

[0106] The output control module is used to input the stress magnitude and electric field strength of the piezoelectric laminated beam system at the current moment into the Bouc-Wen model to obtain the output force at the current moment, and input the output force into the adaptive PID closed-loop controller to obtain the control signal, and control and adjust the piezoelectric actuator of the piezoelectric device based on the control signal.

[0107] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0108] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0109] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0110] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing vibration intelligent control parameters of a piezoelectric laminated beam, characterized in that, The specific steps include: Step 1: Randomly generate a layout scheme for piezoelectric devices, with the optimization objectives of maximizing vibration suppression and energy harvesting efficiency. Optimize the array layout of the piezoelectric devices using a multi-objective genetic algorithm. Step 2: Based on the piezoelectric device array layout determined by the genetic algorithm, obtain the geometric and material properties of the piezoelectric laminate beam composed of piezoelectric devices, manufacture piezoelectric devices based on the geometric and material properties, and establish an adaptive PID closed-loop controller. Step 3: Collect the operating status data of the piezoelectric laminated beam system in the piezoelectric device. The operating status data includes the stress magnitude, electric field strength, electric displacement and output force of the piezoelectric laminated beam system. During the operation of the piezoelectric laminated beam system, the output force is used as the evaluation target to optimize the parameters of the adaptive PID closed-loop controller. Step 4: Perform correlation analysis on the operating status data of the piezoelectric laminated beam system, and establish a Bouc-Wen model with stress magnitude and electric field strength as inputs and output force as output. Step 5: Input the stress magnitude and electric field strength of the piezoelectric laminated beam system at the current moment into the Bouc-Wen model to obtain the output force at the current moment, and input the output force into the adaptive PID closed-loop controller to obtain the control signal. Based on the control signal, control and adjust the piezoelectric actuator of the piezoelectric device.

2. The method for optimizing vibration intelligent control parameters of a piezoelectric laminated beam according to claim 1, characterized in that: The principle underlying the optimization of the piezoelectric device array layout using a multi-objective genetic algorithm in step 1 is as follows: Each piezoelectric device layout is considered as an individual, and a fitness function is generated for each individual. The formula for generating the fitness function is as follows: F(a) = w1·f1(a) + w2·f2(a) Where F(a) represents the fitness function of the a-th individual, a represents the index of the individual, f1(a) represents the vibration suppression effect of the a-th individual, f2(a) represents the energy harvesting efficiency of the a-th individual, w1 and w2 represent the weights of vibration suppression effect and energy harvesting efficiency, respectively, w1+w2=1, and w1>w2; Individuals are selected using a roulette wheel algorithm and arranged in descending order of fitness. Adjacent pairs of individuals form parents, and genes are exchanged between parents to generate offspring. Genes are randomly selected from the offspring to be modified, and the fitness function of the individuals is recalculated. This genetic operation is repeated until a preset number of iterations is reached, and the layout scheme with the highest fitness is selected as the array layout of the piezoelectric devices. The gene represents the control parameters that need to be optimized.

3. The method for optimizing vibration intelligent control parameters of a piezoelectric laminated beam according to claim 1, characterized in that: The geometric properties include the length, width, and thickness of the piezoelectric laminate beam, and the material properties include the Young's modulus, density, piezoelectric constant, and dielectric constant of the piezoelectric material.

4. The method for optimizing vibration intelligent control parameters of a piezoelectric laminated beam according to claim 1, characterized in that: The expression for the Bouc-Wen model is as follows: D(t)=δ1·d·σ(t)+δ2·ε·E(t) G(t) = α·D(t) + β·z(t) Where t is the time variable, D(t) represents the electric displacement at time t, d represents the piezoelectric constant of the piezoelectric material, σ(t) represents the stress magnitude at time t, ε represents the dielectric constant of the piezoelectric material, E(t) represents the electric field strength at time t, and δ1 and δ2 are the first weighting coefficient and the second weighting coefficient, respectively. Where G(t) represents the output force of the piezoelectric laminated beam system at time t, z(t) represents the nonlinear hysteresis variable inside the system at time t, α represents the linear coefficient, and β represents the nonlinear coefficient. Let A represent the time derivative of the electric displacement at time t, i.e., the velocity at time t; let B represent the velocity-related proportionality coefficient; let C represent the interaction coefficient between velocity and internal variables; and let C represent the degree of nonlinearity of the hysteresis response. Among them, δ1, δ2, α, β, A, B, and C are all parameters to be optimized in the Bouc-Wen model.

5. The method for optimizing vibration intelligent control parameters of a piezoelectric laminated beam according to claim 4, characterized in that: The process of optimizing the parameters of the adaptive PID closed-loop controller is as follows: e(t) = r(t) - G(t) e(t+1) = r(t+1) - G(t+1) Where e(t) represents the output force error at time t, and r(t) represents the expected output force at time t; Where u(t) represents the control signal sent by the adaptive PID closed-loop controller to the piezoelectric actuator at time t, and K p (t) represents the proportional gain at time t, K i (t) represents the integral gain at time t, K d (t) represents the differential gain at time t; Where r(t+1) represents the expected output force at time t+1, G(t+1) represents the output force generated by the piezoelectric laminated beam system under the influence of the control signal u(t), and e(t) represents the output force error at time t+1. Among them, K p (t+1) represents the optimized proportional gain at time t+1, and K i (t+1) represents the optimized integral gain at time t+1, K d (t+1) represents the differential gain at time t+1 after optimization, μ1 represents the weight of the output force error, μ2 represents the weight of the rate of change of the output force error, and μ1+μ2=1 and μ1>μ2; Wherein, ε1, ε2, and ε3 are the first optimization weight, the second optimization weight, and the third optimization weight, respectively, and ε1 > ε2 > ε3.

6. A vibration intelligent control parameter optimization system for a piezoelectric laminated beam, characterized in that: The system is used to implement the vibration intelligent control parameter optimization method for piezoelectric laminated beams according to any one of claims 1-5, specifically including: The array optimization module is used to randomly generate layout schemes for piezoelectric devices, with the optimization objectives of maximizing vibration suppression and energy harvesting efficiency. The array layout of the piezoelectric devices is optimized through a multi-objective genetic algorithm. The PID control module is used to obtain the geometric and material properties of the piezoelectric laminate beam composed of piezoelectric devices based on the piezoelectric device array layout determined by the genetic algorithm, manufacture piezoelectric devices based on the geometric and material properties, and establish an adaptive PID closed-loop controller. The parameter optimization module is used to collect the operating status data of the piezoelectric laminated beam system in the piezoelectric device. The operating status data includes the stress magnitude, electric field strength, electric displacement and output force of the piezoelectric laminated beam system. During the operation of the piezoelectric laminated beam system, the output force is used as the evaluation target to optimize the parameters of the adaptive PID closed-loop controller. The model building module is used to perform correlation analysis on the operating status data of the piezoelectric laminated beam system and establish a Bouc-Wen model with stress magnitude and electric field strength as inputs and output force as output. The output control module is used to input the stress magnitude and electric field strength of the piezoelectric laminated beam system at the current moment into the Bouc-Wen model to obtain the output force at the current moment, and input the output force into the adaptive PID closed-loop controller to obtain the control signal, and control and adjust the piezoelectric actuator of the piezoelectric device based on the control signal.

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