Intelligent closed-loop vibration control system and method of FBG and MFC

Abstract

The application provides a smart closed-loop vibration control system and method of FBG and MFC. The system comprises an MFC actuator, a first FBG vibration sensor, a second FBG vibration sensor, a signal converter, a nonlinear mapping module, a Smith predictor and a controller; the MFC actuator and the first FBG vibration sensor are arranged on a controlled cantilever plate; the second FBG vibration sensor is arranged on a sample cantilever plate; the first FBG vibration sensor and the second FBG vibration sensor are connected with the signal converter; the signal converter is connected with the nonlinear mapping module, the Smith predictor and the controller in sequence; the controller receives the vibration signal processed by the nonlinear mapping module and the Smith predictor, and outputs a control voltage to the MFC actuator; the MFC actuator generates a suppression force opposite to the phase of the vibration of the controlled cantilever plate under the drive of the control voltage. The scheme can improve the real-time performance and control effect of vibration control.

Description

Technical Field

[0001] This invention relates to the field of structural vibration control technology, and more specifically to an intelligent closed-loop vibration control system and method using FBG and MFC. Background Technology

[0002] With the rapid development of intelligent structures and active vibration control technology, flexible structures are being used more and more widely in civil engineering, aerospace, marine engineering, unmanned equipment, and intelligent manufacturing. However, flexible structures are susceptible to vibration during operation due to external excitation, fluid disturbances, and environmental noise. Long-term vibration can not only reduce structural stability and control accuracy, but may also cause structural fatigue, loose connections, and noise radiation, seriously affecting equipment safety and service life.

[0003] Active vibration control systems in related technologies suffer from the following problems: On the one hand, traditional vibration monitoring methods often employ piezoelectric sensors or accelerometers for structural response detection. However, these sensors suffer from poor electromagnetic interference resistance, complex wiring, and difficulty in achieving large-area distributed measurements. On the other hand, some systems use macrofiber composite materials (MFCs) to apply vibration control. As a flexible piezoelectric actuator, MFCs have advantages such as light weight, high flexibility, high driving efficiency, and ease of fitting curved structures. They can achieve active vibration reduction by applying voltage to generate reverse piezoelectric strain. However, existing MFC active control systems generally suffer from control time delay, temperature drift interference, and insufficient control stability under complex operating conditions, making it difficult to meet the high-precision real-time control requirements of complex flexible structures.

[0004] In view of this, the present invention is hereby proposed. Summary of the Invention

[0005] The present invention is proposed in view of the above-mentioned problems. According to one aspect of the present invention, an intelligent closed-loop vibration control system based on FBG and MFC is provided, comprising: an MFC actuator, a first FBG vibration sensor, a second FBG vibration sensor, a signal converter, a nonlinear mapping module, a Smith predictor, and a controller; the MFC actuator and the first FBG vibration sensor are both disposed on the controlled cantilever plate; the second FBG vibration sensor is disposed on a sample cantilever plate, the sample cantilever plate and the controlled cantilever plate are in the same temperature environment and do not vibrate; The first FBG vibration sensor and the second FBG vibration sensor are connected to the signal converter. The signal converter receives the vibration light signals detected by the first FBG vibration sensor and the second FBG vibration sensor respectively, and generates a vibration electrical signal based on the difference between the two sets of vibration light signals. The signal converter is sequentially connected to the nonlinear mapping module, the Smith predictor and the controller. The controller receives the vibration signal processed by the nonlinear mapping module and the Smith predictor, and outputs a control voltage to the MFC actuator based on the vibration signal. The MFC actuator generates a suppressive force opposite to the vibration phase of the controlled cantilever plate under the drive of the control voltage.

[0006] The above technical solution combines MFC and FBG, leveraging the advantages of MFC actuators (lightweight, highly flexible, fast-response, easy to fit structural surfaces, and active cancellation of structural vibration energy) and FBG sensors (strong anti-electromagnetic interference capability, high measurement accuracy, and good flexibility) to form a closed-loop vibration control, improving vibration reduction efficiency and structural lifespan. This solution is suitable for curved structures and complex working conditions. By using the difference between the first and second FBG vibration sensors, temperature compensation can be applied to the FBG sensor detection results, avoiding temperature drift and reducing measurement errors. Furthermore, by introducing a nonlinear mapping module and a Smith predictor, the system's nonlinear characteristics and time delays can be compensated, improving the closed-loop control response speed and stability. In summary, this solution integrates FBG sensing, intelligent prediction, temperature compensation, and time delay compensation technologies to construct an integrated intelligent closed-loop control system encompassing "sensing-prediction-compensation-control." This system addresses issues in existing flexible structure vibration control systems, such as separation of sensing and control, significant temperature drift, large control time delay, and insufficient adaptability to complex operating conditions. It enables real-time monitoring, intelligent prediction, and active vibration reduction of structural vibration states, exhibiting fast response, high control accuracy, and strong anti-interference capabilities. It is suitable for vibration monitoring and active control of flexible plate and shell structures, unmanned surface vessel structures, submarine appendage structures, and other streamlined flexible structures. Furthermore, the neural network prediction module and controller parameters can be flexibly adjusted according to different structures and operating conditions to achieve efficient closed-loop control.

[0007] For example, a data acquisition module is connected in series between the signal converter and the nonlinear mapping module. The data acquisition module is used to convert the vibration electrical signal output by the signal converter into a wavelength drift based on a preset mapping relationship; and input the currently obtained wavelength drift and the historical wavelength drift into the nonlinear mapping module. The preset mapping relationship is obtained through pre-calibration.

[0008] The above scheme converts vibration electrical signals based on a preset mapping relationship. It can then reversely calculate the voltage into a real physical strain state quantity in real time based on this mapping relationship, and transmit this state quantity as standard input data to subsequent modules. This ensures that the controller can ultimately accurately calculate the active control voltage based on a state vector with clear physical meaning.

[0009] For example, the nonlinear mapping module processes the input data by inputting the input data into a pre-trained neural network model to obtain the predicted structural state of the controlled cantilever plate. The neural network model is one or more of a BP neural network, an RNN neural network, or an LSTM neural network; the input data is a real-time strain history data sequence composed of the currently obtained wavelength drift and the historical wavelength drift.

[0010] For example, both the MFC actuator and the first FBG vibration sensor are located in the region of the controlled cantilever plate near the fixed base. By performing vibration monitoring and suppression in the high-strain region of the controlled cantilever plate, not only can the vibration monitoring and suppression effects be improved, but also, since the MFC actuator and the first FBG vibration sensor are located in the same region, the spatial transmission hysteresis between the sensor and the actuator can be eliminated, ensuring the instantaneity of the feedback signal. This physically eliminates the time delay of vibration waves in structural transmission, further achieving efficient control of structural vibration.

[0011] For example, the signal converter includes a filter, a photoelectric converter, and a signal conditioning circuit. The first FBG vibration sensor and the second FBG vibration sensor are respectively connected to the filter, and the filter is sequentially connected to the photoelectric converter and the signal conditioning circuit.

[0012] For example, during the data calibration stage, the photoelectric converter is an optical fiber demodulator; during the online closed-loop control stage, the photoelectric converter is a photodiode. This reduces system complexity and equipment cost while improving system real-time performance.

[0013] For example, the controller outputs a control voltage based on the vibration signal by converting the vibration signal into a control voltage using a control algorithm; the control algorithm is one or more of a PID control algorithm, an LQR optimal control algorithm, a CDM control algorithm, or a sliding mode control algorithm.

[0014] For example, a high-voltage amplifier is connected in series between the controller and the MFC actuator, and the control voltage output by the controller is amplified by the high-voltage amplifier and then input to the MFC actuator.

[0015] For example, it also includes a vibrator; the vibrator is mounted on the free end of the controlled cantilever plate. This facilitates system calibration.

[0016] According to another aspect of the present invention, an intelligent closed-loop vibration control method based on FBG and MFC is also provided, which utilizes the above-described system to control the vibration of the controlled cantilever plate.

[0017] Compared with the prior art, the technical solution of the present invention has at least the following technical effects: 1. By combining MFC with FBG, the advantages of MFC actuators (lightweight, flexible, fast response, easy to fit the structural surface, and able to actively cancel structural vibration energy) and FBG sensors (strong anti-electromagnetic interference capability, high measurement accuracy, and good flexibility) can be combined to form closed-loop vibration control, improve vibration reduction efficiency and structural life, and is suitable for curved structures and complex working conditions. 2. By using the difference between the first FBG vibration sensor and the second FBG vibration sensor, temperature compensation can be performed on the detection results of the FBG sensor to avoid temperature drift and reduce measurement error. 3. By introducing a nonlinear mapping module and a Smith predictor, the nonlinear characteristics and time delay of the system can be compensated, thereby improving the response speed and stability of the closed-loop control.

[0018] 4. In summary, this scheme integrates FBG sensing, intelligent prediction, temperature compensation, and time delay compensation technologies to construct an integrated intelligent closed-loop control system encompassing "sensing-prediction-compensation-control." This system addresses issues in existing flexible structure vibration control systems, such as separation of sensing and control, significant temperature drift, large control time delay, and insufficient adaptability to complex operating conditions. It enables real-time monitoring, intelligent prediction, and active vibration reduction of structural vibration states, exhibiting fast response, high control accuracy, and strong anti-interference capabilities. It is suitable for vibration monitoring and active control of flexible plate and shell structures, unmanned surface vessel structures, submarine appendage structures, and other streamlined flexible structures. Furthermore, the neural network prediction module and controller parameters can be flexibly adjusted according to different structures and operating conditions to achieve efficient closed-loop control.

[0019] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0020] The above and other objects, features, and advantages of the present invention will become more apparent from the more detailed description of the embodiments of the invention in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same parts or steps.

[0021] Figure 1 A schematic diagram of the intelligent closed-loop vibration control system of Embodiment 1 of the present invention is shown.

[0022] Figure 2 A schematic diagram of the controlled cantilever plate of Embodiment 1 of the present invention is shown.

[0023] Figure 3 A schematic diagram of a sample cantilever plate according to Embodiment 1 of the present invention is shown.

[0024] Figure 4 The schematic diagram of the system according to Embodiment 1 of the present invention is shown.

[0025] In the diagram, 1. Controlled cantilever plate; 2. Vibrator; 3. Data acquisition module; 4. Computer; 5. Controller; 6. Power amplifier; 7. Signal converter; 8. High voltage amplifier; 9. Fixed base; 10. Laser stage; 11. Laser source; 12. Fiber optic cable; 13. Sample cantilever plate; 14. First FBG vibration sensor; 15. MFC actuator; 16. Second FBG vibration sensor; 17. Nonlinear mapping module; 18. Smith predictor. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely a part of the embodiments of the present invention, and not all of the embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described herein, all other embodiments obtained by those skilled in the art without inventive effort should fall within the protection scope of the present invention.

[0027] As mentioned above, traditional active vibration damping schemes face problems such as poor electromagnetic interference resistance, complex wiring, and difficulty in achieving large-area distributed measurement. Vibration response suffers from MFC control time delay, temperature drift interference, and insufficient control stability under complex operating conditions. Therefore, this invention provides an intelligent closed-loop vibration control system and method based on FBG and MFC. This scheme helps improve the vibration control accuracy, real-time performance, and stability of flexible structures such as cantilever plates, and is applicable to vibration monitoring and active vibration damping control of flexible plate and shell structures, unmanned surface vessel structures, submarine appendage structures, and other streamlined flexible structures. For ease of description, the scheme of this invention will be described in detail below with reference to embodiments.

[0028] Example 1 This embodiment provides an intelligent closed-loop vibration control system based on FBG and MFC. This system is used for vibration monitoring and active vibration reduction control of streamlined flexible structures. For ease of description, this embodiment uses a cantilever plate as the research object; however, it can be understood that the solution in this embodiment can also be applied to other flexible structures, which will not be elaborated upon.

[0029] Figure 1 A schematic diagram of the intelligent closed-loop vibration control system of Embodiment 1 of the present invention is shown. Figure 2 A schematic diagram of the controlled cantilever plate of Embodiment 1 of the present invention is shown. Figure 3 A schematic diagram of a sample cantilever plate according to Embodiment 1 of the present invention is shown. (See also...) Figure 1-3 The system includes an MFC actuator 15, a first FBG vibration sensor 14, a second FBG vibration sensor 16, a signal converter 7, a nonlinear mapping module 17, a Smith predictor 18, and a controller 5. The first FBG vibration sensor 14 and the second FBG vibration sensor 16 are connected to the signal converter 7. The signal converter 7 is sequentially connected to the nonlinear mapping module 17, the Smith predictor 18, and the controller 5. The controller 5 receives the vibration signal processed by the nonlinear mapping module 17 and the Smith predictor 18, and outputs a control voltage to the MFC actuator 15 based on the vibration signal. Driven by the control voltage, the MFC actuator 15 generates a damping force that is opposite in phase to the vibration of the controlled cantilever plate 1.

[0030] Both the MFC actuator 15 and the first FBG vibration sensor 14 are mounted on the controlled cantilever plate 1. The MFC actuator 15 can be fixed to the controlled cantilever plate 1 by adhesive bonding. One end of the controlled cantilever plate 1 is fixed to the fixed base 9, and the other end is a free end, forming a typical cantilever structure. In this embodiment, to facilitate improved vibration monitoring and suppression, both the MFC actuator 15 and the first FBG vibration sensor 14 are located in the region of the controlled cantilever plate 1 near the fixed base 9. This region is the strain-sensitive area of ​​the cantilever plate, where the first FBG vibration sensor 14 can collect the vibration response of the cantilever plate and output a wavelength drift signal. Simultaneously, the MFC actuator 15 can generate reverse piezoelectric strain in this region to counteract structural vibration.

[0031] Given the advantages of FBG vibration sensors, such as small size, strong resistance to electromagnetic interference, long-distance transmission capability, and applicability to complex curved structures, and their widespread application in flexible structure health monitoring, this embodiment considers using an FBG vibration sensor to monitor vibration and combining it with the MFC actuator 15 to form an integrated closed-loop vibration control. However, further research revealed that FBG sensors are sensitive to both temperature and strain, and are prone to temperature drift in complex environments, leading to increased measurement errors. Therefore, this embodiment considers setting up an identical FBG sensor in a constant temperature environment to collect ambient temperature change signals for temperature compensation. Figure 3 As shown, in this embodiment, a sample cantilever plate 13 is provided. This sample cantilever plate 13 and the controlled cantilever plate 1 are in the same temperature environment and do not vibrate. A second FBG vibration sensor 16 is disposed on the sample cantilever plate 13. In actual use, the signal converter 7 receives the vibration light signals detected by the first FBG vibration sensor 14 and the second FBG vibration sensor 16 respectively, and generates a vibration electrical signal based on the difference between the two sets of vibration light signals. Through this differential operation, the temperature drift in the data detected by the first FBG vibration sensor 14 can be removed.

[0032] In this embodiment, the signal converter 7 includes a filter, a photoelectric converter, and a signal conditioning circuit. The laser source 11 is mounted on the laser stage 10. The laser source 11 is connected to the first FBG vibration sensor 14 and the second FBG vibration sensor 16 via optical fibers 12 to excite the two vibration sensors to generate optical signals. The first FBG vibration sensor 14 and the second FBG vibration sensor 16 are respectively connected to the filter, and the filter is connected to the photoelectric converter and the signal conditioning circuit in sequence. The signal processed by the signal conditioning circuit is output to the subsequent module (data acquisition module 3 in this embodiment).

[0033] In related technologies, the conversion of the optical signal reflected by the FBG into an electrical signal is typically achieved using a fiber optic demodulator. However, such devices are bulky, expensive, and suffer from signal delay, limiting their application in real-time active control. Therefore, in this embodiment, the photoelectric converter is a fiber optic demodulator during the data calibration stage; and a photodiode is used during the online closed-loop control stage. Thus, during the data calibration stage, accurate wavelength drift and the corresponding voltage value (i.e., the vibration electrical signal) can be obtained, facilitating the establishment of a linear mapping relationship (i.e., the preset mapping relationship hereinafter) between the vibration electrical signal and the actual wavelength drift. During the online closed-loop control stage, the photodiode can directly convert the FBG reflected light intensity into a voltage signal. This voltage signal, based on the mapping relationship obtained during the calibration stage, can be used to obtain the actual wavelength drift as the input to the subsequent nonlinear mapping module 17. This reduces system complexity and equipment cost while improving system real-time performance.

[0034] Continue reading Figure 1 A data acquisition module 3 is connected in series between the signal converter 7 and the nonlinear mapping module 17. The data acquisition module 3 is used to convert the vibration electrical signal output by the signal converter 7 into a wavelength drift based on a preset mapping relationship; and input the currently obtained wavelength drift and historical wavelength drift into the nonlinear mapping module 17; wherein the preset mapping relationship is obtained through pre-calibration. The data acquisition module 3 can also be connected to the first FBG vibration sensor 14, the second FBG vibration sensor, and the controller 5 to synchronously acquire and store the outputs of each FBG vibration sensor and the output of the controller 5, for use in subsequent neural network model training, closed-loop control optimization, and system performance evaluation.

[0035] In this embodiment, the nonlinear mapping module 17 processes the input data by inputting it into a pre-trained neural network model to obtain a predicted value of the structural state of the controlled cantilever plate 1. The input data is a real-time strain history data sequence composed of the currently obtained wavelength drift and historical wavelength drift. The neural network model is one or more of a BP neural network, an RNN neural network, or an LSTM neural network. The training samples of the neural network model can be historical data stored in the data acquisition module 3. The neural network model can learn the dynamic characteristics of the cantilever plate based on the historical input-output relationship, and realize the prediction of the current state and future vibration trend, so as to solve the nonlinearity and time delay problems existing in traditional closed-loop systems.

[0036] Considering the system time delay caused by fiber demodulation, data transmission and actuator response, in this embodiment, the data processed by the nonlinear mapping module 17 is input to the Smith predictor 18 for further processing. The Smith prediction is used to compensate for the system delay accumulated in the entire link of sensor perception and recognition, photoelectric signal conversion, neural network calculation and MFC execution drive, so as to improve the stability and real-time performance of the closed-loop control system.

[0037] The data processed by the Smith predictor 18 is fed into the controller 5 for calculating the control voltage. The controller 5 can be a dSPACE controller. In this embodiment, the controller 5 outputs the control voltage based on the vibration signal in the following way: the vibration signal is converted into a control voltage using a control algorithm; the control algorithm is one or more of the following: PID control algorithm, LQR optimal control algorithm, CDM control algorithm, or sliding mode control algorithm.

[0038] In one specific embodiment, the LQR optimal control algorithm is used to calculate the control voltage. The active control force generated by MFC satisfies: . F To provide control for MFC output, d 33 The piezoelectric strain constant is V The input control voltage is given by the LQR control law, which satisfies: u(t) = −Kx(t), where u(t) is the control input, K is the feedback gain matrix, and x(t) is the system state vector. In practical applications, the system's vibration control capability for vibrations of different frequencies and amplitudes can be improved by adjusting the LQR feedback gain, Smith prediction parameters, and neural network prediction model parameters, thereby achieving efficient active suppression of vibrations in complex flexible structures.

[0039] Continue reading Figure 1 A high-voltage amplifier 8 is connected in series between the controller 5 and the MFC actuator 15. The control voltage output by the controller 5 is amplified by the high-voltage amplifier 8 and then input to the MFC actuator 15. The high-voltage amplifier 8 is used to amplify the low-voltage control signal output by the controller 5 and drive the MFC actuator 15 to generate reverse piezoelectric strain, thereby realizing active vibration reduction of the structure.

[0040] Continue reading Figure 1 It also includes a vibrator 2; the vibrator 2 is installed at the free end of the controlled cantilever plate 1. The vibrator 2 can generate an excitation signal under the control of the computer 4. Specifically, the computer 4 can generate an excitation signal and output it to the vibrator 2 via the power amplifier 6. The vibrator 2 applies external excitation to the controlled cantilever plate 1, causing the cantilever plate to produce a vibration response.

[0041] Figure 4 A schematic block diagram of the system according to Embodiment 1 of the present invention is shown. Figure 4As shown, before use, the system first undergoes offline data acquisition and calibration. During the data acquisition and calibration phase, the computer generates an excitation signal, which is output by a power amplifier to the vibrator, which then applies external excitation to the controlled cantilever plate. The laser source is connected to the first FBG vibration sensor via optical fiber. The first FBG vibration sensor senses the vibration of the controlled cantilever plate and generates a corresponding wavelength drift signal. Simultaneously, a second FBG vibration sensor, arranged on the sample cantilever plate, synchronously senses changes in ambient temperature. The outputs of the two FBG vibration sensors are differentially processed and converted into voltage signals in a signal converter (using an optical fiber demodulator). The voltage signal output by the signal converter and the corresponding wavelength drift value are input to the data acquisition module for storage. Simultaneously, this data can be used to establish the correspondence between wavelength drift and output voltage, and a linear mapping relationship between optical and electrical signals can be obtained through fitting. Furthermore, the data acquisition module can collect historical input and output data of the system, providing samples for training the neural network model of the nonlinear mapping module, thereby establishing the system's nonlinear mapping model.

[0042] After calibration, real-time operation is performed, with vibration originating from external environmental disturbances. During the online closed-loop control phase, the first FBG vibration sensor senses the vibration of the controlled cantilever plate and generates a corresponding wavelength drift signal. Simultaneously, a second FBG vibration sensor, positioned on the sample cantilever plate, synchronously senses changes in ambient temperature. The outputs of the two FBG vibration sensors undergo differential calculation in a signal converter (using a fiber optic demodulator) and are converted into voltage signals. The voltage signal output from the signal converter, along with the corresponding wavelength drift value, is input to the data acquisition module for storage. The data acquisition module inputs current and historical data to the nonlinear mapping module to predict the current system state. Next, a Smith predictor is used for time-delay compensation. The Smith predictor output is fed into the controller, which calculates the optimal control voltage based on at least one of PID / LQR / sliding diaphragm control algorithms. During real-time execution, the optimal control voltage input to the controller is amplified by a high-voltage amplifier and input to the MFC actuator, controlling the MFC actuator to generate reverse piezoelectric strain, which acts on the cantilever plate to actively counteract vibration energy.

[0043] Data can be recorded, analyzed, and stored both offline and during implementation phases, synchronously recording data such as control voltage and vibration response to facilitate performance evaluation and model optimization. For example, neural network models can be periodically updated and closed-loop control improved to ensure the effectiveness of the system's closed-loop control.

[0044] This embodiment also provides an intelligent closed-loop vibration control method based on FBG and MFC, which uses the above-described system to control the vibration of the controlled cantilever plate.

[0045] Those skilled in the art can easily understand the implementation principle and beneficial effects of the method by reading the above system. For the sake of brevity, further details will not be elaborated here.

[0046] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of the invention. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of the invention. All such changes and modifications are intended to be included within the scope of the invention as claimed in the appended claims.

[0047] 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 in 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. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0048] In the several embodiments provided by this invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0049] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0050] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of the invention. However, this approach should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with fewer features than all of those in a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0051] Those skilled in the art will understand that, apart from the mutual exclusion of features, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or elements of any method or apparatus so disclosed may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0052] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments can be used in any combination.

[0053] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules in the system according to embodiments of the present invention. The present invention can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing some or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0054] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0055] The above description is merely a specific embodiment of the present invention or an explanation of that embodiment. The scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A smart closed-loop vibration control system based on FBG and MFC, characterized in that, include: The system comprises an MFC actuator, a first FBG vibration sensor, a second FBG vibration sensor, a signal converter, a nonlinear mapping module, a Smith predictor, and a controller. The MFC actuator and the first FBG vibration sensor are both mounted on the controlled cantilever plate. The second FBG vibration sensor is mounted on the sample cantilever plate, which is in the same temperature environment as the controlled cantilever plate and does not vibrate. The first FBG vibration sensor and the second FBG vibration sensor are connected to the signal converter. The signal converter receives the vibration light signals detected by the first FBG vibration sensor and the second FBG vibration sensor respectively, and generates a vibration electrical signal based on the difference between the two sets of vibration light signals. The signal converter is sequentially connected to the nonlinear mapping module, the Smith predictor and the controller. The controller receives the vibration signal processed by the nonlinear mapping module and the Smith predictor, and outputs a control voltage to the MFC actuator based on the vibration signal. The MFC actuator generates a suppressive force opposite to the vibration phase of the controlled cantilever plate under the drive of the control voltage.

2. The system according to claim 1, characterized in that, A data acquisition module is connected in series between the signal converter and the nonlinear mapping module. The data acquisition module is used to convert the vibration electrical signal output by the signal converter into a wavelength drift based on a preset mapping relationship; and input the currently obtained wavelength drift and the historical wavelength drift into the nonlinear mapping module. The preset mapping relationship is obtained through pre-calibration.

3. The system according to claim 2, characterized in that, The nonlinear mapping module processes the input data by inputting the input data into a pre-trained neural network model to obtain the predicted structural state of the controlled cantilever plate. The neural network model is one or more of a BP neural network, an RNN neural network, or an LSTM neural network; the input data is a real-time strain history data sequence composed of the currently obtained wavelength drift and the historical wavelength drift.

4. The system according to claim 1, characterized in that, Both the MFC actuator and the first FBG vibration sensor are located in the area of ​​the controlled cantilever plate near the fixed base.

5. The system according to claim 1, characterized in that, The signal converter includes a filter, a photoelectric converter, and a signal conditioning circuit. The first FBG vibration sensor and the second FBG vibration sensor are respectively connected to the filter, and the filter is sequentially connected to the photoelectric converter and the signal conditioning circuit.

6. The system according to claim 5, characterized in that, During the data calibration stage, the photoelectric converter is an optical fiber demodulator; during the online closed-loop control stage, the photoelectric converter is a photodiode.

7. The system according to claim 1, characterized in that, The controller outputs a control voltage based on the vibration signal in the following manner: the vibration signal is converted into a control voltage using a control algorithm; the control algorithm is one or more of the following: PID control algorithm, LQR optimal control algorithm, CDM control algorithm, or sliding mode control algorithm.

8. The system according to claim 1, characterized in that, A high-voltage amplifier is connected in series between the controller and the MFC actuator. The control voltage output by the controller is amplified by the high-voltage amplifier and then input to the MFC actuator.

9. The system according to claim 1, characterized in that, It also includes a vibrator; the vibrator is installed at the free end of the controlled cantilever plate.

10. A smart closed-loop vibration control method based on FBG and MFC, characterized in that, Vibration control of a controlled cantilever plate is performed using the system described in any one of claims 1-9.

Images