Disturbance observation driven minimum control synthesis adaptive vibration control method and system

Abstract

The invention discloses a disturbance observation driven minimum control synthesis adaptive vibration control method and system. The method comprises the following steps: establishing a nominal model of a structure based on a vibration response signal of the structure; carrying out fusion processing on the nominal model of the structure and the vibration response signal, constructing a disturbance observation framework, estimating various disturbances borne by the structure in real time, and generating a disturbance estimation value; combining the disturbance estimation value with a control law of a minimum control synthesis algorithm in a feedforward compensation mode to construct a driving control instruction; and generating an acting force according to the driving control instruction to adjust the vibration of the structure. According to the method, a cooperative control architecture with strong anti-interference capability is constructed by fusing a disturbance observation technology and a minimum control synthesis algorithm, and disturbance and uncertainty are effectively suppressed while vibration is controlled, so that the vibration control precision is improved.

Description

Technical Field

[0001] This application relates to the field of structural vibration control, and more specifically, to a disturbance observation-driven minimum control synthesis adaptive vibration control method and system. Background Technology

[0002] Vibration control is a critical issue that cannot be ignored in many engineering applications, and its importance is particularly evident in systems such as plate and shell structures and building structures that are susceptible to external disturbances or excitations. In actual operation, the performance, stability, and safety of these systems are closely linked to the effectiveness of vibration control.

[0003] Traditional vibration control methods have long been widely used in engineering practice, with PID control and LQR control being typical examples. PID control, with its simple structure and ease of implementation and debugging, plays a crucial role in many simple vibration control scenarios. It adjusts the control quantity by performing proportional, integral, and derivative operations on the system error, thereby suppressing vibration. LQR control, on the other hand, is based on linear quadratic optimal control theory. It designs the controller by optimizing performance indicators and achieves good control results in linear systems with accurate mathematical models.

[0004] However, with the increasing complexity of engineering systems and the growing number of external disturbances and system uncertainties, the limitations of traditional vibration control methods are becoming increasingly apparent. External disturbances take many forms, including not only common exciter input forces and environmental disturbances, but also sudden impact loads. System uncertainties are also widespread; for example, material properties change with environmental factors such as temperature and humidity, and errors inevitably exist between the actual system model and the theoretical model. When faced with these complex situations, traditional methods often struggle to maintain good control performance. For instance, when material properties change significantly, LQR controllers designed based on fixed models may fail to accurately track the system state, leading to a decline in control performance; PID control faces difficulties in parameter tuning when dealing with multivariable, nonlinear, and time-varying systems, making it difficult to meet the requirements of real-time performance and accuracy.

[0005] To overcome the shortcomings of traditional methods, researchers are constantly exploring new vibration control approaches. Minimum Control Synthesis (MCS) algorithm, as an adaptive control method, has emerged. Based on state feedback and reference signal compensation mechanisms, this algorithm can automatically adjust the control strategy according to the real-time state of the structure and reference signals, exhibiting good stability and performance. Under relatively stable operating conditions, the MCS algorithm can effectively suppress vibrations, maintaining the system in a good operating state. However, the MCS algorithm also has significant drawbacks; its ability to cope with disturbances and system uncertainties is limited. When the structure is subjected to complex and variable external disturbances or experiences significant uncertainties, the MCS algorithm cannot promptly and accurately perceive these changes and make effective adjustments, resulting in a significant reduction in control effectiveness.

[0006] Meanwhile, Disturbance Observer (DOB) technology offers a new approach to vibration control. DOBs can estimate external disturbances during structural vibration in real time and improve system robustness through disturbance compensation. It acts like a "smart sensor," detecting various disturbances in the structure and feeding this information back to the controller, enabling the controller to take proactive measures to mitigate the disturbances. However, using DOBs alone also presents some challenges. In complex systems with multiple disturbances, DOBs may struggle to accurately distinguish the characteristics and impact levels of different disturbances, leading to inaccurate compensation. Especially in nonlinear control and dynamic variations, the system model is difficult to establish precisely, severely impacting the accuracy of DOB-based disturbance estimation and compensation, thus failing to fully leverage its advantage in improving system robustness.

[0007] In view of this, the present invention is hereby proposed. Summary of the Invention

[0008] The primary objective of this application is to provide a disturbance observation-driven minimum control synthesis adaptive vibration control method. This method integrates disturbance observation technology with a minimum control synthesis algorithm to construct a cooperative control architecture with strong anti-interference capabilities. While controlling vibration, it effectively suppresses disturbances and uncertainties, thereby improving the accuracy of vibration control.

[0009] The second objective of this invention is to provide a disturbance observation-driven minimum control synthesis adaptive vibration control system, which improves the robustness of the system by implementing the above-mentioned method.

[0010] This application is made to achieve the above-mentioned technical objectives: This invention provides a disturbance observation-driven minimum control synthesis adaptive vibration control method, comprising the following steps: A nominal model of the structure is established based on the vibration response signal of the structure; By fusing the nominal model of the structure and the vibration response signal, a disturbance observation framework is constructed to estimate the various disturbances experienced by the structure in real time and generate disturbance estimates. The disturbance estimate is combined with the control law of the minimum control synthesis algorithm using a feedforward compensation method to construct the drive control command; The vibration of the structure is adjusted by generating a force according to the drive control command.

[0011] Furthermore, the method for constructing the perturbation observation framework includes: The nominal model is combined with the vibration response signal to establish a state equation with perturbation; Design an intermediate variable z and introduce it into the perturbation-inducible state equation; simultaneously, force the control matrix to satisfy the Hurwitz stability condition through the gain matrix to ensure that the perturbation estimate converges asymptotically, thus obtaining the result. The state equation with perturbation is: ; Among them, B d Let d represent the disturbance distribution matrix, d represent the unknown external disturbance, A represent the state matrix of the nominal model, and B represent the disturbance distribution matrix. u Let X represent the control matrix, C represent the output matrix or observation matrix, x represent the state variables, u represent the input data, and y represent the output data. The disturbance observation framework: ; in, Let represent the disturbance estimate of disturbance d, z represent the differential form of the intermediate variable as the eliminated state variable, and L is the gain matrix.

[0012] Furthermore, the output error value is determined based on the dynamic deviation between the reference model of the preset ideal target and the vibration response signal; Based on the output error value, calculate the adaptive gain of the control law of the minimum control synthesis algorithm; The control law of the minimum control synthesis algorithm is derived by combining the adaptive gain with the reference input in the reference model. The adaptive gain is: ; in, and Indicates adaptive gain. and Indicates the scaling factor. Indicates reference input; The control law for the minimum control synthesis is: ; in, Indicates the control law, and Indicates adaptive gain. This represents the vibration response signal.

[0013] Furthermore, the feedforward compensation method is as follows: Based on the disturbance estimate, the disturbance compensation term of the minimum control synthesis is calculated using the disturbance compensation formula; The disturbance compensation term is embedded into the control law of the minimum control synthesis algorithm to obtain the disturbance-compensated control law; The interference compensation term is calculated as follows: ; in, Indicates the interference compensation term. + It is the pseudo-inverse of matrix B, B d Represents the perturbation distribution matrix. (t) represents the estimate of the disturbance d(t).

[0014] Furthermore, the method for constructing drive control instructions includes: The adaptive gain is combined with the disturbance compensation term to generate the final control input data; The final control input data is: ; in, and Indicates adaptive gain. This represents the vibration response signal.

[0015] Furthermore, the formula for determining the output error value of the dynamic deviation is: ; in, Indicates the error value. The state of the reference model representing the ideal goal. This represents the vibration response signal. Let B represent the output signal error position matrix, B represent the control input matrix, and P represent the positive definite solution of the Lyapunov equation.

[0016] Furthermore, the method for establishing the nominal model of the structure includes: Using a state-space model as the initial model framework, and based on system identification technology, the input signal time series and output signal time series in the vibration response signal are dynamically identified to construct an initial high-order state-space model that can comprehensively describe the initial dynamic characteristics. The initial high-order state-space model is obtained by reducing its order using the balanced truncation method. The nominal model of the structure is: ; Where A represents the state matrix of the nominal model, B u Let represent the control matrix, C represent the output matrix or observation matrix, x represent the state variables, u represent the input data, and y represent the output data.

[0017] Furthermore, the reference model for the preset ideal target is: ; in, A reference model representing the ideal goal, x m A represents the state variables of the reference model. m B represents the state matrix of the reference model. m Let r represent the input matrix of the reference model, and r represent the reference input signal.

[0018] This application also provides a control system based on a disturbance observation-driven minimum control synthesis adaptive vibration control method, comprising: The data acquisition unit collects vibration response signals from acceleration sensors installed at key locations on the structure and converts them into time-series datasets. The disturbance estimation unit, connected to the data acquisition unit, is used to calculate various disturbances experienced by the structure and generate disturbance estimates. A control command synthesis unit is connected to the disturbance estimation unit and calculates and outputs drive control commands. The execution unit is connected to the control command synthesis unit and is equipped with a drive device to convert the drive control command into physical force to regulate the vibration of the structure.

[0019] According to another aspect of this application, a computer-readable storage medium is provided, on which program instructions are stored, which, when executed, are used to perform the above-described disturbance observation-driven minimum control synthesis adaptive vibration control method.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: By innovatively integrating a disturbance observer with a minimum control synthesis algorithm, a collaborative control mechanism combining active anti-interference and adaptive adjustment capabilities is constructed. This mechanism can accurately quantify the magnitude and direction of disturbances and rapidly implement targeted disturbance compensation. This prevents vibration control from deviating significantly due to external disturbances and internal instabilities, substantially improving control accuracy. Simultaneously, the system exhibits stronger stability in the face of various unexpected situations, i.e., enhanced robustness.

[0021] In terms of applicability, existing technologies are often limited by specific structures or materials, resulting in a narrow range of applications. However, the control system of this invention breaks through this limitation. It does not rely on the specific form of the structure or the special properties of the material. Whether it is a plate and shell structure with complex curved surfaces and thin characteristics, a large-scale building structure with complex stress, or a precise mechanical structure with variable dynamic characteristics, this system can achieve effective vibration control.

[0022] Existing technologies cannot respond effectively to the dynamic changes in structural vibration modes and external disturbances in a timely manner. This invention, however, utilizes the feedback, feedforward, and disturbance compensation mechanisms of the control law in a minimum control synthesis algorithm to perceive these changes in real time and quickly adjust the control input parameters. In other words, it automatically optimizes the control strategy based on the real-time state of the structure, ensuring optimal vibration control under different operating conditions and keeping the system in a stable operating state.

[0023] The disturbance observation framework provides real-time estimation of external disturbances, offering the system forward-looking disturbance information. By proactively sensing and accurately assessing external disturbances such as exciter input force and environmental disturbances, this invention can take measures to suppress them in advance, effectively preventing these disturbances from disrupting the vibration control process. This not only improves the stability of vibration control and reduces fluctuations during the control process but also further enhances the accuracy of control. Attached Figure Description

[0024] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the accompanying drawings, the same reference numerals generally represent the same components or steps. Figure 1 A schematic flowchart of the disturbance observation-driven minimum control synthesis adaptive vibration control method provided in the embodiments of this application; Figure 2 A block diagram of the disturbance observation framework provided in the embodiments of this application; Figure 3 A block diagram of the minimum control synthesis algorithm provided in the embodiments of this application; Figure 4 An adaptive control block diagram combining disturbance observation and minimum control synthesis is provided for embodiments of this application; Figure 5 This is a schematic block diagram illustrating the operation of the disturbance observation-driven minimum control synthesis adaptive vibration control method provided in the embodiments of this application.

[0025] Specific implementation methods To make the objectives, technical solutions, and advantages of this application more apparent, exemplary embodiments according to this application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein. Based on the embodiments of this application described herein, all other embodiments obtained by those skilled in the art without inventive effort should fall within the protection scope of this application.

[0026] It should be noted that the terms "original," "target," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] This application provides a disturbance observation-driven minimum control synthesis adaptive vibration control method, see [link to relevant documentation]. Figure 1-4 It can be seen that the method includes the following steps S1-S4: S1: Establish a nominal model of the structure based on the vibration response signal of the structure; Specifically, in this embodiment, accelerometers and actuators are arranged at multiple key locations of the structure to be controlled to detect the vibration response signal of the structure under external disturbances and control inputs, wherein the key locations are on the surface of the structure or at the point of maximum amplitude.

[0028] Next, the actuator input signal is used as input data, and the output signal collected by the accelerometer is used as output data. This data covers the dynamic information of the structure under different operating conditions. Subsequently, the input and output data are stored as a time-series dataset for later use. Furthermore, using a state-space model as the initial model framework, the system identification toolbox in Matlab is used to dynamically identify the time-series dataset, constructing an initial high-order state-space model that can comprehensively describe the initial dynamic characteristics. In terms of model selection, the state-space model can well describe the dynamic behavior of the structure during vibration, clearly expressing the relationship between input data, output data, and state variables.

[0029] The initial high-order state-space model was reduced in order using the balanced truncation method, resulting in the nominal model of the structure. Model reduction is a crucial step, ensuring the accuracy of the model while reducing computational complexity, thus improving its practicality and computational efficiency. By appropriately selecting and reducing the model's order, removing secondary dynamic characteristics, and retaining the primary dynamic characteristics, a nominal model that accurately reflects the structure's dynamic characteristics during vibration is obtained. This provides the necessary mathematical model foundation for the subsequent construction of the disturbance observation framework.

[0030] The nominal model of the structure is: ; Where A represents the state matrix of the nominal model, B u Let represent the control matrix, C represent the output matrix or observation matrix, x represent the state variables, u represent the input data, and y represent the output data.

[0031] S2. The nominal model of the structure and the vibration response signal are fused and processed to construct a disturbance observation framework to estimate the various disturbances that the structure is subjected to in real time and generate disturbance estimates.

[0032] Specifically, the nominal model of the structure is first combined with the collected vibration response signals to construct a perturbation-induced state equation. This is because the initial nominal model only considers the influence of accelerometer signals and controller inputs on the system, without incorporating unavoidable external disturbances during actual operation. To more accurately describe the dynamic characteristics of the structure under actual operating conditions, the original nominal model needs to be extended by introducing external disturbances, thereby obtaining a perturbation-induced state equation that comprehensively reflects the actual operating state of the system.

[0033] The state equation with perturbation is: ; Among them, B d Let d represent the disturbance distribution matrix, d represent the unknown external disturbance, A represent the state matrix of the nominal model, and B represent the disturbance distribution matrix. u Let represent the control matrix, C represent the output matrix or observation matrix, x represent the state variables, u represent the input data, and y represent the output data.

[0034] Next, a perturbation observation framework is constructed, an intermediate variable z is designed, and the intermediate variable z is introduced into the perturbation-induced state equation to eliminate the differential form of the state variable. Simultaneously, using the gain matrix L as the core parameter, the disturbance is continuously iteratively estimated. During this process, it is crucial to ensure that the designed disturbance observation framework meets the convergence condition, namely, the gain matrix L must be Hurwitz stable. Only when this condition is met can the disturbance observation framework operate stably and accurately estimate the disturbance value. As the system continues to operate, the disturbance observation framework continuously receives new vibration response signals and updates the disturbance estimate in real time in conjunction with the nominal model, thereby generating a disturbance estimate that is as close as possible to the actual disturbance situation, providing a crucial basis for subsequent vibration control.

[0035] The disturbance observation framework: ; in, Let represent the disturbance estimate of disturbance d, z represent the intermediate variable, and L be the gain matrix.

[0036] S3. The disturbance estimate is combined with the control law of the minimum control synthesis algorithm using a feedforward compensation method to construct the drive control command.

[0037] Specifically, the dynamic deviation between the reference model state based on the preset ideal target and the state of the vibration response signal is first calculated, and the output error value is obtained from the dynamic deviation between the states.

[0038] The reference model for the preset ideal target is: ; in, A reference model representing the ideal goal, x m A represents the state variables of the reference model. m B represents the state matrix of the reference model. m Let r represent the input matrix of the reference model, and r represent the reference input signal.

[0039] The formula for determining the error value is: ; in, Indicates the error value. The state of the reference model representing the ideal goal. Indicates the vibration response signal. Let B represent the output signal error position matrix, B represent the control input matrix, and P represent the positive definite solution of the Lyapunov equation.

[0040] Next, based on the output error value, the adaptive gain of the control law of the minimum control synthesis algorithm is calculated; The adaptive gain is: ; in, and Indicates adaptive gain. and This represents the scaling factor, which determines the effectiveness of the adaptive effect; both are greater than zero. Typical values ​​for this can be [0.1, 0.01], [1.0, 0.1], etc. Indicates reference input.

[0041] Next, in order to calculate the control law of minimum control synthesis, the adaptive gain is combined with the reference input in the reference model. The control law for the minimum control synthesis is: ; in, Indicates the control law, and Indicates adaptive gain. This represents the vibration response signal.

[0042] The disturbance estimate in S2 is transformed into the disturbance compensation term of the minimum control synthesis through the disturbance compensation formula; The interference compensation term is calculated as follows: ; in, Indicates the interference compensation term. + It is the pseudo-inverse of matrix B, B d Represents the perturbation distribution matrix. (t) represents the estimate of the disturbance d(t).

[0043] The adaptive gain is combined with the disturbance compensation term to generate the final control input data; The final control input data is: ; in, and Indicates adaptive gain. This represents the vibration response signal.

[0044] By injecting the disturbance estimate into the control loop in a feedforward form and forming a cooperative control architecture with the minimum control synthesis algorithm, control performance is improved. The feedforward compensation channel instantly cancels out disturbances, enabling the system to respond to sudden disturbances at the millisecond level, significantly shortening the settling time. The adaptive feedback channel continuously corrects model mismatch and residual errors, allowing the system to maintain high-precision tracking even under parameter changes and external disturbances. The synergistic effect reduces energy consumption while ensuring control effectiveness.

[0045] In addition, this embodiment also verifies the stability of the control law in the control method by using the error of the closed-loop system.

[0046] S4. Generate a force according to the drive control command to adjust the vibration of the structure.

[0047] Specifically, based on the control input data obtained in S3, the control input data is converted into an input signal to become a drive control command, and the driver on the drive structure outputs a corresponding force to adjust the vibration state of the structure.

[0048] To implement the above method, this application also discloses a control system for a disturbance observation-driven minimum control synthesis adaptive vibration control method, which is used to implement the above method.

[0049] This vibration control system specifically includes a data acquisition unit, a disturbance estimation unit, a control command synthesis unit, and an execution unit.

[0050] refer to Figure 5 As shown, the vibration control system of this application achieves precise closed-loop control through a multi-unit collaborative architecture. The data acquisition unit installs accelerometers at key locations on the structure to acquire vibration response signals in real time and stores the processed time-series dataset. The disturbance estimation unit receives the dataset and, in conjunction with a pre-stored nominal model, constructs a dynamic observation framework to calculate various disturbances experienced by the structure, including external environmental excitations, parameter changes, and unmodeled dynamics, and generates accurate disturbance estimates.

[0051] The control command synthesis unit is connected to the disturbance estimation unit. Employing a minimum control synthesis algorithm, it combines the disturbance estimate with a feedforward compensation method and an adaptive control law to calculate and output the drive control command. The control command synthesis unit uses a dual-channel architecture for collaborative processing. On one hand, it generates an adaptive feedback quantity based on the real-time system state using the minimum control synthesis algorithm; on the other hand, it converts the disturbance estimate into a precise cancellation signal through the feedforward compensation channel. The two are then superimposed and fused to generate a composite control command. This feedforward-feedback coupling breaks the coupling dilemma between disturbance and state error in traditional control.

[0052] The execution unit is connected to the control command synthesis unit and is equipped with a driver. This driver converts digital control commands into analog voltage signals, activating the driver to generate corresponding physical forces applied to the structure, thereby achieving active regulation of structural vibration. The units interact at high speed through a real-time control hardware platform, ensuring the system completes a full control loop from signal acquisition to force output within milliseconds. The driver can be any one of the following: an electromagnetic driver, a piezoelectric ceramic actuator, or a macrofiber composite material.

[0053] Therefore, this application constructs a dual-channel cooperative control mechanism that combines feedforward compensation and adaptive adjustment by deeply integrating a disturbance observation framework with a minimum control synthesis algorithm. This design overcomes the technical bottleneck of traditional vibration control methods in dealing with unknown disturbances and model uncertainties. It not only achieves real-time accurate estimation and active cancellation of disturbance signals, shortening the system response time, but also significantly improves control accuracy and system robustness. This architecture greatly reduces the dependence on precise mathematical models, enhances the system's adaptability and practicality in complex engineering environments, and provides an effective vibration cancellation technique for structural vibration suppression.

[0054] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform... Figure 1 The method in the illustrated embodiment.

[0055] According to another aspect of the embodiments of this application, an electronic device is also provided, comprising a processor and a memory. The memory stores a computer program, and the processor executes the computer program to implement the described method.

[0056] Those skilled in the art will readily understand the implementation structure, working principle, and beneficial effects of electronic devices and computer-readable storage media by reading the above methods. For the sake of brevity, further details will not be elaborated here.

[0057] 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 this application. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of this application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.

[0058] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application 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.

[0059] 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.

[0060] 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 this application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0061] It should be noted that the above embodiments are illustrative of this application and not limiting of it, 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.

[0062] The above description is merely a specific embodiment or illustration of the embodiments of this application. The scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. The scope of protection of this application shall be determined by the scope of the claims.

Claims

1. A disturbance observation-driven minimum control synthesis adaptive vibration control method, characterized in that, Includes the following steps: A nominal model of the structure is established based on the vibration response signal of the structure; By fusing the nominal model of the structure and the vibration response signal, a disturbance observation framework is constructed to estimate the various disturbances experienced by the structure in real time and generate disturbance estimates. The disturbance estimate is combined with the control law of the minimum control synthesis algorithm using a feedforward compensation method to construct the drive control command; The vibration of the structure is adjusted by generating a force according to the drive control command.

2. The disturbance observation-driven minimum control synthesis adaptive vibration control method according to claim 1, characterized in that, The method for constructing the disturbance observation framework includes: The nominal model is combined with the vibration response signal to establish a state equation with perturbation; Design an intermediate variable z and introduce it into the perturbation-inducible state equation; simultaneously, force the control matrix to satisfy the Hurwitz stability condition through the gain matrix to ensure that the perturbation estimate converges asymptotically, thus obtaining the result. The state equation with perturbation is: ； Among them, B d Let d represent the disturbance distribution matrix, d represent the unknown external disturbance, A represent the state matrix of the nominal model, and B represent the disturbance distribution matrix. u Let X represent the control matrix, C represent the output matrix or observation matrix, x represent the state variables, u represent the input data, and y represent the output data. The disturbance observation framework: ； in, Let represent the disturbance estimate of disturbance d, z represent the differential form of the intermediate variable as the eliminated state variable, and L is the gain matrix.

3. The disturbance observation-driven minimum control synthesis adaptive vibration control method according to claim 1, characterized in that, The method of combining the disturbance estimate with the control law of the minimum control synthesis algorithm using feedforward compensation includes: The output error value is determined based on the dynamic deviation between the reference model with the preset ideal target and the vibration response signal. Based on the output error value, calculate the adaptive gain of the control law of the minimum control synthesis algorithm; The control law of the minimum control synthesis algorithm is derived by combining the adaptive gain with the reference input in the reference model. The adaptive gain is: ； in, and Indicates adaptive gain. and Indicates the scaling factor. Indicates reference input; The control law for the minimum control synthesis is: ； in, Indicates the control law, and Indicates adaptive gain. This represents the vibration response signal.

4. The disturbance observation-driven minimum control synthesis adaptive vibration control method according to claim 3, characterized in that, The feedforward compensation method is as follows: The disturbance compensation term for minimum control synthesis is calculated based on the disturbance estimate using a disturbance compensation formula. The disturbance compensation term is embedded into the control law of the minimum control synthesis algorithm to obtain the disturbance-compensated control law; The interference compensation term is calculated as follows: ； in, Indicates the interference compensation term. + It is the pseudo-inverse of matrix B, B d Represents the perturbation distribution matrix. (t) represents the estimate of the disturbance d(t).

5. The disturbance observation-driven minimum control synthesis adaptive vibration control method according to claim 4, characterized in that, The method for constructing drive control commands includes: The adaptive gain is combined with the disturbance compensation term to generate the final control input data; The final control input data is: ； in, and Indicates adaptive gain. This represents the vibration response signal.

6. The disturbance observation-driven minimum control synthesis adaptive vibration control method according to claim 3, characterized in that, The formula for determining the output error value of the dynamic deviation is: in, Indicates the error value. The state of the reference model representing the ideal goal. This represents the vibration response signal. Let B represent the output signal error position matrix, B represent the control input matrix, and P represent the positive definite solution of the Lyapunov equation.

7. The disturbance observation-driven minimum control synthesis adaptive vibration control method according to claim 1, characterized in that, The method for establishing the nominal model of the structure includes: Using a state-space model as the initial model framework, and based on system identification technology, the input signal time series and output signal time series in the vibration response signal are dynamically identified to construct an initial high-order state-space model that can comprehensively describe the initial dynamic characteristics. The initial high-order state-space model is obtained by reducing its order using the balanced truncation method. The nominal model of the structure is: ； Where A represents the state matrix of the nominal model, B u Let represent the control matrix, C represent the output matrix or observation matrix, x represent the state variables, u represent the input data, and y represent the output data.

8. The disturbance observation-driven minimum control synthesis adaptive vibration control method according to claim 3, characterized in that, The reference model for the preset ideal target is: ； in, A reference model representing the ideal goal, x m A represents the state variables of the reference model. m B represents the state matrix of the reference model. m Let r represent the input matrix of the reference model, and r represent the reference input signal.

9. A control system based on the disturbance observation-driven minimum control synthesis adaptive vibration control method as described in any one of claims 1-8, characterized in that, include: The data acquisition unit collects vibration response signals from acceleration sensors installed at key locations on the structure and converts them into time-series datasets. The disturbance estimation unit, connected to the data acquisition unit, is used to calculate various disturbances experienced by the structure and generate disturbance estimates. A control command synthesis unit is connected to the disturbance estimation unit and calculates and outputs drive control commands. The execution unit is connected to the control command synthesis unit and is equipped with a drive device to convert the drive control command into physical force to regulate the vibration of the structure.

10. A computer-readable storage medium on which program instructions are stored, characterized in that, The program instructions, when executed, are used to perform the disturbance observation-driven minimum control synthesis adaptive vibration control method as described in any one of claims 1-8.

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