Six-axis vibration table disturbance compensation predictive control method, system, terminal and medium

Abstract

The invention discloses a six-axis vibration table disturbance compensation predictive control method, system, terminal and medium, wherein the method comprises: constructing a high-dimensional global linearized Koopman model of the six-axis vibration table system; concentrating the disturbance to the output end of the Koopman model, and establishing a disturbance observer to obtain the observed disturbance, and then obtaining the posture state under the influence of the disturbance; converting the low-dimensional observed disturbance into a high-dimensional observed disturbance; constructing the prediction equation of each posture based on the high-dimensional observed disturbance; constructing the optimization problem of each posture based on the prediction equation; solving and obtaining the optimal control signal sequence of each posture according to the predicted posture vector and the reference posture vector; extracting the first control signal in the optimal control signal sequence of each posture as the control signal input to the six-axis vibration table system. The invention does not rely on complete system modeling, does not need to establish a high-dimensional disturbance observer, and can significantly improve the anti-disturbance performance and control performance of the six-axis vibration table.

Description

Technical Field

[0001] The present invention relates to the field of engineering structure dynamic test, and in particular to a six-axis vibration table disturbance compensation prediction control method, system, terminal and medium. Background Art

[0002] The six-axis vibration table is an important test equipment in the study of modern structural dynamics and seismic performance. Especially in the field of earthquake simulation testing and vehicle-bridge coupled vibration research, it is crucial to use a six-axis vibration table to simulate the multi-degree-of-freedom response in practical applications. However, during the test, the vibration table will be affected by internal and external disturbances and nonlinear dynamics, which directly affects the control accuracy of the vibration table and reduces the reliability of the test results. Therefore, improving the control accuracy of the exciter so that it can reproduce the reference signal with high precision is the key to improving the quality of the experiment. Therefore, studying the disturbance compensation method and high-precision control of the six-axis vibration table has important scientific significance and practical value.

[0003] At present, researchers have proposed a variety of control methods to solve the control problem of six-axis vibration tables, such as traditional PID control, three-parameter control and model predictive control, etc. These methods can achieve certain control effects after parameter tuning. However, these control methods assume that the vibration table is a linear time-invariant system, ignoring the nonlinearity, uncertainty and coupling characteristics of the vibration table. Especially in complex vibration scenarios with multiple degrees of freedom, when high-frequency vibration waveforms are loaded, the coupling effect of the exciter will increase significantly. The traditional decoupling control strategy cannot meet the control accuracy requirements due to its complex modeling and low precision. Therefore, the existing control systems still face huge challenges in dealing with the interference of complex vibration tables with multiple degrees of freedom. Summary of the invention

[0004] The present invention provides a six-axis vibration table disturbance compensation predictive control method, system, terminal and medium to solve the control accuracy problem of the six-axis vibration table under complex disturbance and uncertain conditions.

[0005] In a first aspect, a six-axis vibration table disturbance compensation predictive control method is provided, comprising the following steps:

[0006] S1: Construct the Koopman model of high-dimensional global linearization of the six-axis shaker system;

[0007] S2: Concentrate the disturbance to the output end of the Koopman model, and establish a disturbance observer to obtain the output end observation disturbance, and then obtain the posture state under the influence of the disturbance;

[0008] S3: Convert low-dimensional observation disturbances into high-dimensional observation disturbances based on the posture state under the influence of disturbances;

[0009] S4: Construct prediction equations for each position of the six-axis shaker system based on high-dimensional observation disturbances;

[0010] S5: Construct the optimization problem of each posture of the six-axis vibration table system based on the prediction equation of each posture;

[0011] S6: solving the optimization problem of each posture according to the predicted posture vector predicted by the prediction equation of each posture and the reference posture vector of each posture to obtain the optimal control signal sequence of each posture;

[0012] S7: extracting the first control signal in the optimal control signal sequence of each posture as the control signal input to the six-axis vibration table system.

[0013] Furthermore, step S1 specifically includes:

[0014] Select pose state As the Koopman model training set state, Indicates that the vibration table system is The pose vector at the moment, Respectively The vector formed by each pose after first and second order differentiation;

[0015] Obtaining the system matrix of the Koopman model of the six-axis vibration table system based on the EDMD method , , ;

[0016] According to the system matrix of the Koopman model, the Koopman model of high-dimensional global linearization of the six-axis vibration table system is established and expressed as follows:

[0017]

[0018] In the formula, , represents the linear state variables of the six-axis vibration table system in the ascending dimensional space; represents the dimension-raising function; Indicates the Koopman model for the pose state The predicted output value of express Control signal at every moment; represents the total external disturbance.

[0019] Furthermore, in step S2, the posture state under the influence of the disturbance is expressed as follows:

[0020]

[0021] In the formula, represents the observed disturbance at the output obtained by the disturbance observer.

[0022] Furthermore, in step S2, the dimension of the disturbance observed by the disturbance observer depends only on the dimension of the training set data, rather than the dimension of the Koopman model, and the disturbance observer is one of an extended state observer, a dedicated disturbance observer, or an equivalent input disturbance observer.

[0023] Furthermore, in step S3, the high-dimensional observation disturbance It is obtained by the following formula:

[0024]

[0025] In the formula, represents the dimension-raising function; , represents the high-dimensional pose state without disturbance.

[0026] Furthermore, in step S4, the prediction equations of each posture are expressed as follows:

[0027]

[0028] in, and Position The predicted pose vector and control signal sequence of The value is , , , , or ; They are the position of the vibration table in the x-axis direction, the position in the y-axis direction, the position in the z-axis direction, the position rotated around the x-axis direction, the position rotated around the y-axis direction, and the position rotated around the z-axis direction; , , ; , For posture The output matrix of , , and They are respectively the prediction time domain, control time domain, dimension of Koopman operator dimensionality increase and dimension of Koopman training data of the six-axis vibration table system.

[0029] Furthermore, in step S5, the optimization goal of the optimization problem of each posture of the six-axis vibration table system is to minimize the sum of the deviation between the predicted posture and the reference posture and the control signal in the entire prediction time domain.

[0030] In a second aspect, a six-axis vibration table disturbance compensation predictive control system is provided, comprising:

[0031] Koopman model building module, used to build a high-dimensional global linear Koopman model of a six-axis shaker system;

[0032] The posture state acquisition module is used to concentrate the disturbance to the output end of the Koopman model, and establish a disturbance observer to obtain the observed disturbance at the output end, and then obtain the posture state under the influence of the disturbance;

[0033] The perturbation dimension-increasing module is used to transform low-dimensional observation perturbations into high-dimensional observation perturbations based on the posture state under the influence of the perturbation;

[0034] Prediction equation building module, used to build prediction equations for each position of the six-axis shaker system based on high-dimensional observation disturbances;

[0035] The optimization problem construction module is used to construct the optimization problem of each posture of the six-axis vibration table system based on the prediction equation of each posture;

[0036] The optimal control signal sequence acquisition module is used to solve the optimization problem of each posture according to the predicted posture vector predicted by the prediction equation of each posture and the reference posture vector of each posture, so as to obtain the optimal control signal sequence of each posture;

[0037] The control signal generation module is used to extract the first control signal in the optimal control signal sequence of each posture as the control signal input to the six-axis vibration table system.

[0038] In a third aspect, an electronic terminal is provided, comprising:

[0039] a memory having a computer program stored thereon;

[0040] The processor is used to load and execute the computer program to implement the six-axis vibration table disturbance compensation predictive control method as described above.

[0041] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the six-axis vibration table disturbance compensation predictive control method as described above is implemented.

[0042] The present invention proposes a six-axis vibration table disturbance compensation predictive control method, system, terminal and medium, which effectively compensates the model prediction accuracy of the vibration table Koopman model under the influence of disturbance and nonlinearity by converting the observed low-dimensional disturbance to high-dimensional disturbance, improves the anti-disturbance ability and robustness of the predictive control, and thus improves the control accuracy and reliability of the system. Compared with the traditional control method, this method does not rely on system modeling, and can obtain high-dimensional disturbance information by designing a low-dimensional disturbance observer. The obtained high-dimensional disturbance compensation prediction model is evolved in a high-dimensional space, which greatly improves the prediction accuracy of the prediction model under disturbance, greatly simplifies the difficulty of obtaining high-dimensional disturbances and the controller design process, has stronger adaptability and robustness, and can significantly improve the anti-disturbance performance and control performance of the six-axis vibration table. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 It is a flow chart of the disturbance compensation predictive control method of a six-axis vibration table provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0046] For the control problem of the six-axis vibration table, the traditional decoupling control strategy cannot meet the control accuracy requirements due to its complex modeling and low precision. Therefore, the existing control system still faces huge challenges in dealing with the interference of complex vibration tables with multiple degrees of freedom. Based on this, the present invention proposes a six-axis vibration table disturbance compensation predictive control method, system, terminal and medium based on data-driven to solve the control accuracy problem of the six-axis vibration table under complex disturbances and uncertain conditions. The technical solution of the present invention is described in detail below in conjunction with specific embodiments.

[0047] like Figure 1 As shown, an embodiment of the present invention provides a six-axis vibration table disturbance compensation predictive control method, comprising the following steps:

[0048] S1: Construct the Koopman model of high-dimensional global linearization of the six-axis shaker system;

[0049] S2: Concentrate the disturbance to the output end of the Koopman model, and establish a disturbance observer to obtain the observed disturbance at the output end, and then obtain the posture state under the influence of the disturbance;

[0050] S3: Convert low-dimensional observation disturbances into high-dimensional observation disturbances based on the posture state under the influence of disturbances;

[0051] S4: Construct prediction equations for each position of the six-axis shaker system based on high-dimensional observation disturbances;

[0052] S5: Construct the optimization problem of each posture of the six-axis vibration table system based on the prediction equation of each posture;

[0053] S6: solving the optimization problem of each posture according to the predicted posture vector predicted by the prediction equation of each posture and the reference posture vector of each posture to obtain the optimal control signal sequence of each posture;

[0054] S7: extracting the first control signal in the optimal control signal sequence of each posture as the control signal input to the six-axis vibration table system.

[0055] The following is a more specific description of the six-axis vibration table disturbance compensation predictive control method provided in the above embodiment.

[0056] The shaking table model for a six-axis earthquake simulation is expressed as follows:

[0057] (1)

[0058] in, For the six-axis vibration table system The pose vector at the moment includes three translation poses and three rotation poses; The expression is ,in for Time vibration table The position in the axis direction, for Time vibration table The position in the axis direction, for Time vibration table The position in the axis direction, for Time vibration table around Direction rotation posture, for Time vibration table around Direction rotation posture, for Time vibration table around Direction rotation posture; is the nonlinear mapping function of the six-axis vibration table, represents external disturbance; express The control signal at the moment.

[0059] Due to the mapping relationship It is difficult to establish an accurate mechanism model. In this embodiment, the Koopman operator is used to fit the six-axis vibration table system model. In order to improve the fitting accuracy of the Koopman operator, this embodiment adopts As the Koopman training set state, They are the pose vectors The vector formed by the first and second order differentials of the elements of the six-axis vibration table system. The velocity term of the six-axis vibration table system cannot be measured, and the translational velocity signal and the high-order signal of rotation can be obtained by means of Kalman filter, signal synthesis or tracking differentiator. The original data set of the six-axis vibration table is converted into a Koopman training set and written in the form of formula (2):

[0060] (2)

[0061] In the formula, , , Represent the input pose state set, output pose state set and control signal set respectively

[0062] Choose a set of basis dimensionality-raising functions Used for posture state dimension upgrade:

[0063] (3)

[0064] In the formula, Indicates dimensional function, subscript Indicates the serial number of the dimension-raising function.

[0065] Based on the dimension-raising function, the posture state data of the six-axis vibration table can be upgraded to a high dimension and expressed as follows:

[0066] (4)

[0067] In the formula, , Respectively , The dataset after dimensionality increase.

[0068] Based on the above-mentioned dimensional state, the system matrix of the Koopman model of the six-axis vibration table system can be solved by the EDMD (Exteneded Dynamic Mode Decomposition) method: , , , and expressed as follows:

[0069] (5)

[0070] In the formula, the superscript Represents a pseudo-inverse operation.

[0071] The Koopman model of high-dimensional global linearization of the six-axis vibration table can be established through equation (5) and expressed as follows:

[0072] (6)

[0073] In the formula, , represents the linear state variables of the six-axis vibration table system in the ascending dimensional space; , , Represents the system matrix of the Koopman model; Indicates the Koopman model for the pose state The predicted output value of .

[0074] Considering the impact of disturbance on the state evolution of the Koopman model, we have:

[0075] (7)

[0076] In the formula, Represents the total external disturbance, including external disturbance and model identification error.

[0077] Since the dimension of the state variable in the dimensional space is too high, it is difficult to design an observer to observe the influence of disturbance on the six-axis vibration table system. In this embodiment, the disturbance is concentrated to the output end of the Koopman model and expressed as follows:

[0078] (8)

[0079] In the formula, represents the output disturbance. By transforming equation (8), the disturbance can be concentrated to the output of the Koopman model, and a disturbance observer is designed based on the Koopman model to observe the disturbance. Dimensions and perturbations The dimensions are inconsistent, even if the perturbation is obtained The value of is also difficult to suppress disturbances through feedback control. In order to make full use of the disturbance observation information, this embodiment combines the Koopman-based disturbance observer with MPC (model predictive control), and reversely obtains the high-dimensional disturbance of the Koopman operator space through the observation value of the disturbance observer in the posture dimension, thereby compensating the prediction accuracy of the Koopman-based MPC when predicting future time steps, thereby improving the control accuracy of the MPC. Based on the above ideas, the dimension of the disturbance observed by the disturbance observer only depends on the dimension of the training set data, rather than the dimension of the Koopman model. In specific implementation, the disturbance observer can select one of the extended state observer, the dedicated disturbance observer, or the equivalent input disturbance observer. This embodiment takes the construction of the disturbance observer by the extended state observer as an example to illustrate, and the extended state observer can be established by formula (8) to obtain the observed disturbance of each posture .

[0080] First, define the expansion state as follows:

[0081] (9)

[0082] Then there is the following expanded state system of the six-axis vibration table:

[0083] (10)

[0084] The extended state observer is established for the extended state system of the six-axis vibration table system, which is expressed as follows:

[0085] (11)

[0086] in, and The observer is Always check the vibration table position and disturbance Observed value of and is the system observation gain coefficient matrix, satisfying , , For the The observer gain corresponding to the degrees of freedom is Since the six-axis vibration table training set contains too many states, there are too many parameters that need to be adjusted. Generally speaking, the parameters of the observation gain coefficient matrix are uniformly adjusted by the pole configuration method, where:

[0087] (12)

[0088] in, is the real axis coefficient, is the imaginary axis coefficient and satisfies , which means placing the poles of the system at and the poles are located inside the unit circle.

[0089] Then the posture state under the influence of disturbance is obtained by the following formula and expressed as follows:

[0090] (13)

[0091] in, is the observed perturbation of each pose obtained based on equation (11).

[0092] The high-dimensional pose state without disturbance is defined as , then the high-dimensional observation disturbance of the system It can be obtained by the following formula:

[0093] (14)

[0094] In the formula, Represents the dimension-raising function of the posture state. Based on formula (14), the transformation from low-dimensional observation perturbation to high-dimensional observation perturbation can be realized.

[0095] By posture For example, define the prediction vector , output vector and the reference pose vector As shown in equations (15), (16) and (17) respectively:

[0096] (15)

[0097] (16)

[0098] (17)

[0099] In the formula, , , Respectively represent the posture The predicted pose vector, control signal vector and reference pose vector of Represents the predicted pose at time k+1 , represents the pose at time k The corresponding control signal, represents the pose at time k The corresponding reference pose, The value is , , , , or ; They are the position of the vibration table in the x-axis direction, the position in the y-axis direction, the position in the z-axis direction, the position rotated around the x-axis direction, the position rotated around the y-axis direction, and the position rotated around the z-axis direction.

[0100] Based on formula (14), the prediction equations of each position of the six-axis vibration table system can be constructed and expressed as follows:

[0101] (18)

[0102] in, , , ; , For posture The output matrix of , , and They are respectively the prediction time domain, control time domain, dimension of Koopman operator dimensionality increase and dimension of Koopman training data of the six-axis vibration table system.

[0103] Based on the above prediction equation, the optimization problem of each posture of the six-axis vibration table system is constructed. The optimization goal of the optimization problem of each posture of the six-axis vibration table system is to minimize the sum of the deviation between the predicted posture and the reference posture and the control signal in the entire prediction time domain, which is specifically expressed as follows:

[0104] (19)

[0105] In the formula, For posture The cost function of and are the error weight matrix and the control weight matrix respectively. Assume that the control signal of the system outside the control time domain remains unchanged, that is, , .

[0106] Based on the above prediction equation and reference pose vector, the optimal control signal sequence can be obtained by solving the optimization problem:

[0107] (20)

[0108] Take the first control signal of the optimal control signal sequence As input to the six-axis vibration table system pose Corresponding control signal. Taking posture x as an example, the first control signal of the optimal control signal sequence corresponding to the posture x of the six-axis vibration table system is It is expressed as follows:

[0109] (twenty one)

[0110] Based on the above method, complete disturbance compensation control of each posture can be achieved under the Koopman model.

[0111] The above embodiment provides a six-axis vibration table disturbance compensation predictive control method, which uses historical data and real-time feedback to build a data-driven model, and effectively compensates the model prediction accuracy of the vibration table Koopman model under disturbance and nonlinear influence by converting the observed low-dimensional disturbance to high-dimensional disturbance, thereby improving the anti-disturbance ability and robustness of predictive control, thereby improving the control accuracy and reliability of the system. Compared with traditional control methods, this method does not rely on system modeling, and can obtain high-dimensional disturbance information by designing a low-dimensional disturbance observer. The obtained high-dimensional disturbance compensation prediction model evolves in a high-dimensional space, which greatly improves the prediction accuracy of the prediction model under disturbance, greatly simplifies the difficulty of obtaining high-dimensional disturbances and the controller design process, has stronger adaptability and robustness, and can significantly improve the anti-disturbance performance and control performance of the six-axis vibration table.

[0112] The embodiment of the present invention further provides a six-axis vibration table disturbance compensation prediction control system, comprising:

[0113] Koopman model building module, used to build a high-dimensional global linear Koopman model of a six-axis shaker system;

[0114] The posture state acquisition module is used to concentrate the disturbance to the output end of the Koopman model, and establish a disturbance observer to obtain the observed disturbance at the output end, and then obtain the posture state under the influence of the disturbance;

[0115] The perturbation dimension-increasing module is used to transform low-dimensional observation perturbations into high-dimensional observation perturbations based on the posture state under the influence of the perturbation;

[0116] Prediction equation building module, used to build prediction equations for each position of the six-axis shaker system based on high-dimensional observation disturbances;

[0117] The optimization problem construction module is used to construct the optimization problem of each posture of the six-axis vibration table system based on the prediction equation of each posture;

[0118] The optimal control signal sequence acquisition module is used to solve the optimization problem of each posture according to the predicted posture vector predicted by the prediction equation of each posture and the reference posture vector of each posture, so as to obtain the optimal control signal sequence of each posture;

[0119] The control signal generation module is used to extract the first control signal in the optimal control signal sequence of each posture as the control signal input to the six-axis vibration table system.

[0120] It should be understood that the functional unit modules in various embodiments of the present invention may be concentrated in one processing unit, or each unit module may exist physically separately, or two or more unit modules may be integrated in one unit module, and may be implemented in the form of hardware or software.

[0121] An embodiment of the present invention further provides an electronic terminal, including:

[0122] a memory having a computer program stored thereon;

[0123] The processor is used to load and execute the computer program to implement the six-axis vibration table disturbance compensation predictive control method as described above.

[0124] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the six-axis vibration table disturbance compensation predictive control method as described above is implemented.

[0125] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0126] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0127] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0128] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0130] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A six-axis vibration table disturbance compensation predictive control method, characterized in that: The steps include: S1: Construct the Koopman model of high-dimensional global linearization of the six-axis shaker system; S2: Concentrate the disturbance to the output end of the Koopman model, and establish a disturbance observer to obtain the observed disturbance at the output end, and then obtain the posture state under the influence of the disturbance; S3: Convert low-dimensional observation disturbances into high-dimensional observation disturbances based on the posture state under the influence of disturbances; S4: Construct prediction equations for each position of the six-axis shaker system based on high-dimensional observation disturbances; S5: Construct the optimization problem of each posture of the six-axis vibration table system based on the prediction equation of each posture; S6: solving the optimization problem of each posture according to the predicted posture vector predicted by the prediction equation of each posture and the reference posture vector of each posture to obtain the optimal control signal sequence of each posture; S7: extracting the first control signal in the optimal control signal sequence of each posture as a control signal input to the six-axis vibration table system; Step S1 specifically includes: Select pose state As the Koopman model training set state, Indicates that the six-axis vibration table system The pose vector at the moment, Respectively The vector formed by each pose after first and second order differentiation; Obtaining the system matrix of the Koopman model of the six-axis vibration table system based on the EDMD method , , ; According to the system matrix of the Koopman model, the Koopman model of high-dimensional global linearization of the six-axis vibration table system is established and expressed as follows: ； In the formula, , represents the linear state variables of the six-axis vibration table system in the ascending dimensional space; represents the dimension-raising function; Indicates the Koopman model for the pose state The predicted output value of express Control signal at every moment; represents the total external disturbance.

2. The six-axis vibration table disturbance compensation predictive control method according to claim 1 is characterized in that: In step S2, the posture state under the influence of disturbance is expressed as follows: ； In the formula, represents the observed disturbance at the output obtained by the disturbance observer.

3. The six-axis vibration table disturbance compensation predictive control method according to claim 2 is characterized in that: In step S2, the dimension of the disturbance observed by the disturbance observer depends only on the dimension of the training set data, rather than the dimension of the Koopman model. The disturbance observer is one of an extended state observer, a dedicated disturbance observer or an equivalent input disturbance observer.

4. The six-axis vibration table disturbance compensation predictive control method according to claim 2 is characterized in that: In step S3, the high-dimensional observation perturbation It is obtained by the following formula: ； In the formula, represents the dimension-raising function; , represents the high-dimensional pose state without disturbance.

5. The six-axis vibration table disturbance compensation predictive control method according to claim 4 is characterized in that: In step S4, the prediction equations for each posture are expressed as follows: ； in, and Position The predicted pose vector and control signal sequence of The value is , , , , or ; They are the position of the vibration table in the x-axis direction, the position in the y-axis direction, the position in the z-axis direction, the position rotated around the x-axis direction, the position rotated around the y-axis direction, and the position rotated around the z-axis direction; , , ; , For posture The output matrix of , , and They are respectively the prediction time domain, control time domain, dimension of Koopman operator dimensionality increase and dimension of Koopman training data of the six-axis vibration table system.

6. The six-axis vibration table disturbance compensation predictive control method according to claim 1 is characterized in that: In step S5, the optimization goal of the optimization problem of each posture of the six-axis vibration table system is to minimize the sum of the deviation between the predicted posture and the reference posture and the control signal in the entire prediction time domain.

7. A six-axis vibration table disturbance compensation predictive control system, characterized in that: The method for realizing the disturbance compensation predictive control method of the six-axis vibration table as claimed in claim 1 comprises: Koopman model building module, used to build a high-dimensional global linear Koopman model of a six-axis shaker system; The posture state acquisition module is used to concentrate the disturbance to the output end of the Koopman model, and establish a disturbance observer to obtain the observed disturbance at the output end, and then obtain the posture state under the influence of the disturbance; The perturbation dimension-increasing module is used to transform low-dimensional observation perturbations into high-dimensional observation perturbations based on the posture state under the influence of the perturbation; Prediction equation building module, used to build prediction equations for each position of the six-axis shaker system based on high-dimensional observation disturbances; The optimization problem construction module is used to construct the optimization problem of each posture of the six-axis vibration table system based on the prediction equation of each posture; The optimal control signal sequence acquisition module is used to solve the optimization problem of each posture according to the predicted posture vector predicted by the prediction equation of each posture and the reference posture vector of each posture, so as to obtain the optimal control signal sequence of each posture; The control signal generation module is used to extract the first control signal in the optimal control signal sequence of each posture as the control signal input to the six-axis vibration table system.

8. An electronic terminal, characterized in that: include: a memory having a computer program stored thereon; A processor is used to load and execute the computer program to implement the six-axis vibration table disturbance compensation predictive control method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the six-axis vibration table disturbance compensation predictive control method according to any one of claims 1 to 6 is implemented.

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