Active control method for electro-hydraulic servo system based on finite time control

Abstract

The invention provides an electro-hydraulic servo system active control method based on finite time control. According to the method, an error boundary is dynamically constrained through a time-varying obstacle Lyapunov function, and a backstepping recursion control architecture and a fractional order convergence item are combined, so that the convergence speed and the anti-interference capability of the system are remarkably improved, and the overshoot problem and the chattering phenomenon of PID control are effectively suppressed; a feedforward compensation and self-adaptive disturbance observation mechanism is designed aiming at the nonlinear characteristic and anti-interference performance problems of the electro-hydraulic servo system, the stability can still be quickly recovered under strong external disturbance, and the accuracy and robustness of control force output are ensured; a scaling experiment platform is constructed based on a similarity criterion, the engineering universality of a control strategy is verified, and an efficient, reliable and easy-to-deploy solution is provided for anti-seismic control of a complex structure.

Description

Technical Field

[0001] The present invention belongs to the technical field of structural engineering vibration control and mechatronics, and in particular relates to an active control method for an electro-hydraulic servo system based on finite time control, which is particularly suitable for earthquake-resistant control scenarios in high-rise buildings. Background Art

[0002] In the field of seismic resistance of building structures, active tuned mass dampers (ATMDs) significantly outperform passive TMD systems by applying control forces in real time to offset seismic energy. Electro-hydraulic servo drive systems have become the mainstream actuation solution for ATMDs due to their high power density and rapid response. However, existing technologies face the following bottlenecks: The inherent nonlinearity of valve flow and friction hysteresis in hydraulic systems make traditional PID control prone to overshoot oscillations; the seismic excitation spectrum is complex and variable, and while existing sliding mode control strategies can improve robustness, high-frequency vibrations can accelerate wear of hydraulic components; full-scale experimental platforms are expensive to build, and existing scaling methods focus solely on geometric similarity, resulting in distorted control force mapping.

[0003] Although previous studies have attempted to improve control performance through adaptive algorithms or traditional optimization methods such as GA, PSO, DE, etc., their convergence speed is difficult to meet the real-time requirements of seismic response. Emerging multi-objective optimization strategies and robust control methods based on Lyapunov functions provide theoretical support and practical paths for high-performance control under complex working conditions. Summary of the Invention

[0004] This invention aims to address the low control accuracy and slow convergence speed of existing electro-hydraulic servo ATMD systems, which suffer from nonlinear characteristics, time lag effects, and insufficient interference immunity. By proposing an active control method for electro-hydraulic servo systems based on finite-time control, this method achieves finite-time convergence and dynamic constraints on tracking errors by designing a time-varying barrier Lyapunov function (TVBLF) combined with a backstepping recursive control architecture. This significantly improves the system's robustness and control efficiency under complex operating conditions.

[0005] The present invention is achieved through the following technical solutions. The present invention proposes an active control method for an electro-hydraulic servo system based on finite time control, the method comprising: S1, establish the electro-hydraulic servo drive ATMD shock absorption structure model, and determine the optimal control force U by comparing the TMD and ATMD control strategies; S2, design the hydraulic system parameters based on the optimal control force U and establish the mathematical model of the electro-hydraulic servo system; S3, designing a finite-time controller based on a time-varying barrier Lyapunov function, dynamically constraining the tracking error through a third-order backstepping recursive architecture, and combining fractional-order terms to achieve overshoot suppression and finite-time convergence; S4, build an electro-hydraulic servo scale-down experimental platform based on similarity criteria, and integrate the controller to conduct multi-condition verification, including step response, sinusoidal tracking and force control experiments under seismic wave excitation.

[0006] Furthermore, the S1 specifically includes: The building structure is simplified into a multi-degree-of-freedom linear system, and the Lagrange equation is used to establish the motion equation:

[0007] Where X is the displacement vector of the structure relative to the ground, H is the control force position matrix, I is the seismic wave position matrix, M, C and K are the mass matrix, damping matrix and stiffness matrix of the ATMD control system respectively, x g is the seismic wave input vector, U is the optimal control force; Establish a vibration response evaluation system that includes the inter-story displacement angle index; The LQR algorithm is used to solve the optimal control force that minimizes the inter-story displacement angle.

[0008] Furthermore, the LQR algorithm design includes: Constructing a quadratic performance index function based on structural dynamic response ; The semi-positive definite matrix Q represents the structural vibration energy index, and the positive definite matrix R reflects the control system energy consumption parameter. The two together constitute the core adjustment parameters of active control. The first term of this function quantifies the total structural vibration energy, and the second term represents the control system energy input. Calculate the feedback gain matrix of the optimal control force ; in is the feedback gain matrix of the control input, which is used to calculate the optimal control force U, P is the solution of the Riccati equation, and B is the input matrix of the system.

[0009] Furthermore, the mathematical model in S2 includes: Dynamic equations of hydraulic cylinder considering servo valve flow ; in is the load pressure (MPa), V t is the sum of the volume of the oil inlet chamber and the oil return chamber (m 3 ), is the effective working area of ​​the hydraulic cylinder piston (m 2 ), is the output displacement of the hydraulic cylinder (m), is the leakage coefficient in the system (m 3 / (s·Pa)), is the equivalent bulk elastic modulus of the oil (MPa); The load force balance equation including the leakage coefficient and the viscous friction coefficient is: ; in The mass of the piston and connected load equivalently converted to the piston rod end (kg), is the viscous damping coefficient of the system (N·s / m), is the stiffness of the elastic element in the system (N / m), is the external load applied to the piston (N); Valve port flow equation for servo valve including flow gain coefficient ; The system transfer function model is obtained by combining the three equations.

[0010] Furthermore, the system transfer function model is: .

[0011] Furthermore, the design method of the finite time controller in S3 includes: Design of a third-order backstepping recursive control architecture , construct the dynamic constraint error boundary of the time-varying barrier function to ensure that the absolute value of z1 is less than this boundary; , is the virtual control law; 、 、 Respectively represent position tracking error, velocity tracking error, and acceleration tracking error; Design of virtual control laws , , combined with fractional-order terms to accelerate convergence; Deriving the actual control law , the finite time convergence of the system is proved by Lyapunov stability theory; the feedforward term of this control law Can offset the inherent dynamics of the system, reduce the feedback gain requirements, and the feedback term Provide damping to actively suppress hydraulic system resonance; fractional order terms Force finite time convergence to ensure that the error returns to zero within the predetermined time; compensation term Suppress unmodeled dynamics and external disturbances.

[0012] Furthermore, in step S3, a virtual control law is designed , Specifically: , The first of these is the exponential convergence term, the second term is the finite time term, and the third term is the Young compensation term;

[0013] New items For active offset The coupling terms in .

[0014] Furthermore, the similarity criteria specifically include: geometric scale ratio 1 / 36, prototype cylinder diameter 0.3072m corresponds to model cylinder diameter 0.05m; force scale ratio 1 / 36, the maximum output force of the original system corresponds to the maximum output force of the scaled system is 2800N, the load prototype is 58t, and the load of the scaled system is 100kg.

[0015] The present invention also proposes an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the active control method of an electro-hydraulic servo system based on finite time control when executing the computer program.

[0016] The present invention also provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the active control method of an electro-hydraulic servo system based on finite time control.

[0017] Beneficial effects of the present invention: The present invention integrates the finite-time control algorithm with the electro-hydraulic servo active tuned mass damper (ATMD) system. By dynamically constraining the error boundary of the time-varying barrier Lyapunov function and combining the backstepping recursive control architecture with the fractional-order convergence term, the system's convergence speed and anti-interference ability are significantly improved, and the overshoot and chattering problems of PID control are effectively suppressed. In view of the nonlinear characteristics and anti-interference problems of the electro-hydraulic servo system, a feedforward compensation and adaptive disturbance observation mechanism are designed, which can quickly restore stability under strong external disturbances, ensuring the accuracy and robustness of the control force output. A scaled-down experimental platform is constructed based on the similarity criterion to verify the engineering universality of the control strategy, providing an efficient, reliable and easy-to-deploy solution for the seismic control of complex structures. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0019] Figure 1 This is a flow chart of an active control method for an electro-hydraulic servo system based on finite time control according to the present invention; Figure 2Schematic diagram of the shock-absorbing structure model of the present invention; Figure 3 Schematic diagram of the electro-hydraulic servo valve-controlled cylinder-driven ATMD system of the present invention; Figure 4 This is a schematic diagram of the overall architecture of the finite time controller of the present invention; Figure 5 This is a detailed view of the scaled-down hydraulic test bench of the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] Under conditions of strong nonlinearity and high-frequency disturbances, the existing electro-hydraulic servo ATMD system has the defects of large overshoot and difficult to eliminate steady-state error due to traditional PID and sliding mode control methods. The present invention adopts a third-order backstepping recursive control framework, introduces a time-varying barrier function to dynamically constrain the error boundary, accelerates the convergence of the system state through fractional-order convergence terms, and combines feedforward compensation with disturbance observation mechanisms to effectively suppress the nonlinear interference and time lag effects of the hydraulic system, ensuring the rapidity and stability of the control force output. When designing a control system for an actual structure, it is necessary to consider the nonlinear characteristics of the system and the randomness of the disturbance. Therefore, when designing a control system, a mathematical model is established in combination with the working principle of the electro-hydraulic servo system, the system is optimized through the DE-PID control strategy, and the system is verified through simulation analysis of tracking control to ensure good control effect.

[0022] Specifically, combined Figure 1-Figure 5 The present invention proposes an active control method for an electro-hydraulic servo system based on finite time control, the method comprising: S1, establish the electro-hydraulic servo drive ATMD shock absorption structure model, and determine the optimal control force U by comparing the TMD and ATMD control strategies; Said S1 specifically includes: The building structure is simplified into a multi-degree-of-freedom linear system, and the Lagrange equation is used to establish the motion equation: ; Where X is the displacement vector of the structure relative to the ground, H is the control force position matrix, I is the seismic wave position matrix, M, C and K are the mass matrix, damping matrix and stiffness matrix of the ATMD control system respectively, x g is the seismic wave input vector, U is the optimal control force; Establish a vibration response evaluation system that includes the inter-story displacement angle index; The LQR algorithm is used to solve the optimal control force that minimizes the inter-story displacement angle. Specifically, through seismic wave (ElCentro wave, Taft wave, etc.) excitation simulation, the displacement response curve of each floor is obtained, and the LQR algorithm is used to calculate the optimal control force, providing a basis for the output force design of the electro-hydraulic servo system.

[0023] The LQR algorithm design includes: Constructing a quadratic performance index function based on structural dynamic response ; The semi-positive definite matrix Q represents the structural vibration energy index, and the positive definite matrix R reflects the control system energy consumption parameter. The two together constitute the core adjustment parameters of active control. The first term of this function quantifies the total structural vibration energy, and the second term represents the control system energy input. Calculate the feedback gain matrix of the optimal control force ; in is the feedback gain matrix of the control input, which is used to calculate the optimal control force U, P is the solution of the Riccati equation, and B is the input matrix of the system.

[0024] S2, design the hydraulic system parameters based on the optimal control force U and establish the mathematical model of the electro-hydraulic servo system; The mathematical model described in S2 includes: Dynamic equations of hydraulic cylinder considering servo valve flow ; in is the load pressure (MPa), V t is the sum of the volume of the oil inlet chamber and the oil return chamber (m 3 ), is the effective working area of ​​the hydraulic cylinder piston (m 2 ), is the output displacement of the hydraulic cylinder (m), is the leakage coefficient in the system (m 3 / (s·Pa)), is the equivalent bulk elastic modulus of the oil (MPa); The load force balance equation including the leakage coefficient and the viscous friction coefficient is: ; in The mass of the piston and connected load equivalently converted to the piston rod end (kg), is the viscous damping coefficient of the system (N·s / m), is the stiffness of the elastic element in the system (N / m), is the external load applied to the piston (N); Valve port flow equation for servo valve including flow gain coefficient ; The system transfer function model is obtained by combining the three equations.

[0025] The system transfer function model is: .

[0026] Differential evolution algorithm (DE) is used to optimize PID parameters, and the objective function is: , with a population size of M=50 and the number of iterations G=100, the optimal PID parameters Kp=10000, Ki=0.2, and Kd=150 are obtained.

[0027] S3, designing a finite-time controller based on a time-varying barrier Lyapunov function, dynamically constraining the tracking error through a third-order backstepping recursive architecture, and combining fractional-order terms to achieve overshoot suppression and finite-time convergence; The design method of the finite time controller described in S3 includes: Design of a third-order backstepping recursive control architecture , construct the dynamic constraint error boundary of the time-varying barrier function to ensure that the absolute value of z1 is less than this boundary; , It is a virtual control law, which is designed step by step by backstepping method; 、 、 Respectively represent position tracking error, velocity tracking error, and acceleration tracking error; Design of virtual control laws , , combined with fractional-order terms to accelerate convergence; Deriving the actual control law , the finite time convergence of the system is proved by Lyapunov stability theory; the feedforward term of this control law Can offset the inherent dynamics of the system, reduce the feedback gain requirements, and the feedback term Provide damping to actively suppress hydraulic system resonance; fractional order terms Force finite time convergence to ensure that the error returns to zero within the predetermined time; compensation term Suppress unmodeled dynamics and external disturbances.

[0028] In step S3, the virtual control law is designed , Specifically: , The first of these is the exponential convergence term, the second term is the finite time term, and the third term is the Young compensation term;

[0029] New items For active offset The coupling terms in .

[0030] S4, build an electro-hydraulic servo scale-down experimental platform based on similarity criteria, and integrate the controller to conduct multi-condition verification, including step response, sinusoidal tracking and force control experiments under seismic wave excitation.

[0031] The similarity criteria specifically include: geometric scale ratio 1 / 36, prototype cylinder diameter 0.3072m corresponds to model cylinder diameter 0.05m; force scale ratio 1 / 36, the maximum output force of the original system corresponds to the maximum output force of the scaled system is 2800N, the load prototype is 58t and the load of the scaled system is 100kg.

[0032] The design method of the scaled-down experimental platform described in S4 includes: Based on the Cauchy number similarity criterion, the hydraulic cylinder bore (prototype 307.2mm → model 50mm) and load mass (prototype 58t → model 100kg) were proportionally reduced; Quantitative indicators include mean square error (MSE), mean absolute error (MAE), coefficient of determination (R²) and overshoot to verify its engineering effectiveness.

[0033] The specifications of the scaled hydraulic test bench in step S4 include: Hydraulic cylinder: rated pressure 20MPa; cylinder diameter 50mm; rod diameter 23mm; effective stroke 400mm; Electro-hydraulic servo valve: RT6615E series four-way two-stage servo valve with valve core and valve sleeve; Translational load: Maximum hanging weight 250kg; Maximum allowable force 5000N; Hydraulic pump: SYDFEE type, maximum displacement 28mL; Control system: SINAMICS S120 AC drive control system; Data acquisition module: NI USB M Series, 16 channels.

[0034] Example The present invention proposes a structural active control method for controlling the drive of an electro-hydraulic servo system based on a finite time controller, the method comprising the following steps: (1) Establish an electro-hydraulic servo-driven ATMD shock absorption structure model and determine the optimal control force U by comparing the TMD and ATMD control strategies; (2) Design the hydraulic system parameters based on the optimal control force U and establish the mathematical model of the electro-hydraulic servo system; (3) Design a finite-time controller based on the time-varying barrier Lyapunov function, dynamically constrain the tracking error through a third-order backstepping recursive architecture, and combine fractional-order terms to achieve overshoot suppression and finite-time convergence; (4) Build an electro-hydraulic servo scale-down experimental platform based on similarity theory, and conduct multi-condition verification of the integrated controller, including step response, sinusoidal tracking, and force control experiments under seismic wave excitation.

[0035] As a preferred embodiment of the present invention, in step (1), the process of determining the optimal control force is as follows: (1-1) The building structure is simplified into a multi-degree-of-freedom linear system, and the Lagrange equation is used to establish the equation of motion:

[0036] Among them, M is the mass matrix, C is the damping matrix, K is the stiffness matrix, and H is the control force position matrix.

[0037] (1-2) Calculate the optimal control force using the LQR algorithm; (1-3) Input the building structure mass and stiffness parameters into the control system to generate the initial state space model.

[0038] (1-4) Select earthquake excitations such as El Centro waves and Taft waves as external excitations, and adjust the amplitude to 0.2 times the gravity acceleration.

[0039] (1-5) Start the real-time control program to monitor the top displacement, control force output and system energy consumption.

[0040] As a preferred embodiment of the present invention, refer to Figure 3 ,In step (2), establishing the mathematical model of the electro-hydraulic servo system includes the following steps: (2-1) The system dynamic model is established. Considering the servo valve flow, hydraulic cylinder leakage and oil compressibility, a relationship model between the hydraulic cylinder output displacement and the valve core control signal is established; combining the piston equivalent mass, viscous damping and external load, the dynamic characteristics of the load are described; and a linear relationship model between the valve core displacement and flow output is established.

[0041] (2-2) The differential evolution algorithm (DE) is used to optimize the PID control parameters. The population size is set to 50 and the number of iterations is set to 100. The objective function comprehensively considers the integral time absolute error (ITAE) to obtain the optimal parameter combination.

[0042] (2-3) After determining the PID parameters, the model is tested in Simulink. Test signals include step, sine, and ramp signals to fully evaluate the dynamic response characteristics of the model.

[0043] As a preferred embodiment of the present invention, refer to Figure 4 ,In step (3), the design of a finite-time controller based on the time-varying barrier Lyapunov ,function includes the following steps: (3-1) Define tracking error, taking the difference between target displacement and actual displacement as error variable z1, and expand it into higher-order errors z2 and z3; (3-2) Construct an exponential decay error boundary function to ensure that the error is always within a safe range.

[0044] (3-3) Design the backstepping recursive control law, design the virtual control law layer by layer, and combine the fractional order terms to accelerate convergence.

[0045] (3-4) Build a high-precision electro-hydraulic servo system simulation platform in the Simulink environment. Through modular packaging, parameterized configuration and dynamic response testing, verify the performance of the controller in terms of time-varying tracking and disturbance suppression, and provide data support for actual system debugging.

[0046] As a preferred embodiment of the present invention, refer to Figure 5 ,In step (4), building an electro-hydraulic servo scale-down experimental platform based on similarity criterion ,includes the following steps: (4-1) Similarity criteria and parameter design: the geometric reduction ratio is set to 1:6, the force reduction ratio is 1:36, the pressure of the prototype and the model is kept consistent (15MPa), the cylinder diameter of the hydraulic cylinder is reduced to 50mm, and the load mass is adjusted to 100kg.

[0047] (4-2) Hardware integration and control deployment: The hydraulic cylinder and servo valve are connected through rigid pipes, and the load simulates the actual mass through the counterweight block; force sensors are deployed, and a real-time control program is developed based on the LabVIEW platform, embedding a finite-time control algorithm.

[0048] (4-3) Test process and verification indicators: input a step signal, record the displacement response curve, and evaluate the overshoot and adjustment time; input a 0.5Hz sinusoidal signal, calculate the mean square error (MSE) and coefficient of determination (R²); load the El Centro and Taft wave optimal control force data, and compare the control force output with the target curve.

[0049] The present invention also proposes an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the active control method of an electro-hydraulic servo system based on finite time control when executing the computer program.

[0050] The present invention also provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the active control method of an electro-hydraulic servo system based on finite time control.

[0051] The memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM). It should be noted that the memory used in the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0052] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disc (SSD)).

[0053] During implementation, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor or by instructions in the form of software. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it will not be described in detail here.

[0054] It should be noted that the processor in the embodiments of the present application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above-described method embodiments can be completed by hardware integrated logic circuits in the processor or by software instructions. The above-described processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components. The methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above-described method.

[0055] The above is a detailed introduction to the active control method of an electro-hydraulic servo system based on finite time control proposed in the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. An active control method for an electro-hydraulic servo system based on finite time control, characterized in that: The method comprises: S1, establish the electro-hydraulic servo drive ATMD shock absorption structure model, and determine the optimal control force U by comparing the TMD and ATMD control strategies; S2, design the hydraulic system parameters based on the optimal control force U and establish the mathematical model of the electro-hydraulic servo system; S3, designing a finite-time controller based on a time-varying barrier Lyapunov function, dynamically constraining the tracking error through a third-order backstepping recursive architecture, and combining fractional-order terms to achieve overshoot suppression and finite-time convergence; S4, build an electro-hydraulic servo scale-down experimental platform based on similarity criteria, and integrate the controller to conduct multi-condition verification, including step response, sinusoidal tracking and force control experiments under seismic wave excitation.

2. The method according to claim 1, characterized in that Said S1 specifically includes: The building structure is simplified into a multi-degree-of-freedom linear system, and the Lagrange equation is used to establish the motion equation: Where X is the displacement vector of the structure relative to the ground, H is the control force position matrix, I is the seismic wave position matrix, M, C and K are the mass matrix, damping matrix and stiffness matrix of the ATMD control system respectively, x g is the seismic wave input vector, U is the optimal control force; Establish a vibration response evaluation system that includes the inter-story displacement angle index; The LQR algorithm is used to solve the optimal control force that minimizes the inter-story displacement angle.

3. The method according to claim 2, characterized in that The LQR algorithm design includes: Constructing a quadratic performance index function based on structural dynamic response ; The semi-positive definite matrix Q represents the structural vibration energy index, and the positive definite matrix R reflects the control system energy consumption parameter. The two together constitute the core adjustment parameters of active control. The first term of this function quantifies the total structural vibration energy, and the second term represents the control system energy input. Calculate the feedback gain matrix of the optimal control force ; in is the feedback gain matrix of the control input, which is used to calculate the optimal control force U, P is the solution of the Riccati equation, and B is the input matrix of the system.

4. The method according to claim 1, wherein The mathematical model described in S2 includes: Dynamic equations of hydraulic cylinder considering servo valve flow ; in is the load pressure (MPa), V t is the sum of the volume of the oil inlet chamber and the oil return chamber (m 3 ), is the effective working area of ​​the hydraulic cylinder piston (m 2 ), is the output displacement of the hydraulic cylinder (m), is the leakage coefficient in the system (m 3 / (s·Pa)), is the equivalent bulk elastic modulus of the oil (MPa); The load force balance equation including the leakage coefficient and the viscous friction coefficient is: ； in The mass of the piston and connected load equivalently converted to the piston rod end (kg), is the viscous damping coefficient of the system (N·s / m), is the stiffness of the elastic element in the system (N / m), is the external load applied to the piston (N); Valve port flow equation for servo valve including flow gain coefficient ; The system transfer function model is obtained by combining the three equations.

5. The method according to claim 4, characterized in that The system transfer function model is: 。 6. The method according to claim 1, characterized in that The design method of the finite time controller described in S3 includes: Design of a third-order backstepping recursive control architecture , construct the dynamic constraint error boundary of the time-varying barrier function to ensure that the absolute value of z1 is less than this boundary; , is the virtual control law; 、 、 Respectively represent position tracking error, velocity tracking error, and acceleration tracking error; Design of virtual control laws , , combined with fractional-order terms to accelerate convergence; Deriving the actual control law , the finite time convergence of the system is proved by Lyapunov stability theory; the feedforward term of this control law Can offset the inherent dynamics of the system, reduce the feedback gain requirements, and the feedback term Provide damping to actively suppress hydraulic system resonance; fractional order terms Force finite time convergence to ensure that the error returns to zero within the predetermined time; compensation term Suppress unmodeled dynamics and external disturbances.

7. The method according to claim 6, characterized in that In step S3, the virtual control law is designed , Specifically: ， The first of these is the exponential convergence term, the second term is the finite time term, and the third term is the Young compensation term; New items For active offset The coupling terms in .

8. The method according to claim 1, characterized in that The similarity criteria specifically include: geometric scale ratio 1 / 36, prototype cylinder diameter 0.3072m corresponds to model cylinder diameter 0.05m; force scale ratio 1 / 36, the maximum output force of the original system corresponds to the maximum output force of the scaled system is 2800N, the load prototype is 58t and the load of the scaled system is 100kg.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium for storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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