High-order all-wheel-drive system control method and system of networked multi-axis motion system

Abstract

The invention belongs to but is not limited to the technical field of motion control, and particularly relates to a high-order all-wheel-drive system control method and system of a networked multi-axis motion system, and the method comprises the steps: S1, analyzing a networked multi-axis motion system model with time delay, system uncertainty and nonlinearity, noise and external interference, and on the basis of giving a differential equation of the networked multi-axis motion system model, carrying out the analysis of the system uncertainty and nonlinearity; discretization is carried out after the system meets all-drive conditions, and a high-order all-drive system model is obtained; s2, designing a high-order all-drive system control law, establishing a system linearization state-space equation, and performing normalization processing on system time delay, system uncertainty and nonlinearity and external interference to obtain system lumped interference; and S3, designing an equivalent input interference estimator based on a filter to realize good estimation of lumped interference, and further designing a controller with an interference elimination function to realize that the networked multi-axis motion system with time delay, system uncertainty and nonlinearity, noise and external interference has control performance.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of motion control technology, and in particular relates to a high-order full-drive system control method and system for a networked multi-axis motion system. Background Art

[0002] In the modern high-end equipment manufacturing industry, the advanced control of multi-axis motion systems has always been one of the main issues in the field of motion control, and achieving high-performance control is a key core technology. However, due to the requirements of complex equipment functions of multi-axis motion systems in industrial application scenarios, the system inevitably has system uncertainty and nonlinearity, noise and external interference, which have a serious impact on the control performance of the system.

[0003] At the same time, with the rapid development of industrial Internet of Things technology, motion systems are also developing in the direction of networking, intelligence, high speed and high precision. Introducing the network into the multi-axis motion system, although it improves the data transmission rate and reliability between the controller and each subsystem, greatly reduces the system wiring, and enhances the system expansion capability, the introduction of the network inevitably brings some new problems, such as uncertainty caused by network induction and network attacks leading to system security. In particular, for a networked multi-axis motion system with composite disturbances, the author treats the uncertainty caused by network induction as network interference, and the external load and other disturbances include matching and mismatching disturbances. Then, a controller based on the improved equivalent input disturbance (EID) estimator is designed, and good control performance of the networked multi-axis motion system with composite disturbances is achieved. For a networked multi-axis motion system with total disturbances, the total disturbance includes uncertainty caused by network induction, adjacent coupling of the system and external disturbances, etc., a linear active disturbance rejection controller based on an extended state observer is designed, and the control of the networked multi-axis motion system with total disturbances is achieved. However, there is still a lack of effective solutions to the problem of simultaneously dealing with system delay, system uncertainty and nonlinearity, and external interference, and the solutions adopted above only solve one aspect of the problem. How to design a high-performance controller solution with time delay, system uncertainty and nonlinearity, noise and external interference to achieve good control performance has not been well solved.

[0004] In view of the above analysis, the technical problem that urgently needs to be solved in the existing technology is: how to design a high-performance controller solution that simultaneously has time delay, system uncertainty and nonlinearity, noise and external interference, so that the system can obtain good control performance. Summary of the invention

[0005] In response to the problems existing in the prior art, the present invention provides a high-order all-wheel drive system control method and system for a networked multi-axis motion system, which can effectively handle the system uncertainty and nonlinearity as well as external interference of the system while processing the system delay, thereby improving the efficiency, system delay, system uncertainty and nonlinearity, and external interference processing and disturbance suppression capabilities of the networked multi-axis motion system.

[0006] The present invention is implemented as follows: a high-order full-drive system control method for a networked multi-axis motion system, comprising:

[0007] S1, analyze the networked multi-axis motion system model with time delay, system uncertainty and nonlinearity, noise and external interference, give its differential equation, discretize it after satisfying the full-drive condition, and obtain its high-order full-drive system model;

[0008] S2, design the high-order all-wheel drive system control law, establish the system linearized state space equation, normalize the system delay, system uncertainty and nonlinearity, and external interference, and treat them as system lumped interference;

[0009] S3, design a filter-based equivalent input disturbance estimator to achieve a good estimation of the lumped disturbance, and then design a controller with interference elimination function to achieve good control performance of the networked multi-axis motion system with time delay, system uncertainty and nonlinearity, noise and external disturbance.

[0010] Furthermore, the networked multi-axis motion system under consideration has time delay, system uncertainty and nonlinearity, noise and external interference. The state space model expression of the i-th axis subsystem in a non-networked environment is expressed as follows:

[0011]

[0012] Among them, x i =[x i1 x i2 ] T ,i=1,...,n, C i =[1 0] is the system matrix, a i ,Δa i ,b i ,Δb i ,b di belongs to the system parameters, Δa i ,Δb i is the system uncertainty and nonlinearity, x i (t),y i (t),u i (t),h i(t) are the system state, system output, system control input, system noise and external interference respectively.

[0013] Furthermore, by analyzing the system model, we get its differential equation:

[0014]

[0015] For the system state x i1 (t) The derivative is

[0016]

[0017] On this basis, the Euler approximation is performed, and the time delay of the system in the network environment is considered to write the differential equation of the system in the kth period:

[0018]

[0019] Among them, 1 / H is is the sampling period, It is the delay in the current cycle, including the long and short delays of the system.

[0020] Further, A c0 =a i H is +1, we get the high-order all-wheel drive system model of the system:

[0021]

[0022] Among them, d i (k) is the total interference of the system,

[0023]

[0024] Furthermore, the control law of the high-order all-wheel drive system is designed as follows:

[0025]

[0026] in, Determined by parametric design method, v i (k) is the controller to be designed.

[0027] Substituting the designed control law (6) into the system (5), the system state space equation is established as follows:

[0028]

[0029] make Then system (7) is rewritten as follows:

[0030]

[0031] Design an equivalent input estimator to obtain d i Estimated value of (k) And filter it to get

[0032] Furthermore, a controller with interference elimination function is designed

[0033]

[0034] Substituting (9) into (8) we can obtain the closed-loop system equation:

[0035]

[0036] Through pole configuration, the closed-loop system matrix The poles are within the unit circle, achieving good control of the subsystem. Finally, it is extended to the entire system to achieve high-performance control of the networked multi-axis motion system.

[0037] Another object of the present invention is to provide a high-order all-wheel drive system control system for a networked multi-axis motion system that implements the high-order all-wheel drive system control method for the networked multi-axis motion system, comprising:

[0038] The high-order all-wheel drive system model acquisition module analyzes the networked multi-axis motion system model with time delay, system uncertainty and nonlinearity, noise and external interference, and discretizes it after the system meets the all-wheel drive conditions based on the given differential equation to obtain its high-order all-wheel drive system model;

[0039] Normalization processing module, designs high-order all-wheel drive system control law, establishes system linearization state space equation, normalizes system delay, system uncertainty and nonlinearity, and external interference, and processes them into system lumped interference;

[0040] The multi-axis motion system control module designs a filter-based equivalent input disturbance estimator to achieve a good estimation of the lumped disturbance, and then designs a controller with interference elimination function to achieve good control performance for the networked multi-axis motion system with time delay, system uncertainty and nonlinearity, noise and external disturbance.

[0041] Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the high-order all-wheel drive system control method of the networked multi-axis motion system.

[0042] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the high-order all-wheel drive system control method of the networked multi-axis motion system.

[0043] Another object of the present invention is to provide an information data processing terminal, which includes the high-order all-wheel drive system control system of the networked multi-axis motion system.

[0044] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0045] First, the present invention analyzes the networked multi-axis motion system model with time delay, system uncertainty and nonlinearity, noise and external interference, gives its differential equation, and discretizes it after satisfying the full drive condition to obtain its high-order full drive system model. Secondly, the high-order full drive system control law is designed, the system linearization state space equation is established, and the system time delay, system uncertainty and nonlinearity and external interference are normalized and processed into system lumped interference. Furthermore, an equivalent input interference estimator based on a filter is designed to achieve a good estimation of the lumped interference, and then a controller with interference elimination function is designed to achieve a networked multi-axis motion system with time delay, system uncertainty and nonlinearity, noise and external interference with good control performance. The present invention can ensure high-performance control of a networked multi-axis motion system with lumped interference, effectively handle network delay and system uncertainty and nonlinearity and the influence of external interference on the system, and significantly improve the flexibility, disturbance handling and suppression capabilities, rapidity and control accuracy of the system, and has the prospect of being promoted to industrial applications.

[0046] Second, the networked multi-axis motion system control method based on a high-order all-wheel drive system proposed in the present invention successfully solves the problems of inaccurate control of multi-axis motion systems, poor anti-interference performance, and difficulty in coping with complex dynamic environments in the prior art, and significantly improves the control accuracy, stability, and applicability of the system. When facing a networked system, traditional multi-axis motion system control methods are limited by network delays, uncertainties, and nonlinear factors, and often cannot ensure the stability and efficient control of the system, resulting in reduced accuracy, delayed response, and reduced reliability. The present invention solves multiple technical problems that are difficult to overcome with traditional technologies through an innovative high-order all-wheel drive system control solution.

[0047] First, by establishing a high-order full-drive system model, the present invention effectively integrates the system's time delay, uncertainty, nonlinear factors, noise and external interference into a controllable lumped interference, achieving a more accurate description of the system's dynamic characteristics. Compared with traditional methods, this lumped processing method simplifies complex control problems and improves the robustness of the system model, enabling the system to stably cope with complex dynamic environments in practical applications.

[0048] Secondly, the equivalent input interference estimator designed by the present invention performs real-time interference estimation for noise and external interference, and uses filter processing to generate stable system state feedback. This design enables the control system to significantly improve its interference elimination ability in practical applications, solving the problem that traditional multi-axis control systems are difficult to effectively resist interference in complex environments. At the same time, the interference estimator can dynamically adapt to different interference situations, achieve more sensitive interference compensation in applications, and keep the system stable and efficient in high-dynamic scenarios.

[0049] In terms of control law design, the present invention uses a high-order full-drive system control law to linearize the state space equation and configure the system poles to ensure the stability of the closed-loop system. This innovative design makes the system control more precise and stable, effectively reduces the lag and fluctuation of the control response, overcomes the problems of low precision and slow response in traditional control methods in complex multi-axis systems, and makes the system more suitable for high-precision industrial control scenarios.

[0050] Finally, the technical solution of the present invention can achieve precise control in complex multi-axis motion systems, providing significant performance improvements and economic benefits for industrial applications. In fields with high demands for multi-axis motion control, such as automation, precision machining, and robot control, this solution can provide significant improvements in control accuracy, enhanced anti-interference, and applicability in networked and heterogeneous environments. These technological advances lay the foundation for the realization of more intelligent and efficient multi-axis control systems, bringing significant advantages to high-precision industrial applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flow chart of a high-order all-wheel drive system control method for a networked multi-axis motion system provided by an embodiment of the present invention;

[0052] Figure 2 is a schematic diagram of a high-order all-wheel drive system control method for a networked multi-axis motion system provided by an embodiment of the present invention;

[0053] Figure 3 It is a structural diagram of a high-order all-wheel drive system control system of a networked multi-axis motion system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] like Figure 1 As shown, the high-order full-drive system control method of the networked multi-axis motion system provided by the embodiment of the present invention includes:

[0056] S1, analyze the networked multi-axis motion system model with time delay, system uncertainty and nonlinearity, noise and external interference, give its differential equation, discretize it after satisfying the full-drive condition, and obtain its high-order full-drive system model;

[0057] S2, design the high-order all-wheel drive system control law, establish the system linearized state space equation, normalize the system delay, system uncertainty and nonlinearity, and external interference, and treat them as system lumped interference;

[0058] S3, design a filter-based equivalent input disturbance estimator to achieve a good estimation of the lumped disturbance, and then design a controller with interference elimination function to achieve good control performance of the networked multi-axis motion system with time delay, system uncertainty and nonlinearity, noise and external disturbance.

[0059] The high-order full-drive system control method of the networked multi-axis motion system of the present invention proposes a systematic control method for complex networked multi-axis motion systems with time delay, system uncertainty, nonlinearity, noise and external interference. First, in the system model analysis and discretization stage, the original multi-axis motion system model is comprehensively analyzed, and its differential equation description is established. By ensuring that the system meets the full-drive conditions, the model is discretized into a high-order full-drive system model. This high-order model can more accurately capture the complex dynamic characteristics of the system in a networked environment, including different time delays and uncertainty factors, thereby laying an accurate model foundation for subsequent control.

[0060] Next, in the control law design and state space linearization stage, the control law of the high-order all-wheel drive system is constructed to achieve precise control of the discretized high-order model. Through the linearization of the state space, the complex factors such as the system's time delay, nonlinearity and external interference are normalized and converted into a lumped interference form. This processing simplifies the impact of multiple interference factors, making the system smoother in control response and able to resist external disturbances, and realizing efficient and unified processing of complex system factors.

[0061] Then, the filter-based equivalent input disturbance estimator is designed to accurately estimate the aggregate disturbance in order to monitor and dynamically compensate for the disturbance inside and outside the system in real time. The disturbance estimator uses a filter to isolate and process the noise and disturbance in the system input, thereby generating an accurate equivalent input disturbance estimate. This estimate is input into the system as a compensation signal to help eliminate the effects of uncertainty and external disturbances, making the system more stable in dynamic response, especially in an environment with high-frequency noise and external disturbances, and still maintaining high control accuracy.

[0062] Finally, by designing a controller with interference elimination function, the precise control of the networked multi-axis motion system is further realized. Based on the feedback information of the equivalent input interference estimator, the controller dynamically adjusts the system input to offset the errors caused by time delay and external interference. This controller can adjust the control strategy in real time to ensure the closed-loop stability and control performance of the system, avoid fluctuations and excessive deviations in the system state, and thus achieve precise positioning and high-stability control of the multi-axis motion system. The entire method is suitable for complex networked industrial control environments and provides an efficient and stable motion control solution.

[0063] The present invention relates to a high-order full-drive system control method for a networked multi-axis motion system, which aims to solve problems such as network delay, uncertainty, nonlinearity, noise and external interference, and ensure that the complex motion system can still maintain high-precision and high-stability control under network control. Through system modeling, control law design, interference estimator design and other steps, the overall control performance of the multi-axis motion system is enhanced.

[0064] Step 1: Modeling a networked multi-axis motion system

[0065] First, a networked multi-axis motion system with delay, uncertainty, nonlinearity, noise and external interference is modeled. The system is affected by network transmission delay, and there may be complex external interference and internal system uncertainty in actual control. Therefore, differential equations are used to describe the multi-axis motion system, analyze its dynamic characteristics, and attribute each influencing factor to the overall control goal of the system. Through modeling, it is ensured that the system meets the full drive conditions, and then discretization is performed to obtain a high-order full drive system model of the system, providing a theoretical basis for subsequent control design.

[0066] Step 2: High-level all-wheel drive system control law design

[0067] After obtaining the high-order all-wheel drive model of the system, the high-order all-wheel drive system control law is then designed. By constructing the linearized state space equation of the system, the complex nonlinear system is converted into an equivalent linear form. In order to deal with the uncertainty and external disturbance of the system, the normalization processing method is adopted to unify these influencing factors into lumped disturbances. This processing method simplifies the complexity of controller design and uniformly considers the dynamic behavior of the system and external disturbances in the control framework, laying the foundation for subsequent high-precision control.

[0068] Step 3: Design of filter-based equivalent input disturbance estimator

[0069] In order to further improve the anti-interference ability of the system, a filter-based equivalent input interference estimator is designed. The interference estimator can accurately estimate the total interference in the system during real-time operation. These interferences include external interference, system delay, uncertainty, noise and other factors. Through the introduction of the estimator, the system can dynamically adjust its control strategy and compensate for the impact of interference on the system in real time, so that the multi-axis motion system can still maintain stable control performance in complex environments.

[0070] Step 4: Handling Delay and System Uncertainty

[0071] Since networked multi-axis motion systems have time delays, a compensation mechanism for time delays must be designed. Combining the time delay term in the state-space equation with the system uncertainty, a corresponding control compensation strategy is designed to effectively suppress the system response lag caused by time delays. At the same time, the uncertainty of the system is handled through a state feedback control strategy, so that the control system can still maintain high robustness and adaptability in the face of uncertainty.

[0072] Step 5: Normalization of noise and external interference

[0073] During the control process, the system will be subject to different forms of noise and external disturbances. To solve this problem, this method normalizes the noise and external disturbances and integrates them into a part of the lumped disturbance model. In this way, the controller can adjust the output signal in real time according to the information provided by the equivalent input disturbance estimator, eliminate the influence of noise and external disturbances on the system accuracy, and ensure that the output of the controller is stable and accurate.

[0074] Step 6: Controller design and performance optimization

[0075] Based on the above processing steps, a controller that adapts to complex network environments is designed to ensure that the networked multi-axis motion system has high-order full-drive control capabilities. The controller comprehensively considers delay, uncertainty, external interference and noise, and achieves precise motion control by dynamically adjusting the feedback control law of the system. Ultimately, the controller design enables the system to not only cope with various complex external interferences and internal uncertainties of the system, but also maintain excellent control performance in a network delay environment, which is suitable for actual complex industrial control scenarios.

[0076] like Figure 2 As shown, a control method for a networked multi-axis motion system based on a high-order all-wheel drive system includes:

[0077] The networked multi-axis motion system under consideration has time delay, system uncertainty and nonlinearity, noise and external interference. The state space model expression of its i-th axis subsystem in a non-networked environment is expressed as follows:

[0078]

[0079] Among them, x i =[x i1 x i2 ] T ,i=1,...,n, C i =[1 0] is the system matrix, a i ,Δa i ,b i ,Δb i ,b di belongs to the system parameters, Δa i ,Δb i is the system uncertainty and nonlinearity, x i (t),y i (t),u i (t),h i (t) are the system state, system output, system control input, system noise and external interference respectively.

[0080] By analyzing the system model, we can obtain its differential equation:

[0081]

[0082] For the system state x i1 (t) The derivative is

[0083]

[0084] On this basis, the Euler approximation is performed, and the time delay of the system in the network environment is considered to write the differential equation of the system in the kth period:

[0085]

[0086] Among them, 1 / H is is the sampling period, It is the delay in the current cycle, including the long and short delays of the system.

[0087] make A c0 =a i H is +1, we get the high-order all-wheel drive system model of the system:

[0088]

[0089] Among them, d i (k) is the total interference of the system,

[0090]

[0091] The control law of the high-order all-wheel drive system is designed as follows:

[0092]

[0093] in, Determined by parametric design method, v i (k) is the controller to be designed.

[0094] Substituting the designed control law (6) into the system (5), the system state space equation is established as follows:

[0095]

[0096] make Then system (7) is rewritten as follows:

[0097]

[0098] Design an equivalent input estimator to obtain d i Estimated value of (k) And filter it to get

[0099] Further design of controller with interference cancellation function

[0100]

[0101] Substituting (9) into (8) we can obtain the closed-loop system equation:

[0102]

[0103] Through pole configuration, the closed-loop system matrix The poles are within the unit circle, achieving good control of the subsystem. Finally, it is extended to the entire system to achieve high-performance control of the networked multi-axis motion system.

[0104] like Figure 3 As shown in the figure, the high-level all-wheel drive system control system of the networked multi-axis motion system includes:

[0105] The high-order all-wheel drive system model acquisition module analyzes the networked multi-axis motion system model with time delay, system uncertainty and nonlinearity, noise and external interference, and discretizes it after the system meets the all-wheel drive conditions based on the given differential equation to obtain its high-order all-wheel drive system model;

[0106] Normalization processing module, designs high-order all-wheel drive system control law, establishes system linearization state space equation, normalizes system delay, system uncertainty and nonlinearity, and external interference, and processes them into system lumped interference;

[0107] The multi-axis motion system control module designs a filter-based equivalent input disturbance estimator to achieve a good estimation of the lumped disturbance, and then designs a controller with interference elimination function to achieve good control performance for the networked multi-axis motion system with time delay, system uncertainty and nonlinearity, noise and external disturbance.

[0108] The networked multi-axis motion system control method based on a high-order full-drive system of the present invention realizes accurate and efficient system control by constructing a high-order full-drive system model and designing advanced control laws to address the time delay, system uncertainty, nonlinearity, noise and external interference problems in the multi-axis motion system. First, the method conducts an in-depth analysis of the system model, establishes a differential equation description of the system, and reveals the dynamic characteristics of the system by deriving the system state. The differential equation is obtained by using the Euler approximation method, which can effectively deal with the system delay in a network environment, taking into account the long delay and short delay factors respectively, thereby ensuring the accuracy of system control.

[0109] Secondly, the present invention designs a high-order all-wheel drive system model, integrates uncertainty, nonlinearity, noise and external interference into lumped interference, and performs normalization processing to simplify the complexity of the model. Through this normalization step, the system has higher stability and robustness when dealing with nonlinear interference. The control law of the high-order all-wheel drive system is further designed, and the key parameters are determined by parametric design to achieve precise control. After the control law is introduced into the high-order all-wheel drive model, the linearized state space equation of the system is established, which lays the foundation for improving the overall control performance.

[0110] In addition, in order to further process the noise and interference in the system, the present invention designs a filter-based equivalent input interference estimator. The estimator can accurately estimate the lumped interference and generate a purer system state feedback signal after filtering. The filtered equivalent input interference estimation value is used as the system input compensation, and the system interference is offset and adjusted through the intelligent control strategy, so that the system can still maintain efficient and stable motion control in a complex networked multi-axis motion environment.

[0111] Finally, the poles of the closed-loop system are adjusted by the pole configuration method to ensure that the poles of the closed-loop system matrix are distributed within the unit circle, thereby ensuring the stability of the system. Based on this method, the control effect of the system has been significantly improved. This technical solution is suitable for multi-axis networked motion systems. By extending it to the overall framework of multi-axis control, high-precision multi-axis collaborative control can be achieved, and it has strong immunity to network delays and external disturbances, meeting the needs of high-performance industrial applications.

[0112] An application embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of a high-order all-wheel drive system control method for a networked multi-axis motion system.

[0113] An application embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of a high-order all-wheel drive system control method for a networked multi-axis motion system.

[0114] An application embodiment of the present invention provides an information data processing terminal, which includes a high-order all-wheel drive system control system of a networked multi-axis motion system.

[0115] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0116] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A high-order all-wheel drive system control method for a networked multi-axis motion system, characterized in that: include: S1, analyze the networked multi-axis motion system model with time delay, system uncertainty and nonlinearity, noise and external interference, give its differential equation, discretize it after satisfying the full-drive condition, and obtain its high-order full-drive system model; S2, design the high-order all-wheel drive system control law, establish the system linearized state space equation, normalize the system delay, system uncertainty and nonlinearity, and external interference, and treat them as system lumped interference; S3, design a filter-based equivalent input disturbance estimator to achieve a good estimation of the lumped disturbance, and then design a controller with interference elimination function to achieve control performance of the networked multi-axis motion system with time delay, system uncertainty and nonlinearity, noise and external disturbance.

2. The high-order all-wheel drive system control method of a networked multi-axis motion system according to claim 1, characterized in that: The networked multi-axis motion system under consideration has time delay, system uncertainty and nonlinearity, noise and external interference. The state space model expression of its i-th axis subsystem in a non-networked environment is expressed as follows: Among them, x i =[x i1 x i2 ] T ,i=1,...,n, C i =[1 0] is the system matrix, a i ,Δa i ,b i ,Δb i ,b di belongs to the system parameters, Δa i ,Δb i is the system uncertainty and nonlinearity, x i (t),y i (t),u i (t),h i (t) are the system state, system output, system control input, system noise and external disturbance respectively.

3. The high-order all-wheel drive system control method of a networked multi-axis motion system as claimed in claim 1, characterized in that: By analyzing the system model, we can obtain its differential equation: For the system state x i1 (t) The derivative is On this basis, the Euler approximation is performed, and the time delay of the system in the network environment is considered to write the differential equation of the system in the kth period: Among them, 1 / H is is the sampling period, It is the delay in the current cycle, including the long and short delays of the system.

4. The high-order all-wheel drive system control method of a networked multi-axis motion system as claimed in claim 1, characterized in that: make Then the high-order all-wheel drive system model of the system is obtained: Among them, d i (k) is the total interference of the system, 5. The high-order all-wheel drive system control method of a networked multi-axis motion system as claimed in claim 1, characterized in that: The control law of the high-order all-wheel drive system is designed as follows: in, Determined by parametric design method, v i (k) is the controller to be designed; Substituting the designed control law (6) into the system (5), the system state space equation is established as follows: make Then system (7) is rewritten as follows: Design an equivalent input estimator to obtain d i Estimated value of (k) And filter it to get 6. The high-order all-wheel drive system control method of a networked multi-axis motion system as claimed in claim 1, characterized in that: Designing a controller with interference cancellation Substituting (9) into (8) we can obtain the closed-loop system equation: Through pole configuration, the closed-loop system matrix The poles are within the unit circle, achieving good control of the subsystem; finally, it is extended to the entire system to achieve high-performance control of the networked multi-axis motion system.

7. A high-order all-wheel drive system control system for a networked multi-axis motion system that implements the high-order all-wheel drive system control method for a networked multi-axis motion system as claimed in any one of claims 1 to 6, characterized in that: include: The high-order all-wheel drive system model acquisition module analyzes the networked multi-axis motion system model with time delay, system uncertainty and nonlinearity, noise and external interference, and discretizes it after the system meets the all-wheel drive conditions based on the given differential equation to obtain its high-order all-wheel drive system model; Normalization processing module, designs high-order all-wheel drive system control law, establishes system linearization state space equation, normalizes system delay, system uncertainty and nonlinearity, and external interference, and processes them into system lumped interference; The multi-axis motion system control module designs a filter-based equivalent input disturbance estimator to achieve a good estimation of the lumped disturbance, and then designs a controller with interference elimination function to achieve good control performance for the networked multi-axis motion system with time delay, system uncertainty and nonlinearity, noise and external disturbance.

8. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the high-order all-wheel drive system control method for a networked multi-axis motion system as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the high-order all-wheel drive system control method for a networked multi-axis motion system as claimed in any one of claims 1 to 6.

10. An information data processing terminal, comprising the high-order all-wheel drive system control system of the networked multi-axis motion system according to claim 7.

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