A high-order all-wheel drive system control method and system for a networked multi-axis motion system

Abstract

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, including: S1, analyzing a networked multi-axis motion system model with time delay, system uncertainty and nonlinearity, noise and external interference, giving its differential equation, discretizing it so that the system meets the full-drive conditions, and obtaining its high-order full-drive system model; S2, designing a high-order full-drive system control law, establishing a system linearized state space equation, normalizing the system time delay, system uncertainty and nonlinearity, and external interference, and processing them into system lumped interference; S3, designing a filter-based equivalent input interference estimator to achieve a good estimation of the lumped interference, and then designing a controller with interference elimination function to ensure 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 all-wheel drive system control method and system for a networked multi-axis motion system. Background Art

[0002] In the modern high-end equipment manufacturing industry, advanced control of multi-axis motion systems has always been a major challenge in the field of motion control, and achieving high-performance control is a key core technology. However, due to the complex equipment functions required of multi-axis motion systems in industrial applications, the system inevitably suffers from system uncertainty and nonlinearity, noise, and external interference, which seriously affect the system's control performance.

[0003] At the same time, with the rapid development of industrial Internet of Things (IIoT) technology, motion systems are also moving towards networking, intelligent, high-speed, and high-precision systems. While the introduction of networks into multi-axis motion systems improves the data transmission rate and reliability between the controller and various subsystems, significantly reduces system wiring, and enhances system scalability, the introduction of networks inevitably introduces new challenges, such as uncertainty in system security caused by network induction and cyberattacks. Specifically, this paper addresses a networked multi-axis motion system with composite disturbances, including network-induced uncertainty and external load disturbances. The network-induced uncertainty is treated as network interference, and external load disturbances include both matched and mismatched disturbances. A controller based on an improved equivalent input disturbance (EID) estimator is then designed, achieving excellent control performance for the networked multi-axis motion system with composite disturbances. Furthermore, a linear active disturbance rejection controller based on an extended state observer is designed for a networked multi-axis motion system with summed disturbances, including network-induced uncertainty, adjacent system coupling, and external disturbances. However, there is currently a lack of effective solutions for simultaneously addressing system delays, system uncertainties and nonlinearities, and external interference. Furthermore, the solutions previously employed only address one aspect of these issues. Designing a high-performance controller solution that simultaneously addresses delays, system uncertainties and nonlinearities, noise, and external interference, while achieving good control performance, remains a long-standing challenge.

[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, as well as 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 all-wheel 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, make it meet the full drive conditions, and then discretize it to obtain its high-order full drive system model;

[0008] S2, design a 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 cancellation 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 its 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 Belong 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 can obtain its differential equation:

[0014]

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

[0016]

[0017] On this basis, we perform Euler approximation and consider the system's time delay in the network environment to write the system's differential equation in the kth cycle:

[0018]

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

[0020] Further, let 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 high-order all-wheel drive system control law 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 cancellation function is designed

[0033]

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

[0035]

[0036] By configuring the poles, the closed-loop system matrix The poles are inside 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 based on its differential equation, discretizes it to satisfy the all-wheel drive conditions 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 linearized 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 cancellation function to achieve good control performance of 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 a networked multi-axis motion system model with time delay, system uncertainty and nonlinearity, noise, and external interference, and then discretizes it to satisfy the full-drive conditions based on its differential equations, thereby obtaining a high-order full-drive system model. Secondly, a high-order full-drive system control law is designed, and the system linearized state-space equations are established. The system time delay, system uncertainty and nonlinearity, and external interference are normalized and processed into the system lumped interference. Furthermore, a filter-based equivalent input interference estimator is designed to achieve a good estimation of the lumped interference. Furthermore, a controller with interference cancellation function is designed to achieve excellent control performance for the networked multi-axis motion system with time delay, system uncertainty and nonlinearity, noise, and external interference. The present invention can ensure high-performance control of the networked multi-axis motion system with lumped interference, effectively handle the effects of network time delay, system uncertainty and nonlinearity, and external interference on the system, while significantly improving the system's flexibility, disturbance handling and suppression capabilities, rapidity, and control accuracy, and has the potential for promotion to industrial applications.

[0046] Secondly, 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 existing technology, and significantly improves the control accuracy, stability and applicability of the system. When faced with a networked system, traditional multi-axis motion system control methods are limited by network delays, uncertainties, and nonlinear factors, and often cannot ensure stable and efficient control of the system, resulting in reduced accuracy, delayed response, and decreased 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 all-wheel drive system model, this invention effectively integrates the system's time delay, uncertainty, nonlinear factors, noise, and external interference into a controllable lumped disturbance, achieving a more accurate description of the system's dynamic characteristics. Compared with traditional methods, this lumped approach 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 disturbance estimator designed in this invention performs real-time interference estimation for noise and external disturbances and uses filtering to generate stable system state feedback. This design significantly improves the control system's ability to eliminate disturbances in practical applications, resolving the difficulty of traditional multi-axis control systems in effectively rejecting disturbances in complex environments. Furthermore, the disturbance estimator dynamically adapts to varying disturbance conditions, enabling more sensitive disturbance compensation in applications and maintaining stable and efficient system operation even in highly dynamic scenarios.

[0049] In terms of control law design, this invention utilizes a high-order all-wheel drive system control law to linearize the state-space equations and configure the system poles to ensure closed-loop system stability. This innovative design enables more precise and stable system control, effectively reducing lag and fluctuation in control response. This overcomes the low accuracy and slow response issues often associated with traditional control methods in complex multi-axis systems, making the system more suitable for high-precision industrial control scenarios.

[0050] Finally, the technical solution of this invention enables 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 robotic control, this solution can provide significant improvements in control accuracy, enhanced noise immunity, and applicability in networked and heterogeneous environments. These technological advances lay the foundation for more intelligent and efficient multi-axis control systems, bringing significant advantages to high-precision industrial applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This 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 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 This 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 solutions 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 intended to limit the present invention.

[0055] like Figure 1 As shown, the embodiment of the present invention provides a high-order all-wheel drive system control method for a networked multi-axis motion system, including:

[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, make it meet the full drive conditions, and then discretize it to obtain its high-order full drive system model;

[0057] S2, design a 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 cancellation 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 present invention proposes a high-order all-wheel drive system control method for a networked multi-axis motion system, which proposes a systematic control approach for complex networked multi-axis motion systems characterized by time delays, system uncertainties, nonlinearities, noise, and external interference. First, during the system model analysis and discretization phase, the original multi-axis motion system model is comprehensively analyzed, and its differential equation description is established. This model is then discretized into a high-order all-wheel drive system model by ensuring that the system meets all-wheel drive conditions. This high-order model can more accurately capture the complex dynamic characteristics of the system in a networked environment, including various time delays and uncertainty factors, thereby laying a precise model foundation for subsequent control.

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

[0061] Next, a filter-based equivalent input disturbance estimator is designed to accurately estimate the aggregate disturbance, enabling real-time monitoring and dynamic compensation for both internal and external disturbances. The disturbance estimator utilizes filters to isolate and process noise and disturbances in the system input, generating an accurate equivalent input disturbance estimate. This estimate is fed into the system as a compensation signal, helping to eliminate the effects of uncertainty and external disturbances, resulting in more stable system dynamic response. This ensures high control accuracy, particularly in environments with high-frequency noise and external disturbances.

[0062] Finally, a controller with interference cancellation functionality was designed to further achieve precise control of networked multi-axis motion systems. Based on feedback from an equivalent input disturbance estimator, the controller dynamically adjusts the system input to offset errors caused by time delays and external disturbances. This controller can adjust the control strategy in real time to ensure closed-loop stability and control performance, avoiding fluctuations in system state and excessive deviations. This approach achieves precise positioning and highly stable control of the multi-axis motion system. This approach is applicable to complex networked industrial control environments and provides an efficient and stable motion control solution.

[0063] This invention relates to a high-order all-wheel-drive system control method for a networked multi-axis motion system. It aims to address issues such as network latency, uncertainty, nonlinearity, noise, and external interference, ensuring that complex motion systems maintain high precision and stability under networked control. Through system modeling, control law design, and disturbance estimator design, 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 time delay, uncertainty, nonlinearity, noise, and external interference is modeled. This system is affected by network transmission delay, and actual control may involve complex external interference and internal system uncertainties. Therefore, differential equations are used to describe the multi-axis motion system, analyze its dynamic characteristics, and attribute various influencing factors to the overall control objective of the system. Through modeling, the system is ensured to meet all-wheel drive requirements. Discretization is then performed to obtain a high-order all-wheel drive system model, providing a theoretical basis for subsequent control design.

[0066] Step 2: Design of high-level all-wheel drive system control law

[0067] After obtaining the system's high-order all-wheel drive model, the next step is to design a high-order all-wheel drive system control law. By constructing the system's linearized state-space equations, the complex nonlinear system is converted into an equivalent linear form. To address system uncertainties and external disturbances, a normalization approach is employed, reducing these influencing factors to a lumped disturbance. This approach simplifies the complexity of controller design and integrates the system's dynamic behavior and external disturbances into the control framework, laying the foundation for subsequent high-precision control.

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

[0069] To further enhance the system's anti-interference capabilities, a filter-based equivalent input disturbance estimator was designed. This disturbance estimator accurately estimates the aggregate disturbances in the system during real-time operation. These disturbances include external disturbances, system delays, uncertainty, and noise. By incorporating this estimator, the system can dynamically adjust its control strategy to compensate for the effects of disturbances in real time, enabling the multi-axis motion system to maintain stable control performance even in complex environments.

[0070] Step 4: Handling Delay and System Uncertainty

[0071] Because networked multi-axis motion systems exhibit time delays, a compensation mechanism must be devised. By combining the time delay term in the state-space equation with system uncertainty, a corresponding control compensation strategy was designed to effectively suppress the system response lag caused by time delays. Furthermore, system uncertainty is addressed through a state feedback control strategy, enabling the control system to 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 is subject to various forms of noise and external disturbances. To address this issue, this method normalizes noise and external disturbances and integrates them into a lumped disturbance model. This allows the controller to adjust its output signal in real time based on information provided by the equivalent input disturbance estimator, eliminating the effects of noise and external disturbances on system accuracy and ensuring stable and accurate controller output.

[0074] Step 6: Controller Design and Performance Optimization

[0075] Based on the above processing steps, a controller was designed to adapt to complex network environments, ensuring that the networked multi-axis motion system possesses high-level all-wheel drive control capabilities. This controller comprehensively considers time delay, uncertainty, external interference, and noise, and achieves precise motion control by dynamically adjusting the system's feedback control law. Ultimately, the controller design enables the system to not only cope with various complex external interferences and internal system uncertainties, but also maintain excellent control performance in environments with network delays, making it suitable for practical and 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 Belong 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, we perform Euler approximation and consider the system's time delay in the network environment to write the system's differential equation in the kth cycle:

[0085]

[0086] Among them, 1 / H is is the sampling period, 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) yields the closed-loop system equation:

[0102]

[0103] By configuring the poles, the closed-loop system matrix The poles are inside 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 based on its differential equation, discretizes it to satisfy the all-wheel drive conditions 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 linearized 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 cancellation function to achieve good control performance of the networked multi-axis motion system with time delay, system uncertainty and nonlinearity, noise and external disturbance.

[0108] The present invention's networked multi-axis motion system control method, based on a high-order all-wheel drive system, addresses the issues of time delay, system uncertainty, nonlinearity, noise, and external interference in multi-axis motion systems. By constructing a high-order all-wheel drive system model and designing advanced control laws, it achieves precise and efficient system control. First, this method conducts an in-depth analysis of the system model, establishes a differential equation description of the system, and reveals the system's dynamic characteristics by taking the derivative of the system state. Using the Euler approximation method to obtain the differential equation, this method effectively addresses system time delays in a network environment, taking both long and short delay factors into account, thereby ensuring the accuracy of system control.

[0109] Secondly, the present invention designs a high-order all-wheel drive system model, integrating uncertainty, nonlinearity, noise, and external interference into a lumped disturbance and normalizing it, thereby simplifying the model's complexity. This normalization step improves the system's stability and robustness when dealing with nonlinear interference. Furthermore, a high-order all-wheel drive system control law is designed, using a parametric design approach to determine key parameters for precise control. After introducing the control law into the high-order all-wheel drive model, a linearized state-space equation for the system is established, laying the foundation for improving overall control performance.

[0110] Furthermore, to further address noise and interference in the system, the present invention designs a filter-based equivalent input disturbance estimator. This estimator accurately estimates the aggregated disturbance and, through filtering, generates a purer system state feedback signal. The filtered equivalent input disturbance estimate serves as system input compensation, offsetting and regulating system disturbances through an intelligent control strategy. This enables the system to maintain efficient and stable motion control even in complex networked multi-axis motion environments.

[0111] Finally, the poles of the closed-loop system are adjusted using a pole placement method to ensure that the poles of the closed-loop system matrix are distributed within the unit circle, thereby ensuring system stability. This method significantly improves the system's control performance. This technical solution is applicable to multi-axis networked motion systems. By expanding it into the overall multi-axis control framework, it can achieve high-precision multi-axis coordinated control with 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 portion can be implemented using dedicated logic; the software portion can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art will appreciate that the above-mentioned devices and methods can be implemented using computer-executable instructions and / or contained in processor control code, for example, 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. The devices and 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, 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, or can be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0116] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection 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, make it meet the full drive conditions, and then discretize it to obtain its high-order full drive system model; S2, design a 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 disturbance cancellation function to achieve control performance for networked multi-axis motion systems with time delay, system uncertainty and nonlinearity, noise and external disturbances; 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 Belong 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 system state, system output, system control input, system noise and external interference respectively; By analyzing the system model, we can obtain its differential equation: For the system state x i1 (t) The derivative is On this basis, we perform Euler approximation and consider the system's time delay in the network environment to write the system's differential equation in the kth period: Among them, 1 / H is is the sampling period, The delay in the current cycle, including the long and short delays of the system; make Then we get the high-order all-wheel drive system model of the system: Among them, d i (k) is the total interference of the system, 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 2. The high-order all-wheel drive system control method of a networked multi-axis motion system according to claim 1, characterized in that: Designing a controller with interference cancellation Substituting (9) into (8) yields the closed-loop system equation: l i (k+1)=(A λi -B λi K i )l i (k) (10) By configuring the poles, the closed-loop system matrix (A λi -B λi K i ) 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.

3. 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 2, 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 based on its differential equation, discretizes it to satisfy the all-wheel drive conditions to obtain its high-order all-wheel drive system model; Normalization processing module, designs high-order all-wheel drive system control law, establishes system linearized 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 cancellation function to achieve good control performance of the networked multi-axis motion system with time delay, system uncertainty and nonlinearity, noise and external disturbance.

4. 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 2.

5. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to execute the steps of the high-order all-wheel drive system control method for a networked multi-axis motion system according to any one of claims 1 to 2.

6. 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 3.

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