Loop Attack Modeling and Stability Compensation Methods for Precision Motion Control

Abstract

This invention relates to a method for loop attack modeling and stability compensation for precision motion control. It establishes a loop attack model incorporating delay injection and noise superposition attacks by real-time acquisition of the operating parameters of the precision motion control loop, and constructs a closed-loop control model including a feedforward channel. Frequency domain analysis is performed based on the closed-loop control model to calculate the phase margin of the control loop. When the phase margin falls below a preset margin threshold, an instability alarm is triggered. In response to the instability alarm, the dynamic behavior of the attacked control loop is modeled as a state equation including a feedforward channel. A feedforward compensation term is designed using stability theory, and the compensation parameters are dynamically adjusted to restore the phase margin of the control loop to a stable range, thereby achieving real-time attack detection and dynamic stability compensation for precision motion control systems in an industrial IoT environment.

Description

Technical Field

[0001] This invention relates to the field of industrial Internet of Things (IoT) security and intelligent control technology, and in particular to a method for loop attack modeling and stability compensation for precision motion control. Background Technology

[0002] With the deep integration of the Industrial Internet of Things (IIoT), precision motion control systems face increasingly complex security threats, especially new attack methods such as delay injection and noise superposition in control loops, revealing significant limitations of traditional defense mechanisms. Existing technologies largely rely on static threshold detection or single-parameter adjustment strategies, such as abnormal fluctuation judgment based on fixed thresholds or adaptive adjustment of conventional PID parameters. While these methods can address known attack patterns, they struggle to dynamically adapt to time-varying disturbances and cannot effectively restore stability when the system's phase margin falls below a critical value due to an attack. More critically, static strategies lack quantitative modeling of the coupling relationship between attack disturbances and system stability, resulting in delayed defense responses and failing to meet the stringent requirements of high-precision equipment (such as semiconductor manufacturing equipment) for control robustness and real-time performance. Summary of the Invention

[0003] The main objective of this invention is to provide a loop attack modeling and stability compensation method for precision motion control, so as to achieve real-time attack detection and dynamic stability compensation for precision motion control systems in the industrial Internet of Things environment.

[0004] To achieve the above objectives, this invention provides a method for loop attack modeling and stability compensation for precision motion control, comprising the following steps:

[0005] Real-time acquisition of operating parameters of precision motion control loop, establishment of loop attack model including delay injection attack and noise superposition attack, and construction of closed-loop control model including feedforward channel;

[0006] Frequency domain analysis is performed based on the closed-loop control model to calculate the phase margin of the control loop. When the phase margin is lower than a preset margin threshold, an instability alarm is triggered.

[0007] In response to the instability alarm, the dynamic behavior of the attacked control loop is modeled as a state equation including a feedforward channel;

[0008] Design a feedforward compensation term, dynamically adjust the compensation parameters, and restore the phase margin of the control loop to the stable range.

[0009] Furthermore, the steps of establishing a loop attack model that includes delay injection attacks and noise superposition attacks, and constructing a closed-loop control model that includes a feedforward channel, include:

[0010] By monitoring the real-time data stream of the control loop, time-varying disturbance terms in the control loop are identified, and these time-varying disturbance terms characterize the disturbance characteristics of delay injection attacks.

[0011] The noise superposition attack is modeled as a random noise term that conforms to historical statistical distribution;

[0012] A closed-loop control model is constructed based on the time-varying disturbance term and random noise term, and the dynamic impact of the attack on the loop output is quantified.

[0013] Furthermore, the steps for constructing a closed-loop control model include:

[0014] The original transfer characteristics of the control loop are fused with the time-varying disturbance term and the random noise term to form a closed-loop control model that includes a feedforward channel.

[0015] The closed-loop control model is used for phase margin analysis and compensation parameter design.

[0016] Further, the step of performing frequency domain analysis based on the closed-loop control model to calculate the control loop phase margin, and triggering an instability alarm when the phase margin is lower than a preset margin threshold, includes:

[0017] Bode plots are generated based on the closed-loop control model, and the initial phase margin of the control loop under no-attack conditions is analyzed and obtained.

[0018] Based on the initial phase margin and the actual equipment stability test results, the preset margin threshold for triggering instability alarms is calibrated.

[0019] An alarm signal is generated when the real-time phase margin is lower than the preset margin threshold.

[0020] Furthermore, in response to the instability alarm, the step of modeling the dynamic behavior of the attacked control loop as a state equation including a feedforward channel includes:

[0021] Upon receiving the instability alarm information, the amplitude crossover frequency point is located on the phase curve of the open-loop transfer characteristic of the control loop;

[0022] The difference between the phase value corresponding to the amplitude crossover frequency point and the critical instability value is calculated to obtain the real-time phase margin;

[0023] When the real-time phase margin is lower than the preset attack threshold within a preset number of consecutive control cycles, a multi-level alarm is triggered and a state equation containing time-varying disturbance terms and random noise terms is constructed.

[0024] Furthermore, the steps of designing feedforward compensation terms using stability theory, dynamically adjusting compensation parameters, and restoring the phase margin of the control loop to the stable range include:

[0025] Based on the phase margin analysis results of the closed-loop control model, the feedforward gain coefficient is dynamically adjusted.

[0026] The state convergence of the compensated control loop is verified by using the derivative constraint of the Lyapunov function.

[0027] The feedforward compensation term is superimposed on the control signal input to generate a disturbance-resistant closed-loop control signal.

[0028] Furthermore, the steps for dynamically adjusting the feedforward gain coefficient include:

[0029] The gain coefficient range is adaptively adjusted based on the real-time monitored time-varying disturbance term and phase margin value.

[0030] After dynamically adjusting the gain coefficient, verify whether the compensated phase margin reaches the stable range;

[0031] If stability is not achieved, the gain adjustment strategy is iteratively optimized based on the changing trend of the time-varying disturbance term until the system stability requirements are met.

[0032] Furthermore, the steps for designing feedforward compensation terms using stability theory also include:

[0033] The computational logic for the feedforward compensation term is deployed via a programmable logic device, which is driven by a high-speed clock signal for low-latency response.

[0034] The low-latency response is used to offset the impact of attack disturbances on the precision motion control loop in real time.

[0035] Furthermore, the steps for designing feedforward compensation terms using stability theory also include:

[0036] The gain parameters of the feedforward compensation term are dynamically loaded in the real-time operating system;

[0037] The gain parameters are adjusted via an online update module to adapt to the stability requirements of different attack scenarios.

[0038] This invention also provides a loop attack modeling and stability compensation system for precision motion control, comprising:

[0039] The model building unit is used to collect the operating parameters of the precision motion control loop in real time, establish a loop attack model including delay injection attack and noise superposition attack, and build a closed-loop control model including feedforward channel.

[0040] The stability analysis unit is used to perform frequency domain analysis based on the closed-loop control model, calculate the phase margin of the control loop, and trigger an instability alarm when the phase margin is lower than a preset margin threshold.

[0041] In response to the instability alarm, the dynamic behavior of the attacked control loop is modeled as a state equation including a feedforward channel;

[0042] The compensation optimization unit is used to design feedforward compensation terms, dynamically adjust compensation parameters, and restore the phase margin of the control loop to the stable range.

[0043] The loop attack modeling and stability compensation method for precision motion control provided by this invention has the following beneficial effects: Firstly, by innovatively integrating attack dynamic modeling with Lyapunov stability theory, this invention constructs a multi-layered defense system, improving the security and robustness of precision motion control systems. Secondly, based on a closed-loop model of time-varying disturbances and random noise, this invention achieves precise quantification of the impact of complex attacks, breaking through the blind spot of traditional static threshold detection for dynamic attacks. Thirdly, through the collaborative design of feedforward compensation terms and phase margin recovery mechanisms, system stability is rapidly reconstructed after an attack is triggered, solving the technical bottleneck that single parameter adjustment cannot maintain phase margin. Furthermore, through a hardware-software co-architecture, the compensation algorithm achieves microsecond-level real-time response and dynamic parameter loading capabilities, enabling the system to adaptively cope with diverse attack modes such as step attacks and ramp attacks. In addition, the multi-level alarm mechanism and iterative optimization strategy proposed in this invention establish a complete defense closed loop for industrial control loops, from attack identification and dynamic modeling to compensation control. This not only significantly reduces false alarm rates and missed detection risks but also provides a universal solution for the long-term reliable operation of high-value equipment, with broad industrial application prospects. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating a loop attack modeling and stability compensation method for precision motion control in one embodiment of the present invention.

[0045] Figure 2 This is a structural block diagram of a loop attack modeling and stability compensation system for precision motion control according to an embodiment of the present invention.

[0046] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0048] Reference Figure 1 This is a flowchart illustrating a loop attack modeling and stability compensation method for precision motion control proposed in this invention, including the following steps:

[0049] S1: Real-time acquisition of the operating parameters of the precision motion control loop, establishment of a loop attack model including delay injection attack and noise superposition attack, and construction of a closed-loop control model including feedforward channel.

[0050] S2, perform frequency domain analysis based on the closed-loop control model to calculate the phase margin of the control loop, and trigger an instability alarm when the phase margin is lower than a preset margin threshold.

[0051] S3, in response to the instability alarm, model the dynamic behavior of the attacked control loop as a state equation including a feedforward channel;

[0052] S4, design a feedforward compensation term, dynamically adjust the compensation parameters, and restore the phase margin of the control loop to the stable range.

[0053] In one embodiment, for step S1,

[0054] The steps for establishing a loop attack model that includes delay injection attacks and noise superposition attacks, and constructing a closed-loop control model that includes a feedforward channel, include:

[0055] By monitoring the real-time data stream of the control loop, time-varying disturbance terms in the control loop are identified, and these time-varying disturbance terms characterize the disturbance characteristics of delay injection attacks.

[0056] The noise superposition attack is modeled as a random noise term that conforms to historical statistical distribution;

[0057] A closed-loop control model is constructed based on the time-varying disturbance term and random noise term, and the dynamic impact of the attack on the loop output is quantified.

[0058] In practical implementation, multi-source sensing units are used to collect the operating parameters of the precision motion control loop in real time: a high-precision grating encoder continuously monitors the Z-axis position feedback signal with a resolution of ±0.1 μm, a Hall current sensor captures the spectral characteristics of the servo motor drive current with a bandwidth of 10 kHz, and an FPGA embedded probe records the transmission delay fluctuations of the position command signal with a timestamp accuracy of 10 ns. These real-time data streams are processed by Kalman filtering for noise reduction and sliding window normalization at edge computing nodes, and then input into the attack feature extraction module for in-depth analysis. Abnormally increased communication delay fluctuations in the control loop are identified. The delay injection attack is modeled as a time-varying disturbance term, characterized by a periodic disturbance with randomly varying amplitude (±0.3 ms) superimposed on the normal communication delay baseline (approximately 0.5 ms). This disturbance term is strongly correlated with the frequency of malicious commands injected by the attacker. Modeling of Noise Superposition Attacks: By analyzing noise data captured in a historical attack sample library and statistically analyzing its amplitude distribution characteristics, it was found that the attack noise conforms to a Gaussian distribution with a mean of zero and a standard deviation of 0.12 μm. This noise was then modeled as a random noise term with the same statistical characteristics. Based on this attack model, the original transfer function of the Z-axis servo system (including physical parameters such as motor inertia and damping coefficient) was fused with the attack disturbance term to construct a closed-loop control model containing a feedforward channel. When building the closed-loop model in the simulation environment, the time-varying disturbance term was embedded in the feedforward channel as a variable transmission delay module, while the random noise term was superimposed on the position feedback signal terminal through a Gaussian noise generator. This embodiment deeply couples real-time data acquisition (grating encoder + current sensor + FPGA probe) with attack feature extraction (time-frequency joint analysis), overcoming the shortcomings of traditional methods in modeling the correlation between attack disturbances and system states. It achieves end-to-end mapping from physical signals to the control model, establishing a scalable technical framework for active defense in the industrial IoT environment.

[0059] In one embodiment, the steps of constructing a closed-loop control model include:

[0060] The original transfer characteristics of the control loop are fused with the time-varying disturbance term and the random noise term to form a closed-loop control model that includes a feedforward channel.

[0061] The closed-loop control model is used for phase margin analysis and compensation parameter design.

[0062] Specifically, we establish the transfer function model of the original control loop, assuming the open-loop transfer characteristic of the Z-axis servo system is as follows:

[0063]

[0064] In the formula, This is the rotor inertia of the motor. The damping coefficient is... For proportional gain, This is the Laplace operator, used to transform time-domain signals to the complex frequency domain for analysis. To counter delay injection attacks, a time-varying perturbation term is introduced into the control command channel. Physically, this manifests as random fluctuations in transmission delay (in actual attack scenarios). Noise superposition attacks are modeled as Gaussian white noise, and their statistical characteristics are calibrated through analysis of historical attack data, with a mean of zero and a variance of zero. Closed-loop transfer function modeling: The coupled impact of attack disturbances on the system is characterized by a closed-loop transfer function model, with the delay disturbance term... This causes phase lag, when At that time, the measured phase lag reached 34°; noise disturbance term The position feedback signal was made to exhibit random fluctuations of ±0.15 μm, consistent with the measured error distribution of the Z-axis grating ruler. Frequency domain analysis and stability verification were performed based on the closed-loop model. The uncompensated phase margin was measured to be 28° using Bode plots, which is significantly lower than the preset safety threshold of 45°. Time domain response tests were conducted, and the position tracking error reached ±5 μm under step input (compared to ±0.5 μm under normal operating conditions), with the overshoot increasing from 3% to 22%.

[0065] In one embodiment, for step S2,

[0066] The steps of performing frequency domain analysis based on the closed-loop control model, calculating the control loop phase margin, and triggering an instability alarm when the phase margin is lower than a preset margin threshold include:

[0067] Bode plots are generated based on the closed-loop control model, and the initial phase margin of the control loop under no-attack conditions is analyzed and obtained.

[0068] Based on the initial phase margin and the actual equipment stability test results, the preset margin threshold for triggering instability alarms is calibrated.

[0069] An alarm signal is generated when the real-time phase margin is lower than the preset margin threshold.

[0070] In practical implementation, the closed-loop control model constructed based on step S1 is used. Bode plots were generated using the control system toolbox to analyze the initial phase margin under attack-free conditions. The initial phase margin of the Bangtou Z-axis servo system under normal operating conditions was experimentally measured to be... The corresponding open-loop transfer function Amplitude crossover frequency At this point, the Z-axis position tracking error is ±0.5μm (measured data from the laser interferometer). The critical value of the phase margin is verified through actual equipment stability testing. (Injection of delay perturbation) With noise Under combined attacks, the Bode plot of the closed-loop control model shows that the phase margin decreases to At this point, the Z-axis position oscillation amplitude reached ±5μm, and the servo motor current fluctuation exceeded the limit (peak value reached 120% of the rated value). Further experiments showed that when the phase margin... At that time, the Lyapunov exponent of the system turned from negative to positive. The condition was determined to be in an unstable critical state, therefore the preset margin threshold was set to [value missing]. An FPGA-based online monitoring module is deployed to calculate the phase margin value in real time, collect open-loop frequency response data every 10 ms, and update the amplitude crossover frequency through Fast Fourier Transform (FFT). Locating on the phase curve of the Bode plot Corresponding phase value Calculate the real-time phase margin When there are 3 consecutive sampling periods When the time is right, a level three instability alarm (yellow / orange / red) is triggered, and the feedforward compensation algorithm in step S3 is started.

[0071] In one embodiment, for step S3,

[0072] In response to the instability alarm, the step of modeling the dynamic behavior of the attacked control loop as a state equation including a feedforward channel includes:

[0073] Upon receiving the instability alarm information, the amplitude crossover frequency point is located on the phase curve of the open-loop transfer characteristic of the control loop;

[0074] The difference between the phase value corresponding to the amplitude crossover frequency point and the critical instability value is calculated to obtain the real-time phase margin;

[0075] When the real-time phase margin is lower than the preset attack threshold within a preset number of consecutive control cycles, a multi-level alarm is triggered and a state equation containing time-varying disturbance terms and random noise terms is constructed.

[0076] In practical implementation, when the online monitoring module detects the real-time phase margin At that time, in open-loop transmission characteristics Precisely locate the amplitude crossover frequency on the Bode plot phase curve (At the point where the gain is 0dB), read the value at this time. Phase value at The real-time phase margin was calculated. When this margin value is within 3 consecutive control periods (periods). (The text appears to be incomplete and contains several grammatical errors. A more accurate translation would require the full context.) At that time, the system will trigger yellow ( ),orange( ),red( The three-level alarm mechanism provides a precise time window for subsequent modeling. Based on this alarm signal, the system immediately initiates the dynamic modeling process, constructing state equations that include feedforward channels. , where state variables ,in This refers to the Z-axis angular displacement of the connector. Angular velocity (measured accuracy of laser encoder ±0.1 μrad). Matrix parameters obtained through system identification, time-varying disturbance term. Driven in real time by delay fluctuation data acquired by FPGA (update rate 10kHz), random noise term Then through variance A Gaussian random number generator is injected. This embodiment uses Bode plot analysis and state-space modeling to ensure both the engineering operability of the phase margin threshold (effectively balancing false alarm suppression and response speed with a 3-cycle delay) and the accurate correspondence between the mathematical model and the physical system (all parameters are derived from the equipment technical manual and measured data), ultimately achieving fully automated processing from instability detection and dynamic modeling to compensation control.

[0077] In one embodiment, for step S4,

[0078] The steps for designing feedforward compensation terms using stability theory, dynamically adjusting compensation parameters, and restoring the phase margin of the control loop to the stable region include:

[0079] Based on the phase margin analysis results of the closed-loop control model, the feedforward gain coefficient is dynamically adjusted.

[0080] The state convergence of the compensated control loop is verified by using the derivative constraint of the Lyapunov function.

[0081] The feedforward compensation term is superimposed on the control signal input to generate a disturbance-resistant closed-loop control signal.

[0082] In practical implementation, based on the phase margin analysis results of the closed-loop control model, when a phase margin is detected... When the temperature drops to 28°, the system activates a dynamic compensation mechanism. A quadratic function is constructed using Lyapunov stability theory. , where state variables For the Z-axis angular displacement of the head, the positive definite matrix P is obtained by solving the Riccati equation. (Pick The function is determined. Based on this function, a feedforward compensation term is designed. The gain coefficient By ensuring the derivative constraint of the Lyapunov function The condition is obtained by reverse deduction. The compensation term is calculated in real time by FPGA hardware at a clock frequency of 200MHz, and the response delay is controlled within 0.8μs. After being superimposed on the control signal, the phase margin is restored to 48°.

[0083] In one embodiment, the step of dynamically adjusting the feedforward gain coefficient includes:

[0084] The gain coefficient range is adaptively adjusted based on the real-time monitored time-varying disturbance term and phase margin value.

[0085] After dynamically adjusting the gain coefficient, verify whether the compensated phase margin reaches the stable range;

[0086] If stability is not achieved, the gain adjustment strategy is iteratively optimized based on the changing trend of the time-varying disturbance term until the system stability requirements are met.

[0087] Specifically, real-time monitoring of time-varying disturbance terms (Sampling rate 10 kHz) and phase margin (Update period 1ms), establish gain coefficient Adaptive adjustment rule: when Fluctuation amplitude exceeding ±0.2 ms or At that time, according to The rate is adjusted to control the gain range; after each adjustment, the phase margin is verified using a real-time updated Bode plot. If the 45° stable range is not reached, then... The historical trend of change (predicted through the ARIMA model) is iteratively optimized until... Stable at Within the range.

[0088] In one embodiment, the step of designing the feedforward compensation term using stability theory further includes:

[0089] The computational logic for the feedforward compensation term is deployed via a programmable logic device, which is driven by a high-speed clock signal for low-latency response.

[0090] The low-latency response is used to offset the impact of attack disturbances on the precision motion control loop in real time.

[0091] Specifically, by using a Xilinx Kintex-7 FPGA to implement the pipelined processing of the compensation algorithm, the critical path delay was optimized to 37 ns, ensuring that the calculation was completed within a 1 ms control cycle. ,in The computational logic is implemented using a hardware description language (VHDL) to meet the requirement of real-time countermeasures against attack disturbances. The transition time for phase margin recovery to 48° is reduced to 1.2 ms through low-latency response.

[0092] In one embodiment, the step of designing the feedforward compensation term using stability theory further includes:

[0093] The gain parameters of the feedforward compensation term are dynamically loaded in the real-time operating system;

[0094] The gain parameters are adjusted via an online update module to adapt to the stability requirements of different attack scenarios.

[0095] Specifically, a dynamically loaded module is deployed in the real-time operating system to support online updates via the EtherCAT bus. The system stores 8 preset parameter sets. It automatically switches to the optimal parameters for different attack modes, such as step attacks and ramp attacks. For step attacks, a higher gain is selected for rapid disturbance suppression, matching the instantaneous characteristics of sudden attacks; for ramp attacks, a dynamically adjusted medium gain is used to balance response speed and long-term stability. Through adaptive parameter switching, the system can intelligently respond to diverse attack modes, avoiding performance limitations caused by single fixed parameters, and significantly improving the comprehensive defense capabilities of the industrial IoT control loop.

[0096] Reference Figure 2 Here is a structural block diagram of a loop attack modeling and stability compensation system for precision motion control according to an embodiment of the present invention, comprising:

[0097] The model building unit is used to collect the operating parameters of the precision motion control loop in real time, establish a loop attack model including delay injection attack and noise superposition attack, and build a closed-loop control model including feedforward channel.

[0098] The stability analysis unit is used to perform frequency domain analysis based on the closed-loop control model, calculate the phase margin of the control loop, and trigger an instability alarm when the phase margin is lower than a preset margin threshold.

[0099] In response to the instability alarm, the dynamic behavior of the attacked control loop is modeled as a state equation including a feedforward channel;

[0100] The compensation optimization unit is used to design feedforward compensation terms, dynamically adjust compensation parameters, and restore the phase margin of the control loop to the stable range.

[0101] For the specific implementation of each unit in the above device example, please refer to the method embodiments described above, and will not be repeated here.

[0102] In summary, this invention establishes a loop attack model that includes delay injection attacks and noise superposition attacks by real-time acquisition of the operating parameters of the precision motion control loop, and constructs a closed-loop control model including a feedforward channel. Frequency domain analysis is performed based on the closed-loop control model to calculate the phase margin of the control loop. When the phase margin is lower than a preset margin threshold, an instability alarm is triggered. In response to the instability alarm, the dynamic behavior of the attacked control loop is modeled as a state equation including a feedforward channel. A feedforward compensation term is designed using stability theory, and the compensation parameters are dynamically adjusted to restore the phase margin of the control loop to a stable range, thereby achieving real-time attack detection and dynamic stability compensation for precision motion control systems in an industrial IoT environment.

[0103] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0104] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0105] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for loop attack modeling and stability compensation for precision motion control, characterized in that, Includes the following steps: Real-time acquisition of operating parameters of precision motion control loop, establishment of loop attack model including delay injection attack and noise superposition attack, construction of closed-loop control model including feedforward channel, including by monitoring the real-time data stream of control loop to identify time-varying disturbance terms in control loop, the time-varying disturbance terms characterizing the disturbance characteristics of delay injection attack; and modeling noise superposition attack as random noise term conforming to historical statistical distribution. A closed-loop control model is constructed based on the time-varying disturbance term and random noise term to quantify the dynamic impact of the attack on the loop output. Frequency domain analysis is performed based on the closed-loop control model to calculate the phase margin of the control loop. When the phase margin is lower than a preset margin threshold, an instability alarm is triggered. In response to the instability alarm, the dynamic behavior of the attacked control loop is modeled as a state equation including a feedforward channel; Design a feedforward compensation term, dynamically adjust the compensation parameters, and restore the phase margin of the control loop to the stable range. This includes dynamically adjusting the feedforward gain coefficient based on the phase margin analysis results of the closed-loop control model; and verifying the state convergence of the compensated control loop through the derivative constraint of the Lyapunov function. The feedforward compensation term is superimposed on the control signal input to generate a disturbance-resistant closed-loop control signal; The step of dynamically adjusting the feedforward gain coefficient includes: adaptively adjusting the gain coefficient range based on the real-time monitored time-varying disturbance term and the real-time monitored phase margin; after dynamically adjusting the gain coefficient, verifying whether the compensated phase margin has reached a stable range; if it has not reached stability, iteratively optimizing the gain adjustment strategy in combination with the changing trend of the time-varying disturbance term until the system stability requirements are met.

2. The loop attack modeling and stability compensation method for precision motion control according to claim 1, characterized in that, The steps for constructing the closed-loop control model include: The original transfer characteristics of the control loop are fused with the time-varying disturbance term and the random noise term to form a closed-loop control model that includes a feedforward channel. The closed-loop control model is used for phase margin analysis and compensation parameter design.

3. The loop attack modeling and stability compensation method for precision motion control according to claim 1, characterized in that, The step of performing frequency domain analysis based on the closed-loop control model to calculate the control loop phase margin, and triggering an instability alarm when the phase margin is lower than a preset margin threshold, includes: Bode plots are generated based on the closed-loop control model, and the initial phase margin of the control loop under no-attack conditions is analyzed and obtained. Based on the initial phase margin and the actual equipment stability test results, a preset margin threshold for triggering an instability alarm is calibrated. An alarm signal is generated when the real-time phase margin is lower than the preset margin threshold.

4. The loop attack modeling and stability compensation method for precision motion control according to claim 1, characterized in that, The step of modeling the dynamic behavior of the attacked control loop as a state equation including a feedforward channel in response to the instability alarm includes: Upon receiving the instability alarm information, the amplitude crossover frequency point is located on the phase curve of the open-loop transfer characteristic of the control loop; The difference between the phase value corresponding to the amplitude crossover frequency point and the critical instability value is calculated to obtain the real-time phase margin; When the real-time phase margin is lower than the preset attack threshold within a preset number of consecutive control cycles, a multi-level alarm is triggered and a state equation containing time-varying disturbance terms and random noise terms is constructed.

5. The loop attack modeling and stability compensation method for precision motion control according to claim 1, characterized in that, The steps for designing the feedforward compensation term also include: The computational logic for the feedforward compensation term is deployed via a programmable logic device, which is driven by a high-speed clock signal for low-latency response. The low-latency response is used to offset the impact of attack disturbances on the precision motion control loop in real time.

6. The loop attack modeling and stability compensation method for precision motion control according to claim 1, characterized in that, The steps for designing the feedforward compensation term also include: The gain parameters of the feedforward compensation term are dynamically loaded in the real-time operating system; The gain parameters are adjusted via an online update module to adapt to the stability requirements of different attack scenarios.

7. A loop attack modeling and stability compensation system for precision motion control, characterized in that, include: The model building unit is used to collect the operating parameters of the precision motion control loop in real time, establish a loop attack model including delay injection attack and noise superposition attack, and build a closed-loop control model including feedforward channel. This includes identifying time-varying disturbance terms in the control loop by monitoring the real-time data stream of the control loop. The time-varying disturbance terms characterize the disturbance characteristics of delay injection attack. The noise superposition attack is modeled as a random noise term that conforms to historical statistical distribution. A closed-loop control model is constructed based on the time-varying disturbance term and random noise term to quantify the dynamic impact of the attack on the loop output. The stability analysis unit is used to perform frequency domain analysis based on the closed-loop control model, calculate the phase margin of the control loop, and trigger an instability alarm when the phase margin is lower than a preset margin threshold. In response to the instability alarm, the dynamic behavior of the attacked control loop is modeled as a state equation including a feedforward channel; The compensation optimization unit is used to design feedforward compensation terms, dynamically adjust compensation parameters, and restore the phase margin of the control loop to the stable range. This includes dynamically adjusting the feedforward gain coefficient based on the phase margin analysis results of the closed-loop control model; and verifying the state convergence of the compensated control loop through the derivative constraint of the Lyapunov function. The feedforward compensation term is superimposed on the control signal input to generate a disturbance-resistant closed-loop control signal; The step of dynamically adjusting the feedforward gain coefficient includes: adaptively adjusting the gain coefficient range based on the real-time monitored time-varying disturbance term and the real-time monitored phase margin; after dynamically adjusting the gain coefficient, verifying whether the compensated phase margin has reached a stable range; if it has not reached stability, iteratively optimizing the gain adjustment strategy in combination with the changing trend of the time-varying disturbance term until the system stability requirements are met.

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