A computer-aided simulation method for aircraft mechanical control systems

Abstract

This application relates to the field of simulation technology, specifically to a computer-aided simulation method for an aircraft mechanical control system. The method comprises: constructing a software simulation model of the aircraft mechanical control system; performing virtual sensor data acquisition and time-series synchronization standardization processing; analyzing nonlinear hysteresis effects and quantitatively evaluating dynamic fidelity; comparing ideal models and quantifying coupling deviations with environmental disturbances; and dynamically optimizing and correcting parameters based on an environmental hysteresis index. This application aims to dynamically trigger adaptive adjustment of cable preload, address simulation distortion issues caused by uneven cable preload distribution, asymmetric friction hysteresis, and environmental parameter drift, and enhance the authenticity of human-machine interaction force feedback and the reliability of fault prediction.

Description

Technical Field

[0001] The present application relates to the field of simulation technology, and in particular to a computer-aided simulation method for an aircraft mechanical control system. Background Art

[0002] As aircraft design becomes increasingly complex, the traditional model of relying on physical prototypes and flight tests has exposed problems such as high costs, lengthy cycles, and safety risks. Computer simulation technology has emerged as a result. Early simulations were primarily based on simplified mathematical models, limited by computing power, and were mostly used for local structural analysis. With breakthroughs in finite element analysis, multi-body dynamics modeling, and real-time simulation platforms, simulation accuracy and real-time performance have significantly improved. By integrating sensor data with artificial intelligence algorithms, high-fidelity virtual control systems can be constructed that accurately replicate dynamic characteristics such as mechanical transmission, hydraulic actuation, and human-machine interaction. Currently, with the development of autonomous flight and new energy aircraft, this technology is evolving towards multi-physics coupled simulation and intelligent decision-making, continuously supporting the intelligent upgrade and green transformation of aviation equipment.

[0003] In computer-aided simulation of aircraft mechanical control systems, the lack of dynamic modeling accuracy for the nonlinear hysteresis effects of mechanical transmission chains is a core challenge. This is due to the difficulty in accurately reproducing the microscopic nonlinear behavior of mechanical components using traditional simulation models. Existing technologies primarily address this issue through multibody dynamics modeling combined with high-order friction models and finite element local refinement analysis. However, the computational complexity of coupled multibody dynamics and finite element simulations increases dramatically, making it difficult to meet real-time requirements. This often forces simplified contact meshes or the use of quasi-static assumptions, resulting in the loss of high-frequency dynamic characteristics. The internal state of the mechanical transmission chain (such as cable preload distribution) is difficult to monitor in all dimensions, and data-driven compensation algorithms can cause model instability in the absence of physical constraints. These limitations mean that simulation systems may still experience errors such as hysteresis loop asymmetry and phase offset when simulating microcontroller conditions, impacting the reliability of ergonomic evaluation and fault prediction. Summary of the Invention

[0004] In order to solve the above technical problems, the present application provides a computer-aided aircraft mechanical control system simulation method to solve the existing problems.

[0005] The present invention provides a computer-aided aircraft mechanical control system simulation method using the following technical solutions:

[0006] One embodiment of the present application provides a computer-aided method for simulating an aircraft mechanical control system, the method comprising the following steps:

[0007] Construct a software simulation model of the aircraft mechanical control system;

[0008] Perform virtual sensor data acquisition and time-series synchronization standardization processing; the collected parameters include: actuator displacement data, feedback force data, environmental parameter data, and cable preload data;

[0009] The hysteresis loop features of the actuator are extracted from the actuator displacement sequence and feedback force sequence. The high-frequency energy proportion of the actuator displacement sequence in the frequency domain is then combined to calculate the fidelity attenuation factor of the mechanical transmission chain simulation at each acquisition moment.

[0010] An idealized software simulation model of the aircraft's mechanical control system is constructed to calculate the idealized fidelity degradation factor of the mechanical transmission chain at each acquisition moment. Dynamic time warping is applied to all simulated and idealized fidelity degradation factors calculated within a preset time period before each acquisition moment. The fidelity degradation sequence of the simulation and the environmental parameter sequence at each acquisition moment are used to predict the predicted value of the fidelity degradation factor at each acquisition moment, thereby constructing an environmental hysteresis index at each acquisition moment.

[0011] By utilizing the degree to which the environmental hysteresis index is greater than its preset threshold, the cable preload at the current moment is amplified and adjusted to the same degree to improve the dynamic fidelity of the simulation system.

[0012] Preferably, the software simulation model of the aircraft mechanical control system includes modeling of the driving control mechanism, the mechanical transmission chain and the actuating mechanism.

[0013] Preferably, the collected data is a sequence of collection parameters within a preset time period before each collection moment.

[0014] Preferably, the extracted hysteresis loop features are: loop width, loop area and asymmetry factor, which correspond to the amplitude, energy dissipation and asymmetry degree of the actuator hysteresis effect respectively.

[0015] Preferably, the hysteresis loop feature extraction adopts the Preisach model.

[0016] Preferably, the method for calculating the fidelity attenuation factor of the mechanical transmission chain simulation at each acquisition moment is: calculating the average value of all hysteresis loop characteristics of the extracted actuator; and taking the ratio of the average value to the high-frequency energy proportion as the fidelity attenuation factor of the mechanical transmission chain simulation at the corresponding acquisition moment.

[0017] Preferably, the method for predicting the predicted value of the fidelity attenuation factor at each acquisition moment is: taking the fidelity attenuation sequence simulated at each acquisition moment as the dependent variable, taking the environmental parameter sequence corresponding to the acquisition moment as the independent variable, and using the random forest algorithm to predict the predicted value of the fidelity attenuation factor at the corresponding acquisition moment.

[0018] Preferably, the environmental hysteresis index at each collection moment is determined by the product of the minimum regularized path distance obtained using dynamic time regularization within a preset time period before the corresponding collection moment and the predicted value predicted at the corresponding collection moment.

[0019] Preferably, a SakoeChiba window constraint is set when using dynamic time warping to limit the matching range of dynamic time warping.

[0020] Preferably, the degree to which the environmental hysteresis index is greater than its preset threshold is used to amplify and adjust the steel cable preload at the current moment to the same degree, including: setting a preset threshold for the environmental hysteresis index, and when the environmental hysteresis index collected at the current moment is greater than its preset threshold, normalizing the degree to which it is greater than the preset threshold and then amplifying the steel cable preload collected at the current moment by a corresponding multiple.

[0021] This application has at least the following beneficial effects:

[0022] 1. To address the problem of high-frequency dynamic characteristics loss caused by the nonlinear hysteresis effect of mechanical transmission chains, this method combines hysteresis loop feature extraction with frequency-domain energy attenuation analysis to comprehensively reflect the nonlinear error and dynamic fidelity of the simulation model, solving the problems of high-frequency dynamic loss and insufficient static nonlinear modeling accuracy caused by mesh simplification or quasi-static assumptions in traditional models.

[0023] 2. To address the coupled effects of environmental disturbances (temperature, vibration) on simulation dynamic characteristics, dynamic time warping is used to compare the cumulative deviations between the measured fidelity decay sequence and the ideal model sequence. The random forest algorithm is then used to predict the intensity of environmental parameter disturbances on fidelity. This quantifies the combined distortion risk of the simulation system due to nonlinear hysteresis, high-frequency dynamic loss, and environmental factors, thereby eliminating the potential impact of environmental disturbances on model stability.

[0024] 3. Based on the environmental hysteresis index, the system dynamically triggers adaptive adjustment of the cable preload force to address simulation distortion issues caused by uneven cable preload distribution, asymmetric friction hysteresis, and environmental parameter drift. This significantly improves the simulation system's ability to reproduce the microscopic nonlinear behavior and high-frequency dynamic characteristics of the mechanical transmission chain, reduces the risk of asymmetric errors and phase shifts in the hysteresis loop, and enhances the authenticity of human-machine interaction force feedback and the reliability of fault prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0026] Figure 1 A flowchart of a computer-aided aircraft mechanical control system simulation method provided in this application;

[0027] Figure 2 This application provides a specific process flow chart for quantifying the deviation between ideal model comparison and environmental disturbance coupling. DETAILED DESCRIPTION

[0028] To further illustrate the technical means and effectiveness of this application's implementation of the intended invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of a computer-assisted aircraft mechanical control system simulation method proposed in this application. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0029] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0030] The following describes in detail a computer-aided aircraft mechanical control system simulation method provided by the present application with reference to the accompanying drawings.

[0031] An embodiment of the present application provides a computer-aided method for simulating an aircraft mechanical control system.

[0032] Specifically, a computer-aided aircraft mechanical control system simulation method is provided as follows. Figure 1 , the method comprises the following steps:

[0033] Step 1: Build a software simulation model of the aircraft mechanical control system.

[0034] The aircraft mechanical control system simulation method of the present application is implemented based on a computer software platform and specifically includes the following modules and corresponding software and algorithm implementations:

[0035] 1. Driving control mechanism modeling:

[0036] MATLAB is used to build a dynamic interaction model of the joystick / steering wheel, and LabVIEW is combined to realize the real-time generation of force feedback signals and closed-loop control logic.

[0037] 2. Mechanical transmission chain modeling:

[0038] Software tools: Adams multi-body dynamics software was used to construct geometric parametric models of the cables, pulleys, and connecting rods. ANSYS Workbench was then used to perform finite element analysis of material properties (elastic modulus, friction coefficient, and gap parameters).

[0039] 3. Actuation mechanism modeling:

[0040] Software tools: Build a nonlinear dynamic model of the hydraulic / mechanical actuator based on the AMESim hydraulic simulation platform, and combine it with Simulink to achieve real-time fitting of the displacement-force characteristic curve.

[0041] Step 2: Perform virtual sensor data acquisition and timing synchronization standardization processing.

[0042] In the computer-aided software simulation model of the aircraft mechanical control system, data acquisition is based on virtual sensing technology, specifically including the acquisition of the following three types of data:

[0043] 1. Actuator displacement and feedback force data: A virtual displacement sensor and force feedback sensor unit are built into the nonlinear dynamic model of the actuator mechanism to record the actuator displacement data and its corresponding feedback force data in real time. This is used to calibrate the boundary conditions of the multi-body dynamics equations and analyze the hysteresis effect.

[0044] 2. Environmental parameter data: By integrating the environmental parameter virtual monitoring module, temperature and vibration intensity data in the simulation environment are collected in real time to quantify the dynamic impact of the external environment on the material properties of the mechanical transmission chain.

[0045] 3. Cable preload data: A distributed virtual strain monitoring module is deployed in the geometric parameterized model of the cable drive chain to dynamically output cable preload data for evaluating the nonlinear characteristics of friction hysteresis and microscopic contact.

[0046] In this example, all data is generated at a simulation step frequency of 1kHz or higher, and multi-channel data synchronization is achieved through trigger signals within the software to ensure phase consistency. Normalization is used to map the collected parameters to dimensionless intervals, providing standardized input for subsequent nonlinear hysteresis modeling and multi-physics coupling analysis.

[0047] Finally, the historical data of actuator displacement, feedback force, temperature, and vibration intensity preprocessed within one second before each acquisition moment are sorted in ascending time order to obtain the acquisition parameter sequences of each acquisition moment.

[0048] Step 3: Nonlinear hysteresis effect analysis and dynamic fidelity quantitative evaluation.

[0049] Due to the nonlinear hysteresis effect and loss of high-frequency dynamic characteristics of the mechanical transmission chain in the aircraft's mechanical control system, such as the uneven distribution of cable preload and the microscopic nonlinear behavior of multi-body contact friction, the simulation model will experience errors such as hysteresis loop asymmetry and phase offset under micro-control conditions, affecting the reliability of human-computer interaction evaluation and fault prediction.

[0050] Therefore, the actuator displacement sequence and feedback force sequence are used as input to extract the hysteresis loop characteristics of the actuator. In this embodiment, the Preisach model is selected and the loop width is defined as The maximum force difference threshold is set to ±15% of the rated load to cover the typical hysteresis range, and the ring area is defined as The integration step is set to 0.01s to ensure the accuracy of energy dissipation and define the asymmetry factor , the final output 、 and The quantitative values ​​of represent the amplitude, energy dissipation, and asymmetry of the hysteresis effect. This approach utilizes the dynamic response data at the output of the mechanical transmission chain model to directly characterize the force-displacement hysteresis characteristics of the actuator at the output of the mechanical transmission chain, reflecting the static nonlinear characteristics of the transmission chain. The analysis process of the Preisach model is well-known and will not be elaborated on here.

[0051] Then, the actuator displacement sequence is used as input, and frequency domain energy attenuation analysis is performed. The frequency band is set to 10-100 Hz to cover the natural vibration modes and high-frequency dynamic response of the mechanical transmission chain. The Hamming window function is selected as the window function, and the decomposition layer number is 6 to balance the resolution and computational efficiency.

[0052] Specifically, the displacement signal is converted from the time domain to the frequency domain using a fast Fourier transform (FFT), and the frequency domain energy distribution of the target frequency band is output for analyzing the overall frequency components of the signal. Wavelet packet decomposition is then used to further refine the frequency domain signal, and the frequency domain energy distribution of the target frequency band is input into the wavelet packet decomposition algorithm. Using the set window function and number of decomposition levels, the wavelet packet decomposition extracts the high-frequency energy and low-frequency energy of the target frequency band, outputs the high-frequency energy ratio R, and quantifies the degree of loss of high-frequency dynamic characteristics. The lower the value, the more serious the high-frequency dynamic loss caused by mesh simplification or quasi-static assumptions in the simulation model. Among them, the Hamming window, fast Fourier transform (FFT), and wavelet packet decomposition algorithm are all well-known technologies and will not be described in detail.

[0053] Based on the above analysis, the fidelity attenuation factor A of the mechanical transmission chain simulation at each acquisition moment is constructed, and its calculation relationship is:

[0054]

[0055] Among them, R is the proportion of high-frequency energy in the frequency domain of the actuator displacement sequence at each acquisition moment. It quantifies the degree of loss of high-frequency dynamic characteristics in the simulation model and reflects the simulation model's ability to reproduce the natural vibration mode and high-frequency dynamic response of the mechanical transmission chain. If the R value is lower, it indicates that the model has seriously lost high-frequency dynamics due to mesh simplification or quasi-static assumptions, and cannot capture the microscopic transient behavior of cable preload fluctuations and multi-body contact friction. 、 、 The amplitude of the static nonlinear hysteresis effect of the actuator, the total energy dissipation of the transmission chain, and the non-uniformity of the dynamic response of the transmission chain are respectively characterized. By averaging the three independent hysteresis characteristics, the multi-dimensional characteristics of the static nonlinearity of the transmission chain are covered, reflecting the severity of the overall nonlinear hysteresis of the transmission chain. The larger the result, the more significant the amplitude deviation, energy dissipation and geometric distortion, which leads to error accumulation of the simulation model under micro-manipulation conditions.

[0056] It should be understood that the fidelity attenuation factor A characterizes the nonlinear modeling error and dynamic fidelity of the simulation model, and represents the comprehensive error intensity caused by the simulation model ignoring high-frequency dynamic characteristics and failing to accurately reproduce static nonlinear hysteresis. The larger the value, the higher the risk of distortion in human-computer interaction force feedback and fault prediction deviation due to uneven distribution of cable preload, high-frequency loss of multi-body contact friction micro-behavior, and asymmetric hysteresis effect under micro-manipulation conditions.

[0057] The fidelity attenuation factors calculated at all acquisition moments within one second before each acquisition moment are sorted in ascending order of time to obtain a fidelity attenuation sequence at each acquisition moment.

[0058] Step 4: Quantify the deviation between the ideal model and the coupling with the environmental disturbance.

[0059] Due to the nonlinear hysteresis effect of the mechanical transmission chain and the loss of high-frequency dynamic characteristics in the aircraft mechanical control system, the simulation model will have errors such as hysteresis loop asymmetry and phase offset under micro-control conditions, which affects the accuracy of dynamic characteristic reproduction.

[0060] In this application, the specific process flow chart of ideal model comparison and environmental disturbance coupling deviation quantification is as shown in the attached Figure 2 As shown, specifically:

[0061] Through the multi-body dynamics and finite element coupling model, an idealized aircraft mechanical control system software simulation model with no nonlinear hysteresis and no high-frequency dynamic loss is established. The construction of the idealized aircraft mechanical control system software simulation model needs to be achieved through the following process: First, in the multi-body dynamics and finite element coupling model of step 1, ideal parameters are defined, including zero friction coefficient (the pulley-cable contact property is set to frictionless in Adams), uniform cable preload (constant tension is applied without distributed fluctuation through parameterized loading in ANSYS Workbench), gapless connecting rod connection (free gap parameters are eliminated in Adams articulated joints and set as rigid constraints), ideal elastic material properties (constant elasticity is defined in the ANSYS material library), and the like. modulus and no hysteresis effect), and the hydraulic actuator has no dead zone / leakage (the valve core nonlinearity is removed and the oil is set to be incompressible in AMESim); then, the ideal parameters are synchronously injected into each software module through the joint simulation interface (such as Adams-Control-Simulink) to ensure that the model operates in an ideal state without hysteresis and high-frequency loss; then, based on the standardized process of step 2, the actuator displacement, feedback force and environmental parameter data under the ideal model are collected, and the Preisach model in step 3 is used to extract the ideal hysteresis loop characteristics and wavelet packet decomposition to calculate the ideal high-frequency energy ratio. The ideal model sequence with the same frequency and source as the real model is finally generated to quantify the actual simulation error.

[0062] Therefore, the fidelity decay sequence simulated at each acquisition moment and the ideal model sequence are used as input. Dynamic time warping (DTW) is used, and the SakoeChiba window constraint is set with a width of 10 to limit the path offset to match the actual manipulation timing characteristics. The minimum warped path distance D is output. Its value reflects the cumulative deviation between the measured dynamic characteristics and the ideal model on the time axis. The larger the deviation, the more serious the deviation of the transmission chain dynamic response from the expected one.

[0063] Subsequently, the simulated fidelity decay sequence and environmental parameter sequence at each acquisition moment were used as input. Specifically, the fidelity decay sequence was set as the dependent variable label, and the environmental parameter data as the independent variable label. A random forest algorithm was used for stability prediction, with the number of decision trees set to 200 and the maximum depth to 15. The predicted value S of the fidelity decay factor at each acquisition moment was output. The prediction process of the random forest algorithm is well-known and will not be further described.

[0064] Based on the above analysis, the environmental hysteresis index B at each acquisition moment is constructed, and its calculation relationship is:

[0065]

[0066] Among them, D is the minimum regularized path distance between the fidelity attenuation sequence simulated at each acquisition moment and the ideal model sequence, quantifying the cumulative deviation between the dynamic characteristics of the simulation model and the ideal model in the time dimension, reflecting the degree of dynamic response distortion of the mechanical transmission chain caused by nonlinear hysteresis (such as cable preload fluctuations and multi-body contact friction) and high-frequency dynamic loss (such as the failure to reproduce the natural vibration mode). The larger the D value, the more significant the delay, phase offset and energy dissipation of the transmission chain in the actual simulation; S is the predicted value of the fidelity attenuation factor simulated at each acquisition moment. Through the correlation between environmental parameters (temperature, vibration) and historical fidelity attenuation data, the disturbance intensity of the current environment on the dynamic fidelity of the simulation model is predicted, and the coupling effect of environmental sensitive parameters on the nonlinear hysteresis and high-frequency dynamic characteristics of the transmission chain is characterized. The increase in S value reflects that such environmental factors aggravate the distortion risk of the simulation model.

[0067] The environmental hysteresis index B expresses the dynamic error intensity of the mechanical transmission chain under environmental disturbances, and quantifies the comprehensive distortion risk of the simulation system caused by nonlinear hysteresis, high-frequency dynamic loss, and environmental coupling effects. If the B value is high, it indicates that the preload fluctuation of the cable transmission chain, the asymmetric hysteresis of the pulley friction, and the material parameter drift caused by temperature work together to cause the human-machine interaction force feedback phase offset and the control surface deflection angle prediction inaccurate.

[0068] Step 5: Dynamically optimize and modify parameters based on the environmental hysteresis index.

[0069] Due to inaccurate modeling of the nonlinear hysteresis effects of mechanical transmission chains and the loss of high-frequency dynamic characteristics, simulation systems exhibit errors such as hysteresis loop asymmetry and phase offset under micromanipulation conditions, impacting the reliability of ergonomic assessments and fault prediction. The core reason for this is that in traditional multibody dynamics and finite element coupled simulations, simplified contact meshes and quasi-static assumptions weaken high-frequency response capabilities, making it difficult to monitor the cable preload distribution in real time. Data-driven compensation, lacking physical constraints, can easily lead to model instability.

[0070] Therefore, based on the environmental hysteresis index B, the model parameters are optimized and closed-loop correction is performed for technical problems. Specifically, the preload force of the steel cable is adjusted by the environmental hysteresis index. This embodiment sets a preset threshold value of the environmental hysteresis index. 0.5, ensuring timely intervention when hysteresis nonlinearity or stability risk exceeds the safety margin to avoid error accumulation. When the environmental hysteresis index B collected at the current moment is greater than the preset threshold When the B value is used to trigger the preload force distribution adaptive adjustment algorithm, the specific formula is:

[0071]

[0072] in, is the cable preload collected at the current moment, and are the environmental hysteresis index calculated at the current moment and the preset threshold, is the preload force of the steel cable after adjustment at the current moment, The sigmoid function is used to normalize the value.

[0073] It should be understood that when the measured indicator B exceeds the preset threshold When the dynamic deviation or stability risk increases, the cable preload distribution is dynamically adjusted through the normalized function to make It increases with the increase of B, and approaches the initial cable preload. When B increases, increasing the preload can suppress the hysteresis nonlinearity caused by cable relaxation, thereby reducing the asymmetry error of the hysteresis loop and controlling the phase offset.

[0074] Through the above improvements, the adjustment of the steel cable preload force was realized. Through dynamic parameter optimization and nonlinear compensation, the simulation system's high-fidelity reproduction capability of the microscopic nonlinear behavior of the mechanical transmission chain was significantly improved.

[0075] The above technical features constitute the best embodiment of the present application, which has strong adaptability and optimal implementation effect. Non-essential technical features can be added or removed according to actual needs to meet the requirements of different situations.

Claims

1. A computer-aided method for simulating an aircraft mechanical control system, characterized in that: The method comprises the following steps: Construct a software simulation model of the aircraft mechanical control system; Perform virtual sensor data acquisition and time-series synchronization standardization processing; the acquired parameters include: actuator displacement data, feedback force data, environmental parameter data, and cable preload data; The hysteresis loop features of the actuator are extracted from the actuator displacement sequence and feedback force sequence. The high-frequency energy proportion of the actuator displacement sequence in the frequency domain is then combined to calculate the fidelity attenuation factor of the mechanical transmission chain simulation at each acquisition moment. An idealized software simulation model of the aircraft's mechanical control system is constructed to calculate the idealized fidelity degradation factor of the mechanical transmission chain at each acquisition moment. Dynamic time warping is applied to all simulated and idealized fidelity degradation factors calculated within a preset time period before each acquisition moment. The fidelity degradation sequence of the simulation and the environmental parameter sequence at each acquisition moment are used to predict the predicted value of the fidelity degradation factor at each acquisition moment, thereby constructing an environmental hysteresis index at each acquisition moment. By using the degree to which the environmental hysteresis index is greater than its preset threshold, the current cable preload is amplified and adjusted to the same degree, thereby improving the dynamic fidelity of the simulation system. The fidelity attenuation factor of the mechanical transmission chain simulation at each acquisition moment is calculated by: calculating the average value of all hysteresis loop characteristics of the actuator extracted; and using the ratio of the average value to the high-frequency energy ratio as the fidelity attenuation factor of the mechanical transmission chain simulation at the corresponding acquisition moment; Among them, the environmental hysteresis index at each collection moment is determined by the product of the minimum regularized path distance obtained by using dynamic time warping within a preset time period before the corresponding collection moment and the predicted value predicted at the corresponding collection moment.

2. The computer-aided aircraft mechanical control system simulation method according to claim 1, wherein: The software simulation model of the aircraft mechanical control system includes the modeling of the driving control mechanism, the mechanical transmission chain and the actuating mechanism.

3. The computer-aided aircraft mechanical control system simulation method according to claim 1, wherein: The collected data is a sequence of collection parameters within a preset time period before each collection moment.

4. The computer-aided aircraft mechanical control system simulation method according to claim 1, wherein: The extracted hysteresis loop features are: loop width, loop area and asymmetry factor, which correspond to the amplitude, energy dissipation and asymmetry of the actuator hysteresis effect respectively.

5. The computer-aided aircraft mechanical control system simulation method according to claim 4, characterized in that: The hysteresis loop feature extraction adopts the Preisach model.

6. The computer-aided aircraft mechanical control system simulation method according to claim 1, wherein: The method for predicting the predicted value of the fidelity attenuation factor at each acquisition moment is as follows: taking the fidelity attenuation sequence simulated at each acquisition moment as the dependent variable, taking the environmental parameter sequence corresponding to the acquisition moment as the independent variable, and using the random forest algorithm to predict the predicted value of the fidelity attenuation factor at the corresponding acquisition moment.

7. The computer-aided aircraft mechanical control system simulation method according to claim 1, wherein: Set the SakoeChiba window constraint when using dynamic time warping to limit the matching range of dynamic time warping.

8. The computer-aided aircraft mechanical control system simulation method according to claim 1, wherein: The degree to which the environmental hysteresis index is greater than its preset threshold is used to amplify and adjust the steel cable preload force at the current moment to the same degree, including: setting a preset threshold for the environmental hysteresis index, and when the environmental hysteresis index collected at the current moment is greater than its preset threshold, normalizing the degree of the greater than threshold and then amplifying the steel cable preload force collected at the current moment by a corresponding multiple.

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