A universal suspension control logic testing method

Abstract

The present invention discloses a method for testing the launch control logic of a universal suspension, comprising: constructing a simulated launch scenario for the suspension, and establishing a dynamic random environmental excitation model within the simulated launch scenario; utilizing the dynamic random environmental excitation model to apply different types of environmental excitations to the simulated launch scenario, simulating the launch of the suspension under the environmental excitation conditions; constructing an excitation signal vector during the launch process of the suspension, driving the suspension model into a launch state, and constructing an ideal state model of the suspension during the launch process; outputting the ideal state vector during the launch process of the suspension, constructing a control logic decision model during the launch control test of the suspension, and evaluating the logic control accuracy during the launch control process. The present invention analyzes and processes environmental excitation data within a target area, using this data as a basis for applying environmental excitations to the simulated launch scenario during the test. The simulated test results can truly reflect the target scenario, improving the reliability of the test.

Description

Technical Field

[0001] The present invention relates to the field of aircraft control testing, and in particular to a method for testing the launch and control logic of a universal suspension object. Background Art

[0002] In the aerospace and high-altitude aircraft sectors, a growing number of small aircraft and suspended objects can be custom-released and launched at high altitudes. Using large aircraft as carriers, these objects can be launched at opportune moments at high altitudes. For example, the launch of airborne drones can effectively extend the drone's cruising range and range by installing a drone mounting platform within a large aircraft and transporting the drone to a high altitude for launch or release. Another example is the projection of airborne electronic equipment, which can improve the accuracy of electronic device placement. However, the high-altitude environment is complex, and the possibility of suspension objects and the mounting platform becoming detached from each other requires precise control of the suspension and the mounting platform to prevent mutual interference. Achieving precise launch control requires launch control logic testing, which involves debugging the launch control logic under experimental conditions to ensure sufficient accuracy.

[0003] Existing suspension control logic testing usually uses dedicated test equipment and designs fixed test processes for specific types of suspensions. This has the following defects: different types of suspensions require independent test programs, which results in high repeated development costs; it is difficult to cover complex application environments; and manual comparison based on fixed thresholds is easily affected by environmental noise, leading to misjudgments. Summary of the Invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for testing the launch control logic of a universal suspension object, which can realize the testing of the launch control logic of the suspension object in different application scenarios.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0006] A method for testing the control logic of a universal suspension is provided, comprising:

[0007] Step S1: constructing a simulated launch scene of a suspended object and establishing a dynamic random environmental excitation model in the simulated launch scene;

[0008] Step S2: applying different types of environmental excitations to the simulated launch scene using a dynamic random environmental excitation model, establishing a suspended object model in the simulated launch scene, and simulating the launch of the suspended object under the environmental excitation conditions;

[0009] Step S3: constructing an excitation signal vector during the launch of the suspended object, applying the excitation signal vector to the suspended object model, driving the suspended object model into a launch state, obtaining a state vector during the launch of the suspended object, and constructing an ideal state model of the suspended object during the launch process;

[0010] Step S4: Output the ideal state vector during the suspension launch process through the applied excitation signal vector and the ideal state model, construct a control logic decision model during the suspension launch control test process, and evaluate the logic control accuracy during the launch control process.

[0011] Furthermore, the method for establishing a dynamic random environmental incentive model in step S1 includes:

[0012] Step S11: collecting historical environmental excitation data in the target area during the launch of the suspended object to form an environmental excitation data set, and calculating the temporal distribution probability of the environmental excitation data to obtain a distribution probability data set;

[0013] Step S12: setting a distribution probability range that indicates that the distribution density of the collected environmental stimulus data meets the requirements, segmenting the environmental stimulus data according to whether the distribution probability data falls within the distribution probability range, and calculating the fluctuation coefficient of the environmental stimulus data amplitude within the environmental stimulus data segment;

[0014] Step S13: Using the fluctuation coefficient corresponding to the amplitude of the environmental excitation data in all environmental excitation data segments, a polynomial time-varying amplitude modulation model of the environmental excitation data is established, and based on the polynomial time-varying amplitude modulation model, a dynamic differential equation of the environmental excitation is constructed as a dynamic random environmental excitation model.

[0015] Furthermore, step S11 includes:

[0016] Step S111: Collect historical environmental excitation data in the target area during the launch of the suspended object , forming an environmental incentive dataset ,in, n For the types of environmental incentives, m is the number of environmental stimulus data, t m To collect m A moment when the environment inspires data;

[0017] Step S112: Calculating the temporal distribution probability of the environmental stimulus data according to the time interval between the acquisition moments of two adjacent environmental stimulus data;

[0018] ;

[0019] in, Inspiring data for the environment The distribution probability of To collect m -2 moments of environmentally motivated data;

[0020] Step S113: Get the second environmental stimulus data to m -1 distribution probability dataset corresponding to the environmental stimulus data .

[0021] Furthermore, step S12 includes:

[0022] Step S121: Setting the distribution probability range that the distribution density of the collected environmental stimulus data meets the requirements , is the maximum value of the distribution probability that meets the requirements, The minimum distribution probability that meets the requirements , traverse the distribution probability data set , filter out the values that are less than or equal to the minimum value of the distribution probability The distribution probability of is taken as the benchmark distribution probability;

[0023] Step S122: Taking the reference distribution probability as the origin, traverse the continuous distribution probabilities on both sides in turn, and compare the continuous distribution probabilities on both sides with the maximum value. until the distribution probability is greater than the maximum value Stop when , and output the distribution probability of the two sides farthest from the origin ;

[0024] Step S123: Based on the farthest distribution probability Corresponding environmental incentive data , extract environmental stimulus data The environmental excitation data between the two forms an environmental excitation data segment, and the amplitude of the environmental excitation data in the environmental excitation data segment is calculated. The coefficient of fluctuation;

[0025] ;

[0026] in, k is the segment number of the extracted environmental stimulus data, t max To collect the amplitude of environmental stimulus data The corresponding moment, For the k The fluctuation coefficient of the environmental excitation data amplitude of each environmental excitation data segment, t min To stimulate data valleys for the acquisition environment The corresponding moment.

[0027] Furthermore, step S13 includes:

[0028] Step S131: establishing a polynomial time-varying amplitude modulation model of the environmental excitation data according to the fluctuation coefficients corresponding to the amplitudes of the environmental excitation data in all extracted environmental excitation data segments;

[0029] ;

[0030] in, K is the number of environmental stimulus data segments extracted, is a function of time variable, is the amplitude of historical environmental excitation data, is the time-varying amplitude of the environmental excitation, t is the time variable for applying environmental stimulus;

[0031] Step S132: Time-varying amplitude based on environmental excitation data Establish the dynamic differential equation of environmental excitation as a dynamic random environmental excitation model;

[0032] ;

[0033] in, To simulate the environmental excitation applied to the launch scenario, is the drift coefficient, is the basic perturbation coefficient, is the fluctuation period coefficient of the disturbance coefficient, w is the unit fluctuation period of the disturbance coefficient, The weight coefficients of drift fluctuation and disturbance fluctuation respectively, is the Wiener increment, which obeys the normal distribution , T The test duration.

[0034] Furthermore, step S3 includes:

[0035] Step S31: Constructing the excitation signal vector during the launch of the suspended object , e is the type of excitation signal, For the e Excitation signal; Apply the excitation signal vector to the suspension model , driving the suspended object model into the launching state;

[0036] Step S32: Obtain the feedback signal in the time series during the launch of the suspended object and construct the state vector of the suspended object during the launch process , j is the number of status signals, For the j A status signal;

[0037] Step S33: Construct an ideal state model of the suspended object during the launch process. The ideal state model has a state transfer matrix describe;

[0038] ;

[0039] in, g is the ideal number of states, the state transfer matrix Elements in Indicates the slave state o Transfer to state q The conditional function of

[0040] The conditional function is expressed as:

[0041] ;

[0042] in, To stimulate the trigger function, two excitation signals are used The logical combination outputs the excitation signal, The excitation signal vectors are There are two different excitation signal numbers in is the time constraint function, is the state transition time window during the launch of the suspended object, is the state constraint function, which outputs the ideal state signal during the launch of the suspended object through the state constraint function .

[0043] Furthermore, step S4 includes:

[0044] Step S41: Apply the excitation signal vector And the ideal state model, output the ideal state vector during the launch of the suspension , For the j An ideal state signal;

[0045] Step S42: constructing a control logic decision model during the suspended object launch control test;

[0046] ;

[0047] in, is the control logic accuracy determination function, r is the number of the status signal, For the r status signals, For the r An ideal state signal, is the control logic decision function, is the dynamic noise margin function, is the transition slope, t 0 is the critical time of state transfer, is the maximum allowable noise error;

[0048] Step S43: Output the control logic judgment value in the process of suspended object launch control using the control logic judgment model F ,like If the duration of the transmission control is greater than or equal to the effective time, it is determined that the logic control accuracy in the transmission control process is high; otherwise, the logic control accuracy is low.

[0049] The beneficial effects of this invention are as follows: By analyzing and processing the environmental stimulus data within the target area, this data serves as the basis for applying environmental stimulus during the simulated launch scenario during testing. The simulated test results truly reflect the target scenario, improving test reliability. Furthermore, by abstracting the launch control logic, adapting to different models and types of suspended objects requires only updating the state transition matrix and stimulus signal, making the testing method highly versatile. The control logic decision model features adaptive fault tolerance, allowing for a certain amount of noise interference to avoid biased judgments of logical faults, reducing the false positive rate from 12% with the traditional fixed threshold method to 1.5%. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The figure is a flow chart of the control logic test method for general suspension objects.

[0051] Figure 2 Schematic diagram for segmenting environmental stimulus data. DETAILED DESCRIPTION

[0052] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0053] like Figure 1 As shown, a method for testing the control logic of a universal suspension object includes:

[0054] Step S1: Constructing a simulated launch scene of a suspended object and establishing a dynamic random environment excitation model in the simulated launch scene; the specific method of establishing the dynamic random environment excitation model includes:

[0055] Step S11: Collect historical environmental excitation data in the target area during the launch of the suspended object to form an environmental excitation data set, calculate the temporal distribution probability of the environmental excitation data, and obtain a distribution probability data set. Step S11 specifically includes:

[0056] Step S111: Collect historical environmental excitation data in the target area during the launch of the suspended object , forming an environmental incentive dataset ,in, n For the types of environmental incentives, m is the number of environmental stimulus data, t m To collect m The environmental excitation data includes external excitations that affect the launch and flight of the suspension and the flight of the suspension carrier, such as wind load, ambient temperature, ambient humidity, altitude, rainfall, etc.

[0057] Step S112: Calculating the temporal distribution probability of the environmental stimulus data according to the time interval between the acquisition moments of two adjacent environmental stimulus data;

[0058] ;

[0059] in, Inspiring data for the environment The distribution probability of To collect m -2 moments of environmentally motivated data;

[0060] Step S113: Get the second environmental stimulus data to m -1 distribution probability dataset corresponding to the environmental stimulus data .

[0061] The distribution probability represents the distribution density of each environmental stimulus data point over the acquisition time series. A larger distribution probability value indicates a lower distribution density, while a smaller distribution probability value indicates a higher distribution density. A higher distribution density indicates that the environmental stimulus data collected during the same time period is continuous and reliable. Historical environmental stimulus data fluctuates during the acquisition process, often due to randomness, accidental errors, or transmission delays. To improve the reliability of subsequent calculations applying environmental stimulus data to simulated launch scenarios, it is necessary to extract data segments with a higher distribution density for use in data calculations and discard segments with a lower distribution density.

[0062] Step S12: Setting a distribution probability range that indicates that the distribution density of the collected environmental stimulus data meets the requirements, segmenting the environmental stimulus data according to whether the distribution probability data falls within the distribution probability range, and calculating the fluctuation coefficient of the environmental stimulus data amplitude within the environmental stimulus data segment. Step S12 specifically includes:

[0063] Step S121: Setting the distribution probability range that the distribution density of the collected environmental stimulus data meets the requirements , is the maximum value of the distribution probability that meets the requirements, The minimum distribution probability that meets the requirements , traverse the distribution probability data set , filter out values that are less than or equal to the minimum value of the distribution probability The distribution probability of is taken as the benchmark distribution probability;

[0064] Minimum distribution probability and maximum value It can be set according to the amount of historical environmental incentive data collected. If the amount of data is small, the minimum value can be and maximum value Set it a little larger to ensure the amount of data in the environmental stimulus data segment. If the amount of data is large and sufficient accuracy is required, set the minimum value and maximum value Set it a little smaller.

[0065] Step S122: Taking the reference distribution probability as the origin, traverse the continuous distribution probabilities on both sides in turn, and compare the continuous distribution probabilities on both sides with the maximum value. until the distribution probability is greater than the maximum value Stop when , and output the distribution probability of the two sides farthest from the origin ;

[0066] Step S123: Based on the farthest distribution probability Corresponding environmental incentive data , extract environmental stimulus data The environmental incentive data between forms environmental incentive data segments. The principle of segmenting environmental incentive data is as follows: Figure 2 As shown, the amplitude of the environmental excitation data in the environmental excitation data segment is calculated The coefficient of fluctuation;

[0067] ;

[0068] in, k is the segment number of the extracted environmental stimulus data, t max To collect the amplitude of environmental stimulus data The corresponding moment, For the k The fluctuation coefficient of the environmental excitation data amplitude of each environmental excitation data segment, t min Incentivize data valleys for the acquisition environment The corresponding moment.

[0069] Step S13: Using the fluctuation coefficients corresponding to the amplitudes of the environmental excitation data in all environmental excitation data segments, a polynomial time-varying amplitude modulation model of the environmental excitation data is established, and based on the polynomial time-varying amplitude modulation model, a dynamic differential equation of the environmental excitation is constructed as a dynamic random environmental excitation model. Step S13 specifically includes:

[0070] Step S131: establishing a polynomial time-varying amplitude modulation model of the environmental excitation data according to the fluctuation coefficients corresponding to the amplitudes of the environmental excitation data in all extracted environmental excitation data segments;

[0071] ;

[0072] in, K is the number of environmental stimulus data segments extracted, is a function of time variable, is the amplitude of historical environmental excitation data, is the time-varying amplitude of the environmental excitation, t is the time variable for applying environmental stimulus;

[0073] When applying dynamic random environmental excitation to the simulated emission scene using the polynomial time-varying amplitude modulation model, the time variable function Split the application time period of the environmental stimulus into continuous and environmental stimulus data segments k By applying different environmental excitation amplitudes in different time periods, the difference between the simulated environmental excitation amplitude fluctuation and the real environmental excitation amplitude can be ensured to be small, thereby improving the reliability of the simulation test.

[0074] Step S132: Time-varying amplitude based on environmental excitation data Establish the dynamic differential equation of environmental excitation as a dynamic random environmental excitation model;

[0075] ;

[0076] in, To simulate the environmental excitation applied to the launch scenario, is the drift coefficient, which represents the deterministic rate of change of the time-varying amplitude over time, is the basic perturbation coefficient, is the fluctuation period coefficient of the disturbance coefficient, w is the unit fluctuation period of the disturbance coefficient, The weight coefficients of drift fluctuation and disturbance fluctuation respectively, is the Wiener increment, which obeys the normal distribution , TThe test duration.

[0077] This embodiment applies dynamic, random environmental excitation to a simulated transmission scenario. Dynamic drift fluctuations and random perturbations are introduced based on the time-varying amplitude of the environmental excitation, achieving a random, dynamic output of the environmental excitation. The drift coefficient term represents the deterministic trend fluctuations of the time-varying amplitude of the environmental excitation, while the perturbation coefficient term represents the random perturbations of the time-varying amplitude of the environmental excitation.

[0078] Depending on the type of environmental stimulus applied, the weight coefficient The value of is also different. For example, for wind load, in a small launch area in a real launch scenario, the wind load is very random, so the weight coefficient is , can generally be taken For example, the ambient temperature is affected by the light intensity. The ambient temperature fluctuates periodically within a certain range. The weight coefficient is , can generally be taken .

[0079] Step S2: applying different types of environmental excitations to the simulated launch scene using a dynamic random environmental excitation model, establishing a suspended object model in the simulated launch scene, and simulating the launch of the suspended object under the environmental excitation conditions;

[0080] Step S3: Construct an excitation signal vector during the launch process of the suspended object, apply the excitation signal vector to the suspended object model, drive the suspended object model into the launch state, obtain the state vector during the launch process of the suspended object, and construct an ideal state model of the suspended object during the launch process. Step S3 specifically includes:

[0081] Step S31: Constructing the excitation signal vector during the launch of the suspended object , e is the type of excitation signal, For the e Excitation signal; Apply the excitation signal vector to the suspension model , driving the suspended object model into the launching state;

[0082] Step S32: Obtain the feedback signal in the time series during the launch of the suspended object and construct the state vector of the suspended object during the launch process , j is the number of status signals, For the j A status signal;

[0083] Step S33: Construct an ideal state model of the suspended object during the launch process. The ideal state model has a state transfer matrix describe;

[0084] ;

[0085] in, g is the ideal number of states, the state transfer matrix Elements in Indicates the slave state o Transfer to state q The conditional function of

[0086] The conditional function is expressed as:

[0087] ;

[0088] in, To stimulate the trigger function, two excitation signals are used The logical combination outputs the excitation signal, The excitation signal vectors are There are two different excitation signal numbers in is the time constraint function, is the state transition time window during the launch of the suspended object, is the state constraint function, which outputs the ideal state signal during the launch of the suspended object through the state constraint function .

[0089] Step S4: Output the ideal state vector during the pendant launch process using the applied excitation signal vector and the ideal state model, construct a control logic decision model during the pendant launch control test, and evaluate the logic control accuracy during the launch control process. Step S4 specifically includes:

[0090] Step S41: Apply the excitation signal vector And the ideal state model, output the ideal state vector during the launch of the suspension , For the j An ideal state signal;

[0091] Step S42: constructing a control logic decision model during the suspended object launch control test;

[0092] ;

[0093] in, is the control logic accuracy determination function, r is the number of the status signal, For the r status signals, For the r An ideal state signal, is the control logic decision function, is the dynamic noise margin function, is the transition slope, which is used to control the dynamic noise margin change rate. t 0 is the critical time of state transfer, The maximum allowable noise error can be set to 5% in this embodiment;

[0094] Step S43: Output the control logic judgment value in the process of suspended object launch control using the control logic judgment model F ,like If the duration of the transmission control is greater than or equal to the effective time, it is determined that the logic control accuracy in the transmission control process is high; otherwise, the logic control accuracy is low.

[0095] This method analyzes and processes environmental stimulus data within the target area, using it as a basis for applying environmental stimulus during simulated launch scenarios. The simulated test results truly reflect the target scenario, improving test reliability. Furthermore, by abstracting the launch control logic, adapting to different models and types of suspended objects requires only updating the state transition matrix and stimulus signals, making the test method highly versatile. The control logic decision model features adaptive fault tolerance, allowing for a certain amount of noise interference to avoid biased logic fault judgments, reducing the false positive rate from 12% with the traditional fixed threshold method to 1.5%.

Claims

1. A method for testing the control logic of a general suspension, characterized in that: include: Step S1: constructing a simulated launch scene of a suspended object and establishing a dynamic random environmental excitation model in the simulated launch scene; Step S2: applying different types of environmental excitations to the simulated launch scene using a dynamic random environmental excitation model, establishing a suspended object model in the simulated launch scene, and simulating the launch of the suspended object under the environmental excitation conditions; Step S3: constructing an excitation signal vector during the launch of the suspended object, applying the excitation signal vector to the suspended object model, driving the suspended object model into a launch state, obtaining a state vector during the launch of the suspended object, and constructing an ideal state model of the suspended object during the launch process; Step S4: Outputting the ideal state vector during the suspension launch process based on the applied excitation signal vector and the ideal state model, constructing a control logic decision model during the suspension launch control test process, and evaluating the logic control accuracy during the launch control process; The method for establishing a dynamic random environmental excitation model in step S1 includes: Step S11: collecting historical environmental excitation data in the target area during the launch of the suspended object to form an environmental excitation data set, and calculating the temporal distribution probability of the environmental excitation data to obtain a distribution probability data set; Step S12: setting a distribution probability range that indicates that the distribution density of the collected environmental stimulus data meets the requirements, segmenting the environmental stimulus data according to whether the distribution probability data falls within the distribution probability range, and calculating the fluctuation coefficient of the environmental stimulus data amplitude within the environmental stimulus data segment; Step S13: using the fluctuation coefficients corresponding to the amplitudes of the environmental excitation data in all environmental excitation data segments, establishing a polynomial time-varying amplitude modulation model of the environmental excitation data, and constructing a dynamic differential equation of the environmental excitation based on the polynomial time-varying amplitude modulation model as a dynamic random environmental excitation model; The step S3 comprises: Step S31: Constructing the excitation signal vector during the launch of the suspended object , e is the type of excitation signal, For the e Excitation signal; Apply the excitation signal vector to the suspension model , driving the suspended object model into the launching state; Step S32: Obtain the feedback signal in the time series during the launch of the suspended object and construct the state vector of the suspended object during the launch process , j is the number of status signals, For the j A status signal; Step S33: Construct an ideal state model of the suspended object during the launch process. The ideal state model has a state transfer matrix describe; ； in, g is the ideal number of states, the state transfer matrix Elements in Indicates the slave state o Transfer to state q The conditional function of The conditional function is expressed as: ； in, To stimulate the trigger function, two excitation signals are used The logical combination outputs the excitation signal, The excitation signal vectors are There are two different excitation signal numbers in is the time constraint function, is the state transition time window during the launch of the suspended object, is the state constraint function, which outputs the ideal state signal during the launch of the suspended object through the state constraint function .

2. The method for testing the control logic of a universal suspension according to claim 1, characterized in that: The step S11 includes: Step S111: Collect historical environmental excitation data in the target area during the launch of the suspended object , forming an environmental incentive dataset ,in, n For the types of environmental incentives, m is the number of environmental stimulus data, t m To collect m A moment when the environment inspires data; Step S112: Calculating the temporal distribution probability of the environmental stimulus data according to the time interval between the acquisition moments of two adjacent environmental stimulus data; ； in, Inspiring data for the environment The distribution probability of To collect m -2 moments of environmentally motivated data; Step S113: Get the second environmental stimulus data to m -1 distribution probability dataset corresponding to the environmental stimulus data .

3. The method for testing the control logic of a universal suspension according to claim 2, characterized in that: The step S12 includes: Step S121: Setting the distribution probability range that the distribution density of the collected environmental stimulus data meets the requirements , is the maximum value of the distribution probability that meets the requirements, The minimum distribution probability that meets the requirements , traverse the distribution probability data set , filter out the values that are less than or equal to the minimum value of the distribution probability The distribution probability of is taken as the benchmark distribution probability; Step S122: Taking the reference distribution probability as the origin, traverse the continuous distribution probabilities on both sides in turn, and compare the continuous distribution probabilities on both sides with the maximum value. until the distribution probability is greater than the maximum value Stop when , and output the distribution probability of the two sides farthest from the origin ; Step S123: Based on the farthest distribution probability Corresponding environmental incentive data , extract environmental stimulus data The environmental excitation data between the two forms an environmental excitation data segment, and the amplitude of the environmental excitation data in the environmental excitation data segment is calculated. The coefficient of fluctuation; ； in, k is the segment number of the extracted environmental stimulus data, t max To collect the amplitude of environmental stimulus data The corresponding moment, For the k The fluctuation coefficient of the environmental excitation data amplitude of each environmental excitation data segment, t min Incentivize data valleys for the acquisition environment The corresponding moment.

4. The method for testing the control logic of a universal suspension according to claim 3, characterized in that: The step S13 includes: Step S131: establishing a polynomial time-varying amplitude modulation model of the environmental excitation data according to the fluctuation coefficients corresponding to the amplitudes of the environmental excitation data in all extracted environmental excitation data segments; ； in, K is the number of environmental stimulus data segments extracted, is a function of time variable, is the amplitude of historical environmental excitation data, is the time-varying amplitude of the environmental excitation, t is the time variable for applying environmental stimulus; Step S132: Time-varying amplitude based on environmental excitation data Establish the dynamic differential equation of environmental excitation as a dynamic random environmental excitation model; ； in, To simulate the environmental excitation applied to the launch scenario, is the drift coefficient, is the basic perturbation coefficient, is the fluctuation period coefficient of the disturbance coefficient, w is the unit fluctuation period of the disturbance coefficient, The weight coefficients of drift fluctuation and disturbance fluctuation respectively, is the Wiener increment, which obeys the normal distribution , T The test duration.

5. The method for testing the control logic of a universal suspension according to claim 4, characterized in that: The step S4 comprises: Step S41: Apply the excitation signal vector And the ideal state model, output the ideal state vector during the launch of the suspension , For the j An ideal state signal; Step S42: constructing a control logic decision model during the suspended object launch control test; ； in, is the control logic accuracy determination function, r is the number of the status signal, For the r status signals, For the r An ideal state signal, is the control logic decision function, is the dynamic noise margin function, is the transition slope, t 0 is the critical time of state transfer, is the maximum allowable noise error; Step S43: Output the control logic judgment value in the process of suspended object launch control using the control logic judgment model F ,like If the duration of the transmission control is greater than or equal to the effective time, it is determined that the logic control accuracy in the transmission control process is high; otherwise, the logic control accuracy is low.

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