Vehicle simulation washout algorithm combining simulator control logic with filter adjustment

Abstract

The invention discloses a simulator control logic and filter adjustment combined vehicle simulation washout algorithm, which relates to the technical field of vehicle simulation washout algorithms, and comprises the following steps: S1, initialization: generating a time sequence, defining the size of a sliding window, and initializing input parameters and a state structure body; s2, initial data reading: firstly taking the acceleration of the vehicle from the driving simulation software, reading the angular velocity, the velocity and the rotating speed of an engine, then reading the attitude and position data, and storing the data; according to the invention, the motion of the platform can be controlled more quickly and accurately so as to improve the fidelity and response consistency of a motion signal, and meanwhile, the parameter adjustment process of the filter is optimized, so that the filter can be dynamically adjusted according to the stroke ranges of different motion platforms, the application range of a simulation system is expanded, and full application of the motion platforms is realized.

Description

Technical Field

[0001] The invention relates to the technical field of vehicle simulation washout algorithms, and in particular to a vehicle simulation washout algorithm combining simulator control logic with filter adjustment. Background Art

[0002] In the field of vehicle motion simulation, the vehicle simulation washout algorithm is a key technology used in the dynamic simulation of the freedom platform. Due to the limited range of motion of the motion platform, the simulator cannot achieve the long-distance and high-speed motion performance like a real car, so it can only reproduce the vehicle motion feeling as realistically as possible.

[0003] The quality of vehicle simulation washout algorithms directly affects the fidelity of the simulator. However, existing algorithms have difficulty maintaining consistent responses to various dynamic movements while completely avoiding perception delays. This results in deviations between the movements that users may feel during the simulation and the expected physical movements, affecting the authenticity and immersion of the simulation. In addition, under extreme motion conditions, existing washout algorithms may cause excessive attenuation or distortion of some motion signals, especially when performing high-speed dynamic simulations. These distortions may cause users to experience unrealistic motion sensations. And due to the limitations of the platform's physical limits, traditional washout algorithms do not effectively utilize the full mobility of the motion platform, especially when simulating complex or extreme motion scenarios. This limitation reduces the efficiency of training and research, and also limits the scope of application of simulation systems.

[0004] As mentioned above, we have designed a vehicle simulation wash-out algorithm that combines simulator control logic with filter adjustment to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a vehicle simulation wash-out algorithm combining simulator control logic with filter adjustment.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A vehicle simulation washout algorithm combining simulator control logic with filter adjustment includes the following steps:

[0008] S1, Initialization: Generate time series, define sliding window size, initialize input parameters and state structure;

[0009] S2, initial data reading: first obtain the vehicle's acceleration, angular velocity, speed and engine speed from the driving simulation software, then read the attitude and position data, and store the data;

[0010] S3, position solution and adjustment: the position and attitude introduce dynamic filter adjustment mechanism, including: according to the input acceleration, velocity and displacement data, calculate the vehicle position change, use sliding window to smooth the position data, reduce data noise, and use high-pass filter and low-pass filter to filter the acceleration and angular velocity data;

[0011] S4, attitude detection and adjustment: calculate the vehicle's roll angle, pitch angle and yaw angle, perform low-pass filtering on historical data, and use the thresholds of acceleration and speed to determine the triggering conditions for attitude adjustment;

[0012] S5. Output result: The output result step only outputs the position and posture of the current frame and updates the state structure.

[0013] S6: The vehicle position and posture are output and the process ends.

[0014] Preferably, in step S2, the vehicle's acceleration ax, ay, az, angular velocity angx, angy, angz, speed vx and engine speed rpm are read from sensors; then the read data is stored in input and state.

[0015] Preferably, in step S3, introducing the idea of ​​fuzzy logic control to redesign the simulator control logic includes the following steps:

[0016] s1”, fuzzy state design: define different fuzzy states according to different driving states and road conditions of the vehicle;

[0017] s2”, fuzzy rule setting: setting different processing rules for different fuzzy states, including: data directly obtained from the driving simulation software: vehicle acceleration, angular velocity, speed and engine speed data;

[0018] s3”, displacement calculation: obtain displacement data by integrating acceleration data;

[0019] s4”, displacement and speed limit: set the upper limit of motion displacement according to the actual physical limit of the motion platform, reverse the real-time speed upper limit, and determine whether return motion is needed;

[0020] s5”, fuzzy control algorithm: according to the fuzzy state and processing rules, the vehicle in different states is accurately controlled, and the fuzzy control includes processing and adjusting the output data according to the current motion state of the vehicle.

[0021] Preferably, in step S5, the dynamic filter adjustment mechanism dynamically adjusts the filter parameters according to the real-time motion data of the simulator and the effective moving range of different postures of the motion platform.

[0022] Preferably, in step S5, the posture detection and adjustment, the position calculation and adjustment all use a dynamic filter, and the filter expression is:

[0023] The general equation for a high pass filter is:

[0024]

[0025] The high-pass filter usually adopts a second-order function, and may also adopt a third-order function. The present invention selects a second-order high-pass filter, and its transfer function is:

[0026]

[0027] Where A is the filter gain, ζ is the damping ratio, ω n is the natural frequency of the filter in rad / s. The general equation for a low-pass filter is:

[0028]

[0029] The present invention adopts a second-order low-pass filter, and its transfer function is:

[0030]

[0031] where ζ is the damping ratio, ω n is the natural frequency of the filter in rad / s.

[0032] Preferably, in step S6, the vehicle position and posture are output while transferring state parameter data to step S2 for a data solution adjustment cycle.

[0033] Compared with the prior art, the beneficial effects of the present invention are:

[0034] 1. By introducing a set of simulator control logic combined with filter adjustment, the platform movement can be controlled more quickly and accurately to improve the fidelity and response consistency of the motion signal.

[0035] 2. The design of the present invention optimizes the parameter adjustment process of the filter, so that it can be dynamically adjusted according to the travel range of different motion platforms, thereby better utilizing the motion capabilities of the platform. It not only greatly alleviates the problems of perception delay and inconsistent dynamic response, expands the scope of application of the simulation system, but also realizes the full application of the motion platform.

[0036] 3. The vehicle simulation wash-out algorithm proposed in the present invention cleverly utilizes the dynamic characteristics of the simulation platform and the time and physical space correlation of the control logic, repeatedly tests, integrates the perceptual feedback of multiple subjects and continuously optimizes and iterates, so that the seven-axis six-degree-of-freedom mode vehicle motion platform has better realism and user immersion. It is innovative and efficient in design, with a clear structure, distinct levels, and fast response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A flowchart of a vehicle simulation wash-out algorithm combining simulator control logic and filter adjustment proposed by the present invention;

[0038] Figure 2 A diagram of X-direction displacement data after washing out of a vehicle simulation wash-out algorithm combining a simulator control logic and filter adjustment proposed by the present invention;

[0039] Figure 3 A diagram of pitch data after washout of a vehicle simulation washout algorithm combining a simulator control logic with filter adjustment proposed by the present invention;

[0040] Figure 4 A diagram of yaw data after washing out of a vehicle simulation wash-out algorithm combining a simulator control logic with filter adjustment proposed by the present invention;

[0041] Figure 5 A rollover data diagram after washing out of a vehicle simulation wash-out algorithm combining a simulator control logic with filter adjustment proposed by the present invention;

[0042] Figure 6 This is the X-direction acceleration data diagram of the original vehicle sensor of the present invention. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0044] The present invention is aimed at the problems of perception delay and inconsistent dynamic response in the vehicle simulation wash-out algorithm in the existing seven-axis six-degree-of-freedom mode vehicle motion platform during high-speed dynamic simulation. Based on the classic wash-out algorithm, the invention proposes a new vehicle simulation wash-out algorithm based on fuzzy logic control and the introduction of nonlinear dynamic elements, aiming to enhance the realism of the simulator and the user's immersion while adapting to different ranges of motion platforms.

[0045] Example

[0046] Reference Figure 1, a vehicle simulation wash-out algorithm combining simulator control logic with filter adjustment, comprising the following steps:

[0047] S1. Initialization: Generate a time series, define a sliding window size, initialize input parameters and a state structure; more specifically, initialize the sensor data acquisition module, generate a time series time, define a sliding window size window_size, initialize input parameters input and a state structure state, wherein the initialized input parameters are input variable parameters for determining vehicle acceleration, speed, engine speed, and real-time displacement of the platform;

[0048] S2, initial data reading: first get the vehicle's acceleration, angular velocity, speed and engine speed from the driving simulation software, then read the attitude and position data, and store the data; read the vehicle's acceleration ax, ay, az, angular velocity angx, angy, angz, speed vx and engine speed rpm from the sensor. Store the read data in input and state;

[0049] S3, position solution and adjustment: the position and attitude introduce dynamic filter adjustment mechanism, including: according to the input acceleration, velocity and displacement data, calculate the vehicle position change, use sliding window to smooth the position data, reduce data noise, and use high-pass filter and low-pass filter to filter the acceleration and angular velocity data;

[0050] More specifically, in step S3, introducing the idea of ​​fuzzy logic control to redesign the simulator control logic includes the following steps:

[0051] s1”, fuzzy state design: define different fuzzy states according to different driving states and road conditions of the vehicle;

[0052] s2”, fuzzy rule setting: setting different processing rules for different fuzzy states, including: data directly obtained from the driving simulation software: vehicle acceleration, angular velocity, speed and engine speed data;

[0053] s3”, displacement calculation: obtain displacement data by integrating acceleration data;

[0054] s4”, displacement and speed limit: set the upper limit of motion displacement according to the actual physical limit of the motion platform, reverse the real-time speed upper limit, and determine whether return motion is needed;

[0055] s5”, fuzzy control algorithm: according to the fuzzy state and processing rules, the vehicle in different states can be precisely controlled.

[0056] In the above content, based on the input acceleration, velocity and displacement data, the position change of the vehicle in the three directions of x, y and z is calculated. The position data is smoothed using a sliding window to reduce data noise. The acceleration and angular velocity data are filtered using high-pass and low-pass filters to obtain smooth vehicle posture data;

[0057] S4, attitude detection and adjustment: calculate the vehicle's roll angle, pitch angle and yaw angle, perform low-pass filtering on historical data, and use the thresholds of acceleration and speed to determine the triggering conditions for attitude adjustment;

[0058] S5, output results: The output result step only outputs the position and posture of the current frame, and updates the state structure. Using the simulation control algorithm, the vehicle position x, y, z and posture roll, pitch, yaw of the current frame are output. At the same time, the state structure state is updated for the next frame calculation.

[0059] Filter expression:

[0060] The general equation for a high pass filter is:

[0061]

[0062] The high-pass filter usually adopts a second-order function, and may also adopt a third-order function. The present invention selects a second-order high-pass filter, and its transfer function is:

[0063]

[0064] Where A is the filter gain, ζ is the damping ratio, ω n is the natural frequency of the filter in rad / s. The general equation for a low-pass filter is:

[0065]

[0066] The present invention adopts a second-order low-pass filter, and its transfer function is:

[0067]

[0068] where ζ is the damping ratio, ω n is the natural frequency of the filter in rad / s.

[0069] In the above step S5, in the step S5, when monitoring the state of the motion platform in real time, the real-time angle and position of the motion platform are monitored, and the proximity of the current motion state to the safety boundary and physical limitations is evaluated, and then fuzzy logic control is performed: based on real-time monitoring data, a pre-defined fuzzy logic control algorithm is used to adjust the output parameters, and the filter parameters are adjusted: the cutoff frequency and damping coefficient of the filter are dynamically adjusted, and the control algorithm is optimized: the control algorithm parameters are optimized according to the motion requirements, and an adaptive control mechanism is implemented to ensure that the motion does not exceed the platform capabilities.

[0070] S6, the vehicle position and posture are output and the process ends. The vehicle position and posture are output and the state parameter data is transmitted to step S2 for data calculation and adjustment cycle.

[0071] In this embodiment, the idea of ​​fuzzy control is mainly reflected in how to process and adjust the output according to the current motion state (speed, acceleration and displacement). Specifically:

[0072] 1. Speed ​​and displacement have the same sign: If the speed and displacement have the same sign (both positive or negative), their acceleration in the same direction is considered. If the acceleration and speed are in the same direction and the displacement is close to the preset threshold, the system will limit the increase of speed and displacement to prevent exceeding the set boundary.

[0073] 2. Acceleration threshold judgment: When the change in acceleration and the current acceleration are both below a certain threshold, the vehicle is assumed to be in an acceleration motion with constant acceleration, and the system adjusts the speed and displacement according to the normal acceleration ratio. This ensures the vehicle's smooth response under low dynamic changes.

[0074] 3. Limit state processing: When the displacement reaches or exceeds the motion boundary, the system will force the speed and displacement to adjust in the opposite direction to pull it back to a safe range.

[0075] 4. Special case handling: When the displacement and speed have different signs, the system will check whether the movement boundary has been reached. If so, the speed will be limited to the boundary value to prevent excessive displacement and ensure that the vehicle is within a safe range of movement.

[0076] It should be noted that this embodiment includes three key points:

[0077] 1. Reconstruction of simulator control logic: During operation, the control logic of the simulator is redesigned by introducing the idea of ​​fuzzy logic control to achieve fast and precise control of platform movement.

[0078] Specifically, the input and output variables are redefined, which are acceleration, speed, engine speed and real-time displacement of the platform. Different fuzzy states are defined according to the original vehicle under different driving states and different road conditions, and different processing rules are added to different fuzzy results. First, nonlinear fuzzy processing is introduced to obtain the acceleration, angular velocity, speed, engine speed and other data of the vehicle from the sensor, and the displacement data is obtained by integrating the acceleration data. Then, the upper limit of the motion displacement is set according to the actual physical limitations of the motion platform to ensure that the simulated vehicle moves effectively within a reasonable range of motion. Here, the real-time speed upper limit is reversed based on the displacement upper limit, and the speed upper limit is used to judge whether the current motion platform movement is reasonable to distinguish whether the motion platform needs to return to its original position. In general, the wash-out algorithm accurately controls the vehicle in different states based on fuzzy control logic, and the setting of the control rules mainly depends on the displacement settlement results and the executable range of the motion platform.

[0079] 2. Dynamic filter adjustment mechanism: A dynamic filter adjustment mechanism is introduced, which dynamically adjusts the filter parameters according to the real-time motion data of the simulator and the effective movement range of different postures of the motion platform.

[0080] Specifically, the position data is first smoothed using a sliding window to reduce data noise, and the acceleration and angular velocity data are filtered using high-pass and low-pass filters to obtain smoother vehicle posture data. In addition, the historical data is low-pass filtered to obtain smooth posture change data. Finally, filters are designed separately for signals of different postures based on the simulated driving sensations of different subjects and the data characteristics of the motion platform. The design of separate filters is mainly based on the analysis of the basic characteristics of real-time signals such as frequency range, noise level and response time, and the somatosensory feedback of the subjects during the simulated driving process, especially the motion perception information in different directions. The low-pass filter is used to suppress high-frequency noise and enhance the smoothness of the signal. It is suitable for processing signals that affect rolling and yaw. The actual filtering effect is achieved by adjusting the cutoff frequency and damping ratio. The pitch platform motion is used to simulate the push-back somatosensory in real driving, so a high-pass filter is selected to eliminate the low-frequency drift of the signal. In addition, in order to ensure that the somatosensory of postures in all directions is more balanced when acting together, smoothing filters with different window ranges are added according to the somatosensory feedback of the subjects within an acceptable delay range. Overall, this not only reduces the delay in signal processing, but also improves the fidelity of signals at different postures, thereby more effectively reproducing motion scenes at different postures.

[0081] 3. Comprehensive utilization of the motion capability of the platform: In order to fully utilize the motion capability of the motion platform, the present invention adaptively adjusts the physical upper limits of the motion platform in the rolling, pitching, yaw and xyz directions.

[0082] First, by monitoring the motion state, real-time angle and position of the motion platform in real time, the current motion state is constantly evaluated to see how close it is to the preset safety boundaries and physical limits. Based on this data, a pre-defined fuzzy logic control algorithm is used to automatically adjust output parameters, such as filter thresholds for acceleration and velocity, to ensure that the motion does not exceed the range allowed by the motion platform's capabilities. This includes dynamically adjusting the filter cutoff frequency and damping coefficient, as well as optimizing control algorithm parameters based on motion requirements. This adaptive control mechanism not only improves the practicality and flexibility of the simulator, but also enhances the user experience and the authenticity of the simulation.

[0083] In this embodiment, refer to Figure 2 ,It can be seen that the platform translation simulates the instantaneous displacement in the x-axis direction.

[0084] Reference Figure 3 , Figure 4 as well as Figure 5 The sensory requirements of the driver for different postures during driving were successfully simulated, ensuring the realism of the driving simulator. Overall, the effectiveness of the algorithm has been verified based on the result data and the sensory feedback of the subjects. It not only greatly alleviates the problems of perception delay and inconsistent dynamic response, but also realizes the full application of the motion platform.

[0085] The vehicle simulation wash-out algorithm proposed in this invention cleverly utilizes the dynamic characteristics of the simulation platform and the time and physical space correlation of the control logic. Through repeated testing, integration of perceptual feedback from multiple subjects and continuous optimization and iteration, the seven-axis six-degree-of-freedom mode vehicle motion platform has better realism and user immersion. It is innovative and efficient in design, with a clear structure, distinct levels, and fast response speed.

[0086] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0087] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0088] The basic principles, main features and advantages of the present invention are shown and described above. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of the present invention is defined by the attached claims and their equivalents.

Claims

1. A vehicle simulation wash-out algorithm combining simulator control logic with filter adjustment, characterized in that: The following steps are involved: S1, Initialization: Generate time series, define sliding window size, initialize input parameters and state structure; S2, initial data reading: first obtain the vehicle's acceleration, angular velocity, speed and engine speed from the driving simulation software, then read the attitude and position data, and store the data; S3, position solution and adjustment: the position and attitude introduce dynamic filter adjustment mechanism, including: according to the input acceleration, velocity and displacement data, calculate the vehicle position change, use sliding window to smooth the position data, reduce data noise, and use high-pass filter and low-pass filter to filter the acceleration and angular velocity data; S4, attitude detection and adjustment: calculate the vehicle's roll angle, pitch angle and yaw angle, perform low-pass filtering on historical data, and use the thresholds of acceleration and speed to determine the triggering conditions for attitude adjustment; S5. Output result: The output result step only outputs the position and posture of the current frame and updates the state structure. S6: The vehicle position and posture are output and the process ends.

2. A vehicle simulation wash-out algorithm combining simulator control logic with filter adjustment according to claim 1, characterized in that: In step S2, the vehicle's acceleration ax, ay, az, angular velocity angx, angy, angz, speed vx and engine speed rpm are read from the sensor; then the read data is stored in input and state.

3. The vehicle simulation wash-out algorithm combining simulator control logic and filter adjustment according to claim 1, characterized in that: In step S3, introducing the idea of ​​fuzzy logic control to redesign the simulator control logic includes the following steps: s1”, fuzzy state design: define different fuzzy states according to different driving states and road conditions of the vehicle; s2”, fuzzy rule setting: setting different processing rules for different fuzzy states, including: data directly obtained from the driving simulation software: vehicle acceleration, angular velocity, speed and engine speed data; s3”, displacement calculation: obtain displacement data by integrating acceleration data; s4”, displacement and speed limit: set the upper limit of motion displacement according to the actual physical limit of the motion platform, reverse the real-time speed upper limit, and determine whether return motion is needed; s5”, fuzzy control algorithm: according to the fuzzy state and processing rules, the vehicle in different states is accurately controlled, and the fuzzy control includes processing and adjusting the output data according to the current motion state of the vehicle.

4. The vehicle simulation wash-out algorithm combining simulator control logic and filter adjustment according to claim 1, characterized in that: In step S5, the dynamic filter adjustment mechanism dynamically adjusts the filter parameters according to the real-time motion data of the simulator and the effective moving range of different positions of the motion platform.

5. The vehicle simulation wash-out algorithm combining simulator control logic and filter adjustment according to claim 1, characterized in that: In step S5, the posture detection and adjustment, the position calculation and adjustment all use dynamic filters, and the filter expression is: The general equation for a high pass filter is: The high-pass filter usually adopts a second-order function, and may also adopt a third-order function. The present invention selects a second-order high-pass filter, and its transfer function is: Where A is the filter gain, ζ is the damping ratio, ω n is the natural frequency of the filter in rad / s. The general equation for a low-pass filter is: The present invention adopts a second-order low-pass filter, and its transfer function is: where ζ is the damping ratio, ω n is the natural frequency of the filter in rad / s.

6. The vehicle simulation wash-out algorithm combining simulator control logic and filter adjustment according to claim 1, characterized in that: In step S6, the vehicle position and posture are output and the state parameter data is transmitted to step S2 for a data solution adjustment cycle.

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