CN111785124B - Motion perception simulation fidelity evaluation method for continuous load simulator - Google Patents

Motion perception simulation fidelity evaluation method for continuous load simulator Download PDF

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CN111785124B
CN111785124B CN202010734743.7A CN202010734743A CN111785124B CN 111785124 B CN111785124 B CN 111785124B CN 202010734743 A CN202010734743 A CN 202010734743A CN 111785124 B CN111785124 B CN 111785124B
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舒杨
白俊林
罗鹏
宋琼
胡荣华
刘婷婷
牛红攀
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General Engineering Research Institute China Academy of Engineering Physics
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Abstract

The invention discloses a motion perception simulation fidelity evaluation method of a continuous load simulator, which comprises the following steps of: s1: establishing a comment set according to the actual condition of the fidelity; s2: introducing the direction into the perception error, and correspondingly processing the perception error; s3: constructing different membership functions according to the characteristics of different perception intervals by adopting a fuzzy mathematic method; s4: judging the maximum membership principle by adopting an effectiveness index to obtain a specific evaluation result; s5: performing time domain statistics on the evaluation result; s6: quantifying the evaluation result; s7: and calculating a comprehensive evaluation result according to the time length statistical result and the quantitative result corresponding to the evaluation result. The invention takes the perception threshold value as a segmentation standard, so that the evaluation result is more reasonable; in order to make the evaluation criterion stricter, a weak evaluation rule is adopted when the effectiveness is low.

Description

Motion perception simulation fidelity evaluation method for continuous load simulator
Technical Field
The invention belongs to the technical field of load simulators, and particularly relates to a motion perception simulation fidelity evaluation method of a continuous load simulator.
Background
With the development of flight simulators, the continuous load simulator draws more and more attention in the field of flight training because of the realization of continuous high overload simulation, and is widely applied in the field of flight simulation, and the fidelity evaluation of the flight simulator is the final evaluation basis of the flight simulator. Conventional perceptual evaluation methods and evaluations have not been suitable for a persistent load simulator, such as a flight simulator capable of persistent overload.
The existing human perception evaluation about the flight simulator mainly has two aspects of research:
the evaluation of the fidelity of a flight simulator, such evaluation methods mainly aim at visual, manipulability and environmental perception (sound, light, temperature, vibration and the like). The method is mainly characterized in that the evaluation on the kinematics perception is lacked, and in the flight simulator of the continuous load simulator, the kinematics is the perception simulation and is the main characteristic, so that the method is not suitable for the flight simulation evaluation of the continuous load simulator.
The other class performs approximate perception error evaluation mainly through the perception error size output by the flight simulator. The method has high subjectivity, and the method only evaluates the perception of the flight simulator through perception errors, lacks specific quantitative scientific basis and lacks the combination of human perception characteristics.
The existing evaluation method mainly has the following defects:
1. simulator fidelity evaluation of manipulation, environmental perception and the like is not suitable for the continuous load simulator, because the flight simulation focus of the continuous load simulator is the perception of human body overload and direction;
2. the perception characteristic of the human perception existence threshold is ignored only by evaluating through the error magnitude, and the reasonability of evaluation standard design is reduced.
Disclosure of Invention
The invention aims to provide a motion perception simulation fidelity evaluation method for a continuous load simulator, which is used for solving one of the technical problems in the prior art, such as: in the prior art, simulator fidelity evaluation of 1, control, environment perception and the like is not suitable for a continuous load simulator, because the flight simulation focus of the continuous load simulator is perception of human body overload and direction; 2. the perception characteristic of the human perception existence threshold is ignored only by evaluating through the error magnitude, and the reasonability of evaluation standard design is reduced.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a motion perception simulation fidelity evaluation method for a continuous load simulator comprises the following steps:
s1: establishing a comment set according to the actual condition of the fidelity;
s2: introducing the direction into the perception error, and correspondingly processing the perception error;
s3: constructing different membership functions according to the characteristics of different perception intervals by adopting a fuzzy mathematic method;
s4: judging the maximum membership principle by adopting an effectiveness index to obtain a specific evaluation result;
s5: performing time domain statistics on the evaluation result;
s6: quantifying the evaluation result;
s7: and calculating a comprehensive evaluation result according to the time length statistical result and the quantitative result corresponding to the evaluation result.
Further, step S1 is specifically as follows:
establishing a comment set according to the actual situation of the fidelity, wherein the comment set comprises: very true (HR), comparative true (RR), general true (CR), false (NR), very false (HNR), and orientation error (DE).
Further, step S2 is specifically as follows:
introducing the direction into the perception error, and processing the perception error as follows:
Figure BDA0002604439880000021
wherein p is1、p2The actual perception and the expected perception quantity respectively, and pe is the perception error after the direction processing.
Further, step S3 is specifically as follows:
when the perception error is negative, the direction perception reversal occurs, the perception evaluation of the simulator can be reflected immediately, and the membership function is selected to be Z type:
Figure BDA0002604439880000022
wherein x is the perception error, f (x; a, b) is the processed membership; a. b is a membership function parameter;
when the perception error is within the human perception threshold, the output perception of the simulator is in the most ideal state, and the membership function at the moment is selected as a bell type:
Figure BDA0002604439880000031
wherein x is the perception error, f (x; a, b, c) is the processed membership; a. b and c are membership function parameters;
when the direction of the perception error is the same, but the perception of the absolute quantity has an error, a gaussian membership function should be chosen:
Figure BDA0002604439880000032
wherein x is the perceptual error and f (x; sigma, c) is the processed membership; sigma and c are membership function parameters;
when the sensed error is extremely large and cannot be accepted or evaluation is meaningless, processing is carried out uniformly, and the membership function is selected as an S type:
Figure BDA0002604439880000033
wherein x is the perception error, f (x; a, b) is the processed membership; a. b is a membership function parameter;
when the membership function parameters are designed, the following conditions are satisfied:
the ideal position of the comment meets the condition that the membership function is at the peak value;
when the membership degree of a certain function is 1, the membership degrees of other membership degree functions are close to or equal to zero;
the change trend of the membership degree accords with the specific situation of the current comment.
Further, step S4 is specifically as follows:
multiple evaluation results exist for the same input, and are generally determined by the principle of maximum membership, i.e. for a specific comment setB＝{b1,b2,…,bn}, evaluation results:
R＝max1≤i≤n{bi}；
judging a maximum membership principle by adopting an effectiveness index, wherein the effectiveness is defined as 'maximum membership principle effectiveness test in fuzzy evaluation';
Figure BDA0002604439880000041
wherein β ═ max1≤i≤n{bi}，γ＝max1≤j≤n,j≠i{bj}。
Further, step S7 is specifically as follows:
calculating a comprehensive evaluation result according to the duration statistical result and the evaluation quantitative result corresponding to the evaluation result;
Figure BDA0002604439880000042
wherein, PiIs the corresponding time length ratio of the corresponding comment set, muiIs the efficacy value of the comment set.
Compared with the prior art, the invention has the beneficial effects that:
the method has the innovation point that the traditional fuzzy evaluation generally adopts equal division when the segmentation limit of the comment set is selected, but in the specific field of perception evaluation, the specificity of perception evaluation exists, the perception threshold value can influence the perception error, the perception threshold value is used as a segmentation standard in the method, and the evaluation result is more reasonable.
One innovation point of the scheme is that the traditional perception evaluation method only evaluates the absolute value of the error, however, the perception is a vector, and in designing the flight simulator, if the perception has errors in direction, the direction control judgment of a pilot is influenced, so the direction evaluation is very necessary for the flight simulation judgment, namely, whether the perception error has the wrong direction needs to be judged firstly.
One innovation point of the scheme is that in principle, when the maximum membership degree in the fuzzy evaluation is lower than a certain validity, the evaluation result is generally processed according to invalidity.
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FIG. 1 is a schematic diagram of a perceptual error output model according to an embodiment of the present invention.
FIG. 2 is a diagram of a perceptual evaluation membership function according to an embodiment of the present invention.
FIG. 3 is a schematic flow chart of steps in accordance with an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to fig. 1 to 3 of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example (b):
the existing human perception evaluation about the flight simulator mainly has two aspects of research:
the evaluation of the fidelity of a flight simulator, such evaluation methods mainly aim at visual, manipulability and environmental perception (sound, light, temperature, vibration and the like). The method is mainly characterized in that the evaluation on the kinematics perception is lacked, and in the flight simulator of the continuous load simulator, the kinematics is the perception simulation and is the main characteristic, so that the method is not suitable for the flight simulation evaluation of the continuous load simulator.
The other class performs approximate perception error evaluation mainly through the perception error size output by the flight simulator. The method has high subjectivity, and the method only evaluates the perception of the flight simulator through perception errors, lacks specific quantitative scientific basis and lacks the combination of human perception characteristics.
The existing evaluation method mainly has the following defects:
1. simulator fidelity evaluation of manipulation, environmental perception and the like is not suitable for the continuous load simulator, because the flight simulation focus of the continuous load simulator is the perception of human body overload and direction;
2. the perception characteristic of the human perception existence threshold is ignored only by evaluating through the error magnitude, and the reasonability of evaluation standard design is reduced.
Aiming at the situation, the method mainly solves the problem that under the environment that the continuous load simulator carries out flight simulation, the characteristics of a human body perception model are combined, the human body perception evaluation is carried out by adopting a fuzzy mathematical theory, and then the scientific evaluation method for the motion perception of the flight simulator is realized.
The method for solving the technical problem comprises the following steps: the flight simulator simulates a free and infinite motion environment in a limited and safe space, so that the simulation of real flight physical quantity by the flight simulator is unrealistic, but the flight simulator can realize the humanbody perception simulation of the flight kinematics by approximating the human body perception by the flight simulator, which is also the basic theoretical basis of the flight simulator. The perceptual error output model is shown in fig. 1.
The human perception model is not equivalent to a general sensor, and a certain perception threshold exists, and when the motion acceleration or the angular velocity is smaller than the human perception threshold, the human body cannot feel the existence of the acceleration or the angular velocity. The characteristic has important reference significance for perception evaluation of human bodies. The thresholds for human perception are as follows:
TABLE 1 human perception thresholds
Figure BDA0002604439880000051
Figure BDA0002604439880000061
Therefore, in the evaluation, the influence of the error below the threshold value and the error above the threshold value on human perception is not linear, and the perception threshold values in different directions are different. On the other hand, when the overload perceived by the human body is particularly large, more than a certain upper limit, a loss of consciousness (G-LOC) caused by the overload is likely to occur. This is because the overload is an external force to the human vestibular system, so the overload applied to the human vestibular system has a certain sensing effect range. Through the analysis, the perception of the human vestibular system to overload has a certain sensitive area, and a perception lower limit threshold value and a perception upper limit causing perception loss exist on the overload perception. Both aspects should be considered in building a scientifically effective perceptual evaluation model.
1. Establishing a comment set
The human perception evaluation is different from the general scalar evaluation, and both overload and angular velocity exist directions. If the perceived direction is wrong, the fidelity of the simulator should be severely degraded. Through the above analysis, the comments were set as:
v ═ very true (HR), comparative true (RR), general true (CR), or,
Unreal (NR), very unreal (HNR), Directional Error (DE) }
2. Perceptual direction processing
In designing a flight simulator, if a sense of error occurs in the direction, the determination of the pilot's directional control is affected, so directional evaluation is very essential for the flight simulation determination. In order to highlight the directional characteristic of the perception error, the direction is introduced into the perception error, and the perception error is processed as follows:
Figure BDA0002604439880000062
wherein p is1、p2Actual perception and expected perception quantities are respectively, and pe is a perception error after direction processing;
the perception of human body can not be quantized at present, and in order to accurately evaluate the perception effect, the method adopts a fuzzy mathematics method and constructs different membership functions according to the characteristics of different perception intervals.
3. Membership function based on human body perception structure
When the perception error is negative, the direction perception reversal occurs, the perception evaluation of the simulator can be reflected immediately, and the membership function is selected to be Z type:
Figure BDA0002604439880000071
wherein x is the perception error, f (x; a, b) is the processed membership; a. b is a membership function parameter;
when the perception error is within the human perception threshold, the output perception of the simulator is in the most ideal state, the perception error within the threshold is optimal at the moment, the rationality of setting the evaluation limit is enhanced, and the membership function at the moment is selected as a bell type:
Figure BDA0002604439880000072
wherein x is the perception error, f (x; a, b, c) is the processed membership; a. b and c are membership function parameters;
when the perception error direction is the same, but the perception of the absolute quantity is in error, the most common gaussian membership function should be chosen:
Figure BDA0002604439880000073
wherein x is the perceptual error and f (x; sigma, c) is the processed membership; sigma and c are membership function parameters;
when the sensed error is extremely large and cannot be accepted or evaluation is meaningless, processing is carried out uniformly, and the membership function is selected as an S type:
Figure BDA0002604439880000074
wherein x is the perception error, f (x; a, b) is the processed membership; a. b is a membership functionCounting parameters;
when the membership function parameters are designed, the following conditions are satisfied:
the ideal position of the comment meets the condition that the membership function is at the peak value;
when the membership degree of a certain function is 1, the membership degrees of other membership degree functions are close to or equal to zero;
the change trend of the membership degree accords with the specific situation of the current comment.
In summary, the membership function design of the sensing error is shown in FIG. 2,
wherein theta is1Is a threshold value of human perception, theta2Is a threshold value that is too large to be meaningful in evaluation.
4. Maximum membership degree effectiveness processing method
In most cases, there are multiple evaluation results for the same input, and the maximum membership rule is generally adopted to determine, that is, B ═ B for a specific comment set1,b2,…,bn}, evaluation results:
R＝max1≤i≤n{bi}；
however, the principle of maximum membership is effective, and in the case that the membership of the comment set is not very different, the evaluation result is not ideal. Therefore, in order to make the evaluation result more reasonable, the maximum membership principle is judged by using the validity index, and the validity is defined as 'validity test of the maximum membership principle in fuzzy evaluation'.
Figure BDA0002604439880000081
Wherein β ═ max1≤i≤n{bi}，γ＝max1≤j≤n,j≠i{bj}；
Through definition analysis of the validity, it can be found that the greater the validity α is, the stronger the validity of the maximum membership rule is, and the smaller the validity α is, the weaker the validity of the maximum membership rule is. When membership degree evaluation is carried out, in order to obtain a more meaningful evaluation result, a confidence interval sigma of validity is introduced, and when the validity is greater than sigma, the evaluation result is calculated by adopting a maximum membership degree principle; when the validity is less than sigma, the evaluation results with close membership degrees exist, and in this case, in order to obtain a more strict evaluation result, a relatively strict comment is selected as the evaluation result.
Table 2 low availability evaluation rule
Figure BDA0002604439880000082
Figure BDA0002604439880000091
5. Time domain statistics
The evaluation of human perception fidelity is specific to a certain flight mission or a specific flight action, and does not belong to a certain time point, so that after the validity is processed in the previous step, a specific evaluation result is obtained, and time domain statistics needs to be carried out on the evaluation of the flight mission.
6. Quantifying a comment
To quantify the final evaluation result, the comment set needs to be quantified first, and there are many methods for quantifying the comment set, such as expert evaluation methods, statistical methods, and the like. The evaluation result can also be embodied by taking the weight as the score by adopting an analytic hierarchy process.
The analytic hierarchy process is an effective way to convert qualitative problems into quantitative problems, so the analytic hierarchy process can be adopted for scoring design. The scoring value of each comment can be designed by analogy with the weighting method, i.e. the weaker the weight, the lower the score. And constructing a judgment matrix according to the relative expected degrees of the two comments.
7. Time domain cumulative evaluation
And calculating a comprehensive evaluation result according to the time length statistical result and the evaluation quantitative result corresponding to the evaluation.
Figure BDA0002604439880000092
Wherein, PiIs the corresponding time length ratio of the corresponding comment set, muiIs the efficacy value of the comment set.
In order to improve the contrast of the evaluation result, the percent conversion of the current evaluation result can be performed with the full duration of the highest comment as a full mark.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.

Claims (3)

1. A motion perception simulation fidelity evaluation method of a continuous load simulator is characterized by comprising the following steps:
s1: establishing a comment set according to the actual condition of the fidelity;
s2: introducing the direction into the perception error, and correspondingly processing the perception error;
s3: constructing different membership functions according to the characteristics of different perception intervals by adopting a fuzzy mathematic method;
s4: judging the maximum membership principle by adopting an effectiveness index to obtain a specific evaluation result;
s5: performing time domain statistics on the evaluation result;
s6: quantifying the evaluation result;
s7: calculating a comprehensive evaluation result according to a duration statistical result and a quantitative result corresponding to the evaluation result;
step S1 is specifically as follows:
establishing a comment set according to the actual situation of the fidelity, wherein the comment set comprises: very true (HR), comparative true (RR), general true (CR), false (NR), very false (HNR), and misdirection (DE);
step S2 is specifically as follows:
introducing the direction into the perception error, and processing the perception error as follows:
Figure FDA0003350351090000011
wherein p is1、p2Actual perception and expected perception quantities are respectively, and pe is a perception error after direction processing;
step S3 is specifically as follows:
when the perception error is negative, the direction perception reversal occurs, the perception evaluation of the simulator can be reflected immediately, and the membership function is selected to be Z type:
Figure FDA0003350351090000012
wherein x is the perception error, f (x; a, b) is the processed membership; a. b is a membership function parameter;
when the perception error is within the human perception threshold, the output perception of the simulator is in the most ideal state, and the membership function at the moment is selected as a bell type:
Figure FDA0003350351090000021
wherein x is the perception error, f (x; a, b, c) is the processed membership; a. b and c are membership function parameters;
when the direction of the perception error is the same, but the perception of the absolute quantity has an error, a gaussian membership function should be chosen:
Figure FDA0003350351090000022
wherein x is the perceptual error and f (x; sigma, c) is the processed membership; sigma and c are membership function parameters;
when the sensed error is extremely large and cannot be accepted or evaluation is meaningless, processing is carried out uniformly, and the membership function is selected as an S type:
Figure FDA0003350351090000023
wherein x is the perception error, f (x; a, b) is the processed membership; a. b is a membership function parameter;
when the membership function parameters are designed, the following conditions are satisfied:
the ideal position of the comment meets the condition that the membership function is at the peak value;
when the membership degree of a certain function is 1, the membership degrees of other membership degree functions are close to or equal to zero;
the change trend of the membership degree accords with the specific situation of the current comment.
2. The method for evaluating the motion perception simulation fidelity of the continuous loading simulator as claimed in claim 1, wherein the step S4 is as follows:
multiple evaluation results exist in the same input, and the evaluation results are determined by adopting the maximum membership rule, namely, the corresponding membership B ═ B for a specific comment set1,b2,…,bn}, evaluation results:
R＝max1≤i≤n{bi}；
judging a maximum membership principle by adopting an effectiveness index, wherein the effectiveness is defined as 'maximum membership principle effectiveness test in fuzzy evaluation';
Figure FDA0003350351090000031
wherein β ═ max1≤i≤n{bi}，γ＝max1≤j≤n,j≠i{bj}。
3. The method for evaluating the motion perception simulation fidelity of the continuous loading simulator as claimed in claim 2, wherein the step S7 is as follows:
calculating a comprehensive evaluation result according to the duration statistical result and the evaluation quantitative result corresponding to the evaluation result;
Figure FDA0003350351090000032
wherein, PiIs the corresponding time length ratio of the corresponding comment set, muiIs the efficacy value of the comment set.
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