CN108974389B - Intelligent cabin system implementation method for civil aircraft design and airworthiness approval - Google Patents
Intelligent cabin system implementation method for civil aircraft design and airworthiness approval Download PDFInfo
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Abstract
A novel intelligent cabin system platform for civil aircraft design and airworthiness certification is built, an MATLAB/Simulink and C # mixed programming method is adopted to build the intelligent cabin system platform, actual use time of one-time last-approach landing flight task is about 320s, and simulation time on a PC can be shortened to 20s or even shorter. Therefore, the method and the device can generate a large amount of flight sample data which is verified to be reliable in a short time, and improve the operation efficiency and reliability of the system. The effectiveness and the accuracy of the intelligent cockpit system are verified by comparing flight data generated by the operation of the airplane by the real flight unit in the high-simulation cockpit system with flight data generated by automatic driving in the intelligent cockpit system.
Description
Technical Field
The invention relates to the technology in the field of aviation system control, in particular to an intelligent cockpit system implementation method for civil aircraft design and airworthiness approval.
Background
The flight safety is a permanent theme in the field of civil aviation, and each civil aircraft can execute a civil aviation flight task only after acquiring a seaworthiness certificate after being approved by seaworthiness. The Chinese Civil Aviation Regulation (CCAR) proposes a minimum flight crew criterion, which decomposes the mission of a flight crew into mission requirements related to operation to verify the airworthiness compliance of the aircraft.
The intelligent cockpit system describes the dynamic relationship among the aircrew, the aircraft and the environment, and is the only interface for man-machine interaction in the aircraft system. The technology for designing and developing the intelligent cabin system for civil aircraft design and airworthiness approval is very significant for the smooth proceeding of the airworthiness approval of the aircraft.
When the airworthiness is determined, a large amount of comprehensive, comprehensive and reliable flight data based on various flight scenes are needed to ensure the objectivity and accuracy of the airworthiness determination. At various stages of aircraft design, a large amount of verified and reliable comprehensive flight data covering a small probability of flight events is also needed to evaluate the reasonability and effectiveness of aircraft system design. In general, it is of great significance to establish an intelligent cockpit system in the airworthiness approval process of civil aircraft or in each stage of civil aircraft system design.
The development of an intelligent cockpit system directly aims at quickly realizing flight tasks based on various flight scenes according to various flight conditions under various machine types and human-computer loop parameters. The flight data generated by the cabin system quickly and automatically is verified to be reliable and comprehensive through comparison with the flight data generated by the same flight task executed by a real unit in the high-simulation cockpit. The intelligent cockpit system can be used for describing the dynamic relation and adaptability between factors such as flight tasks, unit intervention, workload distribution, abnormal event handling procedures and the like under various flight conditions. Therefore, there is a need to develop an intelligent cockpit system for civil aircraft design and airworthiness approval.
The existing intelligent cockpit control system is only used for simply regulating and controlling a civil aircraft flight control system, but the system has small data coverage and poor portability and reusability. The flight unit cannot be reproduced, the dynamic interaction influence of the environment and the airplane is avoided, necessary automation equipment is lacked, particularly, the difference between the actual data executed by the flight unit and the actual data executed by the actual flight unit is large when the flight task is executed, and the flight unit cannot be used for the actual airworthiness approval and design process of civil aircrafts.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an intelligent cabin system implementation method for civil aircraft design and airworthiness approval, and the intelligent cabin system development method has necessary repeatability and can quickly obtain a large amount of real, comprehensive, reliable and credible flight data.
The invention is realized by the following technical scheme:
the invention comprises the following steps:
And 4, verifying the validity and reliability of the model: and selecting an absolute value of a difference value between an actual landing point and an expected landing point of the airplane in one flight, namely landing deviation as an evaluation index according to flight data generated by the intelligent cockpit system. And by a semi-physical flight test, the effectiveness and the accuracy of the intelligent cockpit system are verified by comparing flight data generated by controlling an airplane by a real flight unit in the high-simulation cockpit system with flight data generated by automatic driving in the intelligent cockpit system.
The intelligent cabin system can quickly generate a large amount of comprehensive, comprehensive and reliable flight data based on various flight scenes by means of the configuration of machine types and the design of a controller in the intelligent cabin system so as to ensure the objectivity and the accuracy of airworthiness approval.
Technical effects
Compared with the prior art, the invention improves the limitation of automatic driving of the existing cabin system, and builds a new intelligent cabin system platform for civil aircraft design and airworthiness approval. An intelligent cabin system platform is built by adopting a MATLAB/Simulink and C # hybrid programming method, the actual time for completing one-time last approach landing flight task is about 320s, and the simulation time on a PC (personal computer) can be shortened to 20s or even shorter. Therefore, the method and the device can generate a large amount of flight sample data which is verified to be reliable in a short time, and improve the operation efficiency and reliability of the system. The effectiveness and the accuracy of the intelligent cockpit system are verified by comparing flight data generated by the operation of the airplane by the real flight unit in the high-simulation cockpit system with flight data generated by automatic driving in the intelligent cockpit system.
Drawings
FIG. 1 is a diagram of a six-degree-of-freedom model of an aircraft;
FIG. 2 is a schematic view of a nonlinear dynamical model of an aircraft;
FIG. 3 is a simulation platform interface layout;
FIG. 4 is a block diagram of a pitch attitude maintenance controller;
FIG. 5 is a block diagram of a height maintenance controller;
FIG. 6 is a block diagram of a speed maintenance controller;
FIG. 7 is a diagram of a last approach and landing phase flight scenario;
FIG. 8 is a primary flight status data plot for a flight;
FIG. 9 is a graph of aircraft landing site frequency distribution;
FIG. 10 is a landing bias sample data diagram;
FIG. 11 is a sample data QQ diagram;
FIG. 12 is a Hill plot of flight sample data;
figure 13 is a GPD fit check chart;
fig. 14 is a diagram of a high simulation cockpit system.
Detailed Description
Example 1
The embodiment specifically comprises the following steps:
In FIG. 1, the NEWTON' S EQUATION process is a modeling EQUATION for the translational degree of freedom of the aircraft; the EULER' S equilibrium process is used for describing the freedom degree of the motion and rotation of the airplane; solving the FORCES and MOMENTS of the translational degree of freedom and the rotational degree of freedom in the FORCES & MOMENTS process; KINEMATIC EQUATIONS, giving quaternion, aircraft attitude angle, direction cosine matrix and the like which need to be initialized when a system model is built; the input part of the model is the PILOT CONTROL process, which directly changes the lift and drag changes during the course of AERO & PROPULSION.
And 3, designing an automatic aircraft controller. The invention designs a corresponding automatic controller in MATLAB/Simulink aiming at three controls of height maintenance, pitching attitude maintenance and speed maintenance of an autopilot in a cabin system. And the other controllers such as the steering engine, the undercarriage, the flap and the like are realized by selecting common controllers of a civil aircraft system.
The automatic controller comprises: a pitch attitude retainer, a altitude retainer controller and a speed retainer controller for flying the aircraft at a set pitch angle and keeping the pitch angle stable.
The pitching attitude retainer is a proportional-integral pitching attitude retainer controller, and the control rate is as follows:
wherein: kθIs a pitch angle proportional link coefficient; t iseIs an integral link time constant; delta thetacIs the pitch angle at the balance point; delta theta is the actual pitch angle of the airplane;
the coefficient of a pitch angle speed feedback link;
is the pitch angle rate, q. A block diagram of a pitch attitude maintenance controller based on the design in simulink is shown in fig. 4. Stable _ delta _ e in the figure is the elevator angle at the equilibrium point. stable _ theta is the pitch angle at the equilibrium point. In order to weaken the influence of hinge moment on a steering engine in an actual airplane control system, a steering engine loop is used for replacing an independent steering engine to carry out control surface deflection operation, and a pitching rudder feedback loop in the figure is used for simulating the control characteristic of an airplane position servo type electric steering engine.
The height maintaining controller directly utilizes height information to perform feedback control, takes the pitching attitude maintaining controller as an inner ring, and then adds a layer of proportional-integral controller for height on an outer ring to realize the control law, wherein the height maintaining controller comprises the following control laws:
wherein KhIs the height ring scale factor; Δ hcIs the fly height at the balance point; and deltah is the actual flying height of the airplane. A block diagram of a height preserving controller based on the design in simulink is shown in fig. 5.
The control rate of the speed maintaining controller is as follows:
wherein KVIs the velocity ring scaling factor; Δ VcIs the aircraft airspeed at the equilibrium point; Δ V is the aircraft actual airspeed; a is the actual acceleration of the airplane; kaIs the acceleration feedback coefficient. A block diagram of a speed maintenance controller based on the design in simulink is shown in fig. 6.
4.1 flight scenario settings
Assume that the aircraft flies along a predetermined glide trajectory until landing during approach landing as shown in fig. 7. The flight scene can be flexibly configured according to different flight tasks, and the invention takes the flight scene of the last approach landing stage with high accident rate as an example for explanation.
4.2 simulation experiment procedure
Certain commercial aircraft parameter settings are shown in table 1. The basic parameters of the airplane can be flexibly configured according to specific airplane models, and various common models of commercial transport passenger airplanes can be characterized.
TABLE 1 aircraft fuselage parameter setting Table
TABLE 2 aircraft aerodynamic coefficient table
4.3 controlled Experimental setup and results analysis
4.3.1) basic and control experiment settings: according to different settings of a contrast experiment group, the selection of a distributed exploration controller, the system time lag, the sensitivity of a height maintenance controller, the influence of factors such as the error precision of an elevator controller on the flight task completion effect are realized, wherein: the setting of the relevant parameters is set according to flight experience.
TABLE 3 basic experiment and control experiment setup method
4.3.2) analysis of experimental results: the cockpit system autopilot simulation experiment generates a large amount of flight data, and a typical main flight state data in a basic experiment is shown in fig. 8.
The landing site frequency distribution for each set of experiments was simulated 1000 times for 5000 total experiments as shown in fig. 9.
Compared with the basic experiment, the weakening of the vertical height controller in the automatic driving system in the contrast experiment 1 has the advantage that the deviation of the average value of the landing point and the safety rate of the result are not better than those of the basic experiment. Indicating that the cabin system should correct for altitude deviations in real time. Compared with the basic experiment, the time lag of the controller in the cockpit system is increased in the contrast experiment 2, and the flight performance quality is not as good as that of the basic experiment. It has been shown that in the autopilot of a cockpit system, time lag is an important control factor affecting flight missions. Compared with the basic experiment, the sensitivity of the height controller of the airplane is higher in the approach landing process, and the experimental result shows that the average deviation of the landing points in the five groups of experiments is the smallest in the control experiment 3, but the safety rate is lower than that of the basic experiment. It is indicated that the autopilot system should avoid unnecessary elevator and aileron yaw adjustments during flight, which is tolerable for situations where the aircraft deviates slightly from the glidepath for a short time during flight. The control experiment 4 has poorer error precision than the elevator controller of the basic experiment, and allows a larger elevator operation amount to possibly cause the landing point to move backwards and bring a lower safety rate, which indicates that the precision guarantee of various steering engine systems in the system is necessary. In a certain development period, the reliable flight data which can flexibly configure flight tasks and adapt to various flight scenes is generated, so that the airworthiness approval of civil aircrafts is guaranteed, and the method has important significance.
Example 2
Reliability verification of the intelligent cabin system: based on a POT model in an extreme value theory and an established intelligent cockpit autopilot system, the absolute value of the difference between the actual landing point and the expected landing point of the airplane in one flight, namely landing deviation, is selected as an evaluation index. Through a semi-physical flight test, the effectiveness and the accuracy of the intelligent cockpit system are verified by comparing flight data generated by controlling an airplane by a real flight unit in the simulation cockpit system and flight data generated by automatic driving in the intelligent cockpit system.
1 extreme value theory and POT model
The extreme theorem proposed by Gnedeno in the 40 th of the 20 th century is the research basis of the extreme theory. The object of extreme value theoretical research is the tail condition of risk distribution, most of the risk distribution with the characteristics of low frequency and high risk presents the characteristic of thick tail distribution, namely the probability of extreme values in the sample data is greater than the occurrence probability of extreme values in normal distribution.
The BMM model and the POT model are two most representative models of extreme value theory. The BMM models the blocked maximum value data of the sample after blocking, and the BMM models are suitable for extreme value problem analysis with obvious blocking characteristics. The POT model is a model for modeling all extreme data in a group of samples exceeding a certain threshold, and can effectively use extreme sample data to be considered as the most effective model in practical application.
The POT model was developed by Pickands in 1975 according to the framework of extreme value theory, and indicates that, in a set of sample data, for a sufficiently large threshold μ, all sample data exceeding the threshold μ obey approximately a GPD distribution, i.e. a generalized pareto distribution.
The GPD distribution is a two-parameter distribution function having the form:
wherein: ζ is a parameter of the shape,λ (λ > 0) is a scale parameter of the GPD distribution. When zeta is greater than or equal to 0, x is greater than or equal to 0; when zeta is less than 0, x is more than or equal to 0 and less than or equal to-lambda/zeta. The GPD conforms to the thick tail property of risk loss distribution if and only if ζ > 0.
Let X1, X2., Xn be n observation sample data independently distributed, the distribution function of the tail of the sample is f (X), μ is a threshold, and the number of samples exceeding the threshold μ is Nu, then the distribution function of the random variable X exceeding the threshold μ: fμ(y)＝P(X-μ≤y|X＞μ)＝[F(μ+y)-F(μ)]/[1-F(μ)]For x-mu ≧ 0,
in the application of the POT model, the threshold μ is first determined. When the threshold is determined, pass (N-N)μ) N to approximate F (mu), Fμ(x- μ) can be approximated with the GPD distribution, thereby calculating the distribution of the probability function f (x) that yields the tail distribution: f (x) ═ 1- (N)μ/n)[1+ζ(x-μ)/λ]-1/ζ。
2 POT model-based flight accident risk measurement
And selecting the aircraft landing deviation in one flight as an evaluation index according to the established intelligent cockpit automatic driving system. In the process of landing the airplane, if the deviation of the landing point is overlarge, the airplane can rush out of the runway, and the flight safety is seriously threatened.
(1) Thick tail inspection
The simulated autopilot flight is performed 500 times, and a landing deviation sample data graph of 500 flights is obtained as shown in fig. 10.
And (3) performing deviation state inspection on the sample data of 500 flight landing deviations, and performing QQ graph analysis, as shown in FIG. 11.
According to fig. 10 and fig. 11, it is seen that the aircraft landing site deviation has a thick tail characteristic, and the tail distribution form of the 500 sample data can be described by using a POT model of extreme value theory. The POT model in the extreme value theory is mainly applied to two contents: the first block is the determination of the threshold μ in the sample data first; the second block is the parameter estimation in the GPD distribution.
(2) Threshold selection in POT model
The selection of the threshold value mu is the first step and is also a key step. There are many ways to estimate the threshold μ, and the present invention estimates the threshold using the commonly used Hill chart method.
Let X1, X2.. gtoren be n positive-value independent identically-distributed random variables arranged in descending order, that is, X1 ≧ X2 ≧ Xn. Definition HkFor its Hill estimation:
wherein: point (k, H)k) The formed curve is the Hill graph. In the Hill diagram, the starting point of the stable region presented by the curve is selected, and the corresponding threshold value mu is determined.
In this embodiment, 500 flight sample data are plotted into a Hill chart as shown in fig. 12. It is observed that near threshold 1000, the curve appears to have a region that tends to stabilize. In this example, the threshold μ in the POT model may be 1000 in the approximation of the present invention.
(3) Estimation of parameters in POT models
There are many methods for estimating parameters in the GPD model, and the present embodiment estimates parameters ζ and λ by using a moment estimation method. The moment estimates of the parameters ζ and λ of the GPD distribution are:
assuming that Y is a random variable obeying GPD distribution, by Y ═ λ/ζ [1-exp (ζ X)]Obtaining:
the moments are estimated to exist when 1- ζ × r > 0, i.e., ζ < 1/r. Taking r as 1 and r as 2 respectively to obtain the first and second moments of Y as follows:
when in use
And s2Mean and variance of the observed sample data. By using
And s2Mean EY and square instead of populationAnd (d) a difference DY. Estimates of the GPD distribution parameters ζ and λ can be derived:
(4] VaR and ES estimation of POT model
VaR (Value at risk) is a risk Value, also known as a risk metric, and refers to the maximum possible loss of an asset or risk for a particular period of time in the future, at some confidence level.
Let X be a random variable with a distribution function of F (X)]At a given confidence level of P, the present invention defines VaR as: VaRpInf { X | f (X ≦ X) > P }, when the function f (X ≦ X) > P }, the function f (X ≦ X) > P]When it is a continuous function, the invention defines F-1P is the confidence interval, which is the inverse of the distribution function. Calculating to obtain VaR based on the definition of POT modelpI.e. the P quantile of the loss distribution:
wherein: VaR is an effective method of risk measurement, but it cannot describe the loss beyond the confidence interval. Based on the research foundation of VaR, artkner proposed the concept of expected lost ES. ES is defined as the VaR when the risk loss is greater than the confidence PpThe conditions of (2) are as follows: ES (ES)p＝E(X|X＞VaRp)(18]
As can be seen from the definition of the POT model, when the threshold μ is large enough, the sample observation data exceeding the threshold approximately obeys the GPD distribution. Obtaining ES by the present inventionpThe estimation of (d) is:
thereby calculating an estimate of the risk value VaR and the expected loss ES.
500 flight data samples generated by the simulation platform are analyzed, and the mean value of the landing deviation of 500 experiments is calculated
Variance s of flight landing deviation2213546.7. Is represented by formula (9)]Estimates of the GPD distribution parameters ζ and λ can be obtained as:
and defining the aviation flight hidden danger event with landing absolute value deviation more than 1000 meters by selecting the threshold value. Figure 13 is a graph of a GPD fit test when the threshold value is 1000, fitted to the estimation of the GPD parameters, based on the selection of the threshold value. In fig. 13, the GPD distribution model is represented by a smooth curve, and the HS empirical distribution values of 45 excess distribution extrema are represented by scatter. It can be seen that the distribution of extrema exceeding the selected threshold 1000 can be better fitted with the GPD distribution, and the GPD fitting model is valid.
The total number of observed samples is n is 500, and the number of samples exceeding the threshold is Nu is 45. Substituting the parameters into a formula (10), and calculating the distribution function of the landing deviation of the airplane in the experiment as follows: (x) 1-0.09[1+6.22 × 10-5(x-1000)]-36.7647x is more than 1000, and VaR is calculated under a given confidence interval PpComprises the following steps: VaRp＝1000+16071.3824{[11.1111(1-p)]-0.0272-1 }; by VaRpFurther obtain ESpThe estimation of (d) is:
when the confidence interval P is 0.95, VaRp＝1259.0110，ESp1715.6174. It is understood that in this flight mission, the probability of the aircraft landing deviation not exceeding 1259.0110 meters is 95%. The landing bias condition when the absolute value of the landing bias is greater than the risk value of 1259.0110 meters with a confidence of 0.95 is expected to be 1715.6174 meters.
When the confidence interval P is 0.975, VaRp＝1569.8189，ESp2035.1157. It can be understood that in this flight mission, the probability that the aircraft landing deviation does not exceed 1569.8189 meters is 97.5%. The landing bias condition when the absolute value of the landing bias is greater than the risk value of 1569.8189 meters with a confidence of 0.975 is expected to be2035.1157 m.
When the confidence interval P is 0.99, VaRp＝1989.7806，ESp2466.8197. It is understood that in this flight mission, the probability of the aircraft landing deviation not exceeding 1989.7806 meters is 99%. The landing bias condition when the absolute value of the landing bias is greater than the risk value of 1989.7806 meters with a confidence of 0.99 is expected to be 2466.8197 meters.
In order to verify the validity of the model, through a semi-physical flight test, flight data of 500 times of the same flight mission generated by operating the aircraft by a real flight crew in the high-simulation cockpit system is collected. Fig. 14 is a real view of the development of an intelligent cockpit system, all experiments being performed on this simulator.
And calculating the number N of times that the absolute value of the landing deviation of the airplane exceeds the value VaR according to the model, wherein the overflow rate is defined as E, and E is equal to N/N, wherein N is the total number of sample data, namely 500. And comparing the calculated overflow rate E with 1-p to judge the validity of the POT-GPD model, wherein the table 4 shows the verification result of the model.
Verification of the model of Table 4
The model verification result shows that the VaR calculation method based on the POT-GPD model is reasonable and effective. The invention can quickly generate a large amount of comprehensive, comprehensive and reliable flight data based on various flight scenes by depending on the configuration of the machine types in the intelligent cabin system and the design of the controller so as to ensure the objectivity and the accuracy of airworthiness approval.
The foregoing embodiments may be modified in many different ways by those skilled in the art without departing from the spirit and scope of the invention, which is defined by the appended claims and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (5)
1. An implementation method of an intelligent cabin system for civil aircraft design and airworthiness approval is characterized by comprising the following steps:
step 1, constructing a six-degree-of-freedom aircraft dynamics model in an intelligent cockpit system, and endowing each aircraft with real and effective parameters to a civil aircraft model to realize an aircraft dynamics simulation model capable of carrying out numerical calculation;
step 2, an intelligent cabin system platform is built by adopting an MATLAB/Simulink and C # hybrid programming method;
step 3, designing an automatic aircraft controller: aiming at three controls of height maintenance, pitching attitude maintenance and speed maintenance of an autopilot in a cabin system, a corresponding automatic controller is designed in MATLAB/Simulink;
and 4, verifying the validity and reliability of the model: according to flight data generated by an intelligent cockpit system, selecting an absolute value of a difference value between an actual landing point and an expected landing point of an airplane in one flight, namely landing deviation as an evaluation index, and verifying the effectiveness and the accuracy of the intelligent cockpit system by comparing flight data generated by controlling the airplane by a real flight unit in the simulation cockpit system and flight data generated by automatic driving in the intelligent cockpit system through a semi-physical flight test;
the automatic controller comprises: a pitch attitude retainer, a altitude retainer controller and a speed retainer controller for flying the aircraft at a set pitch angle and keeping the pitch angle stable;
the pitch attitude retainer is a proportional-integral pitch attitude retainer controller, and the control rate is as follows:
wherein: kθIs a pitch angle proportional link coefficient; t iseIs an integral link time constant; delta thetacIs the pitch angle at the balance point; delta theta is the actual pitch angle of the airplane;
the coefficient of a pitch angle speed feedback link;
is pitch angle velocity;
the height maintaining controller directly utilizes height information to perform feedback control, takes the pitching attitude maintaining controller as an inner ring, and then adds a layer of proportional-integral controller for height on an outer ring to realize, wherein the control rate of the height maintaining controller is as follows:
wherein KhIs the height ring scale factor; Δ hcIs the fly height at the balance point; delta h is the actual flying height of the airplane;
the control rate of the speed maintaining controller is as follows:
wherein KVIs the velocity ring scaling factor; Δ VcIs the aircraft airspeed at the equilibrium point; Δ V is the aircraft actual airspeed; a is the actual acceleration of the airplane; kaIs the acceleration feedback coefficient.
2. The method as claimed in claim 1, wherein the aircraft dynamics simulation model is a model in which the aircraft parameters are flexibly assigned to different civil aircraft models, and which characterizes the most in-service civil aircraft dynamics model.
3. The method according to claim 1, wherein the step 2 specifically refers to: the nonlinear dynamics model of the airplane is realized in MATLAB/Simulink, the model can calculate the change of the state quantity of the airplane according to the input of the control quantity of a control surface and an accelerator, and can be externally connected with an automatic controller to carry out joint numerical simulation, and the interface design and function division work of a cockpit system display platform is mainly completed in C #.
4. The method as claimed in claim 1, wherein the intelligent cockpit system rapidly generates a large amount of comprehensive and reliable flight data based on various flight scenes by means of configuration of models in the intelligent cockpit system and design of controllers, so as to ensure objectivity and accuracy of airworthiness approval.
5. The method of claim 1, wherein the six-degree-of-freedom flight dynamics equation model framework comprises: the method comprises the steps of modeling an equation of the translational degree of freedom of the airplane movement, describing the rotational degree of freedom of the airplane movement, solving forces and moments of the translational degree of freedom and the rotational degree of freedom, and quaternions, airplane attitude angles and direction cosine matrixes which need to be initialized when a system model is built, wherein the input part of the model directly changes the changes of lift force and resistance.
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