CN106647327A - Landing signal officer vertical forced instruction modeling method based on virtual flight experience - Google Patents

Abstract

The present invention discloses a landing signal officer vertical forced instruction modeling method based on virtual flight experience, belonging to the aircraft design and the flight safety management technology field. The method comprises: the step 1, establishing a man-machine closed loop dynamical model with a pilot observation threshold characteristics; the step 2, determining a LSO and pilot agreement control principle; the step 3, taking the flight safety indexes as a target function, taking the agreement control principle as a prerequisite condition, and performing searching optimization solution and calculation of instruction covered wires; and the step 4, collecting the forced instruction covered wires to form a LSO instruction generation system. The landing signal officer vertical forced instruction modeling method based on the virtual flight experience solves the problems that whether there is a LSO forced instruction model or not, provides a modeling means for the generation of the LSO instruction in the multi-man-machine interaction flight safety simulation and lays the foundation for the establishing of the multi-factor coupling shipboard aircraft landing flight safety simulation model.

Description

Based on virtual flight experience warship commander longitudinal direction imperative instruction modeling method

Technical field

The invention belongs to Flight Vehicle Design and flight safety administrative skill field, are mainly used in warship machine integration design and warship Carrier aircraft flight safety is emulated.

Background technology

The command information interaction during warship is closely related with flight safety, and U.S. army just once occurred because of warship commander The flight safety event that (Landing Signal Officer, abbreviation LSO) slips up and produce.And it is to carry out to carry out LSO modelings Various factors coupling the basis of warship flight safety simulation and prediction and multi information interaction research.Therefore, LSO commander's behavior modelings are carried out Research important in inhibiting.In current LSO scale-model investigations, big multipair LSO is simplified.Such as bibliography [1] (Wang Li Roc. carrier landing commanding officer command strategy studies [M]. Harbin Engineering University's master thesis, Harbin, 2012,3.) LSO instructions are simplified, the obscurity model building of seven scales is established.Bibliography [2] (Qu Xiangju, Cui Hailiang, Wang Yangang. carrier-borne aircraft Lower LSO simulation model [R] .GF-A0085884G of artificial control, Beijing：BJ University of Aeronautics ＆ Astronautics, 2005,10) also builds to LSO Mould method is studied, but equally employs fuzz method to LSO instruction modelings.These researchs are served for LSO instruction modelings Particularly important fundamental role, but these researchs are without differentiation info class instruction in detail, the instruction of suggestion class and force class to instruct, Also it is not based on LSO decision mechanisms to be modeled imperative instruction, therefore in many man-machine interaction closed-loop simulations, it is impossible to it is completely anti- Reflect LSO commanding and decision-making processes.Therefore, it is necessary to providing one kind meets LSO decision mechanisms, flight safety emulation needs are met Modeling method is forced in LSO instructions, is solved the above problems.

The content of the invention

The LSO longitudinal directions imperative instruction modeling method based on virtual flight experience that the present invention is provided, specifically includes following step Suddenly：

The first step, sets up man-machine loop's kinetic model that threshold property is observed with pilot.

Second step, determines that LSO and pilot's agreement manipulate principle.

3rd step, with flight safety index as object function, the condition premised on arranging to manipulate principle, optimizing is solved and counted Calculate instruction envelope curve.

4th step, collects imperative instruction envelope curve, forms LSO instruction production systems.

In above-mentioned steps, LSO and pilot's agreement manipulate principle, refer to the manipulation side of pilot and LSO both sides' acquiescence Formula and amplitude size.Flight safety index refers to the standard for judging that flight risk occurs, it may include stern it is quiet it is high, warship deviation etc. Content.Envelope curve is instructed to refer to flight safety as target, LSO instruction issuing scopes, once i.e. aircraft bias state entrance refers to Make envelope curve, then LSO just assigns corresponding pressure thrust command.Envelope curve is instructed to be inclined by height tolerance, deflection ratio in the present invention The suite line that difference, aircraft and mother ship carrier relative position are constituted.

It is an advantage of the current invention that：

(1) there is provided the LSO imperative instruction modeling methods based on virtual flight experience, LSO imperative instruction models are solved The presence or absence of problem.Generate for the LSO instructions in the emulation of many man-machine interaction flight safeties and provide modeling means, be various factors coupling Carrier landing flight safety Building of Simulation Model is laid a good foundation.

(2) present invention sets up model according to human knowledge experience formation mechanism using virtual flight empirical method, and true LSO instruct forming process closer to.

(3) present invention adopts Pilot Mathematical Model, by flight simulation, generates LSO imperative instruction envelope curves, ultimately forms The production instruction system of similar human brain, is capable of achieving in simulations by two-dimensional interpolation, easy to use.

Description of the drawings

Fig. 1 is man-machine loop's kinetic model calculation process schematic diagram that threshold property is observed with pilot.

Fig. 2 is " giving point throttle " instruction envelope curve schematic diagram.

Fig. 3 is that " giving point throttle " instruction allows area's simulation comparison example figure.

Fig. 4 is " recovery thrust " instruction envelope curve schematic diagram.

Fig. 5 is " open out " instruction envelope curve schematic diagram.

Fig. 6 is " opening reinforcing " instruction envelope curve schematic diagram.

The deflection ratio Δ h of Fig. 7 embodiments 1_dot=2m/s instruction envelope curves collect schematic diagram.

Fig. 8 is that LSO commanders descend warship trailing end enlarged drawing in embodiment 2.

Fig. 9 is the lower pilot's emulation manipulated variable of LSO commanders in embodiment 2.

Specific embodiment

Below in conjunction with drawings and Examples, the present invention is described in further detail.

The present invention provides a kind of LSO longitudinal directions imperative instruction modeling method based on virtual flight experience, and methods described includes Following steps：

The first step：Set up the man-machine loop kinetic model (Modified that threshold property is observed with pilot Optimal Control Pilot Model with Adaptive State Estimation and Optimal Weigtings, abbreviation MOCM-AEW).This model will form LSO and instruct envelope curve in subsequent step by virtual flight.Such as Shown in Fig. 1, described MOCM-AEW models include the weight coefficient based on optimum Automobile driving select and MOCM-AE models and Optimal weighting coefficientses connected applications two parts, comprise the following steps that：

Step one：Build the augmentation controlled device of shape such as following formula：

Wherein, x_sIt is the augmented state vector with delay,For x_sFirst derivative, A_s、B_s、C_s、D_s、E_sIt is augmentation system Matrix number, u_pIt is pilot guidance amount, y is output state amount, and w is external disturbance vector.Wherein C_s=[C DC_d], D_s=D,X is aircraft small perturbation equation state vector.x_dIt is to carry The augmented state vector of delay, A_d、B_d、C_dRespectively time delay coefficient matrix.A, B, C, D, E are aircraft small perturbation equation systems Matrix number.

Pilot's actual perceived vector y_obsFor：

y_obs=C_sx_s+D_su_p+v_y (2)

Wherein v_yIt is the noise-aware relevant with recent observation history, is modeled using white Gaussian noise, also referred to as observation noise. Observation noise intensity V_yForWherein ρ_yIt is noise-aware signal to noise ratio, noise-aware letter in typical single channel tracing task Make an uproar and compare ρ_yUsually 0.01, correspondence signal to noise ratio is -20dB,For observation noise variance.

Step 2, according to aerial mission, builds pilot optimal control model target function, sets initial weighting coefficients value Q_yAnd r_u.Pilot optimal control model target function J is built using Quadratic function_pIt is as follows：

Wherein, Q_yIt is to observe vectorial weight coefficient, r_uIt is to manipulate vectorial weight coefficient, f is to manipulate speed weight coefficient, It is u_pFirst derivative.They reflect pilot will to different driving such as information observation, throttle lever, control stick, manipulation speed The attention degree of element.The selection of wherein f depends on given neural power delay constant T_n。E_∞It is that target function steady state period is hoped Value.The problem to be solved in the present invention is exactly to try agriculture products function weight coefficient Q_yAnd r_uValue.

Step 3, calculates pilot's optimum control gain.Control planning can be obtained by the theory of optimal control is：

Wherein,It is pilot optimal operation amount, G_pIt is controller gain vector,It is the estimate of state vector X, K is The unique solution determined by following Riccati equations：

0=(A_o)^TK+KA_o+Q_o-KB_of^-1(B_o)^TK (5)

Wherein,

By X=[x_s u_p]^T=[x x_d u_p]^T(4) formula of substitution, then,

Wherein G_nIt isGain vector,For x_sEstimate, G_n1It isGain vector.Order,

Then,

I_pAs pilot's optimum control gain.Therefore, (7) formula can be written as,

OrderIntroduce and manipulate noise v_u, then

Wherein v_uIt is that intensity is V_uZero mean Gaussian white noise.ρ_uIt is to manipulate noise signal to noise ratio coefficient, It is to manipulate noise variance.

Step 4：Loop iteration solves observation noise variance and manipulates noise variance, calculates Kalman filter gain.

Simultaneous (1) and (11) formula are obtained：

Wherein,It is the first derivative of the augmented state vector X with manipulated variable, W is that external disturbance is vectorial, v_uTo manipulate noise, v_yFor observation noise, C₁ =[C DC_d D]。

The estimate of state vector XCan be obtained by Kalman filter, wherein filtering gain matrix F are：

F=Σ₁(C₁)^T(V_y)^-1 (13)

Wherein, V_yFor observation noise intensity, evaluated error matrix Σ₁It is the unique solution determined by following Riccati equations：

0=A₁Σ₁+Σ₁(A₁)^T+W₁-Σ₁(C₁)^T(V_y)^-1(C₁)Σ₁ (14)

Wherein W₁=diag (W, V_u), W be external disturbance intensity, V_uTo manipulate noise intensity.With the man-machine of state estimation Closed loop states equation is,

Wherein I₁=[I_p, 0], C_δ=[0 C_d1], F is Kalman filter gain matrix, and δ is aircraft rudder surface amount of deflection.

Covariance matrix X_covIt is following Lyapunov non trivial solutions：

Wherein,Q_lyp=diag (W, V_y,V_u), V_yIt is that observation is made an uproar Sound intensity, V_uManipulate noise intensity.

Then, output covariance matrix：

Wherein,

Thus obtain observation noise variance and manipulation noise variance is respectively：

So for given initial manipulation noise intensity V_uWith observation noise intensity V_y, one can be respectively obtainedWith And can respectively be calculated signal to noise ratio coefficient ρ_yAnd ρ_u, it is possible thereby to loop iteration calculating is formed, until signal to noise ratio meets ρ_y= 0.01 and ρ_uTill=0.003.Meanwhile, iteration can obtain Kalman filter gain after terminating.

Step 5：Parameter function J.

Wherein, J_e=Y_cov(1,1), J_u=Y_cov(2,2), Row_u=row_X-row_x-row_x_d, row_X is the line number of vectorial X, and row_x is the line number of vector x, row_x_dIt is vector x_d Line number.

Step 6：Assumed to calculate weight coefficient according to optimum Automobile driving.

According to weight coefficient selection principle and target function calculating principle, pilot can with optimum to Automobile driving, Make target function minimum.Thus, different Q is set_yAnd r_u, can be by conjugate gradient method optimizing until target function J is obtained Minimum of a value.OCM model optimal weighting coefficientses Q are thereby determined that_yAnd r_u。

Step 7：According to optimal weighting coefficientses and dummy pilot target function setting pilot model initial value.

Step 8：Discrete aircraft state equation is calculated, and obtains aircraft current time dynamic response.

Equation (1), equation (11) discretization is as follows：

WhereinH、B_dis、D_disIt is state-transition matrix.Ф_u、B_udis、E_udisIt is to manipulate vectorial discrete equation state vector. Given x_s、y、w、u_pIn k-1 moment initial values, you can calculate aircraft current time dynamic response.

Step 9：Pilot carries out adaptive state estimation according to extraneous perception.Time-varying noise estimator is as follows,

Wherein (k) represents the k moment, (k-1) represents the k-1 moment,To disturb Estimation of Mean,Disturbance variance matrix estimation, ε is new breath vector,It is observation Noise Variance Estimation,It is observation noise Estimation of Mean, d is Gradual forgetting coefficient, d_k-1=(1- b)/(1-b^k), 0<b<1, b is forgetting factor.P is status predication equation matrix, and I is unit matrix, and D (k) is Recursion Operator, expression Formula is as follows：

Sef-adapting filter is：

P (k | k)=[I_n-K_F(k)H(k)]P(k|k-1) (32)

It is the estimate of X, K_FIt is filtering gain.

Step 10：By formula (22) according to the theory of optimal control, pilot optimal operation amount is obtained.

Step 11：The man-machine loop's kinetic model for observing threshold property with pilot builds and finishes.

Second step：Determine that LSO and pilot's agreement manipulate principle.

The thrust of LSO and pilot's agreement is arranged according to airplane throttle characteristic and throttle lever and determined.Assume that carrier-borne aircraft is normal It is trim position ± 10 ° that throttle lever is spacing when downslide is manipulated.Then assume the throttle commands corresponding relation of LSO and pilot's agreement Such as table 1 below：

Table 1 forces thrust command agreement table

Wherein δ_T0It is downslide trim condition throttle lever position.

3rd step：With flight safety index as object function, the condition premised on arranging to manipulate principle, optimizing is solved and counted Calculate instruction envelope curve.

To ensure safety of going around, typically require that stern headroom is at least 3m, therefore flight safety index of the present invention with stern Quiet high 3m is standard.In warship commander, LSO carries out anticipation to state of flight, if quiet height when LSO anticipations aircraft reaches stern Then think dangerous less than 3 meters, then LSO assigns corresponding imperative instruction.Herein, the method for computations envelope curve is, to fly Deflection ratio deviation delta h at machine and preferable relative position X of warship point, position X_dotWith height tolerance Δ h₀For independent variable, to arrive The quiet high 3m of bottom stern is object function, using first step MOCM-AEW model, with a certain imperative instruction as input, using conjugation Gradient method optimizing, then can obtain the corresponding deflection ratio deviation of one group of imperative instruction and height tolerance curve set, that is, refer to Make envelope curve.

4th step：Collect imperative instruction envelope curve, form LSO instruction production systems.

" to point throttle, thrust, open out will be recovered, reinforcing will be opened " four curves to be collected, that is, define LSO and force to refer to Make production system.

Embodiment 1：

Below with " giving point throttle " instruction instruction envelope curve calculating process and beneficial effect of the present invention.

The first step：Set up man-machine loop's kinetic model (MOCM-AEW) that threshold property is observed with pilot.Wherein Model parameter is as shown in table 2.

The MOCM-AEW model parameters of table 2

Second step：Determine that LSO and pilot's agreement manipulate principle as shown in table 1.

3rd step：With flight safety index as object function, the condition premised on arranging to manipulate principle, optimizing is solved and counted Calculate instruction envelope curve.According to the conventional flying experiences of LSO, and " to point a throttle " be according to the bias state and deviation of aircraft occur opportunity to Go out.Analogue simulation is carried out to LSO flying experiences using MOCM-AEW pilot models for this.Result of calculation is as shown in Fig. 2 root According to initial deflection ratio deviation delta h_dot, distance it is preferable warship point apart from X, height tolerance Δ h₀, give " to a throttle " LSO and refer to Order allows area.As shown in Fig. 2 dash area as instructs permission area under every curve.Simultaneously as calculating the target letter for adopting Number is quiet height at stern, therefore it is x=-80m that instruction allows the location boundary in area, i.e., stern point relative ideal warship point coordinates. From emulation, with the increase of deflection ratio, instruction allows area to be gradually reduced.With the close stern of aircraft, LSO instructions are allowed Height tolerance is gradually reduced.This is consistent with actual conditions.To illustrate to instruct the reasonability for allowing area, instruction bag is respectively compared Each inside and outside line some A points and B points in such as Fig. 2, example deflection ratio increment is 4m/s, and deviation position occurs for X=-300m, height Deviation is respectively Δ h_A=-15m and Δ h_B=10m.Warship emulation is carried out, A points, B points and ideal warship and warship track such as Fig. 3 Shown, the quiet height of A points and B points at stern is respectively -4.7423m and 14.8617m (downwards for just).Obviously for the flight of B points State, even if LSO gives " giving point throttle " instruction, aircraft will hit warship.Therefore can be illustrated by the calculating of this instruction, It is accurate and rational that LSO instructions envelope curve of the present invention is calculated.

Step 4：Collect imperative instruction envelope curve, form LSO instruction production systems.Same calculating " recovery thrust ", " plus Throttle ", " opening reinforcing " instruction envelope curve as shown in figures 4-6.With Δ h_dotCompare identical deflection ratio different instruction as a example by=2m/s Instruction envelope curve, as shown in Figure 7.Deflection ratio Δ h is referring to Fig. 7 wherein regions 1,2,3,4_dotDuring=2m/s " to point a throttle ", The LSO instruction areas of " recovery thrust ", " open out " and " opening reinforcing ".And aircraft bias state once enters region 5, even if flight Member opens reinforcing, and aircraft reaches the quiet height of stern and will be less than 3m, generation it is larger warship risk.

LSO after collecting forces the using method of thrust command as follows：

The first step：Into after decision point of going around, condition adjudgement is carried out to each moment (position X).According to position and currently Deflection ratio (X, Δ h_dot), two-dimensional interpolation is carried out, calculating current state is corresponding " to a point throttle, to be recovered thrust, open out, adds Power " envelope curve height tolerance boundary value.

Second step：According to current flight state (X, Δ h, Δ h_dot), according to instruction envelope curve, select LSO instructions.

3rd step：Instructed according to LSO, pilot is manipulated or carries out many man-machine interaction closed-loop simulations according to instruction.

Thus achieve LSO and force thrust command modeling and simulation.

Embodiment 2

Warship emulation is adopted to verify LSO demand models below.If under LSO commander, the landing precision of aircraft It is improved with security, then just illustrate that the LSO modelings provided in the present invention are rational.Simulating, verifying example parameter such as table Shown in 2.Drift correction ability under clearly to compare LSO instructions, it is to avoid the impact that randomness is produced, removes in warship emulation Atmospheric perturbation impact.In LSO forces class thrust command simulation, differential location X=-1429m, initial deviation Δ h are set_dot =4.5m/s, Δ h=20m.Relatively LSO forces the flight simulation result under thrust command as shown in figure 8, showing for directly perceived, only Give end orbit enlarged drawing.Fig. 9 is the pilot guidance comparison diagram under LSO commanders.As seen from Figure 8, command without LSO When, quiet high h=-1.0543m (inverted triangle track) of the aircraft at stern, (the circle when only LSO info class and suggestion class are instructed The locus of points), quiet high h=-1.7147, and LSO is assigned after pressure thrust command, quiet a height of h=-3.9491m (the positive arris rails of aircraft Mark).Reasonability of the LSO imperative instructions in flight safety is guaranteed by this comparative descriptions.Simultaneously Fig. 9 shows that LSO refers to Pilot guidance behavior under waving, it is seen that when away from warship 480m or so, LSO has been assigned and has been opened reinforcing instruction, be thus ensure that winged Machine flight safety.

The present invention cannot truly be instructed with LSO for tradition LSO and set up the present situation for associating.Thrust command is forced to be adopted LSO Obtained with virtual flight experience, establish LSO imperative instruction envelope curves, define LSO instruction production systems.Embodiment shows LSO Commander is improving landing precision, it is ensured that flight safety aspect has important function, and this illustrates LSO demand models modeling of the present invention The reasonability and model accuracy of method.

Claims (5)

1. based on virtual flight experience warship commander longitudinal direction imperative instruction modeling method, it is characterised in that：

The first step, sets up man-machine loop's kinetic model that threshold property is observed with pilot；

Second step, determines that LSO and pilot's agreement manipulate principle；

3rd step, with flight safety index as object function, the condition premised on arranging to manipulate principle, optimizing is solved and calculated and refers to Make envelope curve；Described flight safety index refers to the standard for judging that flight risk occurs；

4th step, collects imperative instruction envelope curve, forms LSO instruction production systems.

2. it is according to claim 1 based on virtual flight experience warship commander longitudinal direction imperative instruction modeling method, its It is characterised by：The first step comprises the following steps that,

Step one：Build the augmentation controlled device of shape such as following formula：

$\left\{\begin{array}{c}{\stackrel{\&CenterDot;}{x}}_s=A_s{x}_s+B_s{u}_p+E_{s}w\\ y=C_s{x}_s+D_s{u}_p\end{array}\right.---\left(1\right)$

Wherein, x_sIt is the augmented state vector with delay,For x_sFirst derivative, A_s、B_s、C_s、D_s、E_sIt is augmentation coefficient matrix, u_pIt is pilot guidance amount, y is output state amount, and w is external disturbance vector, wherein C_s=[C DC_d], D_s=D,X be aircraft small perturbation equation state vector, x_dBe with postpone augmented state to Amount, A_d、B_d、C_dRespectively time delay coefficient matrix, A, B, C, D, E are aircraft small perturbation equation coefficient matrixes,

Pilot's actual perceived vector y_obsFor：

y_obs=C_sx_s+D_su_p+v_y (2)

Wherein v_yIt is observation noise, observation noise intensity V_yForWherein ρ_yIt is noise-aware signal to noise ratio,For observation Noise variance；

Step 2, according to aerial mission, builds pilot optimal control model target function, sets initial weighting coefficients value Q_yWith r_u；Pilot optimal control model target function J is built using Quadratic function_pIt is as follows：

$J_p=E_{\mathrm{\&infin;}}\{y^T{Q}_{y}y+u_p^T{r}_u{u}_p+{\stackrel{\&CenterDot;}{u}}_p^{T}f{\stackrel{\&CenterDot;}{u}}_p\}---\left(3\right)$

Wherein, Q_yIt is to observe vectorial weight coefficient, r_uIt is to manipulate vectorial weight coefficient, f is to manipulate speed weight coefficient,It is u_p First derivative, E_∞It is target function steady state period prestige value；

Step 3, calculates pilot's optimum control gain；

Obtaining control planning by the theory of optimal control is：

${\stackrel{\&CenterDot;}{u}}_p^{*}=-G_p\hat{X}=-\&lsqb;G_n,G_{n1}\&rsqb;\hat{X}=-f^{-1}{\left(B_o\right)}^{T}K\hat{X}---\left(4\right)$

Wherein,It is pilot optimal operation amount, G_pIt is controller gain vector,It is the estimate of state vector X, K is by under The unique solution that row Riccati equations determine：

0=(A_o)^TK+KA_o+Q_o-KB_of^-1(B_o)^TK (5)

Wherein,

$A_o=\left[\begin{array}{ccc}A& {\mathrm{BC}}_d& B\\ 0& A_d& B_d\\ 0& 0& 0\end{array}\right],B_o=\left[\begin{array}{c}0\\ 0\\ 1\end{array}\right],Q_o=\left[\begin{array}{cc}{\left(C_s\right)}^T{Q}_y{C}_s& {\left(C_s\right)}^T{Q}_y{D}_s\\ {\left(D_s\right)}^T{Q}_y{C}_s& {\left(D_s\right)}^T{Q}_y{D}_s+r_u\end{array}\right]---\left(6\right)$

By X=[x_s u_p]^T=[x x_d u_p]^T(4) formula of substitution, then,

${\stackrel{\&CenterDot;}{u}}_p^{*}=-G_n{\hat{x}}_s-G_{n1}{u}_p^{*}---\left(7\right)$

Wherein G_nIt isGain vector,For x_sEstimate, G_n1It isGain vector；Order,

$T_n=G_{n1}^{-1}---\left(8\right)$

Then,

$G_{n1}^{-1}{G}_n=I_p---\left(9\right)$

I_pAs pilot's optimum control gain, therefore, (7) formula is written as,

$T_n{\stackrel{\&CenterDot;}{u}}_p^{*}+I_p{\hat{x}}_s+u_p^{*}=0---\left(10\right)$

OrderIntroduce and manipulate noise v_u, then

${\stackrel{\&CenterDot;}{u}}_p^{*}=-T_n^{-1}{u}_p^{*}+T_n^{-1}{u}_c+T_n^{-1}{v}_u---\left(11\right)$

Wherein v_uIt is that intensity is V_uZero mean Gaussian white noise；ρ_uIt is to manipulate noise signal to noise ratio coefficient,It is to manipulate Noise variance；

Step 4：Loop iteration solves observation noise variance and manipulates noise variance, calculates Kalman filter gain；

Simultaneous (1) and (11) formula are obtained：

$\left.\begin{array}{c}\stackrel{\&CenterDot;}{X}=A_{1}X+B_1{u}_c+E_1{w}_1\\ y_{obs}=C_{1}X+v_y\end{array}\right\}---\left(12\right)$

Wherein,It is the first derivative of the augmented state vector X with manipulated variable, W is that external disturbance is vectorial, v_uTo manipulate noise, v_yFor observation noise, C₁ =[C DC_dD]；

The estimate of state vector XCan be obtained by Kalman filter, wherein filtering gain matrix F are：

F=Σ₁(C₁)^T(V_y)^-1 (13)

Wherein, V_yFor observation noise intensity, evaluated error matrix Σ₁It is the unique solution determined by following Riccati equations：

0=A₁Σ₁+Σ₁(A₁)^T+W₁-Σ₁(C₁)^T(V_y)^-1(C₁)Σ₁ (14)

Wherein W₁=diag (W, V_u), W be external disturbance intensity, V_uTo manipulate noise intensity；Man-machine loop with state estimation State equation is,

$\left.\begin{array}{c}\left[\begin{array}{c}\stackrel{\&CenterDot;}{X}\\ \stackrel{\&CenterDot;}{\hat{X}}\end{array}\right]=\left[\begin{array}{cc}{A}_1& -B_1{I}_1\\ {\mathrm{FC}}_1& A_1-B_1{I}_1-{\mathrm{FC}}_1\end{array}\right]\left[\begin{array}{c}X\\ \hat{X}\end{array}\right]+\left[\begin{array}{cc}{E}_1& 0\\ 0& F\end{array}\right]\left[\begin{array}{c}{w}_1\\ v_y\end{array}\right]\\ \left[\begin{array}{c}{y}_{obs}\\ \mathrm{\&delta;}\end{array}\right]=\left[\begin{array}{cc}{C}_1& 0\\ C_{\mathrm{\&delta;}}& 0\end{array}\right]\left[\begin{array}{c}X\\ \hat{X}\end{array}\right]\end{array}\right\}---\left(15\right)$

Wherein I₁=[I_p, 0], C_δ=[0 C_d1], F is Kalman filter gain matrix, and δ is aircraft rudder surface amount of deflection；

Covariance matrix X_covIt is following Lyapunov non trivial solutions：

$A_{lyp}{X}_{\mathrm{cov}}+X_{\mathrm{cov}}{A}_{lyp}^T+E_{lyp}{Q}_{lyp}{E}_{lyp}=0---\left(16\right)$

Wherein,Q_lyp=diag (W, V_y,V_u), V_yIt is that observation noise is strong Degree, V_uManipulate noise intensity；

Then, output covariance matrix：

$Y_{\mathrm{cov}}=C_{lyp}{X}_{\mathrm{cov}}{C}_{lyp}^T---\left(17\right)$

Wherein,

Thus obtain observation noise variance and manipulation noise variance is respectively：

${\mathrm{\&sigma;}}_y^2=Y_{\mathrm{cov}}(1,1)---\left(18\right)$

${\mathrm{\&sigma;}}_u^2=Y_{\mathrm{cov}}(2,2)---\left(19\right)$

Loop iteration is calculated, until signal to noise ratio meets ρ_y=0.01 and ρ_uTill=0.003；Meanwhile, iteration is obtained after terminating Kalman filter gain；

Step 5：Parameter function J；

$J=J_e+J_u+J_{\stackrel{\&CenterDot;}{u}}---\left(20\right)$

Wherein, J_e=Y_cov(1,1), J_u=Y_cov(2,2),row_u =row_X-row_x-row_x_d, row_X is the line number of vectorial X, and row_x is the line number of vector x, row_x_dIt is vector x_dRow Number；

Step 6：Assumed to calculate weight coefficient according to optimum Automobile driving；

Setting different Q_yAnd r_u, by conjugate gradient method optimizing until target function J obtains minimum of a value；OCM is thereby determined that Model optimal weighting coefficientses Q_yAnd r_u；

Step 7：According to optimal weighting coefficientses and dummy pilot target function setting pilot model initial value；

Step 8：Discrete aircraft state equation is calculated, and obtains aircraft current time dynamic response；

Equation (1), equation (11) discretization is as follows：

$u_p^{*}\left(k\right)=-{\mathrm{\&Phi;}}_u{u}_p^{*}(k-1)+B_{udis}{u}_c(k-1)+E_{udis}{v}_u(k-1)---\left(22\right)$

WhereinH、B_dis、D_disIt is state-transition matrix, Ф_u、B_udis、E_udisIt is to manipulate vectorial discrete equation state vector, gives x_s、y、w、u_pIn k-1 moment initial values, you can calculate aircraft current time dynamic response；

Step 9：Pilot carries out adaptive state estimation according to extraneous perception；

Time-varying noise estimator is as follows,

$\hat{q}\left(k\right)=\hat{q}(k-1)+d_{k-1}\hat{Q}(k-1)D\left(k\right)\mathrm{\&epsiv;}\left(k\right)---\left(23\right)$

$\hat{Q}\left(k\right)=\hat{Q}\left(k-1\right)+d_{k-1}\hat{Q}\left(k-1\right)D\left(k\right)\&lsqb;\mathrm{\&epsiv;}\left(k\right){\mathrm{\&epsiv;}}^T\left(k\right)-H\left(k\right)P\left(k|k-1\right)H^T\left(k\right)-\hat{R}\left(k-1\right)\&rsqb;D^T\left(k\right)\hat{Q}\left(k-1\right)---\left(24\right)$

$\hat{r}\left(k\right)=(1-d_{k-1})\hat{r}(k-1)+d_{k-1}\&lsqb;Y\left(k\right)-H\left(k\right)\hat{X}\left(k\right|k-1)-D_{dis}u\left(k\right)\&rsqb;---\left(25\right)$

$\hat{R}\left(k\right)=(1-d_{k-1})\hat{R}(k-1)+d_{k-1}\{\&lsqb;I-H\left(k\right)K\left(k\right)\&rsqb;\mathrm{\&epsiv;}\left(k\right){\mathrm{\&epsiv;}}^T\left(k\right)\&times;{\&lsqb;I-H\left(k\right)K\left(k\right)\&rsqb;}^T+H\left(k\right)P\left(k\right|k)H^T\left(k\right)\}---\left(26\right)$

Wherein (k) represents the k moment, (k-1) represents the k-1 moment,To disturb Estimation of Mean,Disturbance variance matrix estimates that ε is new Breath vector,It is observation Noise Variance Estimation,It is observation noise Estimation of Mean, d is Gradual forgetting coefficient, d_k-1=(1-b)/ (1-b^k), 0<b<1, b is forgetting factor；P is status predication equation matrix, and I is unit matrix, and D (k) is Recursion Operator, expression formula It is as follows：

$D\left(k\right)=E_{dis}^T{H}^T\left(k\right){\&lsqb;H\left(k\right)P\left(k\right|k-1)H^T\left(k\right)+R(k-1)\&rsqb;}^{-1}---\left(27\right)$

Sef-adapting filter is：

$\mathrm{\&epsiv;}\left(k\right)=Y\left(k\right)-H\left(k\right)\hat{X}\left(k\right|k-1)-D_{dis}u\left(k\right)-\hat{r}(k-1)---\left(30\right)$

$K_F\left(k\right)=P\left(k\right|k-1)H^T\left(k\right)\&times;{\&lsqb;H\left(k\right)P\left(k\right|k-1)H^T\left(k\right)+\hat{R}(k-1)\&rsqb;}^{-1}---\left(31\right)$

P (k | k)=[I_n-K_F(k)H(k)]P(k|k-1) (32)

$\hat{X}\left(k\right|k)=\hat{X}\left(k\right|k-1)+K_F\left(k\right)\mathrm{\&epsiv;}\left(k\right)---\left(33\right)$

It is the estimate of X, K_FIt is filtering gain；

Step 10：By formula (22) according to the theory of optimal control, pilot optimal operation amount is obtained；

Step 11：The man-machine loop's kinetic model for observing threshold property with pilot builds and finishes.

3. it is according to claim 1 based on virtual flight experience warship commander longitudinal direction imperative instruction modeling method, its It is characterised by：LSO and pilot agreement described in second step manipulates principle, refers to the manipulation side of pilot and LSO both sides' acquiescence The thrust of formula and amplitude size, LSO and pilot's agreement is arranged according to airplane throttle characteristic and throttle lever and determined, it is assumed that carrier-borne aircraft It is trim position ± 10 ° that throttle lever is spacing when normal downslide is manipulated, it assumes that the throttle commands that LSO arranges with pilot are corresponding Relation is as follows：

Wherein δ_T0It is downslide trim condition throttle lever position.

4. it is according to claim 1 based on virtual flight experience warship commander longitudinal direction imperative instruction modeling method, its It is characterised by：Flight safety index described in 3rd step refers to that stern headroom is at least 3m.

5. it is according to claim 1 based on virtual flight experience warship commander longitudinal direction imperative instruction modeling method, its It is characterised by：4th step " to point throttle, will recover thrust, open out, open reinforcing " four articles of curves and be collected, that is, define LSO imperative instruction production systems.