CN105938370A - Control system for collaborative flight of morphing aircraft and modeling simulation method thereof - Google Patents

Abstract

The invention discloses a control system for collaborative flight of a morphing aircraft and a modeling simulation method thereof. The invention shows that the morphing aircraft can well complete the deformation collaborative flight of the span height and the span speed through simulation, verifies the rationality and the superiority of the provided control strategy, shows that the morphing aircraft can improve the maneuvering flight performance by the beneficial influence of the structural deformation in a collaborative way, enlarges the flight envelope range and embodies the advantages of the morphing aircraft compared with the conventional aircraft.

Description

Morphing aircraft works in coordination with control system and the modeling and simulating method thereof of flight

Technical field

The present invention relates to morphing aircraft control field, particularly relate to a kind of morphing aircraft and work in coordination with the control system of flight System and modeling and simulating method thereof.

Background technology

The most dual-use aviation proposes the highest requirement to aircraft performance, and aircraft should adapt to fly The change of row environment, execution different task, ensure flying quality again, and be also satisfied cost-effectiveness requirement, and current flying Row device technology cannot meet these requirements simultaneously.Morphing aircraft technology be a kind of potential, can effectively solve this problem Technological approaches.Morphing aircraft be a kind of can with large scale change aerodynamic configuration so that realize multitask flight aviation fly Row device.The research of morphing aircraft has a long history, and as far back as 1916, the U.S. was it has been suggested that " Variable Geometry Wing " Patent application.In recent years, the fast development in the field such as new material, new driving means and new control technology excites people further The enthusiasm of research intelligence morphing aircraft, in the past few decades, countries in the world have been carried out greatly in morphing aircraft technology Quantifier elimination.

Under different flying conditions, in order to obtain optimal performance, morphing aircraft needs to change in sizable scope Become aerodynamic configuration, it is thus impossible to morphing aircraft is carried out Dynamic Modeling as single rigid body as conventional aircraft, and Set up a kind of kinetic model comprising distressed structure.

At present, when morphing aircraft is carried out Dynamic Modeling, mostly use classical Newton mechanics method, aircraft Regard an entirety as, ask for its momentum and its moment of momentum to barycenter, then to time derivation, and then set up aircraft and close outside Translational motion under the effect of power F and the rotational motion equation under resultant moment M effect outside.In the process, it is contemplated that aircraft Deformation, need to ask for the whole aircraft statical moment about reference point by integration, simultaneously need to rotary inertia derivation with The problem solving the rotary inertia change that aircraft deformation brings, it appeared that this method amount of calculation is relatively big, and needs flying Profile and the Mass Distribution of row device model accurately.During it addition, morphing aircraft is carried out dynamic analysis, the most very Difficulty is analyzed in addition to air force changes, variant motion the inertia the caused impact on vehicle dynamics characteristic.

Summary of the invention

The technical problem to be solved is to provide a kind of morphing aircraft and works in coordination with the control system of flight and build Mould emulation mode, it can improve maneuvering flight performance, expand flight envelope.

The present invention solves above-mentioned technical problem by following technical proposals: a kind of morphing aircraft deformation is collaborative to fly Row control system, comprising:

Flight controller, for controlling the state of flight of aircraft, i.e. realizes flight and controls；

Overall situation deformation controller, is connected with flight controller, is controlled distressed structure；

Network-bus, is used for connecting local deformation controller and distributed sensor, as the passage of data communication；

Local deformation controller, is connected with distributed sensor, is used for controlling distressed structure；

Distributed sensor, is connected with local deformation controller, distribution driver, as the hardware configuration controlling system；

Distribution driver, is used for driving distressed structure；

Distressed structure, makes morphing aircraft realize the frame for movement of variant, makes aircraft obtain high pneumatic efficiency；

Sensor, detects the state of distressed structure and information feedback is flown deformation controller.

The present invention also provides for the modeling and simulating method of a kind of morphing aircraft collaborative flight control system of deformation, it include with Lower step:

Step one, have selected some operating points, has worked out corresponding fuzzy rule in morphing aircraft flight envelope；

Step 2, establishes whole envelope longitudinal direction T-S fuzzy model, to describe former nonlinear kinetics mould by linear model Type；

Step 3, after based on T-S fuzzy model, in conjunction with robust H_∞Control thought and PDC principle, it is proposed that based on continuous T- The Fuzzy Robust Controller H of S fuzzy model_∞Control strategy；

Step 4, calculates fuzzy gain matrix by limited LMI condition, it is ensured that the Existence of Global Stable of deformation flight course And robust performance, and the target flight state of energy asymptotic tracking reference signal；

Step 5, by the continuous T-S fuzzy model discretization of morphing aircraft, uses Fuzzy Lyapunov functions method fall Low conservative, in conjunction with Non-PDC principle, it is proposed that DFRHC based on Discrete T-S fuzzy model strategy, by limited LMI bar Part calculates more feasible discrete-time fuzzy gain matrix, better assure that the deformation Existence of Global Stable of flight course, robust performance and Tracking accuracy；

Step 6, introduces morphing aircraft non-linear dynamic model by designed controller, by numerical simulation exhibition Show.

Preferably, described step 2 comprises the following steps:

Step 2 11, sets up the T-S fuzzy model of nonlinear system:

$h_i\left(\mathrm{\η}\right(t\left)\right)=\frac{{\mathrm{\μ}}_i\left(\mathrm{\η}\right(t\left)\right)}{\underset{k=1}{\overset{r}{\Σ}}{\mathrm{\μ}}_k\left(\mathrm{\η}\right(t\left)\right)},i=1,2,...,r$

$0\≤h_i\left(\mathrm{\η}\right(t\left)\right)\≤1,\underset{i=1}{\overset{r}{\Σ}}{h}_i\left(\mathrm{\η}\right(t\left)\right)=1$

In formula, η_j(t), j=1,2 ..., g is former piece variable；It is jth former piece variable η in the i-th rule_jT () is right The fuzzy subset answered；A_i,B_iIt it is the local linear sytem matrix of the i-th rule.For concrete nonlinear system, former piece The target of system self-characteristic and control design case is depended in the selection of variable, the division of fuzzy subset, the quantity etc. of fuzzy rule；

Step 2 12, sets up the Local Linear Model of nonlinear system:

$\left\{\begin{array}{c}\stackrel{\·}{x}\left(t\right)=\underset{i=1}{\overset{r}{\Σ}}{h}_i\left(\mathrm{\η}\left(t\right)\right)(A_{Li}x\left(t\right)+B_{Li}u\left(t\right))\\ y\left(t\right)=x\left(t\right)\end{array}\right.$

In formula,For state vector；For input vector；For output vector；For nonlinear system function, f (x (t))=[f₁(x(t)) f₂(x(t))…f_n(x(t))]^T。

Preferably, described step 3 includes Fuzzy Robust Controller H based on T-S fuzzy system_∞The design of control program, to the most true Determine the Fuzzy Robust Controller H of the tracking reference signal of T-S Design of Fuzzy Systems_∞Control strategy concrete structure is:

$\left\{\begin{array}{c}u\left(t\right)=u_f\left(t\right)+u_b\left(t\right)\\ u_f\left(t\right)=u_e({\mathrm{\η}}^{*},t)\\ u_b\left(t\right)=-K\left(\mathrm{\η}\right)\stackrel{\‾}{e}\left(t\right)\end{array}\right.$

In formula, fuzzy gain matrixFor the constant value matrix of corresponding dimension, K_iFor The fuzzy gain matrix of the i-th rule,For controlled output vector；u_f(t) For fuzzy-feedforward control part, it is therefore an objective to provide baseline stability according to following the tracks of target；u_bT () is fuzzy feedback-control part, mesh The robust stability being to ensure that closed loop system.

Preferably, described step 5 comprises the following steps:

Step 5 11, discrete-time fuzzy robust H based on discrete T-S fuzzy system_∞The design of control program, to uncertain Continuous T-S obscures augmented system and carries out discretization, obtains uncertain Discrete T-S fuzzy augmented system, uses fuzzy Lyapunov Functional based method reduces conservative, in conjunction with Non-PDC principle, proposes a kind of new DFRHC strategy, and concrete design structure is:

$u_b\left(k\right)=-F_{\mathrm{\η}}{G}_{\mathrm{\η}}^{-1}\stackrel{\‾}{e}\left(k\right)=-\left(\underset{i=1}{\overset{r}{\Σ}}{h}_i\right(\mathrm{\η}\left(k\right)\left)F_i\right){\left(\underset{i=1}{\overset{r}{\Σ}}{h}_i\right(\mathrm{\η}\left(k\right)\left)G_i\right)}^{-1}\stackrel{\‾}{e}\left(k\right)$

In formula, F_i,G_i, i=1,2 ... r is the constant value matrix of corresponding dimension.

Step 5 12, discrete-time fuzzy robust H based on discrete T-S fuzzy system_∞Control program substitutes into uncertain discrete T-S obscures augmented system, obtains closed loop system:

$\left\{\begin{array}{c}\stackrel{\‾}{e}(k+1)=({\stackrel{\‾}{A}}_{LD\mathrm{\η}}-{\stackrel{\‾}{B}}_{LD\mathrm{\η}}{F}_{\mathrm{\η}}{G}_{\mathrm{\η}}^{-1})\stackrel{\‾}{e}\left(k\right)+{\stackrel{\‾}{B}}_{wD\mathrm{\η}}w\left(k\right)\\ z\left(k\right)=(\stackrel{\‾}{C}-\stackrel{\‾}{D}{F}_{\mathrm{\η}}{G}_{\mathrm{\η}}^{-1})\stackrel{\‾}{e}\left(k\right)\end{array}\right.$

For uncertain Discrete T-S fuzzy closed loop system, given constant γ ＞ 0, if there is symmetric positive definite real matrix P_i =P_i ^T＞ 0, i=1,2 ..., r, meet:

$\left[\begin{array}{cccc}-P_{\mathrm{\&eta;}+}& 0& P_{\mathrm{\&eta;}+}({\stackrel{\&OverBar;}{A}}_{LD\mathrm{\&eta;}}-{\stackrel{\&OverBar;}{B}}_{LD\mathrm{\&eta;}}{K}_{\mathrm{\&eta;}})& P_{\mathrm{\&eta;}+}{\stackrel{\&OverBar;}{B}}_{wD\mathrm{\&eta;}}\\ *& -I& \stackrel{\&OverBar;}{C}-\stackrel{\&OverBar;}{D}{K}_{\mathrm{\&eta;}}& 0\\ *& *& -P_{\mathrm{\&eta;}}& 0\\ *& *& *& -{\mathrm{\&gamma;}}^{2}I\end{array}\right]<0$

In formula, * represents the corresponding transposition element of coherent element, then closed loop system asymptotically stable in the large and tool in symmetrical matrix There is H_∞Performance indications γ.

For uncertain Discrete T-S fuzzy augmented system, given constant γ ＞ 0, there is DFRHC so that closed loop system Asymptotically stable in the large and there is H_∞The sufficient condition of performance indications γ is to there is real matrixMeet following LMI condition:

${\mathrm{\&Phi;}}_{ii}^l<Q_i^l,i,l=1,2,...,r$

${\mathrm{\&Phi;}}_{ij}^l+{\mathrm{\&Phi;}}_{ji}^l<Q_{ij}^l,i<j,i,j,l=1,2,...,r$

In formula

${\mathrm{\&Phi;}}_{ij}^l=\left[\begin{array}{cccc}-P_l& 0& {\stackrel{\&OverBar;}{A}}_{LDi}{G}_j-{\stackrel{\&OverBar;}{B}}_{LDi}{F}_j& {\stackrel{\&OverBar;}{B}}_{wDi}\\ *& -I& \stackrel{\&OverBar;}{C}{G}_i-\stackrel{\&OverBar;}{D}{F}_i& 0\\ *& *& P_i-G_i-G_i^T& 0\\ *& *& *& -{\mathrm{\&gamma;}}^{2}I\end{array}\right],i,j,l=1,2,\mathrm{...},r.$

The actively progressive effect of the present invention is: by emulation illustrate morphing aircraft can complete well across height, The collaborative flight of deformation across speed, demonstrates reasonability and the superiority of carried control strategy, and indicates morphing aircraft energy The Beneficial Effect of synergetic structure deformation improves maneuvering flight performance, expands flight envelope, embodies morphing aircraft and compare Advantage in conventional aircraft.

Accompanying drawing explanation

Fig. 1 is the structural representation of the collaborative flight control system of morphing aircraft of the present invention deformation.

Fig. 2 is the simulation comparison result figure of flight speed curve of the present invention.

Fig. 3 is the simulation comparison result figure of angle of attack curve of the present invention.

Fig. 4 is the simulation comparison result figure of angle of pitch curve of the present invention.

Fig. 5 is the simulation comparison result figure of rate of pitch curve of the present invention.

Fig. 6 is the simulation comparison result figure of flying height curve of the present invention.

Detailed description of the invention

Provide present pre-ferred embodiments below in conjunction with the accompanying drawings, to describe technical scheme in detail.

As it is shown in figure 1, the collaborative flight control system of morphing aircraft of the present invention deformation includes flight controller, overall situation deformation Controller, network-bus, local deformation controller, distributed sensor, distribution driver, distressed structure, sensor, its In:

Flight controller, for controlling the state of flight of aircraft, i.e. realizes flight and controls；

Overall situation deformation controller, is connected with flight controller, is controlled distressed structure；

Network-bus, is used for connecting local deformation controller and distributed sensor, as the passage of data communication；

Local deformation controller, is connected with distributed sensor, is used for controlling distressed structure；

Distributed sensor, is connected with local deformation controller, distribution driver, as the hardware configuration controlling system；

Distribution driver, is used for driving distressed structure；

Distressed structure, makes morphing aircraft realize the frame for movement of variant, makes aircraft obtain high pneumatic efficiency；

Sensor, detects the state of distressed structure and information feedback is flown deformation controller.

The modeling and simulating method of the collaborative flight control system of morphing aircraft of the present invention deformation comprises the following steps:

Step one, have selected some operating points, has worked out corresponding fuzzy rule in morphing aircraft flight envelope；

Step 2, establishes whole envelope longitudinal direction T-S fuzzy model, to describe former nonlinear kinetics mould by linear model Type；

Step 3, after based on T-S fuzzy model, in conjunction with robust H_∞Control thought and PDC principle, it is proposed that based on continuous T- The Fuzzy Robust Controller H of S fuzzy model_∞Control strategy；

Step 4, calculates fuzzy gain matrix by limited LMI condition, it is ensured that the Existence of Global Stable of deformation flight course And robust performance, and the target flight state of energy asymptotic tracking reference signal；

Step 5, by the continuous T-S fuzzy model discretization of morphing aircraft, uses Fuzzy Lyapunov functions method fall Low conservative, in conjunction with Non-PDC principle, it is proposed that DFRHC based on Discrete T-S fuzzy model strategy, by limited LMI bar Part calculates more feasible discrete-time fuzzy gain matrix, better assure that the deformation Existence of Global Stable of flight course, robust performance and Tracking accuracy；

Step 6, introduces morphing aircraft non-linear dynamic model by designed controller, by numerical simulation exhibition Show.

Wherein, described step 2 comprises the following steps:

Step 2 11, the T-S fuzzy model formula such as following formula (1) of nonlinear system:

$h_i\left(\mathrm{\&eta;}\right(t\left)\right)=\frac{{\mathrm{\&mu;}}_i\left(\mathrm{\&eta;}\right(t\left)\right)}{\underset{k=1}{\overset{r}{\&Sigma;}}{\mathrm{\&mu;}}_k\left(\mathrm{\&eta;}\right(t\left)\right)},i=1,2,...,r$

$0\&le;h_i\left(\mathrm{\&eta;}\right(t\left)\right)\&le;1,\underset{i=1}{\overset{r}{\&Sigma;}}{h}_i\left(\mathrm{\&eta;}\right(t\left)\right)=\mathrm{1...}\left(1\right)$

In formula, η_j(t), j=1,2 ..., g is former piece variable；It is jth former piece variable η in the i-th rule_jT () is right The fuzzy subset answered；A_i,B_iIt it is the local linear sytem matrix of the i-th rule.For concrete nonlinear system, former piece The target of system self-characteristic and control design case is depended in the selection of variable, the division of fuzzy subset, the quantity etc. of fuzzy rule；

Step 2 12, the formula of the Local Linear Model of nonlinear system such as following formula (2):

$\left\{\begin{array}{c}\stackrel{\&CenterDot;}{x}\left(t\right)=\underset{i=1}{\overset{r}{\&Sigma;}}{h}_i\left(\mathrm{\&eta;}\left(t\right)\right)(A_{Li}x\left(t\right)+B_{Li}u\left(t\right))\\ y\left(t\right)=x\left(t\right)\end{array}\right.\mathrm{...}\left(2\right)$

In formula,For state vector；For input vector；For output vector；For nonlinear system function, f (x (t))=[f₁(x(t)) f₂(x(t))…f_n(x(t))]^T。

Described step 3 includes: Fuzzy Robust Controller H based on T-S fuzzy system_∞The design of control program, to uncertain T-S The Fuzzy Robust Controller H of the tracking reference signal of Design of Fuzzy Systems_∞Control strategy concrete structure such as formula (3):

$\left\{\begin{array}{c}u\left(t\right)=u_f\left(t\right)+u_b\left(t\right)\\ u_f\left(t\right)=u_e({\mathrm{\&eta;}}^{*},t)\\ u_b\left(t\right)=-K\left(\mathrm{\&eta;}\right)\stackrel{\&OverBar;}{e}\left(t\right)\end{array}\right.\mathrm{...}\left(3\right)$

In formula, fuzzy gain matrixFor the constant value matrix of corresponding dimension, K_iFor The fuzzy gain matrix of the i-th rule,For controlled output vector；u_f(t) For fuzzy-feedforward control part, it is therefore an objective to provide baseline stability according to following the tracks of target；u_bT () is fuzzy feedback-control part, mesh The robust stability being to ensure that closed loop system.

Described step 5 comprises the following steps:

Step 5 11, discrete-time fuzzy robust H based on discrete T-S fuzzy system_∞The design of control program, to uncertain Continuous T-S obscures augmented system and carries out discretization, obtains uncertain Discrete T-S fuzzy augmented system, uses fuzzy Lyapunov Functional based method reduces conservative, in conjunction with Non-PDC principle, proposes a kind of new DFRHC strategy, concrete design structure such as formula (4):

$u_b\left(k\right)=-F_{\mathrm{\&eta;}}{G}_{\mathrm{\&eta;}}^{-1}\stackrel{\&OverBar;}{e}\left(k\right)=-\left(\underset{i=1}{\overset{r}{\&Sigma;}}{h}_i\right(\mathrm{\&eta;}\left(k\right)\left)F_i\right){\left(\underset{i=1}{\overset{r}{\&Sigma;}}{h}_i\right(\mathrm{\&eta;}\left(k\right)\left)G_i\right)}^{-1}\stackrel{\&OverBar;}{e}\left(k\right)\mathrm{...}\left(4\right)$

In formula, F_i,G_i, i=1,2 ... r is the constant value matrix of corresponding dimension.

Step 5 12, discrete-time fuzzy robust H based on discrete T-S fuzzy system_∞Control program substitutes into uncertain discrete T-S obscures augmented system, obtains closed loop system formula (5):

$\left\{\begin{array}{c}\stackrel{\&OverBar;}{e}(k+1)=({\stackrel{\&OverBar;}{A}}_{LD\mathrm{\&eta;}}-{\stackrel{\&OverBar;}{B}}_{LD\mathrm{\&eta;}}{F}_{\mathrm{\&eta;}}{G}_{\mathrm{\&eta;}}^{-1})\stackrel{\&OverBar;}{e}\left(k\right)+{\stackrel{\&OverBar;}{B}}_{wD\mathrm{\&eta;}}w\left(k\right)\\ z\left(k\right)=(\stackrel{\&OverBar;}{C}-\stackrel{\&OverBar;}{D}{F}_{\mathrm{\&eta;}}{G}_{\mathrm{\&eta;}}^{-1})\stackrel{\&OverBar;}{e}\left(k\right)\end{array}\right.\mathrm{...}\left(5\right)$

For uncertain Discrete T-S fuzzy closed loop system, given constant γ ＞ 0, if there is symmetric positive definite real matrix P_i =P_i ^T＞ 0, i=1,2 ..., r, meet formula (6)

$\left[\begin{array}{cccc}-P_{\mathrm{\&eta;}+}& 0& P_{\mathrm{\&eta;}+}({\stackrel{\&OverBar;}{A}}_{LD\mathrm{\&eta;}}-{\stackrel{\&OverBar;}{B}}_{LD\mathrm{\&eta;}}{K}_{\mathrm{\&eta;}})& P_{\mathrm{\&eta;}+}{\stackrel{\&OverBar;}{B}}_{wD\mathrm{\&eta;}}\\ *& -I& \stackrel{\&OverBar;}{C}-\stackrel{\&OverBar;}{D}{K}_{\mathrm{\&eta;}}& 0\\ *& *& -P_{\mathrm{\&eta;}}& 0\\ *& *& *& -{\mathrm{\&gamma;}}^{2}I\end{array}\right]<\mathrm{0...}\left(6\right)$

In formula, * represents the corresponding transposition element of coherent element, then closed loop system asymptotically stable in the large and tool in symmetrical matrix There is H_∞Performance indications γ.

For uncertain Discrete T-S fuzzy augmented system, given constant γ ＞ 0, there is DFRHC so that closed loop system is complete Office's Asymptotic Stability and there is H_∞The sufficient condition of performance indications γ is to there is real matrixMeet following LMI conditional (7)

${\mathrm{\&Phi;}}_{ii}^l<Q_i^l,i,l=1,2,...,r$

${\mathrm{\&Phi;}}_{ij}^l+{\mathrm{\&Phi;}}_{ji}^l<Q_{ij}^l,i<j,i,j,l=1,2,...,r$

In formula

${\mathrm{\&Phi;}}_{ij}^l=\left[\begin{array}{cccc}-P_l& 0& {\stackrel{\&OverBar;}{A}}_{LDi}{G}_j-{\stackrel{\&OverBar;}{B}}_{LDi}{F}_j& {\stackrel{\&OverBar;}{B}}_{wDi}\\ *& -I& \stackrel{\&OverBar;}{C}{G}_i-\stackrel{\&OverBar;}{D}{F}_i& 0\\ *& *& P_i-G_i-G_i^T& 0\\ *& *& *& -{\mathrm{\&gamma;}}^{2}I\end{array}\right],i,j,l=1,2,\mathrm{...},r.$

If Fig. 2 is to shown in 6, in the wing contraction process adding composite interference, three kinds of controllers all make variant fly Flight speed when row device has returned to initial after wing deforms and flying height, but only RGSC/SMDO can be the most steady Fixed whole deformation flight course, remains the constant, almost without any fluctuation, control accuracy of flight speed and flying height High；OC and GSC then creates bigger deviation process, is constantly in oscillatory regime simultaneously.This shows, conventional OC and GSC lacks The weary rejection ability to composite interference, RGSC/SMDO is then by controlling in guarantee system complete to the observation of composite interference compensation High robust performance is additionally provided outside office's stability.

Particular embodiments described above, solves the technical problem that the present invention, technical scheme and beneficial effect are carried out Further describe, be it should be understood that the specific embodiment that the foregoing is only the present invention, be not limited to The present invention, all within the spirit and principles in the present invention, any modification, equivalent substitution and improvement etc. done, should be included in this Within the protection domain of invention.

Claims (5)

1. the collaborative flight control system of morphing aircraft deformation, it is characterised in that comprising:

Flight controller, for controlling the state of flight of aircraft, i.e. realizes flight and controls；

Overall situation deformation controller, is connected with flight controller, is controlled distressed structure；

Network-bus, is used for connecting local deformation controller and distributed sensor, as the passage of data communication；

Local deformation controller, is connected with distributed sensor, is used for controlling distressed structure；

Distributed sensor, is connected with local deformation controller, distribution driver, as the hardware configuration controlling system；

Distribution driver, is used for driving distressed structure；

Distressed structure, makes morphing aircraft realize the frame for movement of variant, makes aircraft obtain high pneumatic efficiency；

Sensor, detects the state of distressed structure and information feedback is flown deformation controller.

2. the modeling and simulating method of the collaborative flight control system of morphing aircraft deformation, it is characterised in that it includes following Step:

Step one, have selected some operating points, has worked out corresponding fuzzy rule in morphing aircraft flight envelope；

Step 2, establishes whole envelope longitudinal direction T-S fuzzy model, to describe former non-linear dynamic model by linear model；

Step 3, after based on T-S fuzzy model, in conjunction with robust H_∞Control thought and PDC principle, it is proposed that based on continuous T-S mould The Fuzzy Robust Controller H of fuzzy model_∞Control strategy；

Step 4, calculates fuzzy gain matrix by limited LMI condition, it is ensured that the Existence of Global Stable of deformation flight course and Shandong Rod performance, and the target flight state of energy asymptotic tracking reference signal；

Step 5, by the continuous T-S fuzzy model discretization of morphing aircraft, uses Fuzzy Lyapunov functions method to reduce and protects Keeping property, in conjunction with Non-PDC principle, it is proposed that DFRHC based on Discrete T-S fuzzy model strategy, by limited LMI condition meter Calculate more feasible discrete-time fuzzy gain matrix, better assure that the deformation Existence of Global Stable of flight course, robust performance and tracking Precision；

Step 6, is introduced morphing aircraft non-linear dynamic model by designed controller, is shown by numerical simulation.

3. the modeling and simulating method of the collaborative flight control system of morphing aircraft deformation as claimed in claim 2, its feature exists In, described step 2 comprises the following steps:

Step 2 11, sets up the T-S fuzzy model of nonlinear system:

$\begin{array}{cc}{h}_i\left(\mathrm{\&eta;}\right(t\left)\right)=\frac{{\mathrm{\&mu;}}_i\left(\mathrm{\&eta;}\right(t\left)\right)}{\underset{k=1}{\overset{r}{\&Sigma;}}{\mathrm{\&mu;}}_k\left(\mathrm{\&eta;}\right(t\left)\right)},& i=1,2,...,r\end{array}$

$\begin{array}{cc}0\&le;h_i\left(\mathrm{\&eta;}\right(t\left)\right)\&le;1,& \underset{i=1}{\overset{r}{\&Sigma;}}{h}_i\left(\mathrm{\&eta;}\right(t\left)\right)=1\end{array}$

In formula, η_j(t), j=1,2 ..., g is former piece variable；It is jth former piece variable η in the i-th rule_j(t) correspondence Fuzzy subset；A_i,B_iIt it is the local linear sytem matrix of the i-th rule；For concrete nonlinear system, former piece variable Selection, the division of fuzzy subset, the quantity etc. of fuzzy rule depend on the target of system self-characteristic and control design case；

Step 2 12, sets up the Local Linear Model of nonlinear system:

$\left\{\begin{array}{c}\stackrel{\&CenterDot;}{x}\left(t\right)=\underset{i=1}{\overset{r}{\&Sigma;}}{h}_i\left(\mathrm{\&eta;}\left(t\right)\right)(A_{Li}x\left(t\right)+B_{Li}u\left(t\right))\\ y\left(t\right)=x\left(t\right)\end{array}\right.$

In formula,For state vector；For input vector；For output vector；For nonlinear system function, f (x (t))=[f₁(x(t)) f₂(x(t)) … f_n(x(t)) ]^T。

4. the modeling and simulating method of the collaborative flight control system of morphing aircraft deformation as claimed in claim 2, its feature exists In, described step 3 includes Fuzzy Robust Controller H based on T-S fuzzy system_∞The design of control program, system fuzzy to uncertain T-S The Fuzzy Robust Controller H of the tracking reference signal of system design_∞Control strategy concrete structure is:

$\left\{\begin{array}{c}u\left(t\right)=u_f\left(t\right)+u_b\left(t\right)\\ u_f\left(t\right)=u_e({\mathrm{\&eta;}}^{*},t)\\ u_b\left(t\right)=-K\left(\mathrm{\&eta;}\right)\stackrel{\&OverBar;}{e}\left(t\right)\end{array}\right.$

In formula, fuzzy gain matrixFor the constant value matrix of corresponding dimension, K_iIt is i-th The fuzzy gain matrix of rule,For controlled output vector；u_f(t) be Fuzzy-feedforward control part, it is therefore an objective to provide baseline stability according to following the tracks of target；u_bT () is fuzzy feedback-control part, purpose It is to ensure that the robust stability of closed loop system.

5. the modeling and simulating method of the collaborative flight control system of morphing aircraft deformation as claimed in claim 2, its feature exists In, described step 5 comprises the following steps:

Step 5 11, discrete-time fuzzy robust H based on discrete T-S fuzzy system_∞The design of control program, to uncertain continuously T-S obscures augmented system and carries out discretization, obtains uncertain Discrete T-S fuzzy augmented system, uses Fuzzy Lyapunov functions Method reduces conservative, in conjunction with Non-PDC principle, proposes a kind of new DFRHC strategy, and concrete design structure is:

$u_b\left(k\right)=-F_{\mathrm{\&eta;}}{G}_{\mathrm{\&eta;}}^{-1}\stackrel{\&OverBar;}{e}\left(k\right)=-\left(\underset{i=1}{\overset{r}{\&Sigma;}}{h}_i\right(\mathrm{\&eta;}\left(k\right)\left)F_i\right){\left(\underset{i=1}{\overset{r}{\&Sigma;}}{h}_i\right(\mathrm{\&eta;}\left(k\right)\left)G_i\right)}^{-1}\stackrel{\&OverBar;}{e}\left(k\right)$

In formula, F_i,G_i, i=1,2 ... r is the constant value matrix of corresponding dimension；

Step 5 12, discrete-time fuzzy robust H based on discrete T-S fuzzy system_∞Control program substitutes into uncertain discrete T-S mould Stick with paste augmented system, obtain closed loop system:

$\left\{\begin{array}{c}\stackrel{\&OverBar;}{e}(k+1)=({\stackrel{\&OverBar;}{A}}_{LD\mathrm{\&eta;}}-{\stackrel{\&OverBar;}{B}}_{LD\mathrm{\&eta;}}{F}_{\mathrm{\&eta;}}{G}_{\mathrm{\&eta;}}^{-1})\stackrel{\&OverBar;}{e}\left(k\right)+{\stackrel{\&OverBar;}{B}}_{wD\mathrm{\&eta;}}w\left(k\right)\\ z\left(k\right)=(\stackrel{\&OverBar;}{C}-\stackrel{\&OverBar;}{D}{F}_{\mathrm{\&eta;}}{G}_{\mathrm{\&eta;}}^{-1})\stackrel{\&OverBar;}{e}\left(k\right)\end{array}\right.$

For uncertain Discrete T-S fuzzy closed loop system, given constant γ ＞ 0, if there is symmetric positive definite real matrix P_i=P_i ^T ＞ 0, i=1,2 ..., r, meet:

$\left[\begin{array}{cccc}-P_{\mathrm{\&eta;}+}& 0& P_{\mathrm{\&eta;}+}({\stackrel{\&OverBar;}{A}}_{LD\mathrm{\&eta;}}-{\stackrel{\&OverBar;}{B}}_{LD\mathrm{\&eta;}}{K}_{\mathrm{\&eta;}})& P_{\mathrm{\&eta;}+}{\stackrel{\&OverBar;}{B}}_{wD\mathrm{\&eta;}}\\ *& -I& \stackrel{\&OverBar;}{C}-\stackrel{\&OverBar;}{D}{K}_{\mathrm{\&eta;}}& 0\\ *& *& -P_{\mathrm{\&eta;}}& 0\\ *& *& *& -{\mathrm{\&gamma;}}^{2}I\end{array}\right]<0$

In formula, * represents the corresponding transposition element of coherent element in symmetrical matrix, then closed loop system asymptotically stable in the large and have H_∞ Performance indications γ；

For uncertain Discrete T-S fuzzy augmented system, given constant γ ＞ 0, there is DFRHC so that closed loop system is complete Office's Asymptotic Stability and there is H_∞The sufficient condition of performance indications γ is to there is real matrixMeet following LMI condition:

$\begin{array}{cc}{\mathrm{\&Phi;}}_{ii}^l<Q_i^l,& i,l=1,2,...,r\end{array}$

$\begin{array}{ccc}{\mathrm{\&Phi;}}_{ij}^l+{\mathrm{\&Phi;}}_{ji}^l<Q_{ij}^l,& i<j,& i,j,l=1,2,...,r\end{array}$

In formula

$\begin{array}{cc}{\mathrm{\&Phi;}}_{ij}^l=\left[\begin{array}{cccc}-P_l& 0& {\stackrel{\&OverBar;}{A}}_{LDi}{G}_j-{\stackrel{\&OverBar;}{B}}_{LDi}{F}_j& {\stackrel{\&OverBar;}{B}}_{wDi}\\ *& -I& \stackrel{\&OverBar;}{C}{G}_i-\stackrel{\&OverBar;}{D}{F}_i& 0\\ *& *& P_i-G_i-G_i^T& 0\\ *& *& *& -{\mathrm{\&gamma;}}^{2}I\end{array}\right],& i,j,l=1,2,...,r\end{array}.$