CN107908109A - A kind of hypersonic aircraft reentry stage track optimizing controller based on orthogonal configuration optimization - Google Patents
A kind of hypersonic aircraft reentry stage track optimizing controller based on orthogonal configuration optimization Download PDFInfo
- Publication number
- CN107908109A CN107908109A CN201711116197.5A CN201711116197A CN107908109A CN 107908109 A CN107908109 A CN 107908109A CN 201711116197 A CN201711116197 A CN 201711116197A CN 107908109 A CN107908109 A CN 107908109A
- Authority
- CN
- China
- Prior art keywords
- aircraft
- mrow
- angle
- flight
- hypersonic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a kind of hypersonic aircraft reentry stage track optimizing controller based on orthogonal configuration, which is made of aircraft altitude height sensor, aircraft speed sensor, aircraft flight navigation channel obliquity sensor, the horizontal flying distance sensor of aircraft, aircraft micro-control unit (MCU), Aircraft Angle of Attack controller.Aircraft MCU requires automated execution inner orthogonal configuration optimization algorithm according to the height above sea level, speed, flight path angle of setting, obtain making hypersonic aircraft horizontal flight that the control strategy of acquisition are converted to control instruction and be sent to the execution of Aircraft Angle of Attack controller apart from longest track optimizing control strategy, aircraft MCU.The present invention can be quickly obtained track optimizing control strategy according to the different height above sea level of hypersonic aircraft, speed, flight path angle and flight horizontal distance state, hypersonic aircraft is obtained longer horizontal flight distance.
Description
Technical field
It is mainly a kind of to be optimized based on orthogonal configuration the present invention relates to hypersonic aircraft reentry stage track optimizing field
Hypersonic aircraft reentry stage track optimizing controller.Fast height can be provided after hypersonic aircraft reaches reentry stage
Supersonic aircraft track optimizing control strategy is simultaneously converted to Aircraft Angle of Attack control instruction, hypersonic aircraft is obtained more
Long horizontal flight distance.
Background technology
Hypersonic aircraft is to realize long-range quick precision strike and the course of new aircraft that the whole world quickly reaches, in future
Military affairs, there is highly important strategic position in politics and economy, it is extremely heavy to have become one, Global Aerospace field
The developing direction wanted, research and development hypersonic aircraft have very important meaning in terms of exploiting outer space and national security
Justice.
In the research of hypersonic aircraft, track optimizing is that contemporary aircraft design and the important appearance controlled not only have
Beneficial to raising aircraft flight quality to meet assigned tasks requirement, while it is also important guarantee and the realization for completing aerial mission
The necessary condition of maneuvering flight, was constantly subjected to the attention of domestic and international each military power, currently studied both at home and abroad in recent years
Hot and difficult issue.
Due to entering atmosphere from air from outer rim, highly very big with the excursion of speed, hypersonic aircraft face
Face various severe reentry environments, reentry stage track optimizing technology is then to ensure that hypersonic aircraft completes the pass of aerial mission
Key, has prior practical value to improving its strike scope and impact accuracy.Therefore, efficient hypersonic flight is studied
Device reentry stage track optimizing method is particularly important.
The content of the invention
In order to make hypersonic aircraft obtain longer horizontal flight distance, the strike model of hypersonic aircraft is improved
Enclose, the present invention provides a kind of hypersonic aircraft reentry stage track optimizing controller based on orthogonal configuration optimization.
The purpose of the present invention is what is be achieved through the following technical solutions：It is a kind of based on orthogonal configuration optimization it is hypersonic
Aircraft reentry stage track optimizing controller, according to the initial height above sea level of hypersonic aircraft reentry stage, speed, Flight Path
Inclination angle and flight horizontal distance state rapidly obtain track optimizing control strategy, hypersonic by controlling Aircraft Angle of Attack to make
Aircraft obtains longer horizontal flight distance.Flown by aircraft altitude height sensor, aircraft speed sensor, aircraft
The horizontal flying distance sensor of row navigation channel obliquity sensor, aircraft, aircraft micro-control unit (MCU), Aircraft Angle of Attack control
Device processed is formed.Each part is connected by data/address bus in hypersonic aircraft, and the operational process of described device includes：
Step 1)：Aerodynamic Parameter Model, aircraft of the input corresponding to the aircraft in hypersonic aircraft MCU
Can constraints, specified optimization aim；
Step 2)：After hypersonic aircraft reaches reentry stage, aircraft altitude height sensor, aircraft speed are opened
Sensor, aircraft flight navigation channel obliquity sensor and the horizontal flying distance sensor of aircraft, obtain hypersonic aircraft
Current height above sea level, speed, flight path angle and flight horizontal distance status information；
Step 3)：Aircraft MCU is required inside automated execution according to the height above sea level, speed, flight path angle of setting
Orthogonal configuration optimizes algorithm, obtains making hypersonic aircraft horizontal flight apart from longest track optimizing control strategy；
Step 4)：The track optimizing control strategy of acquisition is sent to control strategy output mould by hypersonic aircraft MCU
Block, and be converted to control instruction and be sent to the execution of Aircraft Angle of Attack controller.
The hypersonic aircraft MCU parts include information acquisition module 21, initialization module 22, ODE
Group (Ordinary Differential Equations, abbreviation ODE) orthogonal configuration module 23, Non-Linear Programming (Non-
Linear Programming, abbreviation NLP) problem solver module 24, control instruction output module 25.Wherein, information gathering mould
Block includes aircraft altitude height and speed acquisition, aircraft flight navigation channel inclination angle and flight horizontal distance collection, aircraft sea
Degree of lifting and speed setting collection, the collection of aircraft flight navigation channel angle set, the Aerodynamic Parameter Model of aircraft and performance are about
Beam condition and specified predetermined optimizing target parameter gather five submodules；NLP, which solves module, includes search direction solution, optimizing step-length
Solution, optimizing amendment, NLP convergences judge four submodules.
Hypersonic aircraft reentry stage track optimizing problem can be described as
Max J [u (t)]=x4(tf)
x1(t0)=h0,x2(t0)=v0,x3(t0)=γ0,x4(t0)=r0
x1(tf)=hf,x2(tf)=vf,x3(tf)=γf
G[u(t),x(t),t]≥0
umin≤u(t)≤umax
Wherein t represents the time, and x (t) represents the state variable of hypersonic aircraft, x1(t) represent that aircraft altitude is high
Degree, x2(t) aircraft speed, x are represented3(t) aircraft flight navigation channel inclination angle, x are represented4(t) represent aircraft horizontal flight away from
From u (t) represents the angle of attack controlled quentity controlled variable of hypersonic aircraft, is the control variable of this problem；Represent state variable x (t)
First derivative, F (x (t), u (t), t) is the differential according to hypersonic aircraft reentry stage three-dimensional space motion establishing equation
Equation group mathematical model；t0Represent the time point that reentry stage track optimizing starts, h0Represent the initial of optimization start time aircraft
Height above sea level, v0Represent the initial velocity of optimization start time aircraft, γ0Represent that the initial of optimization start time aircraft flies
Row navigation channel angle, r0Represent the initial level flying distance of optimization start time aircraft, tfAt the end of representing reentry stage track optimizing
Between point, hfRepresent the height above sea level of optimization finish time aircraft, vfRepresent the speed of optimization finish time aircraft, γfRepresent
Optimize the Flight Path angle of finish time aircraft；J [u (t)] represents that the object function of hypersonic aircraft track optimizing is
Optimize the horizontal flight distance of finish time aircraft, G [u (t), x (t), t] is the pact of hypersonic aircraft reentry stage process
Beam condition, uminAnd umaxRepresent the lower limit and upper limit value of angle of attack control range.
The technical solution adopted by the present invention to solve the technical problems is：In hypersonic aircraft micro-control unit
(MCU) orthogonal configuration optimization algorithm (Orthogonal collocation, abbreviation OC) is integrated with, in hypersonic aircraft
The control instruction of fast Aircraft Angle of Attack can be provided after arrival reentry stage, hypersonic aircraft is obtained longer horizontal flight
Distance.
The MCU can be considered as automatic control signal generator, which includes Aerodynamic Parameter Model, aircraft performance
Constraints, specify optimization aim setting module 11, hypersonic aircraft MCU module 12, aircraft altitude height sensor
13, aircraft speed sensor 14, aircraft flight navigation channel obliquity sensor 15, the horizontal flying distance sensor 16 of aircraft,
Aircraft altitude height, speed, flight path angle setting module 17, Aircraft Angle of Attack control 18, each group in the system
Connected into part by data/address bus in controller.
The operational process of the controller is as follows：
Step 1)：The controller is installed on certain type hypersonic aircraft, and is inputted in aircraft MCU 12
Aerodynamic Parameter Model, aircraft performance constraints, specified predetermined optimizing target parameter information 11 corresponding to aircraft；
Step 2)：After hypersonic aircraft reaches reentry stage, aircraft altitude height sensor 13, aircraft speed pass
Sensor 14, aircraft flight navigation channel obliquity sensor 15 and the horizontal flying distance sensor 16 of aircraft, obtain hypersonic fly
The current height above sea level of row device, speed, flight path angle and flight horizontal distance status information；
Step 3)：Aircraft MCU12 is obtained according to aircraft altitude height, speed, flight path angle setting module 17
Control targe information, MCU module 12 perform the orthogonal configuration optimization algorithm of inside, obtain making the horizontal flying distance of aircraft farthest
TRAJECTORY CONTROL strategy；
Step 4)：The control strategy of acquisition is converted to angle of attack control instruction and exported to Aircraft Angle of Attack control by aircraft MCU
Device module 18 processed；
The hypersonic aircraft MCU for being integrated with orthogonal configuration optimization algorithm is the core of the present invention, it is internal including letter
Cease acquisition module 21, initialization module 22, ODE orthogonal configurations module 23, NLP problem solver modules 24, control instruction output mould
Block 25.Wherein, information acquisition module includes current flight device height above sea level and speed acquisition, current flight device flight path angle
Gather, fly with the collection of flight horizontal distance, aircraft altitude height and speed setting collection, aircraft flight navigation channel angle set
The Aerodynamic Parameter Model and performance constraints of row device and specified predetermined optimizing target parameter gather five submodules；NLP solves mould
Block judges four submodules including search direction solution, the solution of optimizing step-length, optimizing amendment, NLP convergences.
The hypersonic aircraft MCU automatically generates the orthogonal configuration optimization algorithm operating procedure of angle of attack control instruction
It is as follows：
Step 1)：After hypersonic aircraft reaches reentry stage, aircraft altitude height sensor, aircraft speed sensing
Device, aircraft flight navigation channel obliquity sensor and the horizontal flying distance sensor of aircraft are opened, and information acquisition module 21 obtains
The current height above sea level of hypersonic aircraft, speed, flight path angle and flight horizontal distance status information；
Step 2)：Initialization module 22 brings into operation, and sets discrete hop count, the angle of attack controlled quentity controlled variable of track optimizing process time
Initial guess u(0)(t), the initial value x of state trajectory(0)(t), setting optimization required precision tol, iterations k is put
Zero；
Step 3)：By ODE orthogonal configurations module 23 by ordinary differential system in time shaft [t0,tf] on all it is discrete；
Step 4)：Required angle of attack control strategy and corresponding states track are obtained by NLP problem solver modules 24, this
Process includes multiple inner iterative, and each iteration will solve search direction and optimizing step-length, and carry out optimizing amendment.To Mr. Yu
The angle of attack controlled quentity controlled variable u that an iteration obtains(k)(t), if it corresponds to target function value J [u(k)(t)] with the mesh of preceding an iteration
Offer of tender numerical value J [u(k-1)(t)] difference is less than required precision tol, then judges convergence sexual satisfaction, and by angle of attack controlled quentity controlled variable u(k)(t) make
Control strategy output module 25 is output to for instruction.
The ODE orthogonal configuration modules, are realized using following steps：
Step 1)：Angle of attack controlled quentity controlled variable u (t), state trajectory x (t) are represented with the linear combination of M rank basic functions, i.e.,：
Wherein N is time shaft [t0,tf] discrete hop count, φ (t) is Lagrange's interpolation basic function, linear combination coefficient
ui,jAnd si,jIt is u (t) and x (t) respectively in collocation point ti,jOn value.
Step 2)：Due to all basic functions derived function expression formula it is known that then the differential equation group of state trajectory by from
Dispersion quantic：
Step 3)：Original differential equation group is replaced with the differential equation group after discretization, NLP problems to be asked will be obtained.
The NLP solves module, is realized using following steps：
Step 1)：By angle of attack controlled quentity controlled variable u(k-1)(t) as some point in vector space, it is denoted as P1, P1Corresponding target
Functional value is exactly J [u(k-1)(t)]；
Step 2)：From point P1Set out, according to the NLP algorithms of selection, construct a search direction in vector space to d(k -1)With step-length α(k-1)；
Step 3)：Pass through formula u(k)(t)=u(k-1)(t)+α(k-1)d(k-1)U is corresponded in construction vector space(k)Another
Point P2So that P2Corresponding target function value J [u(k)(t)] than J [u(k-1)(t)] it is more excellent.
Step 4)：U is corrected using optimizing(k)(t), the point after being correctedIt is denoted as point P3, with seasonSo that P3Corresponding target function value J [u(k)(t)] than J [u(k-1)(t)] it is more excellent；
Step 5)：If the target function value J [u of current iteration(k)(t)] with the target function value J [u of last iteration(k -1)(t)] difference of absolute value is less than precision tol, then judges convergence sexual satisfaction, the control strategy u that current iteration is obtained(k)(t)
Export to control strategy output module 25；If convergence is unsatisfactory for, iterations k increases by 1, by u(k)(t) it is arranged to initial
Value, continues to execute step 2).
Beneficial effects of the present invention are mainly manifested in：Since orthogonal collocation method possesses more accurate capability of fitting, can obtain
Obtain the accurate solution of hypersonic aircraft reentry trajectory optimization；Employ search direction solution, optimizing step-length solves, optimizing is repaiied
Just, NLP convergences determination strategy, the solution of approximate NLP problems will gradually approach the optimal solution of former problem；Since this method is not required to
Dynamical equation is solved repeatedly, can obtain faster convergence rate, reduced and obtained the optimization of hypersonic aircraft reentry trajectory
The calculating time of optimal control policy.The present invention can be such that hypersonic aircraft horizontal flight is attacked apart from longer track optimizing
Angle control instruction, improves hypersonic aircraft strike scope.
Brief description of the drawings
Fig. 1 is the structure diagram of the present invention；
Fig. 2 is hypersonic aircraft MCU internal module structure charts of the present invention；
Fig. 3 is the angle of attack control strategy curve map of embodiment 1；
Fig. 4 is the corresponding horizontal flight distance map of angle of attack control strategy of embodiment 1.
Embodiment
Hypersonic aircraft reentry stage track optimizing problem can be described as
Max J [u (t)]=x4(tf)
x1(t0)=h0,x2(t0)=v0,x3(t0)=γ0,x4(t0)=r0
x1(tf)=hf,x2(tf)=vf,x3(tf)=γf
G[u(t),x(t),t]≥0
umin≤u(t)≤umax
Wherein t represents the time, and x (t) represents the state variable of hypersonic aircraft, x1(t) represent that aircraft altitude is high
Degree, x2(t) aircraft speed, x are represented3(t) aircraft flight navigation channel inclination angle, x are represented4(t) represent aircraft horizontal flight away from
From u (t) represents the angle of attack controlled quentity controlled variable of hypersonic aircraft, is the control variable of this problem；Represent state variable x (t)
First derivative, F (x (t), u (t), t) is the differential according to hypersonic aircraft reentry stage three-dimensional space motion establishing equation
Equation group mathematical model；t0Represent the time point that reentry stage track optimizing starts, h0Represent the initial of optimization start time aircraft
Height above sea level, v0Represent the initial velocity of optimization start time aircraft, γ0Represent that the initial of optimization start time aircraft flies
Row navigation channel angle, r0Represent the initial level flying distance of optimization start time aircraft, tfAt the end of representing reentry stage track optimizing
Between point, hfRepresent the height above sea level of optimization finish time aircraft, vfRepresent the speed of optimization finish time aircraft, γfRepresent
Optimize the Flight Path angle of finish time aircraft；J [u (t)] represents that the object function of hypersonic aircraft track optimizing is
Optimize the horizontal flight distance of finish time aircraft, G [u (t), x (t), t] is the pact of hypersonic aircraft reentry stage process
Beam condition, uminAnd umaxRepresent the lower limit and upper limit value of angle of attack control range.
The technical solution adopted by the present invention to solve the technical problems is：In hypersonic aircraft micro-control unit
(MCU) orthogonal configuration optimization algorithm (Orthogonal collocation, abbreviation OC) is integrated with, in hypersonic aircraft
The control instruction of fast Aircraft Angle of Attack can be provided after arrival reentry stage, hypersonic aircraft is obtained longer horizontal flight
Distance.
The MCU can be considered as automatic control signal generator, the controller as shown in Figure 1, including Aerodynamic Parameter Model,
Aircraft performance constraints, specify optimization aim setting module 11, hypersonic aircraft MCU module 12, aircraft altitude
Height sensor 13, aircraft speed sensor 14, aircraft flight navigation channel obliquity sensor 15, the horizontal flying distance of aircraft
Sensor 16, aircraft altitude height, speed, flight path angle setting module 17, Aircraft Angle of Attack control 18, the system
Interior each part is connected by data/address bus in controller.
The operational process of the controller is as follows：
Step 1)：The controller is installed on certain type hypersonic aircraft, and is inputted in aircraft MCU 12
Aerodynamic Parameter Model, aircraft performance constraints, specified predetermined optimizing target parameter information 11 corresponding to aircraft；
Step 2)：After hypersonic aircraft reaches reentry stage, aircraft altitude height sensor 13, aircraft speed pass
Sensor 14, aircraft flight navigation channel obliquity sensor 15 and the horizontal flying distance sensor 16 of aircraft, obtain hypersonic fly
The current height above sea level of row device, speed, flight path angle and flight horizontal distance status information；
Step 3)：Aircraft MCU12 is obtained according to aircraft altitude height, speed, flight path angle setting module 17
Control targe information, MCU module 12 perform the orthogonal configuration optimization algorithm of inside, obtain making the horizontal flying distance of aircraft farthest
TRAJECTORY CONTROL strategy；
Step 4)：The control strategy of acquisition is converted to angle of attack control instruction and exported to Aircraft Angle of Attack control by aircraft MCU
Device module 18 processed；
The hypersonic aircraft MCU for being integrated with orthogonal configuration optimization algorithm is the core of the present invention, as shown in Fig. 2, its
Inside includes information acquisition module 21, initialization module 22, ODE orthogonal configurations module 23, NLP problem solver modules 24, control
Command output module 25.Wherein, information acquisition module includes current flight device height above sea level and speed acquisition, current flight device fly
Row navigation channel inclination angle and flight horizontal distance collection, aircraft altitude height and speed setting collection, aircraft flight navigation channel inclination angle
Setting collection, the Aerodynamic Parameter Model of aircraft and performance constraints and specified predetermined optimizing target parameter gather five submodules
Block；NLP solves module and judges four submodules including search direction solution, the solution of optimizing step-length, optimizing amendment, NLP convergences.
The hypersonic aircraft MCU automatically generates the orthogonal configuration optimization algorithm operating procedure of angle of attack control instruction
It is as follows：
Step 1)：After hypersonic aircraft reaches reentry stage, aircraft altitude height sensor, aircraft speed sensing
Device, aircraft flight navigation channel obliquity sensor and the horizontal flying distance sensor of aircraft are opened, and information acquisition module 21 obtains
The current height above sea level of hypersonic aircraft, speed, flight path angle and flight horizontal distance status information；
Step 2)：Initialization module 22 brings into operation, and sets discrete hop count, the angle of attack controlled quentity controlled variable of track optimizing process time
Initial guess u(0)(t), the initial value x of state trajectory(0)(t), setting optimization required precision tol, iterations k is put
Zero；
Step 3)：By ODE orthogonal configurations module 23 by ordinary differential system in time shaft [t0,tf] on all it is discrete；
Step 4)：Required angle of attack control strategy and corresponding states track are obtained by NLP problem solver modules 24, this
Process includes multiple inner iterative, and each iteration will solve search direction and optimizing step-length, and carry out optimizing amendment.To Mr. Yu
The angle of attack controlled quentity controlled variable u that an iteration obtains(k)(t), if it corresponds to target function value J [u(k)(t)] with the mesh of preceding an iteration
Offer of tender numerical value J [u(k-1)(t)] difference is less than required precision tol, then judges convergence sexual satisfaction, and by angle of attack controlled quentity controlled variable u(k)(t) make
Control strategy output module 25 is output to for instruction.
The ODE orthogonal configuration modules, are realized using following steps：
Step 1)：Angle of attack controlled quentity controlled variable u (t), state trajectory x (t) are represented with the linear combination of M rank basic functions, i.e.,：
Wherein N is time shaft [t0,tf] discrete hop count, φ (t) is Lagrange's interpolation basic function, linear combination coefficient
ui,jAnd si,jIt is u (t) and x (t) respectively in collocation point ti,jOn value.
Step 2)：Due to all basic functions derived function expression formula it is known that then the differential equation group of state trajectory by from
Dispersion quantic：
Step 3)：Original differential equation group is replaced with the differential equation group after discretization, NLP problems to be asked will be obtained.
The NLP solves module, is realized using following steps：
Step 1)：By angle of attack controlled quentity controlled variable u(k-1)(t) as some point in vector space, it is denoted as P1, P1Corresponding target
Functional value is exactly J [u(k-1)(t)]；
Step 2)：From point P1Set out, according to the NLP algorithms of selection, construct a search direction in vector space to d(k -1)With step-length α(k-1)；
Step 3)：Pass through formula u(k)(t)=u(k-1)(t)+α(k-1)d(k-1)U is corresponded in construction vector space(k)Another
Point P2So that P2Corresponding target function value J [u(k)(t)] than J [u(k-1)(t)] it is more excellent.
Step 4)：U is corrected using optimizing(k)(t), the point after being correctedIt is denoted as point P3, with seasonSo that P3Corresponding target function value J [u(k)(t)] than J [u(k-1)(t)] it is more excellent；
Step 5)：If the target function value J [u of current iteration(k)(t)] with the target function value J [u of last iteration(k -1)(t)] difference of absolute value is less than precision tol, then judges convergence sexual satisfaction, the control strategy u that current iteration is obtained(k)(t)
Export to control strategy output module 25；If convergence is unsatisfactory for, iterations k increases by 1, by u(k)(t) it is arranged to initial
Value, continues to execute step 2).
Embodiment 1
Hypersonic aircraft reaches reentry stage spatial domain, hypersonic aircraft altitude sensor, velocity sensor,
Flight path angle sensor, horizontal flight range sensor and MCU have turned on.Information acquisition module gathers aircraft immediately
Initial height above sea level, speed, flight path angle and horizontal flight distance during into reentry stage, if current initial time t0=
0s, the height above sea level that altitude sensor is passed to MCU are h0=80 000m, the speed that velocity sensor is passed to MCU are v0=
6400m/s, the flight path angle that flight path angle sensor is passed to MCU are γ0=-0.052rad, horizontal flight distance
The horizontal flight distance that sensor sensor is passed to MCU is r0=0m；Final value moment tfHypersonic aircraft needs the condition met
It is set as h for height above sea levelf=24000m, speed are set as vf=760m/s, flight path angle are set as γf=-
0.08rad；With reference to the three-dimensional space motion equation of aircraft, Aerodynamic Parameter Model, aircraft performance constraints and specify excellent
Change target, the mathematical model for obtaining the problem is as follows：
Max J [u (t)]=x4(tf)
CL=-0.15+3.44u (t)
CD=0.29-1.51u (t)+5.87u (t)2
x1(0)=80 × 103,x1(tf)=24 × 103
x2(0)=6.4 × 103,x2(tf)=760
x3(0)=- 0.052, x3(tf)=- 0.08
x4(0)=0
-15≤u(t)≤30
Wherein L represents lift, and D represents resistance, CLRepresent lift coefficient, CDRepresent resistance coefficient.For the ease of statement, adopt
The differential equation group mathematics of hypersonic aircraft reentry stage three-dimensional space motion establishing equation is represented with F (x (t), u (t), t)
Model, i.e.,：
The constraints of hypersonic aircraft reentry stage process is represented using G [u (t), x (t), t], is：
In addition, J [u (t)] represents that the object function of hypersonic aircraft track optimizing optimizes finish time aircraft
Horizontal flight distance.
Hypersonic aircraft MCU automatically generates the orthogonal configuration optimization algorithm of angle of attack control instruction as shown in Fig. 2, it is transported
Row step is as follows：
Step 1)：After hypersonic aircraft reaches reentry stage, aircraft altitude height sensor, aircraft speed sensing
Device, aircraft flight navigation channel obliquity sensor and the horizontal flying distance sensor of aircraft are opened, and information acquisition module 21 obtains
Initial time t0Hypersonic aircraft height above sea level h during=0s0=80 000m, speed v0=6400m/s, Flight Path incline
Angle is γ0=-0.052rad, horizontal flight range sensor sensor horizontal flight distance are arranged to r0=0m；Final value moment tfIt is high
The requirement of supersonic aircraft height above sea level is set as hf=24000m, rate request are set as vf=760m/s, flight path angle
It is required that it is set as γf=-0.08rad；；
Step 2)：Initialization module 22 brings into operation, and the discrete hop count for setting track optimizing process time is 10, angle of attack control
The initial guess u of amount processed(0)(t)=0.5, setting optimization required precision tol=10-8, by iterations k zero setting；
Step 3)：By ODE orthogonal configurations module 23 by ordinary differential system in time shaft [t0,tf] on all it is discrete；
Step 4)：Required angle of attack control strategy and corresponding states track are obtained by NLP problem solver modules 24, this
Process includes multiple inner iterative, and each iteration will solve search direction and optimizing step-length, and carry out optimizing amendment.To Mr. Yu
The angle of attack controlled quentity controlled variable u that an iteration obtains(k)(t), if it corresponds to target function value J [u(k)(t)] with the mesh of preceding an iteration
Offer of tender numerical value J [u(k-1)(t)] difference is less than required precision 10-8, then judge convergence sexual satisfaction, and by angle of attack controlled quentity controlled variable u(k)(t)
Control strategy output module 25 is output to as instruction.
The ODE orthogonal configuration modules, are realized using following steps：
Step 1)：Angle of attack controlled quentity controlled variable u (t), state trajectory x (t) are represented with the linear combination of 3 rank basic functions, i.e.,：
Wherein N is time shaft [t0,tf] discrete hop count, φ (t) is Lagrange's interpolation basic function, linear combination coefficient
ui,jAnd si,jIt is u (t) and x (t) respectively in collocation point ti,jOn value.
Step 2)：Due to all basic functions derived function expression formula it is known that then the differential equation group of state trajectory by from
Dispersion quantic：
Step 3)：Original differential equation group is replaced with the differential equation group after discretization, NLP problems to be asked will be obtained.
The NLP solves module, is realized using following steps：
Step 1)：By angle of attack controlled quentity controlled variable u(k-1)(t) as some point in vector space, it is denoted as P1, P1Corresponding target
Functional value is exactly J [u(k-1)(t)]；
Step 2)：From point P1Set out, according to the NLP algorithms of selection, construct a search direction in vector space to d(k -1)With step-length α(k-1)；
Step 3)：Pass through formula u(k)(t)=u(k-1)(t)+α(k-1)d(k-1)U is corresponded in construction vector space(k)Another
Point P2So that P2Corresponding target function value J [u(k)(t)] than J [u(k-1)(t)] it is more excellent.
Step 4)：U is corrected using optimizing(k)(t), the point after being correctedIt is denoted as point P3, with seasonSo that P3Corresponding target function value J [u(k)(t)] than J [u(k-1)(t)] it is more excellent；
Step 5)：If the target function value J [u of current iteration(k)(t)] with the target function value J [u of last iteration(k -1)(t)] difference of absolute value is less than precision 10-8, then convergence sexual satisfaction, the control strategy u that current iteration is obtained are judged(k)(t)
Export to control strategy output module 25；If convergence is unsatisfactory for, iterations k increases by 1, by u(k)(t) it is arranged to initial
Value, continues to execute step 2).
Finally, the optimization track of acquisition is output to control strategy output module by aircraft MCU as instruction, is converted to control
Instruction processed is sent to angle of attack controller, completes the execution of track optimizing.Fig. 3 is the angle of attack control strategy curve map of embodiment 1；Figure
4 be the corresponding horizontal flight distance map of angle of attack control strategy of embodiment 1.
Above content is that a further detailed description of the present invention in conjunction with specific preferred embodiments, it is impossible to is assert
The specific implementation of the present invention is only limited to these explanations.For general technical staff of the technical field of the invention, not
On the premise of departing from inventive concept, some simple deduction or replace can also be made, should all be considered as belonging to the protection of the present invention
Scope.
Claims (1)
1. a kind of hypersonic aircraft reentry stage track optimizing controller based on orthogonal configuration optimization, flies according to hypersonic
The initial height above sea level of row device reentry stage, speed, flight path angle and flight horizontal distance state rapidly obtain track optimizing
Control strategy, by controlling Aircraft Angle of Attack to make hypersonic aircraft obtain longer horizontal flight distance.It is characterized in that：
It is horizontal winged by aircraft altitude height sensor, aircraft speed sensor, aircraft flight navigation channel obliquity sensor, aircraft
Row distance sensor, aircraft micro-control unit (MCU), Aircraft Angle of Attack controller are formed.Each part is by high ultrasound
Data/address bus connects in fast aircraft, and the operational process of described device includes：
Step 1)：Input corresponds to the Aerodynamic Parameter Model of the aircraft, aircraft performance about in hypersonic aircraft MCU
Beam condition, specify optimization aim；
Step 2)：After hypersonic aircraft reaches reentry stage, aircraft altitude height sensor, aircraft speed sensing are opened
Device, aircraft flight navigation channel obliquity sensor and the horizontal flying distance sensor of aircraft, it is current to obtain hypersonic aircraft
Height above sea level, speed, flight path angle and flight horizontal distance status information；
Step 3)：Aircraft MCU requires automated execution inner orthogonal according to the height above sea level, speed, flight path angle of setting
Configuration optimization algorithm, obtains making hypersonic aircraft horizontal flight apart from longest track optimizing control strategy；
Step 4)：The track optimizing control strategy of acquisition is sent to control strategy output module by hypersonic aircraft MCU, and
Be converted to control instruction and be sent to the execution of Aircraft Angle of Attack controller.
The hypersonic aircraft MCU parts include information acquisition module 21, initialization module 22, ordinary differential system
(Ordinary Differential Equations, abbreviation ODE) orthogonal configuration module 23, Non-Linear Programming (Non-linear
Programming, abbreviation NLP) problem solver module 24, control instruction output module 25.Wherein, information acquisition module includes flying
Row device height above sea level and speed acquisition, aircraft flight navigation channel inclination angle and the collection of flight horizontal distance, aircraft altitude height and
Speed setting collection, the collection of aircraft flight navigation channel angle set, the Aerodynamic Parameter Model of aircraft and performance constraints with
And specified predetermined optimizing target parameter gathers five submodules；NLP, which solves module, to be included search direction solution, the solution of optimizing step-length, seeks
Excellent amendment, NLP convergences judge four submodules.
The hypersonic aircraft MCU automatically generates the orthogonal configuration optimization algorithm operating procedure of angle of attack control instruction such as
Under：
Step 1)：Hypersonic aircraft reach reentry stage after, aircraft altitude height sensor, aircraft speed sensor,
Aircraft flight navigation channel obliquity sensor and the horizontal flying distance sensor of aircraft are opened, and information acquisition module 21 obtains superb
The current height above sea level of velocity of sound aircraft, speed, flight path angle and flight horizontal distance status information；
Step 2)：Initialization module 22 brings into operation, set the discrete hop count of track optimizing process time, angle of attack controlled quentity controlled variable just
Beginning conjecture value u(0)(t), setting optimization required precision tol, by iterations k zero setting；
Step 3)：By ODE orthogonal configurations module 23 by ordinary differential system in time shaft [t0,tf] on all it is discrete；
Step 4)：Required angle of attack control strategy and corresponding states track, this process are obtained by NLP problem solver modules 24
Including multiple inner iterative, each iteration will solve search direction and optimizing step-length, and carry out optimizing amendment.For certain once
The angle of attack controlled quentity controlled variable u that iteration obtains(k)(t), if it corresponds to target function value J [u(k)(t)] with the target letter of preceding an iteration
Numerical value J [u(k-1)(t)] difference is less than required precision tol, then judges convergence sexual satisfaction, and by angle of attack controlled quentity controlled variable u(k)(t) it is used as and refers to
Order is output to control strategy output module 25.
The ODE orthogonal configuration modules, are realized using following steps：
Step 1)：Angle of attack controlled quentity controlled variable u (t), state trajectory x (t) are represented with the linear combination of M rank basic functions, i.e.,：
<mrow>
<mi>u</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&ap;</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>M</mi>
</munderover>
<msub>
<mi>u</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<msubsup>
<mi>&phi;</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
<mrow>
<mo>(</mo>
<mi>M</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<mi>N</mi>
</mrow>
<mrow>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&ap;</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>M</mi>
</munderover>
<msub>
<mi>s</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<msubsup>
<mi>&phi;</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
<mrow>
<mo>(</mo>
<mi>M</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<mi>N</mi>
</mrow>
Wherein N is time shaft [t0,tf] discrete hop count, φ (t) is Lagrange's interpolation basic function, linear combination coefficient ui,jWith
si,jIt is u (t) and x (t) respectively in collocation point ti,jOn value.
Step 2)：Since the derived function expression formula of all basic functions is it is known that then the differential equation group of state trajectory is discretized
Quantic：
<mrow>
<mover>
<mi>x</mi>
<mo>&CenterDot;</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&ap;</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>M</mi>
</munderover>
<msub>
<mi>s</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<msubsup>
<mover>
<mi>&phi;</mi>
<mo>&CenterDot;</mo>
</mover>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
<mrow>
<mo>(</mo>
<mi>M</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<mi>N</mi>
</mrow>
Step 3)：Original differential equation group is replaced with the differential equation group after discretization, NLP problems to be asked will be obtained.
The NLP solves module, is realized using following steps：
Step 1)：By angle of attack controlled quentity controlled variable u(k-1)(t) as some point in vector space, it is denoted as P1, P1Corresponding target function value
It is exactly J [u(k-1)(t)]；
Step 2)：From point P1Set out, according to the NLP algorithms of selection, construct a search direction in vector space to d(k-1)With
Step-length α(k-1)；
Step 3)：Pass through formula u(k)(t)=u(k-1)(t)+α(k-1)d(k-1)U is corresponded in construction vector space(k)Another point P2,
So that P2Corresponding target function value J [u(k)(t)] than J [u(k-1)(t)] it is more excellent.
Step 4)：U is corrected using optimizing(k)(t), the point after being correctedIt is denoted as point P3, with seasonSo that P3Corresponding target function value J [u(k)(t)] than J [u(k-1)(t)] it is more excellent；
Step 5)：If the target function value J [u of current iteration(k)(t)] with the target function value J [u of last iteration(k-1)
(t)] difference of absolute value is less than precision tol, then judges convergence sexual satisfaction, the control strategy u that current iteration is obtained(k)(t) it is defeated
Go out to control strategy output module 25；If convergence is unsatisfactory for, iterations k increases by 1, by u(k)(t) initial value is arranged to,
Continue to execute step 2).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711116197.5A CN107908109B (en) | 2017-11-13 | 2017-11-13 | Hypersonic aircraft reentry section track optimization controller based on orthogonal configuration optimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711116197.5A CN107908109B (en) | 2017-11-13 | 2017-11-13 | Hypersonic aircraft reentry section track optimization controller based on orthogonal configuration optimization |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107908109A true CN107908109A (en) | 2018-04-13 |
| CN107908109B CN107908109B (en) | 2020-02-28 |
Family
ID=61845212
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711116197.5A Expired - Fee Related CN107908109B (en) | 2017-11-13 | 2017-11-13 | Hypersonic aircraft reentry section track optimization controller based on orthogonal configuration optimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107908109B (en) |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108717588A (en) * | 2018-06-07 | 2018-10-30 | 哈尔滨工业大学 | A kind of track optimizing Initialization Algorithms based on Orthogonal Experiment and Design |
| CN109375649A (en) * | 2018-12-19 | 2019-02-22 | 中国商用飞机有限责任公司 | Method and system for controlling an aircraft |
| CN110020488A (en) * | 2019-04-12 | 2019-07-16 | 中南大学 | A kind of track method for customizing of curve trajectory SMART BULLET |
| CN110727285A (en) * | 2019-11-27 | 2020-01-24 | 浙江大学 | Gauss time distribution point control parameterization-based unpowered aviation aircraft control signal generation device and method |
| CN111026140A (en) * | 2019-11-21 | 2020-04-17 | 浙江大学 | Constraint full-process satisfied hypersonic aircraft trajectory optimization self-adaptive optimal controller |
| CN111045447A (en) * | 2019-11-21 | 2020-04-21 | 浙江大学 | High-precision hypersonic aircraft trajectory optimization multi-scale optimal control system |
| CN111061294A (en) * | 2019-11-21 | 2020-04-24 | 浙江大学 | Trajectory optimization self-adaptive optimal control system for nonstationary hypersonic aircraft |
| CN111103795A (en) * | 2019-11-21 | 2020-05-05 | 浙江大学 | Hypersonic aircraft reentry section track optimization controller based on intelligent self-adaptive optimization algorithm |
| CN111123700A (en) * | 2019-11-21 | 2020-05-08 | 浙江大学 | Constraint full-course satisfied optimal control system for obstacle-detouring flight of hypersonic aircraft |
| CN111123960A (en) * | 2019-11-21 | 2020-05-08 | 浙江大学 | Ultra-high precision hypersonic aircraft trajectory optimization optimal control instrument |
| CN111290436A (en) * | 2020-03-16 | 2020-06-16 | 北京机电工程研究所 | Aircraft wireless instruction correction method and system |
| CN111324035A (en) * | 2019-11-21 | 2020-06-23 | 浙江大学 | Adaptive optimal controller for trajectory optimization of hypersonic aircraft |
| CN111338364A (en) * | 2019-11-21 | 2020-06-26 | 浙江大学 | High-precision controller for optimizing trajectory of hypersonic aerocraft with quick response |
| CN113705117A (en) * | 2021-05-25 | 2021-11-26 | 中国航空工业集团公司沈阳飞机设计研究所 | Optimized analysis method for flight performance of hypersonic cruise aircraft |
| CN114488788A (en) * | 2021-11-30 | 2022-05-13 | 浙江大学 | Probability self-adaptive pollination optimized hypersonic aircraft reentry segment controller |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070038335A1 (en) * | 2005-07-22 | 2007-02-15 | Mcintyre Timothy J | Real Power Regulation for the Utility Power Grid Via Responsive Loads |
| CN101464664A (en) * | 2009-01-09 | 2009-06-24 | 浙江工业大学 | Batch reactor optimal control method based on single population and pre-crossed differential evolution algorithm |
| CN101763086A (en) * | 2009-12-29 | 2010-06-30 | 浙江大学 | High-efficiency system and method for dynamic optimization of industrial process |
| CN101763083A (en) * | 2009-12-29 | 2010-06-30 | 浙江大学 | An effective industrial process dynamic optimization system and method capable of controlling variable parameterization |
| CN103273913A (en) * | 2013-06-08 | 2013-09-04 | 浙江大学 | Automobile automatic braking device based on orthogonal configuration optimization |
| CN103309234A (en) * | 2013-06-08 | 2013-09-18 | 浙江大学 | Orthogonal collocation optimization based batch reactor control system |
| CN103324086A (en) * | 2013-06-08 | 2013-09-25 | 浙江大学 | Batch reactor control system based on accurate punishment optimization |
| CN103303299B (en) * | 2013-06-08 | 2015-07-22 | 浙江大学 | Emergency braking signal generation device for high-speed train based on orthogonal collocation optimization |
-
2017
- 2017-11-13 CN CN201711116197.5A patent/CN107908109B/en not_active Expired - Fee Related
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070038335A1 (en) * | 2005-07-22 | 2007-02-15 | Mcintyre Timothy J | Real Power Regulation for the Utility Power Grid Via Responsive Loads |
| CN101464664A (en) * | 2009-01-09 | 2009-06-24 | 浙江工业大学 | Batch reactor optimal control method based on single population and pre-crossed differential evolution algorithm |
| CN101763086A (en) * | 2009-12-29 | 2010-06-30 | 浙江大学 | High-efficiency system and method for dynamic optimization of industrial process |
| CN101763083A (en) * | 2009-12-29 | 2010-06-30 | 浙江大学 | An effective industrial process dynamic optimization system and method capable of controlling variable parameterization |
| CN103273913A (en) * | 2013-06-08 | 2013-09-04 | 浙江大学 | Automobile automatic braking device based on orthogonal configuration optimization |
| CN103309234A (en) * | 2013-06-08 | 2013-09-18 | 浙江大学 | Orthogonal collocation optimization based batch reactor control system |
| CN103324086A (en) * | 2013-06-08 | 2013-09-25 | 浙江大学 | Batch reactor control system based on accurate punishment optimization |
| CN103303299B (en) * | 2013-06-08 | 2015-07-22 | 浙江大学 | Emergency braking signal generation device for high-speed train based on orthogonal collocation optimization |
Non-Patent Citations (1)
| Title |
|---|
| 胡云卿: "基于控制变量参数化的带约束最优控制问题计算方法", 《中国博士学位论文全文数据库信息科技辑》 * |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108717588A (en) * | 2018-06-07 | 2018-10-30 | 哈尔滨工业大学 | A kind of track optimizing Initialization Algorithms based on Orthogonal Experiment and Design |
| CN109375649A (en) * | 2018-12-19 | 2019-02-22 | 中国商用飞机有限责任公司 | Method and system for controlling an aircraft |
| CN109375649B (en) * | 2018-12-19 | 2021-11-19 | 中国商用飞机有限责任公司 | Method and system for controlling an aircraft |
| CN110020488A (en) * | 2019-04-12 | 2019-07-16 | 中南大学 | A kind of track method for customizing of curve trajectory SMART BULLET |
| CN111045447A (en) * | 2019-11-21 | 2020-04-21 | 浙江大学 | High-precision hypersonic aircraft trajectory optimization multi-scale optimal control system |
| CN111061294B (en) * | 2019-11-21 | 2021-04-09 | 浙江大学 | Trajectory optimization self-adaptive optimal control system for nonstationary hypersonic aircraft |
| CN111061294A (en) * | 2019-11-21 | 2020-04-24 | 浙江大学 | Trajectory optimization self-adaptive optimal control system for nonstationary hypersonic aircraft |
| CN111103795A (en) * | 2019-11-21 | 2020-05-05 | 浙江大学 | Hypersonic aircraft reentry section track optimization controller based on intelligent self-adaptive optimization algorithm |
| CN111123700A (en) * | 2019-11-21 | 2020-05-08 | 浙江大学 | Constraint full-course satisfied optimal control system for obstacle-detouring flight of hypersonic aircraft |
| CN111123960A (en) * | 2019-11-21 | 2020-05-08 | 浙江大学 | Ultra-high precision hypersonic aircraft trajectory optimization optimal control instrument |
| CN111045447B (en) * | 2019-11-21 | 2023-08-29 | 浙江大学 | High-precision hypersonic aircraft track optimization multi-scale optimal control system |
| CN111324035A (en) * | 2019-11-21 | 2020-06-23 | 浙江大学 | Adaptive optimal controller for trajectory optimization of hypersonic aircraft |
| CN111338364A (en) * | 2019-11-21 | 2020-06-26 | 浙江大学 | High-precision controller for optimizing trajectory of hypersonic aerocraft with quick response |
| CN111026140A (en) * | 2019-11-21 | 2020-04-17 | 浙江大学 | Constraint full-process satisfied hypersonic aircraft trajectory optimization self-adaptive optimal controller |
| CN111103795B (en) * | 2019-11-21 | 2021-07-13 | 浙江大学 | Hypersonic aircraft reentry section track optimization controller based on intelligent self-adaptive optimization algorithm |
| CN110727285A (en) * | 2019-11-27 | 2020-01-24 | 浙江大学 | Gauss time distribution point control parameterization-based unpowered aviation aircraft control signal generation device and method |
| CN111290436B (en) * | 2020-03-16 | 2023-04-07 | 北京机电工程研究所 | Aircraft wireless instruction correction method and system |
| CN111290436A (en) * | 2020-03-16 | 2020-06-16 | 北京机电工程研究所 | Aircraft wireless instruction correction method and system |
| CN113705117A (en) * | 2021-05-25 | 2021-11-26 | 中国航空工业集团公司沈阳飞机设计研究所 | Optimized analysis method for flight performance of hypersonic cruise aircraft |
| CN113705117B (en) * | 2021-05-25 | 2022-09-20 | 中国航空工业集团公司沈阳飞机设计研究所 | Optimized analysis method for flight performance of hypersonic cruise aircraft |
| CN114488788A (en) * | 2021-11-30 | 2022-05-13 | 浙江大学 | Probability self-adaptive pollination optimized hypersonic aircraft reentry segment controller |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107908109B (en) | 2020-02-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107908109A (en) | A kind of hypersonic aircraft reentry stage track optimizing controller based on orthogonal configuration optimization | |
| CN111103795B (en) | Hypersonic aircraft reentry section track optimization controller based on intelligent self-adaptive optimization algorithm | |
| CN108052008A (en) | A kind of geometry controller design method of tiltrotor aircraft transition state handoff procedure | |
| CN107065897B (en) | Three-degree-of-freedom helicopter explicit model prediction control method | |
| CN111123700B (en) | Constraint full-course satisfied optimal control system for obstacle-detouring flight of hypersonic aircraft | |
| CN111324035A (en) | Adaptive optimal controller for trajectory optimization of hypersonic aircraft | |
| CN106527462A (en) | Unmanned aerial vehicle (UAV) control device | |
| CN107885082A (en) | A kind of Lunar satellite orbit tracking controller based on orthogonal configuration optimization | |
| CN111045447B (en) | High-precision hypersonic aircraft track optimization multi-scale optimal control system | |
| CN113885543A (en) | Design method of navigation body auto-disturbance rejection roll controller under complex sea condition disturbance | |
| CN107992071A (en) | Longitudinal attitude double-fuzzy control system and method for tailstock type unmanned aerial vehicle | |
| CN108459611B (en) | Attitude tracking control method of near space vehicle | |
| CN116859976B (en) | Anti-external interference method and system for cargo transportation suspension unmanned aerial vehicle | |
| CN113220031A (en) | Anti-saturation finite time-based attitude tracking control method for rotary wing type plant protection unmanned aerial vehicle | |
| CN108845508B (en) | CMAC-sliding mode integrated control-based semi-physical simulation control method for unmanned aerial vehicle | |
| CN111061294B (en) | Trajectory optimization self-adaptive optimal control system for nonstationary hypersonic aircraft | |
| Li et al. | Novel fuzzy approximation control scheme for flexible air-breathing hypersonic vehicles with non-affine dynamics and amplitude and rate constraints | |
| CN114610055A (en) | Aircraft control method and aircraft | |
| Kada | Design of flight control systems for a hypersonic aircraft using sliding-pid control | |
| CN106950833A (en) | Based on the attitude of satellite dynamic programming method for improving PIO algorithms | |
| CN114355770A (en) | Hypersonic aircraft reentry segment controller for optimizing invasive weed population distribution | |
| CN114488788B (en) | Hypersonic aircraft reentry section controller with probability self-adaptive pollination optimization function | |
| Sharma et al. | Optimal three dimensional aircraft terrain following and collision avoidance | |
| CN111026140B (en) | Constraint full-process satisfied hypersonic aircraft trajectory optimization self-adaptive optimal controller | |
| CN111338364B (en) | High-precision controller for optimizing trajectory of hypersonic aerocraft with quick response |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200228 Termination date: 20211113 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |