CN107450323A - Hypersonic aircraft reentry stage neutral net Hybrid Learning control method - Google Patents
Hypersonic aircraft reentry stage neutral net Hybrid Learning control method Download PDFInfo
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- G—PHYSICS
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- 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
Abstract
The invention discloses a kind of hypersonic aircraft reentry stage neutral net Hybrid Learning control method, for solving the technical problem of existing hypersonic aircraft reentry stage attitude control method low precision.Technical scheme is the attitude mode for Control-oriented first by hypersonic aircraft reentry stage kinetic description, neutral net is recycled to carry out learning scene controller to system nondeterministic function, finally use online data structure forecast modeling error, and form combination misalignment using tracking error and prediction error and carry out neutral net weight renewal, lift closed loop control process neural network learning performance improvement tracking performance.Due to using neutral net carry out study can lifting system adaptive ability, improve control accuracy；Due to utilizing online data structure forecast error assessment neural network learning performance and combining system tracking error, neutral net weight vectors complex updates are carried out, neural network learning accuracy in closed loop control process is improved, improves systematic tracking accuracy.
Description
Technical field
It is more particularly to a kind of hypersonic winged the present invention relates to a kind of hypersonic aircraft reentry stage attitude control method
Row device reentry stage neutral net Hybrid Learning control method.
Background technology
Hypersonic aircraft is due to its high-speed flight ability so that in case of emergency realizes that " whole world reaches, the whole world is made
War " is possibly realized, therefore by extensive concern both domestic and external；NASA X-43A, which make a successful trial flight, confirms the feasible of this technology
Property.During hypersonic aircraft reenters, the excursion of the angle of attack and angle of heel is big, and system has strong nonlinearity；Meanwhile gas
Dynamic coefficient, the controlled efficiency of thrust reverser it is all serious depend on flight attitude, this causes parameter in system and state all to deposit
In serious Non-linear coupling.The characteristic of hypersonic aircraft reentry stage brings huge challenge to control design case, control
System processed must just have strongly-adaptive learning ability.
Because characteristic, the aircraft manufacturing technologies such as reentry stage strong nonlinearity, fast time variant and strong uncertainty are very multiple
It is miscellaneous.Conventional processing scheme is designed using robust adaptive.A kind of scheme is that the non-linear of system is written as into linear parameterization
Form, and then adaptive design is carried out, the process needs that there is clear cognition to obtain Parameter Expression to the structure of system；Separately
A kind of outer scheme is will to consider the bound of nonlinear function, using the information design robust item to ensure that system is stable, such as
《Adaptive Dynamic Sliding Mode Control for Near Space Vehicles Under Actuator
Faults》(Jing Zhao, Bin Jiang, Peng Shi, Hongtao Liu,《Circuits Systems&Signal
Processing》, 2013, volume 32, the page number：2281-2296) text have studied sliding formwork control for Near Space Flying Vehicles
Device.Due to carrying out sliding mode design using the unknown upper bound information of system in design process, therefore resulting controller is with very strong
Conservative, be unfavorable for high-precision hypersonic aircraft reentry stage gesture stability.
The content of the invention
In order to overcome the shortcomings of existing hypersonic aircraft reentry stage attitude control method low precision, the present invention provides one
Kind hypersonic aircraft reentry stage neutral net Hybrid Learning control method.This method first reenters hypersonic aircraft
Section kinetic description is the attitude mode of Control-oriented, recycles neutral net to carry out learning scene control to system nondeterministic function
Device processed, finally using online data structure forecast modeling error, and form combination misalignment using tracking error and prediction error and enter
Row neutral net weight updates, and lifts closed loop control process neural network learning performance improvement tracking performance.Due to utilizing nerve
Network carry out study can lifting system adaptive ability, reduce the conservative brought of robust control method, improve control essence
Degree；Due to utilizing online data structure forecast error assessment neural network learning performance and combining system tracking error, god is carried out
Through network weight vector complex updates, neural network learning accuracy and rapidity in closed loop control process are improved, improves and is
The tracking accuracy of system.
The technical solution adopted for the present invention to solve the technical problems：A kind of hypersonic aircraft reentry stage neutral net
Hybrid Learning control method, it is characterized in comprising the following steps：
(a) hypersonic aircraft reentry stage kinetic model is established：
The kinetic model includes state variable X=[v, ω]TWith control input U=Mc, wherein v=[α β σ]TFor appearance
State is angularly measured, and α, β, σ represent the angle of attack, yaw angle and inclination angle respectively；ω=[p q r]TFor attitude angular rate vector, p, q, r
Rolling, pitching and yawrate are represented respectively；Mc=[Mx My Mz]TThe control moment of expression system；I represents inertia matrix；
(b) X=[x are defined1 x2]T, x1=v, x2=ω.Then Attitude control model is represented by：
Wherein g1(x1)=R (), f2(x2)=- I-1Ω I ω, g2(x2)=I-1。
(c) posture angle tracking error e is defined1=x1-yd；Wherein yd=[αd βd σd]TGuidance for guidance system generation refers to
Order.Designing virtual controlling amount is：
Wherein k1∈R3×3To control gain matrix,It can further calculateWherein The first derivative and second dervative respectively guidanceed command.
Define attitude angular rate errorDesign control signal McFor：
WhereinFor the estimate of optimal neural network weight vectors, θ2(x2) it is RBF vector；k2∈R3×3For
Control gain matrix,
(d) define
Wherein τd>0 is integrating range.
Structure forecast error isNeutral net Hybrid Learning adaptive lawIt is designed as：
Wherein λ2∈R3×3To learn rate matrix,kω2∈R3×3For weight factor matrix,
(e) according to obtained control input Mc, return to hypersonic aircraft reentry stage kinetic model (1),
(2) control, is tracked to attitude angle.
The beneficial effects of the invention are as follows：This method is towards control first by hypersonic aircraft reentry stage kinetic description
The attitude mode of system, recycle neutral net to carry out learning scene controller to system nondeterministic function, finally use in line number
According to structure forecast modeling error, and form combination misalignment using tracking error and prediction error and carry out neutral net weight renewal,
Lift closed loop control process neural network learning performance improvement tracking performance.It can be lifted due to carrying out study using neutral net and be
The adaptive ability of system, the conservative that robust control method is brought is reduced, improves control accuracy；Due to utilizing online data structure
Make prediction error assessment neural network learning performance and combine system tracking error, it is compound more to carry out neutral net weight vectors
Newly, neural network learning accuracy and rapidity in closed loop control process are improved, improves the tracking accuracy of system.
The present invention is elaborated with reference to the accompanying drawings and detailed description.
Brief description of the drawings
Fig. 1 is the flow chart of hypersonic aircraft reentry stage neutral net Hybrid Learning control method of the present invention.
Embodiment
Reference picture 1.Hypersonic aircraft reentry stage neutral net Hybrid Learning control method specific steps of the present invention are such as
Under：
(a) hypersonic aircraft reentry stage kinetic model is established：
The kinetic model includes state variable X=[v, ω]TWith control input U=Mc, wherein v=[α β σ]TFor appearance
State is angularly measured, and α, β, σ represent the angle of attack, yaw angle and inclination angle respectively；ω=[p q r]TFor attitude angular rate vector, p, q, r
Rolling, pitching and yawrate are represented respectively；Mc=[Mx My Mz]TThe control moment of expression system；
(b) X=[x are defined1 x2]T, x1=v, x2=ω.Then Attitude control model is represented by：
Wherein g1(x1)=R (), f2(x2)=- I-1Ω I ω, g2(x2)=I-1。
(c) posture angle tracking error e is defined1=x1-yd；Wherein yd=[αd βd σd]TGuidance for guidance system generation refers to
Order；Designing virtual controlling amount is：
WhereinIt can further calculateWhereinRespectively make
Lead the first derivative and second dervative of instruction.
Define attitude angular rate errorDesign control signal McFor：
WhereinFor the estimate of optimal neural network weight, θ2(x2) it is RBF basis function vectors；
(d) define
Wherein τd=0.05s.
Structure forecast error isDesign neutral net Hybrid Learning adaptive lawFor：
Wherein
(e) according to obtained control input Mc, return to hypersonic aircraft reentry stage kinetic model (1),
(2) control, is tracked to attitude angle.
Unspecified part of the present invention belongs to art personnel's common knowledge.
Claims (1)
1. a kind of hypersonic aircraft reentry stage neutral net Hybrid Learning control method, it is characterised in that including following step
Suddenly：
(a) hypersonic aircraft reentry stage kinetic model is established：
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The kinetic model includes state variable X=[v, ω]TWith control input U=Mc, wherein v=[α β σ]TFor attitude angle
Vector, α, β, σ represent the angle of attack, yaw angle and inclination angle respectively；ω=[p q r]TFor attitude angular rate vector, p, q, r difference
Represent rolling, pitching and yawrate；Mc=[Mx My Mz]TThe control moment of expression system；I represents inertia matrix；
(b) X=[x are defined1 x2]T, x1=v, x2=ω；Then Attitude control model is represented by：
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(c) posture angle tracking error e is defined1=x1-yd；Wherein yd=[αd βd σd]TFor guidanceing command for guidance system generation；
Designing virtual controlling amount is：
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Wherein k1∈R3×3To control gain matrix,It can further calculateWherein The first derivative and second dervative respectively guidanceed command；
Define attitude angular rate errorDesign control signal McFor：
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(d) defineWherein τd
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Structure forecast error isNeutral net Hybrid Learning adaptive lawIt is designed as：
Wherein λ2∈R3×3To learn rate matrix,kω2∈R3×3For weight factor matrix,
(e) according to obtained control input Mc, kinetic model (1), (2) of hypersonic aircraft reentry stage are returned to, to appearance
State angle is tracked control.
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| CN109164708A (en) * | 2018-10-31 | 2019-01-08 | 南京航空航天大学 | A kind of hypersonic aircraft neural network adaptive fusion method |
| CN110413000A (en) * | 2019-05-28 | 2019-11-05 | 北京航空航天大学 | A kind of hypersonic aircraft based on deep learning reenters prediction and corrects fault-tolerant method of guidance |
| CN111351488A (en) * | 2020-03-03 | 2020-06-30 | 南京航空航天大学 | Intelligent trajectory reconstruction reentry guidance method for aircraft |
| CN112327627A (en) * | 2020-11-14 | 2021-02-05 | 西北工业大学 | Nonlinear switching system self-adaptive sliding mode control method based on composite learning |
| CN112327626A (en) * | 2020-11-14 | 2021-02-05 | 西北工业大学 | Aircraft channel coupling coordination control method based on data analysis |
| CN114859712A (en) * | 2022-04-17 | 2022-08-05 | 西北工业大学 | Aircraft guidance control integrated method facing throttle constraint |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109164708A (en) * | 2018-10-31 | 2019-01-08 | 南京航空航天大学 | A kind of hypersonic aircraft neural network adaptive fusion method |
| CN109164708B (en) * | 2018-10-31 | 2020-08-04 | 南京航空航天大学 | Neural network self-adaptive fault-tolerant control method for hypersonic aircraft |
| CN110413000A (en) * | 2019-05-28 | 2019-11-05 | 北京航空航天大学 | A kind of hypersonic aircraft based on deep learning reenters prediction and corrects fault-tolerant method of guidance |
| CN110413000B (en) * | 2019-05-28 | 2020-10-13 | 北京航空航天大学 | Hypersonic aircraft reentry prediction correction fault-tolerant guidance method based on deep learning |
| CN111351488A (en) * | 2020-03-03 | 2020-06-30 | 南京航空航天大学 | Intelligent trajectory reconstruction reentry guidance method for aircraft |
| CN111351488B (en) * | 2020-03-03 | 2022-04-19 | 南京航空航天大学 | Intelligent trajectory reconstruction reentry guidance method for aircraft |
| CN112327627A (en) * | 2020-11-14 | 2021-02-05 | 西北工业大学 | Nonlinear switching system self-adaptive sliding mode control method based on composite learning |
| CN112327626A (en) * | 2020-11-14 | 2021-02-05 | 西北工业大学 | Aircraft channel coupling coordination control method based on data analysis |
| CN112327627B (en) * | 2020-11-14 | 2022-06-21 | 西北工业大学 | Nonlinear switching system self-adaptive sliding mode control method based on composite learning |
| CN112327626B (en) * | 2020-11-14 | 2022-06-21 | 西北工业大学 | Aircraft channel coupling coordination control method based on data analysis |
| CN114859712A (en) * | 2022-04-17 | 2022-08-05 | 西北工业大学 | Aircraft guidance control integrated method facing throttle constraint |
| CN114859712B (en) * | 2022-04-17 | 2023-08-01 | 西北工业大学 | Aircraft guidance control integrated method oriented to throttle constraint |
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