CN109828464B - Autonomous attitude control method for spacecraft - Google Patents

Autonomous attitude control method for spacecraft Download PDF

Info

Publication number
CN109828464B
CN109828464B CN201910148020.6A CN201910148020A CN109828464B CN 109828464 B CN109828464 B CN 109828464B CN 201910148020 A CN201910148020 A CN 201910148020A CN 109828464 B CN109828464 B CN 109828464B
Authority
CN
China
Prior art keywords
spacecraft
control
attitude
fuzzy
control law
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.)
Active
Application number
CN201910148020.6A
Other languages
Chinese (zh)
Other versions
CN109828464A (en
Inventor
冯佳佳
王佐伟
何刚
姚蘅
李乐尧
张玉洁
沈扬帆
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Institute of Control Engineering
Original Assignee
Beijing Institute of Control Engineering
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Beijing Institute of Control Engineering filed Critical Beijing Institute of Control Engineering
Priority to CN201910148020.6A priority Critical patent/CN109828464B/en
Publication of CN109828464A publication Critical patent/CN109828464A/en
Application granted granted Critical
Publication of CN109828464B publication Critical patent/CN109828464B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Landscapes

  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

A spacecraft autonomous attitude control method comprises the following steps: 1) performing characteristic modeling according to a spacecraft kinematics dynamics model to obtain a maintenance tracking control algorithm, a golden section control algorithm and a fuzzy golden section control algorithm; 2) establishing a self-adaptive fuzzy control algorithm according to a spacecraft kinematics dynamic model; 3) selecting a control algorithm to determine a spacecraft control law according to the type of a control instruction input into the spacecraft; 4) determining a supervision control law according to a decision function; 5) and determining an execution control law according to the spacecraft control law and the supervision control law, and controlling the attitude of the spacecraft according to the execution control law. The method provided by the invention can meet the requirements of multi-level and diversified control tasks and can provide a control system with better control performance and stability.

Description

Autonomous attitude control method for spacecraft
Technical Field
The invention relates to an autonomous attitude control method for a spacecraft, and belongs to the technical field of spacecraft control.
Background
With the development of space technology, spacecraft structures become more complex, models of the spacecraft structures are more difficult to describe by accurate mathematical formulas, and the spacecraft structures often have nonlinearity, uncertainty and the like, and are limited by various constraints and the like. Under the requirement of intelligent autonomous control of a spacecraft, the problem that how to autonomously control the attitude of the spacecraft is solved aiming at the problem that complex spacecrafts with multi-level and diversified control task requirements are more and more.
Faced with such problems, conventional control theory and methods encounter difficulties. Some existing intelligent control methods, such as intelligent adaptive control based on a feature model, perform control according to the combination of the dynamic characteristics, environmental features and control performance requirements of a controlled object; the fuzzy control is based on the control experience of a human being instead of a model depending on a controlled object, and also includes neural network control, expert control, learning control, and the like. These intelligent control methods have been applied to different degrees in actual engineering, but these intelligent control methods still have some problems to be solved, such as the problem of system identification of intelligent adaptive control based on feature models, the problem of system design of fuzzy control, and so on. Therefore, difficulties are still encountered in independently using these intelligent control methods for autonomous attitude control of spacecraft.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the problem that the existing control method can not realize the autonomous attitude control of the flexible spacecraft with multi-level and diversified control task requirements and limited constraint conditions, the autonomous attitude control method of the spacecraft is provided.
The technical solution of the invention is as follows:
a spacecraft autonomous attitude control method comprises the following steps:
1) performing characteristic modeling according to a spacecraft kinematics dynamics model to obtain a maintenance tracking control algorithm, a golden section control algorithm and a fuzzy golden section control algorithm;
2) establishing a self-adaptive fuzzy control algorithm according to a spacecraft kinematics dynamic model;
3) determining a spacecraft control law according to the type of the control instruction input into the spacecraft, the tracking maintenance control algorithm, the golden section control algorithm and the fuzzy golden section control algorithm obtained in the step 1), and the self-adaptive fuzzy control algorithm established in the step 2);
4) determining a supervision control law according to a decision function;
5) determining an execution control law according to the spacecraft control law determined in the step 3) and the supervision control law determined in the step 4), and controlling the attitude of the spacecraft according to the execution control law.
The method for determining the spacecraft control law in the step 3) specifically comprises the following steps:
31) judging the type of a control instruction input into the spacecraft, and entering step 32) when the type of the control instruction is an attitude maneuver instruction; when the control instruction type is an attitude stabilization instruction, entering step 33); when the control command type is a posture rotation command, entering step 34);
32) according to the performance of a spacecraft control systemPlanning a spacecraft attitude maneuver path according to the requirement and the limit constraint condition to obtain an attitude angular velocity, and determining a spacecraft control law u according to the attitude angular velocity obtained by planning the spacecraft attitude maneuver path and maintaining a tracking control algorithm and an adaptive fuzzy control algorithmc；
33) Determining a spacecraft control law u according to the golden section control algorithm and the self-adaptive fuzzy control algorithmc；
34) Determining a spacecraft control law u according to the fuzzy golden section control algorithm and the self-adaptive fuzzy control algorithmc。
Said step 32) determining the spacecraft control law ucThe method specifically comprises the following steps:
uc＝u0+uA，
Figure BDA0001980647690000021
uA＝θTξ(x)，
wherein, ω isr(k) Attitude angular velocity obtained for spacecraft attitude maneuver path planning, which represents the value of k time in discrete state; omega (k) is the attitude angular velocity actually output by the spacecraft, and represents the value of k time in a discrete state; k represents the current time in the discrete state, and k-1 represents the last time in the discrete state; λ is a forgetting factor;
Figure BDA0001980647690000031
modeling parameters for features in discrete states, wherein
Figure BDA0001980647690000032
θ＝[θ1,θ2,…,θN]TIs an adjustable parameter vector; xi is ═ xi1,ξ2,…,ξN]TIs a fuzzy basis function vector; n is a positive integer; and x is the attitude angle and the attitude angular velocity of the spacecraft.
Said step 33) determining the spacecraft control law ucMethod (2)The method specifically comprises the following steps:
uc＝ug+uA，
Figure BDA0001980647690000033
uA＝θTξ(x)，
e(k)＝y(k)-yr(k)，
wherein y (k) is an attitude angle actually output by the spacecraft, and the attitude angle represents a numerical value of k time in a discrete state; y isr(k) Is a spacecraft target attitude angle which represents the value of k time in a discrete state; k represents the current time in the discrete state, and k-1 represents the last time in the discrete state; λ is a forgetting factor;
Figure BDA0001980647690000034
the parameters are modeled for the features in the discrete states,
Figure BDA0001980647690000035
Figure BDA0001980647690000036
θ＝[θ1,θ2,…,θN]Tis an adjustable parameter vector; xi is ═ xi1,ξ2,…,ξN]TIs a fuzzy basis function vector; n is a fuzzy rule number and is a positive integer; and x is the attitude angle and the attitude angular velocity of the spacecraft.
Said step 34) determining the spacecraft control law ucThe method specifically comprises the following steps:
uc＝uf+uA，
Figure BDA0001980647690000037
Figure BDA0001980647690000041
uA＝θTξ(x)，
e(k)＝y(k)-yr(k)，
wherein, muiTo normalize the intensity of the emission, mu1+μ2+…+μl＝1；
Figure BDA0001980647690000042
Is a golden section control law corresponding to the ith T-S fuzzy rule under the l T-S fuzzy rules;
Figure BDA0001980647690000043
modeling parameters for the characteristics under the discrete state corresponding to the ith T-S fuzzy rule,
Figure BDA0001980647690000044
Figure BDA0001980647690000045
ξ＝[ξ1,ξ2,…,ξN]Tis a fuzzy basis function vector; l is a positive integer; n is a fuzzy rule number and is a positive integer; theta is ═ theta1,θ2,…,θN]TIs an adjustable parameter vector; x is the attitude angle and attitude angular velocity of the spacecraft; lambda [ alpha ]iIs a forgetting factor corresponding to the ith T-S fuzzy rule under the l T-S fuzzy rules; y (k) is an attitude angle actually output by the spacecraft, and the attitude angle represents a numerical value of k time in a discrete state; y isr(k) Is a spacecraft target attitude angle which represents the value of k time in a discrete state; k represents the current time in the discrete state, and k-1 represents the last time in the discrete state.
Said step 4) determining the function VcThe method specifically comprises the following steps:
Figure BDA0001980647690000046
Figure BDA0001980647690000047
e＝ym-yout，
wherein the matrix P satisfies Λc TP+PΛcQ is an arbitrary positive definite matrix, Λc＝[0,1；-k2,-k1]，k1,k2Is such that r(s) is s2+k1s+k2Is a Hurwitz stable polynomial; y ismIs a target attitude angle, y, of the spacecraft in a continuous stateoutThe attitude angle actually output by the spacecraft in a continuous state.
Said step 4) determining the supervisory control law usThe method specifically comprises the following steps:
Figure BDA0001980647690000048
wherein the content of the first and second substances,
Figure BDA0001980647690000053
to determine the threshold value, when
Figure BDA0001980647690000054
When the temperature of the water is higher than the set temperature,
Figure BDA0001980647690000055
when in use
Figure BDA0001980647690000051
When the temperature of the water is higher than the set temperature,
Figure BDA0001980647690000052
bc＝[0,b]T，b>0，bLb is 0. ltoreq. bLAny constant less than or equal to b; f. ofU(x) To satisfy | f (x) | less than or equal to fU(x) F (x) is established according to a spacecraft kinematics kinetic equation; k ═ k2,k1]T，k1,k2Is such that r(s) is s2+k1s+k2Is a Hurwitz stable polynomial.
Said step 5) is according to the spacecraftThe method for determining and executing the control law u by the control law and the supervisory control law specifically comprises the following steps: u-uc+us。
Compared with the prior art, the invention has the advantages that:
1) the invention adopts an intelligent self-adaptive control method based on a characteristic model, simultaneously introduces fuzzy knowledge of spacecraft control tasks into a control system in advance in order to fully ensure the control effect of the system, and particularly adds self-adaptive fuzzy control in the control method to fully ensure the control performance of the control system.
2) The method can be used for carrying out targeted attitude control according to the multi-task requirement of the attitude control of the spacecraft. When the system receives a specific control instruction, a specific control algorithm is selected according to the form of the control instruction, and the whole spacecraft attitude control task can achieve a satisfactory control effect.
3) When the spacecraft needs to perform attitude maneuver control, the method utilizes the particle swarm optimization algorithm to plan the attitude path before performing attitude control, and the planning can be performed on line or off line. On one hand, the method can fully ensure the rapidity and the stability of the spacecraft attitude maneuver and ensure the optimal control, and on the other hand, the method can meet the hierarchical requirement of the control task.
4) The method of the invention particularly introduces a supervisory control law in a spacecraft attitude control system, and fully ensures the stability and robustness of the whole control task.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic block diagram of the method of the present invention.
Detailed Description
Aiming at a flexible spacecraft attitude control system with multi-level and diversified control task requirements and limiting constraint conditions; firstly, performing characteristic modeling according to the nonlinear Euler angular kinematics and attitude dynamics of the flexible spacecraft to complete the establishment of an intelligent self-adaptive control algorithm based on a characteristic model, wherein the intelligent self-adaptive control algorithm mainly comprises a golden section control algorithm, a maintenance tracking control algorithm and a fuzzy golden section control algorithm; secondly, establishing a self-adaptive fuzzy control algorithm according to a nonlinear equation established by the nonlinear Euler angular kinematics and the attitude dynamics of the flexible spacecraft; thirdly, because a single control algorithm in the comprehensive control task cannot meet the overall requirements of the control task, when the system receives a specific control instruction, the control algorithm is selected according to the pertinence of the control instruction, wherein the control instruction of the autonomous attitude control of the spacecraft generally comprises attitude maneuver, attitude stability and attitude rotation, and when the control instruction of the attitude maneuver is received, the attitude maneuver path planning is firstly carried out in order to ensure the control performance of the attitude maneuver. Finally, the method of the invention particularly introduces a supervisory control law, and fully ensures the requirement of the stability of the whole control system.
According to the autonomous attitude control method for the spacecraft, provided by the invention, on one hand, the multilevel and diversified requirements can be met on the control task, and on the other hand, the control performance of the overall control task can be ensured to be optimal on the control performance.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The flow chart of the method of the invention is shown in figure 1, and the concrete implementation steps are as follows:
firstly, performing characteristic modeling according to the flexible spacecraft nonlinear Euler angular kinematics and the attitude dynamics, namely performing characteristic modeling according to a spacecraft kinematics dynamics model to obtain a maintenance tracking control algorithm, a golden section control algorithm and a fuzzy golden section control algorithm;
the kinematic dynamics equation of the spacecraft, which is established according to the nonlinear euler angular kinematics and attitude dynamics of the flexible spacecraft, is shown as follows, and the specific content is detailed in satellite attitude dynamics and control, which is compiled by butchery:
Figure BDA0001980647690000061
wherein J ∈ R3×3Is a rotational inertia matrix of the spacecraft, R3×3Expressed as 3 × 3 momentsArraying; eta ∈ RmThe method is characterized in that the method is a spacecraft flexible attachment modal vector, m is a modal order, and m is a positive integer which is not zero; fs∈R3×mIs a coupling coefficient matrix, R, of a spacecraft flexible accessory and a spacecraft central rigid body3×mExpressed as a 3 × m matrix; ξ is the damping coefficient of the flexible attachment; Λ is a spacecraft flexible attachment modal frequency vector which is a diagonal matrix of m × m;
Figure BDA0001980647690000071
the external moments respectively acting on a rolling shaft, a pitching shaft and a yawing shaft of the spacecraft comprise a control moment u and an environmental disturbance moment d; omega ═ omegax ωy ωz]T∈R3For the angular velocity, omega, of a spacecraft in a spacecraft body coordinate systemx,ωy,ωzRolling angular velocity, pitch angular velocity and yaw angular velocity of the spacecraft are respectively; omega×Is an antisymmetric matrix of the angular velocities of the spacecraft,
Figure BDA0001980647690000072
q three-axis Euler attitude angle of spacecraft, specifically
Figure BDA0001980647690000073
Wherein
Figure BDA0001980647690000074
Respectively a rolling angle, a pitch angle and a yaw angle of the spacecraft; b is an Euler angle attitude transformation matrix which is related to the rotation sequence of three coordinate axes and is related to omega and q
Figure BDA0001980647690000075
The spacecraft kinematics dynamics equation described above is sampled for a time TsModeling the features of (1), then:
q(k+1)＝f1q(k)+f2q(k-1)+g0U(k)，
wherein f is1＝2I-F-1GTs，f2＝TsF-1G-1，g0＝F-1Ts 2，F＝(J-FsFs T)B，
Figure BDA0001980647690000076
I is a 3 × 3 identity matrix.
Order:
Figure BDA0001980647690000077
then: q (k +1) ═ phik Tθk，
Figure BDA0001980647690000078
Can be calculated by a least square method and a gradient method.
At this time, the maintenance tracking control algorithm is expressed as:
Figure BDA0001980647690000079
wherein, ω isr(k) An attitude angular velocity, which represents a value of k time in a discrete state, is planned for the spacecraft attitude maneuver path; omega (k) is the attitude angular velocity actually output by the spacecraft, and represents the value of k time in a discrete state; k represents the current time in the discrete state, and k-1 represents the last time in the discrete state; λ is a forgetting factor;
Figure BDA00019806476900000710
modeling parameters for features in discrete states, wherein
Figure BDA0001980647690000081
In the maintenance tracking control, namely when the control instruction type is an attitude maneuver instruction, the controlled quantity of the system is the attitude angular velocity.
The golden section control algorithm is represented as follows:
Figure BDA0001980647690000082
e(k)＝y(k)-yr(k)，
wherein y (k) is an attitude angle actually output by the spacecraft, and the attitude angle represents a numerical value of k time in a discrete state; y isr(k) Is a spacecraft target attitude angle which represents the value of k time in a discrete state; k represents the current time in the discrete state, and k-1 represents the last time in the discrete state; λ is a forgetting factor;
Figure BDA0001980647690000083
modeling parameters for features in discrete states, wherein
Figure BDA0001980647690000084
Figure BDA0001980647690000085
For details of the golden section control algorithm, see "intelligent adaptive control based on feature model" by wu hong xin et al.
The fuzzy golden section control algorithm is expressed as:
Figure BDA0001980647690000086
Figure BDA0001980647690000087
e(k)＝y(k)-yr(k)，
wherein, muiTo normalize the intensity of the emission, mu1+μ2+…+μl＝1；
Figure BDA0001980647690000088
Is a golden section control law corresponding to the ith T-S fuzzy rule under the l T-S fuzzy rules;
Figure BDA0001980647690000089
modeling parameters for the features in the discrete state corresponding to the ith T-S fuzzy rule, wherein
Figure BDA00019806476900000810
Figure BDA00019806476900000811
l is a fuzzy rule number which is a positive integer; lambda [ alpha ]iIs a forgetting factor corresponding to the ith T-S fuzzy rule under the l T-S fuzzy rules; y (k) is an attitude angle actually output by the spacecraft, and the attitude angle represents a numerical value of k time in a discrete state; y isr(k) Is a spacecraft target attitude angle which represents the value of k time in a discrete state; k represents the current time in the discrete state, and k-1 represents the last time in the discrete state. For details of the fuzzy golden section control algorithm, see the von glauca published as "a fuzzy golden section controller design and its applications".
In the embodiment of the invention: moment of inertia of spacecraft
Figure BDA0001980647690000091
The modal order m is 5, and the flexible attachment modal frequency vector Λ is [0.4743,1.194,1.545,2.255,3.231]Matrix of coupling coefficients of spacecraft flexible attachment and spacecraft central rigid body
Figure BDA0001980647690000092
Damping coefficient xi of flexible attachment of spacecraft is 0.005, and environmental disturbance moment
Figure BDA0001980647690000093
Sampling time T of control systems＝0.01，
Figure BDA0001980647690000094
Calculated by the least square method.
And secondly, establishing a self-adaptive fuzzy control algorithm according to a nonlinear equation established by the flexible spacecraft nonlinear Euler angular kinematics and the attitude dynamics, namely according to a spacecraft kinematics dynamics model. For details, refer to the "fuzzy system and fuzzy control" of the Wang Li New Ed.
The spacecraft kinematics kinetic equation is shown below:
Figure BDA0001980647690000095
wherein f is a nonlinear and uncertain function, b is an unknown normal number, u belongs to R, y belongs to R and is respectively the system input and output, and the equivalence thereof is the input torque in the control system and the attitude angle of the spacecraft.
The introduced adaptive fuzzy control law expression is as follows:
uA＝θTξ(x)，
wherein the content of the first and second substances,
Figure BDA0001980647690000096
x is a system state quantity measured by the spacecraft and comprises an attitude angle and an attitude angular velocity of the spacecraft, and theta is [ theta ═ theta1,θ2,…,θN]TFor adjustable parameter vector, xi ═ xi1,ξ2,…,ξN]TIs a fuzzy basis function vector, N is a fuzzy rule number, and N is a positive integer.
Figure BDA0001980647690000097
Wherein k is 1, 2., N,
Figure BDA0001980647690000101
the membership function of the self-adaptive control system is kept unchanged in the whole self-adaptive process, and can be Gauss type, triangular type or other types, and the membership function can be determined in advance according to fuzzy understanding of the controlled object.
The self-adaptive law of the adjustable parameter vector theta in the self-adaptive fuzzy control is selected as follows:
Figure BDA0001980647690000102
where γ is a positive constant, p2Is of the formula Λc TP+PΛc-last column of matrix P in Q, where Λc＝[0,1；-k2,-k1]，k1,k2Is such that r(s) is s2+k1s+k2Is a Hurwitz stable polynomial, and Q is an arbitrary positive definite matrix.
MθIs a preset threshold value to ensure that | theta | is less than or equal to MθThe adaptation law is modified using the projection algorithm as follows:
Figure BDA0001980647690000103
wherein, P { γ eTp2ξ (x) } is the projection operator, which is defined as:
Figure BDA0001980647690000104
in the embodiment of the invention, the learning factor gamma is 5, k1＝2,k21, a pre-designed threshold Mθ＝1.5，
Figure BDA0001980647690000105
The number of fuzzy rules is 6.
Thirdly, when the system receives a specific control instruction, selecting a specific control algorithm according to the pertinence of the control instruction;
according to the attitude control condition of the spacecraft, the attitude control of the spacecraft can be classified into 3 basic working modes: attitude maneuver mode, attitude stabilization mode, and attitude rotation mode.
Judging the type of a control instruction input into the spacecraft after the system receives the control instruction, and entering the fourth step when the type of the control instruction is an attitude maneuver instruction; entering a fifth step when the control instruction type is an attitude stabilization instruction; and when the control command type is a posture rotation command, entering a sixth step.
Fourthly, when an attitude maneuver instruction is received, in order to ensure the control performance of the attitude maneuver, the attitude maneuver path is planned to obtain the attitude angular velocity;
when the spacecraft is subjected to attitude maneuver, the path of the spacecraft is generally required to be planned in order to achieve the best control effect due to the limitation of various constraint conditions. The path planning means that attitude angular velocity of the spacecraft during maneuvering is planned, and at the moment, a control quantity in a control algorithm is angular velocity instead of angle, and a corresponding physical quantity is planned angular velocity omegarAs will be apparent from the following control algorithm and implementation details.
In actual control, the main limiting constraints imposed on spacecraft attitude maneuver are that the control input is bounded, the spacecraft angular velocity is bounded, etc., which are respectively described as:
Ω1＝{u<umax}，
Ω2＝{ω<ωmax}，
wherein u ismaxFor maximum output torque, omega, of spacecraftmaxIs the maximum spacecraft angular velocity.
When the angular velocity of the spacecraft is bounded as a main limiting condition, the attitude maneuver angular velocity under the ideal condition meets the following conditions:
Figure BDA0001980647690000111
wherein, t1For the end of the maneuver acceleration phase, tfTotal time to complete the maneuver path, and t1≤0.5tf。
At this time, if the maneuvering target angle is α, the following are satisfied: t is tfωmax-t1ωmax＝α
When the control input is bounded as a main limiting condition, the attitude maneuver angular velocity ideally satisfies:
Figure BDA0001980647690000112
wherein, ω isMIs the maximum angular velocity during the maneuver of the spacecraft with bounded control inputs, and ωM≤ωmax，t1Is the motor acceleration stage end time.
At this time, if the maneuvering target angle is α, the following are satisfied: t is t1vM＝α。
Thus, for a given maneuvering target angle α, every t1Corresponding to an attitude maneuver path, wherein for each attitude maneuver path, under the limiting condition, the designed control method has the time t for finishing the attitude maneuver and keeping stable, f (k) is selected as an adaptive value, and the particle swarm optimization algorithm is used for finding t which minimizes f (k)1。
When t is1Determined, the attitude maneuver path of the spacecraft is also determined, namely the planned attitude angular velocity omega when the spacecraft maneuversrIs also determined.
Here, the spacecraft attitude maneuver target Angle
Figure BDA0001980647690000121
Maximum output torque umax0.1, and the maximum spacecraft angular velocity is ωmax＝0.01。
Fifthly, when the type of the received control instruction is an attitude stabilization instruction, determining a spacecraft control law u according to the golden section control algorithm and the self-adaptive fuzzy control algorithmcThe method comprises the following steps:
uc＝ug+uA，
Figure BDA0001980647690000122
uA＝θTξ(x)，
e(k)＝y(k)-yr(k)，
wherein y (k) isAttitude angle of the actual output of the antenna, which represents the value at time k in a discrete state; y isr(k) Is a spacecraft target attitude angle which represents the value of k time in a discrete state; k represents the current time in the discrete state, and k-1 represents the last time in the discrete state; λ is a forgetting factor;
Figure BDA0001980647690000123
the parameters are modeled for the features in the discrete states,
Figure BDA0001980647690000124
Figure BDA0001980647690000125
θ＝[θ1,θ2,…,θN]Tis an adjustable parameter vector; xi is ═ xi1,ξ2,…,ξN]TIs a fuzzy basis function vector; n is a fuzzy rule number and is a positive integer; and x is the attitude angle and the attitude angular velocity of the spacecraft.
Sixthly, when the type of the received control command is an attitude rotation command, determining a spacecraft control law u according to the fuzzy golden section control algorithm and the self-adaptive fuzzy control algorithmcThe method comprises the following steps:
uc＝uf+uA，
Figure BDA0001980647690000131
Figure BDA0001980647690000132
uA＝θTξ(x)，
e(k)＝y(k)-yr(k)，
wherein, muiTo normalize the intensity of the emission, mu1+μ2+…+μl＝1；
Figure BDA0001980647690000138
Is a golden section control law corresponding to the ith T-S fuzzy rule under the l T-S fuzzy rules;
Figure BDA0001980647690000133
modeling parameters for the characteristics under the discrete state corresponding to the ith T-S fuzzy rule,
Figure BDA0001980647690000134
Figure BDA0001980647690000135
ξ＝[ξ1,ξ2,…,ξN]Tis a fuzzy basis function vector; l is a positive integer; n is a fuzzy rule number and is a positive integer; theta is ═ theta1,θ2,…,θN]TIs an adjustable parameter vector; x is the attitude angle and attitude angular velocity of the spacecraft; lambda [ alpha ]iIs a forgetting factor corresponding to the ith T-S fuzzy rule under the l T-S fuzzy rules; y (k) is an attitude angle actually output by the spacecraft, and the attitude angle represents a numerical value of k time in a discrete state; y isr(k) Is a spacecraft target attitude angle which represents the value of k time in a discrete state; k represents the current time in the discrete state, and k-1 represents the last time in the discrete state.
Seventhly, in order to fully guarantee the stability of the control system, determining a supervisory control law u according to a judgment functionsAnd determining an execution control law according to the spacecraft control law and the supervision control law, and controlling the attitude of the spacecraft according to the execution control law.
Here, the supervisory control law is defined as usThe supervisory control law takes the form:
Figure BDA0001980647690000136
wherein the content of the first and second substances,
Figure BDA0001980647690000137
e＝ym-youtthe matrix P satisfies Λc TP+PΛcQ is an arbitrary positive definite matrix, Λc＝[0,1；-k2,-k1]，k＝[k2,k1]T，k1,k2Is such that r(s) is s2+k1s+k2Is a Hurwitz stable polynomial; y ismIs a target attitude angle, yo, in a continuous stateutThe actual output attitude angle of the spacecraft in a continuous state;
Figure BDA0001980647690000141
to determine the threshold value, here
Figure BDA0001980647690000142
When in use
Figure BDA0001980647690000143
When the temperature of the water is higher than the set temperature,
Figure BDA0001980647690000144
when in use
Figure BDA0001980647690000145
When the temperature of the water is higher than the set temperature,
Figure BDA0001980647690000146
bc＝[0,b]T，b>0，bLb is 0. ltoreq. bLAny constant less than or equal to b; f. ofU(x) To satisfy | f (x) | less than or equal to fU(x) Wherein f (x) is a non-linear equation function established by the equations of the kinematics dynamics of the spacecraft. Because b is>0, so sgn (e)TPbc) It can be determined that other items can be determined according to the fuzzy understanding of the controlled object in advance.
And finally, determining an execution control law u-u according to the spacecraft control law and the supervision control lawc+usAnd controlling the attitude of the spacecraft according to the execution control law u.
The functional block diagram of the method of the invention is shown in fig. 2, and the design of the whole controller adopts a three-layer structure, including a tissue layer, a coordination layer and a control layer. The organizational layer is specifically control planning, namely, the control of the system is comprehensively planned from control tasks and constraint conditions, so that the overall performance of the control system is optimal, wherein the control planning comprises selection planning of a control algorithm and attitude maneuver path planning; the coordination layer is used for establishing a determined control algorithm aiming at a specific control task so as to carry out targeted control, wherein the control algorithm comprises three parts of characteristic modeling, satellite control law determination and supervision control law determination; the control layer is specifically control execution, that is, a specific control law is executed.
Wherein the specific signal flow directions in fig. 2 are: 1) the organization layer inputs a specific control instruction O to the coordination layer according to the control task condition of the system and the output condition of the spacecraftp(ii) a When the specific control instruction is input as the attitude maneuver, the attitude maneuver path planning is carried out according to the constraint condition of the system, and the planned attitude path omega is input to the coordination layerr. 2) The coordination layer carries out characteristic modeling according to the control input u (k) of the spacecraft and the output omega (k), y (k) of the spacecraft, thereby establishing an intelligent control algorithm u based on the characteristic model0,ug,uf(ii) a According to the control instruction O input by the tissue layerpAnd combines a fuzzy self-adaptive control law u established by fuzzy understanding of spacecraft in advanceADetermining a spacecraft control law uc(ii) a According to the spacecraft control law u at the same timecAnd determining a supervision control law u by a spacecraft kinematics dynamics models. 3) U input by control layer according to coordination layercAnd usAnd determining an actual execution condition e (k) of the control law, determining an execution control law u (k), and acting on the spacecraft to complete the whole control action, wherein u (k) is a discrete form of u.
Those skilled in the art will appreciate that the invention may be practiced without these specific details.

Claims (3)

1. A spacecraft autonomous attitude control method is characterized by comprising the following steps:
1) performing characteristic modeling according to a spacecraft kinematics dynamics model to obtain a maintenance tracking control algorithm, a golden section control algorithm and a fuzzy golden section control algorithm;
2) establishing a self-adaptive fuzzy control algorithm according to a spacecraft kinematics dynamic model;
3) determining a spacecraft control law according to the type of a control instruction input into a spacecraft, the maintenance tracking control algorithm, the golden section control algorithm and the fuzzy golden section control algorithm obtained in the step 1), and the self-adaptive fuzzy control algorithm established in the step 2);
4) determining a supervision control law according to a decision function;
5) determining an execution control law according to the spacecraft control law determined in the step 3) and the supervision control law determined in the step 4), and controlling the attitude of the spacecraft according to the execution control law;
the method for determining the spacecraft control law in the step 3) specifically comprises the following steps:
31) judging the type of a control instruction input into the spacecraft, and entering step 32) when the type of the control instruction is an attitude maneuver instruction; when the control instruction type is an attitude stabilization instruction, entering step 33); when the control command type is a posture rotation command, entering step 34);
32) planning a spacecraft attitude maneuver path according to the performance requirement and the limiting constraint condition of a spacecraft control system to obtain attitude angular velocity, and determining a spacecraft control law u according to the attitude angular velocity obtained by planning the spacecraft attitude maneuver path, a maintenance tracking control algorithm and an adaptive fuzzy control algorithmc；
33) Determining a spacecraft control law u according to the golden section control algorithm and the self-adaptive fuzzy control algorithmc；
34) Determining a spacecraft control law u according to the fuzzy golden section control algorithm and the self-adaptive fuzzy control algorithmc；
Said step 32) determining the spacecraft control law ucThe method specifically comprises the following steps:
uc＝u0+uA，
Figure FDA0003143712850000021
uA＝θTξ(x)，
wherein, ω isr(k) Attitude angular velocity obtained for spacecraft attitude maneuver path planning, which represents the value of k time in discrete state; omega (k) is the attitude angular velocity actually output by the spacecraft, and represents the value of k time in a discrete state; k represents the current time in the discrete state, and k-1 represents the last time in the discrete state; λ is a forgetting factor;
Figure FDA0003143712850000022
modeling parameters for features in discrete states, wherein
Figure FDA0003143712850000023
θ＝[θ1,θ2,…,θN]TIs an adjustable parameter vector; xi is ═ xi1,ξ2,…,ξN]TIs a fuzzy basis function vector; n is a positive integer; x is the attitude angle and attitude angular velocity of the spacecraft;
said step 33) determining the spacecraft control law ucThe method specifically comprises the following steps:
uc＝ug+uA，
Figure FDA0003143712850000024
uA＝θTξ(x)，
e(k)＝y(k)-yr(k)，
wherein y (k) is an attitude angle actually output by the spacecraft, and the attitude angle represents a numerical value of k time in a discrete state; y isr(k) Is a spacecraft target attitude angle which represents the value of k time in a discrete state; k represents the current time in the discrete state, and k-1 represents the last time in the discrete state(ii) a λ is a forgetting factor;
Figure FDA0003143712850000025
the parameters are modeled for the features in the discrete states,
Figure FDA0003143712850000026
Figure FDA0003143712850000027
θ＝[θ1,θ2,…,θN]Tis an adjustable parameter vector; xi is ═ xi1,ξ2,…,ξN]TIs a fuzzy basis function vector; n is a fuzzy rule number and is a positive integer; x is the attitude angle and attitude angular velocity of the spacecraft;
said step 34) determining the spacecraft control law ucThe method specifically comprises the following steps:
uc＝uf+uA，
Figure FDA0003143712850000031
Figure FDA0003143712850000032
uA＝θTξ(x)，
e(k)＝y(k)-yr(k)，
wherein, muiTo normalize the intensity of the emission, mu1+μ2+…+μl＝1；
Figure FDA0003143712850000033
Is a golden section control law corresponding to the ith T-S fuzzy rule under the l T-S fuzzy rules;
Figure FDA0003143712850000034
for the ith strip T-S mouldCharacteristic modeling parameters under the corresponding discrete state under the fuzzy rule,
Figure FDA0003143712850000035
Figure FDA0003143712850000036
ξ＝[ξ1,ξ2,…,ξN]Tis a fuzzy basis function vector; l is a positive integer; n is a fuzzy rule number and is a positive integer; theta is ═ theta1,θ2,…,θN]TIs an adjustable parameter vector; x is the attitude angle and attitude angular velocity of the spacecraft; lambda [ alpha ]iIs a forgetting factor corresponding to the ith T-S fuzzy rule under the l T-S fuzzy rules; y (k) is an attitude angle actually output by the spacecraft, and the attitude angle represents a numerical value of k time in a discrete state; y isr(k) Is a spacecraft target attitude angle which represents the value of k time in a discrete state; k represents the current time in the discrete state, and k-1 represents the last time in the discrete state;
said step 4) determining the supervisory control law usThe method specifically comprises the following steps:
Figure FDA0003143712850000037
wherein the content of the first and second substances,
Figure FDA0003143712850000038
to determine the threshold value, when
Figure FDA0003143712850000039
When the temperature of the water is higher than the set temperature,
Figure FDA00031437128500000310
when in use
Figure FDA00031437128500000311
When the temperature of the water is higher than the set temperature,
Figure FDA00031437128500000312
bc＝[0,b]T，b>0，bLb is 0. ltoreq. bLAny constant less than or equal to b; f. ofU(x) To satisfy | f (x) | less than or equal to fU(x) F (x) is established according to a spacecraft kinematics kinetic equation; k ═ k2,k1]T，k1,k2Is such that r(s) is s2+k1s+k2Is a Hurwitz stable polynomial.
2. A spacecraft autonomous attitude control method according to claim 1, characterized in that step 4) said decision function VcThe method specifically comprises the following steps:
Figure FDA0003143712850000041
Figure FDA0003143712850000042
e＝ym-yout，
wherein the matrix P satisfies Λc TP+PΛcQ is an arbitrary positive definite matrix, Λc＝[0,1；-k2,-k1]，k1,k2Is such that r(s) is s2+k1s+k2Is a Hurwitz stable polynomial; y ismIs a target attitude angle, y, of the spacecraft in a continuous stateoutThe attitude angle actually output by the spacecraft in a continuous state.
3. A spacecraft autonomous attitude control method according to claim 1, characterized in that said step 5) determines a method of executing a control law u according to a spacecraft control law and a supervisory control law, in particular: u-uc+us。
CN201910148020.6A 2019-02-28 2019-02-28 Autonomous attitude control method for spacecraft Active CN109828464B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910148020.6A CN109828464B (en) 2019-02-28 2019-02-28 Autonomous attitude control method for spacecraft

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910148020.6A CN109828464B (en) 2019-02-28 2019-02-28 Autonomous attitude control method for spacecraft

Publications (2)

Publication Number Publication Date
CN109828464A CN109828464A (en) 2019-05-31
CN109828464B true CN109828464B (en) 2021-09-03

Family

ID=66864776

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910148020.6A Active CN109828464B (en) 2019-02-28 2019-02-28 Autonomous attitude control method for spacecraft

Country Status (1)

Country Link
CN (1) CN109828464B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111856944B (en) * 2020-08-05 2022-01-28 重庆大学 Hypersonic aircraft fuzzy control method based on event triggering
CN112434370B (en) * 2020-11-12 2023-07-14 北京控制工程研究所 Feature modeling method for error-free compression of flexible aircraft
CN117262252B (en) * 2023-09-21 2024-06-11 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) Spacecraft autonomous intersection and docking control method capable of realizing fuel optimization

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104483835B (en) * 2014-11-06 2017-03-15 中国运载火箭技术研究院 A kind of flexible spacecraft multiple target integrated control method based on T S fuzzy models
CN105607485B (en) * 2016-02-04 2018-08-14 河北科技师范学院 Flexible liquid filled spacecraft attitude-adaptive fault tolerant control method based on fault signature model
CN105955284B (en) * 2016-05-30 2018-10-02 中国人民解放军国防科学技术大学 A kind of in-orbit filling Spacecraft Attitude Control method
CN105843244A (en) * 2016-06-02 2016-08-10 北京航空航天大学 Output feedback-based flexible spacecraft precise attitude control method
CN107807657B (en) * 2017-11-29 2021-01-26 南京理工大学 Flexible spacecraft attitude self-adaptive control method based on path planning
CN109270946B (en) * 2018-11-13 2020-07-28 北京航空航天大学 Attitude control method of flexible spacecraft, electronic device and readable storage medium

Also Published As

Publication number Publication date
CN109828464A (en) 2019-05-31

Similar Documents

Publication Publication Date Title
Yang et al. Adaptive NN backstepping control design for a 3-DOF helicopter: Theory and experiments
Fu et al. Adaptive robust backstepping attitude control for a multi-rotor unmanned aerial vehicle with time-varying output constraints
CN109828464B (en) Autonomous attitude control method for spacecraft
Gan et al. QPSO-model predictive control-based approach to dynamic trajectory tracking control for unmanned underwater vehicles
Yang et al. Adaptive neural prescribed performance tracking control for near space vehicles with input nonlinearity
Pouzesh et al. Event-triggered fractional-order sliding mode control technique for stabilization of disturbed quadrotor unmanned aerial vehicles
Ho et al. Robust fuzzy tracking control for robotic manipulators
Dai et al. Design of an indirect adaptive controller for the trajectory tracking of UVMS
CN107807657B (en) Flexible spacecraft attitude self-adaptive control method based on path planning
Hu et al. Nussbaum-based fuzzy adaptive nonlinear fault-tolerant control for hypersonic vehicles with diverse actuator faults
Cheng et al. Attitude control of a satellite using fuzzy controllers
Nagi et al. Tuning fuzzy Bang–bang relay controller for satellite attitude control system
Shen et al. Dynamic surface control for tracking of unmanned surface vessel with prescribed performance and asymmetric time-varying full state constraints
Eltayeb et al. Adaptive fuzzy gain scheduling sliding mode control for quadrotor UAV systems
Zhu et al. A neurodynamics control strategy for real-time tracking control of autonomous underwater vehicles
Wei et al. On adaptive attitude tracking control of spacecraft: A reinforcement learning based gain tuning way with guaranteed performance
CN114237267A (en) Flight maneuver decision auxiliary method based on reinforcement learning
Zhao et al. Data-driven formation control for multiple heterogeneous vehicles in air–ground coordination
Cheng et al. Data-driven time-varying formation-containment control for a heterogeneous air-ground vehicle team subject to active leaders and switching topologies
Xie et al. Fixed-time convergence attitude control for a tilt trirotor unmanned aerial vehicle based on reinforcement learning
Shui et al. Event-based adaptive fuzzy tracking control for nonlinear systems with input magnitude and rate saturations
CN112000014B (en) Control method for model prediction and inversion of control mechanical arm
Yu et al. An observer-based indirect adaptive fuzzy control for rolling cart systems
CN110703602B (en) Robust differential game control method for on-orbit assembly attitude stabilization of multiple robots
Sweafford Jr et al. A neuralnetwork model-based control method for a class of discrete-time nonlinear systems

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