CN111812977B - GEO direct fixed-point launching orbit optimization method - Google Patents
GEO direct fixed-point launching orbit optimization method Download PDFInfo
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Abstract
The invention discloses a GEO direct fixed-point launching orbit optimization method, which comprises the following steps: 1) setting NSGA-II optimization parameters; 2) setting an optimized variable value range; 3) generating an initial population of the calculation parameter condition of the emission orbit; 4) generating a transmitting track calculation result population; 5) generating a filial generation population; 6) generating a filial generation population; 7) a next generation parent population; 8) and (5) judging the genetic termination.
Description
Technical Field
The invention relates to a design optimization method of an upper-level orbit for launching of spacecrafts such as satellites.
Background
Geostationary Orbit (GEO) satellite transmission Orbit design and optimization can improve space transmission mission efficiency. In a concept design stage, the optimal overall parameters are determined based on track optimization, so that the design potential can be excavated to the maximum extent; in the actual flight track design stage, the track optimization design can effectively improve the quality of the effective load and reduce the emission cost. Most of the research of experts and scholars in China is generally indirect Orbit GEO satellite launching Orbit and is divided into two research modes of Geosynchronous Transfer Orbit (GTO) optimization and GEO satellite fixed-point Orbit Transfer optimization.
Due to latitude limitations of the launch field, the GTO launch trajectory is usually selected as the launch trajectory with the berthing trajectory, and the launch trajectory optimization design problem is a large-scale complex trajectory optimization design problem. The design method of the GTO launching orbit which is put forward at the earliest in China adopts the optimal orbital transfer process of multi-time orbital transfer at a far place, and puts forward the change rule with the optimal thrust direction and the optimal selection method of the intermediate orbit. On the basis of a GTO orbit, a GEO satellite is generally launched by adopting an indirect-access launching scheme, a carrier rocket sends a satellite carrying an orbital transfer engine and fuel into the GTO, the satellite is ignited and maneuvered for a plurality of times (4 times to 6 times) near a GTO orbit far place, and finally the satellite is fixed to a desired position. Satellites generally optimize fuel consumption by properly allocating the energy of these several orbital transfer processes; and a method for optimally selecting the argument of the near place of the transfer orbit according to different launching months is also adopted, so that the speed increment required by orbit transfer is reduced, and the satellite orbit transfer scheme is optimized.
For the requirement of direct in-orbit launching of the GEO satellite, a scheme of combining a carrier rocket and an upper stage and directly launching the GEO satellite at a fixed point can be adopted, and the GEO satellite can be directly sent to the upper space of the equator needing the fixed point. Firstly, launching the upper stage of a rocket carrying a GEO satellite into a synchronous transfer orbit with the inclination angle of 19-28 degrees of 200km multiplied by 36000 km or a super-synchronous transfer orbit with a slightly higher far place by the rocket; according to the requirement of the fixed point position of the satellite, the upper stage ignites for the 1 st time at the far place to raise the orbit and partially depress the inclination angle of the orbit, enters a phase modulation orbit, and ignites for the 2 nd time at the far place of the phase modulation orbit after flying the phase modulation orbit section, so as to adjust the height of the near place and the inclination angle of the orbit of the flying orbit, and finally enters the GEO orbit and complete the fixed point. The flight trajectory is shown in fig. 1.
The optimization problem comprises a maximization problem and a minimization problem which can be mutually converted, so that the multi-objective optimization problem is identically generalized to a multi-objective minimization problem, and a general mathematical expression of the multi-objective optimization is given as follows:
min F(x)＝(f 1 (x),f 2 (x),…,f m (x)) T
wherein the content of the first and second substances,
is an n-dimensional decision vector, X is an n-dimensional decision space,
is an m-dimensional target vector, Y is an m-dimensional target space, F (X) is an objective function consisting of m objective component functions mapped from X to Y, f i (x) For the ith objective component function of the objective function f (x), g (x), h (x) are p inequality functions and q equality functions, respectively, which constrain the decision space, the constraint functions together determining the feasible domain of the decision vector x.
The improved non-dominated sorting genetic algorithm (NSGA-II algorithm) is a multi-target genetic algorithm based on rapid classification and adopting an elite strategy. First, a parent population P is randomly generated t The population is then rapidly non-winning ranked. Each non-dominant solution is assigned an adaptation value according to its ranking level. Selection, replication and mutation operations are performed to generate a progeny population Q having N individuals t And the parent and the offspring use the elite strategy to construct a new population in a mixed mode, and the cycle is repeated. The algorithm is represented in pseudo code as:
Merging parents and children
R t ＝P t ∪Q t
Contesting the groupFast non-winning class F ═ { F } 1 ,F 2 ,…}
F＝fast-nondo min ated-sort(R t )
Until the paternal population is filled, otherwise calculate F i Classifying into congestion distance, adding parent ith non-dominated front edge, and checking next front edge
until|P t+1 |+|F i |≤N
crowding-dis tan ce-assignment(F i )
P t+1 ＝P t+1 ∪F i
i＝i+1
Using a comparison operator < n Sorting in descending order
Sort(F i ,＜ n )
Adding F i Is pre-N- | P t+1 From | individuals to populations
P t+1 ＝P t+1 ∪F i [1:N-|P t+1 |]
Construction of new populations by selection, crossover and variation
Q t+1 ＝make-new-pop(P t+1 )
The next iteration
t＝t+1
Wherein the comparison operator < n Is defined as:
i＜ n j if(i rank <j rank )or(i rank ＝j rank )and(i distance >j distance )
since the introduction of the constraint-based dominance tournament selection operator facilitates the assignment of fitness values to individuals, no penalty function is required.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the defects and limitations in the prior art are overcome, the GEO direct fixed-point launching orbit optimization method is provided, the carrying capacity of the direct fixed-point launching GEO satellite is improved, and the optimization design is realized.
The technical solution of the invention is as follows: a GEO direct fixed-point launching orbit optimization method comprises the following steps:
step one, setting NSGA-II optimization parameters;
step two, setting an optimized variable value range;
step three, generating an initial population of the calculation parameter condition of the emission orbit; randomly generating N groups of initial variables in the optimized variable range, and forming N groups of launching orbit calculation initial conditions together with the upper-stage weight, the upper-stage engine thrust and the specific impulse;
Step four, generating a transmitting track calculation result population; and taking the N groups of emission initial orbit parameters obtained in the step three as initial values, taking the corresponding N groups of initial phase modulation orbit target parameters as first orbital transfer target quantities, and performing ballistic iteration calculation.
Step five, generating a transmitting track calculation result population; taking N groups of randomly generated initial parameters and corresponding track calculation results as an initial population Pt, and taking the initial population Pt as a parent population;
step six, carrying out binary competition selection, binary intersection simulation and polynomial variation operation on the parent population Pt to generate a population Qt, and taking the Qt as a child population;
step seven, fusing Pt and Qt together to serve as a temporary population Rt with the scale of 2N, carrying out rapid non-dominated sorting and congestion degree calculation on the temporary population Rt, and selecting the optimal N individuals as a parent population Pt in the next generation evolution operation according to the non-dominated sorting layering sequence number of the individuals and the congestion distance of the individuals, wherein t is t + 1;
step eight, judging whether the iteration times are equal to a preset upper limit value Ngen or not, if so, ending the operation, and finishing the optimization design work; otherwise, returning to the step four, and executing the next generation operation.
The first step of optimizing parameters specifically comprises the following steps: setting the number of optimization variables Nvar, the number of optimization objective functions Func, the population scale of Npop, the evolution algebra of Ngen, the cross rate Pcr, the cross distribution index dic, the variation rate Pmut and the variation distribution index dim.
In the first step, the number Nvar of the optimized variables is set to be 4; the number Func of the optimization target functions is set to be 2; the population size Npop is set to 80; the evolution algebra Ngen is set to 10.
In the first step, the optimization variables include: GTO orbit apogee height Ha after separation of base-level rocket GTO GTO orbit near-place amplitude angle Ha GTO Fixed point phase modulation orbit near-site height Hp pho Fixed point phase modulation orbit inclination angle i pho ；
In the second step, Ha GTO Selecting 35786 +/-200 km; ha GTO Selecting a value range from 178 degrees to 180 degrees; hp pho Selecting a value range from 200km to 35786 km; i.e. i pho The value range is selected from 0-28 deg.
The specific ballistic iteration calculation process of the fourth step is as follows:
step A, according to the randomly generated GTO orbit apogee height Ha in N groups of initial variables GTO And argument of perigee omega GTO Fixed point phase modulation orbit near-site height Hp pho And fixed point phase modulation orbit inclination angle i pho Correspondingly setting a target height Hp of the track near point after the first track change in the current ballistic iteration calculation phoT ＝Hp pho And track inclination angle i phoT ＝i pho ；
Step B, setting the time length t of the first sliding section of the upper stage 1 (ii) a Let the first sliding section of the upper stage have a time t 1 Initially setting to be half of a GTO orbit period;
Step C, setting the working time t of the main engine when the upper-stage main engine is ignited for the first time 2 Sum yaw angle correction amount delta psi 1 (ii) a The working time t of the main engine when the first ignition of the upper main engine is started 2 Initially setting the working time of the remaining available propellant; yaw angle correction quantity delta psi 1 Is 0;
calculating the first sliding time t of the upper level by adopting a Newton iterative algorithm 1 The ignition working time t of the first main engine 2 Sum yaw angle correction amount delta psi 1 The newton iterative relationship is:
the iteration termination condition is as follows:
wherein, Oa is pho Trajectory inclination, Oa, for the end of the first firing operation of the current rail engine pho Is t 1 A function of (a); Δ Hp pho The height Hp of the track near point at the moment when the first ignition operation of the current track main engine is finished pho1 And Hp phoT Absolute value of difference, Δ Hp pho Is t 2 Function of, Hp eps The control precision of the height of the remote place is shown, and n is a non-negative integer; Δ i pho The track inclination angle i of the current track main engine at the end moment of the first ignition work pho1 And i phoT Absolute value of difference, Δ i pho Is delta psi 1 Function of i eps Controlling the precision of the track inclination angle;
if the iteration termination condition is met, completing the iterative calculation of the fixed point phase modulation orbit, and entering the step D;
If in the iteration end condition, Oa pho If the condition is not met, returning to the step B, and adjusting the first sliding time length t of the upper stage 1 ；
If in the iteration end condition, Δ Hp pho 、Δi pho If one of the two does not meet the condition, the step four is returned to, and t is adjusted 2 And delta psi 1 Recalculating until an iteration termination condition is met;
step D, setting the second sliding time length t of the upper level 3 (ii) a Let the duration t of sliding 3 The initial setting is half of the fixed point phase modulation orbit period;
step E, setting the main engine of the upper level main engine during the second ignition maneuverEngine operating time t 4 Yaw angle correction amount delta psi 2 And pitch correction amount
The working time t of the main engine when the second ignition of the upper main engine is started 4 Initially setting the working time of the remaining available propellant; yaw angle correction amount delta psi and pitch correction amount
Are all set to 0;
calculating satellite orbit-entering parameters after satellite separation, and solving by adopting a Newton iterative algorithm, wherein the iterative relation is as follows:
judging whether the satellite orbit-entering parameters meet the following conditions:
wherein a is the semi-major axis value of the satellite orbit at the current ballistic trajectory calculation time, a T Completing orbit semi-major axis value for GEO satellite fixed point, delta a is absolute value of difference value of the two, a eps Transmitting orbit semi-major axis control precision for the satellite; i value of inclination angle of orbit of satellite at current ballistic calculation time, i T The value of the inclination of the orbit for the satellite to be launched, Δ i being the absolute value of the difference between the two, i eps Transmitting the orbit inclination angle control precision for the satellite; e is the eccentricity of the satellite orbit at the current ballistic trajectory calculation time; if Oa does not meet the condition, returning to the step D, and adjusting the second-time coasting time length t of the upper stage 3 ；
If one of the three items of delta a, delta i and E does not meet the condition, the step E is returned to, and t is adjusted 4 、δψ、
Recalculating until an iteration termination condition is met;
if the conditions are all met, the iterative calculation process of the track is finished, and the design of one track is finished;
and according to the iterative calculation flow of the orbits, after N groups of initial orbit calculation of emission are completed, N groups of orbit calculation results are obtained.
Compared with the prior art, the invention has the advantages that:
the method adopts a multi-objective optimization algorithm-an improved non-dominated sorting genetic algorithm (NSGA-II algorithm) to optimize the GEO direct fixed-point launching orbit, is different from the existing GEO satellite fixed-point method, and can improve the GEO satellite orbit quality on the basis of greatly shortening the launching period of the GEO satellite from rocket launching and taking off to a fixed point to a target position by optimizing the GTO orbit apogee altitude, the apogee argument, the phase modulation orbit apogee altitude and the orbit inclination angle, thereby realizing the optimized launching orbit design, and finally achieving the effects of improving the GEO satellite engineering launching efficiency and reducing the launching cost.
Drawings
FIG. 1 is a schematic view of an upper stage direct-on-orbit fixed-point launch GEO flight trajectory;
FIG. 2 is a flow chart of NSGA-II emission trajectory optimization;
fig. 3 is a flow chart of an orbit iteration calculation.
Detailed Description
The method comprises two processes of ballistic iteration calculation and NSGA-II emission orbit optimization, wherein the ballistic iteration calculation process is contained in the optimization process, and the detailed description is given below.
As shown in fig. 2, a GEO direct fixed point transmission orbit optimization method includes the following steps:
step one, setting NSGA-II optimization parameters. Setting the number of optimized variables Nvar, and setting the number of the optimized variables Nvar to 4; optimizing the number Func of the target functions, and setting the number of the target functions to be 2 by the method; the population scale is Npop, and the method is set to 80; the evolution algebra is Ngen and is set to be 10; cross rate Pcr, cross distribution index dic, variation rate Pmut, and variation distribution index dim.
Step (ii) ofAnd secondly, optimizing the value range setting of the variables. Setting an optimized variable value range under the condition of considering the carrying capacity of the base level, wherein the method comprises the following steps: GTO orbit apogee height Ha after separation of base-level rocket GTO The value is generally selected from 35786 +/-200 km; GTO orbit near-place argument Ha GTO The value range is generally 178-180 degrees; fixed point phase modulation orbit near-site height Hp pho The value range is selected from 200km to 35786 km; fixed point phase modulation orbit inclination angle i pho And selecting 0-28 degrees according to the general value range of the mature carrier rocket in China.
And step three, emitting the initial population generation of the track calculation parameter condition. And randomly generating N groups of initial variables in the optimized variable range, and combining the N groups of initial variables with the upper-stage weight, the upper-stage engine thrust and the specific impulse to calculate initial conditions for the N groups of launching tracks.
And step four, emitting the track calculation result population generation. The trajectory iteration calculation flow is followed as shown in fig. 3. And taking the N groups of emission initial orbit parameters obtained in the step three as initial values, and taking the corresponding N groups of initial phase modulation orbit target parameters as first orbital transfer target quantities. The trajectory iterative calculation process is as follows:
step A, randomly generating the GTO orbit far-field height Ha in the N groups of emission orbits GTO And argument of perigee omega GTO Fixed point phase modulation orbit near-site height Hp pho And fixed point phase modulation orbit inclination angle i pho Correspondingly setting a target height Hp of the track near point after the first track change in the current ballistic iteration calculation phoT ＝Hp pho And track inclination angle i phoT ＝i pho ；
Step B, setting the time length t of the first sliding section of the upper stage 1 (ii) a Let the first sliding section of the upper stage have a time t 1 Initially setting to be half of a GTO orbit period;
step C, setting the working time t of the main engine when the upper-stage main engine is ignited for the first time 2 Sum yaw angle correction amount delta psi 1 (ii) a The working time t of the main engine when the first ignition of the upper main engine is started 2 Initially set to remaining propellant availableThe working time length; yaw angle correction quantity delta psi 1 Is 0.
Calculating the first sliding time t of the upper level by adopting a Newton iterative algorithm 1 The ignition working time t of the first main engine 2 Sum yaw angle correction amount delta psi 1 The newton iterative relationship is:
the iteration termination condition is as follows:
wherein, Oa is pho Trajectory inclination, Oa, for the end of the first firing operation of the current rail engine pho Is t 1 A function of (a); Δ Hp pho The height Hp of the track near point at the moment when the first ignition operation of the current track main engine is finished pho1 And Hp phoT Absolute value of difference, Δ Hp pho Is t 2 Function of, Hp eps The control precision of the height of the remote place is shown, and n is a non-negative integer; Δ i pho The track inclination angle i of the current track main engine at the end moment of the first ignition work pho1 And i phoT Absolute value of difference, Δ i pho Is delta psi 1 Function of i eps Controlling the precision of the track inclination angle;
if the iteration termination condition is met, completing the iterative calculation of the fixed point phase modulation orbit, and entering the step D;
If in the iteration end condition, Oa pho If the condition is not met, returning to the step B, and adjusting the first sliding time length t of the upper stage 1 ；
If in the iteration end condition, Δ Hp pho 、Δi pho If one of the two does not meet the condition, the step four is returned to, and t is adjusted 2 And delta psi 1 Recalculating until an iteration termination condition is met;
step D, setting the upper surfaceSecond time of grade coast period t 3 (ii) a Let the duration t of sliding 3 The initial setting is half of the fixed point phase modulation orbit period;
step E, setting the working time t of the main engine when the upper-stage main engine is used for the second ignition maneuver 4 Yaw angle correction amount delta psi 2 And pitch correction amount
The working time t of the main engine when the second ignition of the upper main engine is started 4 Initially setting the working time of the remaining available propellant; yaw angle correction amount delta psi and pitch correction amount
Are all set to 0.
Calculating satellite orbit-entering parameters after satellite separation, and solving by adopting a Newton iterative algorithm, wherein the iterative relation is as follows:
judging whether the satellite orbit-entering parameters meet the following conditions:
wherein a is the semi-major axis value of the satellite orbit at the current ballistic trajectory calculation time, a T The orbit half-length axis value (generally 42164.14km) is completed for the fixed point of the GEO satellite, delta a is the absolute value of the difference between the two, and a eps Transmitting orbit semi-major axis control precision for the satellite; i inclination angle value of satellite orbit at current trajectory calculation time, i T The value of the inclination of the orbit for the satellite to be launched, Δ i being the absolute value of the difference between them, i eps Transmitting the orbit inclination angle control precision for the satellite; e is the eccentricity of the satellite orbit at the current ballistic trajectory calculation time; if Oa does not meet the condition, returning to the step D, and adjusting the second-time coasting time length t of the upper stage 3 ；
If one of the three items of delta a, delta i and E does not meet the condition, the step E is returned to, and t is adjusted 4 、δψ、
Recalculating until an iteration termination condition is met;
if the conditions are all met, the track iterative computation process is finished, and one track is designed.
And according to the iterative calculation flow of the orbits, after N groups of initial orbit calculation of emission are completed, N groups of orbit calculation results are obtained.
And step five, generating a parent population. Taking N groups of randomly generated initial parameters and corresponding track calculation results as an initial population Pt, and taking the initial population Pt as a parent population;
and sixthly, generating offspring population. Carrying out binary competition selection, binary intersection simulation and polynomial variation operation on the parent population Pt to generate a population Qt, and taking the Qt as a child population;
and seventhly, preparing the next generation parent population. Fusing Pt and Qt together to serve as a temporary population Rt with the scale of 2N, performing rapid non-dominant sorting and congestion degree calculation on the temporary population Rt, and selecting optimal N individuals as a parent population Pt in next-generation evolution operation according to the non-dominant sorting layering sequence number of the individuals and the congestion distance of the individuals, wherein t is t + 1;
And step eight, genetic optimization termination judgment. Judging whether the iteration times are equal to a preset upper limit value Ngen or not, if so, ending the operation, and finishing the optimization design work; otherwise, returning to the step five, and executing the next generation operation.
The above-described embodiments are merely preferred embodiments of the present invention, and general changes and substitutions by those skilled in the art within the technical scope of the present invention are included in the protection scope of the present invention.
Claims (5)
1. A GEO direct fixed-point launching orbit optimization method is characterized by comprising the following steps:
step one, setting NSGA-II optimization parameters;
step two, setting an optimized variable value range;
step three, generating an initial population of the calculation parameter condition of the emission orbit; randomly generating N groups of initial variables in the optimized variable range, and forming N groups of launching orbit calculation initial conditions together with the upper-stage weight, the upper-stage engine thrust and the specific impulse;
step four, generating a transmitting track calculation result population; and taking the N groups of emission initial orbit parameters obtained in the step three as initial values, taking the corresponding N groups of initial phase modulation orbit target parameters as first orbital transfer target quantities, and performing ballistic iteration calculation.
Step five, generating a parent population; taking N groups of randomly generated initial parameters and corresponding track calculation results as an initial population Pt, and taking the initial population Pt as a parent population;
step six, generating a child population; carrying out binary competition selection, binary intersection simulation and polynomial variation operation on the parent population Pt to generate a population Qt, and taking the Qt as a child population;
step seven, generating a next generation parent population; fusing Pt and Qt together to serve as a temporary population Rt with the scale of 2N, performing rapid non-dominant sorting and congestion degree calculation on the temporary population Rt, and selecting optimal N individuals as a parent population Pt in next-generation evolution operation according to the non-dominant sorting layering sequence number of the individuals and the congestion distance of the individuals, wherein t is t + 1;
step eight, genetic termination judgment; judging whether the iteration times are equal to a preset upper limit value Ngen or not, if so, ending the operation, and finishing the optimization design work; otherwise, returning to the step four, and executing the next generation operation;
the specific ballistic iteration calculation process of the fourth step is as follows:
step A, according to the randomly generated GTO orbit apogee height Ha in N groups of initial variables GTO And argument of perigee omega GTO Fixed point phase modulation orbit near-site height Hp pho And fixed point phase modulation orbit inclination angle i pho Correspondingly setting a target height Hp of the track near point after the first track change in the current ballistic iteration calculation phoT ＝Hp pho And a trackInclination angle i phoT ＝i pho ；
Step B, setting the time length t of the first sliding section of the upper stage 1 (ii) a Let the first sliding section of the upper stage have a time t 1 Initially setting to be half of a GTO orbit period;
step C, setting the working time t of the main engine when the upper-stage main engine is ignited for the first time 2 Sum yaw angle correction amount delta psi 1 (ii) a The working time t of the main engine when the first ignition of the upper main engine is started 2 Initially setting the working time of the remaining available propellant; yaw angle correction quantity delta psi 1 Is 0;
calculating the first sliding time t of the upper level by adopting a Newton iterative algorithm 1 The ignition working time t of the first main engine 2 Sum yaw angle correction amount delta psi 1 The newton iterative relationship is:
the iteration termination condition is as follows:
wherein, Oa is pho Trajectory inclination, Oa, for the end of the first firing operation of the current rail engine pho Is t 1 A function of (a); Δ Hp pho The height Hp of the track near point at the moment when the first ignition operation of the current track main engine is finished pho1 And Hp phoT Absolute value of difference, Δ Hp pho Is t 2 Function of, Hp eps The control precision of the height of the remote place is shown, and n is a non-negative integer; Δ i pho The track inclination angle i of the current track main engine at the end moment of the first ignition work pho1 And i phoT Absolute value of difference, Δ i pho Is delta psi 1 Function of i eps Controlling the precision of the track inclination angle;
if the iteration termination condition is met, completing the iterative calculation of the fixed point phase modulation orbit, and entering the step D;
if in the iteration end condition, Oa pho If the condition is not met, returning to the step B, and adjusting the first sliding time length t of the upper stage 1 ；
If in the iteration end condition, Δ Hp pho 、Δi pho If one of the two does not meet the condition, the step four is returned to, and t is adjusted 2 And delta psi 1 Recalculating until an iteration termination condition is met;
step D, setting the second sliding time length t of the upper level 3 (ii) a Let the duration t of sliding 3 The initial setting is half of the fixed point phase modulation orbit period;
step E, setting the working time t of the main engine when the upper-stage main engine is used for the second ignition maneuver 4 Yaw angle correction amount delta psi 2 And pitch correction amount
The working time t of the main engine when the second ignition of the upper main engine is started 4 Initially setting the working time of the remaining available propellant; yaw angle correction amount delta psi and pitch correction amount
Are all set to 0;
calculating satellite orbit-entering parameters after satellite separation, and solving by adopting a Newton iterative algorithm, wherein the iterative relation is as follows:
Judging whether the satellite orbit-entering parameters meet the following conditions:
wherein a is the currentSemi-major axis value, a, of satellite orbit at trajectory calculation time T Completing orbit semi-major axis value for GEO satellite fixed point, delta a is absolute value of difference value of the two, a eps Transmitting orbit semi-major axis control precision for the satellite; i inclination angle value of satellite orbit at current trajectory calculation time, i T The value of the inclination of the orbit for the satellite to be launched, Δ i being the absolute value of the difference between them, i eps Transmitting the orbit inclination angle control precision for the satellite; e is the eccentricity of the satellite orbit at the current ballistic trajectory calculation time; if Oa does not meet the condition, returning to the step D, and adjusting the second-time coasting time length t of the upper stage 3 ；
If one of the three items of delta a, delta i and E does not meet the condition, the step E is returned to, and t is adjusted 4 、δψ、
Recalculating until an iteration termination condition is met;
if the conditions are all met, the iterative calculation process of the track is finished, and the design of one track is finished;
and according to the iterative calculation flow of the orbits, after N groups of initial orbit calculation of emission are completed, N groups of orbit calculation results are obtained.
2. The GEO direct fixed point transmission orbit optimization method of claim 1, characterized in that: the first step of optimizing parameters specifically comprises the following steps: setting the number of optimization variables Nvar, the number of optimization objective functions Func, the population scale of Npop, the evolution algebra of Ngen, the cross rate Pcr, the cross distribution index dic, the variation rate Pmut and the variation distribution index dim.
3. The GEO direct fixed point transmission orbit optimization method of claim 2, characterized in that: in the first step, the number Nvar of the optimized variables is set to be 4; the number Func of the optimization target functions is set to be 2; the population size Npop is set to 80; the evolution algebra Ngen is set to 10.
4. A method as claimed in claim 2The GEO direct fixed-point launching orbit optimization method is characterized by comprising the following steps: in the first step, the optimization variables include: GTO orbit apogee height Ha after separation of base-level rocket GTO GTO orbit near-place amplitude angle Ha GTO Fixed point phase modulation orbit near-site height Hp pho Fixed point phase modulation orbit inclination angle i pho 。
5. The GEO direct fixed point transmission orbit optimization method of claim 4, characterized in that: in the second step, Ha GTO Selecting 35786 +/-200 km; ha GTO Selecting a value range from 178 degrees to 180 degrees; hp pho Selecting a value range from 200km to 35786 km; i.e. i pho The value range is selected from 0-28 deg.
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| CN104579454A (en) * | 2015-01-17 | 2015-04-29 | 浙江大学 | Multi-objective optimization satellite flow control method based on software defined network |
| CN105445738A (en) * | 2015-11-16 | 2016-03-30 | 电子科技大学 | GEO satellite-machine double-base SAR receiving station flight parameter design method based on genetic algorithm |
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| CN104579454A (en) * | 2015-01-17 | 2015-04-29 | 浙江大学 | Multi-objective optimization satellite flow control method based on software defined network |
| CN105445738A (en) * | 2015-11-16 | 2016-03-30 | 电子科技大学 | GEO satellite-machine double-base SAR receiving station flight parameter design method based on genetic algorithm |
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