CN110562491A - method and system for attitude control of space power station based on population distribution state - Google Patents
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64G—COSMONAUTICS; VEHICLES OR EQUIPMENT THEREFOR
- B64G1/00—Cosmonautic vehicles
- B64G1/22—Parts of, or equipment specially adapted for fitting in or to, cosmonautic vehicles
- B64G1/24—Guiding or controlling apparatus, e.g. for attitude control
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64G—COSMONAUTICS; VEHICLES OR EQUIPMENT THEREFOR
- B64G1/00—Cosmonautic vehicles
- B64G1/22—Parts of, or equipment specially adapted for fitting in or to, cosmonautic vehicles
- B64G1/24—Guiding or controlling apparatus, e.g. for attitude control
- B64G1/244—Spacecraft control systems
- B64G1/245—Attitude control algorithms for spacecraft attitude control
Abstract
The invention discloses a method and a system for attitude control of a space power station based on a population distribution state, wherein the method and the system adopt a PD controller and carry out attitude control adjustment of the space power station by optimizing relevant parameters of the controller; in the parameter optimization process, firstly, relevant controller parameters are used as optimization parameters for attitude control, and after the relevant controller parameters are coded, system optimization parameters are obtained; secondly, obtaining system optimization parameters according to the codes, setting a target function based on a transient control energy calculation formula, and calculating to obtain a plurality of target solutions, wherein a set formed by the target solutions is used as a population, and each individual in the population is evaluated based on a population distribution state to obtain optimal system parameters; in the invention, a strategy based on population distribution state judgment is provided based on a differential evolution algorithm, and the capability of solving the attitude control problem of the space power station is improved.
Description
Technical Field
The invention relates to the field of aerospace and intelligent computing, in particular to a method for continuously optimizing the attitude of a space power station based on a population distribution state and the attitude of the space power station.
Background
The attitude of the space power station is controlled, the aim is to meet the requirement of the space power station on day/ground direction during on-orbit normal operation, and because the output torque is overlarge and violent structure vibration is excited when the feedback gain is overlarge, the transient control energy needs to be a small value as much as possible in order to weaken the excitation of the structure as much as possible.
In the prior art, the optimization of parameters involved in the attitude control process to make the transient control energy need to be as small as possible is a typical single-target numerical optimization problem. The Differential Evolution (DE) is a group-based heuristic search method in which the Population Size (PS) mainly affects the allocation and balance of algorithm resources, an excessively large PS is beneficial to global search but easily consumes a given number of evaluations quickly, which is not beneficial to Population convergence, and an excessively small PS is beneficial to improving the exploration capability of the algorithm but easily causes Population premature convergence. Therefore, proper PS settings are important to improve the computational efficiency and performance of the algorithm. Meanwhile, although the DE algorithm has a prominent expression on global search, the DE algorithm is still lack of local search capability, so that the population convergence speed of the algorithm is slowed down at the later stage of evolution, and the requirement that the algorithm quickly converges to the optimal solution of the problem under the condition of less evaluation times cannot be met.
disclosure of Invention
The technical problem to be solved by the invention is to provide an optimization method and system for adjusting attitude of a space power station based on population distribution state, aiming at the defect that the population convergence speed is slow and the optimal solution of the problem can not be positioned in the later evolution stage of the prior art, and the overall performance of the method is improved by adding some local search strategies in due time.
The technical scheme adopted by the invention for solving the technical problems is as follows: a method for carrying out attitude control on a space power station based on a population distribution state is constructed, and the method comprises the following steps:
S1, optimizing and adjusting the attitude of the space power station by adopting a PD controller, taking the damping ratio and the frequency of the controller as attitude control optimization parameters in the optimizing and adjusting process, and coding the related controller parameters to obtain a system optimization parameter X;
S2, obtaining a system optimization parameter X according to the codes, and setting the population, wherein the initial population size P is set according to a preset objective functionSThen, the calculated target solution is used as an individual of the population; and the evaluation times counter NFES of the population, the evolution algebra g of the population, and the expansion ratio rate of the population1reduced-scale rate of populations2Ratio mean value R of entropy values of preceding and succeeding populationsavgCarrying out initialization setting;
s3, evaluating each individual in the population and recording the optimal solution Xbest；
S4, carrying out variation, crossing and survival selection evolution operation on the population for n generations by using a differential evolution algorithm, wherein after the evolution of each generation is finished, the ratio mean value R of the entropy values of the population of the previous generation and the population of the next generation is usedavgfurther determining the distribution state of the population, after updating the population scale, returning to the step S3, and executing the step S5 when the evaluation times reach a preset threshold value;
when entering the next evaluation and evolution process based on the population after the scale is updated, on one hand, the evaluation frequency counter needs to be updated, and the updating mode is as follows: NFES + PS(ii) a On the other hand, the optimal solution X obtained from the last evaluation record and the current evaluation record needs to be comparedbestUpdating the optimal solution based on a set objective function;
s5, outputting system optimization parameter XbestAnd the attitude of the space power station is further controlled by taking the PD as the optimal control parameter of the PD controller.
Further, in step S4, the variation and cross control parameters CR and F required for the evolution are respectively adoptedGenerating a test vector U by using normal distribution and Cauchy distribution to generate randomly and generating a test vector U based on randomly generated variation and cross control parameters CR and Fi,gaccording to the maximization or minimization property of the set objective function, under the condition of the maximization property, the maximum value is greater than or equal to Ui,gAs the target vector of the g +1 th generation, namely Xi,g+1＝Xi,g(ii) a Will be less than or equal to U with minimized propertiesi,gAs the target vector of the g +1 th generation, namely Xi,g+1＝Xi,gWherein, under the two conditions, the rest Xi,gAll will be stored into the inferior solution set; xi,g、Xi,g+1Target vectors of the g and g +1 generation respectively;
And through the generated test vector, the evolution that the excellent individuals are selected to enter the next generation is further determined, the inferior individuals are used as objects to be removed from the population, and the inferior individuals are deleted from the population when the population size is determined to be reduced, so that the evaluation effectiveness is further ensured.
Further, in step S4, after the evolution of each generation is finished, the population entropy values of the first and second generations are calculated, and based on the ratio R ═ E of the population entropy values of the first and second generationsg+1/EgConfirming the distribution state of the population; wherein the content of the first and second substances,
Also comprises a mean value R of entropy values of the population of the previous generation and the next generationavgThe updating method comprises the following steps: ravg＝Ravg+ R; population evolution algebra and updated front and back two generations population entropy ratio mean RavgFor population size PSPerforming expansion or reduction treatment; because the dispersion and distribution characteristics of the set can be judged through the entropy value, before the population is updated, the ratio mean value R of the entropy values of the previous generation population is usedavgThe method is applied to population scale updating, and the updating precision of the population scale is further improved.
Further, the calculation process of the entropy values of the populations of the first generation and the second generation is as follows:
a41, determining the attitude control problem dimension D of the space power station;
A42, according toAnd in the space range of each dimension, performing subinterval division on the population, wherein j is 1, the other is P for each dimension falling into the subinterval jSAfter counting the number of individuals, the probability p of the individual falling into the subinterval j is calculatedj,g；
A43 based on probability pj,gCalculating the entropy value E of each dimension of the populationi,gThe calculation formula is as follows:
A44, entropy value E of each dimension of the populationi,gcarrying out statistics to obtain an entropy value of the population of the g generation:
Eg＝∏Ei,g(i＝1,…,D)；
Wherein D is the co-counting dimension of the population, and g is more than or equal to 1.
Further, based on the evolution algebra of the population and the updated entropy ratio mean value R of the population of the first generation and the second generationavgPerforming population size PSThe step of expanding or contracting comprises:
B41, let Ravg＝Ravgn; wherein N is an algebra for updating the population entropy;
B42, updated R based on step B41avgUpdating the population scale according to the following updating rule:
in NFES < 0.2 xMaxNFES and rand (0,1) > RavgThen, a min (rate) is randomly generated in the population according to the expansion ratio of the population and the upper limit value of the population scale1×PS,PSmax-PS) (ii) individuals;
In NFES ≧ 0.2 xMaxNFES and rand (0,1) < RavgRandomly selecting min (rate) from the inferior solution set according to the reduction ratio of the population and the lower limit value of the population scale2×PS,PS-PSmin) Individuals are deleted from the population, and the reduction of the population scale is realized;
Wherein, MaxNFES is the upper limit record value of the counter, PSmax、PSminRespectively is the upper limit value and the lower limit value of the population;
B43Finally, let RavgWhen next generation evolution is carried out, the entropy ratio mean value of the populations of the previous generation and the next generation is ensured to be updated according to the population after the scale is updated;
By combining the evaluation count number and the mean value R of the entropy values of the populations of the first generation and the second generationavgThe discrete or distribution state of the population is further determined, the population is selectively expanded or reduced, and based on the determined inferior individuals in the differential evolution algorithm, the individuals in the population are further ensured to be suitable for the next generation of evolution, and the effectiveness and the calculation accuracy of the algorithm are further ensured.
Further, based on the step S4, when the population size is reduced to the population size lower limit, the cyclic process of evaluation and evolution is ended, the local search strategy is triggered, and the optimal solution X is outputbest。
The invention discloses a system for attitude control of a space power station based on a population distribution state, which comprises the following steps: a processor and the storage device; the storage device is used for storing instructions and data for implementing any one of the methods; the processor is configured to load and execute the instructions and data in the storage device to implement any of the methods described above.
In the method and the system for attitude control of the space power station based on the population distribution state, a strategy based on population distribution state judgment is provided based on a DE algorithm, and the capability of solving the attitude control problem of the space power station is improved.
The method and the system for controlling the attitude of the space power station based on the population distribution state have the following beneficial effects that:
1. in order to improve the calculation efficiency, the method simplifies an information entropy formula and applies the information entropy formula to the quantification of the population distribution state so as to obtain a population entropy value;
2. The population scale is adjusted in time through judging the population distribution state, so that the computing resources are more reasonably and effectively utilized;
3. a local search algorithm SQP (Sequential orthogonal Programming) is combined to improve the deficiency of the DE algorithm in local search.
Drawings
the invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of attitude control of a space power station;
FIG. 2 is a flow chart of population entropy calculation and update;
Fig. 3 is a system structure diagram of attitude control of the aerial power station.
Detailed Description
for a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
please refer to fig. 1, which is a flowchart of attitude control for a space power plant, the method includes the following steps:
And S1, optimizing and adjusting the attitude of the space power station by adopting the PD controller, taking the damping ratio and the frequency of the controller as attitude control optimization parameters in the optimizing and adjusting process, and coding the related controller parameters to obtain a system optimization parameter X.
S2, obtaining a system optimization parameter X according to the coding, and setting an objective function, wherein the optimization parameter is set as:In this embodiment, the value range of the parameter is set as:Wherein ω is07.292e-5, ε represents the damping ratio of the PD controller, ω represents the frequency of the PD controller; the optimization objective is set as: minf (X) (f (X) is a calculation formula of transient control energy, an objective function is set as a minimization solving property), and the current optimization objective is to obtain the minimum transient energy;
in order to solve the optimal target solution, in this embodiment, a differential evolution algorithm is adopted, and first, the initial population size P is calculatedSSet to 50, where a set of multiple target solutions is constructedCombining the target solutions into a population, wherein each target solution in the set is used as a single individual of the population; finally, initializing variables needed in the algorithm, including the evaluation times counter NFES 0, the population evolution algebra g 0, and the population expansion ratio rate10.3, reduced population ratio rate20.05, mean value of ratio of entropy values of populations of previous and next generations RavgInitial setting of 0.
s3, according to the objective function f (X)i,g) Evaluating each individual in the population and recording the optimal solution Xbest。
S4, performing variation, crossing and survival selection operations on the population in the evolution process of each generation by using a differential evolution algorithm to select individuals for the next generation of population, wherein in the current operation, variation and crossing control parameters CR and F are randomly generated by adopting normal distribution and Cauchy distribution respectively; and in the generation selection operation, generating a test vector U by using a mutation and crossing operator based on the generated mutation and crossing control parameters CR and Fi,g(ii) a Wherein, if Ui,gSuperior to target vector Xi,g(i.e., U)i,gLess than target vector Xi,g) Then let Xi,g+1＝Ui,g(i.e., the currently obtained test vectors are taken as the individuals included in the next generation population) while X is addedi,gStoring into a bad solution set (i.e. the current target individual Xi,gAs individuals to be removed from a population); otherwise, order Xi,g+1＝Xi,g(namely, the target vector of the current generation is used as the target vector of the next generation, and the survival selection in the next generation evolution process is waited to be carried out); wherein, Xi,g、Xi,g+1target vectors of the g and g +1 generation respectively; before storing data, performing initialization setting on inferior solution set
Currently, after the evolution is finished, entropy values of the two generations of populations are calculated to obtain a ratio R ═ E of the entropy values of the two generations of populationsg+1/Eg(ii) a Wherein E isg、Eg+1Entropy values of the prior and the later generations of populations are respectively.
Further confirming the distribution state of the population based on the population entropy ratio R, and simultaneously carrying out comparison on the mean value R of the entropy ratios of the two generations of the populationavgupdating is carried out in the following mode: ravg＝Ravg+ R; wherein, based on the evolution algebra of the population and the entropy ratio mean R of the population of the first and the second generations after updatingavgFor population size PSUpdating the evaluation frequency counter;
Currently, after the population is updated, returning to the step S3, and entering the next evaluation and evolution process based on the updated population; the updating mode of the evaluation frequency counter is as follows: NFES + PSAnd each time when the population is evaluated, the optimal solution X obtained from the last time and the current evaluation record is comparedbestUpdating the optimal solution based on a set objective function; when the number of evaluations reaches a preset threshold, step S5 is executed.
S5, outputting system optimization parameter XbestAnd the attitude of the space power station is further controlled by taking the PD as the optimal control parameter of the PD controller.
referring to fig. 2, it is a flowchart of calculating and updating population entropy values, and the calculation process of the population entropy values of the first and second generations is as follows:
A41, setting a problem dimension D based on the number of the parameters of the relevant controllers; in this embodiment, D is set to 8 according to the number of relevant control parameters.
A42, dividing the population into P according to the space range of each dimensionSA subinterval, where each dimension is dropped into a subinterval j, h 1SThe number of individuals was recorded as Nj,gSequentially calculating to obtain the probability p that the individual falls into the subinterval jj,gthe calculation formula is as follows: p is a radical ofj,g＝Nj,g/PS。
A43 based on probability pj,gCalculating the entropy value E of each dimension of the populationi,gThe calculation formula is as follows:
A44, entropy value E of each dimension of the populationi,gafter statistical calculation, obtaining an entropy value of the population of the g generation:
Eg＝∏Ei,g(i＝1,…,D)；
Wherein D is the co-counting dimension of the population, and g is more than or equal to 1. In the middle, the evolution algebra N based on the population entropy value, the population entropy value of each generation, and the ratio mean value R of the population entropy values of the first generation and the second generationavgAnd population size PSfor the update, please refer to fig. 2:
b41, let Ravg＝RavgN; n is an algebra of population entropy update, where N is 4 in this embodiment, that is, after a population entropy value of 4 generations is integrated, averaging is performed;
b42, mean value R based on entropy values of two generationsavgUpdating the population scale according to the following updating rule:
In NFES < 0.2 xMaxNFES and rand (0,1) > RavgAt that time, min (rate) is randomly generated in the population1×PS,PSmax-PS) Individual, expanding the population scale;
In NFES ≧ 0.2 xMaxNFES and rand (0,1) < RavgThen, based on the inferior solution set, the min (rate) is deleted from the population2×PS,PS-PSmin) Individual, reducing the population scale; based on the selected deletion number, deleting the individuals stored in the inferior solution set; wherein, MaxNFES is the upper limit record value of the counter, PSmax、PSminRespectively is the upper limit value and the lower limit value of the population;
b43, finally let Ravg＝0。
As a preferred embodiment, under a specific condition, for example, when the population size is reduced to the population size lower limit, the cyclic process of evaluation and evolution is ended, the local search strategy is triggered, and the optimal solution X is outputbest。
the invention discloses a system for attitude control of a space power station based on population distribution state, and the structure diagram of the system refers to fig. 3, and the system comprises: processor L1 and the storage L2; storage L2 is used to store instructions and data for implementing any of the methods described above; the processor L1 is configured to load and execute instructions and data from the memory device to implement any of the methods described above.
In order to prove the effectiveness of the method and the system disclosed by the application, 4 groups of comparative experiments are designed to explain, and relevant parameters, vibration frequency and mass of the initial configuration of the space power station in the experiments are shown in table 1:
TABLE 1 initial configuration parameters of space power station
| Width of battery subarray (m) | truss side length (m) | Scaling factor | frequency (Hz) | Mass (kg) |
| 26.5 | 0.5 | 1 | 0.002720 | 177960 |
in four groups of comparison experiments, the parameter settings of the attitude control optimization method of the space power station based on population distribution state judgment are shown in table 2:
TABLE 2 parameter settings
| Experiment of | population size | maximum number of evaluations | maximum number of iterations |
| A | 20 | 1000 | 17 |
| II | 50 | 5000 | 34 |
| III | 50 | 10000 | 67 |
| Fourthly | 100 | 20000 | 67 |
Optimization results of four groups of comparison experiments and corresponding ground y-axis attitude anglesError, sun-to-sun y-axis attitude angle thetaiThe error and transient control energies (results retain three decimal places) are shown in table 3:
TABLE 3 optimal controller parameters
the four groups of experimental optimization results in table 3 all satisfy constraint conditions, and are all feasible solutions. The results of the four groups of experiments in table 3 are compared, and the transient control energy consumption corresponding to the optimal controller parameter obtained in the third experiment is the lowest and is the optimal solution of the group of experiments. That is, the controller parameters for which the initial space plant configuration satisfies the attitude angle error constraint are optimal as X ═ 0.707,0.705,0.707,0.528,10.003,21.640,10.252,10.076]The transient control energy is at the lowest level 1954189 (n)2m2s)。
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (7)
1. The method for performing attitude control on the space power station based on the population distribution state is characterized by comprising the following steps of:
S1, optimizing and adjusting the attitude of the space power station by adopting a PD controller, taking the damping ratio and the frequency of the controller as attitude control optimization parameters in the optimizing and adjusting process, and coding the related controller parameters to obtain a system optimization parameter X;
S2, obtaining a system optimization parameter X according to the codes, and setting the population, wherein the initial population size P is set according to a preset objective functionSThen, the calculated target solution is used as an individual of the population; and the evaluation times counter NFES of the population, the evolution algebra g of the population, and the expansion ratio rate of the population1reduced-scale rate of populations2Ratio mean value R of entropy values of preceding and succeeding populationsavgCarrying out initialization setting;
S3, for each of the populationThe individual is evaluated and the optimal solution X is recordedbest；
S4, carrying out variation, crossing and survival selection evolution operation on the population for n generations by using a differential evolution algorithm, wherein after the evolution of each generation is finished, the ratio mean value R of the entropy values of the population of the previous generation and the population of the next generation is usedavgFurther determining the distribution state of the population, after updating the population scale, returning to the step S3, and executing the step S5 when the evaluation times reach a preset threshold; when the population based on the updated scale enters the next evaluation and evolution process, on one hand, the evaluation frequency counter needs to be updated, and the updating mode is as follows: NFES + PS(ii) a On the other hand, the optimal solution X obtained from the last evaluation record and the current evaluation record needs to be comparedbestUpdating the optimal solution based on a set objective function;
s5, outputting system optimization parameter XbestAnd the attitude of the space power station is further controlled by taking the PD as the optimal control parameter of the PD controller.
2. a method for attitude control of a space-borne power plant according to claim 1, characterized in that in step S4, the variation and cross control parameters CR and F required for evolution are respectively generated randomly by normal distribution and Cauchy distribution, and a test vector U is generated based on the randomly generated variation and cross control parameters CR and Fi，gAccording to the optimization property of the set objective function, under the condition of maximizing the optimization property, the value is greater than or equal to Ui，gAs the target vector of the g +1 th generation, namely Xi，g+1＝Xi，g(ii) a Will be less than or equal to U with minimized optimized propertiesi，gAs the target vector of the g +1 th generation, namely Xi，g+1＝Xi，gWherein, under the two conditions, the rest Xi，gAll will be stored into the inferior solution set; xi，g、Xi，g+1the g-th generation and the g + 1-th generation are target vectors respectively.
3. A space power plant for attitude control according to claim 2in step S4, after the evolution of each generation is finished, the method calculates the entropy values of the two generations before and after the evolution of each generation, and based on the ratio R ═ E of the entropy values of the two generations before and after the evolution of each generationg+1/EgConfirming the distribution state of the population; wherein, the method also comprises the mean value R of the entropy values of the populations of the previous and next generationsavgThe updating method comprises the following steps: ravg＝Ravg+ R; population evolution algebra and updated front and back two generations population entropy ratio mean RavgFor population size PSAnd performing expansion or reduction processing.
4. A method for attitude control in a space-borne power plant according to claim 3, wherein the calculation process of the entropy values of the populations of the first and second generations is as follows:
A41, determining the attitude control problem dimension D of the space power station;
A42, dividing the population into sub-intervals according to the space range of each dimension, wherein j is 1SAfter counting the number of individuals, the probability p of the individual falling into the subinterval j is calculatedj，g；
A43 based on probability pj，gCalculating the entropy value E of each dimension of the populationi，gThe calculation formula is as follows:
A44, entropy value E of each dimension of the populationi，gCarrying out statistics to obtain an entropy value of the population of the g generation:
Eg＝ΠEi，g(i＝1，…，D)；
Wherein D is the co-counting dimension of the population, and g is more than or equal to 1.
5. A space-power station attitude control method according to claim 3, characterised in that it is based on the evolution generations of the population and the updated mean R of the entropy ratio of the population of the first and second generationsavgPerforming population size PSstep of expanding or contractingThe method comprises the following steps:
B41, let Ravg＝RavgN; wherein N is an algebra for updating the population entropy;
b42, updated R based on step B41augUpdating the population scale according to the following updating rule:
In NFES < 0.2 xMaxNFES and rand (0,1) > RavgRandomly generating the random number in the population according to the expansion ratio of the population and the upper limit value of the population scale(ii) individuals;
in NFES ≧ 0.2 xMaxNFES and rand (0,1) < RavgRandomly selecting from the inferior solution set according to the reduction ratio of the population and the lower limit value of the population scaleindividuals are deleted from the population, and the reduction of the population scale is realized;
wherein, MaxNFES is the upper limit record value of the counter,Respectively is the upper limit value and the lower limit value of the population;
b43, finally let RavgAnd (0) ensuring that the entropy ratio mean value of the two generations of populations is updated according to the population after updating the scale when the next generation of evolution is carried out.
6. a method for attitude control of a space power plant according to claim 1, 3 or 5, characterized in that based on step S4, when the population size decreases to the population size lower limit, the cyclic process of evaluation and evolution is ended, a local search strategy is triggered, and an optimal solution X is outputbest。
7. a system for performing attitude control on a space power station based on a population distribution state is characterized by comprising: a processor and the storage device; the storage device is used for storing instructions and data for realizing any one of the methods of claims 1-6; the processor is used for loading and executing the instructions and data in the storage device for realizing the method of any one of claims 1 to 6.
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