CN110562491B - Method and system for attitude control of space power station based on population distribution state - Google Patents
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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 of attitude control, and system optimization parameters are obtained after the relevant controller parameters are coded; 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. Differential Evolution (DE) is a Population-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, whereas an excessively small PS is beneficial to improve the exploration capability of the algorithm but easily causes Population premature convergence. Therefore, the proper PS setting is significant 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 problem is as follows: a method for carrying out attitude control on a space power station based on a population distribution state is constructed, and comprises the following steps:
s1, adopting a PD controller to carry out attitude optimization adjustment on a space power station, taking the damping ratio and the frequency of the controller as attitude control optimization parameters in the optimization adjustment process, and coding 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 function S Then, 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 population 1 Reduced-scale rate of populations 2 Ratio mean value R of entropy values of preceding and succeeding populations avg Carrying out initialization setting;
s3, evaluating each individual in the population and recording the optimal solution X best ；
S4, carrying out population advancing by utilizing a differential evolution algorithmCarrying out variation, crossing and survival selection evolution operation on the n generations of rows, wherein after the evolution of each generation is finished, the ratio mean value R of the entropy values of the population of the former generation and the later generation is used avg Further 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 = NFES + P S (ii) a On the other hand, the optimal solution X obtained from the last evaluation record and the current evaluation record needs to be compared best Updating the optimal solution based on a set objective function;
s5, outputting the optimal solution X best And 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 evolution are respectively generated randomly by normal distribution and cauchy distribution, and based on the randomly generated variation and cross control parameters CR and F, a test vector U is generated i,g According 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 U i,g As the target vector of the g +1 th generation, namely X i,g+1 ＝X i,g (ii) a Will be less than or equal to U with minimized properties i,g Is the target vector of the g +1 th generation, namely X i,g+1 ＝X i,g Wherein, under the two conditions, the rest X i,g All will be stored into the inferior solution set; x i,g 、X i,g+1 Target vectors of the g and g +1 generation respectively;
through the generated test vector, evolution that excellent individuals are selected to enter the next generation is further determined, 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, the evolution of each generation is endedThen, calculating the entropy values of the populations of the first and second generations based on the ratio R = E of the entropy values of the populations of the first and second generations g+1 /E g Confirming 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 generations avg The updating method comprises the following steps: r avg ＝R avg + R; evolution algebra based on population and entropy ratio mean value R of former and latter two generations of updated population avg For population size P S Performing 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 used avg The 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 comprises the following steps:
a41, determining an 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 each dimension falls into the sub-intervals j, j =1 S After counting the number of individuals, the probability p of the individual falling into the subinterval j is calculated j,g ；
A43, based on probability p j,g Calculating an entropy value E of each dimension of the population i,g The calculation formula is as follows:
a44, entropy value E of each dimension of the population i,g Carrying out statistics to obtain an entropy value of the population of the g generation:
E g ＝∏E i,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 generation avg Performing population size P S The step of expanding or contracting of (2) comprises:
b41, let R avg ＝R avg N; wherein N is an algebra for updating the population entropy;
b42, R updated based on step B41 avg Updating the population scale according to the following updating rule:
in NFES<0.2 XMAXNFES and rand (0, 1)>R avg Randomly 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)<R avg Randomly selecting from the inferior solution set according to the reduction ratio of the population and the lower limit value of the population scale
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,
respectively is the upper limit value and the lower limit value of the population;
b43, finally making R avg =0, and the mean value of entropy values of the front and rear populations is updated according to the population after updating the scale when next generation evolution is carried out;
evaluating the counting times and the mean value R of the entropy values of the populations of the first generation and the second generation by combining avg The 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 evolution of the next generation, and the effectiveness and the calculation precision 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, a local search strategy is triggered, and the optimal solution X is output best 。
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 above 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 performing attitude control on the space power station based on the population distribution state have the following beneficial effects:
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 flow chart of attitude control for a space power plant, the method includes the following steps:
s1, a PD controller is adopted to carry out attitude optimization adjustment on the space power station, in the optimization adjustment process, the damping ratio and the frequency of the controller are used as attitude control optimization parameters, and after relevant controller parameters are coded, system optimization parameters X are obtained.
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 is 0 =7.292e-5, epsilon represents the damping ratio of the PD controller, and 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 calculated S Setting 50, wherein a set formed by a plurality of target solutions is used as a population, and each target solution in the set is used as a single individual of the population; finally, initializing variables needed in the algorithm, including evaluating times counter NFES =0, population evolution algebra g =0, population expansion ratio rate 1 =0.3, population reduction ratio rate 2 =0.05, mean value R of ratio of entropy values of populations of previous and next generations avg Initialization setting of =0.
S3, according to the target function f (X) i,g ) Evaluating each individual in the population and recording the optimal solution X best 。
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 the 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 generating selection operationsGenerating a test vector U by using a mutation crossover operator based on the generated mutation and crossover control parameters CR, F i,g (ii) a Wherein, if U i,g Superior to target vector X i,g (i.e., U) i,g Smaller than the target vector X i,g ) Then let X i,g+1 ＝U i,g (i.e., the currently obtained test vectors are taken as the individuals included in the next generation population) while X is added i,g Storing into a bad solution set (namely the current target individual X) i,g As individuals to be removed from the population); otherwise, order X i,g+1 ＝X i,g (namely, the target vector of the current generation is taken as the target vector of the next generation, and the survival selection in the evolution process of the next generation is waited for); wherein, X i,g 、X i,g+1 Target vectors of the g generation and the 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 the entropy value ratio R = E of the two generations of populations g+1 /E g (ii) a Wherein E is g 、E g+1 Entropy values of the first generation population and the second generation population 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 population avg Updating, wherein the updating mode is as follows: r is avg ＝R avg + R; wherein, based on evolution algebra of population and entropy ratio mean value R of former and latter two generations of population after updating avg To population size P S Updating 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 = NFES + P S And each time when the population is evaluated, the optimal solution X obtained from the last time and the current evaluation record is compared best Updating the optimal solution based on a set objective function; when the number of evaluations reaches a preset threshold value,step S5 is performed.
S5, outputting the optimal solution X best And 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 parameters of the relevant controllers; in this embodiment, D is set to 8 according to the number of relevant control parameters.
A42, evenly dividing the population into P according to the space range of each dimension S Subintervals, where each dimension will fall into subintervals j, j =1 S The number of individuals was recorded as N j,g Sequentially calculating to obtain the probability p that the individual falls into the subinterval j j,g The calculation formula is as follows: p is a radical of j,g ＝N j,g /P s 。
A43, based on probability p j,g Calculating the entropy value E of each dimension of the population i,g The calculation formula is as follows:
a44, entropy value E of each dimension of the population i,g After statistical calculation, obtaining an entropy value of the population of the g generation:
E g ＝∏E i,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 generation avg And population size P S For the update, please refer to fig. 2:
b41, let R avg ＝R avg N; n is an algebra of population entropy update, where N =4 in this embodiment, that is, after 4 generations of population entropy values are integrated, averaging is performed;
b42, based on the mean value R of the entropy values of the two generations of populations avg To population sizeUpdating, wherein the updating rule is as follows:
in NFES<0.2 XMAXNFES and rand (0, 1)>R avg Are randomly generated in the population
Individual, expanding the population scale;
in NFES ≧ 0.2 XMAXNFES and rand (0, 1)<R avg Then, based on the inferior solution set, delete from the population
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,
respectively an upper limit value and a lower limit value of the population;
b43, last order R avg ＝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 output best 。
The invention discloses a system for attitude control of a space power station based on a population distribution state, which is shown in a structural diagram with reference to a figure 3 and comprises: a processor L1 and the storage device L2; the storage device L2 is used for storing instructions and data for implementing any one of the above methods; the processor L1 is configured to load and execute instructions and data in the storage 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 comparative experiments, the attitude control optimization method parameter setting of the space power station based on population distribution state judgment is shown in table 2:
TABLE 2 parameter settings
Optimization results of four groups of comparison experiments and corresponding ground y-axis attitude angles
Error, sun-to-sun y-axis attitude angle theta i Error and transient control energy (results retain three decimal places)) As 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 are X = [0.707,0.705,0.707,0.528,10.003,21.640,10.252,10.076]The transient control energy is the lowest at this time, 1954189 (n) 2 m 2 s)。
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, adopting a PD controller to carry out attitude optimization adjustment on a space power station, taking the damping ratio and the frequency of the controller as attitude control optimization parameters in the optimization adjustment process, and coding related controller parameters to obtain a system optimization parameter X;
s2, obtaining a system optimization parameter X according to the codes, and setting a population, wherein the initial population size P is set according to a preset objective function S Then, 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 population 1 Reduced-proportion rate of population 2 Front, backMean value of ratios of entropy values of generation groups R avg Carrying out initialization setting;
s3, evaluating each individual in the population and recording the optimal solution X best ；
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 entropy values of the population of the previous generation and the next generation is obtained avg Further determining the distribution state of the population, after updating the population scale, returning to the step S3 until the evaluation times reach a preset threshold value, and executing a step S5; when the population after updating the 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 = NFES + P S (ii) a On the other hand, the optimal solution X obtained from the last evaluation record and the current evaluation record needs to be compared best Updating the optimal solution based on a set objective function;
s5, outputting the optimal solution X best And the attitude of the space power station is further controlled by taking the attitude control parameter as the optimal control parameter of the PD controller.
2. A method for attitude control in a space-power plant according to claim 1, wherein 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 F i,g According 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 U i,g As the target vector of the g +1 th generation, namely X i,g+1 ＝X i,g (ii) a Will be less than or equal to U with minimized optimized properties i,g Is the target vector of the g +1 th generation, namely X i,g+1 ＝X i,g Wherein, under the two conditions, the rest X i,g All will be stored into the inferior solution set; x i,g 、X i,g+1 The g generation and the g +1 generation are target vectors respectively.
3. A method for attitude control in a space power plant according to claim 2 wherein in step S4, after the evolution of each generation is over, by calculating the entropy values of the two generations before and after the evolution of each generation, based on the ratio R = E of the entropy values of the two generations before and after g+1 /E g Confirming 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 generations avg The updating method comprises the following steps: r avg ＝R avg + R; evolution algebra based on population and entropy ratio mean value R of former and latter two generations of updated population avg To population size P S And performing expansion or reduction processing.
4. A method for attitude control of a space power station according to claim 3, wherein the calculation process of the entropy values of the two generations of populations comprises the following steps:
a41, determining an 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 each dimension falls into the sub-intervals j, j =1 S After counting the number of individuals, the probability p of the individual falling into the subinterval j is calculated j,g ；
A43, based on probability p j,g Calculating the entropy value E of each dimension of the population i,g The calculation formula is as follows:
a44, entropy value E of each dimension of the population i,g Carrying out statistics to obtain an entropy value of the population of the g generation:
E g ＝∏E i,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 based on population evolution algebra and updated predecessorsEntropy ratio mean value R of two subsequent generations of population avg Performing population size P S The step of expanding or contracting comprises:
b41, let R avg ＝R avg N; wherein N is an algebra for updating the population entropy;
b42, R updated based on the step B41 avg Updating the population scale according to the following updating rule:
in NFES<0.2 XMAXNFES and rand (0, 1)>R avg Randomly 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)<R avg Randomly selecting from the inferior solution set according to the reduction ratio of the population and the lower limit value of the population scale
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,
respectively is the upper limit value and the lower limit value of the population;
b43, finally making R avg And =0, ensuring that the entropy ratio mean value of the two generations of populations before and after the next generation is updated according to the population after updating the scale when the next generation is evolved.
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 is reduced 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 output best 。
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 implementing 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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