An Attitude Maneuvering Path Planning Method Based on an Improved Gray Wolf Algorithm

Abstract

An attitude maneuvering path planning method based on an improved gray wolf algorithm is proposed. The improved algorithm calculates the fitness value of each gray wolf. As the wolf's position is updated, individuals within the wolf pack continuously mutate, leading to constant population renewal and effectively solving the problem of entering local optima. Simultaneously, the high efficiency of the gray wolf algorithm allows for controlled implementation time. By comparing the fitness values ​​of individual gray wolves and applying a penalty function to wolves with poor fitness values, the global search capability is improved. The optimal parameters output by the improved gray wolf algorithm are used to simulate satellite paths and determine the changes in satellite control attitude data.

Description

Technical Field

[0001] This invention relates to an attitude maneuver path planning method based on an improved gray wolf algorithm, belonging to the field of satellite attitude maneuver path planning technology. Background Technology

[0002] During spacecraft attitude maneuvers, it is crucial to prevent light from bright celestial objects (such as the sun) from entering the field of view of certain optical sensors (e.g., infrared sensors or low-light-sensitivity elements), as this could lead to temporary blindness or damage. During maneuvers, the process direction vector of the solar array must be maintained within certain requirements to continuously provide power. These directional constraints significantly limit the feasible domain of spacecraft attitude maneuvers. Furthermore, limitations on angular velocity and control torque also affect the attitude maneuver path. Due to these complex constraints, time-optimal maneuver path planning for spacecraft guidance, navigation, and control systems presents a significant challenge.

[0003] We transform the satellite trajectory planning problem into an optimization problem of finding the optimal time and the shortest path. In recent years, metaheuristic algorithms have gained increasing application due to their simplicity and advantages. The rapid development of computing technology has compensated for computational costs. Many metaheuristic algorithms have been proposed; they can be subdivided into two main categories: biomimetic algorithms and physical algorithms. The first is related to animal biology and behavior, while the second is based on physical laws. The most commonly used algorithms include: genetic algorithms inspired by Darwin's theory of evolution; ant colony optimization, inspired by ant behavior; particle swarm optimization, based on human social behavior and flocking birds; and differential evolution, based on the concepts of mutation, recombination, and selection applicable to populations. Furthermore, researchers have proposed an improved constrained differential evolution (DE) algorithm to generate optimal feasible paths. Additionally, artificially combined evolutionary programming bee colony algorithms, fuzzy quadtree frameworks, improved traveling salesman problem algorithms, and improved pulse-coupled neural network models are also applicable to agent path planning problems. Other algorithms are used to solve similar optimization problems. Moreover, researchers have implemented many methods for finding optimal paths in different environments. However, one problem with metaheuristic algorithms is how to correctly set certain intrinsic parameters, such as the maximum number of iterations, population size, and internal coefficients. These parameters may vary depending on the problem to be optimized, as they significantly affect the outcome of the optimization process. Therefore, this limits the use of such algorithms to expert users in the field. Summary of the Invention

[0004] The technical problem solved by this invention is that, in the existing technology, traditional algorithms for finding the optimal path have too much impact on the optimization process and have limitations in use. Therefore, this invention proposes an attitude maneuvering path planning method based on an improved gray wolf algorithm.

[0005] The present invention solves the above-mentioned technical problem through the following technical solution:

[0006] An attitude maneuver path planning method based on an improved gray wolf algorithm includes:

[0007] Receive satellite attitude input data, perform iterative mutation according to the improved gray wolf algorithm until the number of iterations or the given accuracy is met, and then output the optimal parameters;

[0008] Obtain the simulated satellite path using optimal parameters;

[0009] Based on the simulated satellite path, determine the simulated changes in satellite control attitude data, and complete the satellite path planning.

[0010] The method for iterative mutation and outputting optimal parameters based on the improved Grey Wolf algorithm is as follows:

[0011] Gray wolf population initialization processing is performed on the satellite attitude input data;

[0012] Crossover of individual gray wolves is performed based on the initial values;

[0013] After the crossover process is completed, the gray wolf individuals undergo mutation operation, and the post-mutation fitness value is calculated.

[0014] The fitness value of the posture difference is used to determine whether the given accuracy requirement is met or the preset number of iterations has not been reached. If so, the gray wolf population crossover and mutation operation is repeated until the preset number of iterations is met or the minimum fitness value after mutation obtained from the crossover and mutation operation meets the given accuracy.

[0015] The initialization process for a gray wolf population is as follows:

[0016] Determine the initial value Preset the number of iterations, population size, and probability factor, and determine the satellite attitude control parameters X0 = {U0, W0, Q0} and target attitude parameters based on the initial values;

[0017] Determine the position vector of the prey in generation t. The position vector of the i-th gray wolf in generation t and the distance between each gray wolf individual in generation t and its prey. Specifically:

[0018]

[0019] Update the leader wolf's position based on the distance value to complete the population initialization process;

[0020] Where U is the control torque, w is the angular velocity, and Q is the attitude quaternion.

[0021] The crossover process for individual gray wolves is as follows:

[0022] Select target vector individuals from the gray wolf population and variation vector According to satellite attitude control parameter X 0 ={U 0 ,W0,Q 0}, Evolutionary target vector individual

[0023] The control torque corresponding to the evolved target vector individual is obtained based on the probability factor.

[0024]

[0025] In the formula, CR is the probability factor and j is the random factor; the satellite attitude control parameters corresponding to the evolved target vector individual are determined based on the control torque corresponding to the evolved target vector individual.

[0026] The method for performing mutation operations on gray wolf individuals after crossover is as follows:

[0027] Determine the difference vector based on two different vector individuals, and add the difference vector to the randomly selected t-th generation target vector individual. Above, determine the next generation mutated vector corresponding to the target vector individual after mutation. And after the iteration process begins, it is substituted into the control torque. During the determination process, the mutation vector of the crossover process of the gray wolf individuals is... Update.

[0028] The difference vector D r1,2 for:

[0029] In the formula, For different vector individuals in the t-th generation gray wolf population;

[0030] The mutation process for individual gray wolves is as follows:

[0031] The method for determining the attitude difference fitness value is as follows:

[0032] After confirming that all target vector individuals have completed the mutation operation, determine the satellite attitude control parameters corresponding to the target vector individuals, and extract the attitude quaternions from the satellite attitude control parameters;

[0033] By comparing the attitude quaternion and the target attitude parameters, the function for calculating the attitude difference adaptation value is determined.

[0034] If the calculated attitude difference fitness value is within the given accuracy range, the process stops; otherwise, the gray wolf population crossover and mutation operation is repeated until the preset number of iterations is met.

[0035] The attitude quaternion and the difference q between the target attitude parameters e For: q e =[q e0 q e1 q e2 q e3 ] T ;

[0036] In the formula, q e0 q e1 q e2 q e3 Define the quaternion for angular velocity as q = [q0, q1, q2, q3];

[0037] Attitude difference adaptation value J q The calculation function is:

[0038]

[0039] During the iterative process of the gray wolf population crossover and mutation operation, the posture difference fitness value J in each generation of the gray wolf population is... q The satellite attitude parameters corresponding to the minimum value are output as the obtained optimal parameters.

[0040] After the simulated satellite path is determined based on the optimal satellite attitude parameters, the changes in the satellite attitude control parameters within a specified time period are marked to determine the simulated changes in the satellite control attitude data.

[0041] Among the satellite attitude control parameters, the angular velocity and attitude quaternion are determined based on the control torque at the current moment, and the control torque is determined iteratively based on the improved Grey Wolf algorithm.

[0042] The advantages of this invention compared to the prior art are:

[0043] (1) The present invention provides an attitude maneuver path planning method based on the improved gray wolf algorithm. The improved gray wolf algorithm is used to calculate the fitness value of each gray wolf, and the attitude difference is judged to achieve the optimal fitness value and the selection of the corresponding satellite attitude parameters. Compared with the scheme of using the traditional iterative algorithm to converge, the interaction of information data is reduced, and the limitations of the resource conditions of each satellite are taken into account, which is conducive to reducing communication load and optimizing power consumption.

[0044] (2) The path planning method proposed in this invention performs attitude maneuvers under the condition of limited satellite computing resources, which improves the robustness of the planning system and the overall system operation efficiency. At the same time, attitude planning is based on the improved gray wolf algorithm, and the planned angular rate and control torque change smoothly and simply, reducing energy consumption. Attached Figure Description

[0045] Figure 1 A flowchart of an attitude maneuvering path planning method based on an improved gray wolf algorithm provided for the invention;

[0046] Figure 2 A schematic diagram illustrating the simulated changes in satellite control attitude data provided for the invention; Detailed Implementation

[0047] An attitude maneuvering path planning method based on an improved gray wolf algorithm is proposed. The improved algorithm calculates the fitness value of each gray wolf. As the wolf's position is updated, individuals within the wolf pack continuously mutate, leading to constant population renewal and effectively solving the problem of entering local optima. Simultaneously, the high efficiency of the gray wolf algorithm allows for controlled implementation time. By comparing the fitness values ​​of individual gray wolves and applying a penalty function to wolves with poor fitness values, the global search capability is improved. The optimal parameters output by the improved gray wolf algorithm are used to simulate satellite paths and determine the changes in satellite control attitude data.

[0048] The attitude maneuvering path planning method based on the improved gray wolf algorithm includes the following steps:

[0049] Receive satellite attitude input data, perform iterative mutation according to the improved gray wolf algorithm until the number of iterations or the given accuracy is met, and then output the optimal parameters;

[0050] Obtain the simulated satellite path using optimal parameters;

[0051] Based on the simulated satellite path, determine the simulated changes in satellite control attitude data, and complete the satellite path planning.

[0052] The method for iterative mutation and outputting optimal parameters based on the improved Grey Wolf algorithm is as follows:

[0053] Gray wolf population initialization processing is performed on the satellite attitude input data;

[0054] Crossover of individual gray wolves is performed based on the initial values;

[0055] After the crossover process is completed, the gray wolf individuals undergo mutation operation, and the post-mutation fitness value is calculated.

[0056] The fitness value of the posture difference is used to determine whether the given accuracy requirement is met or the preset number of iterations has not been reached. If so, the gray wolf population crossover and mutation operation is repeated until the preset number of iterations is met or the minimum fitness value after mutation obtained from the crossover and mutation operation meets the given accuracy.

[0057] The following description, in conjunction with the accompanying drawings and preferred embodiments, provides further details:

[0058] In the current embodiment, such as Figure 1 The diagram shown is a flowchart of the attitude maneuver path planning method based on the improved gray wolf algorithm.

[0059] The initialization process for a gray wolf population is as follows:

[0060] Determine the initial value Preset the number of iterations, population size, and probability factor, and determine the satellite attitude control parameters X0 = {U0, W0, Q0} and target attitude parameters based on the initial values;

[0061] Determine the position vector of the prey in generation t. The position vector of the i-th gray wolf in generation t and the distance between each gray wolf individual in generation t and its prey. Specifically:

[0062]

[0063] Update the leader wolf's position based on the distance value to complete the population initialization process;

[0064] Where U is the control torque, w is the angular velocity, and Q is the attitude quaternion.

[0065] The crossover process for individual gray wolves is as follows:

[0066] Select target vector individuals from the gray wolf population and variation vector Based on the satellite attitude control parameters X0={U0,W0,Q0}, the evolutionary target vector individual

[0067] The control torque corresponding to the evolved target vector individual is obtained based on the probability factor.

[0068]

[0069] In the formula, CR is the probability factor and j is the random factor; the satellite attitude control parameters corresponding to the evolved target vector individual are determined based on the control torque corresponding to the evolved target vector individual.

[0070] The method for performing mutation operations on gray wolf individuals after crossover is as follows:

[0071] Determine the difference vector based on two different vector individuals, and add the difference vector to the randomly selected t-th generation target vector individual. Above, determine the next generation mutated vector corresponding to the target vector individual after mutation. And after the iteration process begins, it is substituted into the control torque. During the determination process, the mutation vector of the crossover process of the gray wolf individuals is... Update.

[0072] Difference vector D r1,2 for:

[0073] In the formula, For different vector individuals in the t-th generation gray wolf population;

[0074] The mutation process for individual gray wolves is as follows:

[0075] The method for determining the attitude difference fitness value is as follows:

[0076] After confirming that all target vector individuals have completed the mutation operation, determine the satellite attitude control parameters corresponding to the target vector individuals, and extract the attitude quaternions from the satellite attitude control parameters;

[0077] By comparing the attitude quaternion and the target attitude parameters, the function for calculating the attitude difference adaptation value is determined.

[0078] If the calculated attitude difference fitness value is within the given accuracy range, the process stops; otherwise, the gray wolf population crossover and mutation operation is repeated until the preset number of iterations is met.

[0079] Attitude quaternion and target attitude parameter difference q e For: q e =[q e0 q e1 q e2 q e3 ] T ;

[0080] In the formula, q e0 q e1 q e2 q e3 Define the quaternion for angular velocity as q = [q0, q1, q2, q3];

[0081] Attitude difference adaptation value J q The calculation function is:

[0082]

[0083] During the iterative process of crossover and mutation operations in gray wolf populations, the posture difference fitness value J in each generation of gray wolf population is... q The satellite attitude parameters corresponding to the minimum value are output as the obtained optimal parameters.

[0084] After the simulated satellite path is determined based on the optimal satellite attitude parameters, the changes in the satellite attitude control parameters within a specified time period are marked to determine the simulated changes in the satellite control attitude data.

[0085] Among the satellite attitude control parameters, angular velocity and attitude quaternions are determined based on the control torque at the current moment, and the control torque is determined iteratively using the improved Grey Wolf algorithm. The simulation changes of the determined satellite control attitude data are as follows: Figure 2 As shown.

[0086] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications to the technical solutions of the present invention by utilizing the methods and techniques disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the protection scope of the technical solutions of the present invention.

[0087] The contents not described in detail in this specification are common knowledge to those skilled in the art.

Claims

1. A method for attitude maneuver path planning based on improved grey wolf algorithm, characterized in that The method comprises the following steps: Receiving satellite attitude input data, performing iterative mutation according to the improved grey wolf algorithm, and outputting optimal parameters until the number of iterations is met or the given accuracy is met; Using the optimal parameters to obtain a simulated satellite path; According to the simulated satellite path, the simulation change of the satellite control attitude data is determined, and the satellite path planning is completed; The method for performing iterative mutation according to the improved grey wolf algorithm and outputting optimal parameters is: Grey wolf population initialization processing is performed on the satellite attitude input data; Grey wolf individual crossover processing is performed according to the initial value; The mutated attitude difference fitness value is calculated after the crossover processing of the grey wolf individual is completed; According to the attitude difference fitness value, if the given accuracy requirement is not met and the preset number of iterations is not reached, the grey wolf population crossover mutation operation is repeatedly performed until the preset number of iterations is met or the minimum value of the fitness value after the crossover mutation operation meets the given accuracy; The grey wolf population initialization processing method is: determining an initial value , a preset iteration number, a population size, a probability factor, determining a satellite attitude control parameter X0={U0, W0, Q0} and a target attitude parameter according to the initial value; Determine the first The position vector of the prey , No. The generation The position vector of each gray wolf and the The distance between each individual gray wolf and its prey Specifically: ； The leader wolf position is updated according to the distance value, and the population initialization processing is completed; Wherein, U is the control torque, w is the angular velocity, and Q is the attitude quaternion; The grey wolf individual crossover processing method is: Selecting target vector individuals in a gray wolf population and mutation vectors , according to satellite attitude control parameters X0={U0, W0, Q0}, evolving target vector individuals ; According to the probability factor, a control torque corresponding to the post-evolution target vector individual is obtained ; In the formula, CR is a probability factor, is a random factor; according to the control moment corresponding to the post-evolution target vector individual, the satellite attitude control parameter corresponding to the post-evolution target vector individual is determined.

2. The attitude maneuver path planning method based on the improved grey wolf algorithm according to claim 1, characterized in that: The method for performing mutation operation on the grey wolf individual after crossover processing is: determining a difference vector from the two different vector individuals, adding the difference vector to a randomly selected target vector individual of the tth generation above, determining a next generation mutation vector corresponding to the mutated target vector individual , and substituting into the control moment determination process after the iteration process starts, updating the mutation vector participating in the cross processing of the gray wolf individual .

3. The attitude maneuver path planning method based on the improved grey wolf algorithm according to claim 2, characterized in that: the difference vector is: ; In the formula, is the tth vector individual in the tth generation of the gray wolf population; The gray wolf individual is subjected to a mutation operation as follows: .

4. The attitude maneuver path planning method based on the improved grey wolf algorithm according to claim 3, characterized in that: The attitude difference fitness value judgment method is: After determining that the current target vector individual has completed the mutation operation, the satellite attitude control parameters corresponding to the target vector individual are determined, and the attitude quaternion in the satellite attitude control parameters is extracted; The attitude quaternion and the target attitude parameter are compared to determine the attitude difference fitness value calculation function; The calculated attitude difference fitness value is judged, and if it is within the given accuracy range, the operation is stopped, otherwise the grey wolf population crossover mutation operation is repeatedly performed until the preset number of iterations is met.

5. The attitude maneuver path planning method based on the improved grey wolf algorithm according to claim 4, characterized in that: The attitude quaternion and the target attitude parameter difference are: ; wherein Let q = [q0, q1, q2, q3] be the angular velocity quaternion. posture difference adaptation value The calculation function is: 。 6. The attitude maneuver path planning method based on the improved grey wolf algorithm according to claim 5, characterized in that: During the iterative process of the gray wolf population crossover and mutation operation, the posture difference fitness value in each generation of the gray wolf population is... The satellite attitude parameters corresponding to the minimum value are output as the obtained optimal parameters.

7. The attitude maneuver path planning method based on the improved grey wolf algorithm according to claim 6, characterized in that: After the optimal satellite attitude parameters are determined, the change of the satellite attitude control parameters in the specified time period is marked to determine the simulation change of the satellite control attitude data; Among them, the angular velocity and the attitude quaternion in the satellite attitude control parameters are determined according to the control torque at the current time, and the control torque is determined by iterative calculation according to the improved grey wolf algorithm.

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