CN118094779A - Guided rocket large airspace glide increase Cheng Dandao optimization method based on ant colony algorithm - Google Patents
Guided rocket large airspace glide increase Cheng Dandao optimization method based on ant colony algorithm Download PDFInfo
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Abstract
The invention discloses a guided rocket large airspace glide increase Cheng Dandao optimization method based on an ant colony algorithm, which comprises the steps of firstly establishing a guided rocket three-degree-of-freedom motion model; then establishing a guided rocket state constraint equation and a guided rocket target function; the continuous state space formed based on the guidance rocket state constraint equation is next; initializing pheromones in a search interval according to the division of the search interval, the setting of node numbers and the given ant colony scale; according to the initialized space parameters, calculating the adaptability of the ant colony, recording the iterative optimal solution and the global optimal solution of the ant colony, and updating rules and the pheromones of all nodes; and finally, optimizing to obtain an optimal solution of the guided rocket glide increase Cheng Dandao based on an ant colony algorithm optimization scheme, a guided rocket state constraint equation and an objective function. According to the method, the ant colony trajectory optimization algorithm is adopted to perform trajectory simulation optimization calculation, and the design of the large airspace glide increase Cheng Dandao is used to effectively improve the maximum range of the guided rocket and solve the range requirement of the guided rocket.
Description
Technical Field
The invention belongs to the technical field of rockets, and particularly relates to a guided rocket large airspace glide increase Cheng Dandao optimization method based on an ant colony algorithm.
Background
Guided rocket range elevation is a basic requirement of a remote rocket projectile, and large airspace glide increase Cheng Dandao optimization design is an important design content of overall optimization of the remote rocket. The reasonable optimal trajectory design can effectively improve the firing range of the rocket projectile, reduce the load quality requirement of the rocket, and realize the remote striking capability with long firing range, low cost, light weight and high precision.
In recent years, the intelligent optimization algorithm of the colony is developed rapidly in the aspect of path planning, the ant colony algorithm converts the optimization problem into a combined search problem of pheromone updating optimization, and the optimal solution is searched through pheromone updating iteration, so that a global optimal result can be obtained through optimization. In the aspect of path planning, the early ant colony algorithm realizes the path optimization problem among nodes by arranging a plurality of path nodes and node pheromones, namely the discrete state optimization problem in a two-dimensional or three-dimensional search space.
The problem of optimizing the trajectory of the guided rocket is not only the problem of optimizing the continuous trajectory of the three-dimensional space, but also the problem of optimizing each state quantity of the rocket by considering. Therefore, the ant colony algorithm introduces the problem of optimizing the trajectory of the guided rocket, and the discretization problem of the multi-dimensional continuous state constraint space is required to be faced. The ant colony algorithm is introduced into the guidance rocket glide increase Cheng Dandao optimization problem from the path planning field, and the guidance rocket state optimal solution can be searched through the guidance rocket discretization state space global search, so that the optimal glide increase Cheng Dandao is obtained. The ant colony algorithm has the advantages of high convergence speed, global random search, good state space adaptability, strong complex constraint condition processing capability and the like in the aspect of processing trajectory optimization, and has important research significance and engineering value in the field of aircraft optimization design.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a guided rocket large airspace glide increase Cheng Dandao optimization method based on an ant colony algorithm, which comprises the steps of firstly establishing a guided rocket three-degree-of-freedom motion model; then establishing a guided rocket state constraint equation and a guided rocket target function; then, based on a continuous state space formed by a guided rocket state constraint equation, continuous space discretization processing is carried out; dividing a search interval, setting the number of nodes and giving an ant colony scale according to the discretized discrete state space of the guided rocket, simultaneously calculating a diffusion transformation matrix, and initializing pheromones in the search interval; according to the initialized space parameters, calculating the ant colony fitness, recording an iteration optimal solution and a global optimal solution of the ant colony, and updating the pheromones of all nodes by utilizing the iteration optimal solution and the pheromone updating rule; and finally, optimizing to obtain an optimal solution of the guided rocket glide increase Cheng Dandao based on an ant colony algorithm optimization scheme, a guided rocket state constraint equation and an objective function. According to the method, the ant colony trajectory optimization algorithm is adopted to perform trajectory simulation optimization calculation, and the design of the large airspace glide increase Cheng Dandao is used to effectively improve the maximum range of the guided rocket and solve the range requirement of the guided rocket.
The technical scheme adopted for solving the technical problems is as follows:
Step 1: establishing a guided rocket three-degree-of-freedom motion model according to the flight state of the guided rocket;
step 2: establishing a guided rocket state constraint equation and a guided rocket target function according to the guided rocket index requirements;
step 3: performing continuous space discretization processing based on a continuous state space formed by a guided rocket state constraint equation;
Step 4: dividing a search interval according to the discretized discrete state space of the guided rocket, setting the number of nodes, giving the ant colony scale, simultaneously calculating a diffusion transformation matrix, and initializing pheromones in the search interval;
Step 5: according to the initialized space parameters, calculating the ant colony fitness, recording an iteration optimal solution and a global optimal solution of the ant colony, and updating the pheromones of all nodes by utilizing the iteration optimal solution and the pheromone updating rule;
Step 6: based on an ant colony algorithm optimization scheme, a guided rocket state constraint equation and an objective function, and optimizing to obtain an optimal solution of the guided rocket glide increase Cheng Dandao.
Further, the step 1 specifically includes the following steps:
The guided rocket flight state parameters include: distance between the earth and the heart Longitude/>Latitude/>Projectile velocity/>Ballistic dip/>Course angle/>Lift/>Resistance/>Engine thrust/>Angle of attack/>And sideslip angle/>；
The guided rocket three-degree-of-freedom dynamics model is shown as follows:
；
In the method, in the process of the invention, Representing the guided rocket mass.
Further, the step 2 specifically includes the following steps:
The guided rocket target function is regarded as a function related to the constraint state quantity as follows:
；
Wherein, Representing the state quantity of the guided rocket; /(I)Upper and lower boundary constraints respectively representing the state quantity of the guided rocket,/>Representing the number of guided rocket state quantities included in the objective function,/>A function representing a guided rocket state quantity; /(I)Representing an objective function, and taking a minimum value in the optimization problem;
Establishing a state constraint equation to form a guidance rocket state space search interval; the state constraint equation of the guided rocket comprises a terminal constraint condition equation and a path constraint condition equation, wherein the terminal constraint condition equation comprises the following components: terminal velocity constraints, range constraints, falling angle constraints, and terminal angle of attack constraints:
；
Wherein, Respectively representing terminal speed, range, falling angle and terminal attack angle; respectively representing the constraint lower boundary of the terminal speed, the range, the falling angle and the terminal attack angle; respectively representing the upper bound of the terminal speed, the range, the falling angle and the terminal attack angle;
The path constraint equation includes: active segment overload constraints, passive segment overload constraints, and full ballistic dynamic pressure constraints.
Further, the step 3 specifically includes the following steps:
the continuous spatial discretization method is as follows:
；
Wherein, Respectively represent state quantity/>Maximum and minimum of/(v)Representing the i-th state quantity selectable discrete point; /(I)Representing the node sequence number; a represents the number of nodes;
setting a node number A, and initializing pheromones of all state nodes; calculating a pheromone diffusion transformation matrix ; And parameterizing the problem of optimizing the guided rocket glide increase Cheng Dandao, regarding the guided rocket glide increase Cheng Dandao as an ant, and giving the ant colony scale, namely selecting a corresponding number of glide increases Cheng Dandao as initial trajectory to be optimized.
Further, the step 5 specifically includes:
Step 5-1: recording each glide increase Cheng Dandao iteration optimal solution, and carrying out numerical simulation calculation on ballistic performance parameters to obtain flight trajectory fitness;
Step 5-2: judging whether the iteration times meet the preset iteration times; if not, turning to the step 5-1; if yes, carrying out pheromone updating and pheromone diffusion transformation by using the iterative optimal solution, wherein the pheromone and pheromone diffusion calculation equation is as follows:
；
；
Wherein, Representing the volatilization quantity of pheromone,/>Representing the value of an iterative optimal solution or a global optimal solution,/>And/>Respectively express/>And/>A time pheromone value; /(I)Respectively representing the information element values after diffusion transformation; respectively representing pheromone values before diffusion transformation; /(I) Is a diffusion transformation matrix.
Further, the step 6 specifically includes:
judging whether the search interval adjustment condition under the constraint condition of the guidance rocket state is reached or not; if the condition is met, adjusting to the next state constraint interval, and turning to the step 3; if the condition is not met, namely the global search is finished, outputting an optimal state path, and forming a guidance rocket glide increase Cheng Dandao optimal solution under the ant colony algorithm.
The beneficial effects of the invention are as follows:
The method improves the ant colony algorithm and is applied to the field of guided rocket trajectory optimization design research. The method comprises the steps of discretizing a continuous state space of a guided rocket by improving an ant colony optimization algorithm, designing state constraint discrete nodes, selecting a flight trajectory ant colony scale, and obtaining a flight trajectory global optimal solution by multiple iterative optimization through setting an objective function and a pheromone diffusion rule. The convergence speed of the algorithm is improved, meanwhile, the problem of sinking into a local optimal solution is avoided, and the flying trajectory global searching capability is improved. And finally, the optimal glide increase Cheng Dandao is obtained in a large airspace range by utilizing an ant colony algorithm, so that the range capability of the remote guided rocket is improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
The invention aims to provide a remote guidance rocket large airspace glide increase Cheng Dandao optimizing method based on an ant colony algorithm, which effectively improves the range capability of a guidance rocket under the multi-state constraint guidance condition. The trajectory optimization method based on the ant colony algorithm has high optimization convergence speed, and the probabilistic global search can avoid sinking into a local optimal solution.
As shown in FIG. 1, the guided rocket large airspace glide increase Cheng Dandao optimization method based on the ant colony algorithm comprises the following steps:
Step 1: establishing a guided rocket three-degree-of-freedom motion model according to the flight state of the guided rocket;
The guided rocket flight state parameters include: distance between the earth and the heart Longitude/>Latitude/>Projectile velocity/>Ballistic dip/>Course angle/>Lift/>Resistance/>Engine thrust/>Angle of attack/>And sideslip angle/>；
The guided rocket three-degree-of-freedom dynamics model is shown as follows:
；
step 2: establishing a guided rocket state constraint equation and a guided rocket target function according to the guided rocket index requirements;
The guided rocket target function is regarded as a function related to the constraint state quantity as follows:
；
Establishing a state constraint equation to form a guidance rocket state space search interval; the state constraint equation of the guided rocket comprises a terminal constraint condition equation and a path constraint condition equation, wherein the terminal constraint condition equation comprises the following components: terminal velocity constraints, range constraints, falling angle constraints, and terminal angle of attack constraints:
；
The path constraint equation includes: active segment overload constraints, passive segment overload constraints, and full ballistic dynamic pressure constraints.
Step 3: performing continuous space discretization processing based on a continuous state space formed by a guided rocket state constraint equation;
the continuous spatial discretization method is as follows:
；
Wherein, Respectively represent state quantity/>Maximum and minimum of/(v)Representing the i-th state quantity selectable discrete points, wherein the size of the corresponding pheromone on the discrete points influences the final optimization result; /(I)Representing the node sequence number; a represents the number of nodes;
setting a node number A, and initializing pheromones of all state nodes; calculating a pheromone diffusion transformation matrix ; And parameterizing the problem of optimizing the guided rocket glide increase Cheng Dandao, regarding the guided rocket glide increase Cheng Dandao as an ant, and giving the ant colony scale, namely selecting a corresponding number of glide increases Cheng Dandao as initial trajectory to be optimized.
Step 4: dividing a search interval according to the discretized discrete state space of the guided rocket, setting the number of nodes, giving the ant colony scale, simultaneously calculating a diffusion transformation matrix, and initializing pheromones in the search interval;
Step 5: according to the initialized space parameters, calculating the ant colony fitness, recording an iteration optimal solution and a global optimal solution of the ant colony, and updating the pheromones of all nodes by utilizing the iteration optimal solution and the pheromone updating rule;
Step 5-1: recording each glide increase Cheng Dandao iteration optimal solution, and carrying out numerical simulation calculation on ballistic performance parameters to obtain flight trajectory fitness;
Step 5-2: judging whether the iteration times meet the preset iteration times; if not, turning to the step 5-1; if yes, carrying out pheromone updating and pheromone diffusion transformation by using the iterative optimal solution, wherein the pheromone and pheromone diffusion calculation equation is as follows:
Step 6: based on an ant colony algorithm optimization scheme, a guided rocket state constraint equation and an objective function, and optimizing to obtain an optimal solution of the guided rocket glide increase Cheng Dandao.
Judging whether the search interval adjustment condition under the constraint condition of the guidance rocket state is reached or not; if the condition is met, adjusting to the next state constraint interval, and turning to the step 3; if the condition is not met, namely the global search is finished, outputting an optimal state path, and forming a guidance rocket glide increase Cheng Dandao optimal solution under the ant colony algorithm.
The invention relates to a remote guidance rocket large airspace glide increase Cheng Dandao optimization method based on an ant colony algorithm, which improves the ant colony algorithm and is applied to the field of guidance rocket trajectory optimization design research. The method comprises the steps of discretizing a continuous state space of a guided rocket by improving an ant colony optimization algorithm, designing state constraint discrete nodes, selecting a flight trajectory ant colony scale, and obtaining a flight trajectory global optimal solution by multiple iterative optimization through setting an objective function and a pheromone diffusion rule. The convergence speed of the algorithm is improved, meanwhile, the problem of sinking into a local optimal solution is avoided, and the flying trajectory global searching capability is improved. And finally, the optimal glide increase Cheng Dandao is obtained in a large airspace range by utilizing an ant colony algorithm, so that the range capability of the remote guided rocket is improved.
Claims (6)
1. The guided rocket large airspace glide increase Cheng Dandao optimizing method based on the ant colony algorithm is characterized by comprising the following steps:
Step 1: establishing a guided rocket three-degree-of-freedom motion model according to the flight state of the guided rocket;
step 2: establishing a guided rocket state constraint equation and a guided rocket target function according to the guided rocket index requirements;
step 3: performing continuous space discretization processing based on a continuous state space formed by a guided rocket state constraint equation;
Step 4: dividing a search interval according to the discretized discrete state space of the guided rocket, setting the number of nodes, giving the ant colony scale, simultaneously calculating a diffusion transformation matrix, and initializing pheromones in the search interval;
Step 5: according to the initialized space parameters, calculating the ant colony fitness, recording an iteration optimal solution and a global optimal solution of the ant colony, and updating the pheromones of all nodes by utilizing the iteration optimal solution and the pheromone updating rule;
Step 6: based on an ant colony algorithm optimization scheme, a guided rocket state constraint equation and an objective function, and optimizing to obtain an optimal solution of the guided rocket glide increase Cheng Dandao.
2. The optimization method of the guided rocket large airspace glide increase Cheng Dandao based on the ant colony algorithm according to claim 1, wherein the step 1 is specifically as follows:
The guided rocket flight state parameters include: distance between the earth and the heart Longitude/>Latitude/>Projectile velocity/>Ballistic dip/>Course angleLift/>Resistance/>Engine thrust/>Angle of attack/>And sideslip angle/>；
The guided rocket three-degree-of-freedom dynamics model is shown as follows:
；
In the method, in the process of the invention, Representing the guided rocket mass.
3. The optimization method of the guided rocket large airspace glide increase Cheng Dandao based on the ant colony algorithm according to claim 2, wherein the step 2 is specifically as follows:
The guided rocket target function is regarded as a function related to the constraint state quantity as follows:
；
Wherein, Representing the state quantity of the guided rocket; /(I)Respectively represent the upper and lower boundary constraints of the guided rocket state quantity,Representing the number of guided rocket state quantities included in the objective function,/>A function representing a guided rocket state quantity; /(I)Representing an objective function, and taking a minimum value in the optimization problem;
Establishing a state constraint equation to form a guidance rocket state space search interval; the state constraint equation of the guided rocket comprises a terminal constraint condition equation and a path constraint condition equation, wherein the terminal constraint condition equation comprises the following components: terminal velocity constraints, range constraints, falling angle constraints, and terminal angle of attack constraints:
；
Wherein, Respectively representing terminal speed, range, falling angle and terminal attack angle; respectively representing the constraint lower boundary of the terminal speed, the range, the falling angle and the terminal attack angle; respectively representing the upper bound of the terminal speed, the range, the falling angle and the terminal attack angle;
The path constraint equation includes: active segment overload constraints, passive segment overload constraints, and full ballistic dynamic pressure constraints.
4. The optimization method of the guided rocket large airspace glide increase Cheng Dandao based on the ant colony algorithm according to claim 3, wherein the step 3 is specifically as follows:
the continuous spatial discretization method is as follows:
；
Wherein, Respectively represent state quantity/>Maximum and minimum of/(v)Representing the i-th state quantity selectable discrete point; /(I)Representing the node sequence number; a represents the number of nodes;
setting a node number A, and initializing pheromones of all state nodes; calculating a pheromone diffusion transformation matrix ; And parameterizing the problem of optimizing the guided rocket glide increase Cheng Dandao, regarding the guided rocket glide increase Cheng Dandao as an ant, and giving the ant colony scale, namely selecting a corresponding number of glide increases Cheng Dandao as initial trajectory to be optimized.
5. The optimization method of the guided rocket large airspace glide increase Cheng Dandao based on the ant colony algorithm according to claim 4, wherein the step 5 is specifically:
Step 5-1: recording each glide increase Cheng Dandao iteration optimal solution, and carrying out numerical simulation calculation on ballistic performance parameters to obtain flight trajectory fitness;
Step 5-2: judging whether the iteration times meet the preset iteration times; if not, turning to the step 5-1; if yes, carrying out pheromone updating and pheromone diffusion transformation by using the iterative optimal solution, wherein the pheromone and pheromone diffusion calculation equation is as follows:
；
；
Wherein, Representing the volatilization quantity of pheromone,/>Representing the value of an iterative optimal solution or a global optimal solution,/>And/>Respectively express/>And/>A time pheromone value; /(I)Respectively representing the information element values after diffusion transformation; respectively representing pheromone values before diffusion transformation; /(I) Is a diffusion transformation matrix.
6. The optimization method of the guided rocket large airspace glide increase Cheng Dandao based on the ant colony algorithm according to claim 5, wherein the step 6 is specifically:
judging whether the search interval adjustment condition under the constraint condition of the guidance rocket state is reached or not; if the condition is met, adjusting to the next state constraint interval, and turning to the step 3; if the condition is not met, namely the global search is finished, outputting an optimal state path, and forming a guidance rocket glide increase Cheng Dandao optimal solution under the ant colony algorithm.
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