Full-trajectory intelligent collaborative planning and launching method, electronic device and storage medium for unpowered guided projectiles

Abstract

A full-trajectory intelligent cooperative planning and launching method, electronic device, and storage medium for unpowered guided projectiles belong to the field of automatic control. To solve the problem of full-trajectory intelligent cooperative planning and launching of unpowered guided projectiles with no continuous controllable power and relatively limited control margin, it includes obtaining the flight time difference between any two adjacent guided projectiles reaching the specified airspace at the end section according to the objective function and the sum of the flight times of each projectile; determining the mid-section planned trajectories of multiple guided projectiles according to the optimal set of mid-section ballistic parameters; generating servo control instructions based on the parameter deviations between the actual mid-section trajectories of multiple guided projectiles and the mid-section planned trajectories of multiple guided projectiles. If multiple guided projectiles reach the specified airspace at the end section from the mid-section, autonomous cooperative planning and shooting of the target are carried out in the specified airspace at the end section, and the effect is to improve the ballistic planning efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of automatic control and relates to the design of a full-trajectory intelligent cooperative planning and launching method for a non-powered guided projectile. Background Art

[0002] With the rapid development of gun-launched non-powered guided projectiles, compared with conventional projectiles, they have obvious advantages in terms of long-range, precision, and intelligence. Combined with the characteristics of large ammunition carrying capacity and continuous firepower of artillery weapons, it has become possible to suppress medium- and long-range targets with high cost-effectiveness damage. However, under the conventional single-shot-by-single-shot application mode, restricted by objective factors such as limited penetration ability of a single projectile and low damage effect, the guided projectiles have not yet exerted their expected effectiveness.

[0003] To break through the above dilemmas, in recent years, domestic and foreign have vigorously developed multi-projectile intelligent trajectory cooperative planning methods and achieved certain research results in aspects such as the construction of the basic theoretical system and engineering practice. Currently, the commonly used planning methods include optimal control and proportional guidance, etc. They can concentrate relatively scattered individual units in a certain section (mid-section or end-section) of the full trajectory and initially achieve damage suppression of medium- and long-range key targets at a lower cost.

[0004] However, the existing technical methods still have the following defects:

[0005] First, in terms of the research object, most of the existing methods are for aircraft such as missiles and unmanned aerial vehicles. The essential difference between guided projectiles and them is that they have no continuous controllable power and relatively limited control margin, and are not suitable for large-range maneuvering and orbit-changing in the mid-course guidance section. There are relatively few trajectory cooperative planning methods suitable for gun-launched guided projectiles.

[0006] Second, in terms of method performance, the existing methods are basically only applicable to the mid-section or end-section alone, and the planning time for determining the cooperative trajectory parameters is relatively long, and the effect is not satisfactory. It is difficult to overall implement the cooperative planning at the overall level from the perspective of the full trajectory, and it is necessary to integrate intelligent optimization and modern control methods to further improve the systematicness and effectiveness of trajectory cooperative planning. Summary of the Invention

[0007] To solve the problem of full-trajectory intelligent cooperative planning and launching of non-powered guided projectiles with no continuous controllable power and relatively limited control margin, in a first aspect, according to some embodiments of the present application, a full-trajectory intelligent cooperative planning and launching method for non-powered guided projectiles includes

[0008] S1. Determine the trajectory-related distance parameters, the number parameters of guided projectiles, and the time parameters;

[0009] S2. Determine the mid-course trajectory parameters to be planned and the trajectory parameter domain of the mid-course trajectory parameters;

[0010] S3. Set the objective function, and obtain the flight time difference between any two adjacent guided projectiles reaching the specified airspace at the end stage and the sum of the flight times of each projectile according to the objective function;

[0011] S4. Optimize the mid-course ballistic parameters in the ballistic parameter domain to minimize the value of the objective function. A set of mid-course ballistic parameters that minimizes the value of the objective function is the optimal set of mid-course ballistic parameters;

[0012] S5. Determine the mid-course planned trajectories of multiple guided projectiles according to the optimal set of mid-course ballistic parameters;

[0013] S6. Generate servo control commands according to the parameter deviations between the actual mid-course trajectories of multiple guided projectiles and the mid-course planned trajectories of multiple guided projectiles. If multiple guided projectiles reach the specified airspace from the mid-course to the end stage, execute step S7; otherwise, return to step S2;

[0014] S7. Conduct autonomous cooperative planning for shooting targets in the specified airspace at the end stage.

[0015] According to the full-trajectory intelligent cooperative planning and launching method of unpowered guided projectiles according to some embodiments of the present application, in step S1, determining the ballistic-related distance parameters, the number of guided projectiles parameters, and the time parameters includes determining the target distance and the distance X between the center of the specified airspace at the end stage of the trajectory and the muzzle according to the actual enemy and friendly situation and the range of the guided projectile, and determining the number M of guided projectiles that need to be coordinated and the firing interval time Δt of the artillery according to the firing performance of the artillery.

[0016] According to the full-trajectory intelligent cooperative planning and launching method of unpowered guided projectiles according to some embodiments of the present application, in step S2, the mid-course ballistic parameters include the firing angle θ, the servo scheme deflection angle δ, and the servo starting moment t d , and the ballistic parameter domain is represented by formula (1):

[0017]

[0018] In the formula, θ min represents the minimum firing angle, θ max represents the maximum firing angle, δ max represents the maximum servo scheme deflection angle, t dmin represents the minimum servo starting moment, t dmax represents the maximum servo starting moment.

[0019] According to the full-trajectory intelligent cooperative planning and launching method of unpowered guided projectiles according to some embodiments of the present application, in step S3, the objective function is represented by formula (2):

[0020]

[0021] In the formula, T i represents the flight time of the i-th projectile in the unguided section and the midcourse section.

[0022] According to the full-trajectory intelligent cooperative planning and launching method of the unpowered guided projectile of some embodiments of the present application, in step S4, the midcourse ballistic parameters are optimized to minimize the value of the objective function, which is realized by the intelligent optimization of ballistic parameters method of the microgrid dimensionality reduction particle swarm natural selection method. The intelligent optimization of ballistic parameters method of the microgrid dimensionality reduction particle swarm natural selection method includes:

[0023] S4.1. Divide the value range of each ballistic parameter in formula (1) representing the ballistic parameter domain into N equal intervals evenly, where N represents the microgrid division degree, and reduce the 3M-dimensional ballistic parameter domain to a one-dimensional sequence of N 3M microgrids;

[0024] S4.2. Solve the objective function values of the centers of each microgrid according to the guided projectile ballistic model and the ballistic parameter domain, sort them according to the numerical magnitude, and randomly select L q dominant microgrids from the x q % microgrids before sorting, and randomly select L h potential microgrids from the x h % microgrids after sorting;

[0025] S4.3. Regard a parameter combination method as a particle, and the number of the particle population is the sum of L q and L h . Take the centers of the microgrids with the number of the particle population as the initial positions of the particles, and randomly initialize the velocities of each particle in the population according to the ballistic parameter domain and the microgrid division degree N. The position of the j-th particle is expressed as B j =(b jθ1 , b jδ1 , b jtd1 ,..., b jθM , b jδM , b jtdM ), and the velocity of the j-th particle is expressed as V j =(v jθ1 , v jδ1 , v jtd1 ,..., v jθM , v jδM , v jtdM ). For the j-th particle, where b jθM represents the position of the parameter launch angle θ of the M-th guided projectile, b jδM represents the position of the parameter servo scheme deflection angle δ of the M-th guided projectile, b jtdM represents the position of the parameter servo start time t d of the M-th guided projectile, and v jθMThe velocity, v, representing the parameter launch angle θ of the M-th guided projectile jδM The velocity, v, representing the parameter servo scheme deflection angle δ of the M-th guided projectile jtdM Represents the parameter servo activation time t of the M-th guided projectile d of the velocity;

[0026] S4.4. Based on the guided projectile trajectory model, calculate the flight time of each guided projectile in the uncontrolled section and the midsection for each particle according to Equation (2), and solve the fitness of each particle by Equation (1);

[0027] The position of the j-th particle is stored in the vector P j , and the fitness of the j-th particle is stored in the vector P jbest ; The position of the optimal particle found by the population optimization is stored in the vector P, and the fitness of the optimal particle found by the population optimization is stored in the vector P best ;

[0028] S4.5. Update the velocity V j ' and position B' j .

[0029] V' j = wV j + c 1 |P j - B j |+ c 2 |P - B j | (3)

[0030] B' j = B j + V j ' (4)

[0031] In the formula, the constants c 1 and c 2 represent the learning factors, and the constant w represents the inertia weight;

[0032] S4.5. Calculate the fitness of each particle after updating and compare it with the vector P jbest , and store the value with better fitness in the vector P jbest , and store the position corresponding to the value with better fitness in the vector P j ;

[0033] S4.6. For all particles, compare the values of the vector P jbest and the vector P best , and store the value with better fitness in the vector P best , and store the position corresponding to the value with better fitness in the vector P;

[0034] S4.7. Sort the particles in the particle population according to the fitness based on the natural selection mechanism, and replace the positions and velocities of the other half of the particles with relatively poor fitness in the particle population with the positions and velocities of the half of the particles with relatively better fitness, while keeping the vectors P jbest vector P j and vector P best and vector P unchanged;

[0035] S4.8. If the particles represented by the vectors P best and P meet the operation accuracy set by the parameters of the objective function, output a set of ballistic parameters corresponding to the particles, otherwise return to step S4.4 to continue the optimization.

[0036] According to the full-ballistic intelligent collaborative planning and launching method of the unpowered guided projectile according to some embodiments of the present application, the method of giving the rudder deflection angle section by section is adopted for the rudder control of the guided projectile in the step S5.

[0037] According to the full-ballistic intelligent collaborative planning and launching method of the unpowered guided projectile according to some embodiments of the present application, in the step S6, the servo control command is generated according to the parameter deviation between the actual mid-course ballistic of multiple guided projectiles and the mid-course planned ballistic of multiple guided projectiles. Among them, the parameter deviation is the ballistic height deviation Δy and the vertical velocity deviation Δv y at the same flight distance x, and the servo control command δ c is expressed by Equation (5):

[0038] δ c =K P Δy + K D ΔV y + δ (5)

[0039] In the formula, the constants K P and K D represent the linear control coefficients.

[0040] According to the full-ballistic intelligent collaborative planning and launching method of the unpowered guided projectile according to some embodiments of the present application, the method of autonomously and collaboratively planning the shooting target for the end ballistic in the specified airspace in the step S7 includes

[0041] S7.1. Solve the relative motion parameter signals between the i-th projectile and the target through the relative motion operator, including the projectile-target distance signal R i , the projectile-target approaching rate signal the line-of-sight angle signal θ i and the line-of-sight angle rate signal the projectile-target distance signal R i and the projectile-target approaching rate signal The line-of-sight angle signal θ is transmitted from the relative motion calculator to the cumulative dynamic surface. i The line-of-sight angle rate signal is transmitted from the relative motion calculator to the signal comparator. It is transmitted from the relative motion calculator to the fast dynamic surface;

[0042] S7.2. Set the expected value of the line-of-sight angle The expected value of the line-of-sight angle is transmitted to the signal comparator, and the signal comparator calculates the difference between the expected value of the line-of-sight angle and the line-of-sight angle signal θ i to obtain and then transmits it to the fast dynamic surface;

[0043] S7.3. The cumulative dynamic surface receives the missile-target distance signal R i and the missile-target approaching rate signal from the relative motion calculator and performs arithmetic processing according to equations (7) and (8) to obtain the cumulative dynamic surface s xi , and then transmits it to the distribution planner;

[0044]

[0045]

[0046] where t m represents the time counted from the start of the end-stage planning, dε represents the infinitesimal of t m , g 1 and g 2 are both positive constants, tanh(x) = (e x -e -x ) / (e x +e -x ), l ij represents the communication connection weight between the i-th and j-th projectiles. If they can communicate, then l ij =1, otherwise it is 0;

[0047] S7.4. The distribution planner receives the signal s xi from the cumulative dynamic surface, calculates the distribution planning signal u xi , and the distribution planning signal u xi is represented by equation (9). The distribution planning signal u xi is transmitted to the i-th guided projectile;

[0048]

[0049] where c x and k x are both positive constants, k xThe value range of is max{|a Tix |} ≤ k x where a Tix represents the component of the target acceleration in the line-of-sight direction of the i-th shell;

[0050] S7.5. The fast dynamic surface receives the signal from the signal comparator and the line-of-sight angular rate signal from the relative motion operator and performs arithmetic processing according to Equation (10) to obtain the fast dynamic surface s fi and then transmits it to the independent planner;

[0051]

[0052] In the formula, g 3 and g 4 represent positive constants;

[0053] S7.6. The independent planner receives the signal s from the fast dynamic surface fi and calculates the independent planning signal u fi The independent planning signal u fi is represented by Equation (11), and u fi is transmitted to the i-th guided shell;

[0054]

[0055] In the formula, c f and k f are both positive constants, and the value range of k f is max{|a Tiy / R i |} ≤ k f where a Tiy represents the component of the target acceleration in the normal direction of the line of sight of the i-th shell.

[0056] This application embodiment also provides an electronic device, which includes: one or more processors, a memory, and one or more programs; wherein, the one or more programs are stored in the memory, and the one or more programs include instructions, when the instructions are executed by the electronic device, the electronic device executes the technical solutions of any possible design in the first aspect of this application embodiment.

[0057] This application embodiment also provides a computer-readable storage medium, which includes a computer program, when the computer program runs on an electronic device, the electronic device executes the technical solutions of any possible design in the first aspect of this application embodiment.

[0058] Advantages of the present invention:

[0059] The intelligent optimization of ballistic parameters by the micro-grid dimensionality reduction particle swarm natural selection method of the present invention processes the initial ballistic parameters of multiple guided projectiles through micro-gridding, evaluation, update, fitness value comparison, sorting, replacement, etc. by means of the micro-grid dimensionality reduction particle swarm natural selection method with a relatively high intelligent optimization level, quickly determines a set of optimal ballistic parameters that meet various constraints and have the least total flight time, enables multiple guided projectiles to enter the specified terminal airspace almost simultaneously, improves the efficiency and effect of ballistic planning, and lays a time-domain and airspace foundation for the subsequent distributed cooperative planning of the terminal ballistic trajectory.

[0060] For the distributed cooperative planning of the terminal ballistic trajectory of the present invention, a distributed planner is designed based on the cumulative dynamic surface, enabling the flight times of each projectile to converge within a limited time. In the line-of-sight normal direction, a fast dynamic surface independent controller is constructed by combining important information such as the environmental situation and the line-of-sight angle of the projectile, ensuring that the line-of-sight angle tracking error and line-of-sight angle rate of each projectile quickly converge to zero, and effectively achieving the distributed cooperative planning of the terminal ballistic trajectory.

[0061] The optimization method designed by the present invention successfully solves the problem of long time-consuming for high-dimensional optimization by reducing the optimization dimension, and improves the efficiency of ballistic planning. The terminal autonomous planning fully combines the flight state information for real-time adaptive adjustment. Under the action of the designed full-ballistic intelligent cooperative planning and launching method, the attitudes of the unpowered guided projectiles are basically in a continuous state, which is beneficial to the full-ballistic flight and cooperative target hitting. The above effects are shown in the simulation experiment analysis part of the specific implementation manner of the present invention. For the possible technical effects of the above-mentioned various aspects, please refer to the description of the technical effects that can be achieved for the first aspect above, and will not be repeated here. The additional aspects and advantages of the present invention will be partly given in the following description, partly will become obvious from the following description, or will be understood through the practice of the present invention. Brief Description of the Drawings

[0062] Figure 1 is the main flow chart of the full-ballistic intelligent cooperative planning and launching method of the unpowered guided projectile.

[0063] Figure 2 is the schematic diagram of the principle of the terminal ballistic autonomous cooperative planning.

[0064] Figure 3 is the ballistic trajectory diagram.

[0065] Figure 4 is the ballistic inclination diagram. Detailed Description of the Invention

[0066] Embodiments of the present application will be described in detail below with reference to the accompanying drawings. Examples of the embodiments are shown in the drawings. The present application provides a full-trajectory intelligent collaborative planning and launching method, an electronic device, and a storage medium for a non-powered guided projectile, aiming to solve the problem of full-trajectory intelligent collaborative planning and launching of a non-powered guided projectile with no continuous controllable power and limited control margin. Among them, the method, the device, and the computer-readable storage medium are based on the same technical concept, and the principles of solving problems are similar. Therefore, the implementations of each subject can be referred to each other, and the repeated parts will not be elaborated.

[0067] Figure 1 is the main flowchart of the full-trajectory intelligent collaborative planning and launching method for the non-powered guided projectile of the present invention. As Figure 1 shown, it includes the following steps:

[0068] Step 1: Initialization. Determine a suitable target distance according to the actual enemy and friendly situation and the range of the guided projectile, as well as the distance X between the center of the specified airspace at the end of the trajectory and the muzzle. Determine the number M of guided projectiles that need to be coordinated according to the firing performance of the gun, and the interval time Δt between gun firings.

[0069] Step 2: Determine the ballistic parameter domain. According to the model and technical characteristics of the non-powered guided projectile, determine the 3 parameters that have the greatest impact on ballistic planning as the ballistic parameters to be planned. They are the elevation angle θ, the servo scheme deflection angle δ, and the servo activation time t d .

[0070] Through relevant simulation calculations, it is found that among these 3 parameters, when only one of the parameters increases monotonically and the other 2 remain unchanged, the range will show a continuous change trend of increasing first and then decreasing. Then, within the same range distance, there must be multiple combinations of these 3 ballistic parameters.

[0071] Before planning the trajectory, it is necessary to determine the ballistic parameter domain of the above 3 parameters according to the actual situation of the non-powered guided projectile and the external environment. Among them, θ is mainly based on the safe firing limit of the gun, δ mainly refers to the limit deflection angle value of the servo, and there are also appropriate value ranges for t d during the use of the guided projectile. Combining the indicators of the gun and the projectile and X, determine the ballistic parameter domain formula (1).

[0072]

[0073] In the formula, θ min represents the minimum elevation angle, θ max represents the maximum elevation angle, δ max represents the maximum servo scheme deflection angle, t dmin represents the minimum servo activation time, t dmax represents the maximum servo activation time.

[0074] Step 3: Set the objective function. Through the midcourse trajectory planning, make M guided projectiles continuously launched at different times reach a specified airspace almost simultaneously in the shortest possible time. Set Equation (2) as the objective function, from which the flight time difference between any two adjacent projectiles reaching the specified airspace at the end stage and the sum of the flight times of each projectile can be solved.

[0075]

[0076] In the formula, T i represents the flight time of the i-th projectile in the uncontrolled section and the midcourse section.

[0077] Step 4: Intelligently optimize the trajectory parameters by the microcell dimensionality reduction particle swarm natural selection method. Consider the combination of the M groups of trajectory parameters θ, δ, t d corresponding to M projectiles as a parameter combination method. Then each combination method corresponds to 3M elements. The midcourse trajectory collaborative planning problem essentially belongs to a 3M-dimensional parameter optimization problem. The purpose is to find a set of optimal trajectory parameters that can minimize the value of Equation (2) and minimize the optimization time as much as possible. It specifically includes the following steps:

[0078] (4.1) Microcell dimensionality reduction. The conventional particle swarm method takes a long time to solve the high-dimensional optimization problem. To improve the trajectory planning efficiency, the dimension needs to be reduced, which is achieved by microcell processing of the 3M-dimensional trajectory parameter domain Equation (1). Divide the value range of each parameter into N tiny intervals evenly. The 3M-dimensional domain is equivalently reduced to a one-dimensional sequence of N 3M microcells.

[0079] (4.2) Screen the microcells. The position of the center of each microcell corresponds to a different parameter combination method. Solve the objective function values of the centers of each microcell based on the guided projectile trajectory model and Equation (1), and sort them according to the numerical size. Randomly select L q dominant microcells from the first x q % of the microcells. To avoid falling into the local optimum problem, randomly select L h potential microcells from the last x h % of the microcells after sorting.

[0080] (4.3) Particle initialization. Consider a parameter combination method as a particle, and the number of the particle population is L q +L h . Take the centers of the L q +L h microcells in step (4.2) as the initial positions of the particles. Randomly initialize the velocities of each particle in the population according to the trajectory parameter domain and the microcell division degree N. The position and velocity of the j-th particle are Bj =(b jθ1 , b jδ1 , b jtd1 ,..., b jθM , b jδM , b jtdM ) and V j =(v jθ1 , v jδ1 , v jtd1 ,..., v jθM , v jδM , v jtdM ). Wherein, b jθM represents the position of the parameter launch angle θ of the Mth guided projectile, b jδM represents the position of the parameter servo scheme deflection angle δ of the Mth guided projectile, b jtdM represents the position of the parameter servo starting time t d of the Mth guided projectile, v jθM represents the velocity of the parameter launch angle θ of the Mth guided projectile, v jδM represents the velocity of the parameter servo scheme deflection angle δ of the Mth guided projectile, v jtdM represents the velocity of the parameter servo starting time t d of the Mth guided projectile.

[0081] (4.4) Fitness processing. For each particle, calculate the T of each guided projectile based on the guided projectile trajectory model i , and solve the fitness of the particle from Equation (1).

[0082] The position and fitness of the jth particle are stored in P j and P jbest respectively. The position and fitness of the optimal particle found by the population optimization are stored in P and P best respectively.

[0083] (4.5) Update particle state. For each particle, update its own velocity V j ' and position B' j according to Equations (3) and (4).

[0084] ' j = wV j + c 1 |P j - B j | + c 2 |P - B j | (3)

[0085] B' j = B j + V j ' (4)

[0086] In the formula, the constant c 1 and c 2 are both learning factors, and the constant w is the inertia weight.

[0087] (4.6) Fitness processing after update. For each particle, calculate its fitness after update and compare it with P jbest . Store the value with better fitness and its corresponding position in P jbest and P j respectively.

[0088] For all particles, compare the values of P jbest and P best . Store the value with better fitness and its corresponding position in P best and P respectively.

[0089] (4.7) Natural selection. The conventional particle swarm method is prone to falling into local optimum during the optimization process. To solve this problem, combined with the natural selection mechanism, sort the particles in the population according to their fitness, and replace the positions and velocities of the other half of the particles with worse fitness with the positions and velocities of the half of the particles with better fitness in the population, while keeping P jbest , P j and P best , P unchanged.

[0090] (4.8) If the particles represented (pointed to) by the vector P best , vector P can meet the set operation accuracy, the intelligent optimization of the microgrid dimensionality reduction particle swarm natural selection method ends this time, and a set of ballistic parameters corresponding to the particle are output. Otherwise, return to step (4.4) to continue the optimization.

[0091] Step Five: Plan the cooperative scheme trajectory of M shells. Combining the ballistic model of the guided shell and the "instantaneous balance" hypothesis, plan the cooperative scheme trajectory of M shells according to the ballistic parameters output in Step Four. Considering the traceability of the scheme trajectory in practice, the method adopted for the guided shell to deflect the rudder is to give the rudder deflection angle section by section.

[0092] Step Six: Track the scheme trajectory of M shells. Use the linear control method as shown in formula (5) to track the scheme trajectory, and generate the servo control command δ y based on the ballistic height deviation Δy and the vertical velocity deviation Δv c between the actual trajectory and the scheme trajectory at the same flight distance x.

[0093] δ c = K P Δy + K D ΔV y + δ (5)

[0094] Wherein, the constant K P and K D are both linear control coefficients. If M shells successfully enter the specified airspace in the terminal stage in coordination, go to Step Seven; otherwise, return to Step Two.

[0095] Step Seven: Autonomous Cooperative Trajectory Planning in the Terminal Stage. Figure 2 is a schematic diagram of the principle, mainly composed of the cumulative dynamic surface 1.1, the distribution planner 1.2, the fast dynamic surface 2.1, the independent planner 2.2, the signal comparator 3.1, and the relative motion operator 3.2. It specifically includes the following steps:

[0096] (7.1) Through the relative motion operator 3.2, solve the relative motion parameter signals between the i-th shell and the target, including the distance R i between the shell and the target, the approaching rate of the shell to the target, the line-of-sight angle θ i and the line-of-sight angle rate The signals R i and are transmitted from the relative motion operator 3.2 to the cumulative dynamic surface 1.1, the signal θ i is transmitted from the relative motion operator 3.2 to the signal comparator 3.1, and the signal is transmitted from the relative motion operator 3.2 to the fast dynamic surface 2.1.

[0097] (7.2) Set the expected value of the line-of-sight angle and transmit it to the signal comparator 3.1. The signal comparator 3.1 performs difference processing on and θ i to obtain and then transmit it to the fast dynamic surface 2.1.

[0098] (7.3) The cumulative dynamic surface 1.1 receives the signals R i and from the relative motion operator 3.2 and performs arithmetic processing according to Equations (7) and (8) to obtain the cumulative dynamic surface s xi , and then transmit it to the distribution planner 1.2.

[0099]

[0100]

[0101] Wherein, t m represents the time counted from the start of the terminal-stage planning, dε represents the differential element of t m , g 1 and g 2 are both positive constants, tanh(x)=(e x -e-x ) / (e x +e -x ),l ij represents the communication connection weight between the i-th shell and the j-th shell. If they can communicate, then l ij = 1, otherwise it is 0.

[0102] (7.4) The distribution planner 1.2 receives the signal s from the cumulative dynamic surface 1.1 xi , and to improve the stability performance of the cumulative dynamic surface, the participation in the distribution planning signal u xi is designed. As shown in Equation (9), it can fully combine the flight state information for real-time adaptive adjustment, and transmit u xi to the i-th guided shell. Under the action of the planning signal u xi , the flight times of M shells can converge within a finite time, achieving the cooperative state in the terminal ballistic planning.

[0103]

[0104] In the formula, c x and k x are both positive constants, and the value range of k x is max{|a Tix |} ≤ k x , where a Tix represents the component of the target acceleration in the line-of-sight direction of the i-th shell.

[0105] (7.5) The fast dynamic surface 2.1 receives the signal from the signal comparator 3.1 and the signal from the relative motion operator 3.2 and performs arithmetic processing according to Equation (10) to obtain the fast dynamic surface s fi , and then transmits it to the independent planner 2.2.

[0106]

[0107] In the formula, g 3 and g 4 are both positive constants.

[0108] (7.6) The independent planner 2.2 receives the signal s from the fast dynamic surface 2.1 fi ,, and to improve the stability performance of the fast dynamic surface, the participation in the independent planning signal u fi is designed. As shown in Equation (11), it can fully combine the flight state information for real-time adaptive adjustment, and transmit u fi to the i-th guided shell. Under the action of the planning signal u fiUnder the action of [description missing], each projectile ensures that the line-of-sight angle tracking error and the line-of-sight angle rate quickly converge to zero, effectively achieving the guided attack of each projectile on the target while achieving the end-section ballistic coordination.

[0109]

[0110] In the formula, c f and k f are both positive constants, and the value range of k f is max{|a Tiy / R i |} ≤ k f , where a Tiy represents the component of the target acceleration in the line-of-sight normal direction of the i-th projectile.

[0111] Step Eight: Hit the target and end. If M guided projectiles hit the target at the same time finally, it means that the full-ballistic intelligent cooperative planning is successful; otherwise, it is a failure.

[0112] Key points of the present invention

[0113] Combined with the above scheme, the key points of the technical solution proposed by the present invention are mainly concentrated in the following two aspects:

[0114] One is to intelligently optimize the ballistic parameters by the micro-grid dimensionality reduction particle swarm natural selection method. By optimizing the micro-grid dimensionality reduction particle swarm natural selection method with a relatively high intelligent optimization degree, the initial ballistic parameters of multiple guided projectiles are processed through micro-gridding, evaluation, updating, fitness value comparison, sorting, replacement, etc., quickly determining a set of optimal ballistic parameters that meet various constraints and have the least total flight time, enabling multiple guided projectiles to enter the specified end-section airspace almost simultaneously, improving the efficiency and effect of ballistic planning, and laying a time-domain and airspace foundation for the subsequent distributed cooperative planning of the end-section ballistic.

[0115] The other is the distributed cooperative planning of the end-section ballistic. Based on the cumulative dynamic surface, a distributed planner is designed to make the flight times of each projectile converge in a limited time. In the line-of-sight normal direction, a fast dynamic surface independent controller is constructed by combining important information such as the environmental situation and the line-of-sight angle of the projectile, ensuring that the line-of-sight angle tracking error and the line-of-sight angle rate of each projectile quickly converge to zero, effectively achieving the distributed cooperative planning of the end-section ballistic.

[0116] Effects of the present invention

[0117] To verify the effectiveness of the designed full-ballistic intelligent cooperative planning launch method technical solution, a certain type of guided projectile is taken as the object, and relevant simulation operations are carried out in Matlab based on the fourth-order Runge-Kutta method. The simulation step size is 10 ms, and the main parameter values are shown in Table 1:

[0118] Table 1 Main simulation parameters

[0119]

[0120]

[0121] Through simulation calculations, the optimization time of the ballistic parameters of the microgrid dimensionality reduction particle swarm natural selection method is 0.5 s. The main simulation operation results are shown in Table 2. To demonstrate its effectiveness, the conventional particle swarm method was used as a comparison, and the ballistic parameter optimization was carried out under the same parameter settings. The optimization time was 2.37 s. Obviously, the designed optimization method successfully solved the problem of long time-consuming high-dimensional optimization by reducing the optimization dimension and improved the ballistic planning efficiency.

[0122] Table 2 Simulation operation results

[0123]

[0124] The ballistic trajectory simulation curve is as Figure 3 shown. When the minimum launch interval time is 10 s, the intelligent ballistic parameter optimization and planning of 3 guided projectiles are carried out by the microgrid dimensionality reduction particle swarm natural selection method. The 3 guided projectiles can enter the terminal situation window in a coordinated manner in time and space, and finally through the autonomous planning of the terminal ballistic trajectory, they successfully achieve the coordinated hit of the target at 61 km.

[0125] Figure 4 shows the change trend of the ballistic inclination angle of the 3 guided projectiles. Except for the short-term fluctuations that occurred at the moment of rudder control start, the overall trend of the projectile attitude change is relatively stable and gentle, which indicates that the introduction of the designed planning signal u xi 、u fi is reasonable and effective. It fully combines the flight state information for real-time adaptive adjustment. Under the action of the designed full-ballistic intelligent cooperative planning and launch method, the attitudes of the unpowered guided projectiles are basically in a continuous state, which is beneficial to the full-ballistic flight and the cooperative hit of the target.

[0126] Compared with the existing technology, the effects of the technical solution proposed by the present invention are mainly reflected in the following two aspects:

[0127] First, for the research object, most of the existing methods are aimed at aircraft such as missiles and unmanned aerial vehicles, and it is difficult to be applicable to gun-launched guided projectiles. The present invention fully considers its inherent typical characteristics such as no continuous controllable power and limited control ability, and can effectively cooperate to plan the full ballistic trajectory of multiple gun-launched guided projectiles, providing certain technical support and reference for giving full play to the actual use efficiency of gun-launched guided projectiles.

[0128] Second, it is the method performance. Compared with most of the existing methods that are only applicable to the middle section or the end section alone, the present invention is based on the perspective of overall planning of the entire trajectory, combines the intelligent optimization of micro-grid dimensionality reduction particle swarm natural selection and the sliding mode control method, changes the method of relying on artificial experience to select trajectory parameters in the past, and successfully improves the efficiency and effect of the collaborative planning of multiple missiles' entire trajectories, further enhancing the systematicness and effectiveness of the trajectory collaborative planning.

[0129] Based on the above embodiments, the embodiments of the present application further provide an electronic device, and the electronic device includes: one or more processors, a memory, and one or more programs; wherein, the one or more programs are stored in the memory, and the one or more programs include instructions, when the instructions are executed by the electronic device, the electronic device is caused to execute the method provided in the above embodiments.

[0130] Based on the above embodiments, the embodiments of the present application further provide a computer storage medium, and a computer program is stored in the computer storage medium. When the computer program is executed by the computer, the computer is caused to execute the method provided in the above embodiments.

[0131] Among them, the storage medium can be any available medium that can be accessed by a computer. Taking this as an example but not limited to: the computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, magnetic disk storage medium or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer.

[0132] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0133] The present application is described with reference to the flowcharts and / or block diagrams of the method, device (system), and computer program product according to the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementation in the process Figure 1One or more processes and / or blocks Figure 1 means for the functions specified in one or more blocks.

[0134] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the functions in the process Figure 1 One or more processes and / or blocks Figure 1 specified in one or more blocks.

[0135] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in the process Figure 1 One or more processes and / or blocks Figure 1 specified in one or more blocks.

[0136] It is apparent that those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A full - trajectory intelligent cooperative planning and launching method for unpowered guided projectiles, characterized in that, it includes S1. Determine the ballistic - related distance parameters, the number parameters of guided projectiles, and the time parameters; S2. Determine the mid - trajectory parameters to be planned and the ballistic parameter domain of the mid - trajectory parameters; S3. Set the objective function, and according to the objective function, obtain the flight - time difference between any two adjacent guided projectiles reaching the specified airspace at the end - stage and the sum of the flight times of each projectile; S4. Optimize the mid - trajectory parameters in the ballistic parameter domain to minimize the value of the objective function. A set of mid - trajectory parameters that minimizes the value of the objective function is the optimal set of mid - trajectory parameters; S5. Determine the mid - trajectory planned trajectories of multiple guided projectiles according to the optimal set of mid - trajectory parameters; S6. Generate servo control commands according to the parameter deviation between the actual mid - trajectories of multiple guided projectiles and the mid - trajectory planned trajectories of multiple guided projectiles. If multiple guided projectiles reach the specified airspace from the mid - stage to the end - stage, execute step S7, otherwise return to step S2; S7. Conduct autonomous cooperative planning for shooting targets in the specified airspace at the end - stage; The objective function in step S3 is represented by formula (2): （2） In the formula, represents the flight time of the i-th projectile in the unguided section and the midcourse section, and M represents the number of guided projectiles that need to be coordinated according to the firing performance of the gun.

2. The full - trajectory intelligent cooperative planning and launching method for unpowered guided projectiles according to claim 1, characterized in that, In step S1, the ballistic-related distance parameter, the number parameter of guided projectiles, and the time parameter are determined, including determining the target distance and the distance X between the center of the specified airspace at the end of the ballistic trajectory and the muzzle according to the actual enemy-friend situation and the range of the guided projectiles, determining the number M of guided projectiles that need to cooperate according to the firing performance of the gun, and the interval time between gunfires .

3. The full - trajectory intelligent cooperative planning and launching method for unpowered guided projectiles according to claim 1 or 2, characterized in that, The mid-course ballistic parameters in the step S2 include the firing angle θ, the steering gear scheme deflection angle δ, and the steering gear starting control time t d , and the ballistic parameter domain is represented by Equation (1): （1） In the formula, represents the minimum firing angle, represents the maximum firing angle, represents the maximum deflection angle of the servo scheme, represents the minimum starting time of the servo, represents the maximum starting time of the servo.

4. The full - trajectory intelligent cooperative planning and launching method for unpowered guided projectiles according to claim 3, characterized in that, In step S4, optimizing the mid - trajectory parameters to minimize the value of the objective function is achieved by the intelligent optimization of ballistic parameters method of the micro - grid dimensionality - reduction particle swarm natural selection method. The intelligent optimization of ballistic parameters method of the micro - grid dimensionality - reduction particle swarm natural selection method includes: S4.

1. Uniformly divide the value range of each ballistic parameter in the formula (1) representing the ballistic parameter domain into N intervals, where N represents the micro-grid division degree, and reduce the 3M-dimensional ballistic parameter domain to a one-dimensional sequence of N 3M micro-grids; S4.

2. Solve the objective function values of the centers of each microcell according to the ballistic model and ballistic parameter domain of the guided projectile, sort them according to the numerical magnitude, and randomly select L from the microcells before sorting by x q % of the microcells q advantageous microcells, and randomly select L from the microcells after sorting by x h % of the microcells h potential microcells; S4.

3. Regard a parameter combination method as a particle, and the number of the particle population is L q and L h sum. Take the center of the microcell with the number of the particle population as the initial position of the particle. Randomly initialize the velocities of the particles in the population according to the ballistic parameter domain and the microcell division degree N. The position of the j-th particle is expressed as , and the velocity of the j-th particle is expressed as . For the j-th particle, where represents the position of the parameter launch angle θ of the M-th guided projectile, represents the position of the parameter servo scheme deflection angle δ of the M-th guided projectile, represents the position of the parameter servo starting time t d of the M-th guided projectile, represents the velocity of the parameter launch angle θ of the M-th guided projectile, represents the velocity of the parameter servo scheme deflection angle δ of the M-th guided projectile, represents the velocity of the parameter servo starting time t d of the M-th guided projectile; S4.

4. Based on the guided - projectile ballistic model, calculate the flight time of each guided projectile in the uncontrolled section and the mid - section according to formula (2), and solve the fitness of each particle by formula (1); The position of the j-th particle is stored in the vector , and the fitness of the j-th particle is stored in the vector ; the position of the optimal particle found by the population optimization is stored in the vector , and the fitness of the optimal particle found by the population optimization is stored in the vector ; S4.5 Update the velocity and position of each particle itself according to equations (3) and (4). and position ; （3） （4） where the constants and represent learning factors, and the constant represents the inertia weight; S4.

5. Calculate the fitness of each particle after update and compare it with the vector . Store the value with better fitness in the vector , and store the position corresponding to the value with better fitness in the vector ; S4.

6. For all particles, compare the vectors and the vector values, store the value with better fitness in the vector , and store the position corresponding to the value with better fitness in the vector ; S4.

7. Sort the particles in the particle population according to the fitness by the natural selection mechanism, and replace the positions and velocities of the other half of the particles with inferior fitness with the positions and velocities of the half of the particles with superior fitness in the particle population, while keeping the vector , vector and vector , vector unchanged; S4.

8. If the vectors , vector represent particles that satisfy the operation accuracy of the parameter settings of the objective function, output a set of ballistic parameters corresponding to the particles; otherwise, return to step S4.

4. and continue the optimization.

5. The full - trajectory intelligent cooperative planning and launching method for unpowered guided projectiles according to claim 4, characterized in that, The method of applying rudder to the guided projectile in step S5 is to give the rudder deflection angle section by section.

6. The full - trajectory intelligent cooperative planning and launching method for unpowered guided projectiles according to claim 4, characterized in that, In step S6, a servo control command is generated based on the parameter deviation between the actual mid-course trajectories of multiple guided projectiles and the mid-course planned trajectories of the multiple guided projectiles, where the parameter deviation is the trajectory height deviation at the same flight distance x and the vertical velocity deviation . The servo control command is represented by Equation (5): （5） In the formula, the constants and represent linear control coefficients.

7. The full - trajectory intelligent cooperative planning and launching method for unpowered guided projectiles according to claim 1 or 4, characterized in that, The method of conducting autonomous cooperative planning for shooting targets in the specified airspace at the end - stage in step S7 includes S7.

1. Solve the relative motion parameter signals between the i-th shell and the target through the relative motion calculator, including the distance signal between the shell and the target , the approaching rate signal between the shell and the target , the line-of-sight angle signal and the line-of-sight angle rate signal . The distance signal between the shell and the target and the approaching rate signal between the shell and the target are transferred from the relative motion calculator to the cumulative dynamic surface. The line-of-sight angle signal is transferred from the relative motion calculator to the signal comparator. The line-of-sight angle rate signal is transferred from the relative motion calculator to the fast dynamic surface; S7.

2. Set the expected value of the line-of-sight angle , the expected value of the line-of-sight angle is transmitted to the signal comparator, and the signal comparator compares the expected value of the line-of-sight angle with the line-of-sight angle signal to perform a difference operation to obtain - , and then it is transmitted to the fast dynamic surface; S7.

3. Cumulative dynamic surface receives the missile-target distance signal from the relative motion operator and the missile-target approaching rate signal , and performs arithmetic processing according to equations (7) and (8) to obtain the cumulative dynamic surface , and then transmits it to the distribution planner; （7） （8） wherein, represents the time counted from the start of the plan for entering the end, represents infinitesimal element of, and are both positive constants, , represents the communication connection weight between the i-th shell and the j-th shell. If they can be communicatively connected, then = 1, otherwise 0; S7.

4. The distribution planner receives signals from the cumulative dynamic surface , calculates the distribution planning signal . The distribution planning signal is represented by Equation (9), and the distribution planning signal is transmitted to the i-th guided projectile; （9） Wherein, and are both normal constants, The value range of is represents the component of the target acceleration in the line-of-sight direction of the i-th shell; S7.

5. Rapid dynamic plane receives signals from the signal comparator - , and the line-of-sight angular rate signal of the relative motion operator , and performs arithmetic processing according to Equation (10) to obtain the rapid dynamic plane , and then transmits it to the independent planner; （10） In the formula, and represent positive constants; S7.

6. The independent planner receives signals from the fast dynamic surface , calculates the independent planning signal . The independent planning signal is represented by Equation (11) and is transmitted to the i-th guided projectile; ​ （11） wherein, and are both normal constants, The value range of is indicating the component of the target acceleration in the line-of-sight normal direction of the i-th shell.

8. An electronic device, the electronic device comprises: One or more processors, a memory, and one or more programs; wherein, the one or more programs are stored in the memory, and the one or more programs include instructions. When the instructions are executed by the electronic device, the electronic device executes the launching method according to any one of claims 1 to 7.

9. A computer-readable storage medium, the computer-readable storage medium comprising a computer program, which, when running on an electronic device, causes the electronic device to execute the transmission method according to any one of claims 1 to 7.

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