Aircraft pulse orbit control fuel optimization method, device and equipment based on particle swarm optimization, and medium

Abstract

The invention discloses an aircraft pulse orbit control fuel optimization method, device, equipment and medium based on a particle swarm optimization algorithm, and the method comprises the following steps: considering the pulse orbit control thrust characteristics of an aircraft, and aiming at the demand that a continuous signal needs to be converted into a discrete signal when a guidance instruction is converted into an orbit control pulse thrust instruction; the method comprises the following steps: firstly, parameterizing a switching threshold value of a conversion function, and designing an index vector to characterize parameters in the guidance instruction conversion function; taking the residual fuel as an optimization index, designing a particle fitness function, and proposing a real-time updating strategy; a transformation parameter index vector is randomly generated to serve as an initial population of a particle swarm algorithm, the index vector is optimized through iteration, and an optimal transformation parameter is obtained after iteration is completed. According to the method, an aircraft pulse orbit control particle swarm fuel optimization algorithm is adopted, and the fuel utilization rate is improved through iterative optimization of guidance instruction conversion parameters. The method has the characteristics of simple design method, capability of adapting to limited fuel and the like.

Description

Technical Field

[0001] The present invention belongs to the field of pulse orbit control aircraft guidance system design, and specifically, relates to an aircraft pulse orbit control fuel optimization method, device, equipment and medium based on a particle swarm algorithm. Background Art

[0002] Spacecraft use pulse thrust to adjust attitude and orbit, and realize attitude adjustment and trajectory guidance through direct force control. However, for some miniaturized spacecraft, the fuel they carry is very limited. How to achieve attitude and orbit control performance requirements under fuel constraints and avoid system oscillation problems caused by frequent switching of pulse thrust has important engineering and theoretical research significance.

[0003] As one of the effective solutions, the optimal flight vehicle fuel based on trajectory optimization is a commonly used method. Usually, fuel consumption is used as the optimization index, and the trajectory optimization method is used to solve the problem to achieve trajectory planning under the optimal index condition. This type of method usually focuses on the continuous thrust optimization problem, but it is difficult to apply it to the pulsed spacecraft orbit control fuel optimization problem.

[0004] Space vehicles use pulse engines for guidance and orbit control, which usually have fuel limitations. It is necessary to design appropriate guidance laws and corresponding command conversion functions to reduce fuel consumption while completing target rendezvous. Summary of the invention

[0005] In view of the above technical problems, the present invention designs a method, device, equipment and medium for optimizing the fuel of aircraft pulse orbit control based on particle swarm algorithm. It is a combined parameter optimization algorithm designed for the continuous guidance instruction conversion process, and reduces fuel consumption by optimizing the conversion parameters. Finally, the particle swarm optimization method is used to solve the optimal parameters.

[0006] The present invention provides a method for optimizing aircraft pulse trajectory control fuel based on particle swarm algorithm, comprising the following steps:

[0007] S1: For the guidance command conversion function of pulse orbit control, the conversion function switch threshold is parameterized, and the index vector is designed to characterize the parameters in the guidance command conversion function;

[0008] S2: Based on the remaining fuel and target interception status obtained from a single mission, the fitness function is designed as follows:

[0009] (6)

[0010] in, The number of aircraft that completed the flight mission, is the bonus factor, which is a fixed value and is greater than the sum of the initial mass of the fuel carried by the aircraft; is the remaining fuel mass of the aircraft, is the indicator vector;

[0011] S3: Randomly generate an indicator vector as the initial population, and then iterate the particle swarm optimization algorithm until the iteration termination condition is met, and finally obtain the transformation parameter corresponding to the maximum fitness in the particle swarm space, that is, as the optimal solution output. The transformation parameter refers to the parameter obtained after parameterizing the switch threshold of the transformation function in step S1.

[0012] The present invention also provides an aircraft pulse orbit control fuel optimization device based on a particle swarm algorithm, comprising the following modules:

[0013] A parameterization module is used to parameterize the switch threshold of the guidance command conversion function for pulse orbit control, and to design an index vector to characterize the parameters in the guidance command conversion function;

[0014] The function design module is used to design the fitness function based on the remaining fuel and target interception status obtained from a single mission as follows:

[0015] (6)

[0016] in, The number of aircraft that completed the flight mission, is the bonus factor, which is a fixed value and is greater than the sum of the initial mass of the fuel carried by the aircraft; is the remaining fuel mass of the aircraft, is the indicator vector;

[0017] The iteration module is used to randomly generate an indicator vector as the initial population, and then iterate the particle swarm optimization algorithm until the iteration termination condition is met, and finally obtain the transformation parameter corresponding to the maximum fitness in the particle swarm space, that is, as the optimal solution output. The transformation parameter refers to the parameter obtained by parameterizing the switch threshold of the transformation function in the parameterization module.

[0018] The present invention also provides a computing device, comprising: at least one processor and a memory storing program instructions; when the program instructions are read and executed by the processor, the computing device executes an aircraft pulse orbit control fuel optimization method based on a particle swarm algorithm.

[0019] The present invention also provides a readable storage medium storing program instructions. When the program instructions are read and executed by a computing device, the computing device executes an aircraft pulse orbit control fuel optimization method based on a particle swarm algorithm.

[0020] Beneficial effects of the present invention:

[0021] The present invention aims at the optimization problem of pulse orbit control fuel for spacecraft, and considers the problems of too high dimension of state space and too large amount of data, and uses a heuristic algorithm to search for the optimal parameters of pulse orbit control fuel. The method designed by the present invention is separated from the guidance law design, so any guidance law that outputs continuous instructions can be optimized by this method in the instruction conversion process. The method optimizes the parameters of the continuous guidance instruction conversion process and uses particle swarm optimization to solve, so as to achieve the pulse orbit control requirements under fuel constraints. According to the technical solution of the present invention, for different types of pulse orbit control thrust and guidance law forms, it is only necessary to redesign the state and fitness functions to iteratively update the optimal fuel parameters, which has good applicability. The aircraft pulse orbit control fuel optimization method based on particle swarm algorithm of the present invention can be used for different types of pulse orbit control aircraft and optimize the conversion function parameters to reduce fuel consumption. The present invention adopts particle swarm optimization algorithm, which has the advantages of simple design method and low cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 It is a flow chart of the aircraft pulse orbit control fuel optimization method based on particle swarm algorithm of the present invention;

[0024] Figure 2 It is a schematic diagram of the hysteresis characteristic curve in the conversion function;

[0025] Figure 3 A schematic diagram of a three-dimensional interception scenario of multiple spacecraft against multiple targets;

[0026] Figure 4 This is the simulation schematic diagram before optimization;

[0027] Figure 5 This is the simulation diagram after optimization. DETAILED DESCRIPTION

[0028] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0029] like Figure 1As shown, the steps of the aircraft pulse orbit control fuel optimization method based on particle swarm algorithm of the present invention are as follows: first, based on the characteristics of the aircraft orbit control pulse thrust and the form of the guidance law, the function switch threshold of the guidance instruction converted into the engine instruction is parameterized, and the conversion parameter index vector is designed; then, based on the remaining fuel obtained from a single mission and the target interception state, a fitness function is designed, and the fitness function takes the particle state index vector as input and outputs the fitness of the particle; finally, based on the designed index vector and fitness function, particle swarm optimization is performed, firstly, the initial population is randomly generated and the fitness is calculated, and then iteration is performed to maximize the particle fitness, and the optimal conversion parameters of the particle swarm state space can be obtained after the iteration. It can be seen from the above steps that the evaluation criteria of the conversion parameters mainly depend on the fitness function of the corresponding particle, and the value is determined by simulation. The more remaining fuel, the greater the fitness, which is consistent with the optimization requirements. The specific implementation steps are as follows:

[0030] S1: For the guidance command conversion function of pulse orbit control, the switching threshold of the conversion function is parameterized, and the index vector is designed to characterize the parameters in the guidance command conversion function.

[0031] The guidance command conversion function includes longitudinal and lateral conversion functions, both of which are typical hysteresis processes, such as Figure 2 As shown, its expression is as follows:

[0032] (1)

[0033] in, It is the maximum thrust of the engine or the maximum thrust of orbit control; for The guidance instructions input into the conversion function at any time are generally periodic sampling of continuous instructions, which need to be further discretized through the conversion function. , and is the on or off threshold of the hysteresis loop, which is used to control the output. In particular, the longitudinal conversion function is recorded as , the lateral transformation function is recorded as , both have their own independent parameters.

[0034] Figure 2 middle and is the positive command switch threshold, which is a hysteresis loop. When the engine is not turned on and the input increases from small to large, only when When the output becomes , when the engine is working forward, only when the input decreases to The engine is shut down only when the switching threshold in the reverse direction works similarly. Figure 2 In Indicates the maximum output value.

[0035] The parameters in the conversion function refer to the parameters obtained after the conversion function switch threshold is parameterized. The design indicator vector characterizes the parameters including:

[0036] (2)

[0037] in, They are the parameters in the vertical instruction conversion function, is the lateral transformation function parameter, These are the shared parameters for the longitudinal and lateral conversion functions.

[0038] The relationship between the parameters of the indicator vector and the conversion function is as follows:

[0039] For the vertical conversion function , the parameters of the indicator vector and The parameters satisfy the following relationship:

[0040] (3)

[0041] For the lateral conversion function , the parameters of the indicator vector and The parameters satisfy the following relationship:

[0042] (4)

[0043] For the longitudinal and lateral conversion functions, the indicator vector parameters The value is the same and is still recorded as .

[0044] In one embodiment, the engine is a pulse engine. The engine start command is received at any time, and When the engine shutdown command is received at any time, the thrust change characteristic is:

[0045] (5)

[0046] In the formula, are the longitudinal and lateral thrust values ​​of the engine; It is the maximum thrust value of the engine in steady state operation; is the rise and fall time.

[0047] S2: Design a fitness function based on the remaining fuel and target interception status obtained from a single mission.

[0048] The fitness function can be designed as follows:

[0049] (6)

[0050] in, The number of aircraft that completed the flight mission, is the bonus factor, which is a fixed value and is greater than the sum of the initial mass of fuel carried by all aircraft; is the remaining fuel mass of the aircraft, Indicates the number of each aircraft, is the indicator vector.

[0051] Specifically, the function obtains the simulation result data corresponding to the input parameters by calling a simulation, and then calculates its fitness. Before iteration, the simulation scene and corresponding variables need to be set, and the guidance law and attitude control method need to be determined. During the iteration, the initial scene, variables, and guidance law settings need to be kept consistent, and only the input conversion parameters are different.

[0052] This step may also include: calculating and updating the fitness function in real time through numerical simulation, the steps are as follows:

[0053] Given a simulation scenario and the parameters of the aircraft and target, the scenario and parameters should be consistent during the iteration process. A six-degree-of-freedom simulation model of the aircraft and the target is constructed in the simulation scenario, in which the aircraft intercepts the target. The proportional guidance method is used by default for interception, and a custom guidance law can also be used. When the aircraft successfully intercepts the target or the interception fails, the simulation of the aircraft stops, the fuel mass consumption is no longer calculated, and whether the interception is successful is recorded. When the simulation of all aircraft stops, the simulation is completed. After the simulation is completed, the simulation result data is exported and substituted into the calculation fitness function to obtain the result value.

[0054] S3: Randomly generate an indicator vector as the initial population, and then iterate the particle swarm optimization algorithm until the iteration termination condition is met, and finally obtain the transformation parameter corresponding to the maximum fitness in the particle swarm space, which is output as the optimal solution. The transformation parameter refers to the parameter obtained after parameterizing the switch threshold of the transformation function in step S1.

[0055] The block diagram of the particle swarm optimization algorithm is shown below:

[0056] First, the initialization step is performed, including the definition and assignment of the iterative algorithm control variables. is the total number of iterations, is the particle swarm size, i.e. the total number of initial particles, is the particle state update coefficient, which determines the update direction of the particle state. Initialization also includes the initialization of the particle state, which is usually obtained by random generation. At the same time, the optimal state and the optimal global particle state must be defined for updating in subsequent cycles.

[0057] Then the iteration process is carried out. In each round of iteration, the fitness of each particle needs to be calculated according to formula (6), and then the optimal state of the particle is updated according to the fitness. After the update is completed, the global optimal state is updated according to the optimal state of the particle, and then the particle state update coefficient is updated. , and then update the particle state and particle velocity in turn.

[0058] After completing all iterations, the global optimal state of the particle is the optimal state value, which corresponds to the optimal transformation parameter combination obtained by the optimization algorithm.

[0059] In summary, step S3 may include:

[0060] Initialize the parameters and variables in the particle swarm optimization algorithm, including the number of iterations, population size, upper and lower limits of each dimension of the indicator vector, upper and lower limits of the particle iteration speed, various weight parameters that control the particle swarm algorithm, and initialization of various variables in the iteration process. Randomly initialize the particle swarm and call the fitness function to calculate the fitness of each particle.

[0061] During each iteration, the index vector is updated according to the particle velocity as follows:

[0062] (7)

[0063] in, For the The particle state at the step iteration, is the particle speed.

[0064] The particle velocity is then updated according to the following formula:

[0065] (8)

[0066] in, is the weight factor, which controls the particle update direction; The matrix is ​​randomly allocated to increase the randomness of the iterative process and avoid falling into the local optimum; and Respectively represent the global optimal index vector and the particle optimal index vector until the current iteration round, subscript Indicates the particle number.

[0067] Execute iteration: calculate the fitness of each particle, then update the particle optimal index vector according to the fitness size, after the update is completed, update the global optimal index vector according to the particle optimal index vector, and then update the weight factor , and then update the particle state and particle velocity in turn.

[0068] After completing all iterations, the global optimal index vector of the particle is obtained, and the global optimal index vector corresponds to the optimal transformation parameter.

[0069] In the above iterations, the iteration termination condition may be reaching a preset number of iterations.

[0070] Furthermore, when meeting different task requirements, it is only necessary to adjust the simulation scenario, and the particle swarm optimization will perform optimization based on the simulation results, so it can be applicable to different scenarios.

[0071] Furthermore, when the number of targets is greater than the number of aircraft, the aircraft will select targets for interception according to the established rules, and then apply the scenario where the number of targets is the same as the number of aircraft.

[0072] A device for optimizing aircraft pulse orbit control fuel based on particle swarm algorithm is also provided, comprising the following modules:

[0073] A parameterization module is used to parameterize the switch threshold of the guidance command conversion function for pulse orbit control, and to design an index vector to characterize the parameters in the guidance command conversion function;

[0074] The function design module is used to design the fitness function based on the remaining fuel and target interception status obtained from a single mission as follows:

[0075] (6)

[0076] in, The number of aircraft that completed the flight mission, is the bonus factor, which is a fixed value and is greater than the sum of the initial mass of the fuel carried by the aircraft; is the remaining fuel mass of the aircraft, is the indicator vector;

[0077] The iteration module is used to randomly generate an indicator vector as the initial population, and then iterate the particle swarm optimization algorithm until the iteration termination condition is met, and finally obtain the transformation parameter corresponding to the maximum fitness in the particle swarm space, that is, as the optimal solution output. The transformation parameter refers to the parameter obtained by parameterizing the switch threshold of the transformation function in the parameterization module.

[0078] A computing device comprises: at least one processor and a memory storing program instructions; when the program instructions are read and executed by the processor, the computing device executes an aircraft pulse orbit control fuel optimization method based on a particle swarm algorithm.

[0079] A readable storage medium storing program instructions, when the program instructions are read and executed by a computing device, enables the computing device to execute an aircraft pulse orbit control fuel optimization method based on a particle swarm algorithm.

[0080] The following uses a scenario of a certain aircraft intercepting a non-maneuverable missile as an example to illustrate the effectiveness of the method proposed in the present invention. The maximum thrust generated by the start-up of the orbital control pulse engine is 3000N. The scenario settings are shown in Table 1, and the simulation parameter settings are shown in Table 2. There are 6 aircraft in the simulation settings, each intercepting an independent target. The terminal guidance process scenario of the interception is shown in Figure 3 shown.

[0081] Table 1 Simulation scenario settings

[0082] In Table 1, km, m and s are the units of distance and time respectively.

[0083] Table 2 Simulation parameter settings

[0084] In Table 2, kg, m, and s are the units of mass, distance, and time, respectively. and Respectively represent the minimum and maximum values ​​of each component of the indicator vector, and They represent the minimum and maximum values ​​of the particle update velocity components respectively.

[0085] After the particle swarm optimization with the above parameter settings, the obtained conversion parameters are simulated to obtain the engine output thrust of the six aircraft. Figure 4 The impulse orbit control thrust curve in the simulation result is the one without parameter optimization. The simulation curve after parameter optimization is as follows: Figure 5 As shown in the figure, Fy / N refers to the longitudinal thrust of the aircraft, and Fz / N refers to the lateral thrust of the aircraft. Comparison between the two shows that after optimization, the switching frequency of the engine has been reduced.

[0086] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing aircraft pulse orbit control fuel based on particle swarm algorithm, characterized in that: The following steps are involved: S1: For the guidance command conversion function of pulse orbit control, the conversion function switch threshold is parameterized, and the index vector is designed to characterize the parameters in the guidance command conversion function; S2: Design the fitness function based on the remaining fuel and target interception status obtained from a single mission; S3: Randomly generate an indicator vector as the initial population, and then iterate the particle swarm optimization algorithm until the iteration termination condition is met, and finally obtain the transformation parameter corresponding to the maximum fitness in the particle swarm space, that is, as the optimal solution output. The transformation parameter refers to the parameter obtained after parameterizing the switch threshold of the transformation function in step S1.

2. The method for optimizing aircraft pulse orbit control fuel based on particle swarm algorithm according to claim 1, characterized in that: The designed fitness function is as follows: （6） in, The number of aircraft that completed the flight mission, is the bonus factor, which is a fixed value and is greater than the sum of the initial mass of the fuel carried by the aircraft; is the remaining fuel mass of the aircraft, is the indicator vector.

3. The method for optimizing aircraft pulse orbit control fuel based on particle swarm algorithm according to claim 1, characterized in that: In step S1, the design index vector characterizes the parameters in the guidance instruction conversion function, including: （2） in, They are the parameters in the vertical instruction conversion function, is the lateral transformation function parameter, These are the shared parameters for the longitudinal and lateral conversion functions.

4. The method for optimizing aircraft pulse orbit control fuel based on particle swarm algorithm as claimed in claim 3, characterized in that: In step S1, the longitudinal and lateral conversion functions are both hysteresis processes: （1） in, It is the maximum thrust for orbit control; for Input the guidance instructions of the conversion function at all times; , and is the opening or closing threshold of the hysteresis loop, and the longitudinal conversion function is recorded as , the lateral transformation function is recorded as .

5. The method for optimizing aircraft pulse orbit control fuel based on particle swarm algorithm as claimed in claim 3, characterized in that: In step S1, the relationship between the parameters of the indicator vector and the conversion function is as follows: For the vertical conversion function , the indicator vector parameter is The parameters satisfy the following relationship: （3） For the lateral conversion function , the indicator vector parameter is The parameters satisfy the following relationship: （4） For the longitudinal conversion function and the lateral conversion function, the indicator vector parameter Parameters of value and conversion functions The value is the same and is still recorded as .

6. The method for optimizing aircraft pulse orbit control fuel based on particle swarm algorithm according to claim 4, characterized in that: In step S2, the fitness function is calculated and updated in real time through numerical simulation. The process of numerical simulation includes: Given a simulation scenario and the parameters of the aircraft and the target, a six-degree-of-freedom simulation model of the aircraft and the target is constructed in the simulation scenario, in which the aircraft intercepts the target. When the aircraft successfully intercepts the target or judges that the interception fails, the simulation of the aircraft stops, and the fuel mass consumption is no longer calculated. At the same time, it records whether the interception is successful. When the simulation of all aircraft stops, the simulation is completed. After the simulation is completed, the simulation result data is exported and substituted into the fitness function to obtain the result value.

7. The method for optimizing aircraft pulse orbit control fuel based on particle swarm algorithm as claimed in claim 5, characterized in that: In step S3, the particle swarm algorithm steps are as follows: Initialize the parameters and variables in the particle swarm algorithm, randomly initialize the particle swarm, and call the fitness function to calculate the fitness of each particle; During each iteration, the index vector is updated according to the particle velocity as follows: （7） in, For the The particle state at the step iteration, is the particle velocity; The particle velocity is then updated according to the following formula: （8） in, is the weight factor, which controls the particle update direction; The matrix is ​​randomly allocated to increase the randomness of the iterative process and avoid falling into the local optimum; and They represent the global optimal index vector and the particle optimal index vector up to the current iteration round respectively; Execute iteration: calculate the fitness of each particle, then update the particle optimal index vector according to the fitness size, after the update is completed, update the global optimal index vector according to the particle optimal index vector, and then update the weight factor , and then update the particle state and particle velocity in turn; After the iteration is completed, the global optimal indicator vector is obtained, which corresponds to the optimal transformation parameter.

8. A device for optimizing aircraft pulse orbit control fuel based on particle swarm algorithm, characterized in that: Includes the following modules: A parameterization module is used to parameterize the switch threshold of the guidance command conversion function for pulse orbit control, and to design an index vector to characterize the parameters in the guidance command conversion function; The function design module is used to design the fitness function based on the remaining fuel and target interception status obtained from a single mission as follows: （6） in, The number of aircraft that completed the flight mission, is the bonus factor, which is a fixed value and is greater than the sum of the initial mass of the fuel carried by the aircraft; is the remaining fuel mass of the aircraft, is the indicator vector; The iteration module is used to randomly generate an indicator vector as the initial population, and then iterate the particle swarm optimization algorithm until the iteration termination condition is met, and finally obtain the transformation parameter corresponding to the maximum fitness in the particle swarm space, that is, as the optimal solution output. The transformation parameter refers to the parameter obtained by parameterizing the switch threshold of the transformation function in the parameterization module.

9. A computing device, characterized in that include: at least one processor and a memory storing program instructions; When the program instructions are read and executed by the processor, the computing device executes the aircraft pulse orbit control fuel optimization method based on particle swarm algorithm as described in any one of claims 1 to 7.

10. A readable storage medium storing program instructions, characterized in that: When the program instructions are read and executed by a computing device, the computing device executes the aircraft pulse orbit control fuel optimization method based on particle swarm algorithm as described in any one of claims 1 to 7.

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