Multi-algorithm trajectory optimization method for hypersonic gliding aircraft

Abstract

The invention belongs to the technical field of flight trajectory optimization, and particularly relates to a multi-algorithm trajectory optimization method for a hypersonic gliding aircraft. The method comprises the following steps: S1, setting each optimization algorithm to adopt a unified initialization parameter and a unified to-be-optimized parameter; s2, calculating an attack angle at the current flight speed according to an attack angle piecewise function; s3, calculating the height, the speed, the voyage, the heat flow, the dynamic pressure and the overload at each moment according to the motion equation of the aircraft; s4, infrared radiation energy at the terminal moment is determined according to the infrared radiation calculation model, penalty quantities caused by the height error, the voyage error and the speed error at the terminal moment are superposed, and final energy is obtained; step S5, calculating optimal parameters to be optimized by using each optimization algorithm by taking the minimum final energy as an optimization target and taking the condition that heat flow, dynamic pressure and overload are not higher than set values as constraint conditions; and S6, selecting an optimal solution from the optimization algorithms. According to the invention, the calculation efficiency and robustness are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of flight trajectory optimization, and particularly relates to a multi-algorithm trajectory optimization method for a hypersonic glider vehicle. BACKGROUND

[0002] The hypersonic glider vehicle (HGV) has the characteristics of high speed and high maneuverability, and its trajectory optimization problem has always been a difficulty in flight mechanics research. The trajectory optimization problem of the hypersonic glider vehicle has always been an important research direction in the fields of flight mechanics and control. Traditional optimization methods are usually based on a single type of algorithm, such as genetic algorithm (GA) or particle swarm optimization (PSO), but due to the complexity and multi-constraint characteristics of the hypersonic glider vehicle, a single algorithm is often difficult to balance between global search ability and local refinement ability, and is prone to fall into local optimum, and the convergence speed is slow. In addition, the existing methods usually lack accurate description of complex physical models, such as the aerodynamic characteristics of the vehicle at different altitudes and speeds, atmospheric models, and constraint conditions such as heat flow, dynamic pressure and overload, which makes it difficult to meet the demand of the optimization result for practical application.

[0003] The existing trajectory optimization methods mostly use linearized models or simplified empirical formulas, which can reduce the computational complexity, but have great limitations in accurately describing the nonlinear motion characteristics of the hypersonic glider vehicle. The motion of the hypersonic glider vehicle is affected by multiple factors, including the exponential decay of atmospheric density, aerodynamic heating effect, and aerodynamic lift and drag characteristics under strong attack angle conditions, etc. The interaction of these factors makes the trajectory optimization problem more complex. SUMMARY

[0004] In order to solve the above problems, the application provides a multi-algorithm trajectory optimization method for a hypersonic glider vehicle, which can not only improve the efficiency and accuracy of optimization, but also better adapt to the needs of different flight stages by combining six different optimization algorithms.

[0005] The application provides a multi-algorithm trajectory optimization method for a hypersonic glider vehicle, mainly including:

[0006] Step S1, setting uniform initialization parameters and uniform to-be-optimized parameters for each optimization algorithm, the to-be-optimized parameters including a bank angle, and a first speed and a second speed for segmenting an attack angle;

[0007] Step S2, calculating the attack angle under the current flight speed according to the attack angle segmentation function constructed by the first speed, the second speed, the initial time speed and the terminal time speed;

[0008] Step S3, according to the aircraft motion equation, the height, speed, distance, heat flow, dynamic pressure and overload at each time are calculated;

[0009] Step S4, the infrared radiation energy at the terminal time is determined according to the infrared radiation calculation model, and the penalty amount caused by the height error, distance error and speed error at the terminal time is superimposed to obtain the final energy;

[0010] Step S5, taking the minimum final energy as the optimization goal, and taking the heat flow, dynamic pressure and overload not higher than the set value as the constraint condition, the optimal to-be-optimized parameter is calculated by using each optimization algorithm;

[0011] Step S6, the optimal solution is selected from each optimization algorithm.

[0012] Preferably, the optimization algorithm includes whale optimization algorithm IWOA, particle swarm optimization algorithm PSO, grey wolf optimization algorithm GWO, genetic algorithm GA, differential evolution algorithm DE and random search algorithm SSA.

[0013] Preferably, in step S2, the attack angle segmented function includes:

[0014]

[0015] Wherein, V is the current flight speed, V1 is the first speed, V2 is the second speed, V0 is the initial time speed, V f is the terminal time speed, a max is the maximum attack angle, a ratio is the reference attack angle.

[0016] Preferably, in step S5, the optimization goal of taking the minimum final energy J is represented by the following formula:

[0017]

[0018] Wherein, a is the attack angle, s is the roll angle, W b is the infrared radiation energy at the terminal time determined according to the infrared radiation calculation model, b r is the terminal time height error, b s is the terminal time distance error, b v is the terminal time speed error, t0 is the initial time, t f is the terminal time, w1, w2, w3 are the penalty factors of related constraints.

[0019] Preferably, the terminal time height error, the terminal time distance error and the terminal time speed error are calculated by the following formula:

[0020]

[0021] Wherein, r(tf ) is the height of the terminal at time t, r f is the expected height of the terminal at time t, and△r is the acceptable error of height, S(t f ) is the distance of the terminal at time t, S f is the expected distance of the terminal at time t, and△S is the acceptable error of distance, V(t f ) is the speed of the terminal at time t, V f is the expected speed of the terminal at time t, and△V is the acceptable error of speed.

[0022] Preferably, the constraint conditions include that the heat flow, the dynamic pressure and the overload are not higher than the set values, and the constraint conditions include:

[0023]

[0024] wherein, is the heat flow, is the maximum value of the heat flow, K Q is the heat flow rate constant, and p is the atmospheric density;

[0025]

[0026] wherein, q is the dynamic pressure, q max is the maximum value of the dynamic pressure;

[0027]

[0028] wherein, N l is the overload, N max is the maximum value of the overload, m is the mass of the aircraft, L is the lift, and D is the drag.

[0029] Preferably, the constraint conditions further include:

[0030] the attack angle is less than the set maximum value of the attack angle;

[0031] the roll angle is less than the set maximum value of the roll angle;

[0032] the flight process satisfies the quasi-equilibrium gliding condition:

[0033]

[0034] wherein, r is the relative distance of the aircraft to the center of the earth, and g is the gravitational acceleration.

[0035] The present application achieves a good balance between the accuracy of the physical model and the diversity of the optimization algorithm, and provides strong technical support for the design and control of the hypersonic gliding aircraft. The present application improves the calculation efficiency and robustness. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1is a flow chart of a preferred embodiment of a multi-algorithm trajectory optimization method of a hypersonic glider vehicle of the present application.

[0037] Figure 2 is a schematic diagram of an optimal solution.

[0038] Figure 3 is a schematic diagram of an altitude-velocity profile.

[0039] Figure 4 is a schematic diagram of an altitude-time profile.

[0040] Figure 5 is a schematic diagram of a velocity-time profile.

[0041] Figure 6 is a schematic diagram of infrared radiation variation. DETAILED DESCRIPTION

[0042] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the accompanying drawings in the embodiments of the present application. Identical or similar labels in the drawings represent identical or similar elements or elements with identical or similar functions throughout. The described embodiments are part of the embodiments of the present application, rather than all the embodiments of the present application. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts fall within the scope of protection of the present application. The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0043] The present application provides a multi-algorithm trajectory optimization method of a hypersonic glider vehicle, as shown in Figure 1 mainly includes:

[0044] Step S1, setting uniform initialization parameters and uniform to-be-optimized parameters for each optimization algorithm, the to-be-optimized parameters including a bank angle, and a first speed and a second speed for segmenting an attack angle;

[0045] Step S2, calculating the attack angle at the current flight speed according to an attack angle segmentation function constructed by the first speed, the second speed, the initial time speed and the terminal time speed;

[0046] Step S3, calculating the altitude, speed, range, heat flow, dynamic pressure and overload at each time according to the vehicle motion equation;

[0047] Step S4, determining the infrared radiation energy at the terminal time according to an infrared radiation calculation model, and superimposing a penalty amount caused by the terminal time altitude error, range error and speed error to obtain a final energy;

[0048] Step S5, using each optimization algorithm to calculate the optimal to-be-optimized parameter with the minimum final energy as the optimization target and the heat flow, dynamic pressure and overload not higher than the set value as the constraint condition;

[0049] Step S6, selecting the optimal solution from each optimization algorithm.

[0050] The application proposes a hypersonic glider trajectory optimization method based on multi-algorithm integration. By combining multiple optimization algorithms, an efficient and robust optimization solution is provided. The method can simultaneously optimize the control parameters of the aircraft to meet the terminal constraints (such as target altitude, speed and range) and process constraints (such as heat flow, dynamic pressure and overload limits).

[0051] In some optional embodiments, the optimization algorithm includes whale optimization algorithm IWOA, particle swarm optimization algorithm PSO, grey wolf optimization algorithm GWO, genetic algorithm GA, differential evolution algorithm DE and random search algorithm SSA.

[0052] The application organically integrates six optimization algorithms in a unified framework, selects the optimal solution through multi-algorithm comparison and result analysis. Each algorithm uses unified initialization parameters and constraint conditions to ensure the comparability and consistency of the results. This integrated framework allows users to flexibly select different algorithm combinations to adapt to different optimization needs.

[0053] The objective function of the application considers the Stefan-Boltzmann law infrared radiation characteristics of the aircraft, terminal state error and process constraint violation.

[0054] In some optional embodiments, in step S5, the optimization target with the minimum final energy J is characterized by the following formula:

[0055]

[0056] Where, α is the attack angle, σ is the roll angle, W b is the infrared radiation energy at the terminal time determined according to the infrared radiation calculation model, b r is the altitude error at the terminal time, b s is the range error at the terminal time, b v is the speed error at the terminal time, t0 is the initial time, t f is the terminal time, w1, w2, w3 are the penalty factors of related constraints.

[0057] Where, the radiation W b is determined by the infrared radiation calculation model of the aircraft, as shown in the following formula:

[0058]

[0059] T = T0 + 0.2 x (h - h target ).

[0060] where R n is the equivalent radius of the aircraft nose, h is the initial height of the aircraft, h target is the target height.

[0061] In some optional embodiments, the terminal time height error, the terminal time range error and the terminal time speed error are calculated by the following formula:

[0062]

[0063] where r(t f ) is the height at the terminal time, r f is the expected height at the terminal time, △r is the acceptable height error, S(t f ) is the range at the terminal time, S f is the expected range at the terminal time, △S is the acceptable range error, V(t f ) is the speed at the terminal time, V f is the expected speed at the terminal time, and △V is the acceptable speed error.

[0064] This multi-objective optimization method ensures the performance of the aircraft while ensuring its safety and reliability.

[0065] In order to calculate the parameters of the terminal, the present application describes the motion state of the aircraft, including the changes of position, speed, angle and other parameters, through the motion equation of the hypersonic gliding aircraft. The equation is as follows:

[0066]

[0067] In the formula:

[0068] r represents the relative distance of the aircraft to the center of the earth, with the unit of m;

[0069] θ and φ are the current longitude and latitude, respectively, with the unit of radian, rad;

[0070] V is the relative speed of the aircraft to the earth, with the unit of m / s;

[0071] γ and ψ are the flight path angle and the heading angle, respectively, with the unit of radian, rad;

[0072] L and D are the lift and the drag, respectively, with the unit of N;

[0073] σ is the bank angle, which is one of the optimization parameters of the present application, with the unit of radian, rad;

[0074] m is the mass of the aircraft, in kg;

[0075] g is the earth gravitational acceleration, in m / s 2 .

[0076] The lift and drag force calculation formulas are:

[0077]

[0078] where: p is the atmospheric density, p = p0* e -βh ; h = r - R, R is the average radius of the earth; S ref is the reference area of the aircraft; C L and C D are the lift and drag coefficients of the aircraft, respectively. The aerodynamic characteristics of the aircraft are determined by the angle of attack profile and the aerodynamic coefficients.

[0079] The angle of attack profile is designed as a piecewise function, which is dynamically adjusted according to the flight speed to optimize the lift and drag characteristics. Here, two other parameters to be optimized of the present application are involved: the first speed, the second speed.

[0080] In some optional embodiments, in step S2, the angle of attack piecewise function comprises:

[0081]

[0082] where V is the current flight speed, V1 is the first speed, V2 is the second speed, V0 is the initial time speed, V f is the terminal time speed, a max is the maximum angle of attack, a ratio is the reference angle of attack.

[0083] The calculation of the aerodynamic coefficients (C L ) and (C D ) is based on the angle of attack and Mach number of the aircraft, and the specific example formula is as follows:

[0084] C L = 0.0292a 2 + 0.2103a + 0.0516 * (1 + tan (2.5 * (M - 8)));

[0085]

[0086] where M is the Mach number.

[0087] The reentry maneuver flight constraints of a hypersonic aircraft mainly include terminal constraints, control quantity constraints and process constraints. Typical process constraints include heat flux density constraints, dynamic pressure constraints and overload constraints.

[0088] In some optional embodiments, the constraint condition that the heat flow, the dynamic pressure and the overload are not higher than the set values comprises:

[0089]

[0090] wherein, is the heat flow, is the maximum heat flow, K Q is the heat flow rate constant, and p is the atmospheric density; wherein, K Q = 7.9686 x 10 -5 Js 2 / (m 3.5 kg 0.5 ).

[0091]

[0092] wherein, q is the dynamic pressure, q max is the maximum dynamic pressure;

[0093]

[0094] wherein, N l is the overload, N max is the maximum overload, m is the mass of the aircraft, L is the lift, and D is the drag.

[0095] In some optional embodiments, the constraint condition further comprises:

[0096] the attack angle is less than a set maximum attack angle;

[0097] the roll angle is less than a set maximum roll angle;

[0098] When the hypersonic gliding vehicle is flying in the gliding phase, it also needs to satisfy the quasi-equilibrium glide condition (QEGC), which has the following specific form:

[0099]

[0100] wherein, r is the relative distance from the aircraft to the center of the earth, and g is the gravitational acceleration.

[0101] The present application improves and implements six optimization algorithms to improve the search ability and optimization efficiency of the algorithms.

[0102] Improved Whale Optimization Algorithm (IWOA): Whale Optimization Algorithm (WOA) is a bio-inspired optimization algorithm that simulates the hunting and group behavior of humpback whales. Improved Whale Optimization Algorithm (IWOA) introduces Tent chaos initialization and Rayleigh flight strategy to enhance the global search ability and avoid premature convergence based on standard WOA. Tent chaos initialization strategy enhances the initial distribution of the population and avoids premature convergence. At the same time, the Levey flight global search strategy is introduced to improve the global search ability of the algorithm. The initial population position is generated by Tent chaos sequence. The search behavior of whales includes surrounding prey, surrounding prey and random jumping. The improved IWOA adds a dynamic balance factor and a Rayleigh flight strategy. To enhance the global search ability, IWOA introduces Rayleigh flight strategy.

[0103] Particle Swarm Optimization (PSO): Particle Swarm Optimization (PSO) is a swarm intelligence-based optimization algorithm that simulates the foraging behavior of bird flocks. Tournament selection mechanism and adaptive inertia weight are used to balance the global search and local search ability of the algorithm. Adaptive inertia weight (w) gradually decreases during optimization to enhance the local refinement ability of the algorithm in the later stage. When selecting particles, the tournament selection mechanism is used to randomly select two particles for comparison, and the particle with higher fitness is retained.

[0104] Grey Wolf Optimizer (GWO): Grey Wolf Optimizer (GWO) is an optimization algorithm based on the hunting behavior of grey wolves, simulating the hunting, surrounding and attacking behavior of grey wolves. Improved GWO introduces a difference evolution strategy based on the surrounding coefficient. The difference evolution strategy based on the surrounding coefficient dynamically adjusts the position update of the grey wolf to improve the convergence speed and search efficiency of the algorithm.

[0105] Genetic Algorithm (GA): Genetic Algorithm (GA) is a bio-inspired optimization algorithm that simulates natural selection, crossover and mutation processes. Improved GA uses tournament selection mechanism and polynomial mutation operation. Tournament selection mechanism ensures the inheritance of excellent individuals. SBX crossover and polynomial mutation operation enhance the diversity and adaptability of the population.

[0106] Differential Evolution (DE): Differential Evolution is an evolutionary algorithm based on real-number coding, which realizes population evolution through difference vectors. Improved DE dynamically adjusts mutation and crossover probabilities. Based on the differential mutation and crossover strategy, the mutation and crossover probabilities are dynamically adjusted to improve the search ability and convergence speed of the algorithm.

[0107] Random Search Algorithm (SSA): Random Search Algorithm (SSA) is an optimization algorithm based on random sampling. SSA combines the update mechanism of discoverers, followers and sentinels to balance the global and local search ability of random search.

[0108] The present application provides various result analysis functions, including convergence curve comparison, optimal solution performance comparison and decision variable distribution analysis, to facilitate users to intuitively understand the optimization effect of each algorithm. In addition, detailed analysis of the optimal trajectory is also provided, including height-velocity profile, height change curve, speed change curve and infrared radiation change curve, to intuitively display the characteristics of the optimal trajectory.

[0109] The beneficial effects of the present application mainly include the following aspects: the present application significantly improves the precision and efficiency of hypersonic gliding vehicle trajectory optimization through multi-algorithm integration and multi-constraint optimization. Experimental results show that the present application has good adaptability and robustness in complex environments, providing important technical support for the design and control of hypersonic vehicles. The beneficial effects are as follows: first, the optimization performance is improved, the convergence speed and robustness of the optimization algorithm are significantly improved by combining multiple optimization algorithms and chaotic initialization mechanism. Second, the complex constraint processing capability, which can effectively handle extreme heat flow, dynamic pressure, overload and other complex constraint problems in the flight process of hypersonic vehicles, ensures the safety and task completion of the vehicle. Third, the visualization and analysis function, which provides detailed convergence curve and optimal trajectory analysis function, facilitates users to intuitively understand the optimization process and results. Fourth, the wide applicability, which can be applied to different types of hypersonic vehicles and adapt to various task requirements.

[0110] The following gives the specific process of parameter optimization using each optimization algorithm in the present application.

[0111] Step 1: Initialization phase.

[0112] (1) Initialize optimization parameters (including population size, iteration number, variable range, etc.).

[0113] (2) Initialize vehicle parameters (including mass, reference area, head radius, etc.).

[0114] (3) Set constraints (such as maximum heat flux, dynamic pressure, overload, and other process constraints, as well as height, speed, range, and other terminal constraints).

[0115] (4) Define design variables (first speed, second speed, and roll angle).

[0116] Step 2: Optimization algorithm selection and execution.

[0117] (1) Define a set of optimization algorithms (Improved Whale Optimization Algorithm IWOA, Particle Swarm Optimization PSO, Grey Wolf Optimization GWO, Genetic Algorithm GA, Differential Evolution DE, and Stochastic Search Algorithm SSA, etc.).

[0118] (2) For each algorithm, perform the optimization process, including initializing the population, evaluating the initial fitness, iterating optimization, updating the population position and objective function value, and recording the convergence curve and optimal solution during the optimization process.

[0119] Step 3: Objective function and constraint evaluation.

[0120] (1) Objective function: Consider the infrared radiation, terminal error, and process constraint violation of the aircraft, and obtain the weighted sum.

[0121] (2) Process constraints: Real-time evaluation of heat flux, dynamic pressure, overload, etc. to determine whether they meet the constraint conditions.

[0122] (3) Terminal constraints: Check whether the height, speed, range, etc. meet the tolerance constraints at the end of optimization.

[0123] Step 4: Result analysis and display.

[0124] (1) Visualize the convergence curve: Draw the convergence curve of each algorithm to analyze its convergence speed and robustness.

[0125] (2) Optimal solution comparison and analysis: Compare the optimal solutions of different algorithms and select the optimal trajectory, as shown in Figure 2 .

[0126] (3) Optimal trajectory analysis: Show the height-speed profile of the aircraft, as shown in Figure 3 , show the height-time profile, as shown in Figure 4 , show the speed-time profile, as shown in Figure 5 , and show the infrared radiation characteristics, as shown in Figure 6 .

[0127] Step 5: Result storage and output.

[0128] (1) Store the optimization results as a file (such as a text file or data file) for subsequent analysis.

[0129] (2) Output the optimal design variables and flight trajectory parameters.

[0130] The implementation is as follows:

[0131] (1) Parameter initialization.

[0132] Set the optimization algorithm parameters:

[0133] Population size: 100;

[0134] Maximum number of iterations: 200;

[0135] Number of optimization variables: 3;

[0136] Variable upper and lower limits lb, ub, lb = [4000, 1800, 0], ub = [4800, 4000, 80].

[0137] Set the aircraft parameters:

[0138] Aircraft mass: 907.2 kg;

[0139] Reference area: 0.484 m2;

[0140] Maximum and reference attack angles are α max = 25° and α ratio = 5°, respectively.

[0141] Set the process constraint thresholds:

[0142] Maximum heat flow

[0143] Maximum dynamic pressure q max = 400 kPa;

[0144] Maximum overload N max = 6g.

[0145] Set the terminal constraints:

[0146] Target height h target = 0.5 km;

[0147] Target speed V target = 0.5 km / s;

[0148] Target range S target = 2000 km;

[0149] Height and speed tolerances are △r = 100 m and △V = 100 m / s, respectively.

[0150] Set the initial state:

[0151] Height h = 60 km;

[0152] Trajectory angle γ = -1°;

[0153] Velocity V = 5000 m / s;

[0154] Heading angle ψ = 40°;

[0155] Latitude lat = 0° and longitude lon = 0°.

[0156] (2) Optimization algorithm implementation (take IWOA algorithm as an example).

[0157] Initialize population: generate chaotic sequence using TentSequence() and scale to parameter range [lb, ub];

[0158] Evaluate initial fitness: calculate the objective function value of each individual;

[0159] Iterative optimization: calculate new positions of global search or spiral attack, boundary processing, evaluate new position fitness, update individual and global optimum;

[0160] Record the convergence curve.

[0161] (3) Objective function calculation (as described above).

[0162] (4) Trajectory simulation calculation.

[0163] Define differential equations: calculate the motion state of the aircraft according to the motion equation and differential equation;

[0164] Solve differential equations: solve the flight state using the solver ode45();

[0165] Calculate process constraints: calculate and obtain heat flow, dynamic pressure and overload constraints using process constraint equations.

[0166] (5) Result analysis.

[0167] Extract optimal solution: extract optimal decision variables, optimal fitness and time, altitude, velocity, infrared characteristics and other performance parameters of each algorithm;

[0168] Draw convergence curve: compare the convergence speed of each algorithm;

[0169] Draw performance comparison chart: total objective function value and optimal solution distribution;

[0170] Analyze the optimal trajectory: visualize the altitude-velocity profile, altitude change, velocity change and infrared radiation.

[0171] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A multi-algorithm trajectory optimization method for hypersonic gliders, characterized in that, include: Step S1: Set all optimization algorithms to use the same initialization parameters and the same parameters to be optimized. The parameters to be optimized include the roll angle and the first and second velocities used to segment the angle of attack. Step S2: Calculate the angle of attack at the current flight speed based on the piecewise angle of attack function constructed from the first speed, the second speed, the initial speed, and the terminal speed; Step S3: Calculate the altitude, speed, range, heat flux, dynamic pressure, and overload at each moment based on the aircraft's equation of motion; Step S4: Determine the infrared radiation energy at the terminal moment based on the infrared radiation calculation model, and add the penalty caused by the altitude error, range error and speed error at the terminal moment to obtain the final energy; Step S5: With the goal of minimizing the final energy, and with heat flux, dynamic pressure, and overload not exceeding the set values ​​as constraints, use various optimization algorithms to calculate the optimal parameters to be optimized. Step S6: Select the optimal solution from each optimization algorithm.

2. The multi-algorithm trajectory optimization method for hypersonic gliders as described in claim 1, characterized in that, The optimization algorithms include the Whale Optimization Algorithm (IWOA), Particle Swarm Optimization Algorithm (PSO), Gray Wolf Optimization Algorithm (GWO), Genetic Algorithm (GA), Differential Evolution Algorithm (DE), and Random Search Algorithm (SSA).

3. The multi-algorithm trajectory optimization method for hypersonic gliders as described in claim 1, characterized in that, In step S2, the piecewise angle of attack function includes: Where V is the current flight speed, V1 is the first speed, V2 is the second speed, V0 is the initial speed, and V... f For the velocity at the terminal moment, α max For the maximum angle of attack, α ratio Angle of attack for reference.

4. The multi-algorithm trajectory optimization method for hypersonic gliders as described in claim 1, characterized in that, In step S5, the optimization objective of minimizing the final energy J is represented by the following formula: Where α is the angle of attack, σ is the angle of roll, and W b To determine the infrared radiation energy at the terminal moment based on the infrared radiation calculation model, b r For the terminal's altitude error, b s For the terminal time range error, b v The velocity error at the terminal time is t0, and the initial time is t f At the terminal time, w1, w2, and w3 are the penalty factors for the relevant constraints.

5. The multi-algorithm trajectory optimization method for hypersonic gliders as described in claim 4, characterized in that, The terminal time altitude error, terminal time range error, and terminal time speed error are calculated using the following formulas: Where r(t) f (r) represents the height at the terminal moment. f S(t) represents the desired altitude at the terminal moment, Δr represents the acceptable altitude error, and S(t) represents the desired altitude at the terminal moment. f S represents the flight distance at the terminal time. f V(t) represents the expected range at the terminal time, ΔS represents the acceptable range error, and V(t) represents the range tolerance. f V represents the velocity at the terminal moment. f Let V be the desired velocity at the terminal moment, and ΔV be the acceptable velocity error.

6. The multi-algorithm trajectory optimization method for hypersonic gliders as described in claim 1, characterized in that, Constraints including heat flux, dynamic pressure, and overload not exceeding set values ​​include: in, For heat flow, K represents the maximum heat flux. Q ρ is the heat flow rate constant, and ρ is the atmospheric density. Where q is the dynamic pressure, q max This represents the maximum dynamic pressure. Where, N l For overload, N max Where m is the maximum overload value, L is the mass of the aircraft, and D is the lift.

7. The multi-algorithm trajectory optimization method for hypersonic gliders as described in claim 1, characterized in that, The constraints also include: The angle of attack is less than the set maximum angle of attack. The roll angle is less than the set maximum roll angle; The flight process meets the quasi-equilibrium gliding conditions: Where r is the relative distance between the spacecraft and the Earth's center, and g is the gravitational acceleration.

Images