Optimization method of combined ramjet control law based on meta-heuristic calculation

Abstract

This application provides a metaheuristic calculation-based control law optimization method for a combined ramjet engine, belonging to the field of aerospace engine control technology. The method includes establishing a combined ramjet engine model and determining optimization variables. For each combined ramjet engine control mode, constraints and an objective function based on the optimization variables are determined. The control modes include a steady-state control mode and an acceleration control mode. The steady-state control modes include a minimum fuel consumption control mode, a maximum thrust control mode, and a minimum turbine inlet temperature control mode. Based on the objective function and constraints, a metaheuristic calculation strategy is used to optimize the control law for each control mode to obtain the optimal control variables. The metaheuristic calculation strategy is set to be a reptile search algorithm fused with a multidimensional learning hunting strategy. This application's processing solution improves the convergence speed of combined ramjet engine control law optimization, improving the optimization effect and engine performance.

Description

Technical Field

[0001] The present application relates to the field of aviation engine control technology, and in particular to a method for optimizing control laws of a combined ramjet engine based on meta-heuristic calculation. Background Art

[0002] As the core powerplant for a new generation of hypersonic vehicles, the combined ramjet engine holds strategic significance for cross-speed flight and space-to-space travel. To adapt to the complex operating conditions of a wide speed range and multiple modes (scramjet / scramjet, combined turbine-ramjet), optimizing the engine's comprehensive performance in modal transitions, thermodynamic cycle matching, and extreme aerodynamic and thermal environments is crucial. Engine control laws coordinate parameters such as the inlet control mechanism, fuel distribution strategy, and combustion chamber mode switching to achieve multi-objective optimization, including thrust continuity, specific impulse optimization, and thermal protection. However, despite breakthroughs in combined propulsion control, performance degradation caused by factors such as high-temperature ablation, shock wave interference, and inlet start-up failure during long-term operation remains a significant challenge. Traditional control laws struggle to adapt to the dynamically changing flow field characteristics, leading to thrust oscillations and modal transition instabilities.

[0003] In recent years, metaheuristic optimization algorithms have gained widespread attention due to their unique solution mechanisms. For example, particle swarm optimization, grasshopper optimization, and whale optimization have been applied to optimization problems in complex systems. The reptile algorithm (RSA) can also be replaced with other animal algorithms, with its formulas modified. For example, a metaheuristic optimization algorithm driven by the hunting behavior of crocodiles has attracted attention due to its clear structure, simple implementation, and good stability. However, like other metaheuristic optimization algorithms, RSA also faces challenges such as large key length requirements, high storage and transmission costs, and time-consuming key generation. Therefore, developing an optimization strategy that can address the performance degradation of aircraft engines during long-term operation is of great significance to control law optimization technology. Summary of the Invention

[0004] In view of this, an embodiment of the present application provides a combined ramjet engine control law optimization method based on meta-heuristic calculation, which at least partially solves the problems in the existing technology that engines in long-term service cannot be adaptively adjusted after component degradation and traditional optimization algorithms have low optimization accuracy.

[0005] The present application provides a method for optimizing control laws of a combined ramjet engine based on metaheuristic calculations, the method comprising:

[0006] establishing a combined ramjet engine model, and determining optimization variables based on the combined ramjet engine model;

[0007] determining constraints and an objective function based on optimization variables for each control mode of the combined ramjet engine, wherein the control mode includes a steady-state control mode and an acceleration control mode, and the steady-state control mode includes a minimum fuel consumption control mode, a maximum thrust control mode, and a minimum turbine inlet temperature control mode;

[0008] Based on the objective function and the constraints, a meta-heuristic computing strategy is used to optimize the control law of each control mode to obtain the optimal control variables. The meta-heuristic computing strategy is set as a reptile search algorithm fused with a multi-dimensional learning hunting strategy.

[0009] According to a specific implementation of the embodiment of the present application, the optimization variables are:

[0010] ,

[0011] The constraints in the process of optimizing the control law of each control mode are described as follows:

[0012] ,

[0013] Among them, u is the optimization variable, W f is the fuel flow rate in the combustion chamber of the turbine engine, A et is the throat area of ​​the turbine engine tail nozzle, φ is the fuel equivalence ratio of the ramjet combustion chamber, A nt is the throat area of ​​the ramjet tail nozzle, AI tur is the turbine flow channel opening, AI tam is the stamping runner opening, A it is the throat area of ​​the ramjet inlet, T4 is the total temperature of the combustion chamber outlet section, ζ sm is the turbine engine compressor surge margin, n is the compressor speed, ζ inlet is the intake duct margin, π inlet is the ramjet inlet pressure ratio, P3 is the compressor outlet pressure, subscript min is the minimum value, and subscript max is the maximum value.

[0014] According to a specific implementation of the embodiment of the present application, the objective function and constraint conditions of the minimum fuel consumption control mode are expressed as follows:

[0015] ,

[0016] Among them, min sfc is the minimum fuel consumption rate of the engine, f(u) is the objective function, st is the constraint condition, g i’ (u) is the inequality constraint, h k’(u) is the equality constraint, i' is the i'th inequality constraint, I' is the total number of inequality constraints, k' is the k'th equality constraint, and K' is the total number of equality constraints;

[0017] The objective function and constraints of the maximum thrust control mode are expressed as follows:

[0018] ,

[0019] Among them, max F is the maximum engine thrust;

[0020] The objective function and constraints of the minimum turbine pre-temperature control mode are expressed as follows:

[0021] .

[0022] According to a specific implementation of an embodiment of the present application, the method further includes: using a penalty function to convert the constrained problem of the control law into an unconstrained problem, and the expression of the penalty function is:

[0023] ,

[0024] Among them, F(u,σ) is the objective function that introduces the penalty function, and σ1 and σ2 are infinite positive numbers.

[0025] According to a specific implementation of the embodiment of the present application, the reptile search algorithm integrated with the multi-dimensional learning hunting strategy includes the following steps:

[0026] Initialize the reptile search algorithm and generate a set of candidate solutions;

[0027] Based on the candidate solution set, updating the position of the reptile in the surrounding phase;

[0028] Based on hunting coordination and hunting cooperation strategies, position updates are performed during the hunting phase;

[0029] Based on the multi-dimensional learning hunting strategy, the position of the reptile is updated to improve the quality of the searched individuals and increase the search ability, and the current best position and the current best fitness value are obtained;

[0030] A greedy strategy is used to evaluate the fitness value of the individual in the current best position, and individuals that are valuable for updating the population position are retained to obtain the final global optimal position and best fitness value.

[0031] According to a specific implementation of the embodiment of the present application, the set of candidate solutions is expressed as follows:

[0032] ,

[0033] ,

[0034] Among them, x is the set of candidate solutions, x i,j Indicates the i th The jth solution th positions, N represents the number of candidate solutions, n represents the dimension of the given problem, rand is a random number between 0 and 1, LB is the lower bound of the given problem, and UB is the upper bound of the given problem.

[0035] According to a specific implementation of the embodiment of the present application, the position update equation in the surround phase is:

[0036] ,

[0037] The position update equation in the hunting phase is:

[0038] ,

[0039] in,

[0040] ,

[0041] ,

[0042] ,

[0043] ,

[0044] ,

[0045] Among them, x i,j (t+1) represents the i-th th The jth solution th The updated location, Best j (t) is the jth optimal solution so far th positions, t is the number of current iterations, T is the maximum number of iterations, η (i,t) (t) is the i-th th The jth solution th The hunting operator of each position, β is the first sensitive parameter, α is the second sensitive parameter, R (i,j) (t) is the value used to reduce the search area, ES(t) is the probability ratio, and ES(t) randomly takes a decreasing value between 2 and -2 in the number of iterations. ∈ represents infinitesimal, r1 is a random number between [1N], r2 is a random number between [1N], r3 is a random integer between -1 and 1, P (i,j) is the percentage difference between the optimal solution position and the current solution position, M(x i ) is ith Average position of the solution, UB (j) For the i th The upper bound of a solution, LB (j) For the i th The lower bound of a solution, ∈ ' is an infinitesimal positive integer.

[0046] According to a specific implementation of an embodiment of the present application, updating the position of a reptile based on a multi-dimensional learning hunting strategy includes:

[0047] constructing a radius matrix based on the original position of the reptile and the new position of the reptile;

[0048] Construct a neighborhood matrix based on the radius matrix and the Euclidean distance between the current individual and the candidate individuals;

[0049] New individuals are generated by learning from multiple neighborhood matrices, where the d-th dimension of each new individual is based on a randomly selected reptile position.

[0050] According to a specific implementation of the embodiment of the present application, the radius matrix expression is:

[0051] ,

[0052] The neighborhood matrix expression is:

[0053] ,

[0054] The expression of the generated new individual is:

[0055] ,

[0056] Among them, Radiusi(t) is the radius matrix, Neighbouri(t) is the neighborhood matrix, x i (t) is the current individual, x j (t) is the candidate individual, x new (t+1) is the new position of individual i at the t+1th iteration, D is the spatial dimension of the optimization variable, N is the initial population, x i-DLH,j (t+1) is the new individual generated, x i,d (t) is the updated position of individual i in the dth dimension at the tth iteration, x n,d (t) is the individual i in the tth iteration based on the randomly selected individual in the dth dimension, x r,d (t) is the reference position of individual i in the dth dimension at the tth iteration.

[0057] According to a specific implementation of the embodiment of the present application, the expression of the greedy strategy is:

[0058] ,

[0059] Among them, x i (t+1) is the updated position of individual i at the t+1th iteration, x i-new (t+1) is the position of individual i after the t+1th iteration without executing the multi-dimensional learning hunting strategy, x i-DLH (t+1) is the position of individual i after the t+1th iteration after the multi-dimensional learning hunting strategy is updated, f(x i-new ) is x i-new The corresponding objective function, f(x i-DLH ) is x i-DLH The corresponding objective function.

[0060] Beneficial effects:

[0061] The metaheuristic-based control law optimization method for a combined ramjet engine, described in an embodiment of this application, improves upon the reptile search optimization algorithm by employing a multidimensional learning hunting strategy. This method optimizes the steady-state and acceleration control laws of a combined ramjet engine, enhancing control law optimization and improving engine performance. Simulation results demonstrate that the multidimensional learning hunting strategy, combined with the reptile search algorithm, converges faster and achieves better optimization results than several classic intelligent optimization algorithms. It effectively reduces the combined ramjet engine's fuel consumption and minimum turbine inlet temperature, increases maximum thrust, and shortens acceleration time by 50%. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0063] Figure 1 Flowchart of a method for optimizing a combined ramjet control law based on meta-heuristic calculation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0065] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the features in the following embodiments and embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative work are within the scope of protection of this application.

[0066] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this application, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement an apparatus and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this apparatus and / or practice this method.

[0067] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. The illustrations only show components related to the present application and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0068] Additionally, in the following description, specific details are provided to provide a thorough understanding of the examples. However, one skilled in the art will appreciate that the aspects described can be practiced without these specific details.

[0069] The present application embodiment provides a combined ramjet engine control law optimization method based on meta-heuristic calculation, which is described below with reference to Figure 1 Provide a detailed description.

[0070] In one embodiment, a method for optimizing a combined ramjet control law based on metaheuristic computing includes the following steps:

[0071] establishing a combined ramjet engine model, and determining optimization variables based on the combined ramjet engine model;

[0072] determining constraints and an objective function based on optimization variables for each control mode of the combined ramjet engine, wherein the control mode includes a steady-state control mode and an acceleration control mode, and the steady-state control mode includes a minimum fuel consumption control mode, a maximum thrust control mode, and a minimum turbine inlet temperature control mode;

[0073] Based on the objective function and the constraints, a meta-heuristic computing strategy is used to optimize the control law of each control mode to obtain the optimal control variables. The meta-heuristic computing strategy is set as a reptile search algorithm fused with a multi-dimensional learning hunting strategy.

[0074] In this embodiment, the meta-heuristic computing strategy is set to the reptile search algorithm fused with the multi-dimensional learning hunting strategy (DLH-IRSA). The multi-dimensional learning hunting strategy (DLH, Dimensional Learning Harvesting) is integrated with the improved reptile search algorithm (RSA, Reptile Search Algorithm). The reptile search algorithm is a heuristic optimization algorithm based on the foraging behavior of reptiles in nature.

[0075] In this embodiment, the proposed control law does not directly optimize engine control parameters. Instead, it optimizes controlled parameters (such as thrust and specific impulse) and other adjustable parameters. Adjusting controlled parameters offers the following advantages over directly optimizing control parameters. First, because engines are susceptible to external factors (such as flight altitude and wind gusts), direct optimization of control parameters requires real-time adjustment based on the environment and flight conditions. Existing optimization algorithms struggle to simultaneously meet both precision and real-time requirements. Second, because engine performance parameters cannot be directly measured, conventional control systems cannot directly control them. This embodiment improves the reptile search optimization algorithm by employing a multidimensional learning hunting strategy for optimizing the steady-state and acceleration control laws of a combined ramjet engine. This improves the control law optimization and enhances engine performance. Simulation results show that the multidimensional learning hunting strategy combined with the reptile search algorithm converges faster and achieves better optimization results than several classic intelligent optimization algorithms. It effectively reduces the fuel consumption and minimum turbine inlet temperature of the combined ramjet engine, increases maximum thrust, and shortens acceleration time by 50%.

[0076] Furthermore, for the combined ramjet engine model studied in this embodiment, the optimization variables are:

[0077] (1),

[0078] The constraints in the process of optimizing the control law of each control mode are described as follows:

[0079] (2),

[0080] Among them, u is the optimization variable, W f is the fuel flow rate in the combustion chamber of the turbine engine, A et is the throat area of ​​the turbine engine tail nozzle, φ is the fuel equivalence ratio of the ramjet combustion chamber, A nt is the throat area of ​​the ramjet tail nozzle, AI tur is the turbine flow channel opening, AI tam is the stamping runner opening, A it is the throat area of ​​the ramjet inlet, T4 is the total temperature of the combustion chamber outlet section, ζ sm is the turbine engine compressor surge margin, n is the compressor speed, ζ inlet is the intake duct margin, π inlet is the ramjet inlet pressure ratio, P3 is the compressor outlet pressure, subscript min is the minimum value, and subscript max is the maximum value.

[0081] The expression of the above constraints can be expressed as:

[0082] (3),

[0083] Among them, g i’ (u) is the inequality constraint, and i' represents the i'th inequality constraint.

[0084] In one embodiment, in the minimum fuel consumption control mode, the equality constraint emphasizes achieving the lowest fuel consumption rate while maintaining constant thrust. It is necessary to comprehensively adjust various parameters to improve the efficiency of the fan, compressor, and inlet, increase engine flow, and thus achieve thrust improvement. By solving the nonlinear constraint problem, a suitable combination of control variables is found to achieve the desired engine thrust and operate at the lowest fuel consumption rate. The objective function and constraints of the minimum fuel consumption control mode are expressed as:

[0085] (4),

[0086] Among them, min sfc is the minimum fuel consumption rate of the engine, f(u) is the objective function, st is the constraint condition, g i’ (u) is the inequality constraint, h k’ (u) is the equality constraint, i' is the i'th inequality constraint, I' is the total number of inequality constraints, k' is the k'th equality constraint, and K' is the total number of equality constraints;

[0087] The maximum thrust control mode is mainly used in high thrust demand phases such as aircraft takeoff and accelerated climb. In this mode, the goal is to achieve maximum thrust while ensuring safe operation of the engine. At the turbine engine operating point, the maximum thrust is located at the intersection of the safety boundaries of the minimum compressor surge margin and the maximum total temperature at the combustion chamber outlet; at the ramjet engine operating point, the maximum thrust is located at the intersection of the safety boundaries of the minimum inlet surge margin and the maximum total temperature at the combustion chamber outlet; in the mode transition state, the maximum thrust is at the intersection of the safety boundaries of the inlet and compressor margin and the maximum total temperature at the combustion chamber outlet. The objective function and constraints of the maximum thrust control mode are expressed as:

[0088] (5),

[0089] Among them, max F is the maximum engine thrust;

[0090] The minimum turbine inlet temperature control mode is mainly used in high Mach number flight conditions of turbine engines. In this mode, the goal is to reduce the turbine inlet temperature as much as possible to extend the engine service life and reduce infrared radiation while ensuring that the thrust remains unchanged. This mode mainly reduces the engine turbine temperature by reducing the main fuel flow rate, and improves the engine pressure ratio, efficiency and flow rate by comprehensively adjusting parameters such as the tail nozzle nozzle area, the fan guide vane angle and the compressor guide vane angle, thereby increasing the thrust and ultimately keeping the thrust basically constant. The objective function and constraints of the minimum turbine inlet temperature control mode are expressed as:

[0091] (6).

[0092] In practice, both the minimum fuel consumption control mode and the minimum turbine inlet temperature mode optimization are multi-objective optimizations. The minimum fuel consumption control mode optimization objective is to achieve the desired thrust while minimizing fuel consumption; the minimum turbine inlet temperature optimization objective is to achieve the desired thrust while minimizing turbine inlet temperature. Through stepwise iterative optimization, either Objective 1 or Objective 2 is optimized. By selecting appropriate weights W = [W1, W2], where W1 is the weight for Objective 1 (minimum fuel consumption, minimum turbine inlet temperature) and W2 is the weight for Objective 2 (unchanged thrust), the combined performance of Objectives 1 and 2 is optimized.

[0093] Furthermore, combining formulas (4) to (6), the optimization of the combined ramjet control law can be expressed as the following nonlinear programming problem:

[0094] (7),

[0095] In order to solve constrained optimization problems, a penalty function can be introduced.

[0096] (8),

[0097] Here, F(u,σ) is the objective function that introduces a penalty function, and σ1 and σ2 are infinite positive numbers. This transforms the original constrained problem into an unconstrained one. In the intelligent optimization of the control law for a combined ramjet engine, an intelligent optimization algorithm is required to find the optimal combination of optimization parameters to achieve optimal engine performance.

[0098] In one embodiment, a multi-dimensional learning hunting strategy is integrated with a reptile search optimization algorithm. Through this method, DLH-IRSA overcomes the shortcomings of the original RSA, further improves the optimization speed and solution accuracy, and provides an effective strategy for solving complex problems. The reptile search algorithm integrated with the multi-dimensional learning hunting strategy includes the following steps:

[0099] Initialize the reptile search algorithm and generate a set of candidate solutions;

[0100] Based on the candidate solution set, updating the position of the reptile in the surrounding phase;

[0101] Based on hunting coordination and hunting cooperation strategies, position updates are performed during the hunting phase;

[0102] Based on the multi-dimensional learning hunting strategy, the position of the reptile is updated to improve the quality of the searched individuals and increase the search ability, and the current best position and the current best fitness value are obtained;

[0103] A greedy strategy is used to evaluate the fitness value of the individual in the current best position, and individuals that are valuable for updating the population position are retained to obtain the final global optimal position and best fitness value.

[0104] Furthermore, the set of candidate solutions is expressed as:

[0105] ,

[0106] ,

[0107] Among them, x is the set of candidate solutions, x i,j Indicates the i th The jth solution th positions, N represents the number of candidate solutions, n represents the dimension of the given problem, rand is a random number between 0 and 1, LB is the lower bound of the given problem, and UB is the upper bound of the given problem.

[0108] Furthermore, the position update equation in the surround phase is:

[0109] ,

[0110] The position update equation in the hunting phase is:

[0111] ,

[0112] in,

[0113] ,

[0114] ,

[0115] ,

[0116] ,

[0117] ,

[0118] Among them, x i,j (t+1) represents the i-th th The jth solution th The updated location, Best j (t) is the jth optimal solution so far th positions, t is the number of current iterations, T is the maximum number of iterations, η (i,t) (t) is the i-th th The jth solution th The hunting operator of each position, β is the first sensitive parameter, α is the second sensitive parameter, R (i,j) (t) is the value used to reduce the search area, ES(t) is the probability ratio, and ES(t) randomly takes a decreasing value between 2 and -2 in the number of iterations. ∈ Indicates infinitesimal, r1 is a random number between [1N], r2 is a random number between [1N], r3 is a random integer between -1 and 1, P (i,j) is the percentage difference between the optimal solution position and the current solution position, M(x i ) is i th Average position of the solution, UB (j) For the i th The upper bound of a solution, LB (j) For the i th The lower bound of a solution, ∈ ' is an infinitesimal positive integer.

[0119] Furthermore, the updating of the reptile position based on the multi-dimensional learning hunting strategy includes:

[0120] constructing a radius matrix based on the original position of the reptile and the new position of the reptile;

[0121] Construct a neighborhood matrix based on the radius matrix and the Euclidean distance between the current individual and the candidate individuals;

[0122] New individuals are generated by learning from multiple neighborhood matrices, where the d-th dimension of each new individual is based on a randomly selected reptile position.

[0123] Furthermore, the radius matrix expression is:

[0124] ,

[0125] The neighborhood matrix expression is:

[0126] ,

[0127] The expression of the generated new individual is:

[0128] ,

[0129] Among them, Radiusi(t) is the radius matrix, Neighbouri(t) is the neighborhood matrix, x i (t) is the current individual, x j (t) is the candidate individual, x new (t+1) is the new position of individual i at the t+1th iteration, D is the spatial dimension of the optimization variable, N is the initial population, x i-DLH,j (t+1) is the new individual generated, x i,d (t) is the updated position of individual i in the dth dimension at the tth iteration, x n,d (t) is the individual i in the tth iteration based on the randomly selected individual in the dth dimension, x r,d (t) is the reference position of individual i in the dth dimension at the tth iteration.

[0130] Furthermore, the expression of the greedy strategy is:

[0131] ,

[0132] Among them, x i (t+1) is the updated position of individual i at the t+1th iteration, x i-new (t+1) is the position of individual i after the t+1th iteration without executing the multi-dimensional learning hunting strategy, x i-DLH (t+1) is the position of individual i after the t+1th iteration after the multi-dimensional learning hunting strategy is updated, f(x i-new ) is x i-new The corresponding objective function, f(x i-DLH ) is x i-DLH The corresponding objective function.

[0133] The following is a detailed description of the reptile search algorithm integrated with the multi-dimensional learning hunting strategy using a specific embodiment, which specifically includes the following steps:

[0134] Step 1: Initialization phase

[0135] In the RSA algorithm, the optimization process starts with a set of randomly generated candidate solutions (as shown in formula (9)), and the best solution in each iteration is considered the optimal solution.

[0136] (9),

[0137] Where x is a set of candidate solutions randomly generated using formula (10) as follows:

[0138] (10).

[0139] Step 2: Surround Phase

[0140] The search in this stage is based on two conditions: the locomotion strategy of walking at high altitude is Conditional on the fact that the movement strategy of abdominal walking is and This means that this condition will be satisfied for almost half of the exploration iterations (high-altitude walking) and the other half for belly walking, which are two exploration search methods. Note that the random scaling coefficient is checked for the elements to generate more multivariate solutions and explore the multivariate region. This embodiment adopts the most direct rule to imitate the encirclement behavior of the crocodile and proposes the position update equation in the exploration phase, as shown in formula (11).

[0141] (11),

[0142] Among them, Best j (t) is the jth optimal solution so far th positions, t is the number of current iterations, T is the maximum number of iterations, η (i,t) (t) is the i-th th The jth solution th The hunting operator of each position (calculated by formula (12)), β is the first sensitive parameter, which controls the detection accuracy of the surrounding phase during the iteration (i.e., high-altitude walking), and is fixed at 0.1. (i,j) (t) is the value used to reduce the search area (calculated by formula (13)), the evolutionary sense ES(t) is the probability ratio, and ES(t) randomly takes a decreasing value between 2 and -2 in the number of iterations (calculated by formula (14)).

[0143] (12),

[0144] (13),

[0145] (14),

[0146] In formula (14), 2 is an associated value, r2 is a random number between [1N], r3 is a random integer between -1 and 1, and P (i,j) is the percentage difference between the optimal solution position and the current solution position, calculated by formula (15);

[0147] (15),

[0148] (16),

[0149] M(x i ) is i th The average position of the solution is calculated by formula (16), UB (j) For the i th The upper bound of a solution, LB (j) For the i th The lower bound of a solution, ∈ ' is an infinitesimal positive integer, α is a sensitive parameter, which also controls the exploration accuracy of the hunting cooperation during the iteration (the difference between candidate solutions), and is fixed to 0.1 in the embodiment.

[0150] Step 3: Hunting Phase

[0151] The mechanism of this algorithm is to find the optimal solution by searching the space and method, using two main search strategies (i.e., hunting coordination and hunting cooperation); the model is shown in formula (17). The search condition of this stage is the hunting coordination strategy. and , otherwise, execute the hunting cooperation strategy, when and Note that the random coefficients are considered to generate denser solutions and exploit promising areas (local areas). This embodiment adopts the most direct rule to imitate the hunting behavior of crocodiles. This embodiment proposes the following position update equation for the development stage:

[0152] (17),

[0153] In this regard, when When , the cycle phase (exploration) occurs, otherwise, when When the hunting phase (exploitation) occurs.

[0154] Exploitative search mechanisms (hunting coordination and cooperation) attempt to avoid getting stuck in local optima. These processes help the exploratory search identify the optimal solution while maintaining a diverse pool of candidate solutions. Two parameters (i.e., β and α) are designed so that they generate a random value at each iteration, continuing exploration not only during the first iteration but also during the last iteration. This part of the search is beneficial in cases where a local optimum is stuck, especially in the final iteration.

[0155] Step 4: DLH strategy update location

[0156] Traditional RSA suffers from the problem of low computational efficiency. Therefore, DLH is introduced to improve the quality of search individuals and increase the search capability.

[0157] DLH generates the following replacement individuals:

[0158] (18),

[0159] The radius matrix Radius is generated by subtracting the distance between the original and new positions, and the neighborhood matrix is ​​based on the current individual x i (t) and alternative individual x j (t) is constructed by the Euclidean distance between them.

[0160] (19),

[0161] By learning from many neighborhood matrices, DLH generates new individuals x i-DLH,j (t+1), where the dth dimension of each new individual is based on a randomly selected individual x n,d (t) is updated in the dth dimension.

[0162] (20),

[0163] Recalculate the individual fitness value and retain the current best position and the current best fitness value.

[0164] Finally, a greedy strategy is used to interfere with the current optimal position, evaluate the fitness value of the current optimal individual, and retain individuals that are more valuable for population position updates. Its mathematical model is as follows:

[0165] (twenty one).

[0166] When the current number of iterations does not satisfy t <t max , and finally returns the best fitness value and the global optimal position.

[0167] This paper proposes a metaheuristic-based method for optimizing the control laws of combined ramjet engines. This method incorporates a reptile search algorithm and combines it with a multidimensional learning hunting strategy to enhance the algorithm's global convergence. The goal is to achieve the lowest fuel consumption rate, thereby improving fuel efficiency; achieve maximum thrust when power demand is highest to meet specific flight requirements; maintain the turbine inlet temperature at a minimum level to ensure turbine service life; and ensure that engine system parameters do not exceed their limit values ​​during transient processes, achieving the shortest acceleration time. The specific contributions of this invention are as follows:

[0168] (1) A multi-dimensional learning hunting strategy fused with reptile search algorithm (DLH-IRSA) is proposed to improve the optimization speed and solution accuracy of the algorithm. A multi-dimensional learning hunting strategy is introduced to improve the quality of individuals, enhance the search process and balance the exploration and exploitation stages.

[0169] (2) The DLH-IRSA algorithm and a variety of classic intelligent optimization algorithms were used to conduct numerical simulation verification of the intelligent optimization of the combined ramjet control law to verify the feasibility of the proposed method. As optimization variables, the steady-state control mode and the transition-state control mode are optimized separately.

[0170] The following describes the combined ramjet control law optimization method based on meta-heuristic calculation of the present application with a specific embodiment.

[0171] The DLH-IRSA algorithm and a variety of classic intelligent optimization algorithms are used to perform numerical simulations to verify the feasibility of the proposed method. Optimization is performed for the steady-state control mode and the acceleration control mode respectively.

[0172] (1) Minimum fuel consumption control mode

[0173] At low Mach numbers, the ramjet engine adopts the turbine working mode. Taking the subsonic cruise point as an example, the minimum fuel consumption control method of the ramjet engine is verified by the ramjet engine. The optimization objective function is set as , fitness is fitness, F r is the engine reference thrust, F n The actual thrust of the engine.

[0174] (2) Maximum thrust control mode

[0175] In the ramjet engine mode, taking high Mach constant acceleration flight as an example, the maximum thrust control method of the combined ramjet engine is simulated. The optimization objective function is set to fitness = 10000 / F n .

[0176] (3) Minimum turbine pre-temperature control mode

[0177] The minimum turbine inlet temperature control of a combined ramjet engine is typically used in high-altitude, high-Mach flight. The goal of this minimum turbine inlet temperature control is to ensure that the airflow temperature in front of the turbine does not exceed the maximum allowable temperature of the turbine material, thereby protecting the turbine. Taking supersonic flight as an example, the intelligent optimization algorithm proposed in this invention and various classic intelligent optimization algorithms are applied to optimize the minimum turbine inlet temperature control mode. The optimization objective function is set as fitness = (F r -F n ) 2 +T4.

[0178] The present invention provides an embodiment of a method that improves upon the reptile search optimization algorithm, employing a multidimensional learning hunting strategy for optimizing the steady-state and acceleration control laws of a combined ramjet engine. This method enhances control law optimization and improves engine performance. Simulation results demonstrate that the multidimensional learning hunting strategy combined with the reptile search algorithm converges faster and achieves better optimization results than several classic intelligent optimization algorithms. It effectively reduces the combined ramjet engine's fuel consumption and minimum turbine inlet temperature, increases maximum thrust, and shortens acceleration time by 50%.

[0179] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A combined ramjet engine control law optimization method based on meta-heuristic calculation, characterized in that: The method comprises: establishing a combined ramjet engine model, and determining optimization variables based on the combined ramjet engine model; determining constraints and an objective function based on optimization variables for each control mode of the combined ramjet engine, wherein the control mode includes a steady-state control mode and an acceleration control mode, and the steady-state control mode includes a minimum fuel consumption control mode, a maximum thrust control mode, and a minimum turbine inlet temperature control mode; Based on the objective function and the constraints, a meta-heuristic computing strategy is used to optimize the control law of each control mode to obtain the optimal control variable, wherein the meta-heuristic computing strategy is set to be a reptile search algorithm fused with a multi-dimensional learning hunting strategy; The reptile search algorithm integrates multi-dimensional learning hunting strategy and includes the following steps: Initialize the reptile search algorithm and generate a set of candidate solutions; Based on the candidate solution set, updating the position of the reptile in the surrounding phase; Based on hunting coordination and hunting cooperation strategies, position updates are performed during the hunting phase; Based on the multi-dimensional learning hunting strategy, the position of the reptile is updated to improve the quality of the searched individuals and increase the search ability, and the current best position and the current best fitness value are obtained; A greedy strategy is used to evaluate the fitness value of the individual in the current best position, and individuals that are valuable for updating the population position are retained to obtain the final global optimal position and the best fitness value; The updating of the reptile position based on the multi-dimensional learning hunting strategy includes: constructing a radius matrix based on the original position of the reptile and the new position of the reptile; Construct a neighborhood matrix based on the radius matrix and the Euclidean distance between the current individual and the candidate individuals; New individuals are generated by learning from multiple neighborhood matrices, where the d-th dimension of each new individual is based on a randomly selected reptile position.

2. The combined ramjet control law optimization method based on meta-heuristic calculation according to claim 1 is characterized in that: The optimization variables are: ， The constraints in the process of optimizing the control law of each control mode are described as follows: ， Among them, u is the optimization variable, W f is the fuel flow rate in the combustion chamber of the turbine engine, A et is the throat area of ​​the turbine engine tail nozzle, φ is the fuel equivalence ratio of the ramjet combustion chamber, A nt is the throat area of ​​the ramjet tail nozzle, AI tur is the turbine flow channel opening, AI tam is the stamping runner opening, A it is the throat area of ​​the ramjet inlet, T4 is the total temperature of the combustion chamber outlet section, ζ sm is the turbine engine compressor surge margin, n is the compressor speed, ζ inlet is the intake duct margin, π inlet is the ramjet inlet pressure ratio, P3 is the compressor outlet pressure, subscript min is the minimum value, and subscript max is the maximum value.

3. The combined ramjet engine control law optimization method based on meta-heuristic calculation according to claim 2 is characterized in that: The objective function and constraints of the minimum fuel consumption control mode are expressed as follows: ， Among them, min sfc is the minimum fuel consumption rate of the engine, f(u) is the objective function, st is the constraint condition, g i’ (u) is the inequality constraint, h k’ (u) is the equality constraint, i' is the i'th inequality constraint, I' is the total number of inequality constraints, k' is the k'th equality constraint, and K' is the total number of equality constraints; The objective function and constraints of the maximum thrust control mode are expressed as follows: ， Among them, max F is the maximum engine thrust; The objective function and constraints of the minimum turbine pre-temperature control mode are expressed as follows: ， Among them, min T4 is the lowest total temperature of the combustion chamber outlet section.

4. The combined ramjet engine control law optimization method based on meta-heuristic calculation according to claim 3 is characterized in that: The method further includes: converting the constrained problem of the control law into an unconstrained problem using a penalty function, wherein the expression of the penalty function is: ， Among them, F(u,σ) is the objective function that introduces the penalty function, and σ1 and σ2 are infinite positive numbers.

5. The combined ramjet control law optimization method based on meta-heuristic calculation according to claim 1 is characterized in that: The expression of the set of candidate solutions is: ， ， Among them, x is the set of candidate solutions, x i,j Indicates the i th The jth solution th positions, N represents the number of candidate solutions, n represents the dimension of the given problem, rand is a random number between 0 and 1, LB is the lower bound of the given problem, and UB is the upper bound of the given problem.

6. The combined ramjet control law optimization method based on meta-heuristic calculation according to claim 5 is characterized in that: The position update equation in the surround phase is: ， The position update equation in the hunting phase is: ， in, ， ， ， ， ， Among them, x i,j (t+1) represents the i-th th The jth solution th The updated location, Best j (t) is the jth optimal solution so far th positions, t is the number of current iterations, T is the maximum number of iterations, η (i,t) (t) is the i-th th The jth solution th The hunting operator of each position, β is the first sensitive parameter, α is the second sensitive parameter, R (i,j) (t) is the value used to reduce the search area, ES(t) is the probability ratio, and ES(t) randomly takes a decreasing value between 2 and -2 in the number of iterations. ∈ represents infinitesimal, r1 is a random number between [1N], r2 is a random number between [1N], r3 is a random integer between -1 and 1, P (i,j) is the percentage difference between the optimal solution position and the current solution position, M(x i ) is i th Average position of the solution, UB (j) For the i th The upper bound of a solution, LB (j) For the i th The lower bound of a solution, ∈ ' is an infinitesimal positive integer.

7. The combined ramjet control law optimization method based on meta-heuristic calculation according to claim 6 is characterized in that: The radius matrix expression is: ， The neighborhood matrix expression is: ， The expression of the generated new individual is: ， Among them, Radiusi(t) is the radius matrix, Neighbouri(t) is the neighborhood matrix, x i (t) is the current individual, x j (t) is the candidate individual, x new (t+1) is the new position of individual i at the t+1th iteration, D is the spatial dimension of the optimization variable, N is the initial population, x i-DLH,j (t+1) is the new individual generated, x i,d (t) is the updated position of individual i in the dth dimension at the tth iteration, x n,d (t) is the individual i in the tth iteration based on the randomly selected individual in the dth dimension, x r,d (t) is the reference position of individual i in the dth dimension at the tth iteration.

8. The combined ramjet control law optimization method based on meta-heuristic calculation according to claim 7 is characterized in that: The expression of the greedy strategy is: ， Among them, x i (t+1) is the updated position of individual i at the t+1th iteration, x i-new (t+1) is the position of individual i after the t+1th iteration without executing the multi-dimensional learning hunting strategy, x i-DLH (t+1) is the position of individual i after the t+1th iteration after the multi-dimensional learning hunting strategy is updated, f(x i-new ) is x i-new The corresponding objective function, f(x i-DLH ) is x i-DLH The corresponding objective function.

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