Adaptive cycle engine control rule optimization method based on DLH-IHBA

Abstract

The invention provides a DLH-IHBA-based adaptive cycle engine control law optimization method, and belongs to the technical field of aero-engine control, and the method comprises the steps: building an adaptive cycle engine model, and determining an optimization variable based on the adaptive cycle engine model; according to the control mode of each self-adaptive cycle engine, constraint conditions and an objective function based on optimization variables are determined, the control modes comprise a steady state control mode and a transition state control mode, and the steady state control mode comprises a minimum fuel consumption control mode, a maximum thrust control mode and a minimum turbine front temperature control mode; and based on the objective function and the constraint condition, optimizing the control rule of each control mode by adopting a multi-dimensional learning prey strategy fused with a badger optimization algorithm to obtain an optimal control variable, and adopting Tent mapping as initial particle swarm position mapping. According to the scheme, the convergence speed of engine control rule optimization is increased, and the optimization effect and the engine performance are improved.

Description

Technical Field

[0001] The present application relates to the technical field of aeroengine control, and particularly relates to an optimization method for an adaptive cycle engine control law based on DLH-IHBA. Background Art

[0002] An aeroengine, as the core power device of an aircraft, is of great significance in both civil and military fields. In order to reduce fuel consumption and minimize the impact on the environment, it is crucial to optimize the performance of an aeroengine under various flight conditions and operating states. The engine control law can optimize the engine performance parameters (such as thrust and specific fuel consumption) by adjusting parameters such as the main fuel flow rate, afterburner fuel flow rate, and nozzle area. However, despite the significant research achievements in engine control laws and performance optimization in recent years, performance degradation caused by factors such as wear and dirt during long-term operation remains a challenge. When performance declines, the existing control laws often fail to adapt, thereby affecting the performance potential of the engine and reducing fuel efficiency and adaptability.

[0003] To address this challenge, a performance seeking control method (PSC) for optimizing the engine operating point under actual conditions has been proposed. PSC optimizes the constraint conditions to find the optimal control quantity to achieve the optimization of performance indicators on the premise of safe operation. In this context, the core of performance seeking control lies in the selection of an on-board adaptive model and an optimization algorithm. Linear programming (LP) algorithms and intelligent optimization algorithms are often used as performance seeking algorithms. However, although the LP algorithm can find the optimal solution in some cases, it is prone to falling into local minima, while the real-time performance of intelligent optimization algorithms is often poor, which limits the practical application of performance seeking control. In addition, the practical application of PSC is also restricted because it needs to call the on-board engine model multiple times for iterative calculations to find the optimal control quantity, which is a time-consuming process and difficult to meet the real-time requirements of the aeroengine control system. In recent years, metaheuristic optimization algorithms have attracted people's attention due to their unique solution mechanisms. For example, particle swarm optimization algorithms, grasshopper optimization algorithms, and whale optimization algorithms have been widely used in optimization problems of complex systems. In particular, the honey badger algorithm (HBA) represents a metaheuristic optimization algorithm inspired by the foraging behavior of honey badgers, and the HBA algorithm has attracted people's attention due to its clear algorithm structure, simple implementation, and good stability. However, like other metaheuristic optimization algorithms, HBA faces problems such as lack of global exploration ability, slow convergence speed, low accuracy, and being prone to falling into local optima. Therefore, finding a solution strategy to meet the performance degradation problem of an aeroengine during long-term operation is of great significance for control law optimization technology. Summary of the Invention

[0004] In view of this, an embodiment of the present application provides an optimization method for the control law of an adaptive cycle engine based on DLH-IHBA, which at least partially solves the problems in the prior art that the engine in long-term service cannot adaptively adjust after component degradation and the optimization accuracy of traditional optimization algorithms is low.

[0005] An embodiment of the present application provides an optimization method for the control law of an adaptive cycle engine based on DLH-IHBA, and the method includes: Establish an adaptive cycle engine model, and determine optimization variables based on the adaptive cycle engine model; For each control mode of the adaptive cycle engine, determine the constraint conditions and the objective function based on the optimization variables. The control modes include a steady-state control mode and a transient control mode. 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 constraint conditions, use a multi-dimensional learning hunting strategy to fuse the honey badger optimization algorithm to optimize the control law of each control mode to obtain the optimal control variables; wherein, the multi-dimensional learning hunting strategy to fuse the honey badger optimization algorithm includes using the Tent mapping as the initial particle swarm position mapping.

[0006] According to a specific implementation manner of an embodiment of the present application, the optimization variables are: , The constraint conditions in the process of optimizing the control law of each control mode are described as: , wherein, u is the optimization variable, n c is the compressor speed, π T is the turbine pressure ratio drop, W fb is the afterburner fuel flow rate, A 63 is the area of the after-duct ejector, α f is the fan guide vane angle, α cdfs is the CDFS guide vane angle, α c is the compressor guide vane angle, A 13 is the area of the mode selection valve, A 125 is the area of the front-duct ejector, A 225 is the area of the inlet of the front mixing zone in the bypass duct, T4 is the total temperature in front of the high-pressure turbine, n f is the fan speed, SM f is the fan surge margin, SM cdfs is the CDFS surge margin, SM c is the compressor surge margin, P3 is the compressor outlet pressure, the subscript min is the minimum value, and the subscript max is the maximum value.

[0007] According to a specific implementation manner of an embodiment of the present application, the objective function and constraint conditions of the minimum fuel consumption control mode are expressed as: , where f(u) is the objective function, g i’ (u) is the inequality constraint condition, h k’ (u) is the equality constraint condition, minsfc is the minimum fuel consumption rate, s.t. is the constraint condition, i' represents the i'-th inequality constraint condition, and k' represents the k'-th equality constraint condition; The objective function and constraint conditions of the maximum thrust control mode are expressed as: , where max F is the maximum engine thrust; The objective function and constraint conditions of the minimum turbine inlet temperature control mode are expressed as: , where min T4 is the minimum total temperature at the inlet of the high-pressure turbine.

[0008] According to a specific implementation manner of an embodiment of the present application, the objective function of the transient control mode adopts the adaptive variable weight coefficient method to transform the double-objective function and multi-inequality constraint problem into an unconstrained optimization problem. The objective function expression of the transient control mode is: , where minJ(k,σ) is the objective function of the transient control mode, ω1 and ω2 are the objective function weights respectively, n c (k) is the rotational speed at the current k moment, T4(k) is the total temperature at the inlet of the high-pressure turbine at the current k moment, n c,obj is the rotational speed target value, T 4,obj is the total temperature target value at the inlet of the high-pressure turbine, σ is an infinitely large positive number, and g(x) is the cost function.

[0009] According to a specific implementation manner of an embodiment of the present application, the control law of each control mode is optimized by adopting a multi-dimensional learning hunting strategy to fuse the honey badger optimization algorithm, including: Construct the initial population, the maximum number of iterations, variables, the spatial dimension of the optimization variables, and the boundaries of the search space, initialize the positions of the honey badgers, and use the Tent mapping as the initial particle swarm position mapping; Define the olfactory intensity of the prey; Calculate the density factor that changes according to the quasi-cosine law; Based on the density factor and the olfactory intensity, update the positions of the honey badgers in the excavation stage and the positions of the honey badgers in the honey collection stage; Update the position of the honey badger based on the multi-dimensional learning hunting strategy to improve the quality of the search individuals and increase the search ability, and obtain the current best position and the current best fitness value; Adopt a greedy strategy to interfere with the current best position to obtain the final global optimal position and the best fitness value.

[0010] According to a specific implementation manner of an embodiment of the present application, the mathematical expression of the Tent mapping is: , where μ is the chaos parameter, i is the population number, j is the chaos variable number, is a chaotic sequence randomly generated between (0,1), is a chaotic sequence randomly generated between (0,1) for the jth chaotic variable of the ith population, is a chaotic sequence randomly generated between (0,1) for the (j + 1)th chaotic variable of the ith population; The expression of the olfactory intensity is: , where I i is the olfactory intensity of the prey, r5 is a random number between 0 and 1, S is the source intensity, d i represents the distance between the prey and the honey pot, x i is the position of the honey pot, x prey is the current position of the prey, indicating the best position currently found in the search space; The expression of the density factor varying according to the quasi-cosine law is: , where α is the density factor varying according to the quasi-cosine law, ω is the traditional density factor, t is the current iteration number, t max is the maximum iteration number.

[0011] According to a specific implementation manner of an embodiment of the present application, the action expression of the honey badger in the excavation stage is: , where x new is the updated position of the honey badger, β is the ability of the honey badger to obtain food, F is the flag for changing the search direction, and r1, r2, and r3 are three different random numbers between 0 and 1; The action expression of the honey badger in the honey collection stage is: , where r4 is a random number.

[0012] According to a specific implementation manner of an embodiment of the present application, the updating of the honey badger position based on the multi-dimensional learning hunting strategy includes: Construct a radius matrix based on the original position and the new position of the honey badger; Construct a neighborhood matrix based on the radius matrix and the Euclidean distance between the current individual and the alternative individual; Generate new individuals by learning from multiple neighborhood matrices, where the d-th dimension of each new individual is updated based on the d-th dimension of a randomly selected individual, and the new individual is the position of the honey badger updated based on the multi-dimensional learning hunting strategy.

[0013] According to a specific implementation manner of an embodiment of the present application, the expression of the radius matrix is: , The expression of the neighborhood matrix is: , The expression of the generated new individual is: , where Radiusi(t) is the radius matrix, Neighbouri(t) is the neighborhood matrix, x i (t) is the current individual, x j (t) is the alternative individual, x new (t + 1) is the new position of individual i at the (t + 1)-th iteration, D is the dimension of the optimization variable space, N is the initial population, x i-DLH,j (t + 1) is the generated new individual, x i,d (t) is the updated position of individual i at the d-th dimension at the t-th iteration, x n,d (t) is the individual randomly selected by individual i at the d-th dimension at the t-th iteration, x r,d (t) is the reference position of individual i at the d-th dimension at the t-th iteration, rand is a random function that generates a random number from 0 to 1.

[0014] According to a specific implementation manner of an embodiment of the present application, the expression of the greedy strategy is: , where x i (t + 1) is the updated position of individual i at the (t + 1)-th iteration, x i-new (t + 1) is the position of individual i after the (t + 1)-th iteration without executing the multi-dimensional learning hunting strategy, f(x i-new ) is the objective function corresponding to x i-new , x i-DLH (t + 1) is the position of individual i after the (t + 1)-th iteration updated by the multi-dimensional learning hunting strategy, f(x i-DLH ) is the objective function corresponding to x i-DLH .

[0015] Beneficial effects: In the adaptive cycle engine control law optimization method based on DLH-IHBA in the embodiments of the present application, Tent mapping is introduced into the honey badger algorithm, and combined with the multi-dimensional learning hunting strategy to improve the global convergence of the algorithm, so as to achieve the lowest fuel consumption rate, thereby improving fuel efficiency; achieve the maximum thrust when the power demand is the largest to meet specific flight requirements; and maintain the turbine inlet temperature at the lowest level to ensure the service life of the turbine; ensure that the engine system parameters do not exceed the limit values during the transient process, and achieve the shortest acceleration time. The optimization method of the present application improves the convergence speed and the optimization effect. Brief description of the drawings

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0017] Figure 1 It is a flowchart of an adaptive cycle engine control law optimization method based on DLH-IHBA according to an embodiment of the present invention; Figure 2 It is a schematic diagram of multi-objective optimization according to an embodiment of the present invention; Figure 3 It is a flowchart of the multi-dimensional learning hunting strategy fusion honey badger optimization according to an embodiment of the present invention. Detailed implementation manners

[0018] The embodiments of the present application will be described in detail below with reference to the drawings.

[0019] The following uses specific specific examples to illustrate the implementation manners of the present application. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. Obviously, the described embodiments are only 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 implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts belong to the scope of protection of the present application.

[0020] It should be noted that the following description concerns various aspects of embodiments within the scope of the appended claims. It will 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, those skilled in the art should understand that one 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 the aspects set forth herein can be used to implement a device and / or practice a method. Additionally, this device and / or method can be implemented using other structures and / or functionality in addition to one or more of the aspects described herein.

[0021] It should also be noted that the diagrams provided in the following embodiments only illustrate the basic concept of this application in a schematic manner. The diagrams only show the components related to this application and are not drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0022] In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0023] An embodiment of this application provides an optimization method for the control law of an adaptive cycle engine based on DLH-IHBA. The following is a detailed description with reference to Figures 1 to 3 for details.

[0024] In one embodiment, with reference to Figure 1 , the method includes the following steps: Establish an adaptive cycle engine model (ACE airborne adaptive model), and determine the optimization variables based on the adaptive cycle engine model; For each control mode of the adaptive cycle engine, determine the constraint conditions and the objective function based on the optimization variables. The control modes include a steady-state control mode and a transient control mode. 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. The transient control mode includes acceleration control and deceleration control; Based on the objective function and the constraint conditions, use the multi-dimensional learning hunting strategy to fuse the honey badger optimization algorithm (DLH-IBHA) to optimize the control law of each control mode, and obtain the optimal control variables for output; wherein, the multi-dimensional learning hunting strategy to fuse the honey badger optimization algorithm includes using the Tent mapping as the initial particle swarm position mapping.

[0025] In this embodiment, the Dimensional Learning Harvesting Improved Honey Badger Algorithm (DLH-IBHA) is adopted, which is the integration of the Dimensional Learning Harvesting (DLH) strategy and the Honey Badger Algorithm (HBA). The honey badger algorithm is a heuristic optimization algorithm based on the behaviors of natural organisms. It mimics the indomitable and random search characteristics demonstrated by honey badgers when searching for food. This algorithm belongs to a type of meta-heuristic algorithm and is used to solve complex optimization problems.

[0026] The adaptive control law optimization method proposed in the embodiments of this application does not directly optimize the engine control parameters. Instead, it optimizes the controlled parameters (such as rotational speed and pressure ratio) and other adjustment parameters. Compared with directly optimizing the control parameters, adjusting the controlled parameters has the following advantages: First, since the engine is vulnerable to external factors (such as flight altitude, gusts, etc.), if the control parameters are directly optimized, the control parameters need to be adjusted in real time according to the environment and flight state, and it is difficult for existing optimization algorithms to meet the requirements of both accuracy and real-time performance. Second, since the engine performance parameters cannot be directly measured, the usual control systems cannot directly control the performance parameters. This embodiment proposes the DLH-IHBA optimization algorithm, which improves the honey badger optimization algorithm by adopting the dimensional learning harvesting strategy and is used for optimizing the steady-state and transient control laws of the Adaptive Cycle Engine (ACE). The purpose is to improve the optimization effect of the control law and enhance the performance of the engine. The simulation results show that DLH-IHBA has a faster convergence speed and better optimization effect than several classical intelligent optimization algorithms. It effectively reduces the fuel consumption rate and the minimum turbine inlet temperature of the ACE, increases the maximum thrust, and shortens the acceleration time by 50%.

[0027] Furthermore, for the adaptive cycle engine model studied in this embodiment, the optimization variables are: , (1) The constraint conditions in the process of optimizing the control law for each control mode are described as: , (2) where u is the optimization variable, n c is the compressor rotational speed, π T is the turbine pressure ratio drop, W fb is the afterburner fuel flow rate, A 63 is the area of the after-duct ejector, α f is the fan guide vane angle, α cdfsis the CDFS guide vane angle, α c is the compressor guide vane angle, A 13 is the mode selection valve area, A 125 is the front bypass ejector area, A 225 is the inlet area of the front mixing zone in the bypass duct, T4 is the total temperature in front of the high-pressure turbine, n f is the fan speed, SM f is the fan surge margin, SM cdfs is the CDFS surge margin, SM c is the compressor surge margin, P3 is the compressor outlet pressure, the subscript min is the minimum value, and the subscript max is the maximum value.

[0028] For the expressions of the above constraint conditions, it can be expressed as: , (3) where, g i’ (u) is the inequality constraint condition, and i’ represents the i’th inequality constraint condition.

[0029] In one embodiment, in the minimum fuel consumption control mode, the equality constraint emphasizes achieving the minimum specific fuel consumption under the condition of maintaining a constant thrust. It is necessary to comprehensively adjust various parameters to improve the working efficiency of the fan, compressor, and inlet duct, increase the engine flow rate, and thus achieve an increase in thrust. By solving the non-linear constraint problem, finding a suitable combination of control variables, making the engine thrust reach the expected value and operating at the minimum specific fuel consumption point, the objective function and constraint conditions of the minimum fuel consumption control mode are expressed as: , (4) where, f(u) is the objective function, g i’ (u) is the inequality constraint condition, h k’ (u) is the equality constraint condition, minsfc is the minimum specific fuel consumption, s.t. is the constraint condition, i’ represents the i’th inequality constraint condition, and k’ represents the k’th equality constraint condition; The maximum thrust control mode is mainly applied to high-thrust demand stages such as aircraft takeoff and accelerated climb. In this mode, the goal is to achieve the maximum thrust on the premise of ensuring the safe operation of the engine. At the engine operating point, the global maximum thrust is located at the intersection of the minimum safety boundaries of the fan surge margin and the compressor surge margin. The objective function and constraint conditions of the maximum thrust control mode are expressed as: , (5) where, max F is the maximum engine thrust; The minimum turbine inlet temperature control mode is mainly applied to high Mach number flight conditions. In this mode, the goal is to minimize the turbine inlet temperature as much as possible while maintaining constant thrust, in order to extend the engine's service life and reduce infrared radiation. This mode mainly reduces the engine turbine temperature by decreasing the main fuel flow rate, and increases the engine pressure ratio, efficiency, and flow rate by comprehensively adjusting parameters such as the nozzle area of the exhaust nozzle, the angle of the fan guide vane, and the angle of the compressor guide vane, 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: , (6) where min T4 is the minimum total temperature at the inlet of the high-pressure turbine.

[0030] In specific implementation, both the minimum fuel consumption control mode optimization and the minimum turbine inlet temperature mode optimization are multi-objective optimizations. The goal of the minimum fuel consumption control mode optimization is to achieve the desired thrust while minimizing the specific fuel consumption; the goal of the minimum turbine inlet temperature mode optimization is to achieve the desired thrust while minimizing the turbine inlet temperature. The principle of multi-objective optimization is as Figure 2 shown. By gradually iterating and optimizing, either objective 1 or objective 2 is made optimal. By selecting appropriate weights W = [W1, W2], where W1 is the weight of objective 1 (minimum specific fuel consumption, minimum turbine inlet temperature) and W2 is the weight of objective 2 (constant thrust), the comprehensive performance of objective 1 and objective 2 is made optimal.

[0031] Combining formulas (4) - (6), the intelligent engine adaptive control law optimization can be expressed by the following non-linear programming problem: , (7) To solve the constrained optimization problem, a penalty function can be introduced, , (8) where F(u,σ) is the objective function with the penalty function introduced, and σ1 and σ2 are infinitely large positive numbers. In this way, the original constrained problem is transformed into an unconstrained problem. In the process of intelligent optimization of the engine adaptive control law, an intelligent optimization algorithm is needed to find the best combination of optimization parameters to optimize the engine performance.

[0032] In one embodiment, the objective function of the transient control mode adopts the adaptive variable weight coefficient method, which transforms the double-objective function and multi-inequality constraint problem into an unconstrained optimization problem. The expression of the objective function of the transient control mode is: , where minJ(k,σ) is the objective function of the transient control mode, ω1 and ω2 are the weight values of the objective function respectively, and n c(k) is the rotational speed at the current k moment, T4(k) is the total temperature in front of the high-pressure turbine at the current k moment, n c,obj is the rotational speed target value, T 4,obj is the total temperature target value in front of the high-pressure turbine, σ is an infinitely large positive number, and g(x) is the cost function.

[0033] During specific implementation, for the transient control mode, the key to the engine's acceleration performance lies in achieving the shortest acceleration time, which can be expressed by the following formula: , (9) where, t ac is the acceleration time, I is the rotor inertia, n is the rotor speed, n max is the rotational speed under the maximum condition, n idle is the rotational speed under the idle condition, ΔH ax is the remaining power of the turbine during the acceleration process. To achieve the best acceleration control in each control cycle, the high-pressure rotor speed can be used as the objective function: , (10) where, t’ is the integration time.

[0034] The total temperature T4 in front of the high-pressure turbine can also effectively increase the turbine power. To comprehensively reflect the energy change of the engine during operation, the total temperature T4 in front of the high-pressure turbine and the high-pressure rotor speed are used as the optimization objectives simultaneously. The discrete objective function is defined using the linear weighted method: , (11) where, ω1 and ω2 are the weights of the objective function respectively, n c (k) is the rotational speed at the current k moment, T4(k) is the total temperature in front of the high-pressure turbine at the current k moment, n c,obj is the rotational speed target value, T 4,obj is the total temperature target value in front of the high-pressure turbine.

[0035] By adopting the adaptive variable weight coefficient method, the dual-objective function and multi-inequality constraint problem are transformed into an unconstrained optimization problem: , (12) This method helps to achieve the maximum remaining power of the turbine, reduce the acceleration time, and ensure high specific thrust.

[0036] In one embodiment, referring to Figure 3 , the control law of each control mode is optimized by adopting the multi-dimensional learning hunting strategy to fuse the honey badger optimization algorithm, including: Construct the initial population, the maximum number of iterations, variables, the spatial dimension of the optimization variables, and the boundaries of the search space, initialize the positions of the honey badgers, and use the Tent mapping as the initial particle swarm position mapping; Define the olfactory intensity of the prey; Calculate the density factor that changes according to the quasi-cosine law; Based on the density factor and the olfactory intensity, update the positions of the honey badgers in the excavation stage and the positions of the honey badgers in the honey collection stage; Update the positions of the honey badgers based on the multi-dimensional learning hunting strategy to improve the quality of the search individuals and increase the search ability, and obtain the current best position and the current best fitness value; Use the greedy strategy to interfere with the current best position to obtain the final global optimal position and the best fitness value.

[0037] According to a specific implementation manner of an embodiment of the present application, the mathematical expression of the Tent mapping is: , where μ is the chaos parameter, i is the population number, j is the chaos variable number, is a chaotic sequence randomly generated between (0,1), is the chaotic sequence randomly generated between (0,1) for the j-th chaotic variable of the i-th population, is the chaotic sequence randomly generated between (0,1) for the (j + 1)-th chaotic variable of the i-th population; The expression of the olfactory intensity is: , where I i is the olfactory intensity of the prey, r5 is a random number between 0 and 1, S is the source intensity, d i represents the distance between the prey and the honey pot, x i is the position of the honey pot, x prey is the current position of the prey, representing the best position currently found in the search space; The expression of the density factor that changes according to the quasi-cosine law is: , where α is the density factor that changes according to the quasi-cosine law, ω is the traditional density factor, t is the current iteration number, t max is the maximum number of iterations.

[0038] According to a specific implementation manner of an embodiment of the present application, the action expression of the honey badger in the excavation stage is: , where x newLet \(x\) be the updated position of the honey badger, \(\beta\) be the ability of the honey badger to obtain food, \(F\) be the flag for changing the search direction, and \(r_1\), \(r_2\), and \(r_3\) be three different random numbers between 0 and 1; The action expression of the honey badger in the honey collection stage is: , where \(r_4\) is a random number.

[0039] According to a specific implementation manner of an embodiment of the present application, updating the position of the honey badger based on the multi-dimensional learning hunting strategy includes: Constructing a radius matrix based on the original position of the honey badger and the new position of the honey badger; Constructing a neighborhood matrix based on the radius matrix and the Euclidean distance between the current individual and the alternative individual; Generating new individuals by learning from multiple neighborhood matrices, where the \(d\)-th dimension of each new individual is updated based on the \(d\)-th dimension of a randomly selected individual, and the new individual is the position of the honey badger updated based on the multi-dimensional learning hunting strategy.

[0040] According to a specific implementation manner of an embodiment of the present application, the expression of the radius matrix is: , The expression of the neighborhood matrix is: , The expression of the generated new individual is: , where \(Radius_i(t)\) is the radius matrix, \(Neighb_i(t)\) is the neighborhood matrix, \(x\) i (t) is the current individual, \(x\) j (t) is the alternative individual, \(x\) new (t + 1) is the new position of individual \(i\) at the \((t + 1)\)-th iteration, \(D\) is the dimension of the optimization variable space, \(N\) is the initial population, \(x\) i-DLH,j (t + 1) is the generated new individual, \(x\) i,d (t) is the updated position of individual \(i\) at the \(d\)-th dimension at the \(t\)-th iteration, \(x\) n,d (t) is the individual selected randomly by individual \(i\) at the \(d\)-th dimension at the \(t\)-th iteration, \(x\) r,d (t) is the reference position of individual \(i\) at the \(d\)-th dimension at the \(t\)-th iteration, and \(rand\) is a random function that generates a random number between 0 and 1.

[0041] According to a specific implementation manner of an embodiment of the present application, the expression of the greedy strategy is: , where \(x\) i(t + 1) is the updated position of individual i at the (t + 1)-th iteration, x i-new (t + 1) is the position of individual i that has not executed the multi-dimensional learning hunting strategy after the (t + 1)-th iteration, f(x i-new ) is for x i-new corresponding objective function, x i-DLH (t + 1) is the position of individual i after the multi-dimensional learning hunting strategy update after the (t + 1)-th iteration, f(x i-DLH ) is for x i-DLH corresponding objective function.

[0042] In specific implementation, the multi-dimensional learning hunting strategy integrated honey badger optimization algorithm (DLH-IHBA) The flowchart of DLH-IHBA is as Figure 3 shown. Through this method, DLH-IHBA overcomes the defects of the original HBA, further improves the optimization speed and solution accuracy, and provides an effective strategy for solving complex problems.

[0043] Step 1: Initialization Construct the initial population N, the maximum number of iterations t max , variables, the dimensionality of the space, and the upper and lower search range boundaries, and initialize the positions of the honey badgers. To improve the speed and accuracy of the optimization algorithm, it is necessary to improve the uniformity of the initial population in the search space. This application uses Tent mapping as the mapping of the initial particle swarm position. Compared with the random generation method, the chaotic sequence has more excellent randomness, ergodicity, and regularity. The uniform distribution characteristic of Tent mapping helps to improve the optimization speed and solution accuracy of the algorithm. The mathematical expression of Tent mapping is shown as follows: , (13) where the chaotic parameter μ ∈ (0, 2] is proportional to the chaos, i and j are the population number and the chaotic variable number respectively, is a chaotic sequence randomly generated between (0, 1).

[0044] Due to the high sensitivity of Tent mapping to the selection of the initial value, this embodiment selects a variety of different initial values to generate the corresponding chaotic sequences and converts them into the search space of each individual. In the DLH-IHBA algorithm, the position of the i-th honey badger is expressed as: , (14) where lb and ub are the lower and upper bounds of the search space respectively.

[0045] The position of the i-th honey badger located in the D-dimensional space is expressed as: , (15) Among them, D is the spatial dimension of the optimization variable.

[0046] Step 2: Definition of the olfactory intensity of the prey The olfactory intensity is related to the distance between the prey and the i-th honey badger. I i represents the olfactory intensity of the prey. A high intensity indicates fast movement. I i is defined by the following formula: , (16) Among them, I i is the olfactory intensity of the prey, r5 is a random number between 0 and 1, S is the source intensity, d i represents the distance between the prey and the honey badger, x i is the position of the honey badger, x prey is the current position of the prey, representing the best position currently found in the search space.

[0047] Step 3: Update the position The core idea of the DLH-IHBA algorithm is to continuously update the position of the prey, search among a limited number of N honey badgers, and update the current position x of the prey prey , which is the solution to the problem. This method divides the foraging behavior of honey badgers into a digging stage and a honey collection stage. The digging stage refers to the behavior of honey badgers digging holes to find food, and the honey collection stage refers to the behavior of honey badgers following honeyguides to find beehives. In the digging stage, the movement of honey badgers is similar to a heart-shaped pattern and can be represented by the following formula: , (17) Among them, x new is the updated position of the honey badger, β is the ability of the honey badger to obtain food (β≥1, conventionally set β = 6), r1, r2, and r3 are three different random numbers between 0 and 1, F is a flag to change the search direction, and F is determined by the following formula: , (18) α is the density factor. In the traditional honey badger optimization algorithm, the density factor α tr is determined by the following formula: , (19) Among them, C is a constant equal to 2, t max is the maximum number of iterations.

[0048] The change of the traditional density factor is very gentle, which reduces the convergence speed of the algorithm. Therefore, the present invention proposes a density factor with a quasi-cosine law change, and its mathematical model is as follows: , (20) Among them, α is the density factor with a quasi-cosine law change, ω is the traditional density factor, t is the current iteration number, t maxis the maximum number of iterations.

[0049] The density factor α that varies according to the improved quasi-cosine law rapidly decreases in the early iterations, ensuring that the population searches within a wide range of space and accelerating the convergence speed of the algorithm. In the middle stage of the iteration, when α drops to a small value, it is given a new incremental step size, enabling the population to find the global optimal solution within a wide range again, which is beneficial for the algorithm to jump out of the local optimal value. In the later stage of the iteration, the value of α drops significantly again, allowing the population to quickly approach the global optimal value. When the value of α is very small, the population will be able to conduct in-depth local searches.

[0050] In the honey collection stage, the honey badger follows the honeyguide to find the beehive, which can be simulated as: 。(21) Step 4: Update the position using the DLH strategy Traditional HBA has low convergence, ultimately leading to the dilemma of falling into local optima. Therefore, DLH is introduced to improve the quality of search individuals and increase the search ability.

[0051] DLH generates the following alternative individuals: ,(22) where the radius matrix Radius is generated by subtracting the distance between the original and new positions, and the neighborhood matrix is constructed based on the Euclidean distance between the current individual x i (t) and the alternative individual x j (t).

[0052] ,(23) By learning from many neighborhood matrices, DLH generates a new individual x i-DLH,j (t + 1), where the d-th dimension of each new individual is updated based on the d-th dimension of a randomly selected individual x n,d (t).

[0053] ,(24) Recalculate the individual fitness value, and retain the current best position and the current best fitness value.

[0054] Finally, a greedy strategy is adopted to interfere with the current optimal position, evaluate the fitness value of the current optimal individual, and retain the individual that is more valuable for updating the population position. Its mathematical model is as follows: 。(25) When the current number of iterations does not satisfy t < t max , finally return the best fitness value and the global optimal position.

[0055] Embodiments of the present application address the problems that components of long-serving engines cannot adaptively adjust after degradation and that traditional optimization algorithms have low optimization accuracy. A method for intelligent optimization of the control law of an adaptive cycle engine (ACE) based on an improved honey badger algorithm is proposed. Tent mapping and the quasi-cosine law are introduced, and combined with a multi-dimensional learning hunting strategy to improve the global convergence of the algorithm, aiming to achieve the lowest fuel consumption rate, thereby improving fuel efficiency; achieving maximum thrust when power demand is the greatest to meet specific flight requirements; and maintaining the turbine inlet temperature at the lowest level to ensure the service life of the turbine; ensuring that engine system parameters do not exceed the limit values during transient processes and achieving the shortest acceleration time. The specific contributions of the present invention are as follows: (1) A multi-dimensional learning hunting strategy fused honey badger algorithm (DLH-IHBA) is proposed. Tent mapping is used as the initial particle swarm position mapping to improve the optimization speed and solution accuracy of the algorithm. The density factor of the algorithm is improved using the quasi-cosine law, causing it to rapidly decline in early iterations and accelerating the convergence speed. A multi-dimensional learning hunting strategy is introduced to improve the individual quality, enhance the search process, and balance the exploration and exploitation phases; (2) Numerical simulation verification of the intelligent optimization of the ACE control law is carried out using the DLH-IHBA algorithm and a variety of classical intelligent optimization algorithms to confirm the feasibility of the proposed method. The optimization parameters are selected as the optimization variables, and optimization is carried out separately for the steady-state control mode and the transient control mode.

[0056] The following describes the optimization method for the control law of the adaptive cycle engine based on DLH-IHBA of the present application with a specific embodiment.

[0057] Numerical simulation verification of the intelligent optimization of the control law is carried out using the DLH-IHBA algorithm and a variety of classical intelligent optimization algorithms to confirm the feasibility of the proposed method. The optimization parameters are selected, and optimization is carried out separately for the steady-state control mode and the transient control mode. Among them, n c is the compressor speed, π T is the turbine pressure ratio drop, A8 is the main duct nozzle outlet area, A 38 is the Flade duct nozzle area, α fl is the Flade guide vane angle, α f is the fan guide vane angle, α cdfs is the CDFS guide vane angle, α c is the compressor guide vane angle.

[0058] (1) Lowest fuel consumption control mode At low Mach numbers, the specific fuel consumption of turbojet engines is higher than that of turbofan engines. Due to the large bypass ratio, turbofan engines are not suitable for high Mach number flight conditions. Therefore, taking the subsonic cruise point (H = 5 km, Ma = 0.8, F r = 78000 N, where H is the altitude and Ma is the Mach number) as an example, the minimum fuel consumption control method of the engine is verified, and the three-bypass turbofan mode of ACE is used for verification.

[0059] The optimization objective function is set as: , with the minimum fuel consumption reduced by 1.26%, where F r is the reference thrust and F n is the actual thrust.

[0060] (2) Maximum thrust control mode In this mode, taking the supersonic cruise point (H = 10 km, Ma = 1.5, n c = 100%) as an example, the maximum thrust control method of ACE is simulated. The optimization objective function is set as fitness = 10000 / F n . The maximum thrust is increased by 0.35%.

[0061] (3) Minimum turbine inlet temperature control mode The minimum turbine inlet temperature control of ACE is usually applied to high-altitude and high-Mach flight. The goal of the minimum turbine inlet temperature control is to ensure that the gas temperature in front of the turbine does not exceed the maximum allowable temperature of the turbine material, thus protecting the turbine. Taking the supersonic cruise point (H = 15 km, Ma = 2.4, F r = 80000 N) as an example, the method proposed in the present invention is applied to optimize the minimum turbine inlet temperature control mode. The optimization objective function is set as fitness = (F r -F n ) 2 + T4. The minimum turbine inlet temperature is reduced by 0.23%.

[0062] (4) Transition state control mode The acceleration mode is a typical transition state control mode. In this mode, under the working conditions of an altitude of 18 km and 0.8 Ma, the acceleration process of the three-bypass working mode of ACE is simulated and verified, and the fan percentage speed is accelerated from 82% to 98%.

[0063] The embodiments provided by the present invention propose a novel optimization method for the control law of an adaptive cycle engine based on DLH-IHBA. This method improves the honey badger optimization algorithm and adopts a multi-dimensional learning hunting strategy for optimizing the control laws of the steady state and transient state of the ACE, aiming to improve the optimization effect of the control law and enhance the performance of the engine. The simulation results show that the DLH-IHBA optimization algorithm has a faster convergence speed and better optimization effect than several traditional classic intelligent optimization algorithms. It effectively reduces the fuel consumption rate and the minimum turbine inlet temperature of the ACE, increases the maximum thrust, and shortens the acceleration time by 50%.

[0064] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered within 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. An optimization method for the control law of an adaptive cycle engine based on DLH-IHBA, characterized in that The method includes: Establishing an adaptive cycle engine model and determining optimization variables based on the adaptive cycle engine model; For each control mode of the adaptive cycle engine, determining constraint conditions and an objective function based on the optimization variables, where the control modes include a steady-state control mode and a transient 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 constraint conditions, using a multi-dimensional learning hunting strategy to fuse the honey badger optimization algorithm to optimize the control law of each control mode to obtain optimal control variables; wherein, the multi-dimensional learning hunting strategy to fuse the honey badger optimization algorithm includes using Tent mapping as the initial particle swarm position mapping.

2. The optimized method for the control law of an adaptive cycle engine based on DLH-IHBA according to claim 1, wherein The optimization variables are: ， The constraint conditions in the process of optimizing the control law of each control mode are described as: ， Among them, u is the optimization variable, n c is the compressor speed, π T is the turbine pressure ratio drop, W fb is the afterburner fuel flow rate, A 63 is the area of the after-duct ejector, α f is the fan guide vane angle, α cdfs is the CDFS guide vane angle, α c is the compressor guide vane angle, A 13 is the area of the mode selection valve, A 125 is the area of the front-duct ejector, A 225 is the inlet area of the front mixing zone in the bypass duct, T4 is the total temperature in front of the high-pressure turbine, n f is the fan speed, SM f is the fan surge margin, SM cdfs is the CDFS surge margin, SM c is the compressor surge margin, P3 is the compressor outlet pressure, the subscript min is the minimum value, and the subscript max is the maximum value.

3. The optimization method for the adaptive cycle engine control law based on DLH-IHBA according to claim 2, characterized in that The objective function and constraint conditions of the minimum fuel consumption control mode are expressed as: ， Among them, f(u) is the objective function, g i’ (u) is the inequality constraint condition, h k’ (u) is the equality constraint condition, min sfc is the minimum fuel consumption rate, s.t. is the constraint condition, i’ represents the i’-th inequality constraint condition, and k’ represents the k’-th equality constraint condition; The objective function and constraint conditions of the maximum thrust control mode are expressed as: ， Where max F is the maximum engine thrust; The objective function and constraint conditions of the minimum turbine inlet temperature control mode are expressed as: ， Where min T4 is the minimum total temperature at the inlet of the high-pressure turbine.

4. The optimization method for the adaptive cycle engine control law based on DLH-IHBA according to claim 3, wherein The objective function of the transient control mode adopts an adaptive variable weight coefficient method to transform the double-objective function and multi-inequality constraint problem into an unconstrained optimization problem, and the expression of the objective function of the transient control mode is: ， Among them, minJ(k,σ) is the objective function of the transition state control mode, ω1 and ω2 are the objective function weights respectively, and n c (k) is the compressor speed at the current k moment, T4(k) is the total temperature in front of the high-pressure turbine at the current k moment, and n c,obj is the target value of the compressor speed, and T 4,obj is the target value of the total temperature in front of the high-pressure turbine, σ is an infinitely large positive number, and g(x) is the cost function.

5. The optimization method for the adaptive cycle engine control law based on DLH-IHBA according to claim 1, characterized in that The using of the multi-dimensional learning hunting strategy to fuse the honey badger optimization algorithm to optimize the control law of each control mode includes: Constructing an initial population, a maximum number of iterations, variables, the spatial dimension of the optimization variables, and the boundaries of the search space, initializing the positions of the honey badgers, and using Tent mapping as the initial particle swarm position mapping; Defining the olfactory intensity of the prey; Calculating the density factor that changes according to the quasi-cosine law; Based on the density factor and the olfactory intensity, updating the positions of the honey badgers in the excavation stage and the positions of the honey badgers in the honey collection stage; Updating the positions of the honey badgers based on the multi-dimensional learning hunting strategy to improve the quality of the search individuals and increase the search ability, obtaining the current best position and the current best fitness value; Using a greedy strategy to interfere with the current best position to obtain the final global optimal position and the best fitness value.

6. The optimization method for the adaptive cycle engine control law based on DLH-IHBA according to claim 5, wherein The mathematical expression of the Tent mapping is: ， Among them, μ is the chaotic parameter, i is the population number, j is the serial number of the chaotic variable, and is a chaotic sequence randomly generated between (0, 1). is a chaotic sequence randomly generated between (0, 1) for the j-th chaotic variable of the i-th population. is a chaotic sequence randomly generated between (0, 1) for the (j + 1)-th chaotic variable of the i-th population. The expression of the olfactory intensity is: ， Among them, I i is the olfactory intensity of the prey, r5 is a random number between 0 and 1, S is the source intensity, d i represents the distance between the prey and the honeypot, x i is the position of the honeypot, x prey is the current position of the prey, indicating the best position currently found in the search space; The expression of the density factor that changes according to the quasi-cosine law is: ， Among them, α is the density factor that changes according to the quasi-cosine law, ω is the traditional density factor, t is the current iteration number, and t max is the maximum iteration number.

7. The method for optimizing the control law of an adaptive cycle engine based on DLH-IHBA according to claim 6, wherein The action expression of the honey badger in the excavation stage is: ， where x new is the updated position of the honey badger, β is the ability of the honey badger to obtain food, F is the flag for changing the search direction, and r1, r2, and r3 are three different random numbers between 0 and 1; The action expression of the honey badger in the honey collection stage is: ， Where r4 is a random number.

8. The optimization method for the adaptive cycle engine control law based on DLH-IHBA according to claim 7, wherein The updating of the positions of the honey badgers based on the multi-dimensional learning hunting strategy includes: Constructing a radius matrix based on the original position and the new position of the honey badger; Constructing a neighborhood matrix based on the radius matrix and the Euclidean distance between the current individual and the alternative individuals; Generating new individuals by learning from multiple neighborhood matrices, where the d-th dimension of each new individual is updated based on the d-th dimension of a randomly selected individual, and the new individual is the position of the honey badger updated based on the multi-dimensional learning hunting strategy.

9. The optimization method for the adaptive cycle engine control law based on DLH-IHBA according to claim 8, wherein The expression of the radius matrix is: ， The expression of the neighborhood matrix is: ， The expression of the newly generated individual is: ， Among them, Radiusi(t) is the radius matrix, Neighbouri(t) is the neighborhood matrix, and x i (t) is the current individual, x j (t) is the alternative individual, x new (t + 1) is the new position of individual i at the (t + 1)-th iteration, D is the dimension of the optimization variable space, N is the initial population, and x i-DLH,j (t + 1) is the newly generated individual, x i,d (t) is the updated position of individual i at the t-th iteration in the d-th dimension, x n,d (t) is the individual randomly selected by individual i at the t-th iteration in the d-th dimension, x r,d (t) is the reference position of individual i at the t-th iteration in the d-th dimension, and rand is a random function that generates a random number between 0 and 1.

10. The optimization method of the adaptive cycle engine control law based on DLH-IHBA according to claim 9, wherein The expression of the greedy strategy is: ， where x i (t + 1) is the updated position of individual i at the (t + 1)-th iteration, x i-new (t + 1) is the position of individual i who has not executed the multi-dimensional learning hunting strategy after the (t + 1)-th iteration, f(x i-new ) is the objective function corresponding to x i-new , x i-DLH (t + 1) is the position of individual i after the multi-dimensional learning hunting strategy is updated at the (t + 1)-th iteration, f(x i-DLH ) is the objective function corresponding to x i-DLH .

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