An aircraft skin heating temperature control method, system, device and medium

Abstract

The present invention discloses an aircraft skin heating temperature control method, system, device and medium, which relates to the technical field of anti-icing for aircraft functional skins. The present invention uses the ZN tuning method to determine the initial PID control parameters of the adaptive PID controller at the current moment, and uses an iterative method to optimize the initial PID control parameters to determine the optimal PID control parameters. The adaptive PID controller injected with the optimal PID control parameters is used to determine the PID control output quantity; according to the first membership function, the second membership function and the fuzzy control rule table, the fuzzy control output quantity at the current moment is output. It realizes automatically determining the optimal PID control parameters for each aircraft functional sub-skin. The method has the self-adjusting ability of working parameters and no longer requires manual debugging process, solving the technical problems of poor application range and low control accuracy of the temperature control method in the prior art due to the lack of self-adjusting ability of working parameters in the temperature control method.

Description

Technical Field

[0001] The present invention relates to the technical field of anti-icing for aircraft functional skins, and particularly to a method, system, device and medium for controlling the heating temperature of an aircraft skin. Background Art

[0002] When an aircraft is in flight, due to the low temperature and complex meteorological environment, icing is likely to occur on the airframe, posing a potential safety hazard to flight. Therefore, the aircraft must adopt certain anti-icing methods. Among various anti-icing methods, the electrothermal functional skin anti-icing method has become an ideal solution to the aircraft anti-icing problem due to its outstanding advantages such as stable operation, uniform heat distribution, and little impact on the aerodynamic performance of the aircraft.

[0003] Currently, there are two main methods for controlling the heating temperature of aircraft functional skins. One is a switch-type temperature controller represented by switch control, and the other is a feedback-type temperature controller represented by PID (P - Proportional, I - Integral, D - Differential) control.

[0004] The switch-type temperature controller, as Figure 1 shown, is based on switch control. Through the study of the icing law on the aircraft surface, a suitable anti-icing cycle control law is designed, that is, the number and time of the controller's on and off within an anti-icing cycle, so that the temperature of the functional skin is maintained within a suitable range, realizing the periodic prevention and removal of ice layers.

[0005] The feedback-type temperature controller, as Figure 2 shown, Setpoint is the input value, Output is the output value, Error is the temperature deviation, K p is the proportional adjustment coefficient, K i is the integral adjustment coefficient, K d is the differential adjustment coefficient, and e(t) is the temperature output function. The feedback-type temperature controller is a control method based on deviation. It operates the error signal obtained after feedback through the proportional, integral, and differential links respectively, and then superimposes them to obtain the output signal of the controller, so that the temperature of the functional skin is continuously adjusted towards the set value, thereby realizing the prevention and removal of ice layers.

[0006] For a switching temperature controller, the switching temperature controller uses the anti-icing cycle control law as its working parameter. Its temperature control effect completely depends on the anti-icing cycle control law based on simulation tests and practical experience. This makes most switching temperature controllers show obvious customization characteristics, that is, once the controlled object or its working environment changes, it is necessary to re-customize the anti-icing cycle control law, and it is impossible to adjust the anti-icing cycle control law in real time according to the change of the working environment. Therefore, the switching temperature controller does not have the self-adjustment ability of working parameters.

[0007] For a feedback temperature controller, the feedback temperature controller uses the control parameter as its working parameter, and the selection of the control parameter requires the operator to continuously adjust it by using professional knowledge and engineering experience. When facing a controlled object with nonlinear and time-varying uncertainties, it is difficult to achieve the ideal effect due to the relatively slow adjustment process. Therefore, when facing a large number and various types of aircraft functional skins, the feedback temperature controller cannot adaptively adjust the control parameter in different application scenarios. Therefore, the feedback temperature controller does not have the self-adjustment ability of working parameters.

[0008] In summary, for the existing heating temperature control method of aircraft functional skins, the controller does not have the self-adjustment ability of working parameters, and the control accuracy of the heating temperature needs to be improved. Summary of the Invention

[0009] The purpose of the present invention is to provide a method, system, device and medium for controlling the heating temperature of aircraft skins, which can solve the technical problem that the temperature control method in the above-mentioned existing technology does not have the self-adjustment ability of working parameters, and improve the control accuracy of the heating temperature of aircraft functional skins.

[0010] To achieve the above purpose, the present invention provides the following solutions:

[0011] A method for controlling the heating temperature of aircraft skins, comprising:

[0012] Dividing the aircraft functional skin into multiple aircraft functional sub-skins, and obtaining the skin temperature of each aircraft functional sub-skin at the current moment;

[0013] For any aircraft functional sub-skin, determining the temperature deviation amount of the aircraft functional sub-skin at the current moment according to the skin temperature and the target temperature at the current moment, and taking the derivative of the temperature deviation amount at the current moment to obtain the deviation differential amount of the aircraft functional sub-skin at the current moment;

[0014] Establish the functional skin mathematical model of each aircraft functional sub-skin at the current moment, and based on the functional skin mathematical models of all aircraft functional sub-skins at the current moment, use the ZN (Ziegler-Nichols) tuning method to determine the initial PID control parameters of the adaptive PID controller at the current moment;

[0015] Optimize the initial PID control parameters of the adaptive PID controller at the current moment by using an iterative method with a set step size to determine the optimal PID control parameters of the adaptive PID controller at the current moment; the optimal PID control parameters are the control parameters corresponding to the minimum adjustment time of the output response of the adaptive PID controller;

[0016] Input the temperature deviation amounts of all aircraft functional sub-skins at the current moment into the optimal controller, and the optimal controller outputs the PID control output amount at the current moment, and the control parameters of the optimal controller are the optimal PID control parameters;

[0017] Determine the first fuzzy linguistic value of the input quantity of the fuzzy adaptive controller and the second fuzzy linguistic value of the output quantity of the fuzzy adaptive controller, and determine the first membership function of the input quantity of the fuzzy adaptive controller according to the first fuzzy linguistic value, and determine the second membership function of the output quantity of the fuzzy adaptive controller according to the second fuzzy linguistic value;

[0018] Take the temperature deviation amounts of all aircraft functional sub-skins at the current moment and the differential deviation amounts of all aircraft functional sub-skins at the current moment as the input quantities of the fuzzy adaptive controller, and the fuzzy adaptive controller outputs the fuzzy control output amount at the current moment according to the first membership function, the second membership function and the fuzzy control rule table; the fuzzy control rule table is determined according to the pre-stored fuzzy control rule library;

[0019] Adopt a composite control algorithm to combine the PID control output amount at the current moment and the fuzzy control output amount at the current moment to obtain the final control output amount;

[0020] Control the heating power of the aircraft functional skin at the current moment according to the final control output amount to achieve the control of the heating temperature.

[0021] An aircraft skin heating temperature control system, comprising:

[0022] The skin characteristic acquisition module is used to divide the aircraft functional skin into multiple aircraft functional sub - skins, and acquire the skin temperature of each aircraft functional sub - skin at the current moment; for any aircraft functional sub - skin, determine the temperature deviation amount of the aircraft functional sub - skin at the current moment according to the skin temperature and the target temperature at the current moment, and take the derivative of the temperature deviation amount at the current moment to obtain the deviation differential amount of the aircraft functional sub - skin at the current moment;

[0023] The ZN method base - setting module is used to establish the functional skin mathematical model of each aircraft functional sub - skin at the current moment, and based on the functional skin mathematical models of all aircraft functional sub - skins at the current moment, use the ZN tuning method to determine the initial PID control parameters of the adaptive PID controller at the current moment;

[0024] The iterative discrimination and optimization module is used to optimize the initial PID control parameters of the adaptive PID controller at the current moment by using an iterative method with a set step size, and determine the optimal PID control parameters of the adaptive PID controller at the current moment; the optimal PID control parameters are the control parameters corresponding to the minimum adjustment time of the output response of the adaptive PID controller;

[0025] The PID control output quantity determination module is used to input the temperature deviation amounts of all aircraft functional sub - skins at the current moment into the optimal controller, and the optimal controller outputs the PID control output quantity at the current moment, and the control parameters of the optimal controller are the optimal PID control parameters;

[0026] The variable fuzzification module is used to determine the first fuzzy language value of the input quantity of the fuzzy adaptive controller and the second fuzzy language value of the output quantity of the fuzzy adaptive controller, and determine the first membership function of the input quantity of the fuzzy adaptive controller according to the first fuzzy language value, and determine the second membership function of the output quantity of the fuzzy adaptive controller according to the second fuzzy language value;

[0027] The fuzzy control output quantity determination module is used to use the temperature deviation amounts of all aircraft functional sub - skins at the current moment and the deviation differential amounts of all aircraft functional sub - skins at the current moment as the input quantities of the fuzzy adaptive controller, and the fuzzy adaptive controller outputs the fuzzy control output quantity at the current moment according to the first membership function, the second membership function and the fuzzy control rule table; the fuzzy control rule table is determined according to the pre - stored fuzzy control rule base;

[0028] The final control output quantity determination module is used to use a composite control algorithm to combine the PID control output quantity at the current moment and the fuzzy control output quantity at the current moment to obtain the final control output quantity;

[0029] A heating temperature control module is used to control the heating power of the aircraft functional skin at the current moment according to the final control output quantity, so as to realize the control of the heating temperature.

[0030] An electronic device includes a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned aircraft skin heating temperature control method.

[0031] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned aircraft skin heating temperature control method.

[0032] According to the specific embodiments provided by the present invention, the following technical effects are disclosed by the present invention:

[0033] The present invention establishes a mathematical model of the functional skin, determines the initial PID control parameters of the adaptive PID controller at the current moment by using the ZN tuning method, optimizes the initial PID control parameters of the adaptive PID controller at the current moment by using an iterative method, determines the optimal PID control parameters of the adaptive PID controller at the current moment, and uses the adaptive PID controller injected with the optimal PID control parameters to determine the PID control output quantity according to the temperature deviation quantity, so as to automatically determine the optimal PID control parameters for each aircraft functional sub-skin, have the self-adjusting ability of working parameters, no longer require a manual debugging process, and improve the control accuracy of the heating temperature of the aircraft functional skin; this application uses the temperature deviation quantity of all aircraft functional sub-skins at the current moment and the deviation differential quantity of all aircraft functional sub-skins at the current moment as the input quantities of the fuzzy adaptive controller. The fuzzy adaptive controller outputs the fuzzy control output quantity at the current moment according to the first membership function, the second membership function and the fuzzy control rule table. The introduction of fuzzification realizes a set of general adaptive algorithms without manual participation, and uniformly adapts to different types and characteristics of aircraft functional skins, solving the technical problems of poor applicability range and low control accuracy of the temperature control method in the prior art due to the lack of self-adjusting ability of the working parameters of the temperature control method. Description of the Drawings

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0035] Figure 1 It is the structural diagram of the switch-type temperature controller provided in the background technology;

[0036] Figure 2 The structural diagram of the feedback temperature controller provided in the background art;

[0037] Figure 3 The flowchart of a method for controlling the temperature of an aircraft skin heating provided in the first embodiment of the present invention;

[0038] Figure 4 The heating and heat dissipation model diagram of the aircraft functional skin temperature control provided in the first embodiment of the present invention;

[0039] Figure 5 The theoretical unit step temperature response curve diagram of the aircraft functional skin provided in the first embodiment of the present invention;

[0040] Figure 6 The principle structural diagram of the intelligent temperature controller of a temperature control system provided in the first embodiment of the present invention;

[0041] Figure 7 The operation flowchart of a temperature control system provided in the first embodiment of the present invention;

[0042] Figure 8 The output control weight function diagram provided in the first embodiment of the present invention;

[0043] Figure 9 The steady-state temperature control curve of the functional skin with a power density of 0.0702 W / cm provided in the second embodiment of the present invention 2 ;

[0044] Figure 10 The steady-state temperature control curve of the functional skin with a power density of 0.5601 W / cm provided in the second embodiment of the present invention 2 ;

[0045] Figure 11 The interference response curve of the functional skin with a power density of 0.0702 W / cm provided in the third embodiment of the present invention 2 ;

[0046] Figure 12 The interference response curve of the functional skin with a power density of 0.5601 W / cm provided in the third embodiment of the present invention 2 ; Detailed implementation manners

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0048] The object of the present invention is to provide an aircraft skin heating temperature control method, system, device and medium. By automatically determining the optimal PID control parameters for each aircraft functional sub-skin, it has the ability to self-adjust working parameters, no longer requires manual debugging process, improves the control accuracy of the aircraft functional skin heating temperature, and introduces fuzzyization to implement a general adaptive algorithm without manual participation, and uniformly adaptively controls different types and characteristics of aircraft functional skins, solving the technical problems of poor applicability range and low control accuracy of the temperature control method caused by the lack of self-adjustment ability of working parameters in the prior art.

[0049] To make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] Embodiment 1

[0051] This embodiment provides a temperature control system for applying the aircraft skin heating temperature control method of the present invention, and its principle structure is as Figure 6 shown.

[0052] The overall structure of the temperature control system is a closed-loop feedback control structure, which consists of two main parts. One is based on an adaptive PID controller, and the other is a fuzzy adaptive controller. The two are organically combined by means of a parallel connection with the help of a composite control algorithm. The adaptive PID controller and the fuzzy adaptive controller constitute an intelligent temperature controller.

[0053] Figure 6 The content in the dotted box in Figure 6 is the process of determining the optimal PID control parameters,

[0054] and the other parts in

[0055] are to place the PID adaptive controller and the fuzzy adaptive controller in the closed-loop system to realize the temperature rise and fall of the aircraft functional sub-skin.

[0056] 2) Subtract the skin temperature y measured by the temperature sensor from the target temperature r to obtain the temperature deviation e.

[0057] 3) The temperature deviation e is used as the input of the adaptive PID controller. After being calculated by the adaptive PID controller, the PID control output u is obtained. PID .

[0058] 4) The temperature deviation e is differentiated to obtain the deviation differential ec. e and ec are used as the inputs of the fuzzy adaptive controller. After being calculated by the fuzzy adaptive controller, the fuzzy control output is obtained.

[0059] 5) The PID control output and the fuzzy control output are calculated by the composite control algorithm to obtain the final control output.

[0060] 6) The final control output directly controls the thermoelectric process of the aircraft functional sub-skin, thereby realizing the temperature rise and fall of the aircraft functional sub-skin.

[0061] 7) The temperature sensor collects the skin temperature y' of the aircraft functional sub-skin at the next moment and re-inputs it as the feedback quantity to the intelligent temperature controller.

[0062] As Figure 3 shown, the present invention provides a method for controlling the heating temperature of an aircraft skin, including:

[0063] Step S1: Divide the aircraft functional skin into multiple aircraft functional sub-skins, and obtain the skin temperature of each aircraft functional sub-skin at the current moment.

[0064] As Figure 7 shown, in practical applications, a temperature sensor is attached to the surface of each functional sub-skin to obtain the skin temperature at the current moment. The measured temperature is input to the intelligent temperature controller through a digital transmitter.

[0065] After obtaining the heating power of each aircraft functional skin at the current moment, the heating power is input to each power switch tube corresponding to each aircraft functional skin in the form of a PWM control signal. The PWM control signal can independently control the opening and closing of each power switch tube, thereby realizing the control of the heating temperature of each aircraft functional skin. Among them, the human-machine interface can intuitively monitor the state of the temperature control of the aircraft functional sub-skin. The human-machine interface communicates with the intelligent temperature controller through RS422. The control circuit power supply supplies power to the intelligent temperature controller, and the drive circuit power supply supplies power to the power switch tubes corresponding to each aircraft functional sub-skin.

[0066] Step S2: For any aircraft functional sub-skin, determine the temperature deviation of the aircraft functional sub-skin at the current moment according to the skin temperature and the target temperature at the current moment, and take the derivative of the temperature deviation at the current moment to obtain the deviation differential of the aircraft functional sub-skin at the current moment.

[0067] Step S3: Establish the functional skin mathematical model of each aircraft functional sub-skin at the current moment, and based on the functional skin mathematical models of all aircraft functional sub-skins at the current moment, use the ZN tuning method to determine the initial PID control parameters of the adaptive PID controller at the current moment.

[0068] Step S3 specifically includes:

[0069] Step S31: The adaptive PID controller applies a set power to each aircraft functional sub-skin at the current moment, collects the temperature rise curve of each aircraft functional sub-skin at the current moment, and determines the lag time and inertia time of each aircraft functional sub-skin according to the temperature rise curve.

[0070] Step S32: Establish the functional skin mathematical model of each aircraft functional sub-skin at the current moment according to the lag time and the inertia time. The functional skin mathematical model is a model including a first-order inertia link and a pure delay link.

[0071] Specifically, regarding the model of the first-order inertia link and the pure delay link:

[0072] The aircraft functional skin is a functional skin with a current thermal effect for aircraft anti-icing formed by adding electrothermal layers such as electrothermal coatings, metal wire heating films, carbon fiber heating films, graphene heating films, and conductive polymer heating films on the surface of the aircraft skin.

[0073] The aircraft functional skin can be regarded as a surface resistance. By applying a voltage across the electrothermal layer, an electric current can be formed inside the electrothermal layer. By applying a current with a certain power to the electrothermal layer of the aircraft functional skin, the electrothermal layer of the aircraft functional skin can be heated, thereby increasing the temperature of the aircraft functional skin; at the same time, under general flight conditions, the temperature of the functional skin is required to be higher than the ambient temperature. Therefore, there is heat exchange between the functional skin and the surrounding environment, causing the temperature of the functional skin to drop. Based on this, the characteristics of the functional skin can be analyzed.

[0074] As Figure 4 shown, the actual temperature T of the aircraft functional skin is determined by two input quantities, heating and heat dissipation. Among them, the heating input quantity is the output power P of the controller, and the heat dissipation input quantity is the external ambient temperature T n , until a thermal equilibrium is reached at a certain temperature T. According to the heat balance equation:

[0075] Q + -Q - = mc(T - Tn)

[0076] where m is the mass of the electrothermal layer of the skin, c is the specific heat capacity, Q + is the heating quantity, Q - is the heat dissipation quantity, and at the same time

[0077] Q + = ∫P + dt

[0078] Q - = ∫P - dt

[0079] P + is the heating power, and P - is the heat dissipation power. Therefore, we have

[0080] ∫(P + - P -)dt = mc(T - Tn)

[0081] It can be seen from the above formula that when the external load is constant, the temperature of the electrothermal layer of the aircraft functional skin increases with the increase of the heating power.

[0082] In general engineering situations, any temperature control object has time lag characteristics and time inertia characteristics, which can be quantitatively characterized by the lag time τ and the inertia time constant T respectively. Therefore, a mathematical model of the functional skin with a first-order inertia link plus a pure lag link can be established:

[0083]

[0084] G 0 (s) is the equivalent transfer function of the aircraft functional skin, s is the complex frequency. Under a unit step input, the curve of the aircraft functional skin temperature rising with time is as Figure 5 shown.

[0085] Step S33: Based on the mathematical model of the functional skin of all aircraft functional sub-skins at the current moment, use the ZN tuning method to determine the initial PID control parameters of the adaptive PID controller at the current moment.

[0086] Step S4: Optimize the initial PID control parameters of the adaptive PID controller at the current moment by using an iterative method with a set step size, and determine the optimal PID control parameters of the adaptive PID controller at the current moment; the optimal PID control parameters are the control parameters corresponding to the minimum adjustment time of the output response of the adaptive PID controller.

[0087] Step S4 specifically includes:

[0088] Step S41: For the Nth iteration, establish the PID control functional skin model of the Nth iteration according to the PID control parameters of the Nth iteration, and form the closed-loop feedback system of the Nth iteration according to the PID control functional skin model of the Nth iteration and the mathematical model of the functional skin; N > 1.

[0089] Step S42: Use a unit step signal as the input of the closed-loop feedback system for the Nth iteration, and determine the settling time and the iteration judgment parameter of the output response process of the closed-loop feedback system for the Nth iteration; the iteration judgment parameter includes the overshoot and the steady-state error.

[0090] In practical applications, the iteration judgment parameter is used to determine whether this iteration is effective. When the absolute value of the iteration judgment parameter is greater than the set value, this iteration is invalid, and the settling time of this iteration cannot be used as the optimal settling time, and the next iteration is entered; when the absolute value of the iteration judgment parameter is less than or equal to the set value, this iteration is effective.

[0091] Step S43: Subtract the optimal settling time of the Nth iteration from the settling time of the Nth iteration to obtain the settling time difference of the Nth iteration; the PID control parameters in the Nth iteration process are obtained by proportionally amplifying or reducing the PID control parameters in the (N - 1)th iteration process with a set step size.

[0092] Step S44: When the absolute value of the iteration judgment parameter of the Nth iteration is less than or equal to the set value and the absolute value of the settling time difference of the Nth iteration is less than or equal to the set value, use the PID control parameters of the Nth iteration as the optimal PID control parameters at the current moment.

[0093] Step S45: When the Nth iteration does not satisfy that the absolute value of the iteration judgment parameter is less than or equal to the set value and the absolute value of the settling time difference of the Nth iteration is less than or equal to the set value, amplify or reduce the PID control parameters of the Nth iteration, determine the updated PID control parameters as the PID control parameters of the (N + 1)th iteration, and determine the minimum settling time in the previous N iterations as the optimal settling time of the (N + 1)th iteration.

[0094] Step S5: Input the temperature deviation amounts of all the aircraft functional sub-skins at the current moment into the optimal controller, and the optimal controller outputs the PID control output amount at the current moment, and the control parameters of the optimal controller are the optimal PID control parameters.

[0095] In practical applications, the implementation process of the adaptive PID controller in this embodiment is as follows:

[0096] The adaptive PID controller in this embodiment is based on the conventional PID controller and is implemented by introducing two modules: "intelligent skin characteristic acquisition" and "intelligent control parameter tuning", as Figure 6 shown. "Intelligent skin characteristic acquisition" corresponds to skin characteristic acquisition, and "intelligent control parameter tuning" includes three parts: PID debugging and basing with the Z-N method, iterative discrimination and optimization, and injection of optimal control parameters.

[0097] 1) Through the above engineering analysis of the functional skin mathematical model, the functional skin mathematical model can be written as where T is the inertial time and τ is the lag time. The inertial time and the lag time are measured by an automatic test program, specifically including:

[0098] The adaptive PID controller enters the open-loop mode and applies a quantitative power to the functional skin. This power can be artificially set as needed. The temperature rise curve of the functional skin is collected. The program can directly read the model parameters T and τ within the temperature rise curve, and then obtain the functional skin mathematical model.

[0099] 2) Substitute the obtained functional skin mathematical model parameters into the Z-N tuning formula

[0100] K p = 1.2T / τ

[0101] T i = 2.2τ

[0102] T d = 0.5τ

[0103] It can be obtained that

[0104]

[0105] This is a debugging starting point for the control parameters in the PID controller. This process is called the Z-N method for PID debugging base setting, where K p is the proportional adjustment coefficient, T i is the integral adjustment coefficient, T d is the derivative adjustment coefficient, and G c (s) is the equivalent transfer function of the PID controller.

[0106] 3) Combine the mathematical models G c (s) and G 0 (s) obtained in 1) and 2) to form a closed-loop feedback system, and write a calculation program using the Python language.

[0107] ① Take the unit step signal as the input, set an appropriate sampling period, calculate the output response of the system using the Z-transform, and then the program automatically analyzes to obtain the steady-state error e ss , overshoot σ%, and adjustment time t s .

[0108] ② Proportionally amplify or reduce the PID control parameters K p , T i and T d in a certain step size or variable step size, and calculate again to obtain the new steady-state error e ss ’, overshoot σ%’, and adjustment time ts ’ Compare the new and old sets of data. On the premise that the steady-state error and overshoot meet the index requirements, select the set of PID control parameters with a shorter adjustment time as the retained parameters and discard the other set.

[0109] ③ Continuously repeat step ② until the program has completed all steps or the PID control parameters no longer change. At this time, the optimal PID control parameters of the measured functional skin are obtained. This process is called iterative discrimination optimization.

[0110] 4) Write the optimal PID control parameters into the control chip and save them. This process is called optimal control parameter injection. Input the temperature deviation of all aircraft functional sub-skins at the current moment into the optimal controller, and the optimal controller outputs the PID control output at the current moment. The PID control output is denoted as u pid 。

[0111] In summary, the main function of the adaptive PID controller is to automatically customize the most suitable PID control parameters for each functional skin through a computer program, eliminating the need for manual debugging processes, thus greatly reducing labor and time costs. At the same time, the optimal PID control parameters obtained by the program can also fully utilize the function of the PID control to eliminate the static error, providing excellent steady-state control accuracy for the system. In addition, the ZN tuning method also enables the system to have a certain ability to resist environmental changes and interference, thereby enhancing the environmental adaptability of the system.

[0112] Step S6: Determine the first fuzzy linguistic value of the input quantity of the fuzzy adaptive controller and the second fuzzy linguistic value of the output quantity of the fuzzy adaptive controller, and determine the first membership function of the input quantity of the fuzzy adaptive controller according to the first fuzzy linguistic value, and determine the second membership function of the output quantity of the fuzzy adaptive controller according to the second fuzzy linguistic value.

[0113] Step S7: Use the temperature deviation of all aircraft functional sub-skins at the current moment and the differential deviation of all aircraft functional sub-skins at the current moment as the input quantities of the fuzzy adaptive controller. The fuzzy adaptive controller outputs the fuzzy control output at the current moment according to the first membership function, the second membership function, and the fuzzy control rule table. The fuzzy control rule table is determined according to the pre-stored fuzzy control rule library.

[0114] In practical applications, it also includes: adjusting the quantization factor and proportional factor of the fuzzy adaptive controller.

[0115] Adjusting the quantization factor and proportional factor of the fuzzy adaptive controller specifically includes:

[0116] Determine the third fuzzy linguistic value of the input quantity of the fuzzy adaptive adjuster and the fourth fuzzy linguistic value of the output quantity of the fuzzy adaptive adjuster, and determine the third membership function of the input quantity of the fuzzy adaptive controller according to the third fuzzy linguistic value, and determine the fourth membership function of the output quantity of the fuzzy adaptive controller according to the fourth fuzzy linguistic value.

[0117] Take the temperature deviation of all aircraft functional sub-skins at the current moment and the differential deviation of all aircraft functional sub-skins at the current moment as the input quantity of the fuzzy adaptive adjuster. The fuzzy adaptive adjuster outputs the fuzzy factor adjustment coefficient at the current moment according to the third membership function, the fourth membership function and the fuzzy control rule table.

[0118] Adjust the quantization factor and the proportional factor in the fuzzy adaptive controller according to the fuzzy factor adjustment coefficient.

[0119] In practical applications, the implementation process of the fuzzy adaptive controller in this embodiment is as follows:

[0120] ① Select a fuzzy adaptive controller, whose input and output variables are e, ec, and u.

[0121] ② Determine the fuzzy linguistic values of each variable and the corresponding membership functions, that is, perform fuzzification:

[0122] a) Define the basic domain

[0123] E ∈ [-30, 30] °C, EC ∈ [-2, 2] °C / s, U ∈ [-4095, 4095].

[0124] Define the domains of E, EC, and U as {-6, -5, …, -1, 0, 1, …, 5, 6}, and determine the quantization factor K e and K ec , and the proportional factor K u .

[0125] K e = 6 / 30 = 0.2

[0126] K ec = 6 / 2 = 3

[0127] K u = 4095 / 6 = 682.5

[0128] b) Define fuzzy sets, select 9 fuzzy linguistic values, and the fuzzy sets of E, EC, and U are all {Negative Big (NB), Negative Middle (NM), Negative Small (NS), Negative Zero (NZ), Zero (Z), Positive Zero (PZ), Positive Small (PS), Positive Middle (PM), Positive Big (PB)}.

[0129] c) As shown in Table 1, Table 2, and Table 3, define the membership functions for the fuzzy sets.

[0130] Table 1 Membership Function Table of Fuzzy Adaptive Controller E

[0131]

[0132] Table 2 Membership Function Table of Fuzzy Adaptive Controller EC

[0133]

[0134]

[0135] Table 3 Membership Function Table of Fuzzy Adaptive Controller U

[0136]

[0137] ③ Establish a fuzzy control rule base. The control law of the fuzzy control rule base is usually composed of a set of fuzzy conditional statements in the if-then structure. Finally, summarize the fuzzy control rule table as shown in Table 4.

[0138] Table 4 Fuzzy Control Rule Table of Fuzzy Adaptive Controller

[0139]

[0140] ④ According to the membership functions of E, EC, and U and the fuzzy control rule table, use fuzzy mathematics to solve the fuzzy control query table. The fuzzy control query table is shown in Table 5. The abscissa in the table is the error change amount, calculated by EC = ec * K ec and the ordinate is the error amount, calculated by E = e * K e The fuzzy output U can be obtained by querying this table. Finally, determine the fuzzy control output quantity, and denote the fuzzy control output quantity as u Fuzzy and u Fuzzy = U * K u .

[0141] Table 5 Fuzzy control query table of the fuzzy adaptive controller

[0142]

[0143] The essence of the fuzzy adaptive adjustment mechanism is to establish a second-level fuzzy control structure on the basis of the fuzzy adaptive controller to dynamically adjust the quantization factor K in the fuzzy adaptive controller e 、K ec and the scale factor K u , and its detailed implementation process is as follows:

[0144] ① Select the input variables of the fuzzy adaptive adjustment mechanism as e and ec, and the output variable as m, where m is the fuzzy factor adjustment coefficient.

[0145] ② Determine the fuzzy linguistic values of each variable and the corresponding membership functions, that is, perform fuzzification:

[0146] a) Define the basic domain

[0147] E ∈ [-30, 30] °C, EC ∈ [-2, 2] °C / s, M ∈ [-4, 4].[[]END]]

[0148] Define the domains of E, EC, and M as {-4, -3, -2, -1, 0, 1, 2, 3, 4}.

[0149] b) Define fuzzy sets, select 7 fuzzy linguistic values, E and EC are {Negative Big (NB), Negative Middle (NM), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Middle (PM), Positive Big (PB)}, and M is {Contract High (CH), Contract Middle (CM), Contract Low (CL), OK (OK), Amplify Low (AL), Amplify Middle (AM), Amplify High (AH)}.

[0150] c) As shown in Table 6, Table 7, and Table 8, define the membership functions for the fuzzy sets.

[0151] Table 6 Membership function table of E for the fuzzy adaptive adjustment mechanism

[0152]

[0153] Table 7 Membership function table of EC for the fuzzy adaptive adjustment mechanism

[0154]

[0155] Table 8 Membership function table of the fuzzy adaptive adjustment machine M

[0156]

[0157] ③ Establish a fuzzy control rule base. The control law is usually composed of a set of fuzzy conditional statements in the if-then structure. Finally, the fuzzy control rule table of the fuzzy control adjustment machine is summarized as shown in Table 9.

[0158] Table 9 Fuzzy control rule table of the fuzzy control adjustment machine

[0159]

[0160] ④ According to the membership functions of E, EC, and M and the fuzzy control rule table, use fuzzy mathematics to solve the control query table of the fuzzy adaptive adjustment machine, as shown in Table 10. The abscissa in the table is the error change amount, calculated from EC = ec * K ec and the ordinate is the error amount, calculated from E = e * K e The output M of the fuzzy adjustment machine can be obtained by querying this table.

[0161] Table 10 Fuzzy control query table of the fuzzy adaptive adjustment machine

[0162]

[0163] Subsequently, adjust the quantization factor and proportional factor in the fuzzy adaptive controller according to the following formula, and the fuzzy adaptive controller can have perfect adaptive ability:

[0164] K e = K e * 2 M

[0165] K ec = K ec * 2 M

[0166] K u = K u / 2 M

[0167] The functions of the fuzzy adaptive controller include the following two points:

[0168] First, fuzzy control ensures the basic stability of temperature control, so that the temperature control system will not become unstable when facing complex and strong disturbances during flight, making the system have high reliability and excellent anti-interference ability.

[0169] Second, the introduction of the fuzzy adaptive adjustment mechanism realizes a set of general adaptive control algorithms that do not require manual participation, enabling unified adaptive control of functional skins with different types and characteristics. At the algorithm level, it ensures the universality of the control system and significantly expands the applicable range of the temperature control system.

[0170] Step S8: Adopt a composite control algorithm to combine the PID control output at the current moment and the fuzzy control output at the current moment to obtain the final control output.

[0171] In practical applications, the role of the composite control algorithm is to organically combine the adaptive PID controller and the fuzzy adaptive controller. Its implementation method is to use an output weight coefficient β to allocate the output strengths of the PID control output and the fuzzy control output respectively, and integrate them into the final control output, which is denoted as u. W-ave , and the specific formula is as follows:

[0172] u W-ave = βu PID + (1 - β)u Fuzzy

[0173] Among them, the output weight coefficient β changes continuously with the change of the temperature deviation e. As Figure 8 shown, the values of the shape control parameters a and b of the output weight coefficient function β(e) in the figure can be set as required.

[0174] Step S9: Control the heating power of the aircraft functional skin at the current moment according to the final control output to achieve the control of the heating temperature.

[0175] Embodiment 2

[0176] To verify that the present invention has excellent control capabilities for functional skins within a relatively wide power density range, two types of functional skins with power densities of 0.0702 W / cm 2 and 0.5601 W / cm 2 respectively are selected for temperature control experiments. The power densities of these two types of functional skins differ by nearly 8 times, and their characteristics are very different.

[0177] The ambient temperature is 17.5 °C. Now, the target temperature of the functional skin is set to 50 °C, the intelligent temperature controller is turned on, the upper computer collects data, and the temperature rise process of the functional skin is observed, as Figure 9 and Figure 10 shown.

[0178] First, observe the overall temperature control effect. Although the characteristics of the two functional skins are quite different, their overall temperature control curves are very similar. The final stable temperatures of the functional skins are both maintained around 50°C, indicating that the method proposed in the present invention can use a set of general control algorithms to achieve good control ability for functional skins with very different power densities.

[0179] Secondly, observe the temperature rise adjustment process. For the functional skin with a power density of 0.0702 W / cm 2 the adjustment time t s = 68 s, and the overshoot σ% = 1.3%; for the functional skin with a power density of 0.5601 W / cm 2 the adjustment time t s = 62 s, and the overshoot σ% = 5.2%. It can be seen that the temperature rise process of the functional skin performs excellently, with a short adjustment time, a small overshoot, and good dynamic characteristics.

[0180] Finally, observe the steady-state process. From Figure 9 and Figure 10 the steady-state magnification close-up of the right half part, it can be obtained that for the functional skin with a power density of 0.0702 W / cm 2 the steady-state control accuracy is within the range of ±0.5°C, and for the functional skin with a power density of 0.5601 W / cm 2 the steady-state control accuracy is within the range of ±0.8°C. The temperature control accuracy is excellent, and the steady-state characteristics are good.

[0181] Example 3

[0182] Similarly, select the two functional skins in Example 2, turn on the intelligent temperature controller for the aircraft functional skin, and apply a strong cold air flow after the control enters the steady state to observe the response characteristics of the control system under strong interference to test the interference resistance adjustment ability of the system.

[0183] The steady-state interference responses of the two functional skins are as Figure 11 and Figure 12 shown. After applying the strong cold air flow interference, the temperature of the functional skin shows a rapid downward trend, but the fuzzy control in the control system responds quickly and suppresses the continued decline of the temperature after about 20 s. Subsequently, the temperature rises rapidly, showing a small overshoot. At this time, the role of the fuzzy control weakens, and the effect of the PID control strengthens, and the temperature of the functional skin re-enters the steady state. The entire interference adjustment process only experiences one adjustment cycle and takes about 55 s, which is a relatively rapid adjustment process for the functional skin with characteristics of large inertia and large lag. Therefore, from the data, it can be seen that the present invention has excellent resistance adjustment ability to complex and strong environmental interference.

[0184] Example 4

[0185] To implement the method corresponding to the first embodiment above to achieve the corresponding functions and technical effects, the following provides an aircraft skin heating temperature control system, including:

[0186] A skin characteristic acquisition module, which is used to divide the aircraft functional skin into multiple aircraft functional sub-skins, and acquire the skin temperature of each aircraft functional sub-skin at the current moment; for any aircraft functional sub-skin, determine the temperature deviation amount of the aircraft functional sub-skin at the current moment according to the skin temperature and the target temperature at the current moment, and take the derivative of the temperature deviation amount at the current moment to obtain the deviation differential amount of the aircraft functional sub-skin at the current moment.

[0187] A ZN method base determination module, which is used to establish a functional skin mathematical model of each aircraft functional sub-skin at the current moment, and based on the functional skin mathematical models of all aircraft functional sub-skins at the current moment, use the ZN tuning method to determine the initial PID control parameters of the adaptive PID controller at the current moment.

[0188] In practical applications, the ZN method debugging base module specifically includes:

[0189] A model creation unit, the adaptive PID controller applies a set power to each aircraft functional sub-skin, and collects the temperature rise curves of each aircraft functional sub-skin, and determines the lag time and inertia time of each aircraft functional sub-skin according to the temperature rise curves; establish a functional skin mathematical model of each aircraft functional sub-skin according to the lag time and the inertia time, and the functional skin mathematical model is a model including a first-order inertia link and a pure lag link.

[0190] A debugging base unit, based on the functional skin mathematical models of all aircraft functional sub-skins at the current moment, uses the ZN tuning method to determine the initial PID control parameters of the adaptive PID controller at the current moment.

[0191] An iterative discrimination optimization module, which is used to optimize the initial PID control parameters of the adaptive PID controller at the current moment by using an iterative method with a set step size, and determine the optimal PID control parameters of the adaptive PID controller at the current moment; the optimal PID control parameters are the control parameters corresponding to the minimum adjustment time of the output response of the adaptive PID controller.

[0192] In practical applications, the iterative discrimination optimization module specifically includes:

[0193] A system construction unit, for the Nth iteration, establish a PID control functional skin model of the Nth iteration according to the PID control parameters of the Nth iteration, and form a closed-loop feedback system of the Nth iteration according to the PID control functional skin model of the Nth iteration and the functional skin mathematical model; N>1.

[0194] The system parameter determination unit uses a unit step signal as the input of the closed-loop feedback system for the Nth iteration, determines the settling time and the iteration judgment parameter of the output response process of the closed-loop feedback system for the Nth iteration; subtracts the optimal settling time of the Nth iteration from the settling time of the Nth iteration to obtain the settling time difference of the Nth iteration; the PID control parameters in the Nth iteration process are obtained by proportionally amplifying or reducing the PID control parameters in the (N - 1)th iteration process by a set step size; the iteration judgment parameter includes the overshoot and the steady-state error.

[0195] The iteration discrimination unit, when the absolute value of the iteration judgment parameter of the Nth iteration is less than or equal to the set value and the absolute value of the settling time difference of the Nth iteration is less than or equal to the set value, uses the PID control parameters of the Nth iteration as the optimal PID control parameters at the current moment; when the Nth iteration does not satisfy that the absolute value of the iteration judgment parameter is less than or equal to the set value and the absolute value of the settling time difference of the Nth iteration is less than or equal to the set value, amplifies or reduces the PID control parameters of the Nth iteration, determines the updated PID control parameters as the PID control parameters of the (N + 1)th iteration, and determines the minimum settling time in the previous N iterations as the optimal settling time of the (N + 1)th iteration.

[0196] The PID control output quantity determination module is used to input the temperature deviation quantity of all aircraft functional sub-skins at the current moment into the optimal controller, and the optimal controller outputs the PID control output quantity at the current moment, and the control parameters of the optimal controller are the optimal PID control parameters.

[0197] The variable fuzzification module is used to determine the first fuzzy linguistic value of the input quantity of the fuzzy adaptive controller and the second fuzzy linguistic value of the output quantity of the fuzzy adaptive controller, and determine the first membership function of the input quantity of the fuzzy adaptive controller according to the first fuzzy linguistic value, and determine the second membership function of the output quantity of the fuzzy adaptive controller according to the second fuzzy linguistic value.

[0198] The fuzzy control output quantity determination module is used to use the temperature deviation quantity of all aircraft functional sub-skins at the current moment and the deviation differential quantity of all aircraft functional sub-skins at the current moment as the input quantity of the fuzzy adaptive controller, and the fuzzy adaptive controller outputs the fuzzy control output quantity at the current moment according to the first membership function, the second membership function and the fuzzy control rule table; the fuzzy control rule table is determined according to the pre-stored fuzzy control rule base.

[0199] In practical applications, it also includes a fuzzy factor adjustment module for adjusting the quantization factor and the proportional factor of the fuzzy adaptive controller.

[0200] The fuzzy factor adjustment module specifically includes:

[0201] A double defuzzification unit is used to determine the third fuzzy linguistic value of the input quantity of the fuzzy adaptive adjuster and the fourth fuzzy linguistic value of the output quantity of the fuzzy adaptive adjuster, and determine the third membership function of the input quantity of the fuzzy adaptive controller according to the third fuzzy linguistic value, and determine the fourth membership function of the output quantity of the fuzzy adaptive controller according to the fourth fuzzy linguistic value.

[0202] A fuzzy factor adjustment coefficient determination unit is used to take the temperature deviation of all aircraft functional sub-skins at the current moment and the differential deviation of all aircraft functional sub-skins at the current moment as the input quantity of the fuzzy adaptive adjuster. The fuzzy adaptive adjuster outputs the fuzzy factor adjustment coefficient at the current moment according to the third membership function, the fourth membership function and the fuzzy control rule table.

[0203] A fuzzy factor adjustment unit is used to adjust the quantization factor and the proportional factor in the fuzzy adaptive controller according to the fuzzy factor adjustment coefficient.

[0204] A final control output quantity determination module is used to adopt a composite control algorithm to composite the PID control output quantity at the current moment and the fuzzy control output quantity at the current moment to obtain the final control output quantity.

[0205] A heating temperature control module is used to control the heating power of the aircraft functional skin at the current moment according to the final control output quantity to achieve the control of the heating temperature.

[0206] In practical applications, it also includes a power supply module. The power supply module provides common airborne DC power supply voltages such as 28V, 110V and 270V and various customized AC and DC voltages for the temperature control system as the energy source of the temperature control system.

[0207] In practical applications, it also includes a human-computer interaction module. The operator can use the human-computer interaction interface to intuitively monitor the working state of the functional skin temperature control system and issue instructions to the control system. RS422 communication is used between the human-computer interaction interface and the intelligent temperature controller of the aircraft functional skin.

[0208] Embodiment 5

[0209] This embodiment provides an electronic device, including a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute a method for controlling the heating temperature of an aircraft skin in Embodiment 1.

[0210] Optionally, the above electronic device may be a server.

[0211] In addition, an embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements a method for controlling the heating temperature of an aircraft skin according to Embodiment 1.

[0212] The various embodiments in this specification are described in a progressive manner. The key point of each embodiment is the difference from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and reference can be made to the description in the method part for related parts.

[0213] Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation on the present invention.

Claims

1. A method for controlling the heating temperature of an aircraft skin, characterized in that, it includes: Dividing the aircraft functional skin into multiple aircraft functional sub - skins, and obtaining the skin temperature of each aircraft functional sub - skin at the current moment; For any aircraft functional sub - skin, determining the temperature deviation amount of the aircraft functional sub - skin at the current moment according to the skin temperature and the target temperature at the current moment, and taking the derivative of the temperature deviation amount at the current moment to obtain the deviation differential amount of the aircraft functional sub - skin at the current moment; Establishing the functional skin mathematical model of each aircraft functional sub - skin at the current moment, and based on the functional skin mathematical models of all aircraft functional sub - skins at the current moment, using the ZN tuning method to determine the initial PID control parameters of the adaptive PID controller at the current moment; Taking a set step size, using an iterative method to optimize the initial PID control parameters of the adaptive PID controller at the current moment, and determining the optimal PID control parameters of the adaptive PID controller at the current moment; the optimal PID control parameters are the control parameters corresponding to the minimum adjustment time of the output response of the adaptive PID controller; Inputting the temperature deviation amounts of all aircraft functional sub - skins at the current moment into the optimal controller, the optimal controller outputs the PID control output amount at the current moment, and the control parameters of the optimal controller are the optimal PID control parameters; Determining the first fuzzy linguistic value of the input quantity of the fuzzy adaptive controller and the second fuzzy linguistic value of the output quantity of the fuzzy adaptive controller, and determining the first membership function of the input quantity of the fuzzy adaptive controller according to the first fuzzy linguistic value, and determining the second membership function of the output quantity of the fuzzy adaptive controller according to the second fuzzy linguistic value; Taking the temperature deviation amounts of all aircraft functional sub - skins at the current moment and the deviation differential amounts of all aircraft functional sub - skins at the current moment as the input quantities of the fuzzy adaptive controller, the fuzzy adaptive controller outputs the fuzzy control output amount at the current moment according to the first membership function, the second membership function and the fuzzy control rule table; the fuzzy control rule table is determined according to the pre - stored fuzzy control rule library; Using a composite control algorithm to combine the PID control output amount at the current moment and the fuzzy control output amount at the current moment to obtain the final control output amount; Controlling the heating power of the aircraft functional skin at the current moment according to the final control output amount to achieve the control of the heating temperature.

2. The method for controlling the heating temperature of an aircraft skin according to claim 1, characterized in that, Establishing the functional skin mathematical model of each aircraft functional sub - skin at the current moment, and based on the functional skin mathematical models of all aircraft functional sub - skins at the current moment, using the ZN tuning method to determine the initial PID control parameters of the adaptive PID controller at the current moment, specifically including: The adaptive PID controller applies a set power to each of the aircraft functional sub-skins at the current moment, collects the temperature rise curves of each of the aircraft functional sub-skins at the current moment, and determines the lag time and inertia time of each of the aircraft functional sub-skins according to the temperature rise curves; Establish a functional skin mathematical model of each of the aircraft functional sub-skins at the current moment according to the lag time and the inertia time, and the functional skin mathematical model is a model including a first-order inertia link and a pure lag link; Based on the functional skin mathematical models of all the aircraft functional sub-skins at the current moment, use the ZN tuning method to determine the initial PID control parameters of the adaptive PID controller at the current moment.

3. A method for controlling the heating temperature of an aircraft skin according to claim 2, Characterized in that, Optimize the initial PID control parameters of the adaptive PID controller at the current moment by using an iterative method with a set step size, and determine the optimal PID control parameters of the adaptive PID controller at the current moment, specifically including: For the Nth iteration, establish a PID control functional skin model for the Nth iteration according to the PID control parameters of the Nth iteration, and form a closed-loop feedback system for the Nth iteration according to the PID control functional skin model for the Nth iteration and the functional skin mathematical model; N>1; Use a unit step signal as the input of the closed-loop feedback system for the Nth iteration, and determine the adjustment time and iteration judgment parameters of the output response process of the closed-loop feedback system for the Nth iteration; the iteration judgment parameters include overshoot and steady-state error; Subtract the optimal adjustment time of the Nth iteration from the adjustment time of the Nth iteration to obtain the adjustment time difference of the Nth iteration; the PID control parameters in the Nth iteration process are obtained by proportionally amplifying or reducing the PID control parameters in the (N - 1)th iteration process with a set step size; When the absolute value of the iteration judgment parameter of the Nth iteration is less than or equal to the set value and the absolute value of the adjustment time difference of the Nth iteration is less than or equal to the set value, use the PID control parameters of the Nth iteration as the optimal PID control parameters at the current moment; When the Nth iteration does not satisfy that the absolute value of the iteration judgment parameter is less than or equal to the set value and the absolute value of the adjustment time difference of the Nth iteration is less than or equal to the set value, amplify or reduce the PID control parameters of the Nth iteration, determine the updated PID control parameters as the PID control parameters of the (N + 1)th iteration, and determine the minimum adjustment time in the previous N iterations as the optimal adjustment time of the (N + 1)th iteration.

4. A method for controlling the heating temperature of an aircraft skin according to claim 1, Characterized in that, Further comprising: Adjust the quantization factor and the proportional factor of the fuzzy adaptive controller; The adjustment of the quantization factor and the proportional factor of the fuzzy adaptive controller specifically includes: Determine the third fuzzy linguistic value of the input quantity of the fuzzy adaptive adjuster and the fourth fuzzy linguistic value of the output quantity of the fuzzy adaptive adjuster, and determine the third membership function of the input quantity of the fuzzy adaptive controller according to the third fuzzy linguistic value, and determine the fourth membership function of the output quantity of the fuzzy adaptive controller according to the fourth fuzzy linguistic value; Take the temperature deviation of all aircraft functional sub-skins at the current moment and the differential deviation of all aircraft functional sub-skins at the current moment as the input quantity of the fuzzy adaptive adjuster. The fuzzy adaptive adjuster outputs the fuzzy factor adjustment coefficient at the current moment according to the third membership function, the fourth membership function and the fuzzy control rule table; Adjust the quantization factor and the proportional factor in the fuzzy adaptive controller according to the fuzzy factor adjustment coefficient.

5. An aircraft skin heating temperature control system, Characterized in that, Comprising: A skin characteristic acquisition module, configured to divide the aircraft functional skin into multiple aircraft functional sub-skins, and acquire the skin temperature of each aircraft functional sub-skin at the current moment; for any aircraft functional sub-skin, determine the temperature deviation of the aircraft functional sub-skin at the current moment according to the skin temperature and the target temperature at the current moment, and take the derivative of the temperature deviation at the current moment to obtain the differential deviation of the aircraft functional sub-skin at the current moment; A ZN method based determination module, configured to establish a functional skin mathematical model of each aircraft functional sub-skin at the current moment, and based on the functional skin mathematical models of all aircraft functional sub-skins at the current moment, use the ZN tuning method to determine the initial PID control parameters of the adaptive PID controller at the current moment; An iterative discrimination and optimization module, configured to optimize the initial PID control parameters of the adaptive PID controller at the current moment by using an iterative method with a set step size, and determine the optimal PID control parameters of the adaptive PID controller at the current moment; the optimal PID control parameters are the control parameters corresponding to the minimum adjustment time of the output response of the adaptive PID controller; A PID control output quantity determination module, configured to input the temperature deviation of all aircraft functional sub-skins at the current moment into the optimal controller, and the optimal controller outputs the PID control output quantity at the current moment, and the control parameters of the optimal controller are the optimal PID control parameters; A variable fuzzification module, configured to determine the first fuzzy linguistic value of the input quantity of the fuzzy adaptive controller and the second fuzzy linguistic value of the output quantity of the fuzzy adaptive controller, and determine the first membership function of the input quantity of the fuzzy adaptive controller according to the first fuzzy linguistic value, and determine the second membership function of the output quantity of the fuzzy adaptive controller according to the second fuzzy linguistic value; The fuzzy control output determination module is used to take the temperature deviation of all aircraft functional sub-skins at the current moment and the deviation differential of all aircraft functional sub-skins at the current moment as the input of the fuzzy adaptive controller. The fuzzy adaptive controller outputs the fuzzy control output at the current moment according to the first membership function, the second membership function, and the fuzzy control rule table. The fuzzy control rule table is determined according to the pre-stored fuzzy control rule base. The final control output determination module is used to adopt a composite control algorithm to combine the PID control output at the current moment and the fuzzy control output at the current moment to obtain the final control output. The heating temperature control module is used to control the heating power of the aircraft functional skin at the current moment according to the final control output to achieve the control of the heating temperature.

6. The aircraft skin heating temperature control system according to claim 5, characterized in that the ZN method debugging and baselining module specifically includes: The model creation unit. At the current moment, the adaptive PID controller applies a set power to each aircraft functional sub-skin, and collects the temperature rise curves of each aircraft functional sub-skin at the current moment. According to the temperature rise curves, the lag time and the inertia time of each aircraft functional sub-skin are determined. According to the lag time and the inertia time, the functional skin mathematical model of each aircraft functional sub-skin at the current moment is established. The functional skin mathematical model is a model including a first-order inertia link and a pure lag link. The debugging and baselining unit, based on the functional skin mathematical models of all aircraft functional sub-skins at the current moment, uses the ZN tuning method to determine the initial PID control parameters of the adaptive PID controller at the current moment.

7. The aircraft skin heating temperature control system according to claim 6, characterized in that the iterative discrimination and optimization module specifically includes: The system construction unit. For the Nth iteration, the PID control functional skin model of the Nth iteration is established according to the PID control parameters of the Nth iteration, and the closed-loop feedback system of the Nth iteration is constituted according to the PID control functional skin model of the Nth iteration and the functional skin mathematical model; N>1. The system parameter determination unit takes the unit step signal as the input of the closed-loop feedback system of the Nth iteration, and determines the adjustment time and the iterative judgment parameters of the output response process of the closed-loop feedback system of the Nth iteration. Subtract the optimal adjustment time of the Nth iteration from the adjustment time of the Nth iteration to obtain the adjustment time difference of the Nth iteration. The PID control parameters in the Nth iteration process are obtained by proportionally amplifying or reducing the PID control parameters in the (N - 1)th iteration process by a set step size. The iterative judgment parameters include the overshoot and the steady-state error. An iterative discrimination unit, when the absolute value of the iterative judgment parameter of the Nth iteration is less than or equal to the set value and the absolute value of the adjustment time difference of the Nth iteration is less than or equal to the set value, uses the PID control parameter of the Nth iteration as the optimal PID control parameter at the current moment; when the Nth iteration does not satisfy that the absolute value of the iterative judgment parameter is less than or equal to the set value and the absolute value of the adjustment time difference of the Nth iteration is less than or equal to the set value, magnifies or shrinks the PID control parameter of the Nth iteration, determines the updated PID control parameter as the PID control parameter of the (N + 1)th iteration, and determines the minimum adjustment time among the previous N iterations as the optimal adjustment time of the (N + 1)th iteration.

8. A temperature control system for aircraft skin heating according to claim 5, characterized in that, it further includes a fuzzy factor adjustment module for adjusting the quantization factor and the proportional factor of the fuzzy adaptive controller; The fuzzy factor adjustment module specifically includes: A double fuzzyfication unit for determining the third fuzzy linguistic value of the input quantity of the fuzzy adaptive adjustment machine and the fourth fuzzy linguistic value of the output quantity of the fuzzy adaptive adjustment machine, and determining the third membership function of the input quantity of the fuzzy adaptive controller according to the third fuzzy linguistic value, and determining the fourth membership function of the output quantity of the fuzzy adaptive controller according to the fourth fuzzy linguistic value; A fuzzy factor adjustment coefficient determination unit for using the temperature deviation amount of all aircraft functional sub-skins at the current moment and the deviation differential amount of all aircraft functional sub-skins at the current moment as the input quantity of the fuzzy adaptive adjustment machine, and the fuzzy adaptive adjustment machine outputs the fuzzy factor adjustment coefficient at the current moment according to the third membership function, the fourth membership function and the fuzzy control rule table; A fuzzy factor adjustment unit for adjusting the quantization factor and the proportional factor in the fuzzy adaptive controller according to the fuzzy factor adjustment coefficient.

9. An electronic device, characterized in that, it includes a memory and a processor, the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic device to execute the aircraft skin heating temperature control method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that, it stores a computer program, and when the computer program is executed by a processor, it implements the aircraft skin heating temperature control method according to any one of claims 1 to 4.

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