Airplane automatic landing simulation method and device based on fuzzy theory

Abstract

The invention belongs to the field of flight control, and particularly relates to an aircraft automatic landing simulation method and device based on a fuzzy theory, and the method comprises the steps: S1, obtaining simulation input parameters at a current moment; s2, inputting the simulation input parameters into a pre-constructed glide rule base and a pre-constructed approach rule base to obtain a sinking rate instruction; s3, calculating a subsidence rate deviation according to the subsidence rate instruction and the subsidence rate; s4, inputting the subsidence rate deviation into a pre-constructed pilot rule base to obtain an elevator instruction; s5, determining an accelerator instruction based on a pre-constructed accelerator rule base; and S6, inputting the elevator instruction and the accelerator instruction into an aircraft landing simulation system to obtain simulation input parameters at the next moment. The anti-interference performance of the aircraft is improved, and the landing safety of the aircraft is improved.

Description

Technical Field

[0001] This application belongs to the field of flight control, and particularly relates to an aircraft automatic landing simulation method and device based on fuzzy theory. Background Art

[0002] When an aircraft lands on a moving platform, it is subject to various interferences mainly from the wake wind field of the moving platform. These interferences have a great impact on the landing accuracy of the aircraft, making it extremely difficult to achieve precise landing. The development of the automatic landing system has improved the success rate of aircraft landing in harsh environments, greatly reduced the operation pressure on pilots, and improved flight quality.

[0003] The core of the automatic landing system is the longitudinal guidance law, which is essentially a PID controller. However, during the glide of the aircraft, it will pass through the wake wind field of the moving platform, interfering with the glide motion state of the aircraft. The wake wind field of the moving platform has the characteristics of high intensity, strong nonlinearity, and strong coupling, and its influence cannot be eliminated through simple compensation. The PID controller with fixed parameters lacks flexibility and accuracy when facing the wake wind field of the moving platform, which will have a certain impact on the landing error and landing satisfaction. Summary of the Invention

[0004] This application provides an aircraft automatic landing simulation method and device based on fuzzy theory, introducing a fuzzy controller into the automatic landing system to improve the problem of lack of flexibility in control caused by fixed control parameters of the traditional PID controller.

[0005] The first aspect of this application provides an aircraft automatic landing simulation method based on fuzzy theory, mainly including: Step S1, obtaining the simulation input parameters at the current moment, where the simulation input parameters include angle of attack, altitude, sink rate, normal overload, distance from the landing point, and moving platform motion parameters; Step S2, inputting the simulation input parameters into a pre-constructed glide rule library and approach rule library to obtain a sink rate command; Step S3, calculating the sink rate deviation based on the sink rate command and the sink rate; Step S4, inputting the sink rate deviation into a pre-constructed autopilot rule library to obtain an elevator command; Step S5, determining the throttle command based on a pre-constructed throttle rule library; Step S6, inputting the elevator command and the throttle command into the aircraft landing simulation system to obtain the simulation input parameters at the next moment; Among them, the glide rule library, approach rule library, autopilot rule library, and throttle rule library are all fuzzy rule libraries.

[0006] Preferably, step S2 further includes: Step S21: Input the simulation input parameters into a pre - constructed glide rule base to calculate the first sink rate command; Step S22: Input the simulation input parameters into a pre - constructed approach rule base to calculate the second sink rate command; Step S23: Superimpose the first sink rate command and the second sink rate command to form the sink rate command.

[0007] Preferably, step S21 further includes: Step S211: Calculate the vertical error between the aircraft and the landing point based on the altitude, the distance to the landing point, and the motion parameters of the mobile platform; Step S212: Calculate the first sink rate command according to the vertical error using different glide rule bases.

[0008] Preferably, step S22 further includes: Calculate the second sink rate command according to the angle of attack and the distance to the landing point using different approach rule bases.

[0009] Preferably, step S6 further includes: Step S61: Determine the longitudinal disturbance quantity and the vertical disturbance quantity according to the wind field disturbance model of the mobile platform; Step S62: Update the airspeed and the angle of attack of the aircraft according to the longitudinal disturbance quantity and the vertical disturbance quantity; Step S63: Input the updated airspeed and angle of attack into the aircraft landing simulation system to obtain the simulation input parameters at the next moment.

[0010] The second aspect of the present application provides an aircraft automatic landing simulation device based on fuzzy theory, mainly including: A simulation input parameter acquisition module, configured to acquire the simulation input parameters at the current moment, where the simulation input parameters include the angle of attack, altitude, sink rate, normal overload, the distance to the landing point, and the motion parameters of the mobile platform; A sink rate command calculation module, configured to input the simulation input parameters into a pre - constructed glide rule base and approach rule base to obtain the sink rate command; A sink rate deviation calculation module, configured to calculate the sink rate deviation according to the sink rate command and the sink rate; An elevator command calculation module, configured to input the sink rate deviation into a pre - constructed autopilot rule base to obtain the elevator command; A throttle command calculation module, configured to determine the throttle command based on a pre - constructed throttle rule base; A simulation input parameter update module, configured to input the elevator command and the throttle command into the aircraft landing simulation system to obtain the simulation input parameters at the next moment; Among them, the descent rule library, the approach rule library, the autopilot rule library, and the throttle rule library are all fuzzy rule libraries.

[0011] Preferably, the sink rate command calculation module includes: A first sink rate command calculation unit for inputting the simulation input parameters into a pre-constructed descent rule library to calculate a first sink rate command; A second sink rate command calculation unit for inputting the simulation input parameters into a pre-constructed approach rule library to calculate a second sink rate command; A sink rate command calculation unit for superimposing the first sink rate command and the second sink rate command to form the sink rate command.

[0012] Preferably, the first sink rate command calculation unit includes: A vertical error calculation subunit for calculating the vertical error between the aircraft and the landing point based on the altitude, the distance from the landing point, and the motion parameters of the mobile platform; A descent rule library classification and call subunit for calculating the first sink rate command using different descent rule libraries according to the vertical error.

[0013] Preferably, the second sink rate command calculation unit includes: An approach rule library classification and call subunit for calculating the second sink rate command using different approach rule libraries according to the angle of attack and the distance from the landing point.

[0014] Preferably, the simulation input parameter update module includes: A disturbance amount calculation unit for determining the longitudinal disturbance amount and the vertical disturbance amount according to the wind field disturbance model of the mobile platform; An airspeed and angle of attack update unit for updating the airspeed and angle of attack of the aircraft according to the longitudinal disturbance amount and the vertical disturbance amount; A simulation parameter replacement unit for inputting the updated airspeed and angle of attack into the aircraft landing simulation system to obtain the simulation input parameters for the next moment.

[0015] This application improves the anti-interference performance of the aircraft and improves the safety of aircraft landing. Description of the Drawings

[0016] Figure 1 is a schematic diagram of the simulation process of a preferred embodiment of the aircraft automatic landing simulation method based on fuzzy theory in this application.

[0017] Figure 2 is a schematic diagram of the composition of the membership function of the input parameter of the distance from the landing point.

[0018] Figure 3 It is a schematic diagram composed of member functions of the input parameter of the sinking rate.

[0019] Figure 4 It is a schematic diagram of the force state of the aircraft.

[0020] Figure 5 It is a schematic diagram comparing the effects of FACLS and ACLS. Specific implementation manners

[0021] To make the purpose, technical solutions and advantages of the implementation of this application clearer, the technical solutions in the implementation manners of this application will be described in more detail below with reference to the accompanying drawings in the implementation manners of this application. In the accompanying drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described implementation manners are some but not all of the implementation manners of this application. The implementation manners described below by referring to the accompanying drawings are exemplary and are intended to explain this application and should not be construed as a limitation of this application. Based on the implementation manners in this application, all other implementation manners obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application. The implementation manners of this application will be described in detail below with reference to the accompanying drawings.

[0022] The first aspect of this application provides an aircraft automatic landing simulation method based on fuzzy theory, as Figure 1 shown, mainly including: Step S1, obtain the simulation input parameters at the current moment, where the simulation input parameters include angle of attack, altitude, sinking rate, normal overload, distance from the landing point, and movement parameters of the mobile platform; Step S2, input the simulation input parameters into the pre-constructed glide rule library and approach rule library to obtain a sinking rate command; Step S3, calculate the sinking rate deviation according to the sinking rate command and the sinking rate; Step S4, input the sinking rate deviation into the pre-constructed autopilot rule library to obtain an elevator command; Step S5, determine the throttle command based on the pre-constructed throttle rule library; Step S6, input the elevator command and the throttle command into the aircraft landing simulation system to obtain the simulation input parameters at the next moment; Among them, the glide rule library, approach rule library, autopilot rule library, and throttle rule library are all fuzzy rule libraries.

[0023] The aircraft automatic landing simulation method based on fuzzy theory in this application mainly has 4 independent fuzzy rule libraries: a glide rule library, an approach rule library, an autopilot rule library, and a throttle rule library. Refer to Figure 1, the descent rule base dominates the guiding part of the PID. The approach rule base mainly deals with the influence of the wake wind field disturbance of the mobile platform and the movement of the ideal landing point. All rules are encoded using the Max derivative criterion and the defuzzification method of the centroid. The logical "AND" and logical "OR" use the conventional minimum or maximum criterion. The elevator rule base and the throttle rule base directly output the change amount of the elevator angle and the command of the throttle, aiming to complete the command of responding to the descent rate command of the descent rule base and maintaining the standard angle of attack. In addition to using some non-linear strategies to improve the dynamic performance of the system, the functions of the elevator rule base and the throttle rule base are very similar to the autopilot and approach power compensation in the existing aircraft automatic landing system. In addition Figure 1 The gain K in is used for decoupling compensation.

[0024] This application designs 6 inputs and 2 outputs, and determines the membership functions of the inputs and outputs through the method of fuzzy statistical experiments. The input space of the distance from the landing point is divided into 4 trapezoidal or semi-trapezoidal membership functions, as Figure 2 shown. From right to left, they are the initial state (abbreviation: AS), in the middle (abbreviation: IM), approaching the landing point (abbreviation: IC), at the landing point (abbreviation: AR); in addition, the membership functions of the other 5 inputs are all designed to include 7 membership functions, and these membership functions are symmetric about the vertical axis, as Figure 3 shown in the membership function of the descent rate. Among them, the membership functions of the descent rate and the normal overload are triangular functions, and the membership functions of the angle of attack and the altitude have trapezoidal membership functions near zero and two half-trapezoidal functions representing "positive large" and "negative large", and the rest are triangular membership functions. The 7 membership functions of the above simulation input parameters are respectively divided into seven continuous fuzzy sets on their domains of discourse, and they are: NL (negative large), NM (negative medium), NS (negative small), NZ (zero), PS (positive small), PM (positive medium), PL (positive large).

[0025] The two outputs are respectively the change amount of the elevator angle and the change amount of the throttle. All outputs are classified into 11 triangular relation functions, which are symmetric about the vertical axis. These 11 continuous fuzzy sets are: NL (negative large), NM (negative medium), NML (negative medium large), NS (negative small), NMS (negative medium small), NZ (zero), PMS (positive medium small), PS (positive small), PML (positive medium large), PM (positive medium), PL (positive large).

[0026] With the above membership functions, the simulation input parameters can be calculated.

[0027] In some alternative embodiments, step S2 further includes: Step S21, input the simulation input parameters into a pre-constructed descent rule base to calculate the first descent rate command; Step S22: Input the simulation input parameters into a pre-constructed proximity rule base to calculate the second sink rate command; Step S23: Superimpose the first sink rate command and the second sink rate command to form the sink rate command.

[0028] In some alternative embodiments, step S21 further includes: Step S211: Calculate the vertical error between the aircraft and the landing point based on the altitude, the distance to the landing point, and the motion parameters of the mobile platform; Step S212: Calculate the first sink rate command using different glide rule bases according to the vertical error.

[0029] Reference Figure 1 , the inputs for the glide rule base mainly include the sink rate and the normal overload , and the output is the first sink rate command. In this embodiment, different glide rule bases need to be used according to different vertical errors. The vertical error can be calculated through fuzzy rules using the altitude, the distance to the landing point, and the motion parameters of the mobile platform, or can be calculated through a function using the altitude, the distance to the landing point, and the motion parameters of the mobile platform, and then the calculation result is subjected to fuzzy mapping.

[0030] For example, the motion of the mobile platform is affected by the motion of the waves. Its travel (assuming the motion of the center of mass of the mobile platform is a steady linear motion) and the perturbation motion (heave, sway, surge, yaw, pitch, roll) together constitute the six-degree-of-freedom motion of the mobile platform. According to the statistical results, the amplitudes of sway, surge, and yaw in the perturbation motion of the mobile platform are very small, and the influence on the longitudinal landing process of the aircraft is very weak. Therefore, the above factors can be ignored when establishing the perturbation motion model of the mobile platform, and only the heave motion and pitch motion of the mobile platform are studied in the longitudinal direction. The height change of the ideal landing point is: .

[0031] Where is the horizontal distance between the ideal landing point of the aircraft and the pitch center of the mobile platform, is the pitch angle, represents the height of the mobile platform, is used to calculate the vertical error of the aircraft.

[0032] In step S212, map the vertical error to seven fuzzy sets of NL (negative large), NM (negative medium), NS (negative small), NZ (zero), PS (positive small), PM (positive medium), and PL (positive large), and then use the glide rule bases in Table 1 - Table 7 to calculate the first sink rate command.

[0033] Table 1 Descent Rule Base When the Vertical Error Is Positive and Large

[0034] Table 2 Descent Rule Base When the Vertical Error Is at the Center

[0035] Table 3 Descent Rule Base When the Vertical Error Is Positive and Small

[0036] Table 4 Descent Rule Base When the Vertical Error Is Zero

[0037] Table 5 Descent Rule Base When the Vertical Error Is Negative and Small

[0038] Table 6 Descent Rule Base When the Vertical Error Is Negative and at the Center

[0039] Table 7 Descent Rule Base When the Vertical Error Is Negative and Large

[0040] In some alternative embodiments, step S22 further includes: Calculating the second sink rate command using different approach rule bases according to the angle of attack and the distance from the landing point.

[0041] This embodiment is used to calculate the second sink rate command using different approach rule bases according to different angles of attack and the distance from the landing point. The inputs used in the approach rule bases include the height h and the sink rate , first divide the distance from the landing point into three cases: approaching the wake of the landing point, at the wake of the landing point, and far from the wake of the landing point. Then, for the case of being at the wake of the landing point, divide it into three cases according to the angle of attack: the angle of attack error is zero, the angle of attack error is negative and small, and the angle of attack error is positive and small. The approach rule base used when approaching the wake of the landing point is shown in Table 8, the approach rule base used when at the wake of the landing point and the angle of attack error is zero is shown in Table 9, the approach rule base used when at the wake of the landing point and the angle of attack error is negative and small is shown in Table 10, and the approach rule base used when at the wake of the landing point and the angle of attack error is positive and small is shown in Table 11.

[0042] Table 8 Approach Rule Base When Approaching the Wake of the Landing Point

[0043] Table 9 Approach Rule Base When at the Wake of the Landing Point and the Angle of Attack Error Is Zero

[0044] Table 10 Approach rule base at the wake of the landing point with a negative angle of attack error

[0045] Table 11 Approach rule base at the wake of the landing point with a positive angle of attack error

[0046] With the sink rate command, the sink rate deviation △ can be calculated from the real-time sink rate at the current moment , and the sink rate deviation is also mapped into the above 7 fuzzy sets. Then, in step S4, together with the normal overload , they are input into the autopilot rule base to output the elevator command, as shown in Table 12 below

[0047] Table 12 Autopilot rule base

[0048] On the other hand, in step S5, the angle of attack and the normal overload are input into the throttle rule base to output the throttle command, as shown in Table 13

[0049] Table 13 Throttle rule base

[0050] Finally, in step S6, the simulation parameters are updated based on the pre-constructed aircraft landing simulation system

[0051] Under the condition of symmetric flight in still air, the ground coordinate system is selected to coincide with the aircraft flight plane, and all the lateral parameters of the aircraft are zero. At this time, the force diagram of the aircraft is as Figure 4 shown, and the longitudinal motion equation of the aircraft is .

[0052] In the formula represents the mass of the aircraft represents the flight speed of the aircraft, which is used to calculate the distance of the aircraft from the landing point , , and respectively represent the engine thrust, drag, lift and pitching moment , , and respectively represent the angle of attack, pitch angle, flight path angle and pitch rate represents the acceleration due to gravity represents the aircraft's rotation about The moment of inertia of the shaft Indicates the aircraft altitude and is used to calculate the vertical error Indicates the sink rate. In the figure, X is the longitudinal axis of the aircraft Is the engine installation deviation angle. It is assumed here that the deviation angle , that is, the direction of the engine thrust is the same as the direction of the X-axis. The values of the aerodynamic force and the aerodynamic moment are given by the following equations respectively: .

[0053] Where , , , And Represent the dynamic pressure, air density, wing reference area and mean aerodynamic chord length respectively , , And Represent the thrust function, drag coefficient function, lift coefficient function and pitching moment coefficient function respectively , Represent the throttle opening and elevator deflection angle respectively

[0054] In some alternative embodiments, step S6 further includes: Step S61: Determine the longitudinal disturbance quantity and the vertical disturbance quantity according to the wind field disturbance model of the mobile platform Step S62: Update the airspeed and angle of attack of the aircraft according to the longitudinal disturbance quantity and the vertical disturbance quantity Step S63: Input the updated airspeed and angle of attack into the aircraft landing simulation system to obtain the simulation input parameters at the next moment

[0055] This embodiment is used to add the wake wind field disturbance of the mobile platform to the aircraft landing simulation system. During the aircraft's glide, the wake wind field disturbance of the mobile platform directly affects the speed, angle of attack and altitude parameters of the aircraft. This disturbance is the main factor leading to aircraft landing accidents and go-around escapes. Its longitudinal wake wind field disturbance model includes the longitudinal wind field And the vertical wind field Two types, composed as follows: free atmosphere turbulence component ; steady-state component of the mobile platform wake ; periodic component of the mobile platform wake ; random component of the mobile platform wake . Let the resultant of the horizontal and vertical air flow disturbances be . Calculate the longitudinal disturbance quantity And the vertical disturbance quantity According to the following formula: .

[0056] The longitudinal wind field mainly affects the airspeed, and the vertical wind field mainly affects the angle of attack. In step S62, the airspeed and angle of attack of the aircraft are represented by and respectively, and the calculation formulas are as follows: .

[0057] In step S63, the above-mentioned airspeed and angle of attack are respectively used to replace the airspeed v and angle of attack in the aircraft landing simulation system under static atmosphere, so that the control parameters can be adjusted online according to the interference of the environment to improve the anti-interference performance of the aircraft.

[0058] Simulate the aircraft landing, and compare the rise time, overshoot, and adjustment time of the landing system FACLS of this application and the traditional landing system ACLS under different sink rate step inputs. The comparison results of the sink rate step responses for sink rates of 5 ft / s, 10 ft / s, and 15 ft / s are shown in Table 14 below.

[0059] Table 14 Comparison table of sink rate step responses of FACLS and ACLS:

[0060] As can be seen from the above table, under different step changes, the maximum overshoots of ACLS are 18.9%, 15.6%, and 13.4% respectively, while in the same situation, the overshoots of FACLS do not exceed 3%. Therefore, the overshoot of FACLS is much smaller than that of the general ACLS. This is because FACLS can effectively change the gain and damping of the control system during the maneuvering process. Under the sink rate step input, the average adjustment time of ACLS is 5.1 s, while the average adjustment time of FACLS is 3.5 s. It shows that FACLS can significantly improve the maximum overshoot and adjustment time, while the improvement degree of the rise time is slightly smaller. When the sink rate step input is 5 ft / s, the comparison of the two systems is as Figure 5 shown. This improved sink rate control can be used to suppress the disturbance of the airflow, thereby reducing the aircraft landing error. When approaching the landing point, FACLS can also respond quickly to the motion compensation of the mobile platform.

[0061] This system introduces the fuzzy theory into the aircraft automatic landing system, which can significantly improve the overshoot and adjustment time of the aircraft automatic landing system and improve the dynamic performance of the control system. It overcomes the influence of the wake of the mobile platform on the aircraft's sink, and enables the aircraft to maintain an appropriate glide trajectory and sink rate.

[0062] The second aspect of the present application provides an aircraft automatic landing simulation device based on fuzzy theory corresponding to the above method, mainly including: A simulation input parameter acquisition module, configured to acquire simulation input parameters at the current moment, where the simulation input parameters include angle of attack, altitude, sink rate, normal overload, distance from the landing point, and moving platform motion parameters; A sink rate command calculation module, configured to input the simulation input parameters into a pre-constructed glide rule library and approach rule library to obtain a sink rate command; A sink rate deviation calculation module, configured to calculate a sink rate deviation based on the sink rate command and the sink rate; An elevator command calculation module, configured to input the sink rate deviation into a pre-constructed autopilot rule library to obtain an elevator command; A throttle command calculation module, configured to determine a throttle command based on a pre-constructed throttle rule library; A simulation input parameter update module, configured to input the elevator command and the throttle command into an aircraft landing simulation system to obtain simulation input parameters for the next moment; Wherein, the glide rule library, the approach rule library, the autopilot rule library, and the throttle rule library are all fuzzy rule libraries.

[0063] In some alternative embodiments, the sink rate command calculation module includes: A first sink rate command calculation unit, configured to input the simulation input parameters into a pre-constructed glide rule library to calculate a first sink rate command; A second sink rate command calculation unit, configured to input the simulation input parameters into a pre-constructed approach rule library to calculate a second sink rate command; A sink rate command calculation unit, configured to superimpose the first sink rate command and the second sink rate command to form the sink rate command.

[0064] In some alternative embodiments, the first sink rate command calculation unit includes: A vertical error calculation subunit, configured to calculate the vertical error between the aircraft and the landing point based on altitude, distance from the landing point, and moving platform motion parameters; A glide rule library classification call subunit, configured to calculate the first sink rate command using different glide rule libraries according to the vertical error.

[0065] In some alternative embodiments, the second sink rate command calculation unit includes: An approach rule library classification call subunit, configured to calculate the second sink rate command using different approach rule libraries according to the angle of attack and the distance from the landing point.

[0066] In some alternative embodiments, the simulation input parameter updating module includes: A perturbation amount calculation unit configured to determine a longitudinal perturbation amount and a vertical perturbation amount according to a wind field perturbation model of the mobile platform; An airspeed and angle of attack updating unit configured to update the airspeed and angle of attack of the aircraft according to the longitudinal perturbation amount and the vertical perturbation amount; A simulation parameter replacement unit configured to input the updated airspeed and angle of attack into an aircraft landing simulation system to obtain simulation input parameters for the next moment.

[0067] As described above, the foregoing are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for simulating automatic aircraft landing based on fuzzy theory, characterized in that: include: Step S1, obtaining simulation input parameters at the current moment, wherein the simulation input parameters include angle of attack, altitude, sinking rate, normal overload, distance from the landing point, and motion parameters of the mobile platform; Step S2, inputting the simulation input parameters into a pre-built glide rule library and approach rule library to obtain a sinking rate instruction; Step S3, calculating the sinking rate deviation according to the sinking rate instruction and the sinking rate; Step S4, inputting the sink rate deviation into a pre-built autopilot rule library to obtain an elevator command; Step S5: determining a throttle command based on a pre-built throttle rule library; Step S6, inputting the elevator command and the throttle command into an aircraft landing simulation system to obtain simulation input parameters at the next moment; Among them, the descent rule base, approach rule base, autopilot rule base and throttle rule base are all fuzzy rule bases.

2. The aircraft automatic landing simulation method based on fuzzy theory as claimed in claim 1, characterized in that: Step S2 further comprises: Step S21, inputting the simulation input parameters into a pre-built glide rule library to calculate a first sinking rate instruction; Step S22, inputting the simulation input parameters into a pre-built approach rule library to calculate a second sinking rate instruction; Step S23: superimpose the first sinking rate instruction and the second sinking rate instruction to form the sinking rate instruction.

3. The method for simulating automatic aircraft landing based on fuzzy theory as claimed in claim 2, characterized in that: Step S21 further includes: Step S211, calculating the vertical error between the aircraft and the landing point based on the height, the distance to the landing point and the motion parameters of the mobile platform; Step S212: Calculate the first sink rate instruction using different glide rule libraries according to the vertical error.

4. The method for simulating automatic aircraft landing based on fuzzy theory as claimed in claim 2, characterized in that: Step S22 further includes: The second sink rate instruction is calculated using different approach rule libraries according to the angle of attack and the distance from the landing point.

5. The method for simulating automatic aircraft landing based on fuzzy theory as claimed in claim 1, characterized in that: Step S6 further comprises: Step S61, determining the longitudinal disturbance amount and the vertical disturbance amount according to the wind field disturbance model of the mobile platform; Step S62, updating the airspeed and angle of attack of the aircraft according to the longitudinal disturbance amount and the vertical disturbance amount; Step S63: input the updated airspeed and angle of attack into the aircraft landing simulation system to obtain the simulation input parameters at the next moment.

6. An aircraft automatic landing simulation device based on fuzzy theory, characterized in that: include: A simulation input parameter acquisition module is used to acquire simulation input parameters at the current moment, wherein the simulation input parameters include angle of attack, altitude, sinking rate, normal overload, distance from the landing point, and motion parameters of the mobile platform; A sinking rate instruction calculation module, used for inputting the simulation input parameters into a pre-built glide rule library and approach rule library to obtain a sinking rate instruction; A sinking rate deviation calculation module, used for calculating a sinking rate deviation according to a sinking rate instruction and a sinking rate; an elevator command calculation module, used for inputting the sink rate deviation into a pre-built autopilot rule library to obtain an elevator command; A throttle command calculation module, used for determining a throttle command based on a pre-built throttle rule library; A simulation input parameter updating module, used for inputting the elevator command and the throttle command into the aircraft landing simulation system to obtain the simulation input parameters at the next moment; Among them, the descent rule base, approach rule base, autopilot rule base and throttle rule base are all fuzzy rule bases.

7. The aircraft automatic landing simulation device based on fuzzy theory as claimed in claim 6, characterized in that: The sinking rate instruction calculation module includes: A first sinking rate instruction calculation unit, used for inputting the simulation input parameters into a pre-built glide rule library to calculate a first sinking rate instruction; a second sinking rate instruction calculation unit, used for inputting the simulation input parameters into a pre-built approach rule library to calculate a second sinking rate instruction; The sinking rate instruction calculation unit is used to superimpose the first sinking rate instruction and the second sinking rate instruction to form the sinking rate instruction.

8. The aircraft automatic landing simulation device based on fuzzy theory as claimed in claim 7, characterized in that: The first sinking rate instruction calculation unit includes: A vertical error calculation subunit, used to calculate the vertical error between the aircraft and the landing point based on the height, the distance to the landing point and the motion parameters of the mobile platform; The glide rule library classification calling subunit is used to calculate the first sinking rate instruction using different glide rule libraries according to the vertical error.

9. The aircraft automatic landing simulation device based on fuzzy theory as claimed in claim 7, characterized in that: The second sinking rate instruction calculation unit includes: The approach rule library classification calling subunit is used to calculate the second sink rate instruction using different approach rule libraries according to the angle of attack and the distance from the landing point.

10. The aircraft automatic landing simulation device based on fuzzy theory as claimed in claim 6, characterized in that: The simulation input parameter updating module comprises: A disturbance amount calculation unit, used for determining the longitudinal disturbance amount and the vertical disturbance amount according to the wind field disturbance model of the mobile platform; An airspeed and angle of attack updating unit, used for updating the airspeed and angle of attack of the aircraft according to the longitudinal disturbance amount and the vertical disturbance amount; The simulation parameter replacement unit is used to input the updated airspeed and angle of attack into the aircraft landing simulation system to obtain the simulation input parameters at the next moment.

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