A method and control system for controlling the starting process of an aero-piston engine

Abstract

The application discloses an aviation piston engine starting process control method and a control system, and particularly relates to the following steps: step 1: obtaining engine speed, ambient temperature, cooling liquid temperature and oil pressure; step 2: using fuzzy reasoning to calculate the difference between the ambient temperature, the cooling liquid temperature and the ambient temperature and the engine speed, and obtaining the choke valve opening; step 3: using fuzzy reasoning to calculate the oil pressure and the cooling liquid temperature, and obtaining the target speed; step 4: using a PID algorithm to calculate the throttle opening according to the target speed and the current engine speed. The application can dynamically adjust the opening of the choke valve and the throttle according to the engine operating state, and realize the automatic control of the whole process from the cold engine starting to the warm-up ending.

Description

Technical Field

[0001] This invention belongs to the field of aviation control, and in particular relates to a method and control system for controlling the starting process of an aviation piston engine. Background Technology

[0002] The starting process is a critical stage in the operation of an aircraft piston engine, and the control of the choke and throttle openings is the core of this stage. The choke regulates the engine's intake air volume during starting, improving starting performance. The throttle controls the engine speed (output power); properly controlling the throttle opening based on the engine's operating status during starting can improve starting efficiency and reduce component wear. Currently, both are mostly controlled manually, relying heavily on the operator's experience, skills, and intuitive understanding of the engine's operating status, thus requiring a high level of operator expertise.

[0003] To address the aforementioned issues, patent application CN103032205B discloses an automatic choke digital control device, which uses a microprocessor to extend the choke recovery time based on the number of start-up failures to achieve engine starting. There are also solutions that use bimetallic materials as actuators to achieve automatic choke adjustment. However, due to the low responsiveness of bimetallic materials to temperature changes, when the engine is restarted at a high temperature, the choke recovery time becomes longer, leading to excessive fuel consumption. While electronic control systems can automatically adjust the throttle opening, a single judgment criterion and fixed control strategy cannot meet the real-time needs of the engine under different operating conditions. Summary of the Invention

[0004] Purpose of the invention: In order to solve the problems existing in the prior art, the present invention provides a method and control system for controlling the starting process of an aircraft piston engine.

[0005] Technical solution: This invention provides a method for controlling the starting process of an aircraft piston engine, specifically including the following steps:

[0006] Step 1: Obtain engine speed, ambient temperature, coolant temperature, and oil pressure;

[0007] Step 2: Use fuzzy reasoning to calculate the choke opening based on ambient temperature, the difference between coolant temperature and ambient temperature, and engine speed.

[0008] Step 3: Use fuzzy reasoning to calculate the oil pressure and coolant temperature to obtain the target speed;

[0009] Step 4: Use the PID algorithm to calculate the throttle opening based on the target speed and the current engine speed.

[0010] Furthermore, step 2 specifically includes:

[0011] Step 2.1: Determine the basic universe of discourse, fuzzy universe of discourse, and fuzzy subsets of ambient temperature, the difference between coolant temperature and ambient temperature, engine speed, and choke opening;

[0012] Step 2.2: Map the ambient temperature, the difference between the coolant temperature and the ambient temperature, and the engine speed from the basic universe of discourse to the fuzzy universe of discourse to obtain fuzzy quantities;

[0013] Step 2.3: Calculate the degree to which the fuzzy values ​​of ambient temperature, coolant temperature difference with ambient temperature, and engine speed belong to each fuzzy subset using the membership function;

[0014] Step 2.4: Based on the calculation results of the membership function, infer the membership of each fuzzy subset of the damper opening according to fuzzy rules;

[0015] Step 2.5: Based on the membership degree of each fuzzy subset of the choke opening, obtain the fuzzy output of the choke opening through a refinement process;

[0016] Step 2.6: Defuzzify the fuzzy output of the choke opening to obtain the choke opening.

[0017] Furthermore, in step 2.1, the basic universe of discourse for ambient temperature is [-30℃, 40℃], the fuzzy universe of discourse is [-7, +7], the fuzzy subset is {-7, -5, -3, -1, +1, +3, +5, +7}, and the corresponding linguistic values ​​are {LB, LM, LS, LZ, HZ, HS, HM, HB}; the basic universe of discourse for the difference between coolant temperature and ambient temperature is [0℃, 70℃], the fuzzy universe of discourse is [-7, +7], the fuzzy subset is {-7, -5, -3, -1, +1, +3, +5, +7}, and the corresponding linguistic values ​​are... The linguistic values ​​are {LB,LM,LS,LZ,HZ,HS,HM,HB}; the basic universe of discourse for the engine speed is [700r / min,1200r / min], the fuzzy universe of discourse is [-1,+1], the fuzzy subset is {-1,+1}, and the corresponding linguistic values ​​are set to {L,H}; the basic universe of discourse for the choke opening is [0%,100%], the fuzzy universe of discourse is [-5,+5], the fuzzy subset is {-5,-3,-1,+1,+3,+5}, and the corresponding linguistic values ​​are {LB,LS,LZ,HZ,HS,HB}.

[0018] Furthermore, the fuzzy rules in step 2.4 are specifically as follows: when the engine speed belongs to the fuzzy subset {L}, the fuzzy rules are as shown in Table 1:

[0019] Table 1

[0020]

[0021]

[0022] When the engine speed belongs to the fuzzy subset {H}, the fuzzy rules are shown in Table 2 below:

[0023] Table 2

[0024]

[0025] Furthermore, step 3 specifically includes:

[0026] Step 3.1: Determine the basic universe of discourse, fuzzy universe of discourse, and fuzzy subset for coolant temperature, oil pressure, and target speed;

[0027] Step 3.2: Map the coolant temperature, oil pressure, and target speed from the basic universe of discourse to the fuzzy universe of discourse to obtain fuzzy quantities;

[0028] Step 3.3: Calculate the degree to which the fuzzy quantities of coolant temperature, oil pressure, and target speed belong to each fuzzy subset using membership functions;

[0029] Step 3.4: Based on the calculation results of the membership function, infer the membership degree of each target rotation speed fuzzy subset according to fuzzy rules;

[0030] Step 3.5: Based on the membership degree of each target rotation speed fuzzy subset, obtain the fuzzy output of the target rotation speed through a refinement process;

[0031] Step 3.6: Defuzzify the fuzzy output of the target rotational speed to obtain the target rotational speed;

[0032] Step 3.7: Calculate the throttle opening using the PID controller based on the target speed and the current engine speed.

[0033] Furthermore, the mapping relationship between the basic universe of discourse and the fuzzy universe of discourse is as follows:

[0034]

[0035] Where x is the value in the basic universe of discourse, y is the mapping of x in the fuzzy universe of discourse, n is the maximum value in the fuzzy universe of discourse, a is the minimum value in the basic universe of discourse, and b is the maximum value in the basic universe of discourse.

[0036] Furthermore, the defuzzification is performed using the following formula:

[0037]

[0038] In the formula, y represents the defuzzification result, u represents the fuzzy output quantity, and y max and y minThese are the maximum and minimum values ​​of the defuzzification result, u. max and u min These represent the maximum and minimum values ​​of the fuzzy output, respectively.

[0039] Furthermore, the basic domain of the coolant temperature is [0℃, 50℃], the fuzzy domain is [-5, +5], the fuzzy subset is {-5, -3, -1, +1, +3, +5}, and the corresponding linguistic values ​​are {LB, LS, LZ, HZ, HS, HB}; the basic domain of the oil pressure is [2bar, 3bar], the fuzzy domain is [-1, +1], the fuzzy subset is {-1, +1}, and the corresponding linguistic values ​​are {L, H}; the basic domain of the target speed is [1400r / min 2300r / min], the fuzzy domain is [-3, +3], the fuzzy subset is {-3, -1, +1, +3}, and the corresponding linguistic values ​​are {LB, LS, HS, HB}.

[0040] Furthermore, the fuzzy rules in step 3.4 are shown in Table 3:

[0041] Table 3

[0042]

[0043] A control system for a method of controlling the starting process of an aircraft piston engine is characterized by comprising a data acquisition unit, a target speed fuzzy inference engine, a choke opening fuzzy inference engine, a PID controller, and a drive execution unit.

[0044] The data acquisition unit acquires sensor signals and processes and calculates ambient temperature, engine speed, coolant temperature, and oil pressure.

[0045] The choke opening fuzzy inference engine calculates the choke opening based on ambient temperature, engine speed, and cooling temperature.

[0046] The target speed fuzzy inference engine calculates the target speed based on the coolant temperature and oil pressure.

[0047] The PID controller calculates the throttle opening based on the target speed and the current engine speed.

[0048] Beneficial effects:

[0049] 1. This invention dynamically adjusts the choke and throttle opening based on fuzzy reasoning during engine starting, and the engine's intake air volume can be optimized in real time according to engine speed, coolant temperature and oil pressure, thereby improving engine starting performance.

[0050] 2. This invention can control the engine to enter the idle state from a cold state without the need for manual adjustment of the choke and throttle opening, reducing the burden on operators, improving engine starting efficiency, and reducing the possibility of human error.

[0051] 3. When calculating the choke opening, the present invention takes the ambient temperature into account, enabling the engine to start automatically under any temperature conditions, thus exhibiting good environmental adaptability and reliability.

[0052] 4. This invention can be implemented without modifying the engine structure, has a simple structure, and can be applied to aviation piston engines of different manufacturers and models. Attached Figure Description

[0053] Figure 1 This is a structural block diagram of the control method of the present invention;

[0054] Figure 2 This is a flowchart of the control method of the present invention;

[0055] Figure 3 Flowchart for fuzzy inference of choke opening;

[0056] Figure 4 This is a schematic diagram of the membership functions of various fuzzy subsets of ambient temperature.

[0057] Figure 5 A schematic diagram of the fuzzy rule for choke opening when the engine speed belongs to the fuzzy subset {L};

[0058] Figure 6 A schematic diagram of the fuzzy rules for choke opening when the engine speed belongs to the fuzzy subset {H};

[0059] Figure 7 Flowchart of fuzzy inference for target rotational speed;

[0060] Figure 8 A schematic diagram of the fuzzy rules for the target rotational speed. Detailed Implementation

[0061] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0062] Figure 1 The structural block diagram of the intelligent control method for the starting process of an aviation piston engine provided in the embodiments of the present invention is divided into four parts: engine, data acquisition unit, inference calculation unit and drive execution unit.

[0063] The engine is equipped with various sensors, including an engine speed sensor, a coolant temperature sensor, and an oil pressure sensor. These sensors convert external input signals into electrical signals, which are then provided to the data acquisition unit. In this embodiment, the engine speed sensor is an electromagnetic pulse type speed sensor; the coolant temperature sensor is a negative temperature coefficient thermistor; and the oil pressure sensor is a two-wire pressure sensor.

[0064] The data acquisition unit is used to process sensor output signals and calculate the true value of the measured quantity. It includes an ambient temperature acquisition module, an engine speed acquisition module, a coolant temperature acquisition module, and an oil pressure acquisition module. In this embodiment, the ambient temperature acquisition module is designed based on the SPL06 chip and outputs ambient temperature data through an SPI interface; the engine speed acquisition module shapes the pulse signal output by the speed sensor into a frequency signal with an amplitude of 3.3V; the coolant temperature acquisition module conditions the resistance signal output by the thermistor into a voltage signal of 0–3.3V; and the oil pressure acquisition module conditions the 4–20mA current signal output by the pressure sensor into a voltage signal of 0.4–2V. The processor of the engine electronic controller acquires the outputs of the above modules and calculates the ambient temperature, engine speed, coolant temperature, and oil pressure.

[0065] The inference calculation unit includes a fuzzy inference generator for choke opening, a fuzzy inference generator for target engine speed, and a PID controller. The fuzzy inference generator consists of a fuzzification stage, a fuzzy inference stage, and a defuzzification stage. Specifically: the fuzzification stage fuzzifies the ambient temperature, engine speed, coolant temperature, and oil pressure to obtain a fuzzification result; the fuzzy inference stage performs inference calculations based on fuzzy rules to obtain a fuzzy output quantity; and the defuzzification stage defuzzifies the fuzzy output quantity to obtain the choke opening and target engine speed commands. The target engine speed fuzzy inference generator is activated when the oil pressure is greater than the minimum oil pressure required for normal engine operation; otherwise, it outputs the engine idle speed. The PID controller includes proportional, integral, and derivative components to control the engine speed to reach and maintain the target speed. The deviation between the target speed and the engine speed is eliminated through the combined action of the proportional, integral, and derivative components.

[0066] The drive execution unit consists of a choke control mechanism and a throttle control mechanism, wherein the control mechanism is a servo motor. The choke opening and throttle opening commands are converted into corresponding PWM control signals, and the PWM control signals drive the servo motor to adjust the choke and throttle openings.

[0067] Figure 2The flowchart of the intelligent control method for the starting process of an aircraft piston engine provided in this embodiment of the invention mainly includes the following steps:

[0068] Step S1: Acquire data from each sensor and calculate the current engine operating parameters.

[0069] The sensors include an ambient temperature sensor, a coolant temperature sensor, an engine speed sensor, and an oil pressure sensor. The engine operating parameters are ambient temperature, the difference between the coolant temperature and ambient temperature, engine speed, and oil pressure. The formula for calculating the difference between the coolant temperature and ambient temperature is:

[0070] ΔT=T C -T E (1)

[0071] In the formula, T C T represents the temperature of the coolant. E The ambient temperature is mentioned.

[0072] Step S2: Based on fuzzy reasoning, the ambient temperature, the difference between the coolant temperature and the ambient temperature, and the engine speed are calculated to obtain the choke opening.

[0073] In the specific implementation process, the preferred method is, such as Figure 3 As shown, step S2 includes:

[0074] Step S21: Determine the basic universe of discourse, fuzzy universe of discourse, and fuzzy subset of the ambient temperature, the difference between coolant temperature and ambient temperature, engine speed, and choke opening.

[0075] The fundamental domain of the ambient temperature is [-30℃, 40℃], the fuzzy domain is [-7, +7], and the fuzzy subset is {-7, -5, -3, -1, +1, +3, +5, +7}. The corresponding linguistic values ​​are set to {LB, LM, LS, LZ, HZ, HS, HM, HB}. The fundamental domain of the difference between the coolant temperature and the ambient temperature is [0℃, 70℃], the fuzzy domain is [-7, +7], and the fuzzy subset is {-7, -5, -3, -1, +1, +3, +5, +7}. The corresponding linguistic values ​​are set to {LB, LM, LS, LZ, HZ, HS, HM, HB}. The fundamental domain of the engine speed is [700r / min, 1200r / min], the fuzzy domain is [-1, +1], and the fuzzy subset is {-1, +1}. The corresponding linguistic values ​​are set to {L, H}. The basic universe of discourse for the opening of the choke is [0%, 100%], the fuzzy universe of discourse is [-5, +5], the fuzzy subset is {-5, -3, -1, +1, +3, +5}, and the corresponding language values ​​are set to {LB, LS, LZ, HZ, HS, HB}.

[0076] Step S22: Map the ambient temperature, the difference between the coolant temperature and the ambient temperature, and the engine speed into a fuzzy domain to obtain fuzzy quantities.

[0077] The mapping relationship between the basic universe of discourse and the fuzzy universe of discourse is shown in equation (2).

[0078]

[0079] In the formula, x is the value in the basic universe of discourse, y is the mapping of x in the fuzzy universe of discourse (fuzzy quantity), n is the maximum value in the fuzzy universe of discourse, a is the minimum value in the basic universe of discourse, and b is the maximum value in the basic universe of discourse.

[0080] Step S23: Calculate the degree to which the fuzzy values ​​of ambient temperature, coolant temperature difference with ambient temperature, and engine speed belong to each fuzzy subset using the membership function.

[0081] The membership functions for ambient temperature, the difference between coolant temperature and ambient temperature, and engine speed are all triangular membership functions with a value range of [0,1]. Figure 4 Let the membership function be the ambient temperature. If the mapping of the fuzzy universe of discourse of the ambient temperature is 2.6, then the membership degree of the fuzzy quantity to the fuzzy subset {HZ} is 0.2, and the membership degree to the fuzzy subset {HS} is 0.8.

[0082] Step S24: Based on the calculation result of the membership function, infer the membership degree of each fuzzy subset of the opening degree of the air choke based on fuzzy rules.

[0083] The fuzzy rules total 98, using the format "ifA and B and C then D", where A is a fuzzy subset of the engine speed, B is a fuzzy subset of the difference between the coolant temperature and the ambient temperature, C is a fuzzy subset of the ambient temperature, and D is a fuzzy subset of the choke opening. These 98 fuzzy rules can generate a 2×7×7 three-dimensional fuzzy rule table. For ease of reading, this three-dimensional fuzzy rule table is divided into two two-dimensional fuzzy rule tables, as shown in Table 1 and Table 2. The corresponding three-dimensional diagrams are shown below. Figure 5 and Figure 6 As shown. Based on the fuzzy rules, the Mamdani method is used to perform fuzzy inference to obtain the membership degrees {μ(-5),μ(-3),μ(-1),μ(+1),μ(+3),μ(+5)} of the fuzzy subsets {-5,-3,-1,+1,+3,+5} of each choke opening. μ represents the membership degree, and μ(-5) = 0.5 means that the degree of membership in the fuzzy subset {-5} is 0.5.

[0084] When the engine speed belongs to the fuzzy subset {L}, the fuzzy rules are shown in Table 1 below:

[0085] Table 1

[0086]

[0087] The fuzzy rules for choke opening when the engine speed belongs to the fuzzy subset {H} are shown in Table 2 below:

[0088] Table 2

[0089]

[0090] If A = L, B = LB, and C = HM, then D = LS. When the engine speed belongs to the fuzzy subset {L} (find the rule in Table 1), the difference between the coolant temperature and the ambient temperature belongs to the fuzzy subset {LB} (located in the 2nd row of Table 1), and the ambient temperature belongs to the fuzzy subset {HM} (located in the 8th column of Table 1), the choke opening membership degree is {LS} (corresponding to the 2nd row and 8th column of Table 1).

[0091] Step S25: Based on the membership degree of each fuzzy subset of the choke opening, the fuzzy output of the choke opening is obtained through a refinement process.

[0092] The refinement process employs a weighted average method as shown in equation (3):

[0093]

[0094] In the formula, u is the fuzzy output quantity, x i For the fuzzy subset of the opening of the i-th choke door, μ i (x i ) is x i The corresponding membership degree, where m is the number of fuzzy subsets.

[0095] Step S26: Defuzzify the fuzzy output of the choke opening to obtain the choke opening.

[0096] The calculation formula for defuzzification is shown in equation (4):

[0097]

[0098] In the formula, y is the defuzzification result, u is the fuzzy output quantity, and y max and y min These are the maximum and minimum values ​​of the output, i.e., the defuzzification result, respectively. max and u min These represent the maximum and minimum values ​​of the fuzzy output, respectively.

[0099] Step S3: Based on fuzzy reasoning, the oil pressure and coolant temperature are calculated to obtain the target rotational speed.

[0100] In the specific implementation process, preferably, the process of step S3 is as follows: Figure 7 As shown.

[0101] Step S31: Determine the basic universe of discourse, fuzzy universe of discourse, and fuzzy subset of the coolant temperature, oil pressure, and target speed.

[0102] The fundamental domain of the coolant temperature is [0℃, 50℃], the fuzzy domain is [-5, +5], and the fuzzy subset is {-5, -3, -1, +1, +3, +5}. The corresponding linguistic values ​​are set to {LB, LS, LZ, HZ, HS, HB}. The fundamental domain of the oil pressure is [2bar, 3bar], the fuzzy domain is [-1, +1], and the fuzzy subset is {-1, +1}. The corresponding linguistic values ​​are set to {L, H}. The fundamental domain of the target engine speed is [1400r / min 2300r / min], the fuzzy domain is [-3, +3], and the fuzzy subset is {-3, -1, +1, +3}. The corresponding linguistic values ​​are set to {LB, LS, HS, HB}.

[0103] Step S32: Map the coolant temperature, oil pressure, and target speed from the basic universe of discourse to the fuzzy universe of discourse according to Formula 2 to obtain fuzzy quantities;

[0104] Step S33: Calculate the degree to which the fuzzy quantities of coolant temperature, oil pressure and target speed belong to each fuzzy subset using the membership function;

[0105] Step S34: Based on the calculation result of the membership function, infer the membership degree of each target rotation speed fuzzy subset according to fuzzy rules.

[0106] A total of 12 fuzzy rules are specified, using the form "ifa and b then c", where 'a' is a fuzzy subset of the coolant temperature, 'b' is a fuzzy subset of the oil pressure, and 'c' is a fuzzy subset of the target engine speed. These 12 fuzzy rules generate a two-dimensional fuzzy rule table as shown in Table 3, with a corresponding three-dimensional schematic diagram as shown below. Figure 8 As shown, the membership degrees {μ(-3),μ(-1),μ(+1),μ(+3)} of the fuzzy subsets {-3,-1,+1,+3} of each target rotation speed are obtained by fuzzy inference using the Mamdani method based on fuzzy rules.

[0107] The fuzzy rules for the target rotational speed are shown in Table 3 below:

[0108] Table 3

[0109]

[0110] Step S35: Based on the membership degree of each target rotation speed fuzzy subset, obtain the fuzzy output of the target rotation speed through the refinement process in step 3.

[0111] Step S36: Defuzzify the fuzzy output of the target rotational speed according to Formula 4 to obtain the target rotational speed.

[0112] Step S4: The throttle opening is calculated using the PID controller based on the target speed and the current engine speed.

[0113] The calculation formula for the PID controller is shown in equation (5).

[0114]

[0115] In the formula, n t The target speed is Ng, where Ng is the engine speed and k is k. p k i k d These are the proportional coefficient, integral coefficient, and derivative coefficient of the PID controller, respectively, which are set to 0.05, 0.002, and 0 in this embodiment. u2 is the throttle opening command, and k represents the time.

[0116] In summary, this invention provides an intelligent control method for the starting process of an aviation piston engine, which can dynamically adjust the opening of the choke and throttle valve according to the engine's operating status, realizing automatic control of the entire process from cold start to warm-up. This not only improves the engine's starting efficiency but also reduces the burden on operators, exhibiting good adaptability and reliability.

[0117] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

Claims

1. A method for controlling the starting process of an aircraft piston engine, characterized in that, Specifically, the steps include the following: Step 1: Obtain engine speed, ambient temperature, coolant temperature, and oil pressure; Step 2: Use fuzzy reasoning to calculate the choke opening based on ambient temperature, the difference between coolant temperature and ambient temperature, and engine speed. Step 3: Use fuzzy reasoning to calculate the oil pressure and coolant temperature to obtain the target speed; Step 4: Use the PID algorithm to calculate the throttle opening based on the target speed and the current engine speed.

2. The method for controlling the starting process of an aircraft piston engine according to claim 1, characterized in that, Step 2 specifically involves: Step 2.1: Determine the basic universe of discourse, fuzzy universe of discourse, and fuzzy subsets of ambient temperature, the difference between coolant temperature and ambient temperature, engine speed, and choke opening; Step 2.2: Map the ambient temperature, the difference between the coolant temperature and the ambient temperature, and the engine speed from the basic universe of discourse to the fuzzy universe of discourse to obtain fuzzy quantities; Step 2.3: Calculate the degree to which the fuzzy values ​​of ambient temperature, coolant temperature difference with ambient temperature, and engine speed belong to each fuzzy subset using the membership function; Step 2.4: Based on the calculation results of the membership function, infer the membership of each fuzzy subset of the damper opening according to fuzzy rules; Step 2.5: Based on the membership degree of each fuzzy subset of the choke opening, obtain the fuzzy output of the choke opening through a refinement process; Step 2.6: Defuzzify the fuzzy output of the choke opening to obtain the choke opening.

3. The method for controlling the starting process of an aircraft piston engine according to claim 2, characterized in that, In step 2.1, the basic universe of discourse for ambient temperature is [-30℃, 40℃], the fuzzy universe of discourse is [-7, +7], the fuzzy subset is {-7, -5, -3, -1, +1, +3, +5, +7}, and the corresponding linguistic values ​​are {LB, LM, LS, LZ, HZ, HS, HM, HB}; the basic universe of discourse for the difference between coolant temperature and ambient temperature is [0℃, 70℃], the fuzzy universe of discourse is [-7, +7], the fuzzy subset is {-7, -5, -3, -1, +1, +3, +5, +7}, and the corresponding linguistic values ​​are { The basic universe of discourse for the engine speed is [700r / min, 1200r / min], the fuzzy universe of discourse is [-1, +1], the fuzzy subset is {-1, +1}, and the corresponding linguistic value is set to {L, H}; the basic universe of discourse for the choke opening is [0%, 100%], the fuzzy universe of discourse is [-5, +5], the fuzzy subset is {-5, -3, -1, +1, +3, +5}, and the corresponding linguistic value is {LB, LS, LZ, HZ, HS, HB}.

4. The method for controlling the starting process of an aircraft piston engine according to claim 3, characterized in that, The fuzzy rules in step 2.4 are as follows: when the engine speed belongs to the fuzzy subset {L}, the fuzzy rules are as shown in Table 1: Table 1 When the engine speed belongs to the fuzzy subset {H}, the fuzzy rules are shown in Table 2 below: Table 2 5. The method for controlling the starting process of an aircraft piston engine according to claim 1, characterized in that, Step 3 specifically involves: Step 3.1: Determine the basic universe of discourse, fuzzy universe of discourse, and fuzzy subset for coolant temperature, oil pressure, and target speed; Step 3.2: Map the coolant temperature, oil pressure, and target speed from the basic universe of discourse to the fuzzy universe of discourse to obtain fuzzy quantities; Step 3.3: Calculate the degree to which the fuzzy quantities of coolant temperature, oil pressure, and target speed belong to each fuzzy subset using membership functions; Step 3.4: Based on the calculation results of the membership function, infer the membership degree of each target rotation speed fuzzy subset according to fuzzy rules; Step 3.5: Based on the membership degree of each target rotation speed fuzzy subset, obtain the fuzzy output of the target rotation speed through a refinement process; Step 3.6: Defuzzify the fuzzy output of the target rotational speed to obtain the target rotational speed; Step 3.7: Calculate the throttle opening using the PID controller based on the target speed and the current engine speed.

6. A method for controlling the starting process of an aircraft piston engine according to any one of claims 2 or 5, characterized in that, The mapping relationship between the fundamental universe of discourse and the fuzzy universe of discourse is as follows: Where x is the value in the basic universe of discourse, y is the mapping of x in the fuzzy universe of discourse, n is the maximum value in the fuzzy universe of discourse, a is the minimum value in the basic universe of discourse, and b is the maximum value in the basic universe of discourse.

7. A method for controlling the starting process of an aircraft piston engine according to any one of claims 2 or 5, characterized in that, Defuzzification is performed using the following formula: In the formula, y represents the defuzzification result, u represents the fuzzy output quantity, and y max and y min These are the maximum and minimum values ​​of the defuzzification result, u. max and u min These represent the maximum and minimum values ​​of the fuzzy output, respectively.

8. The method for controlling the starting process of an aircraft piston engine according to claim 5, characterized in that, The basic domain of the coolant temperature is [0℃, 50℃], the fuzzy domain is [-5, +5], the fuzzy subset is {-5, -3, -1, +1, +3, +5}, and the corresponding linguistic values ​​are {LB, LS, LZ, HZ, HS, HB}; the basic domain of the oil pressure is [2bar, 3bar], the fuzzy domain is [-1, +1], the fuzzy subset is {-1, +1}, and the corresponding linguistic values ​​are {L, H}; the basic domain of the target speed is [1400r / min 2300r / min], the fuzzy domain is [-3, +3], the fuzzy subset is {-3, -1, +1, +3}, and the corresponding linguistic values ​​are {LB, LS, HS, HB}.

9. The method for controlling the starting process of an aircraft piston engine according to claim 8, characterized in that, The fuzzy rules in step 3.4 are shown in Table 3: Table 3 10. A control system applied to the starting process control method of an aircraft piston engine as described in claim 1, characterized in that, It includes a data acquisition unit, a target speed fuzzy inference engine, a choke opening fuzzy inference engine, a PID controller, and a drive execution unit; The data acquisition unit acquires sensor signals and processes and calculates ambient temperature, engine speed, coolant temperature, and oil pressure. The choke opening fuzzy inference engine calculates the choke opening based on ambient temperature, engine speed, and coolant temperature. The target speed fuzzy inference engine calculates the target speed based on the coolant temperature and oil pressure. The PID controller calculates the throttle opening based on the target speed and the current engine speed.

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