A control method for exhaust gas recirculation rate of aero-engines considering uncertainties

Abstract

This invention provides a control method for the exhaust gas recirculation (EGR) rate of aero-engines that considers uncertainties. The method includes establishing an EGR rate calculation expression, analyzing and quantifying uncertainties from the expression to derive an uncertainty model, analyzing correction factors for temperature and engine speed, establishing a final EGR rate mathematical model incorporating these uncertainties, obtaining the mean of the final EGR rate, controlling the EGR valve opening using a fuzzy logic controller based on the error between the target and actual EGR rates to achieve the target EGR rate, and repeating the above steps to achieve real-time control of the EGR rate. This invention, by considering the three uncertainties of sensor measurement errors, temperature, and engine speed during actual operation, can more accurately determine the actual EGR rate, effectively improving the accuracy of the control results. Furthermore, the use of fuzzy logic control enables more precise control of the EGR rate.

Description

Technical Field

[0001] This invention relates to the field of exhaust gas recirculation rate control technology, and more specifically to a control method for the exhaust gas recirculation rate of aero-engines that takes into account uncertainties. Background Technology

[0002] With the increasing prominence of global climate change and environmental pollution, the control of aero-engine emissions has become a research focus. EGR (Exhaust Gas Recirculation) technology has been introduced to effectively reduce emissions of nitrogen oxides and other pollutants from engines. However, existing EGR control strategies are mostly based on deterministic models, often neglecting various uncertainties present in actual operation, or failing to comprehensively consider all uncertainties in current technologies. These uncertainties may affect the accuracy of the EGR rate, thereby impacting engine performance and emissions. Summary of the Invention

[0003] To overcome the technical problem in existing technologies that do not consider uncertainties affecting the EGR rate or do not consider uncertainties comprehensively, thus affecting the accuracy of the EGR rate.

[0004] The technical solution adopted by the present invention to achieve the above-mentioned objective is: a control method for the exhaust gas recirculation rate of an aero-engine considering uncertainties, comprising the following steps:

[0005] S1: Establish the EGR rate calculation expression;

[0006] S2: Analyze and quantify the uncertainty factors from the expression for calculating the EGR rate, and derive the uncertainty model;

[0007] S3: Consider the effects of temperature and speed on EGR rate, and analyze the correction factors for temperature and speed;

[0008] S4: Establish a mathematical model for the final EGR rate that includes uncertainties;

[0009] S5: Read the value of oxygen concentration at the engine intake measured by the data sensor, and perform Monte Carlo simulation on the final EGR rate mathematical model to obtain the average value of the final EGR rate;

[0010] S6: Based on the aircraft engine data processor, query the EGR rate MAP to obtain the target EGR rate;

[0011] S7: Calculate the error between the target EGR rate and the mean of the final EGR rate, and use fuzzy logic to control the EGR valve on the aero engine, changing the valve opening to change the EGR rate.

[0012] S8: Repeat steps S5-S7 to control the EGR rate in real time.

[0013] Preferably, the EGR rate calculation expression in step S1 is:

[0014]

[0015] Where: [O2] represents the oxygen concentration in ambient air. intake This is the oxygen concentration value at the engine intake, measured by a data sensor, [O2]. exhaust This represents the oxygen concentration in the exhaust gas, which is negligible, i.e., [O2]. exhaust =0.

[0016] Preferably, in step S1, the expression for calculating the EGR rate includes [O2]. intake The measurement contains errors. Considering the impact of this parameter uncertainty on the actual EGR rate, an uncertainty model is derived, the expression of which is as follows:

[0017]

[0018] Among them: [O2] intake This represents the actual oxygen concentration at the engine intake. ε represents the oxygen concentration at the engine intake as measured by the aircraft engine data processor, and ε is the measurement error of the sensor, which follows a mathematical expectation of 0 and a variance of σ. 2 The Gaussian distribution.

[0019] Preferably, step S3 is: obtaining the influence of engine intake and exhaust temperatures and engine speed on EGR rate from the aero-engine data processor, and obtaining temperature correction factor β1 and speed correction factor β2 through regression analysis.

[0020] Preferably, the mathematical model expression for the EGR rate, which includes uncertainties, in step S4 is:

[0021] EGR final (%) = EGR(%) + β1T + β2RPM

[0022] Substituting the EGR rate calculation expression from step S1 and the uncertainty model from step S2 into the EGR rate mathematical model that includes uncertainty factors, we obtain the final EGR rate mathematical model that includes uncertainty factors, whose expression is:

[0023]

[0024] Wherein: EGR final (%) represents the final EGR rate, and [O2] represents the oxygen concentration in ambient air. This represents the oxygen concentration at the engine intake, as measured by the aircraft engine data processor; [O2] exhaustε is the oxygen concentration in the exhaust gas, β1 is the temperature correction factor, β2 is the speed correction factor, ε is the measurement error of the sensor, T is the temperature, and RPM is the speed.

[0025] Preferably, step S5 includes the following steps:

[0026] S5.1: Uncertainty factor: Sensor error X i Temperature X j and rotational speed X n Assign probability distributions to determine that sensor error and temperature follow a Gaussian distribution, while rotational speed follows a uniform distribution;

[0027] S5.2: Uncertainty factor X from a known probability distribution of sensor error i Temperature X j and rotational speed X n A sample is randomly selected from the data: Substitute the sample into the final EGR rate mathematical model. Based on the value of the oxygen concentration at the engine intake measured by the data sensor at this time, a final EGR rate is calculated and denoted as y1.

[0028] S5.3: Repeat step S5.2 M times to calculate M final EGR rate values: (y1, y2, ..., y M Based on the M final EGR rate values ​​obtained, the probability distribution, mean, and variance of the final EGR rate are obtained.

[0029] Preferably, step S7 includes the following steps:

[0030] S7.1: Calculate the error between the final average EGR rate and the target EGR rate, expressed as follows:

[0031]

[0032] Where: error is the error value, EGR target (%) represents the target EGR rate. This represents the mean of the final EGR rate;

[0033] S7.2: Define fuzzy sets: NB, NS, ZE, PS, and PB represent: very large and negative error, slightly large and negative error, near zero error, slightly large and positive error, and very large and positive error, respectively.

[0034] S7.3: Define fuzzy rules: If the error is NB, the output is to significantly increase the EGR valve opening; if the error is NS, the output is to slightly increase the EGR valve opening; if the error is ZE, the output is to keep the current EGR valve unchanged; if the error is PS, the output is to slightly decrease the EGR valve opening; if the error is PB, the output is to significantly decrease the EGR valve opening.

[0035] Compared with existing technologies, this invention, by considering three uncertainties in actual operation—sensor measurement errors, temperature, and rotational speed—can more accurately determine the actual EGR rate, effectively improving the accuracy of the control results. Furthermore, the use of fuzzy logic control enables more precise control of the EGR rate. Attached Figure Description

[0036] Figure 1 This is a flowchart of a method for controlling the exhaust gas recirculation rate of an aero-engine that takes into account uncertainties, according to the present invention. Detailed Implementation

[0037] A specific implementation of a method for controlling the exhaust gas recirculation rate of an aero-engine that takes into account uncertainty, according to the present invention.

[0038] Includes the following steps:

[0039] S1: Establish the EGR rate calculation expression. The EGR rate calculation expression is as follows:

[0040]

[0041] Where: [O2] represents the oxygen concentration in ambient air. intake This is the oxygen concentration value at the engine intake, measured by a data sensor, [O2]. exhaust This represents the oxygen concentration in the exhaust gas, which is negligible, i.e., [O2]. exhaust =0;

[0042] S2: In the expression for calculating the EGR rate, [O2] intake The measurement contains errors. Considering the impact of this parameter uncertainty on the actual EGR rate, the uncertainty factors are analyzed and quantified from the expression for calculating the EGR rate, and an uncertainty model is derived; its expression is as follows:

[0043]

[0044] Among them: [O2] intake This represents the actual oxygen concentration at the engine intake. ε represents the oxygen concentration at the engine intake as measured by the aircraft engine data processor, and ε is the measurement error of the sensor, which follows a mathematical expectation of 0 and a variance of σ.2 Gaussian distribution;

[0045] S3: Consider the influence of temperature and engine speed on EGR rate, and analyze the correction factors for temperature and engine speed. Specifically: obtain the influence of engine intake and exhaust temperature and engine speed on EGR rate from the aero-engine data processor, and obtain the temperature correction factor β1 and engine speed correction factor β2 through regression analysis.

[0046] S4: Establish a mathematical model for the final EGR rate that includes uncertainties. The expression for the mathematical model of the EGR rate that includes uncertainties is as follows:

[0047] EGR final (%) = EGR(%) + β1T + β2RPM

[0048] Substituting the EGR rate calculation expression from step S1 and the uncertainty model from step S2 into the EGR rate mathematical model that includes uncertainty factors, we obtain the final EGR rate mathematical model that includes uncertainty factors, whose expression is:

[0049]

[0050] Wherein: EGR final (%) represents the final EGR rate, and [O2] represents the oxygen concentration in ambient air. This represents the oxygen concentration at the engine intake, as measured by the aircraft engine data processor; [O2] exhaust ε is the oxygen concentration in the exhaust gas, β1 is the temperature correction factor, β2 is the speed correction factor, ε is the measurement error of the sensor, T is the temperature, and RPM is the speed.

[0051] S5: Read the oxygen concentration value at the engine intake measured by the data sensor, and perform a Monte Carlo simulation on the final EGR rate mathematical model to obtain the average value of the final EGR rate, including the following steps:

[0052] S5.1: Uncertainty factor: Sensor error X i Temperature X j and rotational speed X n Assign probability distributions to determine that sensor error and temperature follow a Gaussian distribution, while rotational speed follows a uniform distribution;

[0053] S5.2: Uncertainty factor X from a known probability distribution of sensor error i Temperature X j and rotational speed X n A sample is randomly selected from the data: Substitute the sample into the final EGR rate mathematical model. Based on the value of the oxygen concentration at the engine intake measured by the data sensor at this time, a final EGR rate is calculated and denoted as y1.

[0054] S5.3: Repeat step S5.2 M times to calculate M final EGR rate values: (y1, y2, ..., y M Based on the M final EGR rate values ​​obtained, the probability distribution, mean, and variance of the final EGR rate are obtained.

[0055] S6: Based on the aircraft engine data processor, query the EGR rate MAP to obtain the target EGR rate;

[0056] S7: Calculate the error between the target EGR rate and the average of the final EGR rate, and use fuzzy logic to control the EGR valve on the aero-engine, changing the valve opening to change the EGR rate, including the following steps:

[0057] S7.1: Calculate the error between the final average EGR rate and the target EGR rate, expressed as follows:

[0058]

[0059] Where: error is the error value, EGR target (%) represents the target EGR rate. This represents the mean of the final EGR rate;

[0060] S7.2: Define fuzzy sets: NB, NS, ZE, PS, and PB represent: very large and negative error, slightly large and negative error, near zero error, slightly large and positive error, and very large and positive error, respectively.

[0061] S7.3: Define fuzzy rules: If the error is NB, the output is to significantly increase the EGR valve opening; if the error is NS, the output is to slightly increase the EGR valve opening; if the error is ZE, the output is to keep the current EGR valve unchanged; if the error is PS, the output is to slightly decrease the EGR valve opening; if the error is PB, the output is to significantly decrease the EGR valve opening.

[0062] S8: Repeat steps S5-S7 to control the EGR rate in real time.

[0063] Compared with existing technologies, this invention, by considering three uncertainties in actual operation—sensor measurement errors, temperature, and rotational speed—can more accurately determine the actual EGR rate, effectively improving the accuracy of the control results. Furthermore, the use of fuzzy logic control enables more precise control of the EGR rate.

[0064] This invention has been described through embodiments. Those skilled in the art will understand that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of this invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, this invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of this invention.

Claims

1. A method for controlling the exhaust gas recirculation rate of an aero-engine considering uncertainties, characterized in that, Includes the following steps: S1: Establish the EGR rate calculation expression: ； in: This refers to the oxygen concentration in ambient air. This is the actual oxygen concentration at the engine intake, measured by a data sensor. This refers to the oxygen concentration in the exhaust gas. S2: Analyze the uncertainties from the expression for calculating the EGR rate. The uncertainty model is then quantified and derived, with the following expression: ； in: The value of oxygen concentration at the engine intake, measured by the aircraft engine data processor. The error measured by this sensor is given by a mathematical expectation of 0 and a variance of . Gaussian distribution; S3: Obtain the influence of engine intake and exhaust temperatures and engine speed on EGR rate from the aircraft engine data processor, and obtain the temperature correction factor through regression analysis. and speed correction factor ; S4: Establish a mathematical model for the final EGR rate that includes uncertainties, specifically: The mathematical model expression for the EGR rate, which includes uncertainties, is as follows: ； Substituting the EGR rate calculation expression from step S1 and the uncertainty model from step S2 into the EGR rate mathematical model that includes uncertainty factors, we obtain the final EGR rate mathematical model that includes uncertainty factors, whose expression is: ； in: For the final EGR rate, For temperature, Rotational speed; S5: Read the value of oxygen concentration at the engine intake measured by the data sensor, and perform Monte Carlo simulation on the final EGR rate mathematical model to obtain the average value of the final EGR rate; S6: Based on the aircraft engine data processor, query the EGR rate MAP to obtain the target EGR rate; S7: Calculate the error between the target EGR rate and the mean of the final EGR rate, and use fuzzy logic to control the EGR valve on the aero engine, changing the valve opening to change the EGR rate. S8: Repeat steps S5-S7 to control the EGR rate in real time.

2. The method for controlling the exhaust gas recirculation rate of an aero-engine considering uncertainties according to claim 1, characterized in that, Step S5 includes the following steps: S5.1: Uncertainty factor: sensor error ,temperature and rotational speed Assign probability distributions to determine that sensor error and temperature follow a Gaussian distribution, while rotational speed follows a uniform distribution; S5.2: Uncertainty factors from which the probability distribution has been determined, such as sensor error. ,temperature and rotational speed A sample is randomly selected from the data: The sample is then input into the final EGR rate mathematical model. Based on the oxygen concentration at the engine intake measured by the data sensor at this time, a final EGR rate is calculated, denoted as . ; S5.3: Repeat step S5.2 Once, the calculation yielded One final EGR rate value: According to the obtained The final EGR rate value is obtained, along with its probability distribution, mean, and variance.

3. The method for controlling the exhaust gas recirculation rate of an aero-engine considering uncertainties according to claim 2, characterized in that, Step S7 includes the following steps: S7.1: Calculate the error between the final average EGR rate and the target EGR rate, expressed as follows: ； in: This is the error value. For the target EGR rate, This represents the mean of the final EGR rate; S7.2: Define fuzzy sets: NB, NS, ZE, PS, and PB represent: very large and negative error, slightly large and negative error, near zero error, slightly large and positive error, and very large and positive error, respectively. S7.3: Define fuzzy rules: If the error is NB, the output is to significantly increase the EGR valve opening; if the error is NS, the output is to slightly increase the EGR valve opening; if the error is ZE, the output is to keep the current EGR valve unchanged; if the error is PS, the output is to slightly decrease the EGR valve opening; if the error is PB, the output is to significantly decrease the EGR valve opening.

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