Active adaptive engine air-fuel ratio delay based on control loop inverse design

Abstract

The application discloses a kind of engine air-fuel ratio delay active adaptation self anti-interference control methods based on control loop reverse design, the air-fuel ratio system model of the present application is established to control, model high precision, small steady-state error, can accurately reflect air-fuel ratio dynamic mixing process and delay process.

Description

Technical Field

[0001] This invention relates to the field of engine control technology, and in particular to an active anti-disturbance control method for engine air-fuel ratio delay based on reverse design of control loop. Background Technology

[0002] In modern automotive engines, precise control of the air-fuel ratio is crucial for achieving efficient engine operation, reducing fuel consumption, and minimizing emissions. Traditional air-fuel ratio control methods are typically based on fixed control strategies, which struggle to adapt to the dynamic changes in engine performance under different operating conditions. Especially when time-delay faults or external disturbances are present, the control effect is significantly impacted, leading to large fluctuations in the air-fuel ratio and affecting engine performance and emissions standards.

[0003] Air-fuel ratio control faces numerous challenges: on the one hand, exhaust delay affects control quality, and the air-fuel ratio measured by the oxygen sensor is delayed compared to the actual in-cylinder value, limiting the bandwidth and adjustment speed of the closed-loop system; on the other hand, the air-fuel ratio system is subject to internal and external disturbances, such as inconsistencies between the target and actual injection quantities, and the impact of exhaust gas recirculation (EGR) on air-fuel ratio measurement. Traditional control methods, such as PID control, struggle to achieve satisfactory dynamic responses under dynamic operating conditions, while model-based control methods, although showing some breakthroughs, are highly dependent on model accuracy, computationally intensive, and difficult to implement in engineering.

[0004] Various solutions have been proposed in the past to address the challenges of air-fuel ratio systems. The most common approach is to treat the dynamics of the air-fuel ratio as a black box and employ a PID controller. The literature (Franceschi EM, Muske KR, Jones JP, et al. An Adaptive Delay-Compensated PID Air Fuel Ratio Controller[J]. Mathematical Analysis, 2007, 1.) uses a PID control algorithm to compensate for the delay caused by the transmission time between the sensor and the actuator. However, a PID controller with fixed gain cannot achieve a satisfactory dynamic response under all operating conditions.

[0005] To address the issue of varying system parameters under dynamic operating conditions, the literature (Ebrahimi B, Tafreshi R, Masudi H, et al. A parameter-varying filtered PID strategy for air-fuel ratio control of spark ignition engines[J]. Control engineering practice, 2012(8):20.) adds a dynamic compensator to the PID control to adapt to changes in engine operating conditions and improve control performance.

[0006] Compared to purely black-box solutions, another representative approach treats the air-fuel ratio system as a gray box and employs model-based control. The literature (Liu Xiao-Liang, Wang Sheng-Chang, Li Mao-Yue. Research on air-fuel ratio control and time delay. Highways and Automotive Applications, 2008, (2): 11-14.) studies the time delay problem in gasoline engine air-fuel ratio control and proposes a state observer-based method to improve the real-time performance of the control system. The literature (Hou Zhi-Xiang, Wu Yi-Hu, Shen Qun-Tai. Advanced control strategies for engine transient air / fuleratio. Transactions of CSICE, 2003, b21 / b(5): 369-373.) explores advanced air-fuel ratio control strategies for automotive gasoline engines under transient operating conditions, improving control performance by introducing a dynamic compensation mechanism. The literature (Wang SW, YuD L, Gomm JB, Page GF, Douglas SS. Adaptive neural network model based predictive control for air-fuel ratio of SI engines. Engineering Applications of Artificial Intelligence, 2006, b19 / b(2): 189-200.) uses an adaptive neural network model based predictive control method to achieve precise control of the air-fuel ratio of spark-ignition engines and effectively cope with various disturbances that may exist in the system.The literature (Zhang F, Grigoriadis KM, Franchek MA, Makki I H. Transient lean burn air-fuel ratio control using input shaping method combined with linear parameter-varying control. In: Proceedings of the 2006 American Control Conference. Minnesota, USA: IEEE, 2006. 3290-3295.) proposes a method that combines input shaping and linear parameter-varying control to solve the problem of transient air-fuel ratio control under lean burn conditions, thereby enhancing the system's disturbance rejection capability.

[0007] Despite the breakthroughs achieved by the aforementioned methods, their control effectiveness is highly dependent on the accuracy of the control-oriented model. Insufficient model accuracy significantly impacts controller performance, while increasing model accuracy inevitably leads to a greater computational burden, making the controller difficult to implement in engineering. Therefore, in practical applications, a balance must be struck between model complexity and computational efficiency to ensure that the designed control system meets both performance requirements and is feasible for engineering implementation. Summary of the Invention

[0008] The purpose of this invention is to address the technical deficiencies in the existing technology by providing an active anti-disturbance control method for engine air-fuel ratio delay based on reverse design of the control loop.

[0009] The technical solution adopted to achieve the purpose of this invention is:

[0010] The active disturbance rejection control method for engine air-fuel ratio delay based on control loop reverse design includes the following steps:

[0011] Step 1: Based on the engine intake and exhaust processes and the sensor responses during these processes, establish an air-fuel ratio system model that connects a first-order inertial element and a pure time-delay element in series. Then, transform this air-fuel ratio system model into a control-oriented air-fuel ratio system model, which includes the total disturbance. ;

[0012] Step 2: Establish a state-space model of the air-fuel ratio system for a pure time-delayed first-order closed-loop system, and set the control input. and system status After Laplace transform, the closed-loop transfer function of the first-order system is obtained. and system output and system status Laplace form of the time delay between Based on closed-loop transfer function Design a controller for the air-fuel ratio system and obtain the controller's tracking error. ;

[0013] Step 3, ignore the total disturbance Establish the closed-loop transfer function of a pure time-delay first-order system. Calculate system state Set the target air-fuel ratio value Calculate tracking error The tracking error is obtained by performing a time-domain transformation. Based on this, the design includes an extended state observer containing the total disturbance and a feedback controller for a portion of the disturbance, wherein the extended state observer contains parameters. and The feedback controller contains parameters ;

[0014] Step 4: Obtain the extended state observer from Step 3 through Laplace transform. After performing a Laplace transform on the feedback controller, the following is obtained: Combined with tracking error air-fuel ratio target value and configure parameters and Through closed-loop transfer function The air-fuel ratio system output is obtained. Then calculate the closed-loop transfer function of the air-fuel ratio system without time delay. ;

[0015] Step 5: Based on the control-oriented air-fuel ratio system model, ignore the total disturbance. Modeling is performed on the Smith predictor to estimate the delay time of the air-fuel ratio system. Let the actual system delay time be... Equal to the modeling delay time Then the Laplace form of the delay time in step 2. The closed-loop transfer function without delay in compensation step 4 A delay time, combined with the target air-fuel ratio value The air-fuel ratio system output with time delay compensation is obtained. .

[0016] In the above technical solution, in step 1, the air-fuel ratio system model is as follows:

[0017]

[0018] in, It is the air-fuel equivalence ratio. Air-fuel ratio , This refers to the actual amount of fuel injected by the injector. For fresh air quality, It is 14.67. Indicates the in-cylinder air equivalence ratio. It is the sensor measurement value of the fuel-air equivalence ratio. It is the air-fuel equivalence ratio after the mixing process but without delay, and the time constant is denoted as... ; This indicates that the transport process of exhaust gas in the exhaust pipe is approximately the delay time of a delay element.

[0019] In the above technical solution, the air-fuel ratio system model for control in step 1 is as follows:

[0020]

[0021] Among them, control input Control system status Control output , and They represent and The estimated values ​​are a and b, which represent two parameters in the control-oriented air-fuel ratio system model.

[0022] In the above technical solution, in step 1, The sum of disturbances caused by internal and external factors in the system is represented as:

[0023]

[0024] in, and They represent and Modeling error, yes Modeling error, Caused by fresh air volume error

[0025] In the above technical solution, in step 2, the air-fuel ratio state-space model can be expressed as:

[0026]

[0027] in, Indicates the air-fuel ratio system status. Indicated derivative, Indicates system output, Indicates control input, Indicates system delay time;

[0028] The result after Laplace transform is:

[0029]

[0030] in, System status For control input The transfer function.

[0031] In the above technical solution, in step 2, the controller of the air-fuel ratio system:

[0032]

[0033] in, Indicates control input, It is a hypothetical polynomial. Indicates system output, Indicates the target value of the control output. System status For control input The closed-loop transfer function, Indicates system delay time. .

[0034] In the above technical solution, in step 3, the transfer function

[0035]

[0036]

[0037] In the above technical solution, in step 3, Convert to time domain form: .

[0038] The extended state observer (ESO) is represented as follows:

[0039]

[0040] in, and This represents the input to ESO. express The derivative, and They represent and The estimated value, and This represents the parameters in the ESO, and the total perturbation. The sum of express The derivative of .

[0041] In the above technical solution, in step 3, the feedback controller is:

[0042]

[0043] in, Indicates control input, Indicates dynamic error The parameters in.

[0044] In the above technical solution, in step 4, a Laplace transform is performed on the extended state observer to eliminate... We can obtain:

[0045]

[0046] Performing a Laplace transform on the feedback controller, we get:

[0047]

[0048] Tracking error = By substituting the Laplace transformed extended state observer into the Laplace transformed feedback controller, we obtain the Laplace transformed control input. :

[0049]

[0050] at the same time,

[0051]

[0052] The parameters are configured as follows:

[0053]

[0054] in, This indicates the bandwidth of the ESO. This indicates the damping of the ESO.

[0055] In the above technical solution, in step 4, the closed-loop transfer function of the system is:

[0056]

[0057] in, This is an estimate of the time constant.

[0058] In the above technical solution, in step 5, the closed-loop transfer function of the system is:

[0059]

[0060] In the above technical solution, in step 5, the Smith predictor is modeled as follows based on the control-oriented air-fuel ratio system model:

[0061]

[0062] in, and They are and The model estimate, yes The estimated value;

[0063] The delayed air-fuel ratio output value is:

[0064]

[0065] Among them, After Laplace transform, .

[0066] Compared with the prior art, the beneficial effects of the present invention are:

[0067] 1. This invention establishes a control-oriented air-fuel ratio system model with high accuracy and small steady-state error, which can accurately reflect the dynamic mixing and delay processes of the air-fuel ratio.

[0068] 2. The control algorithm of this invention can effectively handle multiple interference problems inside and outside the system, improve robustness to delay, and avoid instability caused by controller gain adjustment.

[0069] 3. The angle-discrete Smith predictor improves the accuracy of the prediction results and enhances the dynamic response performance of the system. Attached Figure Description

[0070] Figure 1 This is a rendering of the air-fuel ratio model.

[0071] Figure 2 This is a diagram of the Smith predictor architecture.

[0072] Figure 3 This is a system framework diagram of the control method. Detailed Implementation

[0073] The present invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0074] An active disturbance rejection control algorithm for engine air-fuel ratio delay adaptation based on control loop reverse design includes the following steps:

[0075] like Figures 1-3 As shown, in step 1, based on the engine intake and exhaust processes and the sensor responses during these processes, an air-fuel ratio system model is established that connects a first-order inertial element and a pure time-delay element in series. This air-fuel ratio system model is then transformed into a control-oriented air-fuel ratio system model, which includes the total disturbance. .

[0076] To simplify control design, the air-fuel ratio Converted to air-fuel equivalence ratio ,in, air-fuel equivalence ratio, It is 14.67 For fresh air quality, This refers to the actual amount of fuel injected by the injector (the amount of fuel that participates in combustion). Depend on Calculations show that The value of the air-fuel equivalence ratio measured by the oxygen sensor in the engine exhaust pipe.

[0077] At the start of the engine cycle, the piston moves downward, drawing in a certain amount of fresh air into the cylinder. Simultaneously, a certain amount of gasoline is injected into the cylinder, mixing with the fresh air to form an air-fuel mixture. After the compression and combustion strokes, exhaust gases are formed and expelled from the cylinder through the exhaust pipe. These gases are sensed by an oxygen sensor installed in the exhaust pipe, which measures the actual air-fuel ratio. During the engine cycle, the mixing of exhaust gases in the exhaust pipe can be considered a first-order dynamic process, with a time constant denoted as . The transport process of exhaust gas in the exhaust pipe is approximately a delay phase, with a delay time of [time value missing]. Based on the above mixing and transport processes, to provide a theoretical basis for accurately describing the dynamic characteristics of the engine's air-fuel ratio and for optimizing the design of the engine control system, this embodiment models the air-fuel ratio dynamics as a first-order closed-loop system with pure time delay. The first-order inertial element is combined with the pure time delay element in series. Therefore, the air-fuel ratio system model is:

[0078] (1)

[0079] in, Indicates the air-fuel equivalence ratio in the cylinder. The oxygen sensor reading represents the air-fuel equivalence ratio. The air-fuel equivalence ratio refers to the mixture of gasoline and air injected into the cylinder without any delay. Further expressed as:

[0080] (2)

[0081] in, This refers to the expected fuel injection quantity of the injector. Expected fuel injection volume and actual fuel injection quantity The deviation is caused by factors such as carbon canister and injector errors. This refers to the air-fuel ratio error caused by factors such as fresh air quality error and EGR gas.

[0082] choose As the state of the control system As control input Substituting formula (2) into formula (1), we obtain the state-space model:

[0083] (3)

[0084] Among them, control input Control system status Control output Equivalent oil quantity deviation a and b represent two parameters in the state-space model of the control system. Comparing formulas (1), (2), and (3), we obtain... and The expression is:

[0085] (4)

[0086] (5)

[0087] in, It is a time constant Modeling error, Represents time constant The estimated value, It's the engine speed. , and These are the pressure, volume, and temperature of the exhaust pipe, respectively. The gas constant of the exhaust gas, delay time The model is as follows:

[0088] (6)

[0089] In formula (4) replace ,get and The estimated value:

[0090] (7)

[0091] in, and They represent and Modeling error, and They represent and Substituting the estimated value of formula (7) into formula (3), we obtain the control-oriented air-fuel ratio system model as follows:

[0092] (8)

[0093] in, This represents the sum of disturbances caused by internal and external factors in the system, and its form is:

[0094] (9)

[0095] This embodiment uses test data from an engine bench to verify the effectiveness of the proposed air-fuel ratio system model, such as... Figure 1 As shown, the fuel quantity was stepped at 1.6s and 4.8s respectively. After about 0.2s, the estimated and actual values ​​of the air-fuel ratio system model began to change. It can be seen that the air-fuel ratio system model can accurately reflect the dynamic mixing and delay processes of the air-fuel ratio. The steady-state error of the air-fuel ratio is about 2.9%, indicating that the air-fuel ratio system model has high accuracy and can be used as the control model in this embodiment.

[0096] Step 2: Establish a state-space model of the air-fuel ratio system for a pure time-delayed first-order closed-loop system, and set the control input. and system status After Laplace transform, the closed-loop transfer function of the first-order system is obtained. and system output and system status Laplace form of the time delay between Based on closed-loop transfer function Design a controller for the air-fuel ratio system and obtain the controller's tracking error. .

[0097] For a first-order closed-loop system with pure time delay, if errors and disturbances are neglected, its air-fuel ratio system state-space model can be expressed as:

[0098] (10)

[0099] in, Indicates the air-fuel ratio system status. Indicated derivative, Indicates system output, Indicates control input, Indicates system delay time. and To represent the model parameters, perform a Laplace transform on formula (10) to convert the time domain into the complex frequency domain. The result is:

[0100] (11)

[0101] in, This represents the complex frequency of the Laplace transform of a variable in the complex frequency domain. System status For control input The closed-loop transfer function of a first-order system. Representing the system output, for a first-order closed-loop system with pure time delay, let the controller of the air-fuel ratio system (initial form, the same as the air-fuel ratio expansion state observer described below) be of the following form:

[0102] (12)

[0103] in, This indicates the target air-fuel ratio value of the control output. and Given a assumed polynomial, substituting formula (12) into formula (11) and rearranging, we get:

[0104] (13)

[0105] To improve robustness to delays, we hope There is no If there is an item, then there must be one.

[0106] (14)

[0107] in, If the polynomial is to be determined, then the closed-loop transfer function of the system is:

[0108] (15)

[0109] To ensure that the closed-loop transfer function of equation (15) is stable and has a certain stability margin, the simplest approach is:

[0110] (16)

[0111] Then we can get:

[0112] (17)

[0113] Substituting formula (17) into formula (12) and rearranging, we get:

[0114] (18)

[0115] Formula (18) represents the form that a controller should typically possess. Note that in the above derivation, It only needs to satisfy that it is a rational fractional polynomial and Stability is sufficient; there's no need to constrain its form, therefore... Design different controllers.

[0116] Step 3: Ignore the total disturbance and establish the closed-loop transfer function of the pure time-delay first-order system. And set control input Calculate system state Set the target air-fuel ratio value Calculate tracking error The tracking error is obtained by performing a time-domain transformation. Based on this, an extended state observer and a feedback controller are designed, including a total disturbance, wherein the extended state observer contains parameters. and ;

[0117] For the air-fuel ratio system model formula (8), since Since obtaining the true value is difficult, this embodiment introduces an estimation mechanism to obtain it. An approximate value is obtained, and then compensated for in the control law, thereby effectively suppressing disturbances in the air-fuel ratio system. To simplify the algorithm design of this embodiment, the main dynamic changes of the air-fuel ratio system are captured, and the air-fuel ratio system state is calculated. For control input transfer function At that time, If this item is omitted, then:

[0118] (19)

[0119] (20)

[0120] (twenty one)

[0121] If we want to use the active disturbance rejection algorithm to control the air-fuel ratio system model (8), we must write the control input in the equivalent form of formula (18), that is, the variable entering the extended state observer ESO must be... This reduces the tracking error between the target value and the system state value. for:

[0122] (twenty two)

[0123] Substituting formulas (20) and (21) into formula (22), we get:

[0124] (twenty three)

[0125] Converting formula (23) to its time-domain form, we get:

[0126] (twenty four)

[0127] in, For the target value, To predict the tracking error, we have:

[0128] (25)

[0129] Regarding formula (25), Equivalent to the total disturbance, the air-fuel ratio expansion state observer (ESO) can be designed as follows:

[0130] (26)

[0131] in, and This represents the input to the air-fuel ratio expansion state observer. express The derivative, and This represents the parameters in the air-fuel ratio expansion state observer (ESO). It represents the amount of compensation disturbance in the air-fuel ratio system (a portion of the total compensation disturbance). express The derivative of .

[0132] Furthermore, based on the design methodology of the Extended State Observer (ESO), the feedback controller can be designed as follows:

[0133] (27)

[0134] in, Indicates control input, Indicates dynamic error The parameters in.

[0135] Substituting formula (27) into formula (25), the total disturbance Depend on In Partial compensation Depend on By compensating for the identical parts in the model, the prediction tracking error can be reduced. Transformed into dynamic error:

[0136] (28)

[0137] This is easy to obtain. It must be a negative number. The talent exponent converges to 0; otherwise, its exponent diverges.

[0138] Step 4: Obtain the extended state observer from Step 3 through Laplace transform. After performing a Laplace transform on the feedback controller, the following is obtained: Combined with tracking error air-fuel ratio target value and configure parameters and Through closed-loop transfer function The air-fuel ratio system output is obtained. Then calculate the closed-loop transfer function of the air-fuel ratio system without time delay. .

[0139] Perform a Laplace transform on formula (26) to eliminate We can obtain:

[0140] (29)

[0141] Applying the Laplace transform to formula (27), we can obtain the control input:

[0142] (30)

[0143] Substituting formulas (23) and (29) into formula (30) and rearranging them appropriately, we can obtain the control input:

[0144] (31)

[0145] remember ,in It is a rational polynomial, and it also has , Therefore:

[0146] (32)

[0147] Further analysis reveals:

[0148] (33)

[0149] As can be seen from formula (33), it is the transfer function of the closed-loop system, and there is no delay element in the feedback loop, thus achieving the purpose of eliminating the influence of delay on the control effect.

[0150] In order to eliminate To simplify the transfer function, Need to have zero point Then Substitution The numerator polynomials are:

[0151] (34)

[0152] After sorting, we can obtain:

[0153] (35)

[0154] The above formula is and The relationship that should be satisfied between them. To ensure the stability of the air-fuel ratio expansion state observer (ESO), , Both must satisfy a value greater than zero, therefore we can choose:

[0155] (36)

[0156] make , Then formula (36) can be rewritten as:

[0157] (37)

[0158] Formula (37) is and Configuration method, and These are parameters in the Air-Fuel Ratio Expansion State Observer (ESO). The bandwidth of the air-fuel ratio expansion state observer (ESO) Damping for the air-fuel ratio expansion state observer ESO.

[0159] Substitute formula (35) into The expression contains:

[0160] (38)

[0161] The closed-loop transfer function is now:

[0162] (39)

[0163] when hour, The low-frequency gain of the closed-loop transfer function is 1, therefore It can be designed to be any negative number. In particular, if we let... Then, by canceling out a pair of zeros and poles, the closed-loop transfer function of the stable, no-delay system becomes:

[0164] (40)

[0165] At this point, the closed-loop transfer function is definitely stable, and it is easy to see that it is equivalent to first-order PI control, that is, .

[0166] Step 5: Based on the control-oriented air-fuel ratio system model, ignore the total disturbance. Modeling is performed on the Smith predictor to estimate the delay time of the air-fuel ratio system. Let the actual system delay time be... Equal to the modeling delay time Then the Laplace form of the delay time in step 2. The closed-loop transfer function without delay in compensation step 4 A delay time, combined with the target air-fuel ratio value The air-fuel ratio system output with time delay compensation is obtained. .

[0167] The results of the Smith predictor are as follows: Figure 2 As shown, It is the transfer function of an ideal model of the air-fuel ratio system dynamics. This represents the actual system delay time. The delay time obtained from modeling, This is the output of the Smith predictor. Ideally, the modeling is accurate enough, i.e. (General transfer function of actual air-fuel ratio system). , This is the general form of the controller for a practical air-fuel ratio system. If the output is the actual system output, then the closed-loop transfer function of the system is:

[0168] (41)

[0169] Therefore, according to formulas (40) and (41), , .

[0170] The system result at this time is as follows: Figure 2 As shown, through compensation, the feedback loop no longer contains a delay element, which is equivalent to predicting the feedback signal in advance, thereby eliminating the impact of the delay element on the system stability and improving the system control effect.

[0171] For the air-fuel ratio system model (8), its essence is a first-order inertial element connected in series with a pure time-delay element, because This represents the "total disturbance" caused by various internal and external factors, which is difficult to obtain the true value of. Therefore, it is ignored in the modeling of the Smith predictor, resulting in:

[0172] (42)

[0173] in, and They are and The estimated value of the air-fuel ratio system model. yes The estimated value, the output of the Smith predictor is:

[0174] (43)

[0175] in, That is, the air-fuel ratio with no delay estimated by the Smith predictor, using To estimate ( After Laplace transform, That is, to obtain Calculation method:

[0176] (44)

[0177] When applied to engine control, models of the form of formulas (42) and (44) cannot be directly run on the ECU (Electronic Control Unit) and need to be discretized. Considering the engine's ignition cycle, the Smith predictor is discretized, i.e., formula (44) is discretized to obtain:

[0178] (45)

[0179] in, These are sensor measurements. Given the target value, the solution to be solved is... and .

[0180] by Taking the discretization of [the data] as an example, firstly, the delay time, measured in seconds... and time constant Converting to crankshaft angles, we have:

[0181] (46)

[0182] in, It is the delay time measured in crankshaft angle. It is a time constant measured in crankshaft angles. It is the engine speed. For formula (42), and Use respectively and Representing and performing the Laplace transform, we get:

[0183] (47)

[0184] (48)

[0185] in, , Using bilinear transformation to Converting to discrete form, the formula for the bilinear transform is:

[0186] (49)

[0187] in, The distance between cylinders is the firing interval, which is selected as the firing interval between each cylinder. For a 4-cylinder engine... Substitute formula (49) into , Convert to You can get Discrete form:

[0188] (50)

[0189] Therefore, there is ,Will Substituting and converting to difference form, we get:

[0190] (51)

[0191] To prevent The result is not an integer. and Perform linear interpolation:

[0192] (52)

[0193] in, yes The integer part, yes Substituting formula (52) into formula (51) yields the decimal part. The final discrete form:

[0194] (53)

[0195] Similarly, following the derivation method above, we can obtain Discrete form:

[0196] (54)

[0197] Substituting formulas (53) and (54) into (45) yields the following result. The calculation formula.

[0198] The above description is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A self-disturbance rejection control method for engine air-fuel ratio delay active adaptation based on control loop reverse design, characterized in that, Includes the following steps: Step 1: Based on the engine intake and exhaust process and the sensor response during the intake and exhaust process, establish an air-fuel ratio system model that connects a first-order inertial element and a pure time delay element in series. Transform the air-fuel ratio system model into a control-oriented air-fuel ratio system model, which includes a total disturbance f(t). Step 2: Establish a state-space model of the air-fuel ratio system for a pure time-delay first-order closed-loop system. Define the control input U1(s) and the system state X1(s). After Laplace transform, obtain the first-order system closed-loop transfer function G(s) and the Laplace form of the time delay between the system output Y1(s) and the system state X1(s). A controller for the air-fuel ratio system is designed based on the closed-loop transfer function G(s), and the tracking error E of the controller is obtained. p (s); Step 3, neglecting the total disturbance f(t), establish the closed-loop transfer function G of the pure time-delay first-order system. XU (s) and set the control input U(s), calculate the system state X(s), set the air-fuel ratio target value R(s), and calculate the tracking error E. p (s), after performing a time-domain transformation, yields the tracking error e. p (t), based on this, the design includes an extended state observer containing the total disturbance and a feedback controller containing the partial disturbance, wherein the extended state observer contains built-in defined parameters β1 and β2, and the parameters β1 and β2 are used in the derivation of the feedback controller; Step 4: The extended state observer from Step 3 is transformed using a Laplace transform to obtain Z2(s). The feedback controller is then transformed using a Laplace transform to obtain U2(s). This is combined with the tracking error E. p (s) and the target air-fuel ratio R(s), and configure parameters β1 and β2, through the closed-loop transfer function G XU (s), obtain the air-fuel ratio system output Y(s), and then calculate the time-delayed closed-loop transfer function G of the air-fuel ratio system. YR (s); Step 5: Based on the control-oriented air-fuel ratio system model, ignoring the total disturbance f(t), model the Smith predictor to estimate the delay time τ2 of the air-fuel ratio system. Assume the actual system delay time τ1 is equal to the modeled delay time τ2, and then use the Laplace form of the delay time from Step 2. The closed-loop transfer function G without delay in compensation step 4 YR (s) A delay time, combined with the target air-fuel ratio value R(s), yields the delayed air-fuel ratio system output Y. P (s).

2. The control method according to claim 1, characterized in that, In step 1, the air-fuel ratio system model is as follows: in, It is the air-fuel equivalence ratio. air-fuel ratio m f The actual fuel injection quantity of the injector, m air For fresh air quality, R af It is 14.

67. Indicates the in-cylinder air equivalence ratio. It is the sensor measurement value of the fuel-air equivalence ratio. It is the air-fuel equivalence ratio after the mixing process but without delay, and the time constant is denoted as τ. m ;τ d This indicates that the transport process of exhaust gas in the exhaust pipe is approximately the delay time of a delay element.

3. The control method according to claim 1, characterized in that, In step 1, the air-fuel ratio system model oriented towards control is as follows: Among them, control input Control system status Control output and Let a and b represent the estimated values ​​of a and b, respectively. a and b represent two parameters in the control-oriented air-fuel ratio system model.

4. The control method according to claim 1, characterized in that, In step 1, f(t) represents the sum of disturbances caused by internal and external factors in the system, expressed as: Where, Δ a and Δ b Let Δ represent the modeling errors of a and b, respectively. m It is τ m Modeling error, This is due to errors in the amount of fresh gas.

5. The control method according to claim 1, characterized in that, In step 2, the air-fuel ratio state-space model can be expressed as: Where x1 represents the air-fuel ratio system state, Let x1 represent the derivative, y1 represent the system output, u1 represent the control input, and τ1 represent the system delay time. The result after Laplace transform is: Wherein, G(s) is the transfer function of system state X1(s) with respect to control input U1(s).

6. The control method according to claim 1, characterized in that, In step 2, the controller of the air-fuel ratio system: Where U1(s) represents the control input, c1(s) is the assumed polynomial, Y1(s) represents the system output, R(s) represents the target value of the control output, G(s) is the closed-loop transfer function of the system state X1(s) with respect to the control input U1(s), and τ1 represents the system delay time.

7. The control method according to claim 1, characterized in that, In step 3, in step 3, The transfer function X(s)=G XU (s)U(s) E p (s)=R(s)-X(s), converted to time domain form: e p (t)=r(t)-x(t). The extended state observer (ESO) is represented as follows: Among them, z1 and e p (t) represents the input of ESO. Denotes the derivative of z1. and Let a and b represent the estimated values, respectively. Let β1 and β2 represent the parameters in the ESO. The total perturbation f(t) is β1(e p The sum of z(t) - z1(t) and z2 Let z be the derivative of z2. The feedback controller is: Where u(t) represents the control input, k p Indicates dynamic error The parameters in.

8. The control method according to claim 1, characterized in that, In step 4, performing a Laplace transform on the extended state observer to eliminate z1 yields: Performing a Laplace transform on the feedback controller, we get: Tracking error E p Substituting (s) = R(s) - X(s) and the Laplace transform extended state observer into the Laplace transform feedback controller, we obtain the Laplace transform control input U2(s): at the same time, The parameters are configured as follows: in, Let ξ represent the bandwidth of the ESO, and ξ represent the damping of the ESO.

9. The control method according to claim 1, characterized in that, In step 4, the closed-loop transfer function of the system is: in, This is an estimate of the time constant.

10. The control method according to claim 1, characterized in that, In step 5, the closed-loop transfer function of the system is: Based on the control-oriented air-fuel ratio system model, the Smith predictor is modeled as follows: Where, x m (t) and y m τ(t) and y(t) are the model estimates of x(t) and y(t), respectively. s It is τ d The estimated value; The zero-delay air-fuel ratio output value is: y p (t)=y(t)-y m (t)+x m (t) Among them, y p (t) is transformed into Y by Laplace transform. P (s).

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