Air-fuel ratio control method and system for biogas engine

Abstract

The invention provides an air-fuel ratio control method and system for a biogas engine, and belongs to the technical field of engine control, and the method comprises the steps that operation parameters of the biogas engine are obtained, an air-fuel ratio dynamic model is built based on the operation parameters and an order improvement method, and the air-fuel ratio dynamic model is a second-order time-delay-free model; designing a nonlinear active disturbance rejection controller based on the air-fuel ratio dynamic model; and parameters of the nonlinear active-disturbance-rejection controller are set, a target nonlinear active-disturbance-rejection controller is obtained, and operation of the biogas engine is controlled based on the target nonlinear active-disturbance-rejection controller and the target air-fuel ratio. According to the method, the air-fuel ratio of the biogas engine can be accurately controlled under the conditions of no modeling, variable working conditions, disturbance and fuel component fluctuation.

Description

Technical Field

[0001] The present invention relates to the technical field of engine control, and particularly to an air-fuel ratio control method and system for a biogas engine. Background Art

[0002] Traditional air-fuel ratio control of biogas engines mostly uses PID controllers, which have the following limitations: 1. Poor non-linear adaptability: The PID is a linear controller and it is difficult to cope with the non-linear characteristics (such as time delay and multi-variable coupling) of the air-fuel ratio system of biogas engines; 2. Weak anti-disturbance ability: Fluctuations in biogas components (changes in methane content), intake pressure disturbances, etc. are likely to cause fluctuations in the air-fuel ratio, and the PID cannot compensate in real time; 3. Strong model dependence: Existing models rely on accurate engine dynamics modeling, while there are unmodeled dynamics in the actual system (such as exhaust mixing delay and sensor noise).

[0003] Therefore, there is an urgent need to provide an air-fuel ratio control method and system for a biogas engine to achieve precise control of the air-fuel ratio of the biogas engine without modeling, under variable working conditions, disturbances, and fuel component fluctuations. Summary of the Invention

[0004] In view of this, it is necessary to provide an air-fuel ratio control method and system for a biogas engine to solve the technical problem in the prior art that the air-fuel ratio of the biogas engine is controlled by a linear PID controller, resulting in low control accuracy of the air-fuel ratio under unmodeled, variable working conditions, disturbances, and fuel component fluctuations.

[0005] On the one hand, to solve the above technical problem, the present invention provides an air-fuel ratio control method for a biogas engine, including: Obtaining the operating parameters of the biogas engine, and constructing an air-fuel ratio dynamic model based on the operating parameters and the order elevation method, where the air-fuel ratio dynamic model is a second-order time-delay-free model; Designing a non-linear auto-disturbance rejection controller based on the air-fuel ratio dynamic model; Tuning the parameters of the non-linear auto-disturbance rejection controller to obtain a target non-linear auto-disturbance rejection controller, and controlling the operation of the biogas engine based on the target non-linear auto-disturbance rejection controller and the target air-fuel ratio.

[0006] In a possible implementation manner, the constructing of the air-fuel ratio dynamic model based on the operating parameters and the order elevation method includes: Constructing an initial air-fuel ratio dynamic model based on the operating parameters, where the initial air-fuel ratio dynamic model is a series of time-delay model and first-order inertia model; Eliminating the time-delay model based on the order elevation method to obtain the second-order time-delay-free model.

[0007] In a possible implementation, the air-fuel ratio dynamic model is as follows:

[0008] In the formula, is the excess air coefficient of the mixture at the mixer; is the sum of the engine cycle delay and the gas transmission delay; is the exhaust gas mixing delay and the wide-range oxygen sensor delay; s is the complex frequency variable.

[0009] In a possible implementation, the nonlinear active disturbance rejection controller includes a tracking differentiator, and the control model of the tracking differentiator is as follows:

[0010] In the formula, is the target air-fuel ratio; is the tracking signal after the smooth transition of the target air-fuel ratio; is the differential signal of; is the fastest control function; is the filtering factor used to suppress the influence of high-frequency noise on the differential signal; is the speed factor used to determine the speed of the transition process.

[0011] In a possible implementation, the nonlinear active disturbance rejection controller further includes an extended state observer. The extended state observer is a third-order observer, and the control model of the extended state observer is as follows:

[0012] In the formula, , , are respectively the estimated value output by the extended state observer, the estimated value of the output change rate, and the estimated value of the total disturbance; , and are observer gain coefficients greater than zero, which determine the estimation speed and noise resistance; is a nonlinear function; , is the length of the linear interval of the nonlinear function; y is the output value of the extended state observer; e is the observation error; , are nonlinear factors.

[0013] In a possible implementation, the nonlinear active disturbance rejection controller further includes a nonlinear combination, and the control model of the nonlinear combination is as follows:

[0014] In the formula, e 1 is the error signal; e 2 is the differential signal; , is the nonlinearity coefficient; is the basic control quantity of the nonlinear active disturbance rejection controller; k 1 is the proportional gain; k 2 is the differential gain.

[0015] In a possible implementation, the nonlinear active disturbance rejection controller further includes dynamic compensation, and the control model of the dynamic compensation is:

[0016] In the formula, is the approximate gain of the controlled object; is the control quantity of the nonlinear active disturbance rejection controller.

[0017] In a possible implementation, the parameters of the nonlinear active disturbance rejection controller include a first type of parameters determined based on physical meaning and engineering experience and a second type of parameters that cannot be determined based on physical meaning and engineering experience; then tuning the parameters of the nonlinear active disturbance rejection controller to obtain a target nonlinear active disturbance rejection controller includes: Determining the first type of parameters based on physical meaning and engineering experience; Determining the second type of parameters based on the particle swarm optimization algorithm.

[0018] In a possible implementation, the method further includes: Building a simulation model based on the target nonlinear active disturbance rejection controller and performing simulation verification on the target nonlinear active disturbance rejection controller based on the simulation model.

[0019] In a second aspect, the present invention also provides an air-fuel ratio control system for a biogas engine, including: An air-fuel ratio model construction unit, configured to obtain the operating parameters of the biogas engine and construct an air-fuel ratio dynamic model based on the operating parameters and the order elevation method, and the air-fuel ratio dynamic model is a second-order time-delay-free model; A nonlinear active disturbance rejection controller design unit, configured to design a nonlinear active disturbance rejection controller based on the air-fuel ratio dynamic model; the nonlinear active disturbance rejection controller includes a tracking differentiator, an extended state observer, a nonlinear combination, and dynamic compensation; A parameter tuning and control unit, configured to tune the parameters of the nonlinear active disturbance rejection controller to obtain a target nonlinear active disturbance rejection controller, and control the operation of the biogas engine based on the target nonlinear active disturbance rejection controller and the target air-fuel ratio.

[0020] The beneficial effects of the present invention are as follows: The air-fuel ratio control method for the biogas engine provided by the present invention first constructs a dynamic air-fuel ratio model of a second-order time-delay-free model based on the obtained operating parameters of the biogas engine and the order elevation method. By transforming the complex time-delay link into a more easily processed time-delay-free model, the design complexity of the nonlinear active disturbance rejection controller can be simplified. Through the elevation of the order, the phase lag, dynamic delay, and disturbance sensitivity problems caused by time delay can be compensated more flexibly, thereby improving stability, response speed, and robustness, and thus improving the control accuracy of the air-fuel ratio of the biogas engine.

[0021] Moreover, by setting a nonlinear active disturbance rejection controller, since the nonlinear active disturbance rejection controller classifies all uncertain factors acting on the biogas engine as unknown disturbances and has the characteristics of estimating and compensating the input and output of the controlled object, the nonlinear active disturbance rejection controller does not need to rely on the mathematical model of the biogas engine, has the advantages of strong robustness and strong anti-disturbance ability, and realizes the precise regulation of the air-fuel ratio under unmodeled, variable working conditions, disturbances, and fuel component fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0023] Figure 1 It is a schematic flowchart of an embodiment of the air-fuel ratio control method for the biogas engine provided by the present invention; Figure 2 It is a schematic structural diagram of an embodiment of the nonlinear active disturbance rejection controller provided by the present invention; Figure 3 It is a schematic flowchart of an embodiment of step S101 provided by the present invention; Figure 4 It is a schematic flowchart of an embodiment of the particle swarm optimization algorithm provided by the present invention; Figure 5 It is a schematic diagram of an embodiment for verifying the effectiveness of the target nonlinear active disturbance rejection controller provided by the present invention; Figure 6 It is a schematic structural diagram of an embodiment of the air-fuel ratio control system for the biogas engine provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0025] It should be understood that the schematic drawings are not drawn to scale. The flowcharts used in the present invention illustrate the operations implemented according to some embodiments of the present invention. It should be understood that the operations in the flowchart may not be implemented in sequence, and the steps without logical context relationships may be reversed or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart or remove one or more operations from the flowchart under the guidance of the content of the present invention. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor systems and / or microcontroller systems.

[0026] Referring to "embodiments" herein means that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0027] The present invention provides an air-fuel ratio control method and system for a biogas engine, which will be described separately below.

[0028] Figure 1 It is a schematic flowchart of an embodiment of the air-fuel ratio control method for the biogas engine provided by the present invention. As Figure 1 shown, the air-fuel ratio control method for the biogas engine includes: S101. Obtain the operating parameters of the biogas engine, and construct an air-fuel ratio dynamic model based on the operating parameters and the order enhancement method. The air-fuel ratio dynamic model is a second-order time-delay-free model.

[0029] Among them, the establishment of the air-fuel ratio dynamic model can provide guidance for the subsequent design of the nonlinear active disturbance rejection controller. For example, the extended state observer corresponding to the second-order time-delay-free model needs to be third-order, including two system states and one extended state for disturbance estimation, so as to more efficiently separate the known dynamics and unknown disturbances. Another example is that parameters such as the inertia time constant in the air-fuel ratio dynamic model can provide an initial reference for the bandwidth selection of the tracking differentiator and the extended state observer, shortening the parameter tuning time.

[0030] Among them, the operating parameters refer to the operating parameters related to the air-fuel ratio control, including but not limited to the actual excess air coefficient monitored by the oxygen sensor and the excess air coefficient of the mixture at the mixer.

[0031] S102. Design a nonlinear active disturbance rejection controller based on the air-fuel ratio dynamic model.

[0032] In a specific embodiment of the present invention, as Figure 2 shown, the nonlinear active disturbance rejection controller includes a tracking differentiator, an extended state observer, a nonlinear combination, and a dynamic compensation. Figure 2 The transition process in is the working process of the tracking differentiator. The working process of the nonlinear active disturbance rejection controller is specifically as follows: input the target air-fuel ratio into the tracking differentiator to obtain the transitioned signal, input both the transitioned air-fuel ratio signal and the air-fuel ratio differential signal into the nonlinear combination for nonlinear combination processing to obtain the preliminary control quantity, then expand the original state variables in the extended state observer, convert the total disturbance of the sum of the environmental disturbance and the system disturbance into a new state variable to obtain the estimated output, the estimated output includes the estimated state and the estimated disturbance, and then use the estimated state as the input of the nonlinear combination and the estimated disturbance as the output of the nonlinear combination for the next control.

[0033] S103. Tune the parameters of the nonlinear active disturbance rejection controller to obtain the target nonlinear active disturbance rejection controller, and control the operation of the biogas engine based on the target nonlinear active disturbance rejection controller and the target air-fuel ratio control.

[0034] Among them, parameter tuning refers to the process of determining the parameters of the nonlinear active disturbance rejection controller to ensure the stable and efficient operation of the nonlinear active disturbance rejection controller.

[0035] Among them, the target air-fuel ratio refers to the desired air-fuel ratio.

[0036] It should be noted that the specific control process of controlling the operation of the biogas engine based on the target nonlinear active disturbance rejection controller and the target air-fuel ratio control is: aiming at the target air-fuel ratio, making the air-fuel ratio output by the target nonlinear active disturbance rejection controller the same as or close to the target air-fuel ratio, that is: it is a tracking control process.

[0037] It should be understood that: The air-fuel ratio control method of the biogas engine in the embodiments of the present invention can be implemented in any device based on the air-fuel ratio control method of the biogas engine, such as: biogas transmitter control devices, etc. Specifically, the air-fuel ratio control method of the biogas engine is stored in the above-mentioned device in the form of a prepared program. When the device is started, the program is called, and the air-fuel ratio control method of the biogas engine is implemented.

[0038] Compared with the prior art, the air-fuel ratio control method of the biogas engine provided by the embodiments of the present invention first constructs a second-order time-delay-free model of the air-fuel ratio dynamic model based on the obtained operating parameters of the biogas engine and the order-increasing method, converts the complex time-delay link into a more easily processed time-delay-free model, can simplify the design complexity of the nonlinear active disturbance rejection controller, and can more flexibly compensate for the phase lag, dynamic delay and disturbance sensitivity problems caused by time delay through the increase of the order, thereby improving stability, response speed and robustness, and thus improving the control accuracy of the air-fuel ratio of the biogas engine.

[0039] Moreover, in the embodiments of the present invention, by setting a nonlinear active disturbance rejection controller, since the nonlinear active disturbance rejection controller classifies all uncertain factors acting on the biogas engine as unknown disturbances and has the characteristics of estimating and compensating the input and output of the controlled object, the nonlinear active disturbance rejection controller does not need to rely on the mathematical model of the biogas engine, has the advantages of strong robustness and strong anti-disturbance ability, and realizes the precise regulation of the air-fuel ratio under unmodeled, variable working conditions, disturbances and fuel component fluctuations.

[0040] Furthermore, in the embodiments of the present invention, by setting the nonlinear active disturbance rejection tracker to include a tracking differentiator, step, ramp and other sudden input signals are converted into smooth trajectories, and their differential signals are synchronously extracted, avoiding oscillations or overshoots caused by direct tracking, and further ensuring the precise regulation of the air-fuel ratio.

[0041] In some embodiments of the present invention, as Figure 3 shown, step S101 includes: S301. Construct an initial air-fuel ratio dynamic model based on the operating parameters. The initial air-fuel ratio dynamic model is a series-connected time-delay model and a first-order inertia model.

[0042] Among them, the construction process of the initial air-fuel ratio dynamic model is as follows: First, establish the original air-fuel ratio system model, specifically as follows:

[0043] In the formula, is the actual excess air coefficient monitored by the oxygen sensor; is the excess air coefficient of the mixture at the mixer; is the engine cycle delay; is the gas transmission delay; is the exhaust gas mixing delay; is the wide - range oxygen sensor delay; s is the complex frequency variable.

[0044] Then, model simplification is carried out. Specifically, since is negligible let represent the total time delay; represent the total time constant. Therefore, the model is simplified to:

[0045] Next, feedback linearization processing is carried out. Specifically, the excess air coefficient at the mixer is expressed as:

[0046] where is the gas mass flow rate at the venturi - type mixer; is a function of the biogas flow rate with respect to the valve core opening of the biogas solenoid valve.

[0047] Using the feedback linearization strategy, the direct adjustment of the fuel solenoid valve opening is equivalently converted into the adjustment of the value of the mixture:

[0048] where is the value actually measured by the UEGO oxygen sensor; is the output signal of the controller, representing the target value of the mixture expected to be formed at the throttle position.

[0049] S302. Eliminate the time - delay model based on the order - increasing method to obtain a second - order time - delay - free model.

[0050] Specifically, the air - fuel ratio dynamic model is:

[0051] In the formula, is the excess air coefficient of the mixture at the mixer; is the sum of the engine cycle delay and the gas transmission delay; is the exhaust gas mixing delay and the wide - range oxygen sensor delay; s is the complex frequency variable.

[0052] It should be noted that: in view of the fact that the time - delay object is processed by the order - increasing method, when adjusting the parameters of the active disturbance rejection controller subsequently, the compensation factor needs to be corrected accordingly according to the time - delay size.

[0053] In a specific embodiment of the present invention, the control model of the tracking differentiator is as follows:

[0054] In the formula, is the target air-fuel ratio; is the tracking signal after the smooth transition of the target air-fuel ratio; is the differential signal of; is the fastest control function; is the filtering factor, used to suppress the influence of high-frequency noise on the differential signal; is the speed factor, used to determine the speed of the transition process.

[0055] In a specific embodiment of the present invention, the extended state observer is a third-order observer, and the control model of the extended state observer is as follows:

[0056] In the formula, , , are respectively the estimated value output by the extended state observer, the estimated value of the output change rate, and the estimated value of the total disturbance; , and are observer gain coefficients greater than zero, which determine the estimation speed and noise resistance; is a non-linear function; , is the linear interval length of the non-linear function; y is the output value of the extended state observer; e is the observation error; , are non-linear factors.

[0057] In a specific embodiment of the present invention, the control model of the non-linear combination is as follows:

[0058] In the formula, e 1 is the error signal; e 2 is the differential signal; , are non-linearity coefficients; is the basic control quantity of the non-linear auto-disturbance rejection controller; k 1 is the proportional gain; k 2 is the differential gain.

[0059] Among them, the desired state generated by the tracking differentiator will be the actual state of the system estimated by the extended state observer and its differential The error signal and its differential signal, which reflect the gap between the current operating state and the desired target, are used in the subsequent control law.

[0060] In a specific embodiment of the present invention, the control model for dynamic compensation is:

[0061] In the formula, is the approximate gain of the controlled object; is the control quantity of the nonlinear active disturbance rejection controller.

[0062] Among them, the estimated value of the biogas engine by the extended state observer, , , , will be dynamically injected into the control quantity through the feedback path to form feedforward compensation.

[0063] It should be understood that: the approximate gain of the controlled object is obtained through calibration.

[0064] To improve the efficiency and accuracy of parameter tuning, in some embodiments of the present invention, the parameters of the nonlinear active disturbance rejection controller include the first type of parameters determined based on physical meaning and engineering experience and the second type of parameters that cannot be determined based on physical meaning and engineering experience; then the step of tuning the parameters of the nonlinear active disturbance rejection controller in S103 to obtain the target nonlinear active disturbance rejection controller includes: Determine the first type of parameters based on physical meaning and engineering experience; Determine the second type of parameters based on the particle swarm optimization algorithm.

[0065] Among them, determining the first type of parameters based on physical meaning and engineering experience specifically means: according to the sampling frequency of the wide-range oxygen sensor, determine the sampling step , the filtering factor is equal to the sampling step, . The speed factor By inputting a signal with a step of 1.2 to the tracking differentiator module order, observing the output curves of the tracking signal and the differential signal under different conditions, determine .

[0066] The gain parameters , , related to the estimation speed and accuracy of the state and total disturbance in the ESO are determined by the bandwidth method, , , . is the observation bandwidth of the extended state observer.

[0067] For the non - linear function , control the non - linear intensity ( enhance the gain in the small - error section and suppress the gain in the large - error section when < 1); preferably determine , .

[0068] To avoid system response lag or oscillation caused by too large or too small linear intervals, take this value equal to the system sampling step and set it as: .

[0069] Among them, the second - type parameters include the compensation factor , the proportional gain k1, and the derivative gain k2. Individually tune them, then as Figure 4 shown, based on the particle swarm optimization algorithm, the specific second - type parameters are determined as: Step1: Initialize the particle swarm population size parameter, set the particle position, velocity vector, and fitness evaluation function; Among them, the fitness function is the weighted sum of the tracking error integral term, the control variable change rate term, and the anti - disturbance recovery time term.

[0070] Step2: Establish the dynamic coupling between the particle swarm algorithm and the non - linear active disturbance rejection controller, and iteratively calculate the fitness values of each particle; Step3: Update the individual optimal solution and the global optimal solution based on the fitness ranking; Step4: Iteratively update the particle position and velocity vector according to the velocity update equation; Step5: Non - linearly update the weight factor according to the weight formula; Step6: Judge whether the termination condition is satisfied. If satisfied, stop the iteration and output the optimal solution. If not, return to Step2 for execution.

[0071] After the algorithm basically converges, find a stable optimal solution. After tuning, determine , , .

[0072] Before optimizing the second - type parameters through the particle swarm in the embodiments of the present invention, the first - type parameters are quickly determined based on physical meaning and engineering experience, reducing the number of parameters that need to be tuned by the particle swarm, avoiding the particle swarm algorithm from falling into local optimality, and ensuring the reliability of parameter tuning while improving the parameter tuning efficiency.

[0073] To verify the effectiveness of the target non - linear active disturbance rejection controller determined in the embodiments of the present invention, in some embodiments of the present invention, the air - fuel ratio control method of the biogas engine further includes: A simulation model is built based on the target non - linear auto - disturbance rejection controller, and the target non - linear auto - disturbance rejection controller is simulated and verified based on the simulation model.

[0074] In a specific embodiment of the present invention, an air - fuel ratio control model based on a non - linear controller is built in the form of an S - function to verify whether the target non - linear auto - disturbance rejection controller meets the expectations under the rated working conditions (1500 r / min, 700 kW). The verification results are as Figure 5 , Figure 5 The step - like line in is the target air - fuel ratio curve, and the curved line is the air - fuel ratio curve determined based on the target non - linear auto - disturbance rejection controller. It can be seen from Figure 5 that the difference between the two is not significant and the coincidence degree is relatively high, verifying the effectiveness of the target non - linear auto - disturbance rejection controller in controlling the air - fuel ratio of the biogas engine.

[0075] In summary, for the air - fuel ratio control method of the biogas engine proposed in the embodiment of the present invention, the non - linear auto - disturbance rejection controller can use an extended state observer to estimate and compensate in real - time for the total disturbance suffered by the air - fuel ratio of the biogas engine during operation. Therefore, the non - linear auto - disturbance rejection controller has strong adaptability and robustness and can perform air - fuel ratio control without relying on a specific system model. Two stages are designed based on non - linear auto - disturbance rejection control, namely: parameter tuning based on physical meaning and engineering experience and parameter tuning based on the particle swarm optimization algorithm. While ensuring the design efficiency of the non - linear auto - disturbance rejection controller, its design reliability is improved. Among them, the tracking differentiator mainly tracks and smoothly transitions the input displacement signal, and extracts a continuous differentiable displacement signal from it to prevent overshoot caused by a large initial control output of the system, thereby improving the control accuracy of the non - linear auto - disturbance rejection controller. That is, the embodiment of the present invention realizes precise control of the air - fuel ratio of the biogas engine without modeling, under variable working conditions, disturbances, and fuel component fluctuations.

[0076] To better implement the air - fuel ratio control method of the biogas engine in the embodiment of the present invention, correspondingly, the embodiment of the present invention also provides an air - fuel ratio control system for a biogas engine. As Figure 6 shown, the air - fuel ratio control system 600 of the biogas engine includes: An air - fuel ratio model construction unit 601, configured to obtain the operating parameters of the biogas engine and build an air - fuel ratio dynamic model based on the operating parameters and the order - increasing method. The air - fuel ratio dynamic model is a second - order model without time delay; A non - linear auto - disturbance rejection controller design unit 602, configured to design a non - linear auto - disturbance rejection controller based on the air - fuel ratio dynamic model; the non - linear auto - disturbance rejection controller includes a tracking differentiator, an extended state observer, a non - linear combination, and a dynamic compensation; The parameter tuning and control unit 603 is used to tune the parameters of the non-linear auto-disturbance rejection controller to obtain a target non-linear auto-disturbance rejection controller, and based on the target non-linear auto-disturbance rejection controller and the target air-fuel ratio, control the operation of the biogas engine.

[0077] The air-fuel ratio control system 600 of the biogas engine provided in the above embodiment can implement the technical solutions described in the embodiments of the air-fuel ratio control method of the above biogas engine. The specific implementation principles of the above modules or units can be referred to the corresponding content in the embodiments of the air-fuel ratio control method of the above biogas engine, and will not be elaborated here.

[0078] Those skilled in the art can understand that all or part of the processes for implementing the methods in the above embodiments can be completed by instructing relevant hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium. Among them, the computer-readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory, etc.

[0079] The above has introduced in detail an air-fuel ratio control method and system for a biogas engine provided by the present invention. Specific examples are used in this article to elaborate on the principles and implementation manners of the present invention. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present invention; at the same time, for those skilled in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for controlling the air-fuel ratio of a biogas engine, characterized in that, Including: Obtain the operating parameters of the biogas engine, and construct an air-fuel ratio dynamic model based on the operating parameters and the order enhancement method. The air-fuel ratio dynamic model is a second-order non-time-delay model; Design a nonlinear active disturbance rejection controller based on the air-fuel ratio dynamic model; Tune the parameters of the nonlinear active disturbance rejection controller to obtain a target nonlinear active disturbance rejection controller, and control the operation of the biogas engine based on the target nonlinear active disturbance rejection controller and the target air-fuel ratio.

2. The air-fuel ratio control method of the biogas engine according to claim 1, characterized in that, The constructing of the air-fuel ratio dynamic model based on the operating parameters and the order enhancement method includes: Construct an initial air-fuel ratio dynamic model based on the operating parameters. The initial air-fuel ratio dynamic model is a series of time-delay model and first-order inertia model; Eliminate the time-delay model based on the order enhancement method to obtain the second-order non-time-delay model.

3. The air-fuel ratio control method of the biogas engine according to claim 2, wherein The air-fuel ratio dynamic model is: In the formula, is the excess air coefficient of the mixture gas at the mixer; is the sum of the engine cycle delay and the gas transmission delay; is the exhaust gas mixing delay and the wide - range oxygen sensor delay; s is the complex frequency variable.

4. The air-fuel ratio control method for a biogas engine according to claim 1, characterized in that The nonlinear active disturbance rejection controller includes a tracking differentiator, and the control model of the tracking differentiator is: In the formula, is the target air-fuel ratio; is the tracking signal after smooth transition of the target air-fuel ratio; is the differential signal of; is the fastest control function; is the filtering factor, used to suppress the influence of high-frequency noise on the differential signal; is the speed factor, used to determine the speed of the transition process.

5. The air-fuel ratio control method of the biogas engine according to claim 4, characterized in that, The nonlinear active disturbance rejection controller further includes an extended state observer. The extended state observer is a third-order observer, and the control model of the extended state observer is: Wherein, , , are the estimated values of the output of the extended state observer, the estimated value of the output change rate, and the estimated value of the total disturbance respectively; , and are observer gain coefficients greater than zero, which determine the estimation speed and noise immunity; is a non-linear function; , is the length of the linear interval of the non-linear function; y is the output value of the extended state observer; e is the observation error; , is the non - linear factor.

6. The air-fuel ratio control method of the biogas engine according to claim 5, characterized in that, The nonlinear active disturbance rejection controller further includes a nonlinear combination, and the control model of the nonlinear combination is: Wherein, e 1 is the error signal; e 2 is the differential signal; , are the nonlinearity coefficients; is the basic control quantity of the nonlinear active disturbance rejection controller; k 1 is the proportional gain; k 2 is the differential gain.

7. The air-fuel ratio control method of the biogas engine according to claim 1, characterized in that The nonlinear active disturbance rejection controller further includes a dynamic compensation, and the control model of the dynamic compensation is: wherein, is the approximate gain of the controlled object; is the control quantity of the nonlinear active disturbance rejection controller.

8. The air-fuel ratio control method of the biogas engine according to claim 1, characterized in that, The parameters of the nonlinear active disturbance rejection controller include the first type of parameters determined based on physical meaning and engineering experience and the second type of parameters that cannot be determined based on physical meaning and engineering experience. Then, the tuning of the parameters of the nonlinear active disturbance rejection controller to obtain a target nonlinear active disturbance rejection controller includes: Determine the first type of parameters based on physical meaning and engineering experience; Determine the second type of parameters based on the particle swarm optimization algorithm.

9. The air-fuel ratio control method of the biogas engine according to claim 1, characterized in that The method further includes: Build a simulation model based on the target nonlinear active disturbance rejection controller, and perform simulation verification on the target nonlinear active disturbance rejection controller based on the simulation model.

10. An air-fuel ratio control system for a biogas engine, characterized in that, Including: An air-fuel ratio model construction unit, configured to obtain the operating parameters of the biogas engine, and construct an air-fuel ratio dynamic model based on the operating parameters and the order enhancement method. The air-fuel ratio dynamic model is a second-order non-time-delay model; A nonlinear active disturbance rejection controller design unit, configured to design a nonlinear active disturbance rejection controller based on the air-fuel ratio dynamic model; A parameter tuning and control unit, configured to tune the parameters of the nonlinear active disturbance rejection controller to obtain a target nonlinear active disturbance rejection controller, and control the operation of the biogas engine based on the target nonlinear active disturbance rejection controller and the target air-fuel ratio.

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