Control method, device, engine, aircraft and computer readable storage medium

Abstract

The disclosure provides a control method, device, engine, aircraft and computer readable storage medium, relates to the technical field of control, and the method comprises the following steps: in the case that a controlled system fails, a dynamic model reflecting the state of the controlled system is determined; the difference between the input and the output of the dynamic model is determined; the control model of the controlled system is determined according to the difference, the control model comprises at least one of an additive fault model and a multiplicative fault model; and the controlled system is fault-tolerant controlled according to the control model.

Description

Technical Field

[0001] This disclosure relates to the field of control technology, and in particular to a control method, apparatus, engine, aircraft, and computer-readable storage medium. Background Technology

[0002] Aircraft engines fly for extended periods in complex environments, placing extremely high demands on their safety. Failures in the controlled systems of aircraft engines can affect their performance and potentially lead to accidents. Therefore, aircraft engines require a certain degree of fault-tolerant control capability to ensure their operational stability and safety.

[0003] In related technologies, when a fault occurs in the controlled system, a control model is arbitrarily selected from the existing control models, and then fault-tolerant control is performed on the controlled system based on the selected control model. Summary of the Invention

[0004] The inventors noticed that when the controlled system malfunctions, the stability of the controlled system is low when fault-tolerant control is performed according to methods in related technologies. Through analysis, the inventors realized that fault-tolerant control based on an arbitrarily chosen control model does not consider the actual fault state of the controlled system, resulting in low accuracy of fault-tolerant control based on the chosen model and failing to guarantee the stability of the controlled system's operation.

[0005] To address the aforementioned problems, the present disclosure proposes the following solutions.

[0006] According to one aspect of the present disclosure, a control method is provided, comprising: determining a dynamic model reflecting the state of the controlled system in the event of a fault in the controlled system; determining a difference between the input and output of the dynamic model; determining a control model of the controlled system based on the difference, the control model including at least one of an additive fault model and a multiplicative fault model; and performing fault-tolerant control on the controlled system based on the control model.

[0007] In some embodiments, the input is r(t), the output is y(t), and the difference is δ(t) = y(t) - r(t), where t represents time.

[0008] In some embodiments, determining the control model of the controlled system based on the difference includes: when the difference is constant, determining the control model of the controlled system as the additive fault model.

[0009] In some embodiments, determining the control model of the controlled system based on the difference includes: when the difference is not a constant, determining the control model of the controlled system as the multiplicative fault model.

[0010] In some embodiments, determining the control model of the controlled system based on the difference includes: when the difference includes constants and non-constant numbers, determining that the control model includes the additive fault model and the multiplicative fault model, wherein the additive fault model is g1(t) = r(t) + f a The multiplicative fault model is g2(t) = r(t) + r(t) * f m , where f a f is a constant m The value is not zero; fault-tolerant control of the controlled system according to the control model includes: determining a hybrid model based on the additive fault model and the multiplicative fault model, wherein the hybrid model is g3(t) = r(t) + r(t)*f m +f a Fault-tolerant control is performed on the controlled system based on the hybrid model.

[0011] In some embodiments, determining a dynamic model reflecting the state of the controlled system in the event of a failure of the controlled system includes: determining a failure mode of the controlled system in the event of a failure of the controlled system; and determining the dynamic model based on the failure mode.

[0012] In some embodiments, determining the dynamic model based on the fault mode includes: acquiring an initial dynamic model corresponding to the fault mode and fault test data corresponding to the fault level of the fault mode, wherein the fault test data includes at least one set of test data, each set of test data includes input data and first output data, wherein the first output data is obtained by inputting the input data into the controlled system to perform a fault injection test; and determining the dynamic model based on the initial dynamic model and the fault test data.

[0013] In some embodiments, determining the dynamic model based on the initial dynamic model and the fault test data includes: performing parameter identification on the initial dynamic models of different orders based on the fault test data to obtain multiple intermediate dynamic models of different orders, and obtaining the parameter identification time consumed by each intermediate dynamic model; determining the difference between the second output data obtained by inputting the input data from each set of test data into each intermediate dynamic model and the first output data in the set of test data; determining the model error of each intermediate dynamic model based on the difference; determining the reference parameters of each intermediate dynamic model based on the model error, the parameter identification time, and the order; and determining the dynamic model from the multiple intermediate dynamic models based on the reference parameters.

[0014] In some embodiments, the dynamic model is the intermediate dynamic model with the smallest reference parameter among the plurality of intermediate dynamic models.

[0015] In some embodiments, the reference parameters of each intermediate dynamic model are positively correlated with the model error, parameter identification time, and order of the intermediate dynamic model.

[0016] In some embodiments, the reference parameter J of each intermediate dynamic model n =r1|ε|+r2|ΔT|+r3|n|, where r1, r2, and r3 are the weight coefficients of the model error ε, parameter identification time ΔT, and order n of the intermediate dynamic model, respectively.

[0017] In some embodiments, the dynamic model is a state-space model.

[0018] In some embodiments, the state-space model is a nonlinear polynomial state-space model.

[0019] In some embodiments, the controlled system includes an actuator circuit of an aircraft engine, the actuator circuit including an actuating cylinder.

[0020] According to another aspect of the present disclosure, a control device is provided, comprising: a first determining module configured to determine a dynamic model reflecting the state of the controlled system in the event of a fault in the controlled system; a second determining module configured to determine a difference between the input and output of the dynamic model; a third determining module configured to determine a control model of the controlled system based on the difference, the control model including at least one of an additive fault model and a multiplicative fault model; and a control module configured to control the controlled system according to the control model.

[0021] According to another aspect of the present disclosure, a control device is provided, comprising: a memory; and a processor coupled to the memory, the processor being configured to execute the method described in any of the above embodiments based on instructions stored in the memory.

[0022] According to another aspect of the present disclosure, an engine is provided, including the control device described in any of the foregoing embodiments.

[0023] In some embodiments, the engine further includes a controlled system.

[0024] In some embodiments, the controlled system includes the actuator circuit of the engine, the actuator circuit including an actuating cylinder.

[0025] According to another aspect of the present disclosure, an aircraft is provided, including the engine described in any of the above embodiments.

[0026] According to another aspect of the present disclosure, a computer-readable storage medium is provided, including computer program instructions, wherein the computer program instructions, when executed by a processor, implement the method described in any of the above embodiments.

[0027] The control method provided in this disclosure determines a dynamic model reflecting the state of the controlled system when a fault occurs. Then, based on the difference between the input and output of the dynamic model, a control model for the controlled system is determined, and the system is controlled according to this control model. In this approach, since the difference between the input and output of the dynamic model can characterize the difference between the input and output of the controlled system under fault conditions, the actual fault state of the controlled system is considered during the determination of the control model. This improves the accuracy of fault-tolerant control of the controlled system, thereby enhancing the stability of its operation.

[0028] The technical solutions of this disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart illustrating a control method according to some embodiments of the present disclosure;

[0031] Figure 2 This is a schematic flowchart of a control method according to other embodiments of the present disclosure;

[0032] Figure 3 This is a flowchart illustrating some implementation methods of step 206;

[0033] Figure 4 This is a schematic diagram of curves of experimental data and model data according to some embodiments of this disclosure;

[0034] Figure 5 This is a schematic diagram of the input and output curves of a dynamic model according to some embodiments of the present disclosure;

[0035] Figure 6 This is a schematic diagram of curves of test data and model data according to other embodiments of this disclosure;

[0036] Figure 7 This is a schematic diagram of the input and output curves of a dynamic model according to other embodiments of this disclosure;

[0037] Figure 8 This is a schematic diagram of curves of experimental data and model data according to some embodiments of this disclosure;

[0038] Figure 9 This is a schematic diagram of the input and output curves of a dynamic model according to some embodiments of the present disclosure;

[0039] Figure 10 This is a schematic diagram of the structure of a control device according to some embodiments of the present disclosure;

[0040] Figure 11 This is a schematic diagram of the structure of a control device according to other embodiments of the present disclosure;

[0041] Figure 12 This is a schematic diagram of the structure of an engine according to some embodiments of the present disclosure. Detailed Implementation

[0042] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0043] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of this disclosure.

[0044] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0045] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0046] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0047] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0048] Figure 1 This is a flowchart illustrating a control method according to some embodiments of the present disclosure.

[0049] In step 102, in the event of a failure in the controlled system, a dynamic model reflecting the state of the controlled system is determined.

[0050] In some embodiments, the controlled system may include an actuator circuit of an aircraft engine, which may include an actuator cylinder. In some embodiments, the actuator circuit may also include sensors and servo valves connected to the actuator cylinder.

[0051] In some embodiments, when a fault occurs in the controlled system, the fault mode of the controlled system can be determined first, and then the dynamic model of the controlled system can be determined based on the fault mode. A fault mode may include multiple fault levels, and the fault level can represent different degrees of fault in the fault mode.

[0052] For example, the fault mode could be a servo valve pressure fault mode, and the fault level of the servo valve pressure fault mode could represent a pressure fault when the servo valve pressure decreases by 20% or a pressure fault when the servo valve pressure decreases by 30%, etc.; another example is the actuator load jamming fault mode, and the fault level of the actuator load jamming fault mode could represent an actuator load jamming fault under 4 times the load, etc.; yet another example is the sensor drift fault mode, and the fault level of the sensor drift fault mode could represent a sensor drift fault when the sensor drift amount is negative 30%, etc.

[0053] It should be noted that servo valve pressure failure refers to a failure caused by factors such as oil filter blockage or pipeline leakage, which reduces or increases the servo valve pressure, resulting in a slower actuator displacement speed and inability to reach the designated position; actuator load jamming failure refers to a failure caused by factors such as deformation of the connecting ring, actuator, or bolts, which changes the load, resulting in a slower actuator displacement speed or even an inability to respond to control commands; sensor drift failure refers to a failure caused by zero-point drift of the sensor, which results in an error between the measured displacement value and the actual displacement value of the actuator, making it impossible to control the actuator normally based on the measured value.

[0054] In some embodiments, the dynamic model can be a state-space model, for example, the state-space model can be a nonlinear polynomial state-space model.

[0055] Step 102 can be implemented in different ways, and some implementation methods of step 102 will be given later.

[0056] In step 104, the differences between the input and output of the dynamic model are determined.

[0057] In some embodiments, the input of the dynamic model is r(t) and the output is y(t), and the difference between the input and the output is δ(t) = y(t) - r(t), where t represents time.

[0058] In step 106, the control model of the controlled system is determined based on the differences.

[0059] Here, the control model includes at least one of the additive fault model and the multiplicative fault model.

[0060] The difference between the input and output of a dynamic model may consist of only constants, only non-constant numbers, or both constants and non-constant numbers. The appropriate control model can be determined based on the type of difference.

[0061] In some embodiments, when the difference between the input and output of the dynamic model contains only constants, the control model of the controlled system is determined to be an additive fault model. For example, if the difference δ(t) = y(t) - r(t) = f a f a If the difference is constant and only contains constants, then the control model for the controlled system can be an additive fault model: g1(t) = r(t) + f a .

[0062] In other embodiments, the control model of the controlled system is determined to be a multiplicative fault model when the difference between the input and output of the dynamic model contains only non-constant numbers. For example, if the difference δ(t) = y(t) - r(t) = r(t) * f m f m If the difference is not zero, and the difference contains only one variable non-constant, then the control model for the controlled system can be a multiplicative fault model: g2(t) = r(t) + r(t) * f m .

[0063] In some other embodiments, when the difference between the input and output of the dynamic model contains both constants and non-constant numbers, the control model for the controlled system is determined to include additive fault models and multiplicative fault models. For example, if the difference δ(t) = y(t) - r(t) = r(t) * f m +f a f a f is a constant m The difference is not zero and includes the constant f. a and the non-constant r(t)*f m Then, based on the constant f a Determining the control model of the controlled system includes the additive fault model g1(t) = r(t) + f a And based on the non-constant r(t)*f m Determining the control model of the controlled system also includes the multiplicative fault model g2(t) = r(t) + r(t) * f m f a f is a constant m Not zero.

[0064] In step 108, fault-tolerant control is performed on the controlled system according to the control model.

[0065] When the control model includes only an additive fault model or a multiplicative fault model, fault-tolerant control is performed on the controlled system based on the additive fault model or the multiplicative fault model. When the control model includes both additive and multiplicative fault models, a hybrid model can be determined based on the additive and multiplicative fault models, and then fault-tolerant control is performed on the controlled system based on the hybrid model.

[0066] For example, the control model also includes the additive fault model g1(t) = r(t) + f a Multiplicative fault model g2(t)=r(t)+r(t)*f m In the case of f a f is a constant m If the value is not zero, the mixed model g3(t) = r(t) + r(t) * f can be determined. m +f a Then, fault-tolerant control is performed on the controlled system based on the hybrid model.

[0067] It should be noted that a corresponding controller can be designed based on the control model, so as to perform fault-tolerant control on the controlled system through the designed controller. In the process of designing the controller, the modeling error of the control model can be considered as a disturbance of the system.

[0068] In the above embodiments, when a fault occurs in the controlled system, a dynamic model reflecting the state of the controlled system is determined. Then, a control model for the controlled system is determined based on the difference between the input and output of the dynamic model, and the controlled system is controlled according to the control model. In this approach, since the difference between the input and output of the dynamic model can characterize the difference between the input and output of the controlled system under fault conditions, the actual fault state of the controlled system is considered in the process of determining the control model, improving the accuracy of fault-tolerant control of the controlled system and thus enhancing the stability of the controlled system's operation.

[0069] Figure 2 This is a flowchart of a control method according to other embodiments of this disclosure.

[0070] In step 202, in the event of a failure in the controlled system, the failure mode of the controlled system is determined.

[0071] For a detailed description of the controlled system and fault modes, please refer to the relevant embodiments in step 102, which will not be repeated here.

[0072] In step 204, the initial dynamic model corresponding to the fault mode and the fault test data corresponding to the fault level of the fault mode are obtained.

[0073] In some embodiments, the fault test data includes at least one set of test data, each set of test data including input data and first output data, wherein the first output data is obtained by inputting the input data into the controlled system to perform a fault injection test.

[0074] In some embodiments, the controlled system can be subjected to corresponding fault injection tests in advance according to different fault levels of the fault modes of the controlled system, so as to obtain fault test data corresponding to different fault levels.

[0075] It should be noted that fault injection tests can introduce different faults into the controlled system to obtain fault test data corresponding to different faults. This fault test data reflects the state of the controlled system when a fault exists. The fault test data can serve as reference data for subsequent determination of the dynamic model.

[0076] In some embodiments, corresponding initial dynamic models can be pre-set for different fault modes of the controlled system. The initial dynamic model is a dynamic model with unknown parameters.

[0077] In one embodiment, one fault mode may correspond to one initial dynamic model. In other embodiments, multiple fault modes may correspond to one initial dynamic model.

[0078] It should be understood that when the controlled system is simultaneously in multiple fault modes, corresponding fault injection tests can be performed on the controlled system in advance, based on the fault level of each fault mode, to obtain fault test data when the controlled system is in multiple fault modes simultaneously. For example, a pressure fault with a 20% reduction in servo valve pressure and an actuator load jamming fault under 4 times the load can be simultaneously introduced into the controlled system through fault injection tests to obtain fault test data when the controlled system has both of these faults.

[0079] In step 206, the dynamic model is determined based on the initial dynamic model and the fault test data.

[0080] Step 206 can be implemented in different ways, and some implementation methods of step 206 will be given later.

[0081] In step 208, the difference between the input and output of the dynamic model is determined.

[0082] In step 210, the control model of the controlled system is determined based on the differences.

[0083] In step 212, fault-tolerant control is performed on the controlled system according to the control model.

[0084] The specific implementation methods of steps 208 to 212 are similar to those of steps 104 to 108. For detailed descriptions, please refer to the relevant embodiments in steps 104 to 108, which will not be repeated here.

[0085] In the above embodiments, by combining the initial dynamic model corresponding to the pre-set fault mode and the fault test data obtained by pre-performing fault injection tests for different fault levels of the fault mode, the determined dynamic model can more accurately reflect the actual fault state of the controlled system, thereby more accurately determining the control model of the controlled system, further improving the accuracy of fault-tolerant control of the controlled system, and thus further improving the stability of the controlled system operation.

[0086] Figure 3 This is a flowchart illustrating some implementation methods of step 206.

[0087] In step 302, parameter identification is performed on the initial dynamic models of different orders based on the fault test data to obtain multiple intermediate dynamic models of different orders, as well as the parameter identification time consumed by each intermediate dynamic model.

[0088] It should be understood that by identifying the parameters of the initial dynamic model based on the fault test data, the values ​​of the unknown parameters in the initial dynamic model can be obtained. The intermediate dynamic model is the dynamic model with definite parameter values ​​obtained after identifying the parameters of the initial dynamic model.

[0089] In step 304, the difference between the second output data obtained by inputting the input data from each set of experimental data into each intermediate dynamic model and the first output data from that set of experimental data is determined.

[0090] It should be understood that for each intermediate dynamic model, the obtained difference can be one or more, and the second output data can also be one or more. The number of differences, the number of second output data, and the number of first output data are the same. For example, suppose the fault test data includes 3 sets of test data. Since each set of test data includes input data and first output data, the number of first output data is 3. After inputting the input data of the 3 sets of test data into a certain intermediate dynamic model, 3 second output data can be obtained. Then, the second output data obtained based on the same input data is compared with the first output data to obtain the difference between the two. This comparison is performed 3 times in total, and finally 3 differences are obtained.

[0091] In step 306, the model error of each intermediate dynamic model is determined based on the difference.

[0092] In some embodiments, the number of differences is one, and this difference can be determined as the model error of the intermediate dynamic model.

[0093] In other embodiments, there are multiple differences, and the variance or standard deviation of these multiple differences can be determined as the model error of the intermediate dynamic model.

[0094] In step 308, the reference parameters of each intermediate dynamic model are determined based on the model error, parameter identification time, and order of each intermediate dynamic model.

[0095] Step 308 can be implemented in different ways, and some implementation methods of step 308 will be given later.

[0096] In step 310, a dynamic model is determined from multiple intermediate dynamic models based on reference parameters.

[0097] In some embodiments, the intermediate dynamic model with the smallest reference parameters can be determined as the dynamic model reflecting the state of the controlled system.

[0098] It should be understood that the dynamic model determined from multiple intermediate dynamic models often requires small model error, short parameter identification time, and appropriate model capacity, so as to ensure that the dynamic model can more accurately reflect the state of the controlled system. Using the reference parameters shown in formula (1), the model error, parameter identification time, and model capacity of each intermediate dynamic model can be comprehensively evaluated, so as to select a dynamic model that meets the requirements of the controlled system.

[0099] In the above embodiments, a dynamic model that meets the requirements of the controlled system is determined from multiple intermediate dynamic models based on reference parameters. The model error, parameter identification time, and model capacity of each intermediate dynamic model can be comprehensively evaluated, ensuring the accuracy and applicability of the determined dynamic model from multiple dimensions. In this way, the control model of the controlled system can be determined more accurately, further improving the accuracy of fault-tolerant control of the controlled system, and thus further improving the stability of the controlled system operation.

[0100] In some embodiments, the reference parameters of each intermediate dynamic model are positively correlated with the model error, parameter identification time, and order of the intermediate dynamic model.

[0101] In some embodiments, as shown in formula (1), the reference parameters for each intermediate dynamic model can be:

[0102] J n =r1|ε|+r2|ΔT|+r3|n| (1)

[0103] Where r1, r2, and r3 are the weight coefficients of the model error ε, parameter identification time ΔT, and order n of the intermediate dynamic model, respectively.

[0104] It should be noted that the order n can be used to represent the model's capacity, which characterizes the model's ability to fit the data. If the capacity is insufficient, the model will not be able to represent the data well, easily exhibiting underfitting; if the capacity is too large, the model will overfit the data, easily exhibiting overfitting. The weighting coefficients r1, r2, and r3 can be selected based on the controlled system's requirements for model error, parameter identification time, and model capacity. For example, when the controlled system has high requirements for model error and model capacity, but low requirements for parameter identification time, larger r1 and r3 can be selected, while smaller r2 can be chosen.

[0105] In the above embodiments, by determining that the reference parameters of each intermediate dynamic model are positively correlated with the model error, parameter identification time and order of the intermediate dynamic model, the dynamic model that meets the requirements of the controlled system can be determined more accurately from multiple intermediate dynamic models, which further improves the accuracy of the control model of the controlled system and thus further improves the stability of the operation of the controlled system.

[0106] Next, the control method provided in this disclosure embodiment will be further explained using three actual faults of the controlled system as examples.

[0107] For example, when the fault mode of the controlled system is the servo valve pressure fault mode, the pre-set initial dynamic model can be a nonlinear polynomial state-space model as shown in formula (2):

[0108]

[0109] Where i = 1, 2, ..., n represents the order of the nonlinear polynomial state-space model, t represents time, r(t) represents the input of the controlled system, and y(t) represents the output of the controlled system. Indicates the state of the controlled system, a i1 and a i2 B and C represent the coefficients corresponding to the state of the controlled system.

[0110] Assuming the fault level of this fault mode represents a pressure fault with a 20% reduction in servo valve pressure, the fault test data corresponding to this fault level is obtained. Combined with the initial dynamic model shown in formula (2), the dynamic model of the controlled system can be determined as shown in formula (3) according to the method described above:

[0111]

[0112] Figure 4 This is a schematic diagram of the curves of a set of fault test data corresponding to a pressure fault where the servo valve pressure decreases by 20% and the model data of the dynamic model shown in formula (3).

[0113] like Figure 4 As shown, curve 1 represents the input data in this set of test data, curve 2 represents the first output data obtained by inputting the input data shown in curve 1 into the controlled system, and curve 3 represents the second output data obtained by inputting the input data shown in curve 1 into the dynamic model shown in formula (3). The large difference between curve 1 and curve 2 indicates that when the controlled system malfunctions, its output cannot accurately track the input; curve 3 basically overlaps with curve 2, indicating that when the controlled system experiences a pressure fault with a 20% reduction in servo valve pressure, the dynamic model shown in formula (3) can accurately reflect the state of the controlled system, and the accuracy of this dynamic model is 96.02%.

[0114] Figure 5 Based on the dynamic model shown in formula (3), the input r(t) is selected as... and A schematic diagram of the input and output curves of the obtained dynamic model.

[0115] like Figure 5 As shown, curves 1, 2, and 3 represent the inputs of the dynamic model, respectively. and Curves 4, 5, and 6 respectively represent the input... and The corresponding output obtained after inputting this dynamic model.

[0116] from Figure 5 It can be seen that curve 4 cannot coincide with curve 1 by direct translation. Similarly, curves 5 and 6 cannot coincide with their corresponding curves 2 and 3 by direct translation. Therefore, the difference between the input and output of the dynamic model shown in formula (3) only includes non-constant numbers. At this time, the control model of the controlled system can adopt the multiplicative fault model g2(t)=r(t)+r(t)*f m f m The value is not zero. The parameters of the multiplicative fault model are identified using the least squares method, resulting in the control model for a pressure fault where the servo valve pressure decreases by 20%, as shown in equation (4):

[0117] g2(t)=r(t)-0.0714*r(t) (4)

[0118] The modeling error σ(t) of this control model is ∈ [-0.3434, 0.3434].

[0119] Subsequently, fault-tolerant control can be performed on the controlled system according to the control model shown in formula (4).

[0120] For example, when the fault mode of the controlled system is the actuator load jamming fault mode, the pre-set initial dynamic model can be a nonlinear polynomial state-space model as shown in formula (2).

[0121] Assuming the fault level of this fault mode represents a cylinder load jamming fault under 4 times the load, the fault test data corresponding to this fault level is obtained, and combined with the initial dynamic model shown in formula (2), the dynamic model of the controlled system can be determined as shown in formula (5) according to the method described above:

[0122]

[0123] Figure 6 This is a schematic diagram of the curves of a set of fault test data corresponding to the actuator cylinder load jamming fault under 4 times load and the model data of the dynamic model shown in formula (5).

[0124] like Figure 6 As shown, curve 1 represents the input data in this set of test data, curve 2 represents the first output data obtained by inputting the input data shown in curve 1 into the controlled system, and curve 3 represents the second output data obtained by inputting the input data shown in curve 1 into the dynamic model shown in formula (5). Among them, the difference between curve 1 and curve 2 is large, indicating that when the controlled system malfunctions, its output cannot accurately track the input; curve 3 basically coincides with curve 2, indicating that when the controlled system experiences a load jamming fault of 4 times the load on the actuator cylinder, the dynamic model shown in formula (5) can accurately reflect the state of the controlled system, and the accuracy of the dynamic model is 93.39%.

[0125] Figure 7 Based on the dynamic model shown in formula (5), the input r(t) is selected as... and A schematic diagram of the input and output curves of the obtained dynamic model.

[0126] like Figure 7 As shown, curves 1, 2, and 3 represent the inputs of the dynamic model, respectively. and Curves 4, 5, and 6 respectively represent the input... and The corresponding output obtained after inputting this dynamic model.

[0127] from Figure 7It can be seen that curve 4 cannot coincide with curve 1 by direct translation. Similarly, curves 5 and 6 cannot coincide with their corresponding curves 2 and 3 by direct translation. Therefore, the difference between the input and output of the dynamic model shown in formula (5) only includes non-constant numbers. At this time, the control model of the controlled system can adopt the multiplicative fault model g2(t)=r(t)+r(t)*f m f m The value is not zero. The parameters of the multiplicative fault model are identified using the least squares method, resulting in the control model for the controlled system experiencing a 4x load actuator load jamming fault, as shown in equation (6):

[0128] g2(t)=r(t)+0.0162*r(t) (6)

[0129] The modeling error σ(t) of this control model is ∈ [-0.4826, 0.4391].

[0130] Subsequently, fault-tolerant control can be performed on the controlled system according to the control model shown in formula (6).

[0131] For example, when the fault mode of the controlled system is sensor drift fault mode, the pre-set initial dynamic model can be a nonlinear polynomial state-space model as shown in formula (7):

[0132]

[0133] Where i = 1, 2, ..., n represents the order of the nonlinear polynomial state-space model, t represents time, q represents the sensor drift, r(t) represents the input of the controlled system, and y(t) represents the output of the controlled system. Indicates the state of the controlled system, a i1 and a i2 B and C represent the coefficients corresponding to the state of the controlled system.

[0134] Assuming the fault level of this fault mode represents a sensor drift fault with a sensor drift of -30%, the fault test data corresponding to this fault level is obtained, and combined with the initial dynamic model shown in formula (7), the dynamic model of the controlled system can be determined as shown in formula (8) according to the method described above:

[0135]

[0136] Figure 8 This is a schematic diagram of the curves of a set of fault test data corresponding to a sensor drift fault when the sensor drift amount is negative 30% and the model data of the dynamic model shown in formula (8).

[0137] like Figure 8As shown, curve 1 represents the input data in this set of experimental data, curve 2 represents the first output data obtained by inputting the input data shown in curve 1 into the controlled system, and curve 3 represents the second output data obtained by inputting the input data shown in curve 1 into the dynamic model shown in formula (8). Among them, the difference between curve 1 and curve 2 is large, indicating that when the controlled system malfunctions, its output cannot accurately track the input; curve 3 basically coincides with curve 2, indicating that when the controlled system experiences a sensor drift fault with a sensor drift of -30%, the dynamic model shown in formula (8) can accurately reflect the state of the controlled system, and the accuracy of the dynamic model is 96.89%.

[0138] Figure 9 Based on the dynamic model shown in formula (8), the input r(t) is selected as... and A schematic diagram of the input and output curves of the obtained dynamic model.

[0139] like Figure 9 As shown, curves 1, 2, and 3 represent the inputs of the dynamic model, respectively. and Curves 4, 5, and 6 respectively represent the input... and The corresponding output obtained after inputting this dynamic model.

[0140] from Figure 9 It can be seen that curve 4 can be directly translated to coincide with curve 1. Similarly, curves 5 and 6 can also be directly translated to coincide with their corresponding curves 2 and 3. Therefore, the difference between the input and output of the dynamic model shown in formula (8) only includes constants. At this time, the control model of the controlled system can adopt the additive fault model g1(t)=r(t)+f a f a The parameter is constant. The multiplicative fault model is parameterized using the least squares method, resulting in the control model for a sensor drift fault where the sensor drift is -30%, as shown in equation (9):

[0141] g1(t)=r(t)+1.8889 (9)

[0142] The modeling error σ(t) of this control model is ∈ [-0.3502, 0.3632].

[0143] Subsequently, fault-tolerant control can be performed on the controlled system according to the control model shown in formula (9).

[0144] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus embodiments, since they largely correspond to the method embodiments, the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0145] Figure 10 This is a schematic diagram of the structure of a control device according to some embodiments of the present disclosure.

[0146] like Figure 10 As shown, the control device 1000 includes: a first determining module 1001 configured to determine a dynamic model reflecting the state of the controlled system in the event of a fault in the controlled system; a second determining module 1002 configured to determine the difference between the input and output of the dynamic model; a third determining module 1003 configured to determine a control model of the controlled system based on the difference, the control model including at least one of an additive fault model and a multiplicative fault model; and a control module 1004 configured to control the controlled system according to the control model.

[0147] Figure 11 This is a schematic diagram of the structure of a control device according to other embodiments of the present disclosure.

[0148] like Figure 11 As shown, the control device 1100 includes a memory 1101 and a processor 1102 coupled to the memory 1101. The processor 1102 is configured to execute the method of any of the foregoing embodiments based on instructions stored in the memory 1101.

[0149] The memory 1101 may include, for example, system memory, fixed non-volatile storage media, etc. The system memory may store, for example, an operating system, application programs, a boot loader, and other programs.

[0150] The control device 1100 may also include an input / output interface 1103, a network interface 1104, and a storage interface 1105. These interfaces 1103, 1104, and 1105, as well as the memory 1101 and processor 1102, can be connected, for example, via a bus 1106. The input / output interface 1103 provides a connection interface for input / output devices such as a monitor, mouse, keyboard, and touchscreen. The network interface 1104 provides a connection interface for various networked devices. The storage interface 1105 provides a connection interface for external storage devices such as SD cards and USB flash drives.

[0151] Figure 12 This is a schematic diagram of an engine according to some embodiments of the present disclosure.

[0152] like Figure 12 As shown, the engine 1200 includes the control device 1000 / 1100 of any of the above embodiments.

[0153] In some embodiments, the engine 1200 further includes a controlled system 1201, which includes an actuator circuit of the engine 1200, the actuator circuit including an actuator cylinder. In some embodiments, the actuator circuit may also include sensors and servo valves connected to the actuator cylinder.

[0154] In some embodiments, the engine 1200 may also include a fault monitor that can monitor the controlled system 1201 for faults and send the monitored fault information (e.g., fault mode, fault level, etc.) to the control device 1000 / 1100.

[0155] In some embodiments, the control device 1000 / 1100 may determine the control model of the controlled system 1201 based on the fault information sent by the fault monitor and the control method provided in the present disclosure, and control the controlled system 1201 based on the determined control model.

[0156] According to another aspect of the present disclosure, an aircraft is provided, including the engine described in any of the above embodiments.

[0157] In some embodiments, the aircraft may be an airplane, such as a civil aircraft. As some implementations, the airplane may be a piston-engine airplane, a turboprop airplane, or a jet airplane, etc.

[0158] This disclosure also provides a computer-readable storage medium including computer program instructions that, when executed by a processor, implement the method of any of the above embodiments.

[0159] The embodiments of this disclosure have now been described in detail. To avoid obscuring the concept of this disclosure, some details known in the art have not been described. Those skilled in the art can fully understand how to implement the technical solutions disclosed herein based on the above description.

[0160] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0161] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that the functions specified in one or more flowchart illustrations and / or one or more blocks in a block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate functions for implementing the functions in the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0162] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0163] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0164] While specific embodiments of this disclosure have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments or equivalent substitutions can be made to some technical features without departing from the scope and spirit of this disclosure. The scope of this disclosure is defined by the appended claims.

Claims

1. A control method, comprising: In the event of a failure in the controlled system, a dynamic model reflecting the state of the controlled system is determined; Determine the difference between the input and output of the dynamic model; The control model of the controlled system is determined based on the differences, and the control model includes at least one of an additive fault model and a multiplicative fault model; Fault-tolerant control is performed on the controlled system according to the control model, wherein... The dynamic model that reflects the state of the controlled system in the event of a failure includes: In the event of a failure in the controlled system, the failure mode of the controlled system is determined; The initial dynamic model corresponding to the fault mode and the fault test data corresponding to the fault level of the fault mode are obtained. The initial dynamic model is a dynamic model with unknown parameters. The fault test data includes at least one set of test data. Each set of test data includes input data and first output data. The first output data is obtained by inputting the input data into the controlled system to perform a fault injection test. The initial dynamic model is used to identify parameters using the fault test data in order to determine a dynamic model that reflects the state of the controlled system.

2. The method according to claim 1, wherein, The input is The output is The difference is t represents time.

3. The method according to claim 2, wherein, Determining the control model of the controlled system based on the aforementioned differences includes: If the difference consists only of constants, the control model is determined to be the additive fault model.

4. The method according to claim 2, wherein, Determining the control model of the controlled system based on the aforementioned differences includes: If the difference contains only non-constant numbers, the control model is determined to be the multiplicative fault model.

5. The method according to claim 2, wherein: Determining the control model of the controlled system based on the aforementioned differences includes: When the differences include both constants and non-constant numbers, the control model is determined to include the additive fault model and the multiplicative fault model, wherein the additive fault model is... The multiplicative fault model is ,in, It is a constant. A non-zero constant; Fault-tolerant control of the controlled system based on the control model includes: A hybrid model is determined based on the additive fault model and the multiplicative fault model, wherein the hybrid model is: ; The controlled system is subjected to fault-tolerant control based on the hybrid model.

6. The method according to claim 1, wherein, Using the fault test data to perform parameter identification on the initial dynamic model to determine the dynamic model reflecting the state of the controlled system includes: Based on the fault test data, parameter identification is performed on the initial dynamic models of different orders to obtain multiple intermediate dynamic models of different orders, and the parameter identification time consumed by each intermediate dynamic model is obtained. Determine the difference between the second output data obtained by inputting the input data from each set of experimental data into each intermediate dynamic model and the first output data of that set of experimental data; The model error of each intermediate dynamic model is determined based on the difference. Based on the model error, parameter identification time, and order of each intermediate dynamic model, the reference parameters of the intermediate dynamic model are determined. The dynamic model is determined from the plurality of intermediate dynamic models based on the reference parameters.

7. The method according to claim 6, wherein, The dynamic model is the intermediate dynamic model with the smallest reference parameter among the plurality of intermediate dynamic models.

8. The method according to claim 6, wherein, The reference parameters of each intermediate dynamic model are positively correlated with the model error, parameter identification time, and order of that intermediate dynamic model.

9. The method according to claim 8, wherein: The reference parameters of each intermediate dynamic model ,in, , , These are the model errors of the intermediate dynamic model. Parameter identification time and order The weighting coefficients.

10. The method according to any one of claims 1-9, wherein, The dynamic model is a state-space model.

11. The method according to claim 10, wherein, The state-space model is a nonlinear polynomial state-space model.

12. The method according to any one of claims 1-9, wherein, The controlled system includes the actuator circuit of an aircraft engine, and the actuator circuit includes an actuating cylinder.

13. A control device, comprising: The first determining module is configured to determine a dynamic model reflecting the state of the controlled system in the event of a failure in the controlled system. The second determining module is configured to determine the difference between the input and output of the dynamic model; The third determining module is configured to determine the control model of the controlled system based on the difference, wherein the control model includes at least one of an additive fault model and a multiplicative fault model; The control module is configured to control the controlled system according to the control model. The first determining module is configured to determine the fault mode of the controlled system when a fault occurs in the controlled system; acquire the initial dynamic model corresponding to the fault mode and the fault test data corresponding to the fault level of the fault mode; the initial dynamic model is a dynamic model with unknown parameters; the fault test data includes at least one set of test data, each set of test data includes input data and first output data, and the first output data is obtained by inputting the input data into the controlled system to perform a fault injection test; The initial dynamic model is used to identify parameters using the fault test data in order to determine a dynamic model that reflects the state of the controlled system.

14. A control device, comprising: Memory; as well as A processor coupled to the memory is configured to execute the method of any one of claims 1-12 based on instructions stored in the memory.

15. An engine comprising the control device as described in claim 13 or 14.

16. The engine according to claim 15, further comprising the controlled system.

17. The engine according to claim 16, wherein, The controlled system includes the actuator circuit of the engine, and the actuator circuit includes an actuating cylinder.

18. An aircraft comprising the engine as described in any one of claims 15-17.

19. A computer-readable storage medium comprising computer program instructions, wherein, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1-12.

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