Ion thruster grid state monitoring method based on extended kalman filter

Abstract

The application relates to the technical field of space electric propulsion, in particular to an ion thruster grid state monitoring method based on an extended Kalman filter, which comprises the following steps: step 1, establishing an extended Kalman filter model for ion thruster grid state monitoring; step 2, calculating a state transition matrix; step 3, calculating a predicted value of a next node state; step 4, obtaining measurement data through an online measurement sensor; step 5, setting an offset limit; step 6, updating the state of the Kalman filter model; step 7, judging whether the online measurement sensor misreads or the ion thruster works abnormally according to a comparison result; step 8, calculating the remaining life of the grid according to the state update value; and step 9, repeating steps 3-8 until the task is completed. The application has the functions of dynamically monitoring the aperture of the grid, the etching depth of the grid hole, the abnormal working state and the remaining life of the grid, and the method is simple, the calculation amount is small, and the abnormal diagnosis speed is fast.

Description

Technical Field

[0001] This application relates to the field of aerospace electric propulsion technology, and more specifically, to a method for monitoring the gate state of an ion thruster based on extended Kalman filtering. Background Technology

[0002] During the operation of an ion thruster, the sputtering corrosion of the gate surface by high-energy ions generated under vacuum and high pressure is the main factor affecting the thruster's lifespan. Therefore, predicting the gate's operational parameters, such as aperture size, etching depth, and remaining lifetime, can help detect operational anomalies in a timely manner and make adjustments to prevent the thruster from failing within its lifespan limit.

[0003] Traditional gate lifetime prediction analysis methods include probabilistic analysis and PIC / MCC simulation analysis. Probabilistic analysis is based on the sputtering corrosion mechanism of the accelerated gate by exchange-charge ions, establishing a functional relationship between the gate's operational lifetime and its structural, operational, and material parameters. Then, the Monte Carlo method is used to perform sample simulations to derive the lifetime probability distribution. However, this method primarily estimates gate lifetime during the ion thruster development phase and lacks operational monitoring capabilities. The PIC / MCC method is a particle simulation method based on the plasma transport process of the ion thruster gate system. This method is significant for thruster design optimization, but it involves iterative particle calculations, is complex and computationally intensive, and requires parameters such as the plasma density upstream of the gate, which are difficult to collect during operation, making it unsuitable for dynamic monitoring during operation.

[0004] In addition, due to the extreme operating environment of the thruster and the inherent errors of the measurement sensors, online measurement sensors often fail to measure the true value, and theoretical models also have uncertainties that prevent them from accurately representing the true state. However, using an extended Kalman filter can fuse nonlinear theoretical models and measurement sensor information, eliminate measurement noise and state noise, and make the optimal estimate of the true state, thereby achieving a reliable prediction of the gate state. Summary of the Invention

[0005] This application provides a method for monitoring the gate state of an ion thruster based on extended Kalman filtering, which can monitor the gate state of the ion thruster during operation throughout the entire mission.

[0006] To achieve the above objectives, this application provides a gate state monitoring method for an ion thruster based on an extended Kalman filter, comprising the following steps: Step 1: Task start, establish an extended Kalman filter model for ion thruster gate state monitoring, and input initial model parameters; Step 2: Calculate the state transition matrix; Step 3: Calculate the predicted value of the next node state; Step 4: Reach the next node, acquire measurement data through an online measurement sensor, and obtain the measured value; Step 5: Set an offset limit, and determine whether the gate state is abnormal by calculating the offset of the measured value based on the predicted value. If the offset does not exceed the offset limit, proceed to Step 6; if the offset exceeds the offset limit, it indicates an abnormality, and proceed to Step 7; Step 6: The Kalman filter model uses the measured value to predict the state. The value is corrected, and the status update is completed; Step 7: If the offset exceeds the offset limit, the working parameters and performance parameters of the ion thruster of this node are output and compared with the historical database. Based on the comparison result, it is determined whether it is a misread by the online measurement sensor or an abnormal operation of the ion thruster; If it is a misread by the online measurement sensor, the status value of this node is directly updated to the predicted value, and step 8 is performed; If the ion thruster is abnormal, the abnormal information is output, and a second manual judgment is performed. The working parameters are adjusted according to expert knowledge. After adjustment, it is necessary to return to step 1 to re-enter the model parameters; Step 8: Calculate the remaining lifetime of the gate based on the status update value, output and save it, and return to step 3; Step 9: Repeat steps 3-8 until the task ends to realize the monitoring of the remaining lifetime of the ion thruster gate.

[0007] Furthermore, in step 1, when establishing the extended Kalman filter model for monitoring the gate state of the ion thruster, gate etching is selected as the monitoring object of the gate state of the ion thruster, and the gate aperture and gate etching depth are used as the monitoring states. The state-observation space of the Kalman filter model is established through the gate etching expansion rate equation and the gate etching depth fitting equation.

[0008] Furthermore, in step 5, the offset limit is set to 0.02.

[0009] Furthermore, in step 6, when updating the state using an extended Kalman filter, information is fused between the nonlinear gate state model and the data from the online measurement sensor to achieve a relatively accurate estimation of the gate state.

[0010] Furthermore, in step 8, when calculating the remaining lifetime of the gate, a formula for calculating the remaining lifetime is established using the formula for the mass corrosion rate of the ion thruster.

[0011] The present invention provides a method for monitoring the gate state of an ion thruster based on extended Kalman filtering, which has the following advantages:

[0012] This application presents an ion thruster gate state monitoring method based on extended Kalman filtering, which has the function of dynamically monitoring the gate aperture, gate hole etching depth, abnormal operating state, remaining lifetime, and other states. The method is simple, has low computational load, and fast anomaly diagnosis speed. It eliminates measurement noise and state noise, makes optimal estimation of the true state, and realizes reliable prediction of the gate state. Attached Figure Description

[0013] The accompanying drawings, which form part of this application, are used to provide a further understanding of the application and to make other features, objects, and advantages of the application more apparent. The illustrative embodiments and descriptions of this application are used to explain the application and do not constitute an undue limitation of the application. In the drawings:

[0014] Figure 1 This is a flowchart of an ion thruster gate state monitoring method based on extended Kalman filtering provided in an embodiment of this application;

[0015] Figure 2 This is an aperture state estimation diagram of an ion thruster gate state monitoring method based on extended Kalman filtering provided in the embodiments of this application;

[0016] Figure 3 This is a remaining lifetime estimation diagram of the ion thruster gate state monitoring method based on extended Kalman filtering provided in the embodiments of this application. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0019] In this application, the terms "upper," "lower," "left," "right," "front," "rear," "top," "bottom," "inner," "outer," "middle," "vertical," "horizontal," "lateral," and "longitudinal" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are primarily for the purpose of better describing this application and its embodiments, and are not intended to limit the indicated device, element, or component to having a specific orientation, or to be constructed and operated in a specific orientation.

[0020] Furthermore, in addition to indicating location or positional relationship, some of the aforementioned terms may also have other meanings. For example, the term "above" may also be used in some cases to indicate a certain dependency or connection relationship. Those skilled in the art can understand the specific meaning of these terms in this application based on the specific circumstances.

[0021] In addition, the term "multiple" should mean two or more.

[0022] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] like Figure 1 As shown in the embodiments of this application, the ion thruster gate state monitoring method based on extended Kalman filtering selects the gate etching state as the monitoring object of the ion thruster gate state, and uses the gate aperture and gate etching depth as the monitoring state variables. A state-observation space is established through the gate etching expansion rate equation and the gate etching depth fitting equation. An extended Kalman filter is established to predict the gate state, and then an offset limit is set to monitor the gate etching state: when the offset exceeds the limit, it indicates that the gate etching is abnormally severe. Then, the operating parameters at the abnormal time are compared with the historical database to determine whether it is a misread by the online measurement sensor or an abnormal operating state. In the case of a misread, the state is directly updated to the predicted value; in the case of an abnormal operating state, the output undergoes secondary expert judgment, and the model parameters are updated after adjusting the operating state. Then, the predicted value is updated through the extended Kalman filter, and the gate state model and online measurement sensor data are fused to achieve a relatively accurate estimate of the gate state. Finally, a remaining lifetime calculation formula is established through the ion thruster mass corrosion rate formula to monitor the remaining lifetime of the ion thruster gate.

[0024] More specifically, the ion thruster gate state monitoring method based on extended Kalman filtering provided in this application includes the following steps:

[0025] Step 1: Task begins. An extended Kalman filter model for monitoring the gate state of the ion thruster is established, and initial model parameters are input. Since the central region of the accelerating gate of the ion thruster is etched the fastest and is the first area to cause gate failure, the monitoring parameters are selected as the aperture diameter and etching depth of the central gate aperture. The state-space variable is defined as: x = [x...]. 1 x 2 ] T =[r, h] T Let y be the observed variable, where r represents the accelerated gate aperture and h represents the gate aperture etching depth. Based on the gate aperture radius etching expansion rate equation in theoretical research:

[0026]

[0027] And the etching depth equation fitted based on historical data:

[0028] h = akT

[0029] The state-observation space of the Kalman filter is established as follows:

[0030]

[0031] Where n represents the number of ions bombarding the gate hole wall per unit time, Y represents the sputtering yield of the working gas ions on the gate material molybdenum (Mo), m0 represents the atomic mass of molybdenum, and t g The gate thickness is represented by T, the data acquisition period is represented by W. k =[w 1 w 2 ] T V represents state-process noise. k =[v 1 v 2 ] T This indicates measurement noise.

[0032] Input initial parameters: n0 = 7.65 × 10 11 / s, Y=0.17, m0=95.96×1.667×10 -27 kg, t g =5×10 -4 m, ρ=1.023kg / m 3 r0 = 0.55 mm, T = 500 hours.

[0033] Step 2: Calculate the state transition matrix; the state transition moments are A:

[0034]

[0035] Step 3: Calculate the predicted value of the next node's state; the predicted state and covariance matrix at time k+1 are as follows:

[0036]

[0037]

[0038] in, and ∑ k Let represent the state update value and covariance at time k, respectively.

[0039] Step 4: Arrive at the next node, acquire measurement data through online measurement sensors, and obtain the measurement value.

[0040] Step 5: Set an offset limit. Calculate the offset between the measured value and the predicted value to determine if the gate state is abnormal. If the offset does not exceed the offset limit, proceed to step 6. If the offset exceeds the offset limit, it indicates an abnormality, and proceed to step 7. In step 5, obtain the measured value y at time k+1. k+1 The offset limit is set to:

[0041]

[0042] Based on experimental experience, in this embodiment of the application, the offset limit is preferably set to e0 = 0.02. When the offset exceeds the limit e0, it indicates that the gate hole etching is abnormally severe and the gate is in an abnormal working state.

[0043] Step 6: The Kalman filter model uses the measured values ​​to correct the predicted values, completing the state update; during the update process, the state estimate and covariance at time k+1 are updated as follows:

[0044]

[0045]

[0046] in, Kalman gain State estimation (aperture) such as Figure 2 As shown.

[0047] Step 7: If the offset exceeds the offset limit, output the ion thruster operating parameters and performance parameters of this node and compare them with the historical database. Based on the comparison results, determine whether it is a misread by the online measurement sensor or an malfunction of the ion thruster. If it is a misread by the online measurement sensor, directly update the state value of this node to the predicted value and proceed to Step 8. If it is an malfunction of the ion thruster, output the abnormal information, perform a second manual judgment, adjust the operating parameters based on expert knowledge, and after adjustment, return to Step 1 to re-enter the model parameters.

[0048] Step 8: Calculate the remaining lifetime of the gate based on the status update value, output and save, then proceed to step 3 again; the remaining lifetime of the gate is calculated according to the mass sputtering etching formula, and the sputtering etching mass and mass sputtering etching rate at time k+1 are:

[0049]

[0050]

[0051] in, and Let M0 represent the gate aperture and etching depth at time k+1, respectively, and T be the sampling period. M0 is the maximum sputterable mass, M0 = 4.6 × 10⁻⁶. -6 kg, therefore the remaining gate lifetime can be calculated as follows:

[0052]

[0053] The remaining lifetime of the ion thruster gate can be calculated using the above formula, and the result can be output and stored. Figure 3 As shown.

[0054] Step 9: Repeat steps 3-8 until the task ends to monitor the remaining lifetime (gate state) of the ion thruster gate.

[0055] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An ion thruster grid state monitoring method based on extended Kalman filtering, characterized by, Includes the following steps: Step 1: Task begins. Establish an extended Kalman filter model for ion thruster gate state monitoring and input the initial model parameters. Step 2: Calculate the state transition matrix; Step 3: Calculate the predicted value of the next node's state; Step 4: Upon reaching the next node, acquire measurement data using online measurement sensors to obtain the measurement value; Step 5: Set offset limit. Determine whether the gate state is abnormal by calculating the offset of the measured value based on the predicted value. If the offset does not exceed the offset limit, proceed to step 6. If the offset exceeds the offset limit, it indicates an abnormality, and proceed to step 7. Step 6: The Kalman filter model uses the measured values ​​to correct the predicted values, thus completing the state update; Step 7: If the offset exceeds the offset limit, output the ion thruster operating parameters and performance parameters of this node, compare them with the historical database, and determine whether it is a misreading of the online measurement sensor or an abnormal operation of the ion thruster based on the comparison results. If the online measurement sensor misreads, the state value of that node is directly updated to the predicted value, and step 8 is performed; if the ion thruster is malfunctioning, the abnormal information is output, and a second manual judgment is performed. The working parameters are adjusted based on expert knowledge. After adjustment, it is necessary to return to step 1 and re-enter the model parameters. Step 8: Calculate the remaining lifetime of the gate based on the status update value, output and save it, and then proceed to step 3 again; Step 9: Repeat steps 3-8 until the task ends, to monitor the remaining lifetime of the ion thruster gate.

2. The extended Kalman filter based ion thruster grid state monitoring method of claim 1, wherein, In step 1, when establishing the extended Kalman filter model for monitoring the gate state of the ion thruster, gate etching is selected as the monitoring object of the gate state of the ion thruster, and the gate aperture and gate etching depth are used as the monitoring states. The state-observation space of the Kalman filter model is established through the gate etching expansion rate equation and the gate etching depth fitting equation.

3. The extended Kalman filter based ion thruster grid state monitoring method of claim 1, wherein, In step 5, the offset limit is set to 0.

02.

4. The extended Kalman filter based ion thruster grid state monitoring method of claim 1, wherein, In step 6, when updating the state using an extended Kalman filter, information is fused from the nonlinear gate state model and data from the online measurement sensor to achieve a relatively accurate estimation of the gate state.

5. The extended Kalman filter based ion thruster grid state monitoring method of claim 1, wherein, In step 8, when calculating the remaining lifetime of the gate, a formula for calculating the remaining lifetime is established using the formula for the mass corrosion rate of the ion thruster.

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