Rudder feedback sensing burr real-time detection method based on online estimation

Abstract

The invention discloses a rudder feedback sensing burr real-time detection method based on online estimation. The method comprises the steps that a rudder feedback sensing predicted value at the current moment is obtained according to a sampling value at the rudder feedback preorder moment; and obtaining a detection statistic according to the rudder feedback sensing predicted value and the rudder feedback measured value at the current moment, obtaining an abnormal detection threshold according to the detection statistic and the design margin, and obtaining detection logic of abnormal burrs according to the detection statistic and the abnormal detection threshold. The control performance and the reliability of the rudder system can be remarkably improved by filtering the burr signals.

Description

A Real-Time Detection Method for Rudder Feedback Sensing Glitches Based on Online Estimation Technical Field

[0001] This invention belongs to the field of steering system and fault diagnosis technology, and particularly relates to a real-time detection method for steering feedback sensing glitch based on online estimation. Background Technology

[0002] The rudder system is a key actuator for controlling the attitude stability of an aircraft, and its accuracy and reliability directly affect the stability and safety of the aircraft. Feedback sensors, as important sensing elements of the rudder system, are used to monitor the rudder angle position in real time, providing information to the control system for precise control of the rudder surfaces. However, in the complex aerospace environment, due to the combined effects of factors such as electromagnetic interference, mechanical vibration, temperature changes, and component wear, abnormal glitches often appear in the rudder feedback sensor signals. These can range from minor issues like decreased rudder surface control accuracy to more serious problems like rudder surface flutter, leading to flight attitude instability and endangering the overall safety of the aircraft system.

[0003] However, the burr phenomenon in rudder feedback sensors is difficult to completely avoid by optimizing the sensor structure. This is especially true for contact potentiometers, where there is relative sliding or rolling between the contact brush and the resistive substrate. Optimizing the contact pressure is difficult. If the contact pressure is too high, the substrate will be subjected to excessive compression and friction, accelerating wear. If the contact pressure is too low, the contact will be unstable, increasing contact resistance and causing local overheating, which will also exacerbate wear. Furthermore, during long-term and frequent operation, the potentiometer substrate is subjected to repeated mechanical stresses such as vibration and impact, which can easily lead to wear and thus abnormal burrs when the contact brush passes through the worn area.

[0004] Currently, there are few designs for real-time detection and processing of rudder feedback sensor glitches, and there is no technology to filter out glitches, which affects the control performance and reliability of the rudder system and may lead to catastrophic consequences in some extreme cases. Summary of the Invention

[0005] The technical problem solved by this invention is to overcome the shortcomings of the prior art and provide a real-time detection method for rudder feedback sensing glitch based on online estimation. By filtering out the glitch signal, the control performance and reliability of the rudder system can be significantly improved.

[0006] The objective of this invention is achieved through the following technical solution: a real-time detection method for rudder feedback sensing glitches based on online estimation, comprising: obtaining the rudder feedback sensing prediction value at time k, i.e., the current time, based on the sampled value at the preceding time of rudder feedback. Based on the rudder feedback sensor prediction value at time k, i.e., the current time. Detection statistics were obtained from the measured values ​​of the rudder feedback. According to the detection statistics The anomaly detection threshold is obtained from the design margin. According to the detection statistics and anomaly detection threshold The logic for detecting abnormal spikes is obtained.

[0007] The above-mentioned real-time detection method for rudder feedback sensing glitch based on online estimation also includes: designing anomaly handling logic when the abnormal glitch detection logic detects the existence of an abnormal glitch.

[0008] In the above-mentioned real-time detection method for rudder feedback sensing glitches based on online estimation, the predicted value of the rudder feedback sensing at time k, i.e., the current time, is obtained based on the sampled value of the rudder feedback at the preceding time. Includes: based on the given polynomial order Sliding window length and sampling time Constructing the basis matrix in Vandermonde form According to the basis matrix Calculate the transformation matrix The construction length is Sliding window, register to The rudder feedback sensor value at any given time, where, At the current sampling time; calculate the sliding update fitting coefficient vector based on the rudder feedback sensor value. The rudder feedback sensor prediction value at time k, i.e., the current time, is obtained based on the fitting coefficient vector of the sliding update. .

[0009] In the above-mentioned real-time detection method for rudder feedback sensing glitch based on online estimation, the basis matrix is ​​in the form of a Vandermonde matrix. It can be obtained through the following formula: ;in, Sampling time, For the order of the polynomial, The sliding window length; the transformation matrix. It can be obtained through the following formula: ;in, The basis matrix is ​​in Vandermonde form. Let be the transformation matrix.

[0010] In the above-mentioned real-time detection method for rudder feedback sensing glitch based on online estimation, to The rudder feedback sensor value at any given time is obtained using the following formula: ;in, for to The rudder feedback sensor value at any given moment. for The rudder feedback sensor value at any given moment. for The rudder feedback sensor value at any given moment. for The rudder feedback sensor value at any given moment. The length of the sliding window. This refers to the current moment.

[0011] In the above-mentioned real-time detection method for rudder feedback sensing glitch based on online estimation, the sliding update fitting coefficient vector is obtained by the following formula: ;in, This is the fitted coefficient vector for sliding updates. for to The rudder feedback sensor value at any given moment. Let be the transformation matrix.

[0012] In the above-mentioned real-time detection method for rudder feedback sensor glitch based on online estimation, the predicted value of the rudder feedback sensor at time k is the current time. It can be obtained through the following formula: ; ;in, Let k be the rudder feedback sensor prediction value at time k, which is the current time. This is the fitted coefficient vector for sliding updates. Sampling time, For the order of the polynomial, The length of the sliding window. For the order of the polynomial, Basis vectors are used for online prediction.

[0013] In the above-mentioned real-time detection method for rudder feedback sensing glitch based on online estimation, the detection statistic... It can be obtained through the following formula: ;in, Let k be the rudder feedback sensor prediction value at time k, which is the current time. The measured value of the rudder feedback; the anomaly detection threshold. It can be obtained through the following formula: ;in, For design margin, For the current moment, The number of sampling points; the detection logic for abnormal spikes is as follows: .

[0014] In the above-mentioned real-time detection method for rudder feedback sensing glitch based on online estimation, the anomaly handling logic includes the following steps: Step C1: If an abnormal glitch exists, set the anomaly handling logic flag. If the output value after exception handling is less than the measured value of the rudder feedback, proceed to step C2; otherwise, let the output value after exception handling use the measured value of the rudder feedback; Step C2: If the count value after entering the exception handling logic is less than If the predicted value is better than the actual value, proceed to step C3 to determine if the predicted value is better than the actual value; otherwise, proceed to step C4. The first counting threshold represents the maximum number of replacement frames; Step C3: Utilize time k, i.e., time before the current time... The output value after anomaly handling is used to calculate the rate of change. , as a reference transformation rate ; Calculation based on current predicted values ​​and measured rudder feedback values The rate of change within the sliding window is measured, and the value closest to the reference rate of change is selected as the output value after anomaly handling; where... Sampling time, This is the rudder feedback output value after anomaly handling at time k-1. This is the rudder feedback output value after anomaly handling at time kn. Calculate the number of intervals for the rate of change; Step C4: If the counted value is less than [a certain value] after entering the exception handling logic. If the error is correct, the output value after anomaly handling will be the measured value from the rudder feedback; otherwise, proceed to step C5. The second counting threshold represents the number of frames required to trigger the exception handling exit logic; Step C5: If If an anomaly still exists, the timer value after entering the anomaly handling logic will be reset. If the exception handling logic fails, proceed to step C4; otherwise, exit the exception handling logic and set the exception handling logic flag. And reset the count value after entering the exception handling logic to 0.

[0015] A real-time detection system for rudder feedback sensing glitches based on online estimation includes: a signal estimation module, used to obtain the rudder feedback sensing prediction value at time k, i.e., the current time, based on the sampled value at the preceding time of rudder feedback. The anomaly detection module is used to predict the rudder feedback sensor value based on time k, i.e., the current time. Detection statistics were obtained from the measured values ​​of the rudder feedback. According to the detection statistics The anomaly detection threshold is obtained from the design margin. According to the detection statistics and anomaly detection threshold The logic for detecting abnormal spikes is obtained.

[0016] Compared with the prior art, the present invention has the following beneficial effects: by filtering out glitch signals, the present invention can significantly improve the control performance and reliability of the rudder system, avoid catastrophic consequences under certain extreme conditions, and contribute to the high reliability development of aerospace electromechanical systems. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 is a schematic diagram of the design principle for real-time detection and processing of rudder feedback sensing glitch provided in an embodiment of the present invention; Figure 2 is a flowchart of the anomaly handling design provided in an embodiment of the present invention; Figure 3 is a diagram of the amplitude provided in an embodiment of the present invention. , A magnified view of the glitch filtering effect under the sine wave command; Figure 4 shows the amplitude provided by the embodiment of the present invention. , Performance diagram of glitch filtering control under sine wave command. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] This embodiment provides a real-time detection method for rudder feedback sensing glitches based on online estimation. The method includes: obtaining the predicted rudder feedback sensing value at time k, i.e., the current time, based on the sampled value at the preceding rudder feedback time. Based on the rudder feedback sensor prediction value at time k, i.e., the current time. Detection statistics were obtained from the measured values ​​of the rudder feedback. According to the detection statistics The anomaly detection threshold is obtained from the design margin. According to the detection statistics and anomaly detection threshold The logic for detecting abnormal spikes is obtained.

[0020] The real-time detection method for rudder feedback sensing glitch based on online estimation also includes: designing anomaly handling logic when anomaly glitch is detected in the anomaly glitch detection logic.

[0021] This embodiment designs a dynamic signal estimator based on the sampled values ​​of the rudder feedback at the preceding time step, enabling real-time online estimation of the rudder feedback at time k (the current time). Based on the predicted and measured values ​​of the rudder feedback, it designs detection statistics and thresholds to achieve real-time online detection of abnormal spikes. An anomaly handling logic that balances closed-loop control stability is designed. Within the allowable iteration range of the iterative estimation error, the predicted value replaces the rudder feedback value in the spike signal interval, achieving real-time online smoothing of the rudder feedback curve and safe and stable control. It includes three key design phases: signal estimation module design; anomaly detection module design; and anomaly handling module design.

[0022] Based on the sampled values ​​from the preceding moments of the rudder feedback, the rudder feedback sensor prediction value at time k, i.e., the current moment, is obtained. Includes: based on the given polynomial order Sliding window length and sampling time Constructing the basis matrix in Vandermonde form According to the basis matrix Calculate the transformation matrix The construction length is Sliding window, register to The rudder feedback sensor value at any given time, where, At the current moment; calculate the sliding update fitting coefficient vector based on the rudder feedback sensor values. The rudder feedback sensor prediction value at time k, i.e., the current time, is obtained based on the fitting coefficient vector of the sliding update. .

[0023] Specifically, the rudder feedback sensor prediction value at time k, i.e., the current time, is obtained. The steps include: Step A1: Given the order of the polynomial Sliding window length and sampling time Step A2: Construct the basis matrix in Vandermonde form. Step A3: Based on the basis matrix Calculate the transformation matrix Step A4: Construct a structure with a length of... Sliding window, register to The rudder feedback sensor value at each moment. Step A5: Calculate the fitting coefficient vector for sliding updates. Step A6: Calculate the rudder feedback sensor prediction value at time k, i.e., the current time. .

[0024] Basis matrices in Vandermonde form It can be obtained through the following formula: ;in, Sampling time, For the order of the polynomial, The sliding window length; the transformation matrix. It can be obtained through the following formula: ;in, The basis matrix is ​​in Vandermonde form. Let be the transformation matrix.

[0025] to The rudder feedback sensor value at any given time is obtained using the following formula: ;in, for to The rudder feedback sensor value at any given moment. for The rudder feedback sensor value at any given moment. for The rudder feedback sensor value at any given moment. for The rudder feedback sensor value at any given moment. The length of the sliding window. This refers to the current moment.

[0026] The fitting coefficient vector for sliding updates is obtained using the following formula: ;in, This is the fitted coefficient vector for sliding updates. for to The rudder feedback sensor value at any given moment. Let be the transformation matrix.

[0027] The rudder feedback sensor prediction value at time k, i.e., the current time. It can be obtained through the following formula: ; ;in, Let k be the rudder feedback sensor prediction value at time k, which is the current time. This is the fitted coefficient vector for sliding updates. Sampling time, For the order of the polynomial, The length of the sliding window. For the order of the polynomial, Basis vectors are used for online prediction.

[0028] A detection statistic is constructed using the absolute value of the deviation between the measured and predicted values ​​of the rudder feedback. When the assessment is normal The maximum value of the fluctuation is used as a basis to design an anomaly detection threshold with an added design margin. Design the logic for detecting abnormal spikes.

[0029] Detection statistics It can be obtained through the following formula: ;in, Let k be the rudder feedback sensor prediction value at time k, which is the current time. The measured value of the rudder feedback; the anomaly detection threshold. It can be obtained through the following formula: ;in, For design margin, For the k-th sampling time, This represents the number of sampling points; here, it represents the number of sampling points J at N time points. k The maximum value of , where k represents the k-th sampling time.

[0030] The detection logic for abnormal burrs is as follows: .

[0031] The design incorporates anomaly handling logic that balances closed-loop control stability. Within the allowable iteration range of the iterative estimation error, the predicted value replaces the rudder feedback value in the glitch signal range, achieving real-time online smoothing of the rudder feedback curve and safe, stable control. This includes setting a timer value. Limit the number of times the predicted value is used to replace the actual value, and ensure that the condition is met. After the auction value is calculated, it continues until... ( If no exceptions occur within a specified time, the exception handling process will be triggered to exit; otherwise, it will restart from the beginning. Start timing and continue until exiting the anomaly handling operation. Before using the predicted value as the output of anomaly handling, a performance evaluation step comparing the predicted and measured values ​​is added, utilizing time k (i.e., time before the current time). Calculate the rate of change of the output value after anomaly handling. , as a reference transformation rate .

[0032] The exception handling logic includes the following steps: Step C1: If an exception glitch exists, set the exception handling logic flag. If the output value after exception handling is less than the measured value of the rudder feedback, proceed to step C2; otherwise, let the output value after exception handling use the measured value of the rudder feedback; Step C2: If the count value after entering the exception handling logic is less than If the predicted value is better than the actual value, proceed to step C3 to determine if the predicted value is better than the actual value; otherwise, proceed to step C4. The first counting threshold represents the maximum number of replacement frames; Step C3: Utilize the values ​​from time k before... The output value after anomaly handling is used to calculate the rate of change. , as a reference transformation rate ; Calculation based on current predicted values ​​and measured rudder feedback values The rate of change within the sliding window is measured, and the value closest to the reference rate of change is selected as the output value after anomaly handling; where... Sampling time, This is the rudder feedback output value after anomaly handling at time k-1. This is the rudder feedback output value after anomaly handling at time kn. Calculate the number of intervals for the rate of change; the output value after anomaly handling is: Step C4: If the count value is less than [a certain value] after entering the exception handling logic. If the error is correct, the output value after anomaly handling will be the measured value from the rudder feedback; otherwise, proceed to step C5. The second counting threshold represents the number of frames required to trigger the exception handling exit logic; Step C5: If If an anomaly still exists, the timer value after entering the anomaly handling logic will be reset. If the exception handling logic fails, proceed to step C4; otherwise, exit the exception handling logic and set the exception handling logic flag. And reset the count value after entering the exception handling logic to 0.

[0033] As shown in Figure 1, this embodiment also provides a real-time detection system for rudder feedback sensing glitches based on online estimation. This system includes a signal estimation module, used to obtain the predicted rudder feedback sensing value at time k (i.e., the current time) based on the sampled values ​​from the preceding rudder feedback time. The anomaly detection module is used to predict the rudder feedback sensor value based on time k, i.e., the current time. Detection statistics were obtained from the measured values ​​of the rudder feedback. According to the detection statistics The anomaly detection threshold is obtained from the design margin. According to the detection statistics and anomaly detection threshold The logic for detecting abnormal spikes is obtained.

[0034] The real-time detection system for rudder feedback sensing glitch based on online estimation also includes an anomaly handling module, which is used to design anomaly handling logic when anomaly glitch is detected in the anomaly glitch detection logic.

[0035] It is mainly achieved through the following technical solutions: (1) Signal estimation module design Based on the collected rudder feedback sensor position signal, a dynamic signal estimator is designed by using basis function fitting. The specific steps include: Step A1: Given the order of the polynomial Sliding window length and sampling time .

[0036] Step A2: Construct the basis matrix in Vandermonde form as follows,

[0037] in, Sampling time.

[0038] Step A3: Calculate the transformation matrix as follows,

[0039] Step A4: Construct a length of Sliding window, register to The rudder feedback sensor value at any moment makes

[0040] in, for The rudder feedback sensor value at any given moment.

[0041] Step A5: Calculate the fitting coefficient vector for sliding update. as follows,

[0042] Step A6: Calculate the rudder feedback sensor prediction value at time k, i.e., the current time. as follows,

[0043] in, .

[0044] (2) Anomaly Detection Module Design Based on the real-time estimation results of the above signal estimation module, the anomaly detection logic is designed. The specific steps include: Step B1: Construct the following detection statistics. ,

[0045] in, The deviation between the measured value and the predicted value of the rudder feedback. This is the absolute value operator.

[0046] Step B2: Assess normal conditions The maximum value of the fluctuation is used as a basis to design an anomaly detection threshold with an added design margin. as follows,

[0047] in, For the length of the data used in the evaluation, This is a design margin added by human intervention.

[0048] Step B3: The detection logic for abnormal glitch is designed as follows: .

[0049] (3) Exception handling module design Based on exception detection, the exception handling logic is designed as follows, as shown in Figure 2. The specific steps include: Step C1: If Then set the exception handling logic flag. If the error persists, proceed to step C2; otherwise, use the measured value from the rudder feedback as the output value after exception handling.

[0050] in, This is the output value after exception handling.

[0051] Step C2: If the count value is less than [a certain value] after entering the exception handling logic. If the predicted value is better than the actual value, proceed to step C3 to determine if the predicted value is better than the actual value; otherwise, proceed to step C4.

[0052] Step C3: Utilize time k, i.e., time before the current time. Calculate the rate of change of the output value after anomaly handling. , as a reference transformation rate ; Calculation based on current predicted values ​​and measured rudder feedback values The rate of change within the sliding window is measured, and the value that is closest to the reference rate of change is selected as the output value after anomaly handling.

[0053] Step C4: If the count value is less than [a certain value] after entering the exception handling logic. If the error is correct, the output value after the anomaly handling will be the actual measured value of the rudder feedback; otherwise, proceed to step C5.

[0054] Step C5: If If an anomaly still exists, the timer value after entering the anomaly handling logic will be reset. If the exception handling logic fails, proceed to step C4; otherwise, exit the exception handling logic and set the exception handling logic flag. And reset the count value after entering the exception handling logic to 0.

[0055] An embodiment of the present invention is a rudder system, given an amplitude of , The sine command signal, with parameters set to: polynomial order Sliding window length Sampling time , , , and burrs on the rudder angle Random injection within the interval is used to meet design verification requirements.

[0056] Specifically, at this time the basis matrix The parameterized form is

[0057] Simultaneously, the rudder feedback sensor predicts the value. The parameterized calculation formula is

[0058] Figure 3 shows the amplitude. , A magnified view of the glitch filtering effect under the sine command shows that the feedback curve after anomaly processing and filtering is well filtered out, and the smoothness of the feedback curve is also good.

[0059] Figure 4 shows the amplitude. , The control performance of glitch filtering under sinusoidal commands is shown in the diagram. The control curve at the actual rudder position is observed and compared with the case where glitch is not filtered. After adopting the proposed real-time glitch detection and processing method, the control tracking performance is improved.

[0060] This embodiment employs a basis function fitting method to construct a set of linearly independent polynomial bases. This method uses rudder feedback sample values ​​from a previous moment, employs a sliding window to dynamically fit a curve, and then extrapolates to dynamically predict the rudder feedback value at the current moment. Unlike Savitzky-Golay filtering, this embodiment uses extrapolation, enabling real-time prediction of the sampled value at the current moment. Savitzky-Golay filtering, on the other hand, predicts the median value of the sequence, replacing abnormal spike values, and cannot provide real-time updates.

[0061] In this embodiment, the exception handling design involves setting a timer value. Limit the number of times the predicted value is used to replace the actual value, while satisfying the requirement of achieving If no abnormality is detected within a given observation period, the exit abnormality handling operation is triggered, ensuring the smoothness of the control curve and the safety and stability of the control.

[0062] Before using the predicted value as the output for anomaly handling, this embodiment adds a performance evaluation step between the predicted value and the measured value, which helps to ensure the smoothness of the control curve.

[0063] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications to the technical solutions of the present invention by utilizing the methods and techniques disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the protection scope of the technical solutions of the present invention.

Claims

1. A real-time detection method for rudder feedback sensing glitch based on online estimation, characterized in that... include: The rudder feedback sensor prediction value at the current moment is obtained based on the sampled value of the rudder feedback in the preceding moment; The detection statistics are obtained based on the current rudder feedback sensor prediction value and the actual rudder feedback value. The anomaly detection threshold is obtained based on the detection statistics and the design margin. The detection logic for abnormal spikes is obtained based on the detection statistics and the anomaly detection threshold.

2. The real-time detection method for rudder feedback sensing glitch based on online estimation according to claim 1, characterized in that... It also includes: designing exception handling logic when the exception glitch detection logic detects the existence of exception glitch.

3. The real-time detection method for rudder feedback sensing glitch based on online estimation according to claim 1, characterized in that: The rudder feedback sensor prediction value at the current moment is obtained based on the sampled value at the previous moment of the rudder feedback. Includes: based on the given polynomial order Sliding window length and sampling time Constructing the basis matrix in Vandermonde form According to the basis matrix Calculate the transformation matrix The construction length is Sliding window, register to The rudder feedback sensor value at any given time, where, At the current time; calculate the sliding update fitting coefficient vector based on the rudder feedback sensor value; obtain the rudder feedback sensor prediction value at the current time based on the sliding update fitting coefficient vector. 。 4. The real-time detection method for rudder feedback sensing glitch based on online estimation according to claim 3, characterized in that: Basis matrices in Vandermonde form It can be obtained through the following formula: ;in, Sampling time, For the order of the polynomial, The sliding window length; the transformation matrix. It can be obtained through the following formula: ;in, The basis matrix is ​​in Vandermonde form. Let be the transformation matrix.

5. The real-time detection method for rudder feedback sensing glitch based on online estimation according to claim 3, characterized in that: to The rudder feedback sensor value at any given time is obtained using the following formula: ;in, for to The rudder feedback sensor value at any given moment. for The rudder feedback sensor value at any given moment. for The rudder feedback sensor value at any given moment. for The rudder feedback sensor value at any given moment. The length of the sliding window. This refers to the current moment.

6. The real-time detection method for rudder feedback sensing glitch based on online estimation according to claim 3, characterized in that: The fitting coefficient vector for sliding updates is obtained using the following formula: ;in, This is the fitted coefficient vector for sliding updates. for to The rudder feedback sensor value at any given moment. Let be the transformation matrix.

7. The real-time detection method for rudder feedback sensing glitch based on online estimation according to claim 3, characterized in that: Current rudder feedback sensor prediction value It can be obtained through the following formula: ； ;in, This is the rudder feedback sensor prediction value at the current moment. This is the fitted coefficient vector for sliding updates. Sampling time, For the order of the polynomial, The length of the sliding window. For the order of the polynomial, Basis vectors are used for online prediction.

8. The real-time detection method for rudder feedback sensing glitch based on online estimation according to claim 1, characterized in that: Detection statistics It can be obtained through the following formula: ;in, This is the rudder feedback sensor prediction value at the current moment. The measured value of the rudder feedback; the anomaly detection threshold. It can be obtained through the following formula: ;in, For design margin, For the current moment, The number of sampling time points; the detection logic for abnormal spikes is as follows: 。 9. The real-time detection method for rudder feedback sensing glitch based on online estimation according to claim 2, characterized in that: The exception handling logic includes the following steps: Step C1: If an exception glitch exists, set the exception handling logic flag. If the output value after exception handling is less than the measured value of the rudder feedback, proceed to step C2; otherwise, let the output value after exception handling use the measured value of the rudder feedback; Step C2: If the count value after entering the exception handling logic is less than If the predicted value is better than the actual value, proceed to step C3 to determine if the predicted value is better than the actual value; otherwise, proceed to step C4. The first time-counting threshold is set; Step C3: Utilize the previous time frame... The output value after anomaly handling is used to calculate the rate of change. , as a reference transformation rate ; Calculation based on current predicted values ​​and measured rudder feedback values The rate of change within the sliding window is measured, and the value closest to the reference rate of change is selected as the output value after anomaly handling; where... Sampling time, This is the rudder feedback output value after anomaly handling at time k-1. This is the rudder feedback output value after anomaly handling at time kn. Calculate the number of intervals for the rate of change; Step C4: If the counted value is less than [a certain value] after entering the exception handling logic. If the error is correct, the output value after anomaly handling will be the measured value from the rudder feedback; otherwise, proceed to step C5. The second counting threshold; Step C5: If If an anomaly still exists, the timer value after entering the anomaly handling logic will be reset. If the exception handling logic fails, proceed to step C4; otherwise, exit the exception handling logic and set the exception handling logic flag. And reset the count value after entering the exception handling logic to 0.

10. A real-time detection system for rudder feedback sensing glitch based on online estimation, characterized in that... include: The signal estimation module is used to obtain the current rudder feedback sensor prediction value based on the sampled value of the rudder feedback in the preceding time step. The anomaly detection module is used to obtain detection statistics based on the current rudder feedback sensor prediction value and the actual rudder feedback value, obtain the anomaly detection threshold based on the detection statistics and design margin, and obtain the anomaly glitch detection logic based on the detection statistics and the anomaly detection threshold.

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