Longitudinal Flight Control Safety Estimation Method and System under Random Deviation Conditions

Abstract

This disclosure proposes a method and system for longitudinal flight control safety estimation under random deviation conditions, relating to the field of UAV safety control technology. The longitudinal flight control safety estimation method includes: establishing an actual received measurement signal model of the longitudinal flight control system under network transmission subjected to random spoofing attacks using a measurement signal model corresponding to the dynamic model of the longitudinal flight control system under random deviation in a composite state; establishing a dynamic model of an estimator based on the single-rate system model corresponding to the actual received measurement signal model and the measurement signal model under the composite state; determining a dynamic model of the augmented estimation error corresponding to the longitudinal flight control system based on the single-rate system model and the dynamic model of the estimator; and determining the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error. This method enables longitudinal flight control safety estimation.

Description

Technical Field

[0001] This disclosure relates to the field of unmanned aerial vehicle (UAV) safety control technology, and in particular to a longitudinal flight control safety estimation method and system under random deviation conditions. Background Technology

[0002] The longitudinal flight control system is a core component of aircraft automatic control. Designing estimation methods for the longitudinal flight control system helps to accurately grasp the system's operating status and facilitate the successful completion of flight missions.

[0003] Real-world systems are often affected by different types of disturbances, and the unknown inputs to these disturbed systems can be characterized by random deviations. Considering random deviations during system modeling and effectively handling them by jointly estimating the system state and random deviations during system analysis is of research significance.

[0004] Considering the inconsistency between the system state update cycle and the sensor measurement sampling cycle in practice, researching multi-rate sampling aligns with practical needs. Due to variations in sensor operating conditions and component aging, sensor nonlinearity inevitably occurs, affecting actual measurement output results. Pre-considering sensor nonlinearity ensures the accuracy of measurement signal modeling. While internet technology brings convenience to information transmission, network attacks are frequent, seriously threatening system information security and stable operation. Existing estimation methods cannot directly handle random deviations, multi-rate sampling, sensor nonlinearity, and random spoofing attacks, leading to reduced estimation performance.

[0005] Therefore, this invention takes into account the impact of random deception attacks and designs a secure estimation method and system that can still ensure normal system operation when a deception attack occurs. This solves at least one of the following technical problems: the inability to handle or simultaneously handle random deviations, multi-rate sampling, sensor nonlinearity, random deception attacks, the inability to directly handle the reduction in estimation performance caused by random deviations, multi-rate sampling, sensor nonlinearity, and random deception attacks, or the inability to solve for the unknown gain of the estimator based on random deviations, disturbances, and random deception attacks. Summary of the Invention

[0006] This disclosure proposes a longitudinal flight control safety estimation method and corresponding technical solution for random deviation scenarios.

[0007] According to one aspect of this disclosure, a method for longitudinal flight control safety estimation under random deviation conditions is provided, comprising: establishing an actual received measurement signal model of the longitudinal flight control system under network transmission subjected to random spoofing attacks using a measurement signal model corresponding to the dynamic model of the longitudinal flight control system under random deviation in a composite state; establishing a dynamic model of an estimator based on the actual received measurement signal model and the single-rate system model corresponding to the measurement signal model under the composite state; determining a dynamic model of the augmented estimation error corresponding to the longitudinal flight control system based on the single-rate system model and the dynamic model of the estimator; and determining the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error using finite-time bounded performance analysis, so as to achieve longitudinal flight control safety estimation of the longitudinal flight control system corresponding to the dynamic model under the composite state.

[0008] Preferably, determining the measurement signal model in the composite state includes: determining the dynamic model in the composite state based on the state vector corresponding to the dynamic model of the longitudinal flight control system under random deviation at the state update time and the random deviation corresponding to the dynamic model of random deviation; and determining the measurement signal model in the composite state based on the dynamic model of the longitudinal flight control system under random deviation in the composite state, the preset sampling rate, and the measurement signal model of sensor nonlinearity.

[0009] Preferably, determining the dynamic model of the longitudinal flight control system under random deviation includes: acquiring the state vector, random deviation, and process noise corresponding to the first state update time within a finite time; and using a preset set of matrices to assign weights to the state vector, random deviation, and process noise corresponding to the first state update time to obtain the state vector corresponding to the second state update time corresponding to the next time after the first state update time within the finite time, thereby determining the dynamic model of the longitudinal flight control system under random deviation.

[0010] Preferably, determining the dynamic model of the random deviation includes: obtaining the random deviation corresponding to the first state update time and the noise corresponding to the random deviation; and using the fourth preset given matrix, the random deviation corresponding to the first state update time and the noise corresponding to the random deviation, obtaining the random deviation corresponding to the second state update time corresponding to the next time step after the first state update time.

[0011] Preferably, determining the dynamic model in the composite state based on the state vector corresponding to the dynamic model of the longitudinal flight control system under random deviation at the state update time and the random deviation corresponding to the dynamic model of random deviation includes: constructing a composite state vector corresponding to the first state update time using the state vector and random deviation at the first state update time; constructing a composite process noise vector corresponding to the first state update time using the process noise corresponding to the first state update time, the random deviation at the first state update time, and the noise corresponding to the random deviation; determining a first weight matrix of the composite state vector corresponding to the first state update time and a second weight matrix of the composite process noise vector corresponding to the first state update time; determining a weighted composite state vector corresponding to the first state update time based on the first weight matrix and the composite state vector; determining a weighted composite process noise vector corresponding to the first state update time based on the second weight matrix and the composite process noise vector; and combining the weighted composite state vector corresponding to the first state update time and the weighted composite process noise vector corresponding to the first state update time to obtain the dynamic model in the composite state corresponding to the second state update time at the next time step after the first state update time.

[0012] Preferably, determining the first weight matrix of the composite state vector corresponding to the first state update time includes: using a first preset given matrix and a second preset given matrix to construct a preset given matrix group corresponding to the state vector corresponding to the second state update time, and constructing a fourth preset given matrix of random deviation corresponding to the second state update time, to determine the weight matrix of the composite state vector corresponding to the first state update time.

[0013] Preferably, determining the second weight matrix of the composite process noise vector corresponding to the first state update time includes: constructing a diagonal matrix using a third preset matrix that constructs a preset set of given matrices corresponding to the state vector corresponding to the second state update time and an identity matrix of a set dimension, and determining the second weight matrix of the composite process noise vector corresponding to the first state update time.

[0014] Preferably, determining the measurement signal model in the composite state based on the dynamic model of the longitudinal flight control system under the random deviation in the composite state, the preset sampling rate, and the measurement signal model of the sensor nonlinearity includes: obtaining the measurement signal model corresponding to the preset sampling rate and the sensor nonlinearity; replacing the state vector corresponding to the sampling time in the measurement signal model with the composite state vector corresponding to the first state update time in the dynamic model in the composite state, to obtain the measurement signal model in the composite state corresponding to the sampling time.

[0015] Preferably, determining the measurement signal model corresponding to the preset sampling rate and sensor nonlinearity includes: acquiring the state vector of the longitudinal flight control system at the sampling time, the sensor nonlinearity corresponding to the state vector, and the measurement noise; configuring a fifth preset given matrix and a sixth preset given matrix for the state vector and the measurement noise, respectively, so that the sum of the first product of the fifth preset given matrix and the state vector, the second product of the sixth preset given matrix and the measurement noise, and the sensor nonlinearity is the measurement output of the longitudinal flight control system at the sampling time.

[0016] Preferably, determining the single-rate system model corresponding to the measurement signal model in the composite state includes: using the mapping relationship between the first state update time in the dynamic model in the composite state and the sampling time in the measurement signal model to convert the multi-rate system model corresponding to the measurement signal model in the composite state into a single-rate system model.

[0017] Preferably, determining the actual received measurement signal model includes: acquiring the random variable corresponding to the sampling time, the measurement output of the longitudinal flight control system, and the spoofing signal; calculating the difference between the spoofing signal and the measurement output; performing a multiplication operation on the difference and the random variable to obtain a random difference; and combining the random difference and the measurement output to determine the actual received measurement signal model.

[0018] Preferably, the step of establishing the dynamic model of the estimator based on the actual received measurement signal model and the single-rate system model corresponding to the measurement signal model in the composite state includes: obtaining the actual received measurement signal corresponding to the actual received measurement signal model at the sampling time, the single-rate state vector estimate corresponding to the single-rate state vector of the single-rate system model corresponding to the measurement signal model in the composite state at the sampling time, the single-rate sensor nonlinearity, and the first matching matrix corresponding to the single-rate state vector estimate; calculating the expectation corresponding to the random variable at the sampling time; determining the estimated received measurement signal corresponding to the single-rate state vector estimate using the expectation, the single-rate state vector estimate, and the single-rate sensor nonlinearity; calculating the measurement signal difference between the estimated received measurement signal and the actual received measurement signal; configuring the measurement signal difference into the estimator gain to be designed; performing a multiplication operation on the single-rate state vector using the first matching matrix to obtain a single-rate matched state vector; and establishing the dynamic model of the estimator by combining the single-rate matched state vector and the measurement signal difference after configuring the estimator gain to be designed.

[0019] Preferably, determining the dynamic model of the augmented estimation error corresponding to the longitudinal flight control system based on the single-rate system model and the dynamic model of the estimator includes: obtaining the state vector of the single-rate system model at the sampling time and the state vector of the dynamic model of the estimator at the sampling time and the state vector at the sampling time and the state vector at the sampling time and the state vector at the sampling time and the state vector at the sampling time and the state vector estimation corresponding to the state vector at the sampling time and the state vector estimation at the sampling time and the state vector at the sampling time and the state vector estimation corresponding to the state vector at the sampling time and the state vector at the sampling time and the state vector estimation, and determining the dynamic model of the augmented estimation error at the corresponding time.

[0020] Preferably, determining the dynamic model of the augmented estimation error corresponding to the longitudinal flight control system further includes: updating the state vector at the sampling time and the estimation error at the sampling time in the dynamic model of the augmented estimation error by defining an augmented estimation error vector.

[0021] Preferably, determining the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error using finite-time bounded performance analysis includes: determining the solution inequality corresponding to the estimator gain to be designed in the dynamic model of the augmented estimation error using finite-time bounded performance analysis; calculating the weighted gain matrix and the gain weighting matrix based on the solution inequality corresponding to the estimator gain to be designed, the upper bound constraint condition of the dynamic model of the augmented estimation error, and the initial state constraint condition of the dynamic model of the augmented estimation error; and determining the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error based on the weighted gain matrix and the gain weighting matrix.

[0022] Preferably, determining the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error based on the gain value matrix to be weighted and the gain value weighting matrix includes: multiplying the gain value weighting matrix by the gain value matrix to be weighted to determine the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error.

[0023] According to one aspect of this disclosure, a longitudinal flight control safety estimation system under random deviation conditions is provided, comprising: a first establishing unit, configured to establish an actual received measurement signal model of the longitudinal flight control system subjected to random spoofing attacks during network transmission using a measurement signal model corresponding to the dynamic model of the longitudinal flight control system under random deviation in a composite state; a second establishing unit, configured to establish a dynamic model of an estimator based on the actual received measurement signal model and a single-rate system model corresponding to the measurement signal model under the composite state; a first determining unit, configured to determine a dynamic model of the augmented estimation error corresponding to the longitudinal flight control system based on the single-rate system model and the dynamic model of the estimator; and a second determining unit, configured to determine the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error using finite-time bounded performance analysis, so as to realize the longitudinal flight control safety estimation corresponding to the dynamic model of the longitudinal flight control system under the composite state.

[0024] According to one aspect of this disclosure, a longitudinal flight control safety estimation system under random deviation conditions is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the aforementioned longitudinal flight control safety estimation method under random deviation conditions; or, comprising: a computer-readable storage medium storing a computer program / instructions and a bit stream thereon, wherein the computer program / instructions, when executed by the processor, implement the aforementioned longitudinal flight control safety estimation method under random deviation conditions to generate the bit stream; or, comprising: a computer program product configured with a computer program / instructions, wherein the computer program / instructions, when executed by the processor, implement the aforementioned longitudinal flight control safety estimation method under random deviation conditions.

[0025] According to one aspect of this disclosure, a computer program product is provided, including a computer program / instructions, characterized in that, when the computer program / instructions are executed by a processor, they implement the aforementioned longitudinal flight control safety estimation method under random deviation conditions.

[0026] According to one aspect of this disclosure, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: execute the aforementioned longitudinal flight control safety estimation method under random deviation conditions.

[0027] According to one aspect of this disclosure, a computer-readable storage medium is provided that stores a computer program / instructions and a bit stream thereon, wherein the computer program / instructions, when executed by a processor, generate the bit stream by implementing the aforementioned longitudinal flight control safety estimation method under random deviation conditions.

[0028] In this disclosure, a technical solution is proposed for a longitudinal flight control safety estimation method and system under random deviation conditions. This solution addresses at least one of the following technical problems: the inability to simultaneously handle random deviation, multi-rate sampling, sensor nonlinearity, random deception attacks, the inability to directly handle the performance degradation caused by random deviation, multi-rate sampling, sensor nonlinearity, and random deception attacks, or the inability to solve for the unknown gain of the estimator based on random deviation, disturbance, or random deception attacks.

[0029] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0030] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0031] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0032] Figure 1 A flowchart illustrating a longitudinal flight control safety estimation method under random deviation scenarios according to embodiments of the present disclosure is shown. Figure 2 This is a comparison diagram of the trajectories of the three components of the system state and the corresponding three components of the estimator state in an embodiment of the present invention. Figure 3 This is a comparison diagram of the trajectories of the three components of the system's random deviation and the corresponding trajector states in an embodiment of the present invention. Figure 4 This is the estimation error trajectory of the three components of the system state in this embodiment of the invention; Figure 5 This is the estimated error trajectory of the three components of the system's random deviation in this embodiment of the invention; Figure 6 This invention provides a finite-time bounded performance for augmenting the estimation error under the action of the estimator in this embodiment of the invention. Figure 7 This represents a random occurrence of a deception attack in an embodiment of the present invention; Figure 8 This is a comparison chart of the system state update period and the measurement signal sampling period in an embodiment of the present invention; Figure 9 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment; Figure 10This is a block diagram illustrating an electronic device 1900 according to an exemplary embodiment. Detailed Implementation

[0033] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0034] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0035] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0036] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0037] It is understood that the various method embodiments mentioned above in this disclosure can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this disclosure will not elaborate further.

[0038] In addition, this disclosure also provides a longitudinal flight control safety estimation device or system, electronic equipment, computer-readable storage medium, and program under random deviation conditions. All of the above can be used to implement any of the longitudinal flight control safety estimation methods under random deviation conditions provided in this disclosure. The corresponding technical solutions and descriptions are described in the corresponding section on longitudinal flight control safety estimation methods under random deviation conditions, and will not be repeated here.

[0039] Figure 1 A flowchart illustrating a longitudinal flight control safety estimation method under random deviation scenarios according to embodiments of the present disclosure is shown, such as... Figure 1As shown, the longitudinal flight control safety estimation method under random deviation conditions includes: Step S1: Using the measurement signal model corresponding to the dynamic model of the longitudinal flight control system under random deviation in the composite state, establish the actual received measurement signal model of the longitudinal flight control system under random spoofing attacks during network transmission; Step S2: Based on the actual received measurement signal model and the single-rate system model corresponding to the measurement signal model under the composite state, establish the dynamic model of the estimator; Step S3: Based on the single-rate system model and the dynamic model of the estimator, determine the dynamic model of the augmented estimation error corresponding to the longitudinal flight control system; Step S4: Using finite-time bounded performance analysis, determine the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error, so as to realize the longitudinal flight control safety estimation corresponding to the dynamic model of the longitudinal flight control system under the composite state. To address at least one of the following technical problems: the inability to handle or simultaneously handle random deviations, multi-rate sampling, sensor nonlinearity, random spoofing attacks, the inability to directly handle the performance degradation caused by random deviations, multi-rate sampling, sensor nonlinearity, and random spoofing attacks, or the inability to solve for the unknown gain of the estimator based on random deviations, perturbations, and random spoofing attacks.

[0040] Step S1: Using the measurement signal model corresponding to the dynamic model of the longitudinal flight control system under random deviation in the composite state, establish the actual received measurement signal model of the longitudinal flight control system under random spoofing attacks during network transmission.

[0041] In this embodiment of the disclosure, determining the measurement signal model in the composite state includes: determining the dynamic model in the composite state based on the state vector corresponding to the dynamic model of the longitudinal flight control system under random deviation at the state update time and the random deviation corresponding to the dynamic model of random deviation; and determining the measurement signal model in the composite state based on the dynamic model of the longitudinal flight control system under random deviation in the composite state, the preset sampling rate, and the measurement signal model of sensor nonlinearity.

[0042] In this embodiment of the disclosure, determining the dynamic model of the longitudinal flight control system under random deviation includes: acquiring the state vector, random deviation, and process noise corresponding to the first state update time within a finite time; and using a preset given matrix group to perform weight configuration on the state vector, random deviation, and process noise corresponding to the first state update time to obtain the state vector corresponding to the second state update time corresponding to the next time after the first state update time within the finite time, so as to determine the dynamic model of the longitudinal flight control system under random deviation.

[0043] In this embodiment of the disclosure, determining the dynamic model of the random deviation includes: obtaining the random deviation corresponding to the first state update time and the noise corresponding to the random deviation; and using a fourth preset given matrix, the random deviation corresponding to the first state update time and the noise corresponding to the random deviation, obtaining the random deviation corresponding to the second state update time at the next time step after the first state update time.

[0044] In the embodiments disclosed herein and other possible embodiments, the step of weighting the state vector, random deviation, and process noise corresponding to the first state update time using a preset given matrix group to obtain the state vector corresponding to the second state update time corresponding to the next time of the first state update time within the finite time includes: using the first preset given matrix in the preset given matrix group. A Compared with the first state update time Corresponding state vector Multiplying them together yields the weighted state vector. ; using the second preset given matrix in the preset given matrix group B Compared with the first state update time Corresponding random deviation Multiply to obtain the first weighted random deviation. ; using the third preset given matrix in the preset given matrix group E Compared with the first state update time Corresponding process noise Multiply to obtain the weighted process noise. Combine the weights to configure the state vector First weight random deviation and weighting process noise The first state update time is obtained within the finite time. The second state update time corresponding to the next moment Corresponding state vector .

[0045] In the embodiments disclosed herein and other possible embodiments, obtaining the random deviation corresponding to the second state update time at the next time step of the first state update time using a fourth preset given matrix, the random deviation corresponding to the first state update time, and the noise corresponding to the random deviation includes: using the fourth preset given matrix G Multiply by the first state update time Corresponding random deviation The second weighted random deviation is obtained. Combined with the second weighted random deviation and the first state update time The random deviation Corresponding noise The first state update time is obtained. The second state update time corresponding to the next moment Corresponding random deviation .

[0046] In this embodiment of the disclosure, determining the dynamic model in the composite state based on the state vector corresponding to the dynamic model of the longitudinal flight control system under random deviation at the state update time and the random deviation corresponding to the dynamic model of random deviation includes: constructing a composite state vector corresponding to the first state update time using the state vector and random deviation at the first state update time; constructing a composite process noise vector corresponding to the first state update time using the process noise corresponding to the first state update time, the random deviation at the first state update time, and the noise corresponding to the random deviation; determining a first weight matrix of the composite state vector corresponding to the first state update time and a second weight matrix of the composite process noise vector corresponding to the first state update time; determining a weighted composite state vector corresponding to the first state update time based on the first weight matrix and the composite state vector; determining a weighted composite process noise vector corresponding to the first state update time based on the second weight matrix and the composite process noise vector; and combining the weighted composite state vector corresponding to the first state update time and the weighted composite process noise vector corresponding to the first state update time to obtain the dynamic model in the composite state corresponding to the second state update time at the next time step after the first state update time.

[0047] In this embodiment of the disclosure, determining the first weight matrix of the composite state vector corresponding to the first state update time includes: using a first preset given matrix and a second preset given matrix to construct a preset given matrix group corresponding to the state vector corresponding to the second state update time, and constructing a fourth preset given matrix of random deviation corresponding to the second state update time, to determine the weight matrix of the composite state vector corresponding to the first state update time; and / or, determining the second weight matrix of the composite process noise vector corresponding to the first state update time includes: using a third preset given matrix to construct a preset given matrix group corresponding to the state vector corresponding to the second state update time and an identity matrix of a set dimension to construct a diagonal matrix, to determine the second weight matrix of the composite process noise vector corresponding to the first state update time.

[0048] In the embodiments of this disclosure and other possible embodiments, determining the weighted composite state vector corresponding to the first state update time based on the first weight matrix and the composite state vector includes: performing a multiplication operation on the composite state vector using the first weight matrix to determine the weighted composite state vector corresponding to the first state update time.

[0049] In this disclosure and other possible embodiments, determining the weighted composite process noise vector corresponding to the first state update time based on the second weight matrix and the composite process noise vector includes: using the second weight matrix... Perform a multiplication operation on the composite process noise vector to determine the weighted composite process noise vector corresponding to the first state update time. In the embodiments disclosed herein and other possible embodiments, the first state update time is utilized. Corresponding state vector and the first state update time Corresponding random deviation Construct the first state update moment The corresponding composite state vector Update time using the first state Corresponding process noise and the first state update time The random deviation and the noise corresponding to the random deviation Construct the first state update moment The corresponding composite process noise vector Determine the first state update time. The first weight matrix of the corresponding composite state vector and the first state update time The second weight matrix of the corresponding composite process noise vector Using the first weight matrix Perform a multiplication operation on the composite state vector to determine the first state update time. The corresponding weighted composite state vector Using the second weight matrix Perform a multiplication operation on the composite process noise vector to determine the first state update time. The corresponding weighted composite process noise vector Combined with the first state update time The corresponding weighted composite state vector and the first state update time The corresponding weighted composite process noise vector The first state update time is obtained. The second state update time corresponding to the next moment Dynamic model under the corresponding composite state .

[0050] In the embodiments disclosed herein and other possible embodiments, the first state update time is determined. The first weight matrix of the corresponding composite state vector This includes: using the first preset given matrix corresponding to the preset given matrix group corresponding to the state vector at the second state update time to construct the second state update time. A and the second preset given matrix B Construct a fourth pre-defined matrix for the random deviation corresponding to the second state update time. G Determine the first state update time The weight matrix of the corresponding composite state vector .

[0051] In the embodiments disclosed herein and other possible embodiments, the first state update time is determined. The second weight matrix of the corresponding composite process noise vector This includes: using a third preset matrix corresponding to a preset set of matrixes corresponding to the state vector at the second state update time. E and setting dimensions identity matrix Construct a diagonal matrix to determine the first state update time. The second weight matrix of the corresponding composite process noise vector .

[0052] In this embodiment of the disclosure, determining the measurement signal model in the composite state based on the dynamic model of the longitudinal flight control system under the random deviation in the composite state, the preset sampling rate, and the measurement signal model of the sensor nonlinearity includes: obtaining the measurement signal model corresponding to the preset sampling rate and the sensor nonlinearity; replacing the state vector corresponding to the sampling time in the measurement signal model with the composite state vector corresponding to the first state update time in the dynamic model in the composite state, thereby obtaining the measurement signal model in the composite state corresponding to the sampling time.

[0053] In this embodiment of the disclosure, determining the measurement signal model corresponding to the preset sampling rate and sensor nonlinearity includes: acquiring the state vector of the longitudinal flight control system at the sampling time, the sensor nonlinearity corresponding to the state vector, and the measurement noise; configuring a fifth preset given matrix and a sixth preset given matrix for the state vector and the measurement noise, respectively, so that the sum of the first product of the fifth preset given matrix and the state vector, the second product of the sixth preset given matrix and the measurement noise, and the sensor nonlinearity is the measurement output of the longitudinal flight control system at the sampling time.

[0054] In the embodiments disclosed herein and other possible embodiments, determining the measurement signal model under the combined state based on the dynamic model of the longitudinal flight control system under random deviation, the preset sampling rate, and the measurement signal model of the sensor nonlinearity includes: acquiring the measurement signal model corresponding to the preset sampling rate and the sensor nonlinearity; and using the first state update time in the dynamic model under the combined state. The corresponding composite state vector Replace the sampling time in the measurement signal model Corresponding state vector The sampling time is obtained. The corresponding measurement signal model in the composite state .

[0055] In this disclosure and other possible embodiments, determining the measurement signal model corresponding to the preset sampling rate and sensor nonlinearity includes: obtaining the measurement signal model at the sampling time. The state vector of the longitudinal flight control system The state vector Corresponding sensor nonlinearity and measurement noise ; respectively for the state vector and the measured noise Configure the fifth preset given matrix C and the sixth preset given matrix D To satisfy the fifth preset given matrix C With the state vector First product The sixth preset given matrix D With measurement noise The second product and the nonlinearity of the sensor The sum is the sum of the longitudinal flight control system at the sampling time. Measurement output .

[0056] Step S2: Based on the actual received measurement signal model and the single-rate system model corresponding to the measurement signal model under the composite state, establish the dynamic model of the estimator.

[0057] In this embodiment of the disclosure, determining the single-rate system model corresponding to the measurement signal model in the composite state includes: using the mapping relationship between the first state update time in the dynamic model in the composite state and the sampling time corresponding to the measurement signal model, converting the multi-rate system model corresponding to the measurement signal model in the composite state into a single-rate system model; and / or, In this embodiment of the disclosure, determining the actual received measurement signal model includes: acquiring a random variable corresponding to the sampling time, the measurement output of the longitudinal flight control system, and a spoofing signal; calculating the difference between the spoofing signal and the measurement output; performing a multiplication operation on the difference and the random variable to obtain a random difference; and combining the random difference and the measurement output to determine the actual received measurement signal model.

[0058] In the embodiments disclosed herein and other possible embodiments, determining the single-rate system model corresponding to the measurement signal model in the composite state includes: using the first state update time in the dynamic model in the composite state. With respect to the sampling time in the measured signal model The corresponding mapping relationship ( , The mapping relationship is (times), the measurement signal model in the composite state at the first state update time. and sampling time Corresponding multi-rate system model Converted to sampling time Corresponding single-rate system model .

[0059] In this disclosure and other possible embodiments, determining the actual received measurement signal model includes: acquiring the sampling time. Corresponding random variable The measurement output of the longitudinal flight control system and deceptive signals ; Calculate the deception signal With the measurement output The difference between ; regarding the difference and the random variable Perform a multiplication operation to obtain a random difference. Combined with the aforementioned random difference and the measurement output Determine the actual received measurement signal model .

[0060] Step S3: Based on the single-rate system model and the dynamic model of the estimator, determine the dynamic model of the augmented estimation error corresponding to the longitudinal flight control system.

[0061] In this embodiment of the disclosure, the step of establishing a dynamic model of the estimator based on the actual received measurement signal model and the single-rate system model corresponding to the measurement signal model in the composite state includes: obtaining the actual received measurement signal corresponding to the actual received measurement signal model at the sampling time, the single-rate state vector estimate corresponding to the single-rate state vector of the single-rate system model corresponding to the measurement signal model in the composite state at the sampling time, the single-rate sensor nonlinearity, and the first matching matrix corresponding to the single-rate state vector estimate; calculating the expectation corresponding to the random variable at the sampling time; using the expectation, the single-rate state vector estimate, and the single-rate sensor nonlinearity to determine the estimated received measurement signal corresponding to the single-rate state vector estimate; calculating the measurement signal difference between the estimated received measurement signal and the actual received measurement signal; configuring the measurement signal difference into the estimator gain to be designed; performing a multiplication operation on the single-rate state vector using the first matching matrix to obtain a single-rate matched state vector; and combining the single-rate matched state vector and the measurement signal difference after configuring the estimator gain to be designed to establish a dynamic model of the estimator.

[0062] In the embodiments disclosed herein and other possible embodiments, the step of establishing the dynamic model of the estimator based on the actual received measurement signal model and the single-rate system model corresponding to the measurement signal model in the composite state includes: obtaining the actual received measurement signal model at the sampling time. Corresponding actual received measurement signal The single-rate system model corresponding to the measurement signal model in the composite state at the sampling time... single-rate state vector Corresponding single-rate state vector estimation Single-rate sensor nonlinearity and the single-rate state vector estimation The corresponding first matching matrix ; Calculate at sampling time random variables Corresponding expectations ; Utilizing the aforementioned expectation The single-rate state vector estimation The single-rate sensor is nonlinear. Determine the single-rate state vector estimate Corresponding estimated received measurement signal ; Calculate the estimated received measurement signal With the actual received measurement signal The difference between the measured signals Configure the difference in the measured signal into the gain of the estimator to be designed. K ( ); using the first matching matrix For a single-rate state vector Perform a multiplication operation to obtain the single-rate matching state vector. Combined with the single-rate matching state vector and configuration of the estimator gain to be designed K The difference in the measured signal after ( ), and establish a dynamic model of the estimator.

[0063] In this embodiment of the disclosure, determining the dynamic model of the augmented estimation error corresponding to the longitudinal flight control system based on the single-rate system model and the dynamic model of the estimator includes: obtaining the state vector of the single-rate system model at the sampling time and the state vector of the dynamic model of the estimator at the sampling time and the state vector at the sampling time and the state vector at the sampling time and the state vector at the sampling time and the state vector at the sampling time and the state vector estimation corresponding to the state vector at the sampling time and the state vector estimation at the sampling time and the state vector at the sampling time and the state vector estimation corresponding to the state vector at the sampling time and the state vector at the sampling time and the state vector estimation ...

[0064] In this embodiment of the disclosure, determining the dynamic model of the augmented estimation error corresponding to the longitudinal flight control system further includes: updating the state vector at the sampling time and the estimation error at the sampling time in the dynamic model of the augmented estimation error by defining an augmented estimation error vector.

[0065] In embodiments disclosed herein and other possible embodiments, determining the dynamic model of the augmented estimation error corresponding to the longitudinal flight control system based on the single-rate system model and the dynamic model of the estimator includes: obtaining the single-rate system model at the sampling time. At the next sampling time state vector and the dynamic model of the estimator at the sampling time At the next sampling time state vector State vector estimation Based on the next sampling time state vector and the next sampling time state vector Corresponding state vector estimation A dynamic model for determining the augmented estimation error at the corresponding time point. .

[0066] In the embodiments disclosed herein and other possible embodiments, determining the dynamic model corresponding to the augmented estimation error of the longitudinal flight control system further includes: defining an augmented estimation error vector. The sampling time in the dynamic model of the augmented estimation error state vector and the sampling time estimation error Update.

[0067] Step S4: Using finite-time bounded performance analysis, determine the gain of the estimator to be designed corresponding to the dynamic model of the augmented estimation error, so as to realize the longitudinal flight control safety estimation corresponding to the dynamic model of the longitudinal flight control system in the compound state.

[0068] In this embodiment of the disclosure, determining the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error using finite-time bounded performance analysis includes: determining the solution inequality corresponding to the estimator gain to be designed in the dynamic model of the augmented estimation error using finite-time bounded performance analysis; calculating the weighted gain matrix and the gain weighting matrix based on the solution inequality corresponding to the estimator gain to be designed, the upper bound constraint condition of the dynamic model of the augmented estimation error, and the initial state constraint condition of the dynamic model of the augmented estimation error; and determining the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error based on the weighted gain matrix and the gain weighting matrix.

[0069] In this embodiment of the disclosure, determining the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error based on the gain value matrix to be weighted and the gain value weighting matrix includes: multiplying the gain value weighting matrix by the gain value matrix to be weighted to determine the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error.

[0070] In the embodiments disclosed herein and other possible embodiments, this invention addresses the technical problem that existing estimation methods cannot simultaneously handle random deviations, multi-rate sampling, sensor nonlinearity, and random spoofing attacks, thus failing to guarantee estimation performance. The invention sequentially establishes a dynamic model of the longitudinal flight control system under random deviations, a measurement signal model with a preset sampling rate and sensor nonlinearity, a single-rate system model, an actual received measurement signal model subjected to random spoofing attacks during network transmission, and a dynamic model of the estimator. Then, the estimation error is calculated to obtain a dynamic model of the augmented estimation error. Next, the unknown gain of the dynamic model of the estimator is solved. Finally, the gain is substituted into the dynamic model of the estimator to achieve a safe estimation of the dynamic model of the longitudinal flight control system under random deviations.

[0071] Specifically, the longitudinal flight control safety estimation method under random deviation includes the following steps: S100, establishing a dynamic model of the longitudinal flight control system under random deviation, and combining the system state (control system state) and the dynamic model of random deviation to obtain a dynamic model of the composite state; S200, establishing a measurement signal model with a preset sampling rate and sensor nonlinearity under the composite state based on the dynamic model of the longitudinal flight control system obtained in step S100; S300, converting the multi-rate system model (multi-rate control system model) composed of the dynamic model of the composite state obtained in step S100 and the measurement signal model obtained in step S200 into a single-rate system model (single-rate control system model); S400, establishing a measurement signal model based on the measurement signal model obtained in step S200. The actual received measurement signal model is subjected to random spoofing attacks during network transmission; S500, based on the actual received measurement signal model obtained in step S400 and the single-rate system model obtained in step S300, a dynamic model of the estimator is established; S600, according to the single-rate system model obtained in step S300 and the dynamic model of the estimator obtained in step S500, a dynamic model of the augmented estimation error is obtained; S700, using finite-time bounded performance analysis, according to the dynamic model of the augmented estimation error obtained in step S600, the gain of the dynamic model of the estimator obtained in step S500 is solved; S800, the gain obtained in step S700 is substituted into the dynamic model of the estimator obtained in step S500 to achieve a secure estimation of the dynamic model of the composite state obtained in step S100.

[0072] In this invention, for any positive integer , express Vie Euclidean space; for any square matrix , , and They represent The reverse, It is a positive definite and positive semi-definite matrix; for any matrix , express transpose; Indicates the Kronecker product; Represents the identity matrix, with dimension . For any random item , express Take the expected value; Indicates that the elements on the diagonal are and A diagonal matrix.

[0073] Step S100 specifically includes: state update time. , In a limited time The dynamic model of the longitudinal flight control system affected by random deviations is as follows:

[0074] In the formula, Indicates the longitudinal flight control system in The state vector corresponding to the state update time. Indicates the longitudinal flight control system in The state vector corresponding to the state update time. , , , They represent in At the moment of state update, the pitch angle, pitch rate, and normal velocity of the longitudinal flight control system are displayed; the update cycle of the system state (longitudinal flight control system state) is... ; It is the process noise of the longitudinal flight control system, which satisfies the bounded condition. ; This represents the first upper bound of the process noise boundary condition; This indicates a random deviation from the longitudinal flight control system; , and Let N and N represent given matrices of appropriate dimensions; N is a preset upper bound for time.

[0075] The dynamic model for random deviation is:

[0076] In the formula, The noise in the random deviation of the longitudinal flight control system satisfies the bounded condition. , ; Represents a given matrix with appropriate dimensions.

[0077] The state vector in augmented equation (1) and the random deviation in equation (2) yield the dynamic model of the composite state as follows:

[0078] In the formula, ; The dimension is The identity matrix.

[0079] Step S200 specifically includes: The measurement signal model with a preset sampling rate and sensor nonlinearity is as follows:

[0080] In the formula, The system (longitudinal flight control system) indicates the sampling time. The measurement output; the sampling period of the measurement signal model is , The sampling period of the measurement signal model is an integer, obtained from the update period of the system state (longitudinal flight control system state) obtained in step S100. of times; Indicates the longitudinal flight control system at the sampling time Corresponding state vector The sensor is nonlinear; Indicates the longitudinal flight control system at the sampling time Corresponding state vector The measurement noise satisfies the bounded condition. ; This represents the second upper bound of the measurement noise boundary condition; and Let each represent a given matrix of appropriate dimensions.

[0081] The constraints to be satisfied are:

[0082] In the formula, and Represents a given matrix with appropriate dimensions.

[0083] For the composite state (3) in step S100 In the substitution formula (4) The equivalent form of equation (4) (the measurement signal model in the composite state) is:

[0084] In the formula,

[0085] Step S300 specifically includes: For the multi-rate system model composed of the dynamic model (3) of the composite state obtained in step S100 and the measurement signal model (6) obtained in step S200: .

[0086] Define the state vector of a single-rate system as follows: The multi-rate system model is transformed into a single-rate system model as follows:

[0087] In the formula,

[0088] express The corresponding power is .

[0089] Step S400 specifically includes: Based on the measurement signal model (6) obtained in step S200, the actual received measurement signal of the longitudinal flight control system subjected to random deception attacks during network transmission ( The model is:

[0090] In the formula, It is a random variable that follows a Bernoulli distribution and has an expected value of ( (where is a known scalar), and the variance is... ; Indicates the longitudinal flight control system at the sampling time The deceptive signal satisfies the bounded condition. . Indicates the longitudinal flight control system at the sampling time The measurement output.

[0091] Step S500 specifically includes: Based on the actual received measurement signal model (8) obtained in step S400 and the single-rate system model (7) obtained in step S300, the dynamic model of the estimator is constructed as follows:

[0092] In the formula, Represents the state vector of a single-rate system Corresponding estimates; This represents the gain of the estimator to be designed for the longitudinal flight control system.

[0093] Step S600 specifically includes: Define the estimation error corresponding to the state vector of the longitudinal flight control system. (State vector of a single-rate system model) , representing the state vector of a single-rate system. Corresponding estimates Augmented noise vectors of the longitudinal flight control system and the longitudinal flight control system Organize the single-rate system model (7) It is in the form of an augmented noise vector, and is based on the single-rate system model (7) obtained in step S300 and the dynamic model (9) of the estimator obtained in step S500. The dynamic model for calculating the estimation error yields the following results:

[0094] In the formula,

[0095] In equation (10) The expression contains There is a problem of inconsistent vector notation, which is addressed by defining an augmented estimation error vector. This will be addressed. The dynamic model for the augmented estimation error is as follows:

[0096] In the formula,

[0097] Step S700 specifically includes: Using the dynamic model (11) of the augmented estimation error obtained in step S600, and through finite-time bounded performance analysis based on Lyapunov stability theory, the gain of the estimator to be designed is obtained by solving a set of matrix inequalities. : Based on the dynamic model of estimation error (11) and Lyapunov stability theory, the gain of the estimator to be designed is determined. The corresponding inequality to be solved is:

[0098] Using Lyapunov stability theory, the estimator gain value that makes the augmented estimation error finite-time bounded is obtained. Through the formula:

[0099] Calculate the estimator gain; In the formula,

[0100] in, Given a scalar, For scalars, matrices ( , )and It is a matrix with appropriate dimensions.

[0101] This invention provides a safety estimation method and system for a longitudinal flight control system under random deviation conditions. It simultaneously considers the impact of random deviation, multi-rate sampling, sensor nonlinearity, and random spoofing attacks on estimation performance. The finite-time bounded criterion includes important information such as parameters in the dynamic evolution of random deviation, the multiple of the measurement sampling period relative to the state update period, and the probability of attack occurrence. Compared with current estimation methods for networked systems, the estimation method of this invention simultaneously handles random deviation, multi-rate sampling, sensor nonlinearity, and random spoofing attacks. It solves for the unknown gain of the estimator based on the augmented estimation error, achieving the purpose of resisting random deviation, disturbances, and random spoofing attacks. It also has the advantages of being easy to solve and apply.

[0102] The simulation experiments corresponding to the technical solution of this invention are as follows: This invention estimates the physical quantities of pitch angle, pitch rate, and normal velocity in a longitudinal flight control system. To further verify this invention, the finite-time bounded performance analysis described in step S700 is based on the finite-time boundedness theorem of Lyapunov stability theory:

[0103] get

[0104] In the formula:

[0105] in, for Lyapunov function at time t, for The Lyapunov function at time t.

[0106] Simulations performed using the method described in this invention yield the following system parameters:

[0107]

[0108] nonlinear functions The expression is:

[0109] In the formula, yes The Each element. and Constraint (5) is satisfied.

[0110] Estimator gain calculation: Solve the matrix inequalities (12)-(14) to obtain the estimator gain. as follows:

[0111] The initial states of the system, random deviation, and estimator are:

[0112] The noise and spoofing signals are:

[0113]

[0114] The estimation performance of the estimator, in Figures 2-8 The explanation is as follows.

[0115] Figure 2 This is a comparison diagram of the trajectory of the three components of the system state and the corresponding trajectory of the three components of the estimator state in a longitudinal flight control system under random deviation conditions, according to an embodiment of the present invention. Figure 2 It can be seen that the estimator can accurately track the evolution trajectory of the three components of the system state. Figure 3 This is a comparison diagram of the safety estimation method for a longitudinal flight control system under random deviation conditions according to an embodiment of the present invention, and the trajectories of the three components of the random deviation of the system and the corresponding three components of the estimator state. Figure 3 It can be seen that the estimator can approximate the evolution trajectory of the three randomly deviated components very well, indicating that the invented estimator design method is effective.

[0116] Figure 4 This invention relates to a method for estimating the safety of a longitudinal flight control system under random deviation conditions, and the estimation error trajectory of the three components of the system state. Figure 4It can be seen that the estimation errors of the three components of the system state are respectively in the interval [missing information]. , , Within this range, the estimation error is very small. Figure 5 This invention relates to a method for estimating the safety of a longitudinal flight control system under random deviation conditions, and the estimation error trajectory of the three components of the random deviation in the system. For example... Figure 5 As shown, the estimation errors of the three components of the system's random deviation are respectively in the interval [missing information]. , , Within this range, the estimation error is also very small.

[0117] Figure 6 This invention relates to a safety estimation method for a longitudinal flight control system under random deviation conditions, and the finite-time bounded performance of the system in terms of augmented estimation error under the action of the estimator. For example... Figure 6 As shown, In a limited time The interior is bounded, and its size is within the interval. The effectiveness of the invented finite-time estimator design method is further revealed.

[0118] Figure 7 This invention relates to a method for estimating the safety of a longitudinal flight control system under random deviation conditions and addresses the random occurrence of deception attacks in the system. For example... Figure 7 As shown, when When this occurs, it indicates that a spoofing attack has occurred during network transmission, and the estimator actually receives a spoofing signal injected by the attacker; when... When the signal is true, it indicates that no spoofing attack occurs during network transmission, and the estimator actually receives the original measurement signal.

[0119] Figure 8 This is a comparison diagram of a safety estimation method for a longitudinal flight control system under random deviation conditions according to an embodiment of the present invention, and the update period of the system state and the sampling period of the measurement signal in the system. Figure 8 As shown, the measurement signal sampling period of the multi-rate system in this invention is 4 times the system state update period.

[0120] Depend on Figures 2 to 8 It is evident that, for longitudinal flight control systems that consider random deviations, multi-rate sampling, sensor nonlinearity, and random deception attacks, the invented estimator design method accurately approximates the system state and random deviations.

[0121] The entity executing the longitudinal flight control safety estimation method under random deviation scenarios can be a longitudinal flight control safety estimation device or system under random deviation scenarios. For example, the longitudinal flight control safety estimation method under random deviation scenarios can be executed by a terminal device, server, or other processing device. The terminal device can be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, wearable device, etc. In some possible implementations, this longitudinal flight control safety estimation method under random deviation scenarios can be implemented by a processor calling computer-readable instructions stored in memory.

[0122] Those skilled in the art will understand that, in the longitudinal flight control safety estimation method under the above-described random deviation scenario in the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0123] This disclosure also proposes a longitudinal flight control safety estimation system under random deviation conditions, characterized by comprising: a first establishment unit, used to establish an actual received measurement signal model of the longitudinal flight control system under random deviation conditions by utilizing the measurement signal model corresponding to the dynamic model of the longitudinal flight control system under composite conditions; a second establishment unit, used to establish a dynamic model of the estimator based on the actual received measurement signal model and the single-rate system model corresponding to the measurement signal model under composite conditions; a first determination unit, used to determine the dynamic model of the augmented estimation error corresponding to the longitudinal flight control system based on the single-rate system model and the dynamic model of the estimator; and a second determination unit, used to determine the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error using finite-time bounded performance analysis, so as to realize the longitudinal flight control safety estimation corresponding to the dynamic model of the longitudinal flight control system under composite conditions.

[0124] This disclosure also provides a longitudinal flight control safety estimation system under random deviation conditions, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the aforementioned longitudinal flight control safety estimation method under random deviation conditions; or, comprising: a computer-readable storage medium storing a computer program / instructions and a bit stream thereon, wherein the computer program / instructions, when executed by the processor, implement the aforementioned longitudinal flight control safety estimation method under random deviation conditions to generate the bit stream; or, comprising: a computer program product configured with a computer program / instructions, wherein the computer program / instructions, when executed by the processor, implement the aforementioned longitudinal flight control safety estimation method under random deviation conditions.

[0125] This disclosure also provides a computer program product, including a computer program / instruction, characterized in that, when the computer program / instruction is executed by a processor, it implements the above-described longitudinal flight control safety estimation method under random deviation conditions.

[0126] This disclosure also provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: execute the above-described longitudinal flight control safety estimation method under random deviation conditions.

[0127] This disclosure also provides a computer-readable storage medium storing a computer program / instructions and a bit stream thereon, wherein the computer program / instructions, when executed by a processor, generate the bit stream by implementing the aforementioned longitudinal flight control safety estimation method under random deviation conditions.

[0128] Figure 9 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, or other terminal.

[0129] Reference Figure 9 The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0130] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0131] Memory 804 is configured to store various types of data to support the operation of electronic device 800. Examples of such data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0132] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0133] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0134] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0135] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0136] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 can detect the on / off state of electronic device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0137] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0138] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0139] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions that can be executed by a processor 820 of an electronic device 800 to perform the above-described method.

[0140] Figure 10 This is a block diagram illustrating an electronic device 1900 according to an exemplary embodiment. For example, the electronic device 1900 may be provided as a server. (Refer to...) Figure 10 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0141] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output (I / O) interface 1958. Electronic device 1900 can operate on an operating system stored in memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0142] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of an electronic device 1900 to perform the above-described method.

[0143] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0144] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0145] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0146] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0147] Various aspects of this disclosure are described herein 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 each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0148] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0149] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0150] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0151] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method and system for estimating the safety of longitudinal flight control under random deviation conditions, characterized in that, include: By utilizing the measurement signal model corresponding to the dynamic model of the longitudinal flight control system under random deviation in the composite state, an actual received measurement signal model of the longitudinal flight control system under random spoofing attacks during network transmission is established. Based on the actual received measurement signal model and the single-rate system model corresponding to the measurement signal model under the composite state, a dynamic model of the estimator is established; Based on the single-rate system model and the dynamic model of the estimator, determine the dynamic model of the augmented estimation error corresponding to the longitudinal flight control system; By using finite-time bounded performance analysis, the gain of the estimator to be designed corresponding to the dynamic model of the augmented estimation error is determined, so as to realize the longitudinal flight control safety estimation corresponding to the dynamic model of the longitudinal flight control system in the compound state.

2. The longitudinal flight control safety estimation method under random deviation conditions according to claim 1, characterized in that, Determining the measurement signal model under the composite state includes: determining the dynamic model under the composite state based on the state vector corresponding to the dynamic model of the longitudinal flight control system under random deviation at the state update time and the random deviation corresponding to the dynamic model of random deviation; determining the measurement signal model under the composite state based on the dynamic model of the longitudinal flight control system under random deviation, the preset sampling rate, and the measurement signal model of sensor nonlinearity; and / or, Determining the dynamic model of the longitudinal flight control system under random deviation includes: acquiring the state vector, random deviation, and process noise corresponding to a first state update time within a finite time; configuring the weights of the state vector, random deviation, and process noise corresponding to the first state update time using a preset set of matrices to obtain the state vector corresponding to the second state update time corresponding to the next time step of the first state update time within the finite time, thereby determining the dynamic model of the longitudinal flight control system under random deviation; and / or, determining the dynamic model of the random deviation includes: acquiring the random deviation and the noise corresponding to the random deviation corresponding to the first state update time; and using a fourth preset matrix, the random deviation and the noise corresponding to the first state update time to obtain the random deviation corresponding to the second state update time corresponding to the next time step of the first state update time.

3. The longitudinal flight control safety estimation method under random deviation conditions according to claim 2, characterized in that, The step of determining the dynamic model in the composite state based on the state vector corresponding to the dynamic model of the longitudinal flight control system under random deviation at the state update time and the random deviation corresponding to the dynamic model of random deviation includes: constructing a composite state vector corresponding to the first state update time using the state vector and random deviation corresponding to the first state update time; constructing a composite process noise vector corresponding to the first state update time using the process noise corresponding to the first state update time, the random deviation at the first state update time, and the noise corresponding to the random deviation; determining a first weight matrix of the composite state vector corresponding to the first state update time and a second weight matrix of the composite process noise vector corresponding to the first state update time; determining a weighted composite state vector corresponding to the first state update time based on the first weight matrix and the composite state vector; determining a weighted composite process noise vector corresponding to the first state update time based on the second weight matrix and the composite process noise vector; and combining the weighted composite state vector corresponding to the first state update time and the weighted composite process noise vector corresponding to the first state update time to obtain the dynamic model in the composite state corresponding to the second state update time at the next time step of the first state update time; and / or, Determining the first weight matrix of the composite state vector corresponding to the first state update time includes: using a first preset given matrix and a second preset given matrix to construct a preset given matrix group corresponding to the state vector corresponding to the second state update time, and constructing a fourth preset given matrix for random deviation corresponding to the second state update time, to determine the weight matrix of the composite state vector corresponding to the first state update time; and / or, determining the second weight matrix of the composite process noise vector corresponding to the first state update time includes: using a third preset given matrix to construct a preset given matrix group corresponding to the state vector corresponding to the second state update time and an identity matrix of a set dimension to construct a diagonal matrix, to determine the second weight matrix of the composite process noise vector corresponding to the first state update time.

4. The longitudinal flight control safety estimation method under random deviation conditions according to any one of claims 2 or 3, characterized in that, The determination of the measurement signal model under the combined state based on the dynamic model of the longitudinal flight control system under the random deviation, the preset sampling rate, and the measurement signal model of the sensor nonlinearity in the combined state includes: acquiring the measurement signal model corresponding to the preset sampling rate and the sensor nonlinearity; replacing the state vector corresponding to the sampling time in the measurement signal model with the combined state vector corresponding to the first state update time in the dynamic model under the combined state to obtain the measurement signal model under the combined state corresponding to the sampling time; and / or, Determining the measurement signal model corresponding to the preset sampling rate and sensor nonlinearity includes: acquiring the state vector of the longitudinal flight control system at the sampling time, the sensor nonlinearity corresponding to the state vector, and the measurement noise; configuring a fifth preset given matrix and a sixth preset given matrix for the state vector and the measurement noise, respectively, so that the sum of the first product of the fifth preset given matrix and the state vector, the second product of the sixth preset given matrix and the measurement noise, and the sensor nonlinearity is the measurement output of the longitudinal flight control system at the sampling time.

5. The longitudinal flight control safety estimation method under random deviation conditions according to any one of claims 1-4, characterized in that, Determining the single-rate system model corresponding to the measurement signal model in the composite state includes: using the mapping relationship between the first state update time in the dynamic model under the composite state and the sampling time corresponding to the measurement signal model, converting the multi-rate system model corresponding to the measurement signal model under the composite state into a single-rate system model; and / or, Determining the actual received measurement signal model includes: acquiring the random variable corresponding to the sampling time, the measurement output of the longitudinal flight control system, and the spoofing signal; calculating the difference between the spoofing signal and the measurement output; performing a multiplication operation on the difference and the random variable to obtain a random difference; and combining the random difference and the measurement output to determine the actual received measurement signal model.

6. The longitudinal flight control safety estimation method under random deviation conditions according to any one of claims 1-5, characterized in that, The step of establishing a dynamic model of the estimator based on the actual received measurement signal model and the single-rate system model corresponding to the measurement signal model under the composite state includes: obtaining the actual received measurement signal corresponding to the actual received measurement signal model at the sampling time, the single-rate state vector estimate corresponding to the single-rate state vector of the single-rate system model corresponding to the measurement signal model under the composite state at the sampling time, the single-rate sensor nonlinearity, and the first matching matrix corresponding to the single-rate state vector estimate; calculating the expectation corresponding to the random variable at the sampling time; using the expectation, the single-rate state vector estimate, and the single-rate sensor nonlinearity to determine the estimated received measurement signal corresponding to the single-rate state vector estimate; calculating the measurement signal difference between the estimated received measurement signal and the actual received measurement signal; configuring the measurement signal difference into the estimator gain to be designed; performing a multiplication operation on the single-rate state vector using the first matching matrix to obtain a single-rate matched state vector; and combining the single-rate matched state vector and the measurement signal difference after configuring the estimator gain to be designed to establish a dynamic model of the estimator.

7. The longitudinal flight control safety estimation method under random deviation conditions according to any one of claims 1-6, characterized in that, The step of determining the dynamic model of the augmented estimation error corresponding to the longitudinal flight control system based on the single-rate system model and the dynamic model of the estimator includes: The state vector estimation is performed by obtaining the state vector of the single-rate system model at the sampling time and the state vector of the dynamic model of the estimator at the sampling time and the next sampling time; based on the state vector at the next sampling time and the corresponding state vector estimation, the dynamic model of the augmented estimation error at the corresponding time is determined; and / or, Determining the dynamic model of the augmented estimation error corresponding to the longitudinal flight control system further includes: updating the state vector at the sampling time and the estimation error at the sampling time in the dynamic model of the augmented estimation error by defining the augmented estimation error vector.

8. The longitudinal flight control safety estimation method under random deviation conditions according to any one of claims 1-7, characterized in that, The step of determining the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error using finite-time bounded performance analysis includes: determining the solution inequality corresponding to the estimator gain to be designed in the dynamic model of the augmented estimation error using finite-time bounded performance analysis; calculating the weighted gain matrix and the gain weighting matrix based on the solution inequality corresponding to the estimator gain to be designed, the upper bound constraint condition of the dynamic model of the augmented estimation error, and the initial state constraint condition of the dynamic model of the augmented estimation error; determining the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error based on the weighted gain matrix and the gain weighting matrix; and / or, The step of determining the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error based on the gain value matrix to be weighted and the gain value weighting matrix includes: multiplying the gain value weighting matrix by the gain value matrix to be weighted to determine the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error.

9. A longitudinal flight control safety estimation system under random deviation conditions, characterized in that, include: The first establishment unit is used to establish the actual received measurement signal model of the longitudinal flight control system under random deviation under the dynamic model of the longitudinal flight control system in the composite state, and to establish the actual received measurement signal model of the longitudinal flight control system under random deception attack during network transmission. The second establishment unit is used to establish a dynamic model of the estimator based on the actual received measurement signal model and the single-rate system model corresponding to the measurement signal model under the composite state. The first determining unit is used to determine the dynamic model of the augmented estimation error corresponding to the longitudinal flight control system based on the single-rate system model and the dynamic model of the estimator. The second determining unit is used to determine the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error using finite-time bounded performance analysis, so as to realize the longitudinal flight control safety estimation corresponding to the dynamic model of the longitudinal flight control system in the compound state.

10. A longitudinal flight control safety estimation system under random deviation conditions, characterized in that, include: processor; A memory for storing processor-executable instructions; wherein the processor is configured to invoke instructions stored in the memory to execute the longitudinal flight control safety estimation method under random deviation conditions as described in any one of claims 1-8; or, Includes: a computer-readable storage medium storing a computer program / instructions and a bit stream thereon, wherein the computer program / instructions, when executed by a processor, implement the longitudinal flight control safety estimation method for random deviations as described in any one of claims 1-8 to generate the bit stream; or, Includes: a computer program product configured with a computer program / instruction, which, when executed by a processor, implements the longitudinal flight control safety estimation method under random deviation conditions as described in any one of claims 1-8.

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