Parameter estimation and adaptive control method, device, medium and product for high-performance aircraft pitch angular velocity control system with stability margin self-configuration

Abstract

The invention discloses a parameter estimation and adaptive control method, device, medium and product of a high-performance aircraft pitch angular velocity control system with stability margin self-configuration, and relates to the field of aircraft control, the method comprises the following steps: collecting the pitch angular velocity of an aircraft and the deflection angle of an elevator in real time, and calculating a tracking error; constructing a relation equation of system parameters and stability margin based on a PI controller, and performing approximate simplification by adopting a numerical approximation method; estimating system parameters and time delay on line by adopting an approximate nonlinear least square algorithm; and substituting the target stability margin, the system parameter estimation value and the time delay estimation value into a system frequency domain characteristic equation for solving to obtain a proportional gain and an integral time constant of a PI controller, and adjusting an elevator deflection angle of the aircraft in combination with a tracking error so as to control the pitch angular velocity of the aircraft. According to the invention, the autonomous configuration of the stability margin is realized, and the stability, robustness and dynamic adaptation capability of the control system are improved.

Description

Technical Field

[0001] This application relates to the field of aircraft control, and in particular to a parameter estimation and adaptive control method, device, medium and product for a high-performance aircraft pitch rate control system with stability margin self-configuration. Background Technology

[0002] When designing aircraft control systems, safety is the primary concern for designers. Given that aircraft encounter various internal and external disturbances during flight and need to perform large-scale maneuvers, the aircraft control system must ensure the aircraft remains stable and controllable. Therefore, stability margin is considered a crucial reference indicator when designing aircraft system controllers in engineering. In addition, time delays in high-performance aircraft control loops can also pose a significant threat to system safety, generally requiring sufficient phase margin for compensation. However, due to the complexity of calculating system frequency domain characteristics, accurate configuration of stability margin not only depends on precise information about the system structure and parameters but also requires complex calculations. Therefore, the configuration of stability margins in aircraft control systems is currently primarily performed offline, and efficient and accurate configuration methods are lacking.

[0003] Existing stability margin configuration methods are either designed based on relatively simple low-order inertial elements, lacking engineering applicability, or they are unable to cope with strong system parameter uncertainties, and cannot ensure that the performance of the control system meets expectations under complex operating conditions. Summary of the Invention

[0004] The purpose of this application is to provide a parameter estimation and adaptive control method, device, medium and product for a high-performance aircraft pitch rate control system with self-configuration of stability margin, which can realize the autonomous configuration of stability margin under the condition of unknown aircraft parameters and time delay, while ensuring time-domain command tracking.

[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a parameter estimation and adaptive control method for a high-performance aircraft pitch rate control system with stability margin self-configuration, including: The aircraft's pitch rate and elevator deflection angle are collected in real time, and the tracking error is calculated based on the pitch rate and the desired reference signal. The relationship equation between system parameters and stability margin of the aircraft control system is constructed based on the proportional-integral controller, and the relationship equation is simplified by numerical approximation method to obtain the system frequency domain characteristic equation. Based on the pitch angular velocity and the elevator deflection angle, the system parameters and time delay of the aircraft control system are estimated online using an approximate nonlinear least squares algorithm, resulting in estimated system parameters and estimated time delay. Substitute the target stability margin, the estimated system parameters, and the estimated time delay into the system frequency domain characteristic equation and solve for the proportional gain and integral time constant of the proportional-integral controller. Based on the proportional gain and integral time constant of the proportional-integral controller, the elevator deflection angle of the aircraft is adjusted according to the tracking error to control the pitch rate of the aircraft.

[0006] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described parameter estimation and adaptive control method for a high-performance aircraft pitch rate control system with stable margin self-configuration.

[0007] Thirdly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described parameter estimation and adaptive control method for a high-performance aircraft pitch rate control system with stability margin self-configuration.

[0008] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described parameter estimation and adaptive control method for a high-performance aircraft pitch rate control system with stable margin self-configuration.

[0009] According to the specific embodiments provided in this application, this application has the following technical effects: By collecting motion parameters in real time and calculating tracking errors, it provides real-time and accurate feedback for control adjustments; by constructing the relationship equation between system parameters and stability margin and simplifying it numerically to obtain the frequency domain characteristic equation, the stability margin of the system can be solved online with low computational overhead, and a mapping from system parameters and desired stability margin to proportional-integral (PI) controller parameters is obtained. The PI controller calculated through this mapping can configure the system stability margin to near the desired value while ensuring the system's time-domain command tracking characteristics. Furthermore, by employing an approximate nonlinear least squares algorithm, system parameters and time delays can be accurately estimated online, adapting to the parameter change characteristics of the aircraft's dynamic operating conditions, realizing dynamic self-configuration of controller parameters, and ensuring that the system always conforms to the target stability margin requirements. Finally, the elevator deflection angle is adjusted according to the parameters to accurately compensate for tracking errors, effectively improving the system's adaptability to changes in operating conditions and ensuring high performance and high stability of pitch rate control. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating a parameter estimation and adaptive control method for a high-performance aircraft pitch rate control system with stability margin self-configuration, provided as an embodiment of this application.

[0012] Figure 2 for Bode plot of the nominal control system under certain conditions.

[0013] Figure 3 for The response curve of the nominal control system under the given conditions.

[0014] Figure 4 for The parameter estimation results of the adaptive control system under the given conditions are shown in the figure.

[0015] Figure 5 for Bode plot of adaptive control system under certain conditions.

[0016] Figure 6 for The response curve of the adaptive control system under the given conditions.

[0017] Figure 7 for Bode plot of the nominal control system under certain conditions.

[0018] Figure 8 for The response curve of the nominal control system under the given conditions.

[0019] Figure 9 for The parameter estimation results of the adaptive control system under the given conditions are shown in the figure.

[0020] Figure 10 for Bode plot of adaptive control system under certain conditions.

[0021] Figure 11 for The response curve of the adaptive control system under the given conditions. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] This invention combines a stability margin configuration algorithm with an online parameter estimator to solve the problem of stability margin configuration under parameter uncertainty.

[0024] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] In one exemplary embodiment, such as Figure 1 As shown, a parameter estimation and adaptive control method for a high-performance aircraft pitch rate control system with stability margin self-configuration is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In the embodiments of this application, the method includes steps 101 to 105.

[0026] Step 101: Real-time acquisition of the aircraft's pitch rate and elevator deflection angle, and calculation of tracking error based on the pitch rate and desired reference signal.

[0027] The control method provided in this application is applied to a small disturbance model of the longitudinal channel of an aircraft, and its system model can be represented by a second-order transfer function with time delay: (1) in, The value is the second-order transfer function. For system parameters; K Indicates the system's static gain; It represents the first-order zero-point time constant, in seconds (s), and determines the finite zero-point locations of the transfer function; It represents the undamped natural angular frequency, with the unit being rad / s; it is the natural oscillation rate of a second-order system, which determines the speed of the response. represents the damping ratio, which is dimensionless and characterizes the oscillation damping characteristics of the system. For time delay, ; The pitch angular velocity is expressed in rad / s. Elevator deflection angle; s For the Laplace operator.

[0028] The system output is the aircraft's pitch rate. , is the quantity that is desired to be controlled; the control input is the elevator deflection angle. Its deflection command is issued by the controller.

[0029] Step 102: Construct the relationship equation between the system parameters and stability margin of the aircraft control system based on the proportional-integral controller, and use a numerical approximation method to approximate and simplify the relationship equation to obtain the system frequency domain characteristic equation.

[0030] Stability margin is a parameter used in the control field to describe the stability reserve of a system; a sufficient stability margin implies higher system safety. However, stability margin is a frequency domain indicator, and its accurate calculation involves solving complex equations. This application employs certain approximation methods to greatly simplify the calculation while ensuring the accuracy of the stability margin solution, thereby achieving accurate configuration while ensuring computational simplicity.

[0031] The stability margin includes phase margin and amplitude margin. First, the amplitude-frequency characteristics of the aircraft control system are constructed based on the proportional-integral controller. Phase frequency characteristics : (2) (3) in, The angular frequency of the signal characterizes how fast the signal changes. The amplitude-frequency response represents the gain of the system control loop on the signal as the frequency of the input system's reference signal changes. The phase-frequency response represents the phase lag of the system control loop on the signal as the frequency of the input system's reference signal changes. j It is an imaginary number. For the proportional gain of the proportional-integral controller, This is the integral time constant of the proportional-integral controller.

[0032] Phase margin and gain margin The definition is as follows: (4) (5) in, The amplitude crossover frequency is defined as the frequency point at which the amplitude response crosses 0dB, that is, the frequency point at which the gain of the signal input to the system drops below 1. This represents the phase crossover frequency, corresponding to the phase frequency response curve and The frequency of intersection. It is important to note that the two common expressions of gain margin must be distinguished. The ratio-based definition... It is generally preferred for deriving integral laws, while the definition based on decibels is preferred. It is more commonly used in the formulation of industrial standards.

[0033] Specifically, and Satisfy the following equation: (6) (7) The above formula reflects the relationship between system parameters and stability margin. In order to... and To determine the expected value, the conventional method is to directly solve the above system of equations to obtain the expected stability margin. and System parameters And the controller parameters that need to be tuned ( , The quantitative relationship between the system parameters and the stability margin is established. With this quantitative relationship, given the desired stability margin and known system parameters, the controller parameters can be directly calculated, and controller parameter tuning can be completed. However, directly solving the above equation is extremely complex and difficult to implement in engineering. This application employs an appropriate numerical approximation method to obtain an approximate solution. Specifically, the following function is used to approximate and simplify the above equation relating system parameters and stability margin: (8) (9) (10) (11) in, x These are the variables that need to be substituted. Using formula (8). and It can be simplified to and Using formula (9). and It can be approximated as and Using formula (10). It can be simplified to Using formula (11). It can be simplified to It should be noted that employing the above approximation methods requires a systematic approach. and It is large enough, which is relatively easy to meet in engineering.

[0034] Applying the above method, the frequency domain characteristics of the system can be simplified to the following system frequency domain characteristic equation: (12) (13) (14) (15) By directly solving the above formulas (12) to (15), the properties of the proportional-integral controller can be determined. and The parsing expression: (16) (17) in, For the target gain margin, This represents the target phase margin.

[0035] Furthermore, in system parameters Given the given conditions, the desired phase margin for the system configuration is determined. and gain margin The parameters of the PI controller can be determined according to formulas (16) and (17).

[0036] Step 103: Based on the pitch angular velocity and the elevator deflection angle, the system parameters and time delay of the aircraft control system are estimated online using an approximate nonlinear least squares algorithm to obtain the estimated system parameters and estimated time delay values.

[0037] The nominal control law formulas (16) and (17) show that accurate knowledge of the system parameters and time delay is crucial for configuring the stability margin near the desired value. However, this information may not be directly available. Therefore, an online parameter estimator is needed to address the uncertainties in the system model. To facilitate the description of the subsequent parameter update law design, the system model needs to be written in the following form: (18) (19) (20) in, for t The pitch angular velocity at any given moment, This refers to the elevator deflection angle. t For time, It is the starting moment. P ( s )and Indicated by the Laplace operator s The polynomial formed, Indicates to use P ( s The signal obtained after processing Similarly The relative order of this system is denoted as... .

[0038] Considering and If this is not possible, this application employs a filter to obtain the derivative information of the signal. A stable filter is selected. By performing operations on both sides of formula (18), the equation can be rewritten as: (twenty one) in, To use a stable filter Filtered pitch velocity To use a stable filter The elevator deflection angle after filtering. and To establish a stable transfer function, the linear regression equation can be constructed as follows: (twenty two) in, For the system parameter vector, for t The regression vector that contains input-output data pairs at any given time: (twenty three) (twenty four) like Known means If it is measurable, then conventional linear estimation algorithms (such as gradient method and least squares method) can be used to estimate it. However, the input delay considered in this application is unknown, causing conventional linear estimation algorithms to fail. Therefore, a definition is provided. and They are respectively and The estimated value is obtained, and thus the estimation error is derived: (25) (26) in, for t The time estimation error, for t The estimated values ​​of the system parameter vector at time step. for t The regression vector estimate that contains input-output data pairs at any given time. for tThe estimated time delay at any given moment.

[0039] and All require online updates. To simplify implementation, a... t Generalized parameter vector to be estimated at time step Its truth value is represented as : (27) in, , , , for t The estimated system parameters at time t, i.e. for t time The estimated value, for t time The estimated value, for t time K The estimated value, for t time The estimated value.

[0040] Conventional online parameter identification algorithms typically require the system to be represented as a linear regression equation in the form of equation (22). However, due to unknown time delays... exist The introduction of nonlinear characteristics makes it impossible to... The system is constructed in a linear regression form. This means that conventional parameter identification methods cannot simultaneously determine unknown system parameters and time delays. Therefore, this application employs an approximate nonlinear least squares algorithm to estimate the system parameters. and time delay Therefore, Defined as about The negative gradient, i.e.: (28) Similar to linear least squares, nonlinear least squares also involves minimizing the cost function. : (29) in, Forgetting factor, Forgetting factor An exponential time-weighted mechanism was introduced into the cost function. It is a weight matrix used to prevent Deviation from initial value Too many, and , t s It is the integrand in the integral. As the normalization factor, This design paradigm assigns greater importance to recent estimation errors by distributing increasing weights over time, while gradually reducing the impact of historical errors. Therefore, this parameter estimation algorithm exhibits time-varying tracking capabilities with adjustable memory depth, thereby promoting adaptive behavior over time.

[0041] The derivation of the online parameter estimator depends on the relative order of the system to be identified. For the relative order... For the system, the parameter update law of formula (1) can be constructed as follows: (30) (31) in, for The derivative, for The derivative, for t The gain of the parameter estimator at time t will adjust the rate of change of the estimated parameters as time changes. When the estimation error is very small, It will become very small, making the estimate stable.

[0042] Step 104: Substitute the target stability margin, the estimated system parameters, and the estimated time delay into the system frequency domain characteristic equation to solve for the proportional gain and integral time constant of the proportional-integral controller.

[0043] Based on the parameter tuning structure formulas (16) to (17) of the nominal control law, and combined with the parameter estimator formulas (30) to (31), the adaptive control law designed in this application is as follows: (32) (33) (34) (35) in, This is an estimate of the time delay. , , , These are the estimated values ​​of the system parameters, i.e. for The estimated value, for The estimated value, forK The estimated value, for The estimated value.

[0044] In summary, this application considers the presence of unknown factors. , , , and The system model proposes an adaptive PI control law. and By tuning using formulas (34) to (35), the phase margin can be adjusted. and gain margin Approximately configured in and Nearby, while ensuring time-domain instruction tracking.

[0045] Step 105: Based on the proportional gain and integral time constant of the proportional-integral controller, adjust the elevator deflection angle of the aircraft according to the tracking error to control the pitch rate of the aircraft.

[0046] The control objective of this application is the pitch rate of the aircraft. The control input is the aircraft. t elevator deflection angle at any moment Assume the desired pitch rate tracks a reference signal. The aircraft measured by the sensor t The pitch angular velocity at time t is Therefore, the difference between the expected reference signal and the actual measured signal is... t Tracking error at time : (36) To achieve the control objective, a proportional-integral controller is used as the base control structure. t elevator deflection angle at any moment for: (37) in, For the integration variable, iterate from 0 to t All moments. Integral term. Indicates error e From the initial moment to t The accumulation of time in a given moment.

[0047] PI controller transfer function for: (38) The control objectives of this application include: (1) Explicit configuration of stability margin: by calculating the parameters of the PI controller ( This establishes specific gain and phase margins, thereby quantizing the robustness of the system in the frequency domain.

[0048] (2) Tracking performance indicators: The controller must ensure that the system output asymptotically converges to the reference input and has no steady-state error.

[0049] This application can handle significant system uncertainties, ensuring time-varying command tracking in the time domain and configuring the system's stability margin to the desired value in the frequency domain. First, a widely applicable approximation method is proposed, capable of solving for the system's stability margin online with low computational overhead, thereby obtaining a mapping from system parameters and the desired stability margin to PI controller parameters. The PI controller calculated through this mapping can configure the system's stability margin near the desired value while ensuring the system's time-domain command tracking characteristics. Subsequently, the aforementioned control law is integrated with an indirect adaptive control framework. An approximate nonlinear least squares method is used to identify the system's unknown parameters and time delays online, and the PI controller parameters are updated online based on the estimation results. This achieves autonomous configuration of the stability margin under unknown aircraft parameters and time delays, while ensuring time-domain command tracking.

[0050] The effectiveness of the parameter estimation and adaptive control method for the high-performance aircraft pitch rate control system with stability margin self-configuration provided in this application is verified through numerical simulation. To simplify controller tuning, the response of the aircraft pitch rate loop can be characterized as a second-order system with time delay, i.e., a low-order equivalent system method. An experimental verification is conducted using the pitch rate control system of an aircraft. (39) in, The pitch angular velocity of the aircraft. This refers to the elevator deflection angle of the aircraft.

[0051] To ensure robustness under complex disturbances or maneuver commands, the aircraft control system must possess at least the following capabilities: stability margin Under conditions of significant uncertainty regarding flight conditions, wear, and nonlinearity, the above requirements allow for a 50% reduction in margin (i.e., from...). Down to To demonstrate the effectiveness of the adaptive margin configuration algorithm, two sets of stability margins were selected, and the configuration performance under nominal and adaptive conditions was evaluated respectively.

[0052] For simplicity, gain margin is selected. The configuration error is quantified based on a ratio definition. Under adaptive conditions, the system parameters and time delay are set to be unknown. The estimator parameters are initialized as follows: The pitch command is designed as a square wave signal with an amplitude of 0.5 and a period of 6 seconds, thus providing excitation for online estimation and maintaining a steady-state constant value in subsequent stages. The initial parameter estimates deviate from their nominal values ​​by a factor of 1.5, while the initial time delay estimates are perturbed by a factor of 1.2.

[0053] First, the expected stability margin Set as a common standard . Figure 2 and Figure 3 It displays the configuration results and system response under the nominal controller, while Figures 4 to 6 This demonstrates the performance of the adaptive controller. The nominal controller will control the system... and The settings were 2.009 (6.06dB) and 45.9 respectively. The configuration errors were only 0.7% and 2%, thus verifying the accuracy of the stability margin configuration scheme.

[0054] Figure 6 The convergence behavior of the system parameters and delay estimates is demonstrated, validating the effectiveness of this application. The accurate online estimation results lay a solid foundation for subsequent stability margin configuration. Figure 4 As shown, the phase margin and gain margin are configured to 49. The values ​​are 2.19 (6.82 dB) and 2.19 (6.82 dB), with configuration errors of 9.7% and 8.8%, respectively. The expected time-domain dynamic tracking performance of the system under the desired stability margin is as follows: Figure 5 As shown, the quality of the time-domain response improves as the parameter estimates converge to their true values.

[0055] It is important to emphasize that the dynamic response characteristics of a control system are strongly correlated with its stability margin. Therefore, when the system's stability margin is configured within a preset range, its dynamic behavior is inherently determined. Typically, a larger phase margin leads to higher damping characteristics. To achieve low-damping characteristics in the system response, the desired stability margin is adjusted to... Meanwhile, other parts of the adaptive algorithm remain unchanged. Figures 7 to 8 It displays the configuration results and system response under the nominal controller, while Figures 9 to 11 The performance of the adaptive control scheme is demonstrated.

[0056] The nominal PI controller will Configured as The configuration errors were 12.9% and 3.3%, respectively. Figure 8The results show that reducing the phase margin results in a faster convergence speed, but also increases the overshoot. Therefore, a trade-off must be struck between stability margin and other response metrics.

[0057] In summary, numerical simulations have verified the effectiveness of the parameter estimation and adaptive control method for a high-performance aircraft pitch rate control system with self-configuration and stability margin proposed in this application. The results show that this application can achieve accurate stability margin configuration under different desired indices. Compared with the nominal controller, this method maintains good performance even with parameter uncertainties, thus ensuring robustness and adaptability.

[0058] This application proposes a novel stability margin configuration method based on indirect adaptive PI control for second-order systems with unknown parameters and input delays. Compared to existing adaptive control methods, it not only handles the complex nonlinearities caused by the simultaneous unknown system parameters and input delays, but also explicitly configures the phase margin and gain margin online to desired values, ensuring the robustness of the closed-loop control system in the frequency domain. First, an adaptive PI control law utilizing approximate mapping is proposed. This law replaces the complex solution of transcendental equations with a newly derived analytical approximation formula, directly establishing the mapping relationship between the desired stability margin and the controller parameters, greatly reducing the computational burden of stability margin configuration and making it easy to implement online. Subsequently, a time-delay-aware parameter estimator based on nonlinear least squares is proposed, overcoming the limitation of conventional linear regression algorithms in simultaneously identifying unknown delays and system parameters, achieving synchronous online estimation of system parameters and delays. These methods ensure accurate time-domain command tracking of the closed-loop system with low computational overhead, while automatically meeting preset phase and gain margin requirements in the frequency domain.

[0059] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0060] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0061] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0062] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0063] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0064] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, etc., and are not limited to these.

[0065] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0066] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A parameter estimation and adaptive control method for a high-performance aircraft pitch rate control system with stability margin self-configuration, characterized in that, The parameter estimation and adaptive control method for the high-performance aircraft pitch rate control system with stability margin self-configuration includes: The aircraft's pitch rate and elevator deflection angle are collected in real time, and the tracking error is calculated based on the pitch rate and the desired reference signal. The relationship equation between system parameters and stability margin of the aircraft control system is constructed based on the proportional-integral controller, and the relationship equation is simplified by numerical approximation method to obtain the system frequency domain characteristic equation. Based on the pitch angular velocity and the elevator deflection angle, the system parameters and time delay of the aircraft control system are estimated online using an approximate nonlinear least squares algorithm, resulting in estimated system parameters and estimated time delay. Substitute the target stability margin, the estimated system parameters, and the estimated time delay into the system frequency domain characteristic equation and solve for the proportional gain and integral time constant of the proportional-integral controller. Based on the proportional gain and integral time constant of the proportional-integral controller, the elevator deflection angle of the aircraft is adjusted according to the tracking error to control the pitch rate of the aircraft.

2. The parameter estimation and adaptive control method for a high-performance aircraft pitch rate control system with stability margin self-configuration as described in claim 1, characterized in that, The stability margin includes phase margin and magnitude margin.

3. The parameter estimation and adaptive control method for a high-performance aircraft pitch rate control system with stability margin self-configuration according to claim 2, characterized in that, The equation relating the system parameters and stability margin of an aircraft control system is as follows: ； ； ； ； in, For the magnitude margin, For phase margin, For the proportional gain of the proportional-integral controller, The integral time constant of the proportional-integral controller. For phase crossing frequency, For amplitude crossover frequency, K , , and These are all system parameters. K Represents the system's static gain. Represents the first-order zero-point time constant. Indicates the undamped natural angular frequency. Indicates the damping ratio. This is a time delay.

4. The parameter estimation and adaptive control method for a high-performance aircraft pitch rate control system with stability margin self-configuration as described in claim 2, characterized in that, The system's frequency domain characteristic equation is: ； ； ； ； in, For the magnitude margin, For phase margin, For the proportional gain of the proportional-integral controller, The integral time constant of the proportional-integral controller. For phase crossing frequency, For amplitude crossover frequency, K , , and These are all system parameters. K Represents the system's static gain. Represents the first-order zero-point time constant. Indicates the undamped natural angular frequency. Indicates the damping ratio. This is a time delay.

5. The parameter estimation and adaptive control method for a high-performance aircraft pitch rate control system with stability margin self-configuration according to claim 1, characterized in that, The system parameter estimates and time delay estimates are obtained using the following formulas: ； ； ； ； ； ； in, for The derivative, for t The generalized parameter vector to be estimated at time step 1 for The derivative, for t The gain of the parameter estimator at time t. for about The negative gradient, for t The time estimation error, for t The pitch angular velocity at any given moment, for t The estimated values ​​of the system parameter vector at time step. for t The regression vector estimate that contains input-output data pairs at any given time. , , , for t System parameter estimates at time 10:

00. for t The estimated time delay at a given moment. For stabilizing the filter, , For the Laplace operator, To use a stable filter Filtered pitch velocity To use a stable filter The elevator deflection angle after filtering. This refers to the elevator deflection angle. Forgetting factor, As the normalization factor, , , t For time.

6. The parameter estimation and adaptive control method for a high-performance aircraft pitch rate control system with stability margin self-configuration according to claim 2, characterized in that, The proportional gain and integral time constant of the proportional-integral controller are obtained using the following formulas: ； ； in, For the proportional gain of the proportional-integral controller, The integral time constant of the proportional-integral controller. For the target gain margin, For the target phase margin, This is an estimate of the time delay. , , , These are estimated values ​​for system parameters.

7. The parameter estimation and adaptive control method for a high-performance aircraft pitch rate control system with stability margin self-configuration according to claim 1, characterized in that, The elevator deflection angle of the aircraft is adjusted using the following formula: ； in, for t Elevator deflection angle at any given moment For the proportional gain of the proportional-integral controller, The integral time constant of the proportional-integral controller. for t Tracking error at any time, t For time, It is the integral variable.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the parameter estimation and adaptive control method for a high-performance aircraft pitch rate control system with stability margin self-configuration as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the parameter estimation and adaptive control method for a high-performance aircraft pitch rate control system with stability margin self-configuration as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the parameter estimation and adaptive control method for a high-performance aircraft pitch rate control system with stability margin self-configuration as described in any one of claims 1-7.

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