Autonomous reconstruction method for abnormal operation state of aviation turbine engine

Abstract

The invention discloses an autonomous reconstruction method for an abnormal operation state of an aviation turbine engine, and relates to the technical field of health management and intelligent control of aviation engines, and the method comprises the steps: constructing a physical model of operation state evolution; determining a predicted value of a running state variable at the current moment based on the physical model of running state evolution; constructing a second-order expansion state observer of the state evolution power system based on the predicted value of the operation state variable at the current moment by adopting a super-spiral sliding mode control algorithm; constructing a Lyapunov function based on a second-order extended state observer; when the Lyapunov function meets a preset condition, determining a sliding mode control law; constructing an abnormal state autonomous reconstruction observer based on a sliding mode control law; and determining an estimated value of the abnormal operation state of the aviation turbine engine at the current moment based on the abnormal state autonomous reconstruction observer. According to the method, the problem that the abnormal operation state is difficult to reconstruct accurately due to uncertain factors is effectively solved.

Description

Technical Field

[0001] This application relates to the field of aero-engine health management and intelligent control technology, and in particular to a method for autonomous reconfiguration of abnormal operating states of an aero-engine turbine. Background Technology

[0002] As the core power unit of aircraft, the stability and reliability of aero-turbine engines directly affect flight safety. However, in actual operation, engines face complex conditions such as strong nonlinearity, multivariable coupling, and external disturbances, posing significant challenges to the real-time detection and reconstruction of their abnormal operating states. Traditional methods for estimating engine operating states and diagnosing faults rely on precise mathematical models, but the nonlinear characteristics and uncertainties of engines can easily lead to the accumulation of estimation errors, affecting reconstruction accuracy. Furthermore, conventional control algorithms lack robustness under strong disturbance environments, making it difficult to achieve rapid convergence of operating states and disturbance suppression.

[0003] In related technologies, observer-based state estimation methods are widely used in engine condition monitoring. However, traditional observers struggle to handle system uncertainties and external disturbances in a unified manner, resulting in limited estimation accuracy for complex disturbances. While sliding mode control technology possesses strong robustness, its fixed-threshold control update strategy may lead to resource waste or control lag. This is particularly problematic in systems like aero-engines, which have extremely high requirements for real-time performance and resource efficiency, where traditional control strategies are insufficiently adaptable. Furthermore, existing abnormal state reconstruction methods are mostly based on model matching or parameter identification. In scenarios with coupled multi-source disturbances, it is difficult to quickly separate the true abnormal state components from complex dynamics, leading to response delays and misjudgment risks in the health management system.

[0004] Therefore, it is necessary to provide a method for autonomous reconfiguration of abnormal operating states of aero-turbine engines to solve the above problems. Summary of the Invention

[0005] The purpose of this application is to provide an autonomous reconstruction method for the abnormal operating state of an aero-turbine engine, solving the problem that it is difficult to accurately reconstruct the abnormal operating state of an aero-turbine engine caused by uncertain factors.

[0006] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for autonomous reconfiguration of abnormal operating states of an aero-turbine engine, including: Obtain the measured values ​​of the operating state variables of the aero-turbine engine at the previous moment; Based on the measured values ​​of the operating state variables of the aero-turbine engine at the previous moment, a physical model of the operating state evolution of the aero-turbine engine is constructed. Based on the measured values ​​of the operating state variables of the aero-turbine engine at the previous moment and the physical model of the evolution of the operating state of the aero-turbine engine, the predicted values ​​of the operating state variables of the aero-turbine engine at the current moment are determined. A second-order extended state observer for the state evolution dynamic system of an aero-turbine engine is constructed by adopting a super-helical sliding mode control algorithm based on the predicted values ​​of the operating state variables of the aero-turbine engine at the current moment. A second-order extended state observer based on the state evolution dynamic system of an aero-turbine engine is used to construct a Lyapunov function. When the Lyapunov function satisfies the preset conditions, the sliding control law of the aero-turbine engine at the current moment is determined based on the predicted value of the operating state variables of the aero-turbine engine at the current moment. Based on the sliding film control law of the aero-turbine engine at the current moment, an autonomous reconfiguration observer for the abnormal state of the aero-turbine engine is constructed. A matrix decoupling algorithm is adopted, based on the autonomous reconstruction observer of the abnormal state of the aero-turbine engine, to determine the estimated value of the abnormal operating state of the aero-turbine engine at the current moment, thereby realizing the autonomous reconstruction of the abnormal state of the aero-turbine engine.

[0007] Optionally, based on the measured values ​​of the operating state variables of the aero-turbine engine at the previous moment and the physical model of the evolution of the operating state of the aero-turbine engine, the predicted values ​​of the operating state variables of the aero-turbine engine at the current moment are determined, specifically including: Based on the physical model of the operational state evolution of the aero-turbine engine, the derivatives of the first operational state variable and the second operational state variable of the aero-turbine engine at the current moment are determined respectively. Based on the measured value of the first operating state variable of the aero-turbine engine at the previous moment and the derivative of the first operating state variable of the aero-turbine engine at the current moment, the predicted value of the first operating state variable of the aero-turbine engine at the current moment is determined. Based on the measured value of the second operating state variable of the aero-turbine engine at the previous moment and the derivative of the second operating state variable of the aero-turbine engine at the current moment, the predicted value of the second operating state variable of the aero-turbine engine at the current moment is determined.

[0008] Optionally, a superspiral sliding mode control algorithm is employed to construct a second-order extended state observer for the state evolution dynamic system of the aero-turbine engine based on the predicted values ​​of the engine's operating state variables at the current moment. Specifically, this includes: Define the state variables of the observer; the state variables of the observer include: the first observer state variable and the second observer state variable; The super-helical sliding mode control algorithm is adopted. Based on the predicted value of the first operating state variable of the aero-turbine engine at the current moment and the first observer state variable at the current moment, the first nonlinear mapping function and the second nonlinear mapping function at the current moment are calculated respectively. Based on the first nonlinear mapping function at the current time, the second nonlinear mapping function at the current time, the predicted value of the first operating state variable of the aero-turbine engine at the current time, the first observer state variable at the current time, and the second observer state variable at the current time, a second-order extended state observer of the state evolution dynamic system of the aero-turbine engine is constructed.

[0009] Optionally, based on the second-order extended state observer of the state evolution dynamic system of an aero-turbine engine, a Lyapunov function is constructed, specifically including: Based on the second-order extended state observer of the state evolution dynamic system of the aero-turbine engine and the observer state variables of the previous time step, the estimated values ​​of the observer state variables at the current time step are determined. The observation error at the current moment is determined based on the estimated values ​​of the observer state variables at the current moment and the predicted values ​​of the operating state variables of the aero-turbine engine at the current moment. Based on the observation error and positive definite matrix at the current moment, the Lyapunov function is constructed.

[0010] Optionally, the preset condition is that the derivative of the Lyapunov function is less than zero; Based on the predicted values ​​of the operating state variables of the aero-turbine engine at the current moment, the sliding film control law of the aero-turbine engine at the current moment is determined, specifically including: Determine whether the trigger signal at the current moment meets the adaptive event triggering condition; If so, the sliding surface is determined based on the observation error, and the sliding control law at the current moment is determined based on the predicted values ​​of the sliding surface and the operating state variables of the aero-turbine engine at the current moment. If not, the control law at the previous trigger moment will be used as the synovial control law at the current moment.

[0011] Optionally, the expression for the adaptive event triggering condition is: ; in, for t The trigger signal at a given moment; for t Adaptive trigger threshold at any given time; For bias terms The predicted values ​​of the operating state variables of the aero-turbine engine at time t; It is a norm.

[0012] Alternatively, the expression for the synovial control law is as follows: ; in, This is the synovial control law; It is a positive number; It is a symbolic function; This is the control law value at the previous trigger moment.

[0013] Optionally, the adaptive trigger threshold for the current moment can be dynamically adjusted based on the observation error at the current moment.

[0014] Optionally, when dynamically adjusting the adaptive trigger threshold at the current moment, the formula for calculating the rate of change of the adaptive trigger threshold is: ; in, To adapt the trigger threshold at t Rate of change over time; The initial adaptive trigger threshold; For adjustment coefficients; It is the hyperbolic tangent function.

[0015] Optionally, the expression for the estimated value of the abnormal operating state of the aero-turbine engine at the current moment is: ; in, For aircraft turbine engines t The estimated value of the abnormal operating state at any given time; This is an estimate of the external disturbance; To control the input gain matrix; The inverse matrix of the control input gain matrix; It is a rotation matrix; For aircraft turbine engines t The estimated values ​​of the running state variables at time t; Let be the derivative of the predicted value of the operating state variable of the aero-turbine engine at time t.

[0016] According to the specific embodiments provided in this application, this application has the following technical effects: This application discloses an autonomous reconstruction method for abnormal operating states of an aero-turbine engine. By integrating an extended state observer for nonlinear systems with a super-helical sliding mode control algorithm, a second-order extended state observer for the state evolution dynamic system of the aero-turbine engine is constructed. This method can effectively overcome parameter perturbations and external disturbances during aero-turbine engine operation. The second-order extended state observer for the state evolution dynamic system of the aero-turbine engine can accurately estimate system uncertainties. Combined with the design of sliding mode control law, the operating state of the aero-turbine engine can be rapidly converged, achieving high-precision reconstruction of abnormal operating states of the turbine engine. This significantly improves the reliability and stability of the engine in complex environments, providing strong protection for aviation safety. Attached Figure Description

[0017] 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.

[0018] Figure 1 This is a schematic flowchart of an autonomous reconfiguration method for abnormal operating states of an aero-turbine engine provided in an embodiment of this application. Detailed Implementation

[0019] 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.

[0020] This invention relates to an autonomous reconstruction technology for abnormal operating states of aero-turbine engines, which aims to solve the problem of difficulty in accurately reconstructing abnormal operating states caused by parameter perturbations and external interference during aero-turbine engine operation. It can also significantly improve the reliability and efficiency of engine operation and provide key technical support for the health management of aero-turbine engines.

[0021] 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.

[0022] In one exemplary embodiment, such as Figure 1 As shown, an autonomous reconfiguration method for abnormal operating states of an aero-turbine engine is provided, including the following steps: Wherein: Step S1: Obtain the measured values ​​of the operating state variables of the aero-turbine engine at the previous moment.

[0023] Specifically, running state variables This represents the operating state variables of an aircraft turbine engine. For running state variables; This is the first running state variable; This is the second operating state variable; the operating state variable is a physical parameter characterizing the core operating characteristics of the engine, including but not limited to combinations of dynamic parameters such as speed, pressure, temperature, vibration amplitude, or flow rate. The specific physical meaning of the state variable can be adaptively selected according to the engine type (such as turbofan, turbojet), operating stage (such as takeoff, cruise), or fault monitoring target (such as abnormal vibration, excessive temperature), and is not limited here.

[0024] Step S2: Based on the measured values ​​of the operating state variables of the aero-turbine engine at the previous moment, construct a physical model of the operating state evolution of the aero-turbine engine.

[0025] Specifically, a physical model describing the operational state evolution of an aero-turbine engine is adopted using a second-order nonlinear dynamic system, and its expression is as follows: (1) in, For aircraft turbine engines t The derivative of the first running state variable at time t; t For time; For aircraft turbine engines t The second running state variable at time t; For aircraft turbine engines t The derivative of the second running state variable at time t; For aircraft turbine engines t The first running state variable at any given time; For aircraft turbine engines t The second running state variable at time t; It is a nonlinear state function; This is an external disturbance.

[0026] Step S3: Based on the measured values ​​of the operating state variables of the aero-turbine engine at the previous moment and the physical model of the evolution of the operating state of the aero-turbine engine, determine the predicted values ​​of the operating state variables of the aero-turbine engine at the current moment.

[0027] As an optional implementation, step S3 specifically includes: Step S31: Based on the physical model of the evolution of the operating state of the aero-turbine engine, determine the derivatives of the first operating state variable of the aero-turbine engine at the current moment and the derivatives of the second operating state variable of the aero-turbine engine at the current moment.

[0028] Step S32: Based on the measured value of the first operating state variable of the aero-turbine engine at the previous moment and the derivative of the first operating state variable of the aero-turbine engine at the current moment, determine the predicted value of the first operating state variable of the aero-turbine engine at the current moment.

[0029] Step S33: Based on the measured value of the second operating state variable of the aero-turbine engine at the previous moment and the derivative of the second operating state variable of the aero-turbine engine at the current moment, determine the predicted value of the second operating state variable of the aero-turbine engine at the current moment.

[0030] Step S4: Using the super-helical sliding mode control algorithm, a second-order extended state observer of the state evolution dynamic system of the aero-turbine engine is constructed based on the predicted values ​​of the operating state variables of the aero-turbine engine at the current moment.

[0031] As an optional implementation, step S4 specifically includes: Step S41, define the observer's state variables; the observer's state variables include: a first observer state variable and a second observer state variable. ,in, For the first observer's state variable, This is the state variable of the second observer.

[0032] Step S42: Using the super-helical sliding mode control algorithm, based on the predicted value of the first operating state variable of the aero-turbine engine at the current moment and the first observer state variable at the current moment, calculate the first nonlinear mapping function and the second nonlinear mapping function at the current moment, respectively.

[0033] Specifically, the expression for the first nonlinear mapping function is as follows: (2) The expression for the second nonlinear mapping function is as follows: (3) in, and For observer coefficients, It is a symbolic function; This is the predicted value of the first operating state variable of the aero-turbine engine at the current moment; This is a constant that determines the characteristics of the nonlinear term.

[0034] Step S43: Based on the first nonlinear mapping function at the current time, the second nonlinear mapping function at the current time, the predicted value of the first operating state variable of the aero-turbine engine at the current time, the first observer state variable at the current time, and the second observer state variable at the current time, a second-order extended state observer of the state evolution dynamic system of the aero-turbine engine is constructed.

[0035] Specifically, based on the strong robustness of sliding mode control structures, a second-order extended state observer for the state evolution dynamic system of an aero-turbine engine is constructed. This observer can treat system uncertainties and external disturbances as extended states for estimation, thereby providing accurate state information for subsequent control law design. Its mathematical structure is shown below: (4) in, The derivative of the state variable of the first observer. It is the derivative of the state variable of the second observer.

[0036] Step S5: Construct the Lyapunov function based on the second-order extended state observer of the state evolution dynamic system of the aero-turbine engine.

[0037] As an optional implementation, step S5 specifically includes: Step S51: Based on the second-order extended state observer of the state evolution dynamic system of the aero-turbine engine and the observer state variables of the previous time step, determine the estimated value of the observer state variables at the current time step.

[0038] Step S52: Based on the estimated values ​​of the observer state variables at the current moment and the predicted values ​​of the operating state variables of the aero-turbine engine at the current moment, determine the observation error at the current moment.

[0039] The formula for calculating observation error is: (5) in, for The observation error at any given time; These are the predicted values ​​of the operating state variables of the aero-turbine engine at the current moment; This is the estimated value of the observer's state variables at the current moment.

[0040] Step S53: Based on the observation error and positive definite matrix at the current time, the Lyapunov function is constructed.

[0041] Specifically, by establishing a Lyapunov function with a specific structure, the system stability is analyzed to ensure the observer operates stably under perturbation conditions. Based on Lyapunov stability theory, a quadratic structure is used to establish the Lyapunov function. As shown in the following formula: (6) in, It is a positive definite symmetric matrix. These are components of the Lyapunov function.

[0042] Step S6: When the Lyapunov function satisfies the preset conditions, the sliding control law of the aero-turbine engine at the current moment is determined based on the predicted value of the operating state variable of the aero-turbine engine at the current moment.

[0043] As an optional implementation, in step S6, the preset condition is that the derivative of the Lyapunov function is less than zero. A derivative of the Lyapunov function being less than zero indicates that the second-order extended state observer of the state evolution dynamic system of the aero-turbine engine is stable.

[0044] In step S6, based on the predicted values ​​of the operating state variables of the aero-turbine engine at the current moment, the sliding film control law of the aero-turbine engine at the current moment is determined, specifically including: Step S61: Determine whether the trigger signal at the current moment meets the adaptive event triggering condition.

[0045] To ensure control accuracy while avoiding resource waste, an adaptive event triggering mechanism is introduced. Based on system observation errors, the adaptive triggering threshold is dynamically adjusted, and parameter updates and control law calculations are performed only when the adaptive event triggering conditions are met. This effectively reduces data transmission and computational overhead, saves communication resources, and improves system operating efficiency. The corresponding expression for the adaptive event triggering condition is: (7) in, for The trigger signal at a given moment reflects the difference between the current operating status of the aero-turbine engine and the status at the last trigger moment; for Adaptive trigger threshold at any given time; For bias terms; For aircraft turbine engines The predicted values ​​of the running state variables at any given time; It is a norm.

[0046] Step S62, if yes, then based on the observation error, determine the sliding surface, and based on the predicted values ​​of the sliding surface and the operating state variables of the aero-turbine engine at the current moment, determine the sliding control law at the current moment.

[0047] Step S63: If not, then the control law at the previous trigger moment shall be used as the sliding control law at the current moment.

[0048] Specifically, judging the physical model When bounded conditions are met, a sliding diaphragm control law can be designed. To offset system uncertainties and suppress disturbances, the operating state of the aero-turbine engine is rapidly converged to a predetermined sliding mode, achieving effective control over the aero-turbine engine's operating state. To ensure good dynamic response performance of the system in the sliding mode, a sliding surface is defined. for: (8) in, for The derivative of the observation error at time; A row vector used to specify the shape of the sliding surface.

[0049] In order to quickly reach the operating state of the aero-turbine engine, the desired sliding surface is obtained. And maintain motion on the sliding surface, the sliding control law is designed as shown in equation (9): (9) in, This is the synovial control law; It is a positive number; It is a symbolic function; This is the control law value at the previous trigger moment.

[0050] As an optional implementation, in step S62, the adaptive trigger threshold for the current time is dynamically adjusted based on the observation error at the current time. The rate of change of the adaptive trigger threshold is related to the observation error; when the observation error increases, As the rate of change of the adaptive trigger threshold increases, the adaptive trigger threshold also increases; similarly, when the observation error decreases, the adaptive trigger threshold also decreases.

[0051] The formula for calculating the rate of change of the adaptive trigger threshold when dynamically adjusting the adaptive trigger threshold at the current moment is as follows: (10) in, To adapt the trigger threshold at Rate of change over time; The initial adaptive trigger threshold; For adjustment coefficients; It is the hyperbolic tangent function.

[0052] The threshold is dynamically adjusted based on changes in observation error, enabling the adaptive triggering threshold condition to adapt to different system operating conditions. The sliding mode control law is updated only when the adaptive triggering condition is met, generating sufficient control force to drive the aero-turbine engine's operating state to overcome uncertainties and disturbances, and quickly converge to the sliding mode surface. When the adaptive triggering condition is not met, the previous control law (the control law at the previous triggering moment) is used, thereby significantly improving resource utilization efficiency while ensuring control effectiveness.

[0053] Step S7: Based on the sliding film control law of the aero-turbine engine at the current moment, construct an autonomous reconfiguration observer for the aero-turbine engine's abnormal state.

[0054] Specifically, the autonomous reconfiguration observer for the anomalous state of an aero-turbine engine can accurately estimate the true state of the system while compensating for disturbances. The expression for the autonomous reconfiguration observer for the anomalous state of an aero-turbine engine is as follows: (11) in, For aircraft turbine engines The derivative of the estimated value of the running state variable at time t; For aircraft turbine engines The estimated values ​​of the operating state variables at any given time (the operating state variables of an aero-turbine engine (such as rotor speed, temperature, etc.) usually cannot be directly measured in full and need to be estimated by an observer). Let be a rotation matrix. To control the input gain matrix Represents the coefficients of the extended state observer. For aircraft turbine engines The estimated value of the first running state variable at time t.

[0055] Step S8 involves employing a matrix decoupling algorithm, based on an autonomous reconstruction observer for the aero-turbine engine's abnormal state, to determine the estimated value of the aero-turbine engine's abnormal operating state at the current moment, thereby achieving autonomous reconstruction of the aero-turbine engine's abnormal state. The matrix decoupling algorithm separates the engine's abnormal state from the complex system dynamics, realizing the reconstruction of the engine's healthy state system's abnormal state.

[0056] As an optional implementation, in step S8, the expression for the estimated value of the abnormal operating state of the aero-turbine engine at the current moment is: (12) in, For aircraft turbine engines t The estimated value of the abnormal operating state at any given time; This is an estimate of the external disturbance; To control the input gain matrix; The inverse matrix of the control input gain matrix; It is a rotation matrix; For aircraft turbine engines t The estimated values ​​of the running state variables at time t; Let be the derivative of the predicted value of the operating state variable of the aero-turbine engine at time t.

[0057] 1) This application realizes the autonomous reconstruction of the abnormal operating state of aero-turbine engines, and can estimate the operating state of aero-turbine engines in real time and accurately under complex and uncertain environments, providing key technical support for engine health management and effectively solving the problem of difficulty in accurately reconstructing abnormal operating states caused by uncertain factors.

[0058] 2) This application effectively overcomes parameter perturbations and external disturbances during aero-engine operation by integrating a nonlinear system extended state observer with a super-helical sliding mode control algorithm. It utilizes a second-order extended state observer to accurately estimate system uncertainties, and combined with sliding mode control design, enables rapid convergence of the aero-turbine engine's operating state. This achieves high-precision reconstruction of abnormal turbine engine operating states, significantly improving the engine's reliability and stability in complex environments, and providing strong protection for aviation safety.

[0059] 3) This application introduces an adaptive event triggering mechanism, which dynamically adjusts the triggering threshold based on system observation errors to achieve intelligent control of observer parameter and control law updates. Update operations are performed only when necessary, significantly reducing data transmission and computation, effectively saving communication resources, and lowering system operating energy consumption. Simultaneously, this adaptive event triggering mechanism improves system response efficiency, making the engine health management system operate more efficiently and reducing maintenance costs and resource waste.

[0060] 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.

[0061] 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).

[0062] 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, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0063] 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.

[0064] 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 method for autonomously reconstructing abnormal operating states of an aero-turbine engine, characterized in that, The autonomous reconstruction method for abnormal operating states of the aero-turbine engine includes: Obtain the measured values ​​of the operating state variables of the aero-turbine engine at the previous moment; Based on the measured values ​​of the operating state variables of the aero-turbine engine at the previous moment, a physical model of the operating state evolution of the aero-turbine engine is constructed. Based on the measured values ​​of the operating state variables of the aero-turbine engine at the previous moment and the physical model of the evolution of the operating state of the aero-turbine engine, the predicted values ​​of the operating state variables of the aero-turbine engine at the current moment are determined. A second-order extended state observer for the state evolution dynamic system of an aero-turbine engine is constructed by adopting a super-helical sliding mode control algorithm based on the predicted values ​​of the operating state variables of the aero-turbine engine at the current moment. A second-order extended state observer based on the state evolution dynamic system of an aero-turbine engine is used to construct a Lyapunov function. When the Lyapunov function satisfies the preset conditions, the sliding control law of the aero-turbine engine at the current moment is determined based on the predicted value of the operating state variables of the aero-turbine engine at the current moment. Based on the sliding film control law of the aero-turbine engine at the current moment, an autonomous reconfiguration observer for the abnormal state of the aero-turbine engine is constructed. A matrix decoupling algorithm is adopted, based on the autonomous reconstruction observer of the abnormal state of the aero-turbine engine, to determine the estimated value of the abnormal operating state of the aero-turbine engine at the current moment, thereby realizing the autonomous reconstruction of the abnormal state of the aero-turbine engine.

2. The method for autonomous reconfiguration of abnormal operating states of an aero-turbine engine according to claim 1, characterized in that, Based on the measured values ​​of the operating state variables of the aero-turbine engine at the previous moment and the physical model of the evolution of the aero-turbine engine's operating state, the predicted values ​​of the operating state variables of the aero-turbine engine at the current moment are determined, specifically including: Based on the physical model of the operational state evolution of the aero-turbine engine, the derivatives of the first operational state variable and the second operational state variable of the aero-turbine engine at the current moment are determined respectively. Based on the measured value of the first operating state variable of the aero-turbine engine at the previous moment and the derivative of the first operating state variable of the aero-turbine engine at the current moment, the predicted value of the first operating state variable of the aero-turbine engine at the current moment is determined. Based on the measured value of the second operating state variable of the aero-turbine engine at the previous moment and the derivative of the second operating state variable of the aero-turbine engine at the current moment, the predicted value of the second operating state variable of the aero-turbine engine at the current moment is determined.

3. The method for autonomous reconfiguration of abnormal operating states of an aero-turbine engine according to claim 2, characterized in that, Using a superspiral sliding mode control algorithm, a second-order extended state observer for the state evolution dynamic system of an aero-turbine engine is constructed based on the predicted values ​​of the engine's operating state variables at the current moment. Specifically, this includes: Define the state variables of the observer; the state variables of the observer include: the first observer state variable and the second observer state variable; The super-helical sliding mode control algorithm is adopted. Based on the predicted value of the first operating state variable of the aero-turbine engine at the current moment and the first observer state variable at the current moment, the first nonlinear mapping function and the second nonlinear mapping function at the current moment are calculated respectively. Based on the first nonlinear mapping function at the current time, the second nonlinear mapping function at the current time, the predicted value of the first operating state variable of the aero-turbine engine at the current time, the first observer state variable at the current time, and the second observer state variable at the current time, a second-order extended state observer of the state evolution dynamic system of the aero-turbine engine is constructed.

4. The method for autonomous reconfiguration of abnormal operating states of an aero-turbine engine according to claim 3, characterized in that, A second-order extended state observer based on the state evolution dynamics system of an aero-turbine engine is constructed, including the Lyapunov function, specifically: Based on the second-order extended state observer of the state evolution dynamic system of the aero-turbine engine and the observer state variables of the previous time step, the estimated values ​​of the observer state variables at the current time step are determined. The observation error at the current moment is determined based on the estimated values ​​of the observer state variables at the current moment and the predicted values ​​of the operating state variables of the aero-turbine engine at the current moment. Based on the observation error and positive definite matrix at the current moment, the Lyapunov function is constructed.

5. The method for autonomous reconfiguration of abnormal operating states of an aero-turbine engine according to claim 4, characterized in that, The prerequisite is that the derivative of the Lyapunov function is less than zero; Based on the predicted values ​​of the operating state variables of the aero-turbine engine at the current moment, the sliding film control law of the aero-turbine engine at the current moment is determined, specifically including: Determine whether the trigger signal at the current moment meets the adaptive event triggering condition; If so, the sliding surface is determined based on the observation error, and the sliding control law at the current moment is determined based on the predicted values ​​of the sliding surface and the operating state variables of the aero-turbine engine at the current moment. If not, the control law at the previous trigger moment will be used as the synovial control law at the current moment.

6. The method for autonomous reconfiguration of abnormal operating states of an aero-turbine engine according to claim 5, characterized in that, The expression for the adaptive event triggering condition is: ； in, for t Trigger signal at time ； Let be the adaptive trigger threshold at time t; For bias terms The predicted values ​​of the operating state variables of the aero-turbine engine at time t; It is a norm.

7. The method for autonomous reconfiguration of abnormal operating states of an aero-turbine engine according to claim 6, characterized in that, The expression for the synovial control law is as follows: ； in, This is the synovial control law; It is a positive number; It is a symbolic function; This is the control law value at the previous trigger moment.

8. The method for autonomous reconfiguration of abnormal operating states of an aero-turbine engine according to claim 7, characterized in that, The adaptive trigger threshold is dynamically adjusted based on the observation error at the current moment.

9. The method for autonomous reconfiguration of abnormal operating states of an aero-turbine engine according to claim 8, characterized in that, When dynamically adjusting the adaptive trigger threshold at the current moment, the formula for calculating the rate of change of the adaptive trigger threshold is: ； in, To adapt the trigger threshold at Rate of change over time; The initial adaptive trigger threshold; For adjustment coefficients; It is the hyperbolic tangent function.

10. The method for autonomous reconfiguration of abnormal operating states of an aero-turbine engine according to claim 9, characterized in that, The expression for the estimated value of the abnormal operating state of the aero-turbine engine at the current moment is: ； in, For aircraft turbine engines t The estimated value of the abnormal operating state at any given time; This is an estimate of the external disturbance; To control the input gain matrix; The inverse matrix of the control input gain matrix; It is a rotation matrix; For aircraft turbine engines t The estimated values ​​of the running state variables at time t; Let be the derivative of the predicted value of the operating state variable of the aero-turbine engine at time t.

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