A method and system for fault-tolerant control of aircraft

Abstract

The present invention relates to an aircraft fault-tolerant control method and system, and relates to the field of aircraft control. The method obtains the state information of the aircraft physical entity in the physical space; uses the digital twin technology to build an aircraft virtual model matching the aircraft physical entity in the digital space according to the state information of the aircraft physical entity; uses the model prediction static programming technology to correct the parameters of the aircraft virtual model according to the state information of the aircraft physical entity to obtain the model parameter correction amount; and performs fault-tolerant control on the aircraft physical entity according to the model parameter correction amount. By combining the digital twin technology and the model prediction static programming technology and applying them to the field of aircraft fault-tolerant control, dynamic management and control of the operating state of the aircraft physical entity is achieved, effectively improving the flight safety and control efficiency.

Description

Technical Field

[0001] The present invention relates to the field of aircraft control, and in particular to an aircraft fault-tolerant control method and system. Background Art

[0002] The aging of aircraft sensors, actuators, and aircraft structures can easily lead to failures, which, once they occur, often result in economic losses or even catastrophic consequences. Therefore, when a failure occurs, a more effective and rapid control strategy is needed to ensure that the system can still fly safely after the failure and avoid accidents. Fault-tolerant control, as an effective control method for dealing with the impact of failures, has become a hot topic for scholars in the control field.

[0003] Digital twin technology is to establish a virtual space-time system model of the physical system, complete the mapping of physical entities in the virtual space, and further integrate, analyze, simulate and mine the acquired multi-physical quantity information, so as to achieve the purpose of system monitoring, fault-tolerant control, etc. At present, digital twin technology is mainly concentrated in the field of intelligent manufacturing, and there is no relevant public technical information on the application of digital twin technology in the field of aircraft control systems for fault-tolerant controller design.

[0004] Model predictive static programming combines the characteristics of model predictive control and approximate dynamic programming. This method introduces optimal control theory into the control framework. For typical two-point boundary value problems in optimal control problems, only static co-state vectors are needed to update the control quantity, and the co-state vectors can be recursively calculated using the sensitivity matrix. Since there is no numerical optimization process in the calculation, an analytical solution is directly given, which has significant advantages in real-time performance. Summary of the invention

[0005] The purpose of the present invention is to provide an aircraft fault-tolerant control method and system, which combines digital twin technology and model prediction static planning technology and applies them to the field of aircraft fault-tolerant control, thereby realizing dynamic management and control of the operating status of the aircraft's physical entity, and effectively improving flight safety and control efficiency.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for fault-tolerant control of an aircraft, comprising:

[0008] Obtaining status information of the physical entity of the aircraft in the physical space;

[0009] According to the state information of the physical entity of the aircraft, a virtual model of the aircraft matching the physical entity of the aircraft is constructed in the digital space by using the digital twin technology; the virtual model of the aircraft includes an aircraft geometry model, an aircraft physical model, an aircraft behavior model and an aircraft rule model;

[0010] According to the state information of the physical entity of the aircraft, the parameters of the virtual model of the aircraft are corrected by using the model prediction static programming technology to obtain the model parameter correction amount;

[0011] The aircraft physical entity is subjected to fault-tolerant control according to the model parameter correction amount.

[0012] Optionally, the using digital twin technology to build an aircraft virtual model matching the aircraft physical entity in a digital space according to the state information of the aircraft physical entity specifically includes:

[0013] Mapping the geometric dimensions of the aircraft in the state information and the positional relationship between the aircraft control surfaces into a digital space using digital twin technology to obtain a geometric model of the aircraft;

[0014] Mapping the aircraft structural parameters in the state information to the digital space using the digital twin technology to obtain the aircraft physical model;

[0015] The aircraft flight link data and flight status data in the status information are mapped to the data space using digital twin technology to obtain the aircraft behavior model and the aircraft rule model.

[0016] Optionally, the modifying the parameters of the aircraft virtual model by using a model prediction static programming technology according to the state information of the aircraft physical entity to obtain the model parameter modification amount specifically includes:

[0017] Obtaining a prediction error according to the state information of the aircraft physical entity and the prediction value output by the aircraft flocculation model;

[0018] Performing Taylor expansion and parameter estimation on the prediction error to obtain the relationship between the system parameter error and the prediction output error;

[0019] Determine an augmented objective function according to the relationship between the system parameter error and the predicted output error and a sensitivity matrix;

[0020] The augmented objective function is solved to obtain the model parameter correction amount.

[0021] Optionally, the expression of the augmented objective function is:

[0022]

[0023] in, is the augmented objective function, N is the final step in a discrete time interval, k is the discrete time node, T is the matrix transpose, dv k is the system parameter error, dyN Prediction output error, R k is a positive definite matrix, λ is a static Lagrangian operator, B k is the sensitivity matrix.

[0024] An aircraft fault-tolerant control system, comprising:

[0025] An acquisition module, used to acquire status information of a physical entity of an aircraft in a physical space;

[0026] A building module, used to build an aircraft virtual model matching the aircraft physical entity in a digital space by using digital twin technology according to the state information of the aircraft physical entity; the aircraft virtual model includes an aircraft geometric model, an aircraft physical model, an aircraft behavior model and an aircraft rule model;

[0027] A correction module, used to correct the parameters of the aircraft virtual model by using a model prediction static programming technology according to the state information of the aircraft physical entity, so as to obtain a model parameter correction amount;

[0028] A fault-tolerant control module is used to perform fault-tolerant control on the physical entity of the aircraft according to the model parameter correction amount.

[0029] Optionally, the building module specifically includes:

[0030] An aircraft geometric model determination unit, used to map the positional relationship between the aircraft geometric dimensions and the aircraft control surfaces in the state information into a digital space using digital twin technology to obtain the aircraft geometric model;

[0031] An aircraft physical model determination unit, configured to map the aircraft structural parameters in the state information to a digital space using digital twin technology to obtain the aircraft physical model;

[0032] The aircraft behavior model and aircraft rule model determination unit is used to map the aircraft flight link data and flight status data in the status information to the data space using the digital twin technology to obtain the aircraft behavior model and the aircraft rule model.

[0033] Optionally, the correction module specifically includes:

[0034] A prediction error determination unit, configured to obtain a prediction error according to the state information of the aircraft physical entity and the prediction value output by the aircraft flocculation model;

[0035] A Taylor expansion and parameter estimation unit, used to perform Taylor expansion and parameter estimation on the prediction error to obtain a relationship between the system parameter error and the prediction output error;

[0036] An augmented objective function determination unit, used to determine an augmented objective function according to a relationship between the system parameter error and the predicted output error and a sensitivity matrix;

[0037] The solving unit is used to solve the augmented objective function to obtain the model parameter correction value.

[0038] Optionally, the expression of the augmented objective function is:

[0039]

[0040] in, is the augmented objective function, N is the final step in a discrete time interval, k is the discrete time node, T is the matrix transpose, dv k is the system parameter error, dy N Prediction output error, R k is a positive definite matrix, λ is a static Lagrangian operator, B k is the sensitivity matrix.

[0041] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0042] The present invention obtains the state information of the physical entity of the aircraft in the physical space; uses the digital twin technology to build an aircraft virtual model matching the physical entity of the aircraft in the digital space according to the state information of the physical entity of the aircraft; the aircraft virtual model includes an aircraft geometric model, an aircraft physical model, an aircraft behavior model and an aircraft rule model; uses the model prediction static programming technology to correct the parameters of the aircraft virtual model according to the state information of the physical entity of the aircraft to obtain the model parameter correction amount; and performs fault-tolerant control on the physical entity of the aircraft according to the model parameter correction amount. By combining the digital twin technology and the model prediction static programming technology and applying them to the field of fault-tolerant control of aircraft, dynamic management and control of the operating state of the physical entity of the aircraft is achieved, effectively improving the safety and control efficiency of flight. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0044] Figure 1 A flow chart of the aircraft fault-tolerant control method provided by the present invention;

[0045] Figure 2An overview of the implementation process of the aircraft fault-tolerant control method provided by the present invention;

[0046] Figure 3 A schematic diagram of the general principle of the aircraft fault-tolerant control method provided by the present invention;

[0047] Figure 4 A schematic diagram of data information exchange for the aircraft fault-tolerant control method provided by the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] The purpose of the present invention is to provide an aircraft fault-tolerant control method and system, which combines digital twin technology and model prediction static planning technology and applies them to the field of aircraft fault-tolerant control, thereby realizing dynamic management and control of the operating status of the aircraft's physical entity, and effectively improving flight safety and control efficiency.

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] like Figure 1 As shown, the present invention provides an aircraft fault-tolerant control method, comprising:

[0052] Step 101: Obtain status information of the physical entity of the aircraft in the physical space.

[0053] Physical Space

[0054] The physical entity of an aircraft refers to a physical entity in the real environment that has sensors, actuators, and aircraft control surfaces. It mainly serves as a data acquisition device and the main data source of various models of the aircraft digital twin. The physical entity of the aircraft should have multi-source heterogeneous data perception capabilities, and collect entity perception information through sensors, including flight environment variables (including but not limited to flight altitude, airspeed, temperature) and flight state variables (including but not limited to flight speed, flight attitude angle, attitude angular acceleration), etc., and upload them to the digital twin database.

[0055] The digital twin of an aircraft is a one-to-one mapping model of all elements of a physical entity, which can reproduce the operating status of the physical entity. In order to ensure its accuracy, the transformer entity data stored in the digital twin must be authentic and reliable. Then, the digital twin is constructed based on the collected data information, which lays the foundation for the fault-tolerant control of the aircraft.

[0056] Step 102: Based on the status information of the physical entity of the aircraft, a virtual aircraft model matching the physical entity of the aircraft is constructed in the digital space using the digital twin technology; the virtual aircraft model includes an aircraft geometry model, an aircraft physical model, an aircraft behavior model, and an aircraft rule model. Based on the digital twin technology, a digital twin aircraft virtual model matching the physical entity of the aircraft is constructed on the simulation platform, and the simulation working environment of the model is configured.

[0057] Data is the key in the application of digital twin technology and the driving force of the digital twin implementation process. The data of the digital twin of the aircraft is mainly stored in the database. The twin database dynamically updates the attributes of the aircraft virtual model based on the result data of data analysis and fusion processing by the algorithm, including but not limited to updating the time-varying characteristics, dynamic performance and state matrix parameters of the aircraft virtual model, and then generates a fault-tolerant control strategy. The digital twin database includes but is not limited to the physical entity data of the aircraft, real-time physical data, simulation data of the digital twin, and all derivative data information generated in this process. Physical entity data includes but is not limited to physical attribute data such as size, material, shape, structure, assembly relationship of each component of the aircraft, and perceived real-time operation status data; real-time physical data includes but is not limited to all data information of the geometry, physics, behavior and rule models of the virtual aircraft, control surface fault data collected by the data acquisition system, and simulation data of the aircraft fault-tolerant controller established based on the above model to simulate its real operation. Derivative data includes derivative data generated after data conversion, preprocessing and fusion analysis in the process of parameter identification by using intelligent algorithms to build a real-time aerodynamic parameter identification model for aircraft.

[0058] In addition to transmitting information, the connection between the aircraft's physical space, digital space, and database also requires updating and correcting various models in the digital space to facilitate real-time interactive mapping, so that the aircraft's digital twin can simulate its operating status more accurately.

[0059] The connection between the various parts of the aircraft fault-tolerant control system based on digital twins and model prediction static planning makes it a closed-loop whole, realizing the dynamic flow of data and real-time update and iteration of data. The established aircraft digital twin is closer to the physical entity of the aircraft. By analyzing the real-time aerodynamic parameters of the aircraft, the dynamic flight characteristics of the aircraft can be optimized, and adjustments can be made quickly and efficiently when a fault occurs.

[0060] When an aircraft fails or a model deviation occurs, the actual deviation of the aircraft virtual model is estimated online using model-based predictive static planning technology, and the model correction results are sent to the twin database.

[0061] Step 102 specifically includes:

[0062] The positional relationship between the aircraft geometric dimensions and the aircraft control surfaces in the state information is mapped to the digital space using the digital twin technology to obtain the aircraft geometric model. The aircraft structural parameters in the state information are mapped to the digital space using the digital twin technology to obtain the aircraft physical model. The aircraft flight link data and flight state data in the state information are mapped to the data space using the digital twin technology to obtain the aircraft behavior model and the aircraft rule model.

[0063] Step 103: Correct the parameters of the aircraft virtual model using the model prediction static programming technology according to the state information of the aircraft physical entity to obtain the model parameter correction amount.

[0064] The physical data generated by the operation of the aircraft's physical entity and the virtual data generated by the operation of the aircraft's virtual model are received through the digital main line, and a large amount of heterogeneous physical data and virtual data are analyzed and fused. Parameters are estimated based on the model prediction static planning technology to obtain the parameter update amount dv, update the aircraft's virtual model, and quickly simulate to obtain the aircraft's state prediction for a period of time in the future.

[0065] Step 103 specifically includes:

[0066] A prediction error is obtained based on the state information of the physical entity of the aircraft and the predicted value of the output of the aircraft flocculation model; the prediction error is subjected to Taylor expansion and parameter estimation to obtain the relationship between the system parameter error and the prediction output error; an augmented objective function is determined based on the relationship between the system parameter error and the prediction output error and a sensitivity matrix; the augmented objective function is solved to obtain a model parameter correction.

[0067] Wherein, the expression of the augmented objective function is:

[0068]

[0069] in, is the augmented objective function, N is the final step in a discrete time interval, k is the discrete time node, T is the matrix transpose, dv k is the system parameter error, dy N Prediction output error, R k is a positive definite matrix, λ is a static Lagrangian operator, B k is the sensitivity matrix.

[0070] In order to achieve fast and efficient fault-tolerant control of aircraft, a parameter estimation algorithm is given based on the model prediction static planning technology, based on the establishment of a hyper-realistic aircraft digital twin model in the digital space mentioned above. Through this algorithm, a parameter update dv is obtained, and the system dynamics equation is updated to minimize the prediction error, thereby obtaining a new estimate. And it is transmitted to the twin database in real time to avoid accidents caused by the failure state lasting too long.

[0071] Step 104: Perform fault-tolerant control on the aircraft physical entity according to the model parameter correction amount.

[0072] Based on the latest model, the optimal control strategy is obtained to dynamically manage and control the operation status of the aircraft physical entity, and when a fault occurs, the control instructions are adjusted in time. According to the fault-tolerant control strategy, the control surfaces of the aircraft physical entity are controlled through several actuators in the aircraft physical entity.

[0073] Aiming at the problems in the prior art that the aircraft fault-tolerant controller has low working efficiency, is difficult to keep up with the dynamic and time-varying operating state and performance of the aircraft, cannot update the control strategy in a targeted manner to match the time-varying aircraft, resulting in untimely control, and cannot discover and warn some potential dangers in advance, the present invention provides a design method for an aircraft fault-tolerant controller based on digital twin and model prediction static planning. The method innovatively introduces digital twin technology, and synchronously monitors the operating data of two aircraft in an actual working environment and a simulation working environment after building an aircraft virtual machine model that is completely consistent with the aircraft physical entity. When the aircraft physical entity fails, the state parameters are estimated using the model prediction static planning technology. After minimizing the prediction error, the virtual engine model is dynamically updated accordingly, and the optimal control strategy is obtained based on the latest model to achieve dynamic management and control of the operating state of the aircraft physical entity, effectively improving the safety and control efficiency of the aircraft, and effectively solving the problems in the prior art that the aircraft fault-tolerant controller has low working efficiency, is difficult to keep up with the dynamic and time-varying operating state and performance of the aircraft, cannot update the control strategy in a targeted manner to match the time-varying aircraft, resulting in untimely control, and cannot discover and warn some potential dangers in advance.

[0074] First, we need to establish a digital twin system for the aircraft; then, we need to collect data and information generated by the real aircraft entity, and use data analysis, intelligent iteration and other technologies to perform analysis and calculations to achieve monitoring, evaluation, prediction and control of the aircraft's operating status.

[0075] The core components of this method are physical space, digital space, digital twin library, parameter estimation, and the connection between the components, such as Figure 2 As shown. The aircraft is taken as an aircraft physical entity in the actual working environment. The aircraft physical entity is an aircraft in the real physical space, which can be any common aircraft such as a fixed-wing aircraft, a non-powered glider, and an airship that is controlled by the aerodynamic force generated by the rudder. When working, the external environment and the body parameters change in real time, such as Figure 2 An overview of the implementation process.

[0076] The fault-tolerant control method is implemented as follows:

[0077] (1) Model creation

[0078] Since the aircraft control system is very complex, with strong nonlinearity and uncertainty, it is difficult to establish the object model using the mechanism analysis method. Therefore, it is considered to use historical data to model the aircraft object, and comprehensively consider the factors affecting the change of the object output and the delay and inertia of the controlled object to determine the input and output of the model.

[0079] (2) Model deployment

[0080] Model deployment is the process of connecting the twin system with the aircraft entity through real-time communication of data, including virtual models and parameter estimation modules, as well as online controllers.

[0081] (3) Model initialization

[0082] Before fault-tolerant control, the model needs to be initialized using an online simulator so that the process object model reaches the current state of the real process object. The specific implementation method is to use the actual process measurement value as the set point of the virtual model, and the object model will be quickly driven to the current state of the controlled object. When the virtual model is in the same state as the physical entity, the initialization phase is completed.

[0083] (4) Model update optimization

[0084] At this stage, the initialized simulation model runs in parallel with the actual physical entity, both of which are controlled by the actual control system, and the error between the measured values ​​of the real loop and the model output can be observed in real time.

[0085] Due to the change of flight status, the characteristics of the aircraft change, and the virtual model must be updated and optimized in time to reduce the model error. In order to obtain more accurate prediction results, the parameter estimation module based on the model static planning technology is connected to the process object model, which can quickly simulate and obtain the aircraft state prediction for a period of time in the future. If a fault occurs, the control instructions can be adjusted in time.

[0086] This method builds a digital twin aircraft virtual model that matches the physical entity of the aircraft on a simulation platform based on digital twin technology. It can also be called an aircraft digital twin. It is a high-fidelity aircraft mathematical model that exists in a computer virtual digital space. It can be corrected and simulated in real time based on the received parameter signals and parameter estimation signals.

[0087] The simulation working environment of the aircraft virtual model is configured to be consistent with the actual working links of the aircraft physical entity. The physical data generated by the operation of the aircraft physical entity and the operation of the aircraft virtual model are received by the data acquisition device through the digital bus. The aircraft virtual model is updated according to the processing results, and the corresponding optimal control strategy under the latest model is obtained based on the calibrated performance and current status of the aircraft physical entity in combination with online simulation technology. According to the optimal control strategy, the aircraft physical entity is fault-tolerantly controlled.

[0088] Step 1: Collecting information about the physical state of the aircraft

[0089] For aircraft, what exists in physical space is the physical entity of the aircraft that is controlled by generating aerodynamic force through the rudder in the real environment. It mainly serves as a data acquisition device and the main data source of various models of the aircraft digital twin. The physical entity of the aircraft should have multi-source heterogeneous data perception capabilities, and collect entity perception information through sensors, including flight environment variables (including but not limited to flight altitude, airspeed, temperature) and flight state variables (including but not limited to flight speed, flight attitude angle, attitude angular acceleration), etc., and upload them to the digital twin database.

[0090] The digital twin of an aircraft is a one-to-one mapping model of all elements of a physical entity, which can reproduce the operating status of the physical entity. In order to ensure its accuracy, the transformer entity data stored in the digital twin must be authentic and reliable. Then, the digital twin is constructed based on the collected data information, which lays the foundation for the fault-tolerant control of the aircraft.

[0091] Step 2: Establishing the Digital Twin of the Aircraft

[0092] like Figure 3As shown in the figure, the digital twin of an aircraft is a digital mapping of a physical entity in a virtual space. It can simulate the performance and behavior of an aircraft in the real world in real time, establish the description and association of aircraft knowledge information, and use simulation software and other means to establish all elements of the model. After combining the two, it is driven by real-time physical data to build a hyper-realistic digital twin model of an aircraft. The aircraft digital twin is the virtual model of the aircraft.

[0093] In practical applications, the geometric model of the aircraft digital twin includes but is not limited to the aircraft geometric dimensions such as the aircraft wing reference area, wingspan, chord length, and the distance from the center of gravity of the aircraft to the leading edge of the average geometric chord; the positional relationship between the aircraft control surfaces. The physical model of the aircraft digital twin stores the aircraft structural parameters such as the mass, moment of inertia, and product of inertia of the aircraft in the real environment; the behavior model of the aircraft digital twin is to use Newton's second law to establish the linear motion of the aircraft under the action of the external force and the angular motion equation under the action of the external torque, that is, the aircraft dynamics equation and the aircraft kinematics equation. The aircraft dynamics equation includes the center of mass dynamics equation and the center of mass dynamics equation. The center of mass dynamics equation includes but is not limited to the calculation models of the earth's gravity, aerodynamic force, thrust, centrifugal inertia force, and Coriolis inertia force. The center of mass dynamics equation includes but is not limited to the aerodynamic moment vector and the additional Coriolis force vector acting on the aircraft.

[0094] The aircraft kinematic model includes the aircraft center of mass motion equation and the aircraft kinematic equation around the center of mass.

[0095] By inputting flight link data and flight status data into the model, the real-time operating status of the aircraft can be simulated, allowing the aircraft digital twin to simulate the actual operating conditions in a hyper-realistic manner.

[0096] In the design, the regular model of the digital twin of the aircraft defines that for any set of Mach number, angle of attack and rudder deflection values, the corresponding aerodynamic derivative values ​​can be obtained. The aerodynamic parameters of the longitudinal and lateral modes required for aircraft modeling include the lift coefficient C L , drag coefficient C D , lateral force coefficient C Y , rolling moment coefficient C l , pitch moment coefficient C m and the yaw moment coefficient C n , which are all expressed as Mach number M, angle of attack α, sideslip angle β, left elevator deflection angle δ e 、Right elevator deflection angle δ a and the rudder angle δ r The fifth-order polynomial function of can be obtained by the following calculation expression:

[0097] Lift coefficient C L =CLa +C L,de +C L,da +C L,dc ;

[0098] Among them, C La is the basic lift increment coefficient of the aircraft, C L,de is the lift increment coefficient of the left elevator of the aircraft, C L,da is the lift increment coefficient of the right elevator of the aircraft, C L,dc is the lift increment coefficient of the aircraft canard.

[0099] Drag coefficient C D =C Da +C D,de +C D,da +C D,dr +C D,dc ;

[0100] Among them, C Da is the basic drag increment coefficient of the aircraft, C D,de is the aircraft left elevator drag increment coefficient, C D,da is the aircraft right elevator drag increment coefficient, C D,dr is the aircraft rudder drag increment coefficient, C D,dc is the aircraft canard drag increment coefficient.

[0101] Side force coefficient C Y =C Ya +C Y,de +C Y,da +C Y,dr ;

[0102] Among them, C Ya is the basic side force increment coefficient of the aircraft, C Y,de is the aircraft left elevator side force increment coefficient, C Y,da is the aircraft right elevator side force increment coefficient, C D,dr is the aircraft rudder side force increment coefficient.

[0103] Rolling moment coefficient

[0104] in, is the derivative of the rolling moment and sideslip angle of the aircraft, C l,de is the aircraft left elevator rolling moment increment coefficient, C l,da is the aircraft right elevator rolling moment increment coefficient, C l,dr is the aircraft rudder rolling moment increment coefficient, is the rolling moment and rolling angular velocity derivative of the aircraft, is the derivative of the rolling moment yaw rate of the aircraft. p is the aircraft's roll rate, r is the aircraft's yaw rate, b is the aircraft's span, and V is the aircraft's current flight speed.

[0105] Pitch moment coefficient

[0106] Among them, C ma is the basic pitch moment increment coefficient of the aircraft, C m,de is the aircraft left elevator pitch moment increment coefficient, C m,da is the aircraft right elevator pitch moment increment coefficient, C m,dr is the aircraft rudder pitch moment increment coefficient, C m,dc is the aircraft canard pitch moment increment coefficient, is the pitch moment of the aircraft and the derivative of the pitch angular velocity, q is the pitch angular rate of the aircraft, and c is the average aerodynamic chord length.

[0107] Yaw moment coefficient

[0108] in, is the derivative of the aircraft's yaw moment and sideslip angle, C n,de is the aircraft left elevator yaw moment increment coefficient, C n,da is the aircraft right elevator yaw moment increment coefficient, C n,dr is the aircraft rudder yaw moment increment coefficient, is the derivative of the rolling angular velocity of the aircraft yaw moment, is the derivative of the yaw moment and yaw rate of the aircraft.

[0109] The aerodynamic force R of the aircraft (including lift L, drag D and side force Y) can be calculated according to the following formula:

[0110] L=QSC L

[0111] D=QSC D

[0112] Y=QSC Y

[0113] Where S is the reference area of ​​the aircraft and Q is the dynamic pressure of the aircraft.

[0114] The specific expression of the aerodynamic moment of the aircraft (including rolling moment l, pitching moment m and yaw moment n) is:

[0115] l=QbSC l

[0116] m=QcSC m +x cg(Dsinα+Lcosα)

[0117] n=QbSC n -x cg Y

[0118] Among them, x cg is the distance between the aerodynamic focus and the center of gravity of the aircraft, and α is the current flight angle of attack of the aircraft.

[0119] Through the definition of the rule model, the aerodynamic parameters of the longitudinal and lateral modes of the aircraft can be obtained, and then based on these aerodynamic parameters, the lift L, drag D, side force Y, rolling moment l, pitch moment m and yaw moment n under different flight conditions can be calculated. These values ​​are important components in the process of establishing the digital twin of the aircraft.

[0120] Step 3: Parameter estimation algorithm based on model prediction static programming technology

[0121] When the aircraft fails or the model deviates, the actual deviation of the aircraft virtual model is estimated online using the model prediction static planning technology, and the model correction results are sent to the twin database for data analysis and fusion processing. The data in the digital space is corrected. The virtual mapping of the physical model is first established in the digital space. When the physical entity changes, the basic rule model will not change, but the parameters will change. This part is to find the change of parameters and make adjustments to achieve fault tolerance. The data in the digital twin is divided into the environmental variables of the aircraft (including the physical quantities of the aircraft body, such as span, reference area, and dynamic pressure), which are defined by the geometric model and the behavioral model, and the state variables of the aircraft, which are defined by the kinematic model and the rule model. When the physical entity changes, that is, the aircraft fails or the model deviates, the algorithm can use the data of the physical entity to find the changes in the aerodynamic parameters of the rule model in the digital twin, and then regenerate a new control vector.

[0122] The algorithm uses the equations for the dynamics of a discrete system that progresses over time with explicit parameters to be estimated:

[0123] x k+1 =F k (x k ,u k ,v k );

[0124] Where: x∈R n is the state vector; u∈R n is the control vector; v∈R p is a parameter vector; k = 1, 2, ..., N-1, where N is the final step in a discrete time interval, F kThe system dynamics equation used to describe the time k, where the system refers to the series of formulas established in step 2 to describe the kinematic model of the real-time operation state of the aircraft. To update the current parameters, select x N is the state quantity at the current moment, consider the following output equation:

[0125] y k =h(x k )(If k=1,2,...,N-1, each x k All included in x N In, x N is a general term, x k is the specific value of each point).

[0126] Where: y∈R q is the output vector considered in parameter estimation. The prediction error between the measured value and the predicted value of the output vector at the current moment is defined as:

[0127]

[0128] Where: is the output vector measurement value obtained by the sensor in the physical entity of the aircraft; N Predicted values ​​for the aircraft digital twin.

[0129] The algorithm obtains a parameter update dv and updates the system discrete equation to minimize the prediction error Δy N , and thus a new estimate is obtained. Assuming that the prediction error is small, it can be obtained by a small amount of approximation.

[0130]

[0131] By performing Taylor expansion on the system equations and taking into account the small amount approximation, the error at the k+1 node can be expressed as:

[0132]

[0133] Since the time nodes considered by the parameter estimation algorithm are all past time nodes and the control vector is known and determined, the change of the control quantity is not considered, that is, du k =0, the above formula can be simplified to:

[0134]

[0135] Similarly, at the current time node, that is, N nodes have

[0136]

[0137] Substitution After that, you can get

[0138]

[0139] General dx in k (k=1,2,...,N-1) keep substituting to get

[0140] dy N =Adx1+B1dv1+B2dv2+...+B N-1 dv N-1 ;

[0141] Where:

[0142]

[0143]

[0144] Assuming that the initial state remains unchanged, then dx1 = 0, which can be simplified to

[0145]

[0146] Mode Indicates the system parameter error dv k The predicted output error dy N The relationship between. Sensitivity matrix B k The amount of calculation can be greatly reduced by reverse recursive calculation:

[0147]

[0148] In the formula: superscript 0 represents the intermediate variable in the calculation process. In order to construct a complete optimal control problem structure, the following objective function is selected:

[0149]

[0150] Where: R k is a positive definite matrix. The selection of matrix elements generally depends on the numerical size ratio of the estimated parameters. The reason for selecting this objective function is that when the aircraft parameters change, the parameters are generally still near the nominal parameters. When correcting the parameters, the minimum correction amount of the parameters should be considered. In addition, the objective function also contains the required quadratic structure. By introducing this objective function and considering the formula A constrained static optimization problem can be established. The augmented objective function is the objective function of the parameter estimation algorithm. The obtained parameters will be sent to the digital twin database, which will then drive the digital twin to perform iterative optimization and further affect the physical entity. The augmented objective function is constructed as:

[0151]

[0152] Where: λ is the static Lagrangian operator. The necessary conditions for obtaining the optimal solution are solved by static optimization theory, and assuming that dv is equal at each step, we can get:

[0153]

[0154] Solving the parameter correction dv with the above formula yields:

[0155]

[0156] In the formula

[0157] This gives the estimated values ​​of the parameters as:

[0158] v=v p +dv;

[0159] Where: v is the updated parameter value; v p is the parameter value before updating. It is worth noting that although the assumption that the parameters are time-invariant within the estimation interval is made in the derivation process, due to the fast update characteristics of the algorithm itself, time-varying parameters can be estimated. p +dv, after compensating the parameter correction dv, the corresponding system prediction output error is also compensated. Among them, the parameter value before updating is the aerodynamic parameter. After the aerodynamic parameter is updated, the twin model can be closer to the physical entity, thereby realizing fault-tolerant control.

[0160] In summary, the completion Figure 4 The data information shown exchanges between modules, thereby completing fault-tolerant control.

[0161] The present invention also provides an aircraft fault-tolerant control system, comprising:

[0162] The acquisition module is used to obtain the status information of the physical entity of the aircraft in the physical space.

[0163] A building module is used to build an aircraft virtual model that matches the aircraft physical entity in a digital space based on the status information of the aircraft physical entity using digital twin technology; the aircraft virtual model includes an aircraft geometric model, an aircraft physical model, an aircraft behavior model and an aircraft rule model.

[0164] The correction module is used to correct the parameters of the aircraft virtual model by using the model prediction static programming technology according to the state information of the aircraft physical entity to obtain the model parameter correction amount.

[0165] A fault-tolerant control module is used to perform fault-tolerant control on the physical entity of the aircraft according to the model parameter correction amount.

[0166] As an optional implementation, the building module specifically includes:

[0167] The aircraft geometric model determination unit is used to map the positional relationship between the aircraft geometric dimensions and the aircraft control surfaces in the state information into a digital space using digital twin technology to obtain the aircraft geometric model.

[0168] The aircraft physical model determination unit is used to map the aircraft structural parameters in the state information to the digital space using the digital twin technology to obtain the aircraft physical model.

[0169] The aircraft behavior model and aircraft rule model determination unit is used to map the aircraft flight link data and flight status data in the status information to the data space using the digital twin technology to obtain the aircraft behavior model and the aircraft rule model.

[0170] As an optional implementation, the correction module specifically includes:

[0171] The prediction error determination unit is used to obtain the prediction error according to the state information of the aircraft physical entity and the prediction value output by the aircraft flocculation model.

[0172] The Taylor expansion and parameter estimation unit is used to perform Taylor expansion and parameter estimation on the prediction error to obtain the relationship between the system parameter error and the prediction output error.

[0173] The augmented objective function determination unit is used to determine the augmented objective function according to the relationship between the system parameter error and the predicted output error and the sensitivity matrix.

[0174] The solving unit is used to solve the augmented objective function to obtain the model parameter correction value.

[0175] As an optional implementation, the expression of the augmented objective function is:

[0176]

[0177] in, is the augmented objective function, N is the final step in a discrete time interval, k is the discrete time node, T is the matrix transpose, dv k is the system parameter error, dy N Prediction output error, R k is a positive definite matrix, λ is a static Lagrangian operator, B k is the sensitivity matrix.

[0178] The present invention regards the aircraft as an aircraft physical entity in an actual working environment, first builds a digital twin aircraft virtual model that matches the aircraft physical entity on a simulation platform based on the digital twin technology, configures the simulation working environment of the model, and then receives the physical data generated by the operation of the aircraft physical entity and the virtual data generated by the operation of the aircraft virtual model through the digital main line, and performs data analysis and fusion processing on a large amount of heterogeneous physical data and virtual data, and updates the aircraft virtual model according to the processing results, and obtains the corresponding optimal control strategy under the latest model based on the aircraft control instructions and model prediction static planning technology, and controls the aircraft physical entity through the control rudder according to the optimal control theory, and updates the aircraft virtual model and fault-tolerant control strategy in real time, so as to form dynamic rolling data exchange and realize fault-tolerant control.

[0179] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0180] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for fault-tolerant control of an aircraft, characterized in that: include: Obtaining status information of the physical entity of the aircraft in the physical space; According to the state information of the physical entity of the aircraft, a virtual model of the aircraft matching the physical entity of the aircraft is constructed in the digital space by using the digital twin technology; the virtual model of the aircraft includes an aircraft geometry model, an aircraft physical model, an aircraft behavior model and an aircraft rule model; According to the state information of the physical entity of the aircraft, the parameters of the virtual model of the aircraft are corrected by using the model prediction static programming technology to obtain the model parameter correction amount; Performing fault-tolerant control on the physical entity of the aircraft according to the model parameter correction amount; The method of correcting the parameters of the aircraft virtual model by using the model prediction static programming technology according to the state information of the aircraft physical entity to obtain the model parameter correction amount specifically includes: Obtaining a prediction error according to the state information of the aircraft physical entity and the prediction value output by the aircraft flocculation model; Performing Taylor expansion and parameter estimation on the prediction error to obtain the relationship between the system parameter error and the prediction output error; Determine an augmented objective function according to the relationship between the system parameter error and the predicted output error and a sensitivity matrix; Solving the augmented objective function to obtain model parameter corrections; The expression of the augmented objective function is: in, is the augmented objective function, N is the final step in a discrete time interval, k is the discrete time node, T is the matrix transpose, dv k is the system parameter error, dy N Prediction output error, R k is a positive definite matrix, λ is a static Lagrangian operator, B k is the sensitivity matrix.

2. The aircraft fault-tolerant control method according to claim 1, characterized in that: The step of using the digital twin technology to build a virtual model of the aircraft matching the physical entity of the aircraft in a digital space according to the state information of the physical entity of the aircraft specifically includes: Mapping the geometric dimensions of the aircraft in the state information and the positional relationship between the aircraft control surfaces into a digital space using digital twin technology to obtain a geometric model of the aircraft; Mapping the aircraft structural parameters in the state information to the digital space using the digital twin technology to obtain the aircraft physical model; The aircraft flight link data and flight status data in the status information are mapped to the data space using digital twin technology to obtain the aircraft behavior model and the aircraft rule model.

3. An aircraft fault-tolerant control system, characterized in that: include: An acquisition module, used to acquire status information of a physical entity of an aircraft in a physical space; A building module, used to build an aircraft virtual model matching the aircraft physical entity in a digital space by using digital twin technology according to the state information of the aircraft physical entity; the aircraft virtual model includes an aircraft geometric model, an aircraft physical model, an aircraft behavior model and an aircraft rule model; A correction module, used to correct the parameters of the aircraft virtual model by using a model prediction static programming technology according to the state information of the aircraft physical entity, so as to obtain a model parameter correction amount; A fault-tolerant control module, used for performing fault-tolerant control on the physical entity of the aircraft according to the model parameter correction amount; The correction module specifically includes: A prediction error determination unit, configured to obtain a prediction error according to the state information of the aircraft physical entity and the prediction value output by the aircraft flocculation model; A Taylor expansion and parameter estimation unit, used to perform Taylor expansion and parameter estimation on the prediction error to obtain a relationship between the system parameter error and the prediction output error; An augmented objective function determination unit, used to determine an augmented objective function according to a relationship between the system parameter error and the predicted output error and a sensitivity matrix; A solving unit, used for solving the augmented objective function to obtain a model parameter correction value; The expression of the augmented objective function is: in, is the augmented objective function, N is the final step in a discrete time interval, k is the discrete time node, T is the matrix transpose, dv k is the system parameter error, dy N Prediction output error, R k is a positive definite matrix, λ is a static Lagrangian operator, B k is the sensitivity matrix.

4. The aircraft fault-tolerant control system according to claim 3, characterized in that: The building module specifically includes: An aircraft geometric model determination unit, used to map the positional relationship between the aircraft geometric dimensions and the aircraft control surfaces in the state information into a digital space using digital twin technology to obtain the aircraft geometric model; An aircraft physical model determination unit, configured to map the aircraft structural parameters in the state information to a digital space using digital twin technology to obtain the aircraft physical model; The aircraft behavior model and aircraft rule model determination unit is used to map the aircraft flight link data and flight status data in the status information to the data space using the digital twin technology to obtain the aircraft behavior model and the aircraft rule model.

Images