CN116610137A - Hypersonic aircraft strong disturbance rejection control method based on disturbance prediction - Google Patents
Hypersonic aircraft strong disturbance rejection control method based on disturbance prediction Download PDFInfo
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Abstract
The invention relates to a hypersonic aircraft strong disturbance rejection control method based on disturbance prediction, which belongs to the technical field of aircraft control, and aims at the problem of high-precision attitude control of a hypersonic aircraft under multi-source disturbance such as model uncertainty, atmospheric turbulence, control surface degradation and the like, and firstly, an aircraft three-degree-of-freedom attitude system model is established; secondly, decomposing the three-degree-of-freedom attitude system model into three subsystem models; thirdly, designing a finite-time interference observer to estimate model uncertainty; thirdly, designing an extended state observer based on a self-learning mechanism to estimate lumped items of control surface efficiency and other interference; and finally, designing a strong anti-interference controller based on a self-learning mechanism according to the estimated values of the finite-time interference observer, the self-learning mechanism and the extended state observer, and completing the design of a hypersonic aircraft strong anti-interference control method based on interference prediction. The invention is suitable for controlling the posture of the hypersonic aircraft in extreme environments.
Description
Technical Field
The invention belongs to the technical field of aircraft control, and particularly relates to a hypersonic aircraft strong disturbance rejection control method based on disturbance prediction, which is mainly applied to the disturbance online prediction and high-precision attitude control of hypersonic aircraft under multi-source disturbance.
Background
The hypersonic aircraft is an advanced aircraft which works in near space of 20-100 km for most of the time and has the flight speed of more than 5 Mach. Due to the task characteristics of a large airspace, a wide speed domain, high dynamics and diversification, the flight safety and the system performance of the hypersonic aircraft are threatened by multi-source heterogeneous interference moments such as atmospheric turbulence, component degradation, inter-channel coupling and the like. For example, due to the current state of development of numerical simulation and ground test techniques, there is often a large uncertainty in model pneumatic parameters of hypersonic aircrafts; because the hypersonic speed aircraft has extremely high speed, the airflows are subjected to strong compression and friction in the flight process, so that the aircraft body, the control surface and the like can be subjected to high-temperature ablation with different degrees, and further the execution efficiency of the control surface and other parts is degraded to a certain degree; the integrated configuration design of hypersonic aircrafts results in mutual coupling between their aerodynamic/structural/propulsion forces, resulting in strong uncertainties in the aircraft model and parameters. These features greatly increase the difficulty in designing the attitude control system of the hypersonic aircraft, and the traditional control method is often difficult to apply. Thus, the strong disturbance rejection attitude control of hypersonic aircraft is a very challenging theoretical and engineering challenge.
At present, aiming at the problems of strong coupling, strong nonlinearity, multi-source interference and the like of a hypersonic aircraft attitude system, an extended state observer and a neural network are mainly adopted to estimate or approximate corresponding unknown interference and dynamics. In the Chinese patent application CN110377045A, the attitude dynamics model is converted into integral series connection through linear state conversion, an extended state observer is designed to rapidly estimate the total disturbance of the aircraft, and an active disturbance rejection controller is designed by combining proportional-differential control, so that a better control effect is achieved. The neural network technology is introduced into the Chinese patent application CN110488852A to approach the unknown nonlinearity in the attitude system, and the attitude control law of the hypersonic aircraft with the full profile is designed by a backstepping method, so that the controller is prevented from switching in the full-profile flight process. The literature (Du Lifu, li Dong, zhang Rui, etc. ESO-based hypersonic aircraft decoupling control [ J ]. Flight control and detection, 2021, 4 (05): 21-26) proposes a full-channel decoupling control method based on an extended state observer aiming at the channel coupling problem of the hypersonic aircraft, and the influence caused by strong coupling is largely eliminated through compensation control. Existing methods deal with different sources and different types of disturbances/uncertainties on hypersonic aircrafts, which are often regarded as lumped disturbances, are highly conservative and lack online predictions of the different types of disturbances. Therefore, there is a need to explore a hypersonic aircraft strong immunity control method with tamper predictive capability.
Disclosure of Invention
Aiming at the problems of poor anti-interference capability, low control precision, insufficient interference online prediction capability and the like of a hypersonic aircraft attitude control method, the invention provides a hypersonic aircraft strong anti-interference control method based on interference prediction, and adopts a strategy combining interference observation, state estimation and parameter self-learning, thereby realizing online prediction of uncertainty, external interference and control surface efficiency of an aircraft model and effectively improving autonomy, adaptability and safety of an aircraft attitude control process.
In order to achieve the above purpose, the technical solution adopted by the invention is as follows:
a hypersonic aircraft strong immunity control method based on interference prediction comprises the following steps:
firstly, building an aircraft three-degree-of-freedom attitude system model containing model uncertainty, atmospheric turbulence, pneumatic control surface degradation and other multi-source interference according to attitude dynamics characteristics of a hypersonic aircraft;
secondly, decomposing the hypersonic aircraft into three subsystem models of pitching, yawing and rolling based on a hypersonic aircraft three-degree-of-freedom attitude system model, and establishing a hypersonic aircraft attitude equivalent model facing control;
thirdly, designing a finite time disturbance observer based on a hypersonic aircraft gesture equivalent model facing control, and estimating model uncertainty;
fourthly, designing a self-learning mechanism and an extended state observer based on a hypersonic aircraft gesture equivalent model facing control, and estimating lumped items of control surface efficiency and other interference;
fifthly, designing a self-learning mechanism and a strong disturbance rejection controller according to the model uncertainty, the control surface efficiency and the lumped disturbance estimated value, and completing the design of a hypersonic aircraft strong disturbance rejection control method based on disturbance prediction.
Further, in the first step, the hypersonic aircraft three-degree-of-freedom attitude system model is built as follows:
，
，
，
wherein ,、/>、/>respectively representing attack angle, sideslip angle and roll angle; />、/>、/>Respectively representing the rolling angle rate, the pitch angle rate and the yaw angle rate; />、/>、/>Representing uncertainty in the coupling of the aircraft centroid kinematic state into the pose; />、/>Respectively representing the track inclination angle and the track deflection angle; />、/>、/>、/>、/>、/>、/>、/>Respectively->、/>、/>、/>、/>、/>、/>、/>First derivative with respect to time; />、/>、/>Representing the rotational inertia of the aircraft body around the three axes of the machine body coordinate system; />、/>、/>The disturbance moment caused by external disturbance such as atmospheric turbulence is represented; />、/>、/>Respectively representing roll moment, pitch moment and yaw moment, wherein the specific expression is as follows:
，
in the above-mentioned method, the step of,for pneumatic reference area->For wing extension, ->For average aerodynamic chord length +.>In order to achieve a speed of the aircraft,is of atmospheric density>、/>、/>Respectively rolling, pitching and yawing moment coefficients;
considering control surface degradation, and ignoring terms with smaller influence, the moment coefficient polynomial fitting model of the aircraft is as follows:
，
in the above-mentioned method, the step of,、/>、/>represents the deflection angle of the triaxial rudder>、/>、/>、/>、/>、/>、/>、/>、、/>Fitting parameters for aerodynamic moment coefficients, +.>、/>、/>Represents the degradation factor of the triaxial control surface and satisfies +.>，，/>。
Further, the second step includes:
the following variables are defined:
，
，
，
in the above-mentioned method, the step of,、/>、/>representing the efficiency of the triaxial control surface>、/>、/>、/>、/>、/>Representing the state of the gesture system->、/>、/>The expanded state of the channel attitude subsystem, which is pitch, yaw and roll, contains coupling between channels, external disturbance moments and other disturbances, which are considered lumped disturbances. In combination with the physical properties of the aircraft, assume an expanded state +.>、/>、/>The rate of change is bounded, i.e.)>、/>、/>First derivative of time>、/>、/>Presence, defined as->，/>，/>。
The three-degree-of-freedom attitude system model in the first step is converted into a control-oriented multichannel model as follows:
the pitch channel model is:
，
the yaw passage model is as follows:
，
the rolling channel model is as follows:
，
further, the third step includes:
based on pitching channel modelDesigning a finite time interference observer as follows:
，
wherein ,representing status->Estimated value of ∈10->Representation model uncertainty +.>Estimated value of ∈10-> and />Respectively-> and />First derivative of time, +.>、/>、/>For interfering observer->Gain of->A representative sign function defined as: for a real number->，
，
Yaw channel modelDesigning a finite time interference observer as follows:
，
wherein ,representing status->Estimated value of ∈10->Representation model uncertainty +.>Estimated value of ∈10-> and />Respectively-> and />Time first derivative>、/>、/>For interfering observer->Is a gain of (2);
rolling channel modelDesigning a finite time interference observer as follows:
，
wherein ,representing status->Estimated value of ∈10->Representation model uncertainty +.>Estimated value of ∈10-> and />Respectively-> and />First derivative of time, +.>、/>、/>For interfering observer->Is provided.
Further, the fourth step includes:
based on pitching channel modelThe design of the expansion state observer is as follows:
，
wherein , and />Respectively indicate status->Total interference->Estimated value of ∈10-> and />Respectively is and />First derivative of time, +.>For observer->Bandwidth of->For control surface efficiency->The self-learning mechanism of the on-line estimation is designed as follows:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is set, and the design parameters of (a) are set.
Yaw channel modelThe design of the expansion state observer is as follows:
，
wherein , and />Respectively indicate status->Total interference->Estimated value of ∈10-> and />Respectively-> and />First derivative of time, +.>For observer->Bandwidth of->Is effective for control surfaceRate->The self-learning mechanism of the on-line estimation is designed as follows:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is set, and the design parameters of (a) are set.
Rolling channel modelThe design of the expansion state observer is as follows:
，
wherein , and />Respectively indicate status->Total interference->Estimated value of ∈10-> and />Respectively-> and />First derivative of time, +.>For observer->Bandwidth of->For control surface efficiency->The self-learning mechanism of the on-line estimation is designed as follows:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is set, and the design parameters of (a) are set.
Further, the fifth step includes:
observer based on finite time interference、/>、/>Extended state observer->、/>、/>And self-learning mechanisms->、/>、/>Estimate of +.>、/>、/>、/>、/>、/>、/>、/>、/>Model uncertainty in the attitude System of an aircraft>、/>、/>Lumped interference->、/>、/>And control surface efficiency->、/>、/>Performing online prediction; the following variables are defined:
，
，
，
wherein ,、/>、/>respectively indicate->、/>、/>Reference instruction of->、/>、/>Respectively->、/>、/>First derivative of time, +.>、/>、/>Is a design parameter;
observer based on expansion stateThe output design pitch channel attitude controller of (1) is:
，
wherein ,、/>、/>for controller->Design parameters of->Is->The self-learning mechanism of the on-line estimation is designed as follows:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is a design parameter of (1);
based on observerThe output design yaw path attitude controller of (1) is:
，
wherein ,、/>、/>for controller->Design parameters of->Is->The self-learning mechanism of the on-line estimation is designed as follows:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is a design parameter of (1);
based on observerThe output design roll channel attitude controller of (a) is:
，
wherein ,、/>、/>for controller->Design parameters of->Is->The self-learning mechanism of the on-line estimation is designed as follows:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is set, and the design parameters of (a) are set.
Compared with the prior art, the invention has the beneficial effects that:
the invention fully combines a finite time observer, a state observer and a self-learning mechanism, realizes the on-line prediction of multi-source interference and the high-precision attitude control of the aircraft, overcomes the limitation that the traditional control method is difficult to simultaneously perform on-line prediction on interference and control surface efficiency, has the advantages of high precision, strong robustness and self-adaption, and is suitable for the attitude control of the hypersonic aircraft under extreme environments.
Drawings
FIG. 1 is a flow chart of a design of a hypersonic aircraft strong immunity control method based on interference prediction;
fig. 2 is a control block diagram of a hypersonic aircraft strong immunity control method based on interference prediction.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings and examples.
As shown in fig. 1 and 2, the specific implementation steps of the hypersonic aircraft strong immunity control method based on interference prediction are as follows:
firstly, according to the attitude dynamics characteristics of the hypersonic aircraft, a hypersonic aircraft three-degree-of-freedom attitude system model containing multi-source interference such as model uncertainty, atmospheric turbulence, pneumatic control surface efficiency degradation and the like is established, and the three-degree-of-freedom attitude system model is shown as follows:
，
，
，
wherein ,、/>、/>respectively representing attack angle, sideslip angle and tilting angle, and respectively taking initial values of 0 rad, 0 rad and 0 rad; />、/>、/>Respectively representing the rolling angle rate, the pitch angle rate and the yaw angle rate, and taking initial values of 0 rad/s, 0 rad/s and 0 rad/s respectively; />、/>、/>Representing uncertainty in the coupling of the aircraft centroid kinematic state into the pose; />、/>Respectively representing the track inclination angle and the track deflection angle, and respectively taking initial values of 0 rad and 0 rad; />、/>、/>、/>、/>、/>、/>、Respectively->、/>、/>、/>、/>、/>、/>、/>First derivative with respect to time; />、/>、/>Representing the rotational inertia of the aircraft body around the three axes of the machine body coordinate system; />、/>、/>The disturbance moment caused by external disturbance such as atmospheric turbulence is represented;、/>、/>respectively representing roll moment, pitch moment and yaw moment, wherein the specific expression is as follows:
，
in the above-mentioned method, the step of,for pneumatic reference area->For wing extension, ->For average aerodynamic chord length +.>In order to achieve a speed of the aircraft,is of atmospheric density>、/>、/>Respectively roll, pitch, yaw moment coefficients. Considering control surface degradation, and ignoring some less influencing terms, the moment coefficient polynomial fitting model of the aircraft is as follows:
，
in the above-mentioned method, the step of,、/>、/>is the deflection angle of the triaxial rudder>、/>、/>、/>、/>、/>、/>、/>、/>、Fitting parameters for aerodynamic moment coefficients, +.>、/>、/>Represents the degradation factor of the triaxial control surface and satisfies +.>，，/>。
In the present embodiment, the triaxial moment of inertia is set to be respectively、、/>The pneumatic reference area is set asWing span is set to +.>The average aerodynamic chord length is set to +.>The initial value of the aircraft speed is set to +.>The three-axis control surface degradation factors are respectively set to +.>、、/>。
Second, define the following variables:
，
，
，
in the above-mentioned method, the step of,、/>、/>representing the efficiency of the triaxial control surface>、/>、/>、/>、/>、/>Representing the state of the gesture system->、/>、/>The expanded state of the channel attitude subsystem, which is pitch, yaw and roll, contains coupling between channels, external disturbance moments and other disturbances, which are considered lumped disturbances. In combination with the physical properties of the aircraft, assume an expanded state +.>、/>、/>The rate of change is bounded, i.e.)>、/>、/>First derivative of time>、/>、/>Presence, defined as->，/>，/>。
Then, the three-degree-of-freedom attitude system model in the first step is converted into a control-oriented multichannel model as follows:
the pitch channel model is:
，
the yaw passage model is as follows:
，
the rolling channel model is as follows:
，
third, designing a finite time disturbance observer, comprising:
based on pitching channel modelDesigning a finite time interference observer as follows:
，
wherein ,representing status->Estimated value of ∈10->Representation model uncertainty +.>Estimated value of ∈10-> and />Respectively-> and />First derivative of time, +.>、/>、/>For interfering observer->Gain of->A representative sign function defined as: for a real number y of the number,
，
yaw channel modelDesigning a finite time interference observer as follows:
，
wherein ,representing status->Estimated value of ∈10->Representation model uncertainty +.>Estimated value of ∈10-> and />Respectively-> and />Time first derivative>、/>、/>For interfering observer->Is a gain of (2);
rolling channel modelDesigning a finite time interference observer as follows:
，
wherein ,representation ofStatus->Estimated value of ∈10->Representation model uncertainty +.>Estimated value of ∈10-> and />Respectively-> and />First derivative of time, +.>、/>、/>For interfering observer->Is provided.
In this embodiment, the gain of the interference observer is taken as、/>、/>、、/>、/>、/>、/>、/>。
Fourth, designing an extended state observer based on a self-learning mechanism, comprising:
based on pitching channel modelThe design of the expansion state observer is as follows:
，
wherein , and />Respectively indicate status->Total interference->Estimated value of ∈10-> and />Respectively is and />First derivative of time, +.>For observer->Bandwidth of->For control surface efficiency->The self-learning mechanism of the on-line estimation is designed as follows:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is set, and the design parameters of (a) are set.
Yaw channel modelThe design of the expansion state observer is as follows:
，
wherein , and />Respectively indicate status->Total interference->Estimated value of ∈10-> and />Respectively-> and />First derivative of time, +.>For observer->Bandwidth of->For control surface efficiency->The self-learning mechanism of the on-line estimation is designed as follows:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is set, and the design parameters of (a) are set.
Rolling channel modelThe design of the expansion state observer is as follows: />
，
wherein , and />Respectively indicate status->Total interference->Estimated value of ∈10-> and />Respectively-> and />First derivative of time, +.>For observer->Bandwidth of->For control surface efficiency->The self-learning mechanism of the on-line estimation is designed as follows:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is set, and the design parameters of (a) are set.
In this embodiment, the bandwidths of the extended state observer are respectively taken as、/>、The design parameters of the self-learning mechanism are respectively taken as +.>、/>、/>、、/>、/>。
Fifthly, designing a hypersonic aircraft strong disturbance rejection attitude control law based on a self-learning mechanism, which comprises the following steps:
observer based on finite time interference、/>、/>Extended state observer->、/>、/>And self-learning mechanisms->、/>、/>Estimate of +.>、/>、/>、/>、/>、/>、/>、/>、/>Model uncertainty in the attitude System of an aircraft>、/>、/>Lumped interference->、/>、/>And control surface efficiency->、/>、/>And carrying out online prediction. The following variables are defined:
，
，
，
wherein ,、/>、/>respectively indicate->、/>、/>Reference instruction of->、/>、/>Respectively->、/>、/>First derivative of time, +.>、/>、/>Is a design parameter.
Observer based on expansion stateThe output design pitch channel attitude controller of (1) is:
，
wherein ,、/>、/>for controller->Design parameters of->Is->The self-learning mechanism of the on-line estimation is designed as follows:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is set, and the design parameters of (a) are set.
Based on observerThe output design yaw path attitude controller of (1) is: />
，
wherein ,、/>、/>for controller->Design parameters of->Is->Is provided with a self-learning mechanismThe method comprises the following steps:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is set, and the design parameters of (a) are set.
Based on observerThe output design roll channel attitude controller of (a) is:
，
wherein ,、/>、/>for controller->Design parameters of->Is->The self-learning mechanism of the on-line estimation is designed as follows:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is set, and the design parameters of (a) are set.
In this embodiment, the controller parameters are respectively taken as、/>、/>、/>、/>、、/>、/>、/>、/>、/>、/>、/>、、/>、/>、/>、/>。
What is not described in detail in the present specification belongs to the prior art known to those skilled in the art.
Claims (6)
1. The hypersonic aircraft strong disturbance rejection control method based on disturbance prediction is characterized by comprising the following steps of:
firstly, building an aircraft three-degree-of-freedom attitude system model containing model uncertainty, atmospheric turbulence and multisource interference of pneumatic control surface degradation according to attitude dynamics characteristics of a hypersonic aircraft;
secondly, decomposing the hypersonic aircraft into three subsystem models of pitching, yawing and rolling based on a hypersonic aircraft three-degree-of-freedom attitude system model, and establishing a hypersonic aircraft equivalent model facing control;
thirdly, designing a finite time disturbance observer based on a hypersonic aircraft equivalent model facing control, and estimating model uncertainty;
step four, designing a self-learning mechanism and an extended state observer based on a hypersonic aircraft equivalent model facing control, and estimating lumped items of control surface efficiency and other interference;
and fifthly, designing a strong anti-interference controller based on a self-learning mechanism according to the model uncertainty, the control surface efficiency and the estimation value of lumped interference, and completing the design of a hypersonic aircraft strong anti-interference control method based on interference prediction.
2. The hypersonic aircraft strong immunity control method based on interference prediction according to claim 1, wherein in the first step, the established three-degree-of-freedom attitude system model of the aircraft is:
，
，
，
wherein ,、/>、/>respectively representing attack angle, sideslip angle and roll angle; />、/>、/>Respectively representing the rolling angle rate, the pitch angle rate and the yaw angle rate; />、/>、/>Representing uncertainty in the coupling of the aircraft centroid kinematic state into the pose; />、/>Respectively representing the track inclination angle and the track deflection angle; />、/>、/>、/>、/>、/>、/>、/>Respectively->、/>、/>、/>、/>、/>、/>、/>First derivative with respect to time; />、/>、/>Representing the rotational inertia of the aircraft body around the three axes of the machine body coordinate system; />、/>、/>Representing disturbance moment caused by external disturbance; />、/>、/>Respectively representing roll moment, pitch moment and yaw moment, wherein the specific expression is as follows:
，
in the above-mentioned method, the step of,for pneumatic reference area->For wing extension, ->For average aerodynamic chord length +.>For aircraft speed, +.>Is of atmospheric density>、/>、/>Respectively rolling, pitching and yawing moment coefficients;
considering control surface degradation and ignoring terms with smaller influence, the aerodynamic moment coefficient polynomial fitting model of the aircraft is as follows:
，
in the above-mentioned method, the step of,、/>、/>is the deflection angle of the triaxial rudder>、/>、/>、/>、/>、/>、/>、/>、/>、/>Fitting parameters for aerodynamic moment coefficients, +.>、/>、/>Represents the degradation factor of the triaxial control surface and satisfies +.>，，/>。
3. The hypersonic aircraft strong immunity control method based on interference prediction as set forth in claim 2, wherein the second step includes:
the following variables are defined:
，
，
，
in the above-mentioned method, the step of,、/>、/>representing the efficiency of the triaxial control surface>、/>、/>、/>、/>、/>Representing the state of the gesture system->、/>、/>The expansion state of the channel attitude subsystem is pitch, yaw and roll, and comprises coupling among channels, external interference moment and other interference, and the expansion state is regarded as lumped interference; in combination with the physical properties of the aircraft, assume an expanded state +.>、/>、/>The rate of change is bounded, i.e.)>、/>、/>First derivative of time>、/>、/>Presence, defined as->，/>，/>；
The three-degree-of-freedom attitude system model in the first step is converted into a control-oriented multichannel model as follows:
the pitch channel model is:
，
the yaw passage model is as follows:
，
the rolling channel model is as follows:
。
4. a hypersonic aircraft strong immunity control method based on interference prediction as set forth in claim 3, wherein the third step includes:
based on pitching channel modelDesigning a finite time interference observer as follows:
，
wherein ,representing status->Estimated value of ∈10->Representation model uncertainty +.>Estimated value of ∈10-> and />Respectively is and />First derivative of time, +.>、/>、/>For interfering observer->Gain of->A representative sign function defined as: for a real number y of the number,
，
yaw channel modelDesigning a finite time interference observer as follows:
，
wherein ,representing status->Estimated value of ∈10->Representation model uncertainty +.>Estimated value of ∈10-> and />Respectively is and />Time first derivative>、/>、/>For interfering observer->Is a gain of (2);
rolling channel modelDesigning a finite time interference observer as follows:
，
wherein ,representing status->Estimated value of ∈10->Representation model uncertainty +.>Estimated value of ∈10-> and />Respectively is and />First derivative of time, +.>、/>、/>For interfering observer->Is provided.
5. The hypersonic aircraft strong immunity control method based on interference prediction as set forth in claim 4, wherein the fourth step includes:
based on pitching channel modelThe design of the expansion state observer is as follows:
，
wherein , and />Respectively indicate status->Total interference->Estimated value of ∈10-> and />Respectively->Andfirst derivative of time, +.>For observer->Bandwidth of->For control surface efficiency->The self-learning mechanism of the on-line estimation is designed as follows:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is a design parameter of (1);
yaw channel modelThe design of the expansion state observer is as follows:
，
wherein , and />Respectively indicate status->Total interference->Estimated value of ∈10-> and />Respectively is and />First derivative of time, +.>For observer->Bandwidth of->For control surface efficiency->The self-learning mechanism of the on-line estimation is designed as follows:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is a design parameter of (1);
rolling channel modelThe design of the expansion state observer is as follows:
，
wherein , and />Respectively indicate status->Total interference->Estimated value of ∈10-> and />Respectively is and />First derivative of time, +.>For observer->Bandwidth of->For control surface efficiency->The self-learning mechanism of the on-line estimation is designed as follows:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is set, and the design parameters of (a) are set.
6. The hypersonic aircraft strong immunity control method based on interference prediction as set forth in claim 5, wherein the fifth step includes:
observer based on finite time interference、/>、/>Extended state observer->、/>、/>And self-learning mechanisms、/>、/>Estimate of +.>、/>、/>、/>、/>、/>、/>、/>、/>Model uncertainty in the attitude System of an aircraft>、/>、/>Lumped interference->、/>、/>And control surface efficiency->、/>、/>Performing online prediction; the following variables are defined:
，
，
，
wherein ,、/>、/>respectively indicate->、/>、/>Reference instruction of->、/>、/>Respectively->、/>、/>First derivative of time, +.>、/>、/>Is a design parameter;
observer based on expansion stateThe output design pitch channel attitude controller of (1) is:
，
wherein ,、/>、/>for controller->Design parameters of->Is->The self-learning mechanism of the on-line estimation is designed as follows:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is a design parameter of (1);
based on observerThe output design yaw path attitude controller of (1) is:
，
wherein ,、/>、/>for controller->Design parameters of->Is->The self-learning mechanism of the on-line estimation is designed as follows:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is a design parameter of (1);
based on observerThe output design roll channel attitude controller of (a) is:
，
wherein ,、/>、/>for controller->Design parameters of->Is->The self-learning mechanism of the on-line estimation is designed as follows:
，
in the formula ,is->First derivative of time, +.> and />Is self-learning mechanism->Is set, and the design parameters of (a) are set.
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