CN104597911A

CN104597911A - Adaptive optimal butt joint trajectory tracking flying control method for air refueling receiving machine

Info

Publication number: CN104597911A
Application number: CN201410712667.4A
Authority: CN
Inventors: 袁锁中; 甄子洋; 张进; 龚全铨
Original assignee: Nanjing University of Aeronautics and Astronautics
Current assignee: Nanjing University of Aeronautics and Astronautics
Priority date: 2014-11-28
Filing date: 2014-11-28
Publication date: 2015-05-06

Abstract

The invention relates to an adaptive optimal butt joint trajectory tracking flying control method for an air refueling receiving machine. The method comprises a receiver model calculation module, an uncertainty interference neural network identifier module, a tracking trajectory generation module, a state estimation module, an adaptive updating law module, an adaptive control law module and an optimal control law module and further comprises a signal acquisition sensor, an arithmetic unit for calculation and a storage. A projection operator performs adaptive updating and estimation on control parameters of an adaptive system, adaptive updating and estimation are performed on parameters of an adaptive control algorithm at the next moment, and accordingly, zero generalized errors and accurate butt joint of the receiver and a refueling tapered sleeve are achieved.

Description

Self-adaptive optimal butt joint trajectory tracking flight control method for aerial refueling oil receiving machine

Technical Field

The invention relates to the technical field of flight control of aircrafts, in particular to a flight control method for self-adaptive optimal butt joint trajectory tracking of an aerial oiling machine and an oil receiving machine.

Background

The air refueling technology is generalized to a flight technology that one aircraft refuels another aircraft or several aircraft in the air, and the flight duration is prolonged and the flight range is increased. Under the attack and defense requirements of the modern air combat middle and large fields, the air refueling technology can greatly increase the fighting capacity of the aircraft participating in the battle, and gradually becomes one of the important development directions for increasing national defense and military forces of unmanned aerial vehicles, reconnaissance aircraft, fighters and the like and the basic functions of the modern military aircraft in all countries. At present, the air refueling technology has gradually become an indispensable air backup platform due to the outstanding advantages and important military values, and changes some once unimaginable combat tasks in modern war into reality. It is shown from the relevant data that airborne fueling technology has become more widely used in high-tech local wars that have occurred in recent years.

The aircrafts participating in the air refueling operation are divided into two types of oiling machines and oil receiving machines, and the research and development requirements of the oiling machines are brought by the rapid development of the air refueling technology. The aerial oiling machine is used as an aerial oiling support platform and is an airplane which is specially used for loading fuel oil and providing oiling operation. And the oil receiving machine is mainly responsible for tracking the oiling machine in the air oiling process, is accurately butted with the oiling equipment of the oiling machine and receives the fuel oil conveyed by the oiling machine, so that the voyage of the oil receiving machine is increased, and the dead time of the oil receiving machine is prolonged.

In the process of butt joint and fuel oil delivery by the close distance of the oil dispenser and the oil filling taper sleeve, except the influence of atmospheric turbulence, uncertain factors of parameters such as the trailing vortex action and control failure of the oil dispenser cannot be ignored. Because an accurate oil receiving machine model is difficult to establish, the anti-interference capability, the dynamic performance and the robust performance of an oil receiving machine control system need to be improved urgently, the quick self-adaptive capability of the oil receiving machine control system is improved, the oil receiving machine can stably track the docking guidance law, meanwhile, the expected transient response is achieved, the performance of a closed-loop system is ensured, and the requirement on stability is met.

Disclosure of Invention

In the process of close-range docking and fuel oil conveying of the oil receiving machine and the oil filling taper sleeve, the air oil filling docking and tracking maintenance are influenced by uncertain dynamic interferences of the oil receiving machine, such as atmospheric turbulence, oil filling wake vortex interference, failure of an actuating mechanism and the like, the quick self-adaption capability of the oil receiving machine control system is poor, expected transient response cannot be achieved while tracking docking guidance law is carried out on the oil receiving machine, and the requirements on the performance and the stability of a closed-loop system are difficult to guarantee. The present invention is directed to solve the above problems of the prior art, and provides the following technical solutions.

The technical scheme of the invention provides a self-adaptive optimal butt joint trajectory tracking flight control method for an aerial refueling and oil receiving machine, which is characterized by comprising the following steps of:

the oil receiving machine model calculation module is used for storing pneumatic parameters of the oil receiving machine and generating a linear model system matrix signal and a control matrix signal of the oil receiving machine, the linear model system matrix signal and the control matrix signal are sent to the optimal control module, and the linear model system matrix signal and the control matrix signal are sent to the state estimation module;

the sensor is used for acquiring flight state signals of the aerial oil receiving machine and sending the acquired flight state signals to the optimal control law module, the uncertain disturbance neural network identifier module, the self-adaptive updating law module and the self-adaptive control law module;

the uncertain interference neural network identifier module generates uncertain interference signals of the oil receiving machine system and sends the uncertain interference signals to the state estimation module;

the tracking track generating module is used for generating an ideal flight track signal of the oil receiver, wherein an oil receiving plug of the oil receiver is in butt joint with a refueling taper sleeve of the oiling machine, and sending the ideal flight track signal to the optimal control law module and the state estimation module;

the state estimation module generates an interference estimation value signal of the oil receiving machine and sends the interference estimation value signal to the self-adaptive control law module; generating a flight state estimator signal of the oil receiving machine, and sending a flight state error signal obtained by carrying out operation processing on the flight state estimator signal and a flight state signal acquired by a sensor to the self-adaptive updating law module;

the self-adaptive updating law module generates a flight state control parameter estimation value signal of the oil receiving machine and sends the flight state control parameter estimation value signal to the self-adaptive control law module and the state estimation module;

the self-adaptive control law module generates a self-adaptive control quantity signal of the oil receiving machine and sends the signal to the state estimation module and the self-adaptive updating law module;

and the optimal control law module generates an optimal control quantity signal of the oil receiving machine and sends the optimal control quantity signal to the state estimation module, and the optimal control quantity signal and the self-adaptive control quantity signal output by the self-adaptive control law module are synthesized and then output to the control surface controller of the oil receiving machine.

The invention also provides a further improved technical scheme as follows.

Further, the oil receiver model calculation module comprises the following equation:

the oil receiving machine is simplified into a decoupled six-dimensional freedom aircraft model, the motion equation of the model is divided into three subsystems, and the model is described as follows in the form of a state equation:

X_i(0)＝X_i0，

Y_{i} (t) = c_{i}^{T} X_{i} (t),

wherein i is 1, 2, 3;and Λ_l≤Λ_i≤Λ_u；Representing the uncertain disturbance variable signal of the tail vortex of the oiling machine. Analyzing the data acted on the oil receiver in the wind tunnel test of the oil feeder to obtain the uncertain disturbance delta of the tail vortex of the oil feeder_i(Y) sample values of the rate of change, and fitting by an uncertain disturbance neural network identifier module;

the input quantity X (t) and the output quantity Y (t) of the control external circuit of the oil receiving machine are respectively as follows:

X (t) = {[\begin{matrix} X_{1}^{T} & X_{2}^{T} & X_{3}^{T} (t) \end{matrix}]}^{T},

Y(t)＝[Y₁(t) Y₂(t) Y₃(t)]^T＝[l(t) h(t) y(t)]^T，

wherein, the first subsystem uses the thrust input of the engine of the oil receiver to control the horizontal distance between the oil receiver and the oil filling taper sleeve, and the state vector, the system state matrix and the control input are respectively:

X_{1} = [\begin{matrix} l \\ V \end{matrix}],

wherein l is the forward distance of the oil receiver relative to the oiling machine, V is the flying speed of the oil receiver,_Tis the throttle input quantity of the oil receiving machine,is the pneumatic parameter of the oil receiver.

The second subsystem uses the elevator input of the oil receiver to control the vertical distance between the oil receiver and the oil filling taper sleeve, and the state vector, the system state matrix and the control input of the second subsystem are respectively

X₂＝[α θ q h]^T

Wherein alpha is the attack angle of the oil receiver, theta is the pitch angle of the oil receiver, q is the pitch angle rate of the oil receiver, h is the relative height between the oil receiver and the oiling machine,_eis the elevator input. Z_α，M′_α，M′_q，Is the pneumatic parameter of the oil receiving machine.

The third subsystem uses the flap input of the oil receiver to control the lateral distance between the oil receiver and the oil filling taper sleeve, and the state vector, the system matrix and the control input of the third subsystem are respectively

X₃＝[φ β p r y]^T

B_{3} = [\begin{matrix} 0 \\ 0 \\ 1 \\ 0 \\ 0 \end{matrix}],

C_{3} = [\begin{matrix} 0 \\ 0 \\ 0 \\ 0 \\ 1 \end{matrix}],

In the formula, beta is the side slip angle of the oil receiver, phi is the rolling angle of the oil receiver, and p and r are the axial angular rate of the oil receiver body;_αthe input quantity of the ailerons of the oil receiving machine is the input quantity of the ailerons of the oil receiving machine; y is_(.)，L_(.)，N_(.)，M_(.)Is the pneumatic parameter of the oil receiving machine. Theta₀，γ₀Is a reference pitch angle and a track angle V of an oil receiver₀The reference speed of the oil receiving machine is g, and the gravity acceleration is g.

Further, the uncertain disturbance neural network identifier module comprises the following equations:

uncertain interference delta to oil receiving machine system by adopting Radial Basis Function (RBF) neural network_i(Y) fitting to obtain a fitted equivalent delta_i(Y) is Δ_i(Y)＝w_i ^TΦ_i(Y)+_i(Y)，

||_i(Y)||≤_i ^*，

Y∈D_i，

Wherein phi_i(Y) represents p_iA x 1-dimensional vector, which is a gaussian distribution function matrix of output Y;representing an unknown constant weight matrix, and performing update estimation through a self-adaptive update law module;_i ^*representative set D_iA uniformly bounded approximation error;

gaussian distribution function matrix phi_i(Y) the expression of the ith element is

In the formula, the parameter κ_iAnd upsilon_iRepresenting the center and width of a predefined neural network, is selected by analyzing the fuel dispenser's wake vortex effect sample results.

Further, the tracking trajectory generation module includes the following equations:

the relative position of the distance between the oil filling taper sleeve and the oil receiving plug in the inertial space at the initial moment is (X)_d，Y_d，Z_d) Requiring the oil receiver to be at t_fCompleting the butt joint within time; setting an initial displacement ofThe final required position is (0, 0, 0), namely the butt joint of the oil receiving plug and the oil filling taper sleeve is realized, and the reference trajectory equation is

x_ref(t)＝f(t)α_x，

y_ref(t)＝f(t)α_y，

z_ref(t)＝f(t)α_z，

Wherein f (t) t ≡ t⁴ t⁵ t⁶ t⁷]，

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The reference trajectory is divided into two phases: the first stage is at t₁Finish Y by oil receiving machine within time_N，Z_NCorrecting the deviation in the direction to enable the oil receiving plug to be aligned to the oil filling taper sleeve in the flight direction; and the second stage eliminates the relative distance difference of the oil receiving machine in the flight direction, so that the oil receiving plug is butted with the oil filling taper sleeve.

Further, the state estimation module is used for dynamically estimating the state of the oil receiving machine, the corresponding dynamic response is the expected dynamic response of the oil receiving machine, and the state equation is

\hat{y} (t) = c^{T} \hat{x} (t),

\hat{x} (0) = x_{0},

Wherein,representing the adaptive parameter estimate.

Further, the adaptive update law module includes the following equations:

<math> <mrow> <msub> <mover> <mover> <mi>Λ</mi> <mo>^</mo> </mover> <mo>·</mo> </mover> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>ΓProj</mi> <mo>[</mo> <msub> <mover> <mi>Λ</mi> <mo>^</mo> </mover> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <mo>-</mo> <msub> <mi>u</mi> <msub> <mi>ad</mi> <mi>i</mi> </msub> </msub> <msubsup> <mover> <mi>x</mi> <mo>~</mo> </mover> <mi>i</mi> <mi>T</mi> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <msub> <mi>PB</mi> <mi>i</mi> </msub> <mo>]</mo> <mo>,</mo> <msub> <mover> <mi>Λ</mi> <mo>^</mo> </mover> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mn>0</mn> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mover> <mi>Λ</mi> <mo>^</mo> </mover> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> <mo>,</mo> </mrow> </math>

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<math> <mrow> <msubsup> <mover> <mover> <mi>w</mi> <mo>^</mo> </mover> <mo>·</mo> </mover> <mi>i</mi> <mi>T</mi> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>ΓProj</mi> <mo>[</mo> <msubsup> <mover> <mi>w</mi> <mo>^</mo> </mover> <mi>i</mi> <mi>T</mi> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <mo>-</mo> <msub> <mi>Φ</mi> <mi>i</mi> </msub> <mo>[</mo> <mi>Y</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>]</mo> <msubsup> <mover> <mi>x</mi> <mo>~</mo> </mover> <mi>i</mi> <mi>T</mi> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <msub> <mi>PB</mi> <mi>i</mi> </msub> <mo>]</mo> <mo>,</mo> <msubsup> <mover> <mi>w</mi> <mo>^</mo> </mover> <mi>i</mi> <mi>T</mi> </msubsup> <mrow> <mo>(</mo> <mn>0</mn> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mover> <mi>w</mi> <mo>^</mo> </mover> <mrow> <mi>i</mi> <mn>0</mn> </mrow> <mi>T</mi> </msubsup> <mo>,</mo> </mrow> </math>

wherein,for the system tracking error, the self-adaptive rate is represented by more than 0 and can be set to a larger value, so that the quick self-adaptive performance of the system is ensured; p ═ P^TThe expression of Lyapunov equation > 0And Q is_iIs greater than 0; and Proj (·, ·) is a projection operator.

Further, the adaptive control law module comprises the following equation:

adaptive control quantityThe expression is

u_{{ad}_{i}} (s) = - k_{i} D_{i} (s) r_{u_{i}} (s),

Wherein k is_i> 0 represents the adaptive system feedback gain;representing an input signalIs expressed as

Transfer function D_i(s) ensuring a closed-loop equivalent transfer function C_i(s) strictly homeostatic, C_i(s) is

Wherein i is 1, 2, 3; and a low-pass gain C_i(0)＝1。

Further, the optimal control law module comprises the following equations:

X_i(0)＝X_i0，

Y_{i} (t) = c_{i}^{T} X_{i} (t),

wherein i is 1, 2, 3;

introducing integral error variables

<math> <mrow> <msub> <mi>y</mi> <mi>I</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mi>t</mi> </msubsup> <mo>[</mo> <msub> <mi>Y</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>τ</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>Y</mi> <mrow> <mi>cm</mi> <msub> <mi>d</mi> <mi>i</mi> </msub> </mrow> </msub> <mrow> <mo>(</mo> <mi>τ</mi> <mo>)</mo> </mrow> <mo>]</mo> <mi>dτ</mi> </mrow> </math>

Obtaining the dynamic equation of the system after the augmentation

Y_{i} (t) = c_{i}^{T} X_{i} (t),

Defining a cost functionBy selecting Q_iAnd R_iSolving the Ricitti equation to obtain the optimal control quantity

u_{lqri} = K_{i} [\begin{matrix} X_{i} (t) \\ y_{I} (t) \end{matrix}] = [\begin{matrix} - k_{P_{i}} & - k_{I_{i}} \end{matrix}] [\begin{matrix} X_{i} (t) \\ y_{I} (t) \end{matrix}] .

The tracking flight control method is applied to the butt joint track tracking flight control of the air refueling oil receiving machine, establishes an accurate oil receiving machine model, realizes the air refueling butt joint and tracking maintenance of the oil receiving machine under the uncertain dynamic interference conditions of atmospheric turbulence, refueling wake vortex interference, actuator failure and the like, and ensures that a control system has quick self-adaptive response capability.

Advantageous effects

1) The design of the ideal butt joint tracking track ensures that the reference flight track is feasible, the control saturation of a control surface is avoided, the flight track of the oil receiving machine is smooth, and the oil receiving machine can smoothly approach the oil filling taper sleeve and butt joint.

2) An appropriate feedback gain matrix is selected in the optimal control law, so that the closed-loop system can be ensured to have expected dynamic response characteristics and stability under the interference of oiling wake vortexes and the like; and when the dynamic changes of the oiling wake vortex and the like exist and uncertain interferences of failure of an actuating mechanism and the like exist, the self-adaptive control signal is not zero, and the parameters of the self-adaptive controller are continuously updated on line by a self-adaptive law, so that the error signal of the system is converged to zero under the control of the self-adaptive control signal.

3) Aiming at uncertain interference factors in the butt joint tracking process, the butt joint tracking control system of the oil receiving machine is designed by adopting a self-adaptive control method, the performances of air refueling butt joint and tracking maintenance of the oil receiving machine under the conditions of uncertain interference, failure of an actuating mechanism and the like are improved, the dynamic performance, the self-adaptive updating rate and the robust performance of the system are improved, and the method is suitable for the autonomous air refueling butt joint tracking control process under large dynamic interference.

Drawings

FIG. 1 is a schematic diagram of a self-adaptive optimal docking trajectory tracking control method of a fuel receiving machine.

Fig. 2 shows a reference butt joint trajectory curve of the invention and an actual butt joint trajectory curve of an oil receiving machine.

FIG. 3 shows a trace of the butt joint of the oil receiving machine and the oil filling taper sleeve.

Detailed Description

In order to clarify the technical solution and technical object of the present invention, the present invention will be further described with reference to the accompanying drawings and the detailed description.

The design idea of the adaptive control algorithm is that a state estimation module is adopted to replace a fixed reference model, so that the state estimation module can better represent the expected dynamic performance of the oil receiving machine under the actual docking condition. The control parameters of the self-adaptive system are adaptively updated and estimated by introducing a projection operator, the generated self-adaptive signals are filtered after passing through a low-pass filter to obtain corresponding self-adaptive signal increments, the increments are simultaneously acted on the oil receiving machine and a corresponding state estimation module, and then the parameters of the self-adaptive control algorithm at the next control moment are adaptively updated and estimated according to the errors of the self-adaptive signal increments and the actual state quantity of the oil receiving machine, so that the control targets that the generalized error is zero and the oil receiving machine and an oil filling taper sleeve are accurately butted and tracked are finally realized.

The adaptive control method has many advantages: (1) the method has quick self-adaptive capacity and is only limited by the computing speed of system hardware; (2) the self-adaptive capacity and the robustness of the control system can be decoupled; (3) the system is ensured to have good transient performance; (4) the transient response with uniform boundary is not affected by initial conditions and changes of unknown parameter values.

As shown in fig. 1, the adaptive optimal docking trajectory tracking flight control method for an oil receiving machine for airborne refueling of the invention includes an oil receiving machine model calculation module, an uncertain disturbance neural network identifier module, a tracking trajectory generation module, a state estimation module, an adaptive update law module, an adaptive control law module and an optimal control law module, and further includes a sensor for signal acquisition, and an arithmetic unit and a memory for calculation processing. The oil receiving machine model calculation module is used for storing pneumatic parameters of the oil receiving machine and generating a linear model system matrix signal and a control matrix signal of the oil receiving machine, the linear model system matrix signal and the control matrix signal are sent to the optimal control module, and the linear model system matrix signal and the control matrix signal are sent to the state estimation module; the sensor is used for acquiring flight state signals of the aerial oil receiving machine and sending the acquired flight state signals to the optimal control law module, the uncertain disturbance neural network identifier module, the self-adaptive updating law module and the self-adaptive control law module; the uncertain interference neural network identifier module generates uncertain interference signals of the oil receiving machine system and sends the uncertain interference signals to the state estimation module; the tracking track generating module is used for generating an ideal flight track signal of the oil receiver, wherein an oil receiving plug of the oil receiver is in butt joint with a refueling taper sleeve of the oiling machine, and sending the ideal flight track signal to the optimal control law module and the state estimation module; the state estimation module generates an interference estimation value signal of the oil receiving machine and sends the interference estimation value signal to the self-adaptive control law module; generating a flight state estimator signal of the oil receiving machine, and sending a flight state error signal obtained by carrying out operation processing on the flight state estimator signal and a flight state signal acquired by a sensor to the self-adaptive updating law module; the self-adaptive updating law module generates a control parameter estimation value signal of the flight state of the oil receiving machine and sends the control parameter estimation value signal to the self-adaptive control law module and the state estimation module; and the self-adaptive control law module generates a self-adaptive control quantity signal of the oil receiver, sends the self-adaptive control quantity signal to the state estimation module and the self-adaptive update law module optimal control law module, generates an oil receiver optimal control quantity signal, and sends the oil receiver optimal control quantity signal to the state estimation module, wherein the optimal control quantity signal is synthesized with the self-adaptive control quantity signal output by the self-adaptive control law module and then is output to the oil receiver control surface controller.

(1) Receiving oil machine model calculation module

In order to carry out the design work of the adaptive control algorithm, an oil receiver is regarded as a decoupled six-degree-of-freedom airplane model, a motion equation of the oil receiver is divided into three subsystems, and the three subsystems are described as follows in the form of a state equation:

X_i(0)＝X_i0，

Y_{i} (t) = c_{i}^{T} X_{i} (t),

wherein i is 1, 2, 3;and Λ_l≤Λ_i≤Λ_u；Representing the uncertain disturbance variable signal of the tail vortex of the oiling machine. Analyzing the data acted on the oil receiver in the wind tunnel test of the oil feeder to obtain the uncertain disturbance delta of the tail vortex of the oil feeder_i(Y) sample values of the rate of change and fitting by the uncertain disturbance neural network identifier module.

X (t) = {[\begin{matrix} X_{1}^{T} & X_{2}^{T} & X_{3}^{T} (t) \end{matrix}]}^{T},

Y(t)＝[Y₁(t) Y₂(t) Y₃(t)]^T＝[l(t) h(t) y(t)]^T，

X_{1} = [\begin{matrix} l \\ V \end{matrix}],

X₂＝[α θ q h]^T

Wherein alpha is the attack angle of the oil receiver, theta is the pitch angle of the oil receiver, q is the pitch angle rate of the oil receiver, h is the relative height between the oil receiver and the oiling machine,_eis input into the elevator of the oil receiving machine. Z_α，M′_α，M′_q，Is the pneumatic parameter of the oil receiving machine.

X₃＝[φ β p r y]^T

B_{3} = [\begin{matrix} 0 \\ 0 \\ 1 \\ 0 \\ 0 \end{matrix}],

C_{3} = [\begin{matrix} 0 \\ 0 \\ 0 \\ 0 \\ 1 \end{matrix}],

In the formulaBeta is the side slip angle of the oil receiver, phi is the rolling angle of the oil receiver, and p and r are the axial angular rate of the oil receiver body;_αthe input quantity of the ailerons of the oil receiving machine is the input quantity of the ailerons of the oil receiving machine; y is_(.)，L_(.)，N_(.)，M_(.)Is the pneumatic parameter of the oil receiving machine. Theta₀，γ₀Is a reference pitch angle and a track angle V of an oil receiver₀The reference speed of the oil receiving machine is g, and the gravity acceleration is g.

When the input signal of the oil receiving machine system isThe feedback parameter matrix of the LQR track controller is K_iThen, the state equation of the ith subsystem is:

Y_{i} (t) = c_{i}^{T} x_{i} (t)

wherein, i is 1, 2, 3,representing the reference docking track input of the oil receiver,Δ_i[Y(t)]represents an uncertain dynamic disturbance of the system; lambda_iRepresenting the effective parameter value of the oil receiving machine actuator, when the non-control fails, the lambda_i1. On the basis of the outer loop LQR control, it is assumed thatThe state equation of the oil receiver can be modified as follows:

Y(t)＝c^Tx(t)

wherein k is_gRepresents the corresponding reference input matrix and is,

(2) uncertain interference neural network identifier module

Uncertain dynamic interference delta for oil receiving machine system_i(Y) recognition using neural networksThe device module performs approximation and dynamic fitting, selects a Radial Basis Function (RBF) neural network model, and approximates the uncertain interference of the actual docking process with high precision. The network is divided into three layers, wherein the first layer is an input layer consisting of signal source nodes and only transmits signals; the second layer is a hidden layer, the transformation function of the hidden unit is an RBF function, a nonlinear optimization strategy is adopted, and the learning speed is low; the third layer is an output layer, responds to the action of an input mode, adjusts linear weight, adopts a linear optimization strategy and has higher learning speed. The parameters solved in the modeling learning algorithm are as follows: radial basis function center, variance, and weight from hidden layer to output layer. If the expected output value of the sample is d, the total number of the samples is P, the number of nodes of the hidden layer is h, and the Gaussian function is a radial basis function, the activation function is

Wherein the parameter c_iAnd σ_iRepresenting a predefined spiritThe result of the influence of the oiling wake vortex is selected through the center and width of the network.

Uncertain interference delta to oil receiving machine system by adopting Radial Basis Function (RBF) neural network_i(Y) fitting to obtain a fitted equivalent delta_i(Y) is

Δ(Y)＝w_i ^TΦ_i(Y)+_i(Y)，

||_i(Y)||≤_i ^*，

Y∈D_i，

Uncertain oiling wake vortex interference delta in control algorithm design_iThe fitting result of (Y) generally affects the performance of the control algorithm, and therefore the fitting range and requirements of the neural network need to be given. Assume a set of fitting errors D based on radial basis function neural network distribution_iComprises the following steps:

wherein σ_iThe value > 0 represents an arbitrary small normal number, and the other three parameter values respectively represent the transient tracking error boundary and uncertainty of the reference signalAn error bound for the interference factor fit and a tracking error bound for the closed-loop reference system.

When designing the adaptive control algorithm, the fitting error range of the neural network belongs to the set D_iIn the process, the uncertain interference fitting of the system to the oiling wake vortex can be considered to be accurate and can be used as one of reference inputs of the control signal.

(3) State estimation module

The state estimation module is used for dynamically estimating the state of the oil receiver, the corresponding dynamic response is the expected dynamic response of the oil receiver, and the state equation is

\hat{y} (t) = c^{T} \hat{x} (t),

\hat{x} (0) = x_{0},

Wherein,representing the estimated value of the adaptive parameter, and continuously calculating and updating by an adaptive updating law module in the adaptive control calculation to obtain the value.

(4) Adaptive update law module

Defining the state quantity tracking error of the system as

\tilde{x} (t) = \hat{x} (t) - x (t)

The projection operator is introduced to carry out self-adaptive updating on the adjustable mechanism parameters, and the self-adaptive law of the estimated values of the adjustable system parameters comprises the following equation

Wherein, the adaptive rate is more than 0 and can be set to a larger value, thereby ensuring the quick adaptive performance of the system; p ═ P^TThe expression of Lyapunov equation > 0And Q is_iIs greater than 0; proj (·, ·) represents the projection operators for both.

(5) Adaptive control law module

The output quantity of the adjustable controller becomes an adaptive control signal u input to the oil receiving machine after being filtered by a low-pass filter_adThe adaptive control signals of each subsystem are designed as follows:

adaptive control outputThe expression is

u_{{ad}_{i}} (s) = - k_{i} D_{i} (s) r_{u_{i}} (s),

Wherein i is 1, 2, 3; low pass gain C_i(0)＝1；Λ_iRepresenting efficiency of each subsystem actuator, during normal operation_i1. By adding a low-pass filtering system C in the feedback path_i(s) capable of attenuating high-frequency oscillation generated in the control signal.

(6) Optimal control law module

In the design of an outer loop control system, a control algorithm based on a state feedback Linear Quadratic Regulator (LQR) is used for ensuring the basic stability of an oil engine trajectory control system under certain interference of oiling wake vortexes.

When the interference of uncertainty factor is zero, i.e.' A_i1 and Δ_iWhen 0, the subsystem state equation is

X_i(0)＝X_i0，

Y_{i} (t) = c_{i}^{T} X_{i} (t),

Wherein i is 1, 2, 3;

introducing integral error variables

The above integration error is added to the state quantity of the system to obtain the dynamic characteristics of the system after the expansion as follows

Y_{i} (t) = c_{i}^{T} X_{i} (t),

Control signal u in each subsystem_iHas the following forms:

u_{i} = u_{{lin}_{i}} (t) + u_{{ad}_{i}} (t) = - k_{I_{i}} y_{I} (t) - k_{P_{i}} X_{i} (t) + u_{{ad}_{i}} (t)

wherein,a control signal representing the ith subsystem base control algorithm,representing the adaptive control signal.

When the basic control algorithm is the LQR control algorithm, the gain can be fed back by solving the state

K_{i} = - [\begin{matrix} k_{I_{i}} & k_{P_{i}} \end{matrix}]

Minimizing the cost function and defining a cost function

<math> <mrow> <msub> <mi>J</mi> <mi>i</mi> </msub> <mo>=</mo> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mo>∞</mo> </msubsup> <mo>{</mo> <mo>[</mo> <msubsup> <mi>X</mi> <mi>i</mi> <mi>T</mi> </msubsup> <msub> <mi>y</mi> <mi>I</mi> </msub> <mo>]</mo> <msub> <mi>Q</mi> <mi>i</mi> </msub> <msup> <mrow> <mo>[</mo> <msubsup> <mi>X</mi> <mi>i</mi> <mi>T</mi> </msubsup> <msub> <mi>y</mi> <mi>I</mi> </msub> <mo>]</mo> </mrow> <mi>T</mi> </msup> <mo>+</mo> <msubsup> <mi>u</mi> <msub> <mi>lin</mi> <mi>i</mi> </msub> <mn>2</mn> </msubsup> <msub> <mi>R</mi> <mi>i</mi> </msub> <mo>}</mo> <mi>dt</mi> <mo>,</mo> </mrow> </math>

Wherein Q is_iAnd R_iThe design parameters representing the LQR control algorithm in each subsystem are positive definite matrixes and meet the Riccati equation.

From this, the state equation of the closed loop system is derived as follows:

let x be_i(t)＝[X_i(t) y_i(t)]^TAnd obtaining the state equation of the augmented closed-loop system as follows:

Y_{i} (t) = c_{i}^{T} X_{i} (t)

according to the analysis of the formula, the feedback gain matrix K suitable for the target selection of the outer loop LQR track control algorithm design_iTherefore, under certain oiling wake vortex interference, the closed loop system can be ensured to have expected dynamic response characteristic and stability. When the oiling wake vortex is dynamically changed and uncertain interferences such as failure of an actuating mechanism exist, the self-adaptive control signal u_adAnd (t) is not zero, and the adjustable control algorithm parameters are continuously updated on line by a self-adaptive law, so that the error signal is converged to zero under the control of the self-adaptive control signal.

(7) Tracking track generation module

The purpose of the tracking track generation module is to generate a feasible track which can drive the plug of the oil receiving machine to be connected with the oil filling taper sleeve. Suppose the docking engine is in the time interval (t)₀，t_f) And (4) completing the process. To ensure that the reference trajectory is feasible and does not lead to control saturation of the control surface, the docking time t may be set_fAnd setting the offset as a function of the initial offset between the oil receiving machine and the oil filling taper sleeve.

The distance between the oil filling taper sleeve and the oil receiving machine plug at the initial time is in the inertial space (O)_NX_NY_NZ_N) Relative position of the lower part is (X)_d，Y_d，Z_d) Requiring the oil receiver to be at t_fAnd the butt joint is completed within time. And the reference flight track of the oil receiving machine is designed, so that the oil receiving plug of the oil receiving machine can smoothly approach the oil filling taper sleeve and be in butt joint.

According to the constraint of the state and control of the oil receiving machine, the final time t_fComprises the following steps:

<math> <mrow> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>10</mn> <mo>*</mo> <mi>ceil</mi> <mrow> <mo>(</mo> <mfrac> <mrow> <mo>|</mo> <msub> <mover> <mi>x</mi> <mo>&OverBar;</mo> </mover> <mi>d</mi> </msub> <mo>|</mo> <mo>+</mo> <mo>|</mo> <msub> <mover> <mi>y</mi> <mo>&OverBar;</mo> </mover> <mi>d</mi> </msub> <mo>|</mo> <mo>+</mo> <mo>|</mo> <msub> <mover> <mi>z</mi> <mo>&OverBar;</mo> </mover> <mi>d</mi> </msub> <mo>|</mo> </mrow> <msub> <mi>γ</mi> <mi>f</mi> </msub> </mfrac> <mo>)</mo> </mrow> </mrow> </math>

where the "ceil" function is rounding the result in parentheses to the nearest integer, and γ_fIs a design parameter which is selected so that the approach rate between the oil receiver and the cone sleeve becomes sufficiently small. The approach rate is typically less than 1.2m/s, depending on the soft refuelling equipment specifications. T since the dispenser and the receiver have the same initial speed and direction₀The initial offset of the time is a constant. The initial displacement is defined as:

<math> <mrow> <msub> <mi>x</mi> <mi>d</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mover> <mi>x</mi> <mo>&OverBar;</mo> </mover> <mi>d</mi> </msub> </mrow> </math>

<math> <mrow> <msub> <mi>y</mi> <mi>d</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mover> <mi>y</mi> <mo>&OverBar;</mo> </mover> <mi>d</mi> </msub> </mrow> </math>

<math> <mrow> <msub> <mi>z</mi> <mi>d</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mover> <mi>z</mi> <mo>&OverBar;</mo> </mover> <mi>d</mi> </msub> </mrow> </math>

the reference trajectory equation is

x_ref(t)＝f(t)α_x，

y_ref(t)＝f(t)α_y，

z_ref(t)＝f(t)α_z，

Wherein f (t) t ≡ t⁴ t⁵ t⁶ t⁷]，

α_xMay be passed through the final offsetAnd t_fThe third derivative of the velocity, acceleration and position at the moment is all zero and is determined as:

<math> <mrow> <msub> <mi>a</mi> <mi>x</mi> </msub> <mo>=</mo> <msup> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <msup> <mi>f</mi> <mo>′</mo> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <msup> <mi>f</mi> <mrow> <mo>′</mo> <mo>′</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <msup> <mi>f</mi> <mrow> <mo>′</mo> <mo>′</mo> <mo>′</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mover> <mi>x</mi> <mo>&OverBar;</mo> </mover> <mi>d</mi> </msub> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> </mtr> </mtable> </mfenced> </mrow> </math>

reference track is y_ref，z_refThe definition is as follows:

y_ref(t)＝f(t)α_y

z_ref(t)＝f(t)α_z

wherein alpha is_yAnd alpha_zIs also determined by the four coefficients at t₁The final offset of the moment, the initial zero velocity, the acceleration and the derivative of the acceleration are calculated to generate:

<math> <mrow> <msub> <mi>a</mi> <mi>y</mi> </msub> <mo>=</mo> <msup> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <msup> <mi>f</mi> <mo>′</mo> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <msup> <mi>f</mi> <mrow> <mo>′</mo> <mo>′</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <msup> <mi>f</mi> <mrow> <mo>′</mo> <mo>′</mo> <mo>′</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mover> <mi>y</mi> <mo>&OverBar;</mo> </mover> <mi>d</mi> </msub> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> <msub> <mi>a</mi> <mi>z</mi> </msub> <mo>=</mo> <msup> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <msup> <mi>f</mi> <mo>′</mo> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <msup> <mi>f</mi> <mrow> <mo>′</mo> <mo>′</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <msup> <mi>f</mi> <mrow> <mo>′</mo> <mo>′</mo> <mo>′</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msub> <mover> <mi>z</mi> <mo>&OverBar;</mo> </mover> <mi>d</mi> </msub> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> </mtr> </mtable> </mfenced> </mrow> </math>

the reference trajectory is divided into two phases: the first stage is at t₁Finish Y by oil receiving machine within time_N，Z_NAnd deviation rectification in the direction enables the oil receiving plug to be aligned to the oil filling taper sleeve in the flight direction. And the second stage eliminates the relative distance difference of the oil receiving machine in the flight direction, so that the oil receiving plug is butted with the oil filling taper sleeve.

First stage

The purpose of the first phase of the docking maneuver is to have the oil receiver in Y_NAnd Z_NThe direction is consistent with the taper sleeve. The first stage is from t₀Last until t₁，t₁Determined by a fractional factor of the total time, i.e.

t₁＝γ₁t_f

Selecting a suitable parameter gamma₁Ensuring smooth change of the reference track, namely enabling the oil receiver to have enough effective operation margin to track the reference track and ensuring that the following boundary conditions are met

(II) second stage

In the phase, the oil receiver must track the disturbance change of the position of the taper sleeve in the lateral and vertical directions, and the phase is t₁Last until t_fAnd finally, the inertial position of the oil filling taper sleeve is reached to complete butt joint. Suppose that the inertial position of the taper sleeve in this stage is:

<math> <mrow> <msub> <mi>y</mi> <mi>d</mi> </msub> <mo>=</mo> <msub> <mover> <mi>y</mi> <mo>&OverBar;</mo> </mover> <mi>d</mi> </msub> <mo>+</mo> <mi>Δ</mi> <msub> <mi>y</mi> <mi>d</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <msub> <mi>z</mi> <mi>d</mi> </msub> <mo>=</mo> <msub> <mover> <mi>z</mi> <mo>&OverBar;</mo> </mover> <mi>d</mi> </msub> <mo>+</mo> <mi>Δ</mi> <msub> <mi>z</mi> <mi>d</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </math>

definition of t₁The trace after the time instant is:

<math> <mrow> <msub> <mi>y</mi> <mi>ref</mi> </msub> <mo>=</mo> <msub> <mover> <mi>y</mi> <mo>&OverBar;</mo> </mover> <mi>d</mi> </msub> <mo>+</mo> <msub> <mi>K</mi> <mi>ref</mi> </msub> <mi>Δ</mi> <msub> <mi>y</mi> <mi>d</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </math>

<math> <mrow> <msub> <mi>z</mi> <mi>ref</mi> </msub> <mo>=</mo> <msub> <mover> <mi>z</mi> <mo>&OverBar;</mo> </mover> <mi>d</mi> </msub> <mo>+</mo> <msub> <mi>K</mi> <mi>ref</mi> </msub> <mi>Δ</mi> <msub> <mi>z</mi> <mi>d</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </math>

wherein the gain K_refIs defined as follows:

by K_refThe definition of (2) achieves a smooth transition from the first phase to the second phase, i.e. there is no break point in the designed reference trajectory. By gain K_refIs defined as_refAt t₁Gradually increasing from 0 to t₂The time is 1. t is t₂Is determined by the following formula:

t₂＝γ₂t_f

wherein the parameter gamma₂Is obtained by design.

If the movement of the filling cone is not taken into account in Y_N、Z_NAnd then the trajectory design of the second stage can be simplified as: when the oil receiver enters the second stage, the relative position in the lateral direction and the vertical direction is not changed, and only the oil receiver is sleeved in the X relative to the taper sleeve_NThere is a relative velocity in the direction.

The self-adaptive butt joint tracking control system is subjected to simulation verification under the comprehensive dynamic interference, namely under the action of simultaneous existence of severe atmospheric turbulence interference, uncertain oiling wake vortex interference and control failure, the air oiling height is set to be 6000 meters, and the corresponding atmospheric turbulence fluctuation amplitude is sigma_u＝σ_v＝σ_w＝3(m/s)。

The air refueling docking tracking initial moment takes an oil receiving machine as a reference coordinate point, namely the coordinate value of the oil receiving machine is (0, 0, 0) m, and the relative position of a refueling taper sleeve is (l)₀，y₀，h₀) And (100, 50, 50) m, in the butt joint tracking process of 0-50 s, the self-adaptive butt joint tracking controller controls the oil receiving machine to approach the oil filling taper sleeve according to a certain smooth guiding rule, so that the initial relative position deviation is overcome, and the accurate butt joint of the oil receiving plug and the oil filling taper sleeve is completed. When the comprehensive dynamic interference exists, the nonlinear simulation result of the adaptive docking controller of the oil receiving machine is shown in fig. 2-3.

Through theoretical modeling and simulation analysis, the achievement of the invention achieves the expected purpose: under the comprehensive dynamic interferences of severe atmospheric turbulence interference, uncertain oiling wake vortex interference, control failure and the like, the adaptive controller can accurately estimate uncertain interference factors and generate corresponding adaptive signals so as to weaken the dynamic comprehensive interference acting on the oil receiving machine, the docking errors are smaller than the docking errors, the docking machine can be considered as a successful docking maneuver, the flying task of accurate docking and tracking of the oil receiving machine and the oiling taper sleeve is ensured to be completed, and in the process of docking and tracking, each control quantity and state quantity of the oil receiving machine are within a feasible range.

The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the foregoing description only for the purpose of illustrating the principles of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the invention as defined by the appended claims, specification, and equivalents thereof.

Claims

1. An adaptive optimal butt joint trajectory tracking flight control method for an aerial refueling oil receiving machine is characterized by comprising the following steps:

the tracking track generating module is used for generating an ideal flight track signal of the oil receiver, wherein the ideal flight track signal of the oil receiver is formed by butting an oil receiving plug of the oil receiver with a refueling taper sleeve of the oiling machine and sending the ideal flight track signal to the optimal control law module and the state estimating module;

2. The adaptive optimal docking trajectory tracking flight control method for the aerial refueling and oil receiving machine as claimed in claim 1, wherein the oil receiving machine model calculation module comprises the following equations:

X_i(0)＝X_i0，

Y_{i} (t) = c_{i}^{T} X_{i} (t),

wherein i is 1, 2, 3;and Λ_l≤Λ_i≤Λ_u；Representing the uncertain disturbance variable signal of the tail vortex of the oiling machine. Analyzing the data acted on the oil receiver in the wind tunnel test of the oil feeder to obtain the uncertain disturbance delta of the tail vortex of the oil feeder_i(Y) rate of changeSample values are fitted through an uncertain disturbance neural network identifier module;

X (t) = {[\begin{matrix} X_{1}^{T} (t) & X_{2}^{T} (t) & X_{3}^{T} (t) \end{matrix}]}^{T},

Y(t)＝[Y₁(t) Y₂(t) Y₃(t)]^T＝[l(t) h(t) y(t)]^T，

X_{1} = [\begin{matrix} l \\ V \end{matrix}],

wherein l is the forward distance of the oil receiver relative to the oiling machine, and V is the flying speed of the oil receiver，_TIs the throttle input quantity of the oil receiving machine,is the pneumatic parameter of the oil receiver.

B_{3} = [\begin{matrix} 0 \\ 0 \\ 1 \\ 0 \\ 0 \end{matrix}],

C_{3} = [\begin{matrix} 0 \\ 0 \\ 0 \\ 0 \\ 1 \end{matrix}],

In the formula, beta is the side slip angle of the oil receiver, phi is the rolling angle of the oil receiver, and p and r are the axial angular rate of the oil receiver body;_athe input quantity of the ailerons of the oil receiving machine is the input quantity of the ailerons of the oil receiving machine; y is_(.)，L_(.)，N_(.)，M_(.)Is the pneumatic parameter of the oil receiving machine. Theta₀，γ₀Is a reference pitch angle and a track angle V of an oil receiver₀The reference speed of the oil receiving machine is g, and the gravity acceleration is g.

3. The adaptive optimal docking trajectory tracking flight control method for an airborne fueling engine as set forth in claim 1 wherein said uncertain disturbance neural network identifier module comprises the following equations:

uncertain interference delta to oil receiving machine system by adopting Radial Basis Function (RBF) neural network_i(Y) fitting to obtainTo a fitted equivalent of_i(Y) is

Δ_i(Y)＝w_i ^TΦ_i(Y)+_i(Y)，

||_i(Y)||≤_i ^*，

Y∈D_i，

In the formula, the parameter κ_iAnd upsilon_iRepresenting predeterminedThe center and width of the neural network are selected by analyzing the influence of the oiling wake vortex of the oiling machine on the sample result.

4. The adaptive optimal docking trajectory tracking flight control method for the aerial refueling truck as recited in claim 1, wherein the tracking trajectory generating module comprises the following equations:

the relative position of the distance between the oil filling taper sleeve and the oil receiving plug in the inertial space at the initial moment is (X)_d，Y_d，Z_d) Requiring the oil receiver to be at t_fCompleting the butt joint within time; setting an initial displacement ofThe final required position of the side is (0, 0, 0), namely the butt joint of the oil receiving plug and the oil filling taper sleeve is realized, and the reference track equation is

x_ref(t)＝f(t)a_x，

y_ref(t)＝f(t)a_y，

z_ref(t)＝f(t)a_z，

Wherein

<math> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&equiv;</mo> <mfenced open='[' close=']'> <mtable> <mtr> <mtd> <msup> <mi>t</mi> <mn>4</mn> </msup> </mtd> <mtd> <msup> <mi>t</mi> <mn>5</mn> </msup> </mtd> <mtd> <msup> <mi>t</mi> <mn>6</mn> </msup> </mtd> <mtd> <msup> <mi>t</mi> <mn>7</mn> </msup> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow> </math>

5. The adaptive optimal docking trajectory tracking flight control method for the aerial refueling oil receiving machine as recited in claim 1, wherein the state estimation module is configured to dynamically estimate the state of the oil receiving machine, the corresponding dynamic response is the expected dynamic response of the oil receiving machine, and the state equation is

\hat{y} (t) = c^{T} \hat{x} (t),

\hat{x} (0) = x_{0},

Wherein,representing the adaptive parameter estimate.

6. The adaptive optimal docking trajectory tracking flight control method for the aerial refueling truck as recited in claim 1, wherein the adaptive update law module comprises the following equations:

<math> <mrow> <msub> <mover> <mover> <mi>k</mi> <mo>^</mo> </mover> <mo>·</mo> </mover> <msub> <mi>x</mi> <mi>i</mi> </msub> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>ΓProj</mi> <mo>[</mo> <msub> <mover> <mi>k</mi> <mo>^</mo> </mover> <msub> <mi>x</mi> <mi>i</mi> </msub> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <mo>-</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <msubsup> <mover> <mi>x</mi> <mo>~</mo> </mover> <mi>i</mi> <mi>T</mi> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <msub> <mi>PB</mi> <mi>i</mi> </msub> <mo>]</mo> <mo>,</mo> <msub> <mover> <mi>k</mi> <mo>^</mo> </mover> <msub> <mi>x</mi> <mi>i</mi> </msub> </msub> <mrow> <mo>(</mo> <mn>0</mn> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mover> <mi>k</mi> <mo>^</mo> </mover> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> </msub> <mo>,</mo> </mrow> </math>

7. The adaptive optimal docking trajectory tracking flight control method for the aerial refueling truck as recited in claim 1, wherein the adaptive control law module comprises the following equation:

adaptive control outputThe expression is

u_{{ad}_{i}} (s) = - k_{i} D_{i} (s) r_{u_{i}} (s),

Wherein i is 1, 2, 3; and a low-pass gain C_i(0)＝1。

8. The adaptive optimal docking trajectory tracking flight control method for the aerial refueling receiving machine as recited in claim 1, wherein the optimal control law module comprises the following equations:

X_i(0)＝X_i0，

Y_{i} (t) = c_{i}^{T} X_{i} (t),

wherein i is 1, 2, 3;

introducing integral error variables

<math> <mrow> <msub> <mi>y</mi> <mi>I</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mi>t</mi> </msubsup> <mo>[</mo> <msub> <mi>Y</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>τ</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>Y</mi> <msub> <mi>cmd</mi> <mi>i</mi> </msub> </msub> <mrow> <mo>(</mo> <mi>τ</mi> <mo>)</mo> </mrow> <mo>]</mo> <mi>dτ</mi> </mrow> </math>

Obtaining the dynamic equation of the system after the augmentation

Y_{i} (t) = c_{i}^{T} X_{i} (t),

Defining a cost functionBy selecting Q_iAnd R_iSolving the Ricitti equation to obtain the optimal control quantity output

u_{lqri} = K_{i} [\begin{matrix} X_{i} (t) \\ y_{I} (t) \end{matrix}] = [\begin{matrix} - k_{P_{i}} & - k_{I_{i}} \end{matrix}] [\begin{matrix} X_{i} (t) \\ y_{I} (t) \end{matrix}] .