CN115097854A - Fixed-wing unmanned aerial vehicle attitude self-adaptive control method based on model correction - Google Patents
Fixed-wing unmanned aerial vehicle attitude self-adaptive control method based on model correction Download PDFInfo
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Abstract
The invention discloses a fixed wing unmanned aerial vehicle attitude self-adaptive control method based on model correction, and belongs to the technical field of unmanned aerial vehicle flight control. The method comprises the following steps: constructing a fixed wing unmanned aerial vehicle attitude nonlinear model, designing a correction term by utilizing output information of a reference model and the unmanned aerial vehicle attitude nonlinear model, and correcting the reference model; taking the expected attitude angle information of the unmanned aerial vehicle, the output information of the reference model and the unmanned aerial vehicle attitude nonlinear model as radial basis function neural network input to obtain an approximate value of model uncertainty; designing an extended state observer and obtaining an interference estimation value; and designing an unmanned aerial vehicle attitude controller and a neural network adaptive law based on the obtained model uncertainty approximation value and the interference estimation value. The method can enable the unmanned aerial vehicle to asymptotically track the expected reference track under the consideration of model uncertainty, control input saturation and wind interference influence, and has strong anti-interference capability.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle flight control, and particularly relates to a fixed-wing unmanned aerial vehicle attitude self-adaptive control method based on model correction.
Background
With the wide application of the unmanned aerial vehicle, the task requirements are increased day by day, the task execution environment is more complex, larger maneuvering flight actions need to be completed, and larger control input is needed at the moment. But unmanned aerial vehicle can't provide required control volume because its self physics limits, leads to control input saturation to take place to reduce unmanned aerial vehicle flight control performance. In addition, unmanned aerial vehicle external interference and model uncertainty can also reduce unmanned aerial vehicle flight control performance.
Therefore, in order to ensure the flight quality of the unmanned aerial vehicle and complete the flight task, an effective unmanned aerial vehicle control method is designed, and the flight control performance of the unmanned aerial vehicle under the conditions of uncertainty of a model, saturated control input, wind interference and the like is very necessary to be enhanced.
Disclosure of Invention
Aiming at the problems of uncertainty of an unmanned aerial vehicle model, control input saturation, wind interference and the like, the invention provides a fixed wing unmanned aerial vehicle attitude self-adaptive control method based on model correction, which enhances the flight control performance of an unmanned aerial vehicle under the conditions of uncertainty of the model, control input saturation, wind interference and the like, can track an expected reference track gradually and has strong anti-interference capability.
In order to achieve the purpose, the invention adopts the following technical scheme:
a self-adaptive control method for attitude of a fixed-wing unmanned aerial vehicle based on model correction comprises the following steps:
step 1: simultaneously considering model uncertainty and unknown external interference, constructing a fixed wing unmanned aerial vehicle attitude nonlinear model, designing a correction term by utilizing output information of a reference model and the unmanned aerial vehicle attitude nonlinear model, and correcting the reference model;
step 2: taking the unmanned aerial vehicle expected attitude angle information, the reference model and the output information of the unmanned aerial vehicle attitude nonlinear model as RBF (Radial Basis Function) neural network input to obtain a model uncertainty approximation value;
and step 3: based on the model uncertainty approximation value obtained in the step (2), designing an extended state observer by using output information and control input information of the unmanned aerial vehicle attitude nonlinear model, and obtaining an interference estimation value;
and 4, step 4: and (3) designing an unmanned aerial vehicle attitude controller and a neural network adaptive law based on the model uncertainty approximation value and the interference estimation value obtained in the steps 2 and 3.
Preferably, in step 1, considering model uncertainty and unknown external disturbances, the fixed-wing drone attitude non-linear model is as shown in equation (1):
wherein X ═ γ θ ψ] T Is an attitude angle vector, wherein gamma, theta and psi are respectively a roll angle, a pitch angle and a yaw angle of the unmanned aerial vehicle, f X As a known part of the model,. DELTA.f X For model uncertainty, u is the control input, d is the unknown external disturbance, g X To control the input gain matrix, the expression:
in the formula, Q is the dynamic pressure of free flow, S is the wing area of the unmanned aerial vehicle, L is the wing span, b A For the mean aerodynamic chord length of the wing, I x 、I y 、I z Is moment of inertia, I xy Is the product of the inertia, and is,
for the aileron control surface efficiency,
in order to be able to control the surface efficiency of the rudder,
control surface efficiency for elevators;
considering the unmanned aerial vehicle attitude nonlinear model in the formula (1), defining a tracking error:
in the formula (I), the compound is shown in the specification,
as the error between the attitude angle vector and the reference model output vector,
error between the desired attitude angle vector and the reference model output vector; x d To the desired attitude angle vector, X is the attitude angle vector, X r Is the output vector of the reference model;
the following reference model was designed:
in the formula, lambda is more than 0, and lambda is a design parameter;
from the tracking errors and their derivatives in equations (3) and (4), an error function is obtained:
in the formula (I), the compound is shown in the specification,
respectively the tracking error
A derivative of (a); xi, xi rd Respectively the tracking error
An error function of (a); designing a correction term a xi according to the error function xi, and correcting the reference model shown in the formula (5), wherein the corrected reference model is as follows:
wherein a is more than 0, and a is a design parameter; when control input saturation occurs, the actual control input requirements cannot be met, resulting in tracking errors
When the correction value becomes larger, the correction term a xi is also increased, the reference model is adjusted, and the reference model outputs X r The change is carried out, the error between the change and the attitude angle vector X is reduced, the required control input is reduced, the actual control input requirement can be met, and the unmanned aerial vehicle can exit the saturation area; as can be seen from equation (8), when the tracking error is small
When the model disappears, the error function xi disappears, and the corrected reference model (8) becomes the original form (5), so that the unmanned aerial vehicle not only progressively tracks the modified reference model, but also progressively tracks the original reference model.
Preferably, in step 2, uncertainty Δ f in the non-linear model for the unmanned aerial vehicle attitude X Designing and using an RBF neural network to carry out approximation, wherein the RBF neural network algorithm is as follows:
Δf X ＝W *T h(Γ)+ε (10)；
in the formula, h j As a hidden layer j th The output of each neuron, exp represents the logarithm of the base e exponential in parentheses, [ Γ ═ Γ [ ] 1 ,…,Γ n ] T As input vectors to the network, c j ＝[c j1 ,…,c jn ]Is a network j th Central vector of Gaussian basis function of individual neuron, b j Is j th The width of the Gaussian basis function of each neuron; w * Is an ideal weight of the neural network, h ═ h 1 (Γ),…,h m (Γ)] T The output of the hidden layer of the neural network is shown, and epsilon is an approximation error;
select an input as
The neural network outputs are:
in the formula (I), the compound is shown in the specification,
estimating weights for the neural network;
i.e. uncertainty Δ f X Is calculated.
Preferably, in step 3, the uncertainty Δ f is derived based on step 2 X The approximation value of (2), then the model of the drone (1) is written as:
in the formula (I), the compound is shown in the specification,
the method comprises the following steps of (1) including neural network approximation error and unknown external interference; estimating an approximation error epsilon of the neural network by the extended state observer, and simultaneously estimating unknown external interference d by the extended state observer; based on the formula (1) and the neural network output formula (11), the extended state observer is designed as follows:
in the formula, z 1 、z 2 、z 3 For expanding the output of the state observer, X, B,
An estimated value of (d); beta is a 1 、β 2 、β 3 To extend the state observer gain.
Preferably, in step 4, model uncertainty approximation values obtained in step 2 and step 3 are used as basis
And interference estimate z 3 Designing unmanned aerial vehicle attitude controller and neural network self-adaptation law, the controller expression is:
in the formula, K X ＞0，K X Is a controller gain matrix;
the RBF neural network self-adaptation law is as follows:
wherein G is G T ＞0，σ X > 0 is a design parameter.
The invention has the following beneficial technical effects:
the method takes the nonlinear model of the fixed wing unmanned aerial vehicle with model uncertainty, control input saturation and wind interference into consideration, improves the nonlinear model on the basis of model reference adaptive control, corrects the reference model, adjusts the reference model according to an error signal when the control input saturation occurs, enables the unmanned aerial vehicle to exit a saturation area, and solves the problem of control input saturation; the method is combined with a neural network and an interference observer, the RBF neural network is used for approximating model uncertainty, on the basis, an extended state observer is introduced to estimate unknown external interference and neural network approximation errors, and the outputs of the neural network and the extended state observer are added into a controller to eliminate the influence of the model uncertainty and the unknown external interference; the invention can effectively solve the problem of stable flight control of the unmanned aerial vehicle under the conditions of uncertainty of the model, saturated control input, wind interference and the like.
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FIG. 1 is a schematic block diagram of a method for adaptive control of attitude of a fixed-wing drone based on model correction.
Detailed Description
The invention is described in further detail below with reference to the following figures and detailed description:
as shown in fig. 1, the method for self-adaptive control of attitude of fixed-wing drone based on model correction of the present invention specifically includes the following steps:
step 1: meanwhile, considering model uncertainty and unknown external interference, constructing a fixed wing unmanned aerial vehicle attitude nonlinear model, designing a correction term by utilizing output information of a reference model and the unmanned aerial vehicle attitude nonlinear model, and correcting the reference model;
considering model uncertainty and unknown external interference, the non-linear model of the attitude of the fixed-wing drone is described as follows:
wherein X ═ γ θ ψ] T Is an attitude angle vector, wherein gamma, theta and psi are respectively a roll angle, a pitch angle and a yaw angle of the unmanned aerial vehicle, f X As a known part of the model,. DELTA.f X For model uncertainty, u is the control input, d is the unknown external disturbance, g X To control the input gain matrix, the expression:
in the formula, Q is the dynamic pressure of free flow, S is the wing area of the unmanned aerial vehicle,l is wing span, b A For the mean aerodynamic chord length of the wing, I x 、I y 、I z Is moment of inertia, I xy Is the product of the inertia, and is,
for the aileron control surface efficiency,
in order to be able to control the surface efficiency of the rudder,
control surface efficiency for elevators;
considering the nonlinear model of the unmanned aerial vehicle attitude in the formula (1), defining a tracking error:
in the formula (I), the compound is shown in the specification,
as the error between the attitude angle vector and the reference model output vector,
error between the desired attitude angle vector and the reference model output vector; x d To the desired attitude angle vector, X is the attitude angle vector, X r Is the output vector of the reference model;
the following reference model was designed:
in the formula, lambda is more than 0 as a design parameter;
the error function is derived from the tracking error and its derivative in equations (3) and (4):
in the formula (I), the compound is shown in the specification,
respectively the tracking error
A derivative of (a); designing a correction term a xi according to the error function xi, and correcting a reference model shown in an equation (5), wherein the corrected reference model is as follows:
wherein a is more than 0, and a is a design parameter; when control input saturation occurs, the actual control input requirements cannot be met, resulting in tracking errors
When the correction value becomes larger, the correction term a xi is also increased, the reference model is adjusted, and the reference model outputs X r The change is carried out, the error between the change and the attitude angle vector X is reduced, the required control input is reduced, the actual control input requirement can be met, and the unmanned aerial vehicle can exit the saturation area; as can be seen from equation (8), when the tracking error is small
When the model disappears, the error function xi disappears, and the corrected reference model (8) becomes the original form (5), so that the unmanned aerial vehicle not only progressively tracks the modified reference model, but also progressively tracks the original reference model.
Step 2: step 2: taking the unmanned aerial vehicle expected attitude angle information, the reference model and the output information of the unmanned aerial vehicle attitude nonlinear model as RBF (Radial Basis Function) neural network input to obtain a model uncertainty approximation value;
uncertainty Δ f in non-linear model for unmanned aerial vehicle attitude X Designing and using an RBF neural network to carry out approximation, wherein the RBF neural network algorithm is as follows:
Δf X ＝W *T h(Γ)+ε (10)；
in the formula, h j For the hidden layer j th The output of each neuron, exp represents the logarithm of the base e exponential in parentheses, [ Γ ═ Γ [ ] 1 ,…,Γ n ] T As input vectors to the network, c j ＝[c j1 ,…,c jn ]Is a network j th Central vector of Gaussian basis function of individual neuron, b j Is j th The width of the Gaussian basis function of each neuron; w * Is an ideal weight of the neural network, h ═ h 1 (Γ),…,h m (Γ)] T For the neural network hidden layer output, ε is the approximation error.
Select an input as
The neural network outputs are:
in the formula (I), the compound is shown in the specification,
estimating weights for the neural network;
i.e. uncertainty Δ f X Is calculated.
And step 3: based on the approximation value obtained in the step (2), designing an extended state observer by utilizing the output information and the control input information of the unmanned aerial vehicle attitude nonlinear model, and obtaining an interference estimation value;
obtaining uncertainty Δ f based on step 2 X Then the model of the drone (1) can be written as:
in the formula (I), the compound is shown in the specification,
the method comprises the following steps of (1) including neural network approximation error and unknown external interference; estimating an approximation error epsilon of the neural network by the extended state observer, and simultaneously estimating unknown external interference d by the extended state observer; based on the formula (1) and the neural network output formula (11), the extended state observer is designed as follows:
in the formula, z 1 、z 2 、z 3 For expanding the output of the state observer, X, B,
An estimated value of (d); beta is a beta 1 、β 2 、β 3 To extend the state observer gain.
And 4, step 4: designing an unmanned aerial vehicle attitude controller and a neural network adaptive law based on the model uncertainty approximation value and the interference estimation value obtained in the step 2 and the step 3;
model uncertainty approximation value obtained based on step 2 and step 3
And interference estimate z 3 Designing unmanned aerial vehicle attitude controller and neural network self-adaptation law, the controller expression is:
in the formula, K X The gain matrix of the controller is more than 0;
the RBF neural network self-adaptation law is as follows:
wherein G is G T ＞0，σ X > 0 is a design parameter.
In conclusion, when the control input saturation occurs, the reference model is adjusted according to the error signal, so that the unmanned aerial vehicle exits the saturation area; the method is combined with a neural network and an extended state observer, the influence of model uncertainty and unknown external interference is eliminated, stable operation of the unmanned aerial vehicle can be effectively guaranteed under the influence of uncertainty, control input saturation and wind interference of an unmanned aerial vehicle model, and the method has strong robustness.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make variations, modifications, additions and substitutions within the spirit and scope of the present invention.
Claims (5)
1. A self-adaptive control method for attitude of a fixed-wing unmanned aerial vehicle based on model correction is characterized by comprising the following steps: the method comprises the following steps:
step 1: simultaneously considering model uncertainty and unknown external interference, constructing a fixed wing unmanned aerial vehicle attitude nonlinear model, designing a correction term by utilizing output information of a reference model and the unmanned aerial vehicle attitude nonlinear model, and correcting the reference model;
step 2: taking the expected attitude angle information of the unmanned aerial vehicle, the output information of the reference model and the unmanned aerial vehicle attitude nonlinear model as radial basis function neural network input to obtain a model uncertainty approximation value;
and step 3: based on the model uncertainty approximation value obtained in the step 2, designing an extended state observer by using output information and control input information of the unmanned aerial vehicle attitude nonlinear model, and obtaining an interference estimation value;
and 4, step 4: and (3) designing an unmanned aerial vehicle attitude controller and a neural network adaptive law based on the model uncertainty approximation value and the interference estimation value obtained in the steps 2 and 3.
2. The model correction-based attitude adaptive control method for the fixed-wing drone according to claim 1, characterized in that: in step 1, considering model uncertainty and unknown external interference, the non-linear model of the attitude of the fixed-wing drone is shown in formula (1):
wherein X is [ γ θ ψ ]] T Is an attitude angle vector, wherein gamma, theta and psi are respectively a roll angle, a pitch angle and a yaw angle of the unmanned aerial vehicle, f X As a known part of the model,. DELTA.f X For model uncertainty, u is the control input, d is the unknown external disturbance, g X To control the input gain matrix, the expression:
in the formula, Q is the dynamic pressure of free flow, S is the wing area of the unmanned aerial vehicle, L is the wing span, b A For the mean aerodynamic chord length of the wing, I x 、I y 、I z Is moment of inertia, I xy Is the product of the inertia, and is,
for the efficiency of the aileron control surface,
in order to be able to control the surface efficiency of the rudder,
control surface efficiency for elevators;
considering the unmanned aerial vehicle attitude nonlinear model in the formula (1), defining a tracking error:
in the formula (I), the compound is shown in the specification,
as the error between the attitude angle vector and the reference model output vector,
error between the desired attitude angle vector and the reference model output vector; x d To the desired attitude angle vector, X is the attitude angle vector, X r Is the output vector of the reference model;
the following reference model was designed:
in the formula, lambda is more than 0, and lambda is a design parameter;
from the tracking errors and their derivatives in equations (3) and (4), an error function is obtained:
in the formula (I), the compound is shown in the specification,
respectively the tracking error
A derivative of (a); xi, xi rd Respectively the tracking error
An error function of (a); designing a correction term a xi according to the error function xi, and correcting the reference model shown in the formula (5), wherein the corrected reference model is as follows:
wherein a is more than 0, and a is a design parameter; when control input saturation occurs, the actual control input requirements cannot be met, resulting in tracking errors
When the correction value becomes larger, the correction term a xi is also increased, the reference model is adjusted, and the reference model outputs X r The change is carried out, the error between the change and the attitude angle vector X is reduced, the required control input is reduced, the actual control input requirement can be met, and the unmanned aerial vehicle can exit the saturation area; as can be seen from equation (8), when the tracking error is small
When the model disappears, the error function xi disappears, and the corrected reference model (8) becomes the original form (5), so that the unmanned aerial vehicle not only progressively tracks the modified reference model, but also progressively tracks the original reference model.
3. According to the claims2, the self-adaptive control method for the attitude of the fixed-wing unmanned aerial vehicle based on model correction is characterized by comprising the following steps: in step 2, uncertainty Δ f in the non-linear model for unmanned aerial vehicle attitude X The RBF neural network is designed to be used for approximation, and the algorithm of the RBF neural network is as follows:
Δf X ＝W *T h(Γ)+ε (10)；
in the formula, h j For the hidden layer j th The output of each neuron, exp represents the logarithm of the base e exponential in parentheses, [ Γ ═ Γ [ ] 1 ,…,Γ n ] T As input vectors to the network, c j ＝[c j1 ,…,c jn ]Is a network j th Central vector of Gaussian basis function of individual neuron, b j Is j is th The width of the Gaussian basis function of each neuron; w * Is an ideal weight of the neural network, h ═ h 1 (Γ),…,h m (Γ)] T Is the output of the hidden layer of the neural network, and epsilon is the approximation error;
select an input as
The neural network outputs are:
in the formula (I), the compound is shown in the specification,
estimating weights for the neural network;
i.e. uncertainty Δ f X Of the estimated value of (c).
4.The model correction-based attitude adaptive control method for the fixed-wing drone according to claim 1, characterized in that: in step 3, the uncertainty Δ f is derived based on step 2 X The approximation value of (2), then the model of the drone (1) is written as:
in the formula (I), the compound is shown in the specification,
the method comprises the following steps of (1) including neural network approximation error and unknown external interference; estimating an approximation error epsilon of the neural network by the extended state observer, and simultaneously estimating unknown external interference d by the extended state observer; based on the formula (1) and the neural network output formula (11), the extended state observer is designed as follows:
in the formula, z 1 、z 2 、z 3 For expanding the output of the state observer, X, B,
An estimated value of (d); beta is a 1 、β 2 、β 3 To extend the state observer gain.
5. The adaptive control method for attitude of a fixed-wing drone based on model modification of claim 1, characterized by: in step 4, model uncertainty approximation values obtained based on step 2 and step 3
And interference estimate z 3 Designing unmanned aerial vehicle attitude controller and neural network self-adaptation law, the controller expression is:
in the formula, K X ＞0，K X Is a controller gain matrix;
the RBF neural network self-adaptation law is as follows:
wherein G is G T ＞0，σ X > 0 is a design parameter.
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| CN116627145B (en) * | 2023-07-25 | 2023-10-20 | 陕西欧卡电子智能科技有限公司 | Autonomous navigation control method and system for unmanned pleasure boat |
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