CN117666358A - An aircraft attitude prediction control method with adaptive disturbance observation compensation - Google Patents
An aircraft attitude prediction control method with adaptive disturbance observation compensation Download PDFInfo
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Abstract
一种自适应扰动观测补偿的飞行器姿态预测控制方法，属于飞行器控制技术领域。所述方法为：构建飞行器姿态动力学与运动学模型，与姿态指令作差形成姿态控制误差模型；对于飞行器俯仰、偏航和滚转三个通道，分别设计自适应观测增益的扰动观测器；利用扰动观测值，进行变增益补偿控制，获得补偿控制量；定义积分型性能指标，采用可变预测周期的Critic网络预测该指标，并基于预测值更新Actor网络，以获得近似最优控制量；补偿控制量与近似最优控制量相加，获得飞行器总的姿态控制量。变增益的扰动观测补偿方法可提高飞行器对飞行过程中所受各类干扰的准确观测与补偿，提高飞行稳定性。
An aircraft attitude prediction control method with adaptive disturbance observation compensation belongs to the field of aircraft control technology. The method is as follows: constructing an aircraft attitude dynamics and kinematics model, and comparing it with the attitude command to form an attitude control error model; designing disturbance observers with adaptive observation gains for the three channels of aircraft pitch, yaw and roll respectively; Use the disturbance observation value to perform variable gain compensation control to obtain the compensation control amount; define an integral performance index, use the Critic network with a variable prediction period to predict the index, and update the Actor network based on the predicted value to obtain an approximately optimal control amount; The compensation control amount is added to the approximately optimal control amount to obtain the total attitude control amount of the aircraft. The variable-gain disturbance observation and compensation method can improve the aircraft's accurate observation and compensation of various disturbances encountered during flight and improve flight stability.
Description
Technical Field
The invention belongs to the technical field of aircraft control, and relates to an aircraft attitude prediction control method for self-adaptive disturbance observation compensation.
Background
In the process of executing a task, the aircraft can be subjected to various types of disturbance such as pneumatic disturbance, wind disturbance and the like due to the complexity of the flight environment, so that the accuracy and stability of attitude control are seriously influenced, and in order to realize high-accuracy control, the aircraft is required to be capable of adaptively overcoming the influence of various disturbance, and the flight stability is ensured; meanwhile, in order to realize high-precision control as much as possible under the condition of limited control resources, the attitude control law of the aircraft needs to have optimality, and the accuracy of predicting and evaluating the quality of the current control law according to flight data is a key for guiding the on-line optimization of flight control.
Disclosure of Invention
The invention provides an aircraft attitude prediction control method for self-adaptive disturbance observation compensation, which aims to solve the problems of self-adaptive disturbance influence overcoming, accurate prediction and control law evaluation in the background art.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
an aircraft attitude prediction control method for self-adaptive disturbance observation compensation, which comprises the following steps:
step one: constructing an aircraft attitude dynamics and kinematics model, and forming an attitude control error model by making a difference with an attitude instruction;
step two: for three channels of pitching, yawing and rolling of the aircraft, respectively designing disturbance observers of self-adaptive observation gains;
step three: performing variable gain compensation control by using the disturbance observation value to obtain compensation control quantity;
step four: defining an integral performance index, predicting the index by adopting a Critic network with a variable prediction period, and updating an Actor network based on a predicted value to obtain an approximate optimal control quantity;
step five: the compensation control quantity is added with the approximate optimal control quantity to obtain the total attitude control quantity of the aircraft, and the total attitude control quantity u=u of the aircraft a +U b 。
Compared with the prior art, the invention has the beneficial effects that: the disturbance observation compensation method of the variable gain can improve the accurate observation and compensation of various disturbances suffered by the aircraft in the flight process, and improve the flight stability; the prediction control method of the variable prediction period can integrate current data and historical sampling time data of the aircraft, improves the prediction precision of performance indexes, further improves the control optimality, reduces control deviation and saves control energy.
Drawings
FIG. 1 is a flow chart of an aircraft attitude prediction control method for adaptive disturbance observation compensation according to the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making creative efforts based on the embodiments of the present invention are all within the protection scope of the present invention.
Example 1:
an aircraft attitude prediction control method for adaptive disturbance observation compensation, as shown in fig. 1, comprises the following steps:
step one: constructing an aircraft attitude dynamics and kinematics model, and forming an attitude control error model by making a difference with an attitude instruction; the first step is specifically as follows:
the method comprises the following steps: the aircraft attitude dynamics and kinematics model is as follows:
in the formula (1), the components are as follows,is the first derivative of Ω with respect to time; />Is an attitude angle vector of the aircraft; />Is a pitch angle; psi is the yaw angle; gamma is the roll angle;
r is a gesture conversion matrix, and the gesture conversion matrix,
is the first derivative of ω with respect to time; omega= [ omega ] x ,ω y ,ω z ]Is the attitude angular velocity vector of the aircraft; omega x Is the roll angle speed; omega y Is yaw rate; omega z Is pitch angle rate; j represents a rotational inertia matrix of the aircraft; omega × A cross matrix representing ω; b (B) 1 Representing a control moment coefficient matrix; delta = [ delta ] x ,δ y ,δ z ]Representing a control input quantity; delta x Is the deflection angle of the aileron; delta y Is the deflection angle of the rudder; delta z Is the deflection angle of the elevator; d= [ d ] x ,d y ,d z ]The aerodynamic moment and the disturbance moment are items; d, d x The aerodynamic moment and the disturbance moment which act in the rolling direction are items; d, d y The aerodynamic moment and the disturbance moment are items acting in the yaw direction; d, d z The aerodynamic moment and the disturbance moment which act in the pitching direction are items;
step two: setting attitude angle change instructions for aircraft
In the formula (2): omega shape cx Is a roll channel instruction; omega shape cy A yaw path command; omega shape cz A pitch channel command;
defining the attitude angle tracking error as
In the formula (3):is pitch angle tracking error; x is x 1ψ Tracking error for yaw angle; x is x 1γ Is roll angle tracking error;
continuously obtaining two-order derivatives of the formula (3), and obtaining an attitude control error model as
In the formula (4):represents x 1 First derivative with respect to time; x is x 2 Representing the first derivative of the attitude angle tracking error, an Is the first derivative of pitch tracking error; x is x 2ψ Is the first derivative of yaw tracking error; x is x 2γ Is the first derivative of the roll angle tracking error; />Represents x 1 Second derivative with respect to time; u is the control quantity, and u=rj -1 B 1 δ＝[u x ,u y ,u z ]；u x For the roll direction control amount, this will be given by step 5; u (u) y For yaw direction control amount, will be given by step 5; u (u) z As pitch direction control amount, will be given by step 5; h is total disturbance, and->H x The total disturbance quantity is the rolling direction; h y The total disturbance quantity in the yaw direction; h z The total disturbance quantity in the pitching direction;
step two: for three channels of pitching, yawing and rolling of the aircraft, respectively designing disturbance observers of self-adaptive observation gains; the second step is specifically as follows:
step two,: taking a pitch channel as an example, a distended state observer of the following form is designed
In formula (5): e, e z For observer pairIs determined by the estimation error of (a); />For->Is determined by the estimation of (a); />For->Is determined by the estimation of (a);to H z Is determined by the estimation of (a); beta 01z The adaptive gain coefficient of the first order correction term of the pitching channel expansion state observer; beta 02z The adaptive gain coefficient of the second-order correction term of the pitching channel expansion state observer; beta 03z The adaptive gain coefficient of the third-order correction term of the pitching channel expansion state observer;
β 01z ,β 02z and beta 03z The expression of (2) is
In formula (6): k (k) sz > 0 is the scaling factor, co E (1, 1.5) is the power factor, delta 0iz > 0, i=1, 2,3 is a threshold value;
the form of the extended state observer adopted by the yaw channel and the form of the pitch channel are the same, wherein the extended state observer of the yaw channel is that
In the formula (7): e, e y For observer pair x 1ψ Is determined by the estimation error of (a); z 1ψ Is of the pair x 1ψ Is determined by the estimation of (a); z 2ψ Is of the pair x 2ψ Is determined by the estimation of (a); z 3ψ To H y Is determined by the estimation of (a); beta 01y The adaptive gain coefficient of the first order correction term of the yaw channel extended state observer; beta 02y The adaptive gain coefficient of the second-order correction term of the yaw channel extended state observer; beta 03y The adaptive gain coefficient of the third-order correction term of the yaw channel extended state observer is obtained;
β 01y ,β 02y and beta 03y The expression of (2) is
In formula (8): k (k) sy > 0 is the scaling factor, delta 0iy > 0, i=1, 2,3 is a threshold value;
the extended state observer of the rolling channel is
In the formula (9): e, e x For observer pair x 1γ Is determined by the estimation error of (a); z 1γ Is of the pair x 1γ Is determined by the estimation of (a); z 2γ Is of the pair x 2γ Is determined by the estimation of (a); z 3γ To H x Is determined by the estimation of (a); beta 01x An adaptive gain coefficient for a first order correction term of the rolling channel extended state observer;β 02x the adaptive gain coefficient of the second-order correction term of the rolling channel expansion state observer is obtained; beta 03x The self-adaptive gain coefficient of the third-order correction term of the rolling channel expansion state observer;
finally, the extended state observer output of the pitch, yaw and roll channels is defined as:
step three: performing variable gain compensation control by using the disturbance observation value to obtain compensation control quantity; the third step is specifically as follows:
step three: the gain matrix of the compensation control is recorded asWherein->Control gain, K, for compensation of pitch channel bψ Control gain, K, for compensation of yaw path bγ The gain is controlled for compensation of the roll channel;
K bψ and K is equal to bγ The expression of (2) is
In the formula (10): epsilon (0.5, 1) is a power coefficient;
step three, two: multiplying the gain of the compensation control by the disturbance observed value to obtain a compensation control quantity U b ＝K b Z 3 ；
Step four: defining an integral performance index, predicting the index by adopting a Critic network with a variable prediction period, and updating an Actor network based on a predicted value to obtain an approximate optimal control quantity; the fourth step is specifically as follows:
step four, first: definition of integral Performance index V
In the formula (11): t is t 0 =0 is the integration initial time; τ is an integral variable; q (Q) x And R is R u All are positive definite matrixes; x is x 1 (τ) represents τ time x 1 Is a value of (2); u (τ) represents the value of U at τ;
step four, two: critic network is designed as
In the formula (12):an estimated value of an integral performance index for the Critic network; />Is Critic network weight; phi (x) 1 ) Is an activation function;
and step four, three: designing the weight update law of the variable prediction period of Critic network as
In the formula (13):the derivative of Critic network weights; η (eta) c > 0 is learning rate; Γ is a positive gain matrix; omega c An estimated residual error of the Critic network at the current sampling moment; omega ci An estimated residual error of the ith prediction period Critic network; v > 0 is regularized gain; />Wherein the index i represents the value of the variable at the i-th prediction period; k is the adaptive prediction period;
and step four: defining a regression matrix, and adaptively adjusting a prediction period according to the rank of the regression matrix
Defining regression matricesAt each sampling moment, calculating whether the regression matrix is full; if the rank is full, k does not need to be adjusted; if the rank is not full, at the next sampling time, k=k+1;
step four, five: designing an Actor network, and iteratively updating to obtain approximate optimal control quantity
The Actor network is designed asWherein->The weight of the Actor network; iterative updating of the Actor network aims at minimizing normalized prediction error E c (t)：
Approximately optimal control amount U a Is that
Step five: the compensation control quantity is added with the approximate optimal control quantity to obtain the total attitude control quantity of the aircraft, and the total attitude control quantity u=u of the aircraft a +U b 。
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (5)
1. An aircraft attitude prediction control method for self-adaptive disturbance observation compensation is characterized by comprising the following steps of: the method comprises the following steps:
step one: constructing an aircraft attitude dynamics and kinematics model, and forming an attitude control error model by making a difference with an attitude instruction;
step two: for three channels of pitching, yawing and rolling of the aircraft, respectively designing disturbance observers of self-adaptive observation gains;
step three: performing variable gain compensation control by using the disturbance observation value to obtain compensation control quantity;
step four: defining an integral performance index, predicting the index by adopting a Critic network with a variable prediction period, and updating an Actor network based on a predicted value to obtain an approximate optimal control quantity;
step five: the compensation control quantity is added with the approximate optimal control quantity to obtain the total attitude control quantity u=u of the aircraft a +U b 。
2. The method for controlling the prediction of the attitude of an aircraft with adaptive disturbance observer compensation according to claim 1, wherein: the first step is specifically as follows:
the method comprises the following steps: the aircraft attitude dynamics and kinematics model is as follows:
in the formula (1), the components are as follows,is the first derivative of Ω with respect to time; />Is an attitude angle vector of the aircraft; />Is a pitch angle; psi is the yaw angle; gamma is the roll angle;
r is a gesture conversion matrix, and the gesture conversion matrix,
is the first derivative of ω with respect to time; omega= [ omega ] x ,ω y ,ω z ]Is the attitude angular velocity vector of the aircraft; omega x Is the roll angle speed; omega y Is yaw rate; omega z Is pitch angle rate; j represents a rotational inertia matrix of the aircraft; omega × A cross matrix representing ω; b (B) 1 Representing a control moment coefficient matrix; delta = [ delta ] x ,δ y ,δ z ]Representing a control input quantity; delta x Is the deflection angle of the aileron; delta y Is the deflection angle of the rudder; delta z Is the deflection angle of the elevator; d= [ d ] x ,d y ,d z ]The aerodynamic moment and the disturbance moment are items; d, d x The aerodynamic moment and the disturbance moment which act in the rolling direction are items; d, d y To act on yawAerodynamic moment and disturbance moment terms of the direction; d, d z The aerodynamic moment and the disturbance moment which act in the pitching direction are items;
step two: setting attitude angle change instructions for aircraft
Ω c ＝[Ω cz ,Ω cy ,Ω cx ] (2)
In the formula (2): omega shape cx Is a roll channel instruction; omega shape cy A yaw path command; omega shape cz A pitch channel command;
defining the attitude angle tracking error as
In the formula (3):is pitch angle tracking error; x is x 1ψ Tracking error for yaw angle; x is x 1γ Is roll angle tracking error;
continuously obtaining two-order derivatives of the formula (3), and obtaining an attitude control error model as
In the formula (4):represents x 1 First derivative with respect to time; x is x 2 Representing the first derivative of the attitude angle tracking error, an Is the first derivative of pitch tracking error; x is x 2ψ First order for yaw tracking errorDerivative; x is x 2γ Is the first derivative of the roll angle tracking error; />Represents x 1 Second derivative with respect to time; u is the control quantity, and u=rj -1 B 1 δ＝[u x ,u y ,u z ]；u x Is the rolling direction control quantity; u (u) y Is the yaw direction control quantity; u (u) z Is a pitch direction control amount; h is total disturbance, and->H x The total disturbance quantity is the rolling direction; h y The total disturbance quantity in the yaw direction; h z Is the total disturbance quantity in the pitching direction.
3. The method for controlling the prediction of the attitude of an aircraft with adaptive disturbance observer compensation according to claim 1, wherein: the second step is specifically as follows:
step two,: taking pitch channel as an example, a distended state observer is designed
In formula (5): e, e z For observer pairIs determined by the estimation error of (a); />For->Is determined by the estimation of (a); />For->Is determined by the estimation of (a); />To H z Is determined by the estimation of (a); beta 01z The adaptive gain coefficient of the first order correction term of the pitching channel expansion state observer; beta 02z The adaptive gain coefficient of the second-order correction term of the pitching channel expansion state observer; beta 03z The adaptive gain coefficient of the third-order correction term of the pitching channel expansion state observer;
β 01z ,β 02z and beta 03z The expression of (2) is
In formula (6): k (k) sz > 0 is the scaling factor, co E (1, 1.5) is the power factor, delta 0iz > 0, i=1, 2,3 is a threshold value;
the form of the extended state observer adopted by the yaw channel and the form of the pitch channel are the same, wherein the extended state observer of the yaw channel is that
In the formula (7): e, e y For observer pair x 1ψ Is determined by the estimation error of (a); z 1ψ Is of the pair x 1ψ Is determined by the estimation of (a); z 2ψ Is of the pair x 2ψ Is determined by the estimation of (a); z 3ψ To H y Is determined by the estimation of (a); beta 01y The adaptive gain coefficient of the first order correction term of the yaw channel extended state observer; beta 02y The adaptive gain coefficient of the second-order correction term of the yaw channel extended state observer; beta 03y The adaptive gain coefficient of the third-order correction term of the yaw channel extended state observer is obtained;
β 01y ,β 02y and beta 03y The expression of (2) is
In formula (8): k (k) sy > 0 is the scaling factor, delta 0iy > 0, i=1, 2,3 is a threshold value;
the extended state observer of the rolling channel is
In the formula (9): e, e x For observer pair x 1γ Is determined by the estimation error of (a); z 1γ Is of the pair x 1γ Is determined by the estimation of (a); z 2γ Is of the pair x 2γ Is determined by the estimation of (a); z 3γ To H x Is determined by the estimation of (a); beta 01x An adaptive gain coefficient for a first order correction term of the rolling channel extended state observer; beta 02x The adaptive gain coefficient of the second-order correction term of the rolling channel expansion state observer is obtained; beta 03x The self-adaptive gain coefficient of the third-order correction term of the rolling channel expansion state observer;
finally, the extended state observer output of the pitch, yaw and roll channels is defined as:
4. the method for controlling the prediction of the attitude of an aircraft with adaptive disturbance observer compensation according to claim 1, wherein: the third step is specifically as follows:
step three: the gain matrix of the compensation control is recorded asWherein->Control gain, K, for compensation of pitch channel bψ Control gain, K, for compensation of yaw path bγ The gain is controlled for compensation of the roll channel;
K bψ and K is equal to bγ The expression of (2) is
In the formula (10): epsilon (0.5, 1) is a power coefficient;
step three, two: multiplying the gain of the compensation control by the disturbance observed value to obtain a compensation control quantity U b ＝K b Z 3 。
5. The method for controlling the prediction of the attitude of an aircraft with adaptive disturbance observer compensation according to claim 1, wherein: the fourth step is specifically as follows:
step four, first: definition of integral Performance index V
In the formula (11): t is t 0 =0 is the integration initial time; τ is an integral variable; q (Q) x And R is R u All are positive definite matrixes; x is x 1 (τ) represents τ time x 1 Is a value of (2); u (τ) represents the value of U at τ;
step four, two: critic network is designed as
In the formula (12):an estimated value of an integral performance index for the Critic network; />Is Critic network weight; phi (x) 1 ) Is an activation function;
and step four, three: designing the weight update law of the variable prediction period of Critic network as
In the formula (13):the derivative of Critic network weights; η (eta) c > 0 is learning rate; Γ is a positive gain matrix; omega c An estimated residual error of the Critic network at the current sampling moment; omega ci An estimated residual error of the ith prediction period Critic network; v > 0 is regularized gain; />Wherein the index i represents the value of the variable at the i-th prediction period; k is the adaptive prediction period;
and step four: defining a regression matrix, and adaptively adjusting a prediction period according to the rank of the regression matrix
Defining regression matricesAt each sampling moment, calculating whether the regression matrix is full; if the rank is full, k does not need to be adjusted; if the rank is not full, at the next sampling time, k=k+1;
step four, five: designing an Actor network, and iteratively updating to obtain approximate optimal control quantity
Actor networkThe network is designed asWherein->The weight of the Actor network; iterative updating of the Actor network aims at minimizing normalized prediction error E c (t)：
Approximately optimal control amount U a Is that
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| CN118915435A (en) * | 2024-07-09 | 2024-11-08 | 哈尔滨工业大学 | Variant aircraft terminal sliding mode control method based on cyclic neural network observer |
| CN119781523A (en) * | 2024-12-27 | 2025-04-08 | 中国科学院沈阳自动化研究所 | A lateral control method for a micro flapping-wing aircraft |
| CN120066107A (en) * | 2025-02-21 | 2025-05-30 | 中国科学院力学研究所 | Aircraft attitude control method, system, equipment and medium |
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