Fixed-wing unmanned aerial vehicle formation distributed predictive control method based on disturbance compensation

Abstract

This invention relates to a distributed predictive control method for fixed-wing UAV formations based on disturbance compensation. It establishes a generalized dynamic model of heterogeneous fixed-wing UAVs, designs the communication topology connection conditions in the heterogeneous fixed-wing UAV formation, designs a cost function based on the flight objectives of the formation, and introduces a fixed-time disturbance observer into the controller of each fixed-wing UAV to compensate for various uncertainties encountered during flight. An MPC-constrained optimization problem is established and rolled time-domain optimization is performed to obtain the optimal control force and torque acting on the fixed-wing UAVs. An actuator control quantity allocation strategy combining offline and online optimization is designed to correspond to the optimal control force and torque. This invention improves the real-time performance of the optimized controller designed based on the complex dynamic model, enhances the disturbance rejection performance of the overall formation flight system, enables it to handle uncertainties to a certain extent, and optimizes computational burden and communication energy consumption.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of non-electric variable control or regulation system, and particularly relates to a disturbance compensation-based distributed predictive control method for formation of fixed-wing unmanned aerial vehicles (UAVs) for position, course, altitude or attitude control of land, water, air or space vehicles. BACKGROUND

[0002] With the continuous development of science and technology in the past decade, the unmanned aerial vehicle (UAV) technology is becoming mature. Due to the advantages of easy operation and low cost, the UAV has been widely used in various military and civilian fields. However, with the increasing complexity of tasks, the limitations of a single UAV are increasingly prominent, and therefore the automatic control of formation flight of multiple UAV systems has gradually become a research hotspot in the academic and industrial fields.

[0003] Under the current classification, the UAVs can be mainly divided into three categories of rotary-wing UAVs, vertical take-off and landing UAVs and fixed-wing UAVs. From the performance point of view, the fixed-wing UAV has specific advantages such as stable cruising and long endurance, and therefore the formation flight control of fixed-wing UAVs has high research value in practical applications. However, the current automatic control strategies for formation flight of fixed-wing UAVs are mainly based on a point mass model or a simple dynamics model considering only acceleration as the driving force, which cannot consider the mechanical constraints of the actual execution mechanism of the UAV and the driving model, nor can it consider the external disturbances in actual flight. In addition, the control method of the above-mentioned model is generally designed based on homogeneous UAVs, while the actual formation is often composed of multiple heterogeneous types, which also makes the existing research have great challenges in practical applications.

[0004] On the other hand, in the formation flight, efficient information exchange between neighbor UAVs needs to be realized and the real-time computing problem of the overall system needs to be solved. Considering the various constraints of the system, the conventional centralized and decentralized controllers usually face high communication and computing burden, and cannot make the system achieve the desired effect. SUMMARY

[0005] The present application proposes a disturbance compensation-based distributed predictive control method for formation of fixed-wing UAVs in order to solve the existing problems in the current technology.

[0006] The technical solution adopted by the present application is a disturbance compensation-based distributed predictive control method for formation of fixed-wing UAVs, which comprises the following steps:

[0007] Step 1: establishing a dynamics model of heterogeneous fixed-wing UAVs;

[0008] Step 2: designing a communication topology connection condition in the formation of heterogeneous fixed-wing UAVs;

[0009] Step 3: Design a cost function based on the flight objectives of the heterogeneous fixed-wing UAV formation;

[0010] Step 4: Introduce a fixed-time disturbance observer into the controller of each fixed-wing UAV to compensate for various uncertainties encountered during flight;

[0011] Step 5: Establish an MPC-constrained optimization problem and perform rolling time-domain optimization to obtain the result acting on the fixed-wing UAV v i The optimal control force and torque;

[0012] Step 6: Design an actuator control quantity allocation strategy that combines offline and online optimization to correspond to the optimal control force and torque.

[0013] Preferably, in step 1, let Let be a state variable, let To control the quantity, Let the three-axis positions of the center of mass of the fixed-wing UAV in the inertial coordinate system be given. For the three-axis velocities of the fixed-wing UAV relative to the body coordinate system. For the rotational motion of a fixed-wing UAV relative to the inertial coordinate system within the body coordinate system, (This refers to the Euler angles.) Let ω be the angular velocity relative to the aircraft's coordinate system, and T be the engine thrust. τ is the resultant external force of the three-axis pneumatic system, and τ is the rotational torque acting on the machine body.

[0014] Define any fixed-wing UAV d represents various uncertainties in formation flying, f represents... c (·) represents the model function;

[0015] Let there be M heterogeneous fixed-wing UAVs, and let the discrete dynamics system of the i-th fixed-wing UAV at time k+1 be x. i (k+1)=f i (x i (k),u i (k),d i (k)), where f i (·) is the model function describing the discrete system corresponding to the i-th fixed-wing UAV.

[0016] Preferably, a constraint is set for the i-th fixed-wing UAV in the heterogeneous fixed-wing UAVs, x i,min ≤x i ≤x i,max ,u i,min ≤u i ≤u i,max , where x i,min x i,max and u i,min u i,maxThese are the lower and upper limits of the state variables and the lower and upper limits of the control variables, respectively.

[0017] Preferably, in step 2, the communication topology connection condition is as follows:

[0018]

[0019] Where r max This indicates the maximum permissible communication distance. To be able to communicate with the i-th fixed-wing UAV v i A collection of neighboring fixed-wing drones that exchange information

[0020] Preferably, in step 3, the flight target of the heterogeneous fixed-wing UAV formation is v for each fixed-wing UAV. i Track its corresponding expected trajectory Maintain a constant speed during formation flight and stable attitude Meanwhile, fixed-wing UAVs with undirected connectivity communication topology v i and v j The specified geometric distance s is maintained between the formations according to the pre-defined offline formation structure. ij , i = 1, 2, ..., M; superscript here This represents a vector in both the inertial coordinate system and the body coordinate system. The superscript "d" indicates the expected value and is used to distinguish it from regular variables.

[0021] Preferably, in step 4, the observer is,

[0022]

[0023] Among them, κ i =ζ i -x i , ζ i For the observer state, T is the disturbance estimate. s p is the sampling time. d To adjust the parameter, λ 1,i , λ 2,i and λ 3,i For the weighting coefficients, under the fixed-time perturbation observer estimation, the perturbation estimation error will converge to 0 within a fixed time T0.

[0024] Preferably, λ 2,i >0, Among them, ||d i ||≤d i,max d i It is a collection of various uncertain factors.

[0025] Preferably, in step 5, the fixed-wing UAV v at time k... i Construct a constrained optimization problem.

[0026]

[0027] stx i (t+1|k)=f i (x i (t|k),u i (t|k),0)

[0028] x i,min ≤x i ≤x i,max

[0029] u i,min ≤u i ≤u i,max

[0030] x i (0|k)=x i (0)

[0031] in, Let x be the optimal control sequence obtained by solving Problem_i at time k. i (0|k)=x i (0) represents the initial state, and N represents the MPC prediction time domain;

[0032] Defined as the actual action on the fixed-wing UAV v at time k. i Controller in heterogeneous formation flight Get fixed-wing drones v i Optimal control force and torque and That is, the optimal thrust, lift, drag, lateral force, and rotational torque.

[0033] Preferably, at time k, the fixed-wing UAV v i Optimal actuator control quantity satisfy

[0034] Thrust T at time k i (k)=δ p,i (k)·T max,i T max,i For v i The maximum thrust that the engine can provide, and the optimal throttle opening at time k.

[0035] by To obtain the optimal control surface deflection, the following conditions must be met:

[0036]

[0037]

[0038]

[0039] Where, δ e,i δ r,i and δ a,i v i Elevator, rudder and ailerons, delta e,i , delta a,i , delta r,i and For v i The constraint boundaries of each implementing agency.

[0040] Preferably, in step 6, L i D i Y i , and At certain intervals μ i Values ​​are taken within the constraints, and all values ​​are processed according to {L,D,Y, The groups are arranged as offline datasets.

[0041] To each The optimal control surface deflection is obtained by constraining and optimizing the data within the system. As offline callable data;

[0042] If the optimal control force and torque obtained at time k during heterogeneous formation flight are... The difference between the norm of the search result and one of the data sets is less than σ. i Then directly call the corresponding This serves as the optimal control surface deflection at the current moment; otherwise, online optimization is performed to obtain... As the optimal control surface deflection.

[0043] This invention relates to a distributed predictive control method for fixed-wing UAV formations based on disturbance compensation. It establishes a generalized dynamic model of heterogeneous fixed-wing UAVs, designs the communication topology connection conditions in the heterogeneous fixed-wing UAV formation, designs a cost function based on the flight objectives of the heterogeneous fixed-wing UAV formation, introduces a fixed-time disturbance observer into the controller of each fixed-wing UAV to compensate for various uncertainties encountered during flight, establishes an MPC-constrained optimization problem and performs rolling time-domain optimization to obtain the control parameters acting on the fixed-wing UAVs v.i To determine the optimal control force and torque; and to design an actuator control quantity allocation strategy that combines offline and online optimization to achieve the optimal control force and torque.

[0044] The beneficial effects of this invention are as follows:

[0045] (1) By combining the generalized dynamics model of the heterogeneous fixed-wing UAV formation with the allocation strategy of offline and online optimization of specific actuators, the real-time performance of the optimized controller designed based on the complex dynamics model is improved.

[0046] (2) A fixed-time disturbance observer was introduced into the controller of each fixed-wing UAV to effectively estimate the disturbance, which improved the anti-disturbance performance of the overall formation flight system and gave it a certain ability to handle uncertain factors.

[0047] (3) The overall formation control method based on distributed MPC optimizes the computational burden and communication energy consumption. Attached Figure Description

[0048] Figure 1 This is a flowchart of the method of the present invention;

[0049] Figure 2 This is a schematic diagram of a heterogeneous fixed-wing UAV formation using six UAVs as an example in this invention;

[0050] Figure 3 This is a schematic diagram of the actuator of the UAV in this invention;

[0051] Figure 4 This is a flowchart illustrating the implementation of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] like Figure 1As shown, this embodiment discloses a distributed predictive control method for fixed-wing UAV formation based on disturbance compensation. Its main execution part is implemented on the onboard computer of each fixed-wing UAV in the heterogeneous formation, and includes the following three main stages: a) Parameter setting, setting the MPC weighting matrix, prediction time domain, maximum allowable communication distance, desired speed, desired attitude, and desired formation structure; b) Offline debugging, constraining and solving the control forces and torques within the constraint range to obtain the corresponding offline dataset; c) Online operation, performing MPC rolling time domain optimization until the end of formation flight.

[0054] The specific application steps are as follows:

[0055] 1) Establish a generalized dynamic model for heterogeneous fixed-wing UAVs:

[0056] make Let the inertial reference frame (IRF) be represented by... This represents the body reference frame (BRF) of the UAV, where... and Pointing to the north, east, and the Earth's center respectively. and These represent the roll axis, pitch axis, and yaw axis, respectively.

[0057] According to Newton's second law, the rotational and translational motions of a fixed-wing UAV are as follows:

[0058]

[0059]

[0060] in This represents the Euler angles (roll, pitch, yaw) of the UAV's rotational motion relative to the IRF within the BRF. It is the angular velocity relative to the BRF. yes The antisymmetric matrix, J = diag(J x J y J z Diagonal inertia matrix, It is the rotational torque acting on the machine body. This indicates the three-axis position of the drone's center of mass under IRF (Inductively Coupled Radio Frequency) conditions. The three-axis velocity of the drone relative to the BRF. The linear velocity is relative to the IRF. The combined external forces of the three-axis aerodynamic system (lift, drag, and lateral force) are: g is the acceleration due to gravity, and T is the engine thrust. and Let be a rotation matrix, where The other symbols are similar.

[0061] According to formula (2) Differentiating it yields:

[0062]

[0063] make As a state variable, let As control variables, the fixed-wing unmanned aerial vehicle system described by equations (1)-(3) is reconstructed as follows:

[0064]

[0065] Where d represents various uncertainties during formation flying, including external disturbances, measurement noise, and model mismatch, etc., f c (·) is the model function of the system corresponding to the descriptive formulas (1)-(3).

[0066] Considering that the flight formation consists of M heterogeneous fixed-wing UAVs, the discrete dynamics system of the i-th (i = 1, 2, ..., M) UAV can be represented as:

[0067] x i (k+1)=f i (x i (k),u i (k),d i (k)) (5)

[0068] Where f i (·) is the model function describing the discrete system corresponding to the i-th UAV.

[0069] In this embodiment, each UAV in heterogeneous formation flight inevitably faces various complex constraints during flight. When the pitch angle is too large, the fixed-wing UAV will not be able to obtain sufficient lift and will enter a stall state. When performing turning maneuvers, an excessive roll angle will increase the risk of collision with neighboring UAVs. Regarding control forces and torques, the different actuators will also result in different control input constraints for each UAV. The constraints are summarized, and the state and input constraints for each UAV are set as follows:

[0070] x i,min ≤x i ≤x i,max ,u i,min ≤u i ≤u i,max (6)

[0071] Where x i,min (x i,max ) and u i,min (u i,max These are the lower limit (upper limit) of the state variable and the lower limit (upper limit) of the control variable, respectively.

[0072] 2) Design the communication topology connection conditions in heterogeneous UAV formations:

[0073] For heterogeneous UAV flight formations, it is assumed that there is an undirected communication topology between UAVs that meets the following conditions.

[0074]

[0075] Where, r max This indicates the maximum permissible communication distance for drones in heterogeneous formations. i (i = 1, 2, ..., M), which can communicate with undirected connected drones v in a communication topology. j To conduct information exchange, among which Defined as being able to interact with v i A collection of neighboring drones that exchange information and are always present

[0076] Specifically, when M=6 is selected in the embodiment, the overall heterogeneous fixed-wing UAV formation is shown as follows. Figure 2 As shown. The dashed lines indicate that there is an undirected communication topology between the drones, and they can exchange information. Figure 2 The example shown represents one possible and reasonable communication topology connection scenario in the embodiments.

[0077] 3) Design the cost function based on the flight objectives of the heterogeneous UAV formation:

[0078] In this embodiment, the objective of heterogeneous formation flight is set as follows: a) Each UAV v i Track its corresponding expected trajectory Maintain a constant speed during formation flight and stable attitude Unmanned aerial vehicle (UAV) with undirected connection communication topology i and v j The specified geometric distance s is maintained between the formations according to the pre-defined offline formation structure. ij Where i = 1, 2, ..., M,

[0079] For drones in heterogeneous formations i The cost function that simultaneously satisfies the above objectives is set as follows:

[0080]

[0081] in, N is the prediction time domain, Q i R i W i and P i Let x be a diagonal weighted matrix. i (t|k) represents the predicted state at current time k with respect to the future time k+t, and similarly, u i (t|k) is the predictive control input.

[0082] 4) Introduce a fixed-time disturbance observer into each UAV controller to estimate various uncertainties encountered during flight:

[0083] When heterogeneous fixed-wing UAVs fly in formation, they will inevitably be subject to various unknown disturbances from the outside world and measurement noise when various sensors fuse information. In addition, the identification deviation of UAV physical parameters and aerodynamic parameters will also bring certain errors to the established model.

[0084] The collective term for the aforementioned disturbances, noise, and errors is called uncertainty d. i and believe d i,max and All are positive constants.

[0085] To address the impact of uncertainties during formation flight on individual UAV systems and the overall formation system, a fixed-time disturbance observer is introduced to estimate the uncertainties, thereby compensating for their effects. For the UAV system v described by equation (5) i (i = 1, 2, ..., M), introduce the following observer:

[0086]

[0087] Among them κ i =ζ i -x i , ζ i For the observer state, T is the disturbance estimate. s λ is the sampling time. 1,i , λ 2,i and λ 3,i For weighting coefficients, and designed with λ 2,i >0 and For system (5), under the fixed-time perturbation observer estimation described by equation (9), the perturbation estimation error will converge to 0 within a fixed time T0:

[0088]

[0089] in And the minimum convergence time T0 will be at Obtained at that time.

[0090] 5) Establish an MPC-constrained optimization problem and perform rolling time-domain optimization to obtain the result acting on the UAV v i Optimal control force and torque:

[0091] For the unmanned aerial vehicle v described by equations (5)-(6) i (i = 1, 2, ..., M), construct the following constrained optimization problem:

[0092]

[0093] in Let x be the optimal control sequence obtained at time k. i (0|k)=x i (0) represents the initial state.

[0094] Based on the fundamental principles of MPC, the optimal control sequence at time k is obtained. The first item This will affect the unmanned aerial vehicle system v i However, the above optimal control sequence is based on ignoring the uncertainty factor d. i The results obtained under the premise of [previous conditions] do not effectively compensate for the uncertainties of the actual system. To enable the drone [v...] i It exhibits good anti-interference performance. In the embodiment, the actual interference effect on the UAV system v at time k is defined. i The optimal control force and torque are shown below:

[0095]

[0096] Equation (12) above is the unmanned aerial vehicle system v i In heterogeneous formation flight, the controller obtains the optimal control force and torque at each k-time according to equations (11)-(12) to maintain the stable formation and attitude of the M heterogeneous fixed-wing UAVs: and

[0097] The optimal control force and torque here are obtained based on the generalized dynamics model of each heterogeneous UAV, and need to be implemented in the execution drive mechanism of the UAV.

[0098] 6) Design an actuator control quantity allocation strategy that combines offline and online optimization to achieve optimal control force and torque:

[0099] For fixed-wing UAVs, their actuators mainly include: engine (thrust system), elevator δe rudder δ r and aileron δ a For heterogeneous fixed-wing UAV formations, there are various models with different sizes and fuselage structures.

[0100] by Figure 3 For example, in the three unconventional fixed-wing aircraft configurations shown in the diagram, their elevators, rudders, and ailerons are not completely independent as in conventional aircraft; rather, they are coupled to some extent. However, regardless of the configuration, there must be some connection between them. Figure 3 The transformation relationship shown is similar to that of the control surface deflection relationship.

[0101] Consider unmanned aerial vehicle (UAV) systems i The engine is controlled by the throttle opening δ p,i For control purposes, the thrust at time k is given by the following formula:

[0102] T i (k)=δ p,i (k)·T max,i (13)

[0103] Where T max,i For v i The maximum thrust that the engine can provide. Therefore, the optimal throttle opening at time k is...

[0104] The effects of the elevator, rudder, and ailerons on the net external forces and net external moments on the three axes of the UAV. i Physical parameters, real-time status x i Aerodynamic parameters identified by wind tunnel tests The relationships between them are as follows:

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111] Where S i b i and c i The drones are v i The wing area, wingspan, and mean aerodynamic chord length, V iα i and β i The drones are v i The airspeed, pitch angle, and sideslip angle, and have α i =tan -1 (w 1,i / u 1,i ) and β i =sin -1 (v 1,i / V i ).

[0112] Let ξ i =[δ e,i δ a,i δ r,i ] T To represent the deflection of the UAV control surfaces, the above equations (14)-(15) are integrated into the following mapping function:

[0113]

[0114] Based on the summarized mapping relationship, the following constrained optimization problem is solved at time k:

[0115]

[0116] Get drone v i The optimal elevator, rudder, and aileron deflection, among which To obtain the optimal control surface deflection, delta e,i , delta a,i , delta r,i and For v i The constraint boundaries of each implementing agency.

[0117] Thus, the v of the UAV system at time k can be obtained through equations (13) and (17). i Optimal actuator control quantity

[0118]

[0119] To reduce the computational burden when allocating actuators, the embodiment uses... The following strategy, combining offline and online optimization, is defined to obtain L: a) For L i D i Y i , and At certain intervals μi Take values ​​within the constraints, and apply all values ​​according to... The groups are arranged as offline datasets. b) respectively The data within is obtained by constrained optimization using equation (17). c) As offline callable data; if the optimal control force and torque obtained at time k during heterogeneous formation flight are... The difference between the norm of the search result and one of the data sets is less than σ. i Then directly call the corresponding The optimal control surface deflection at the current moment is determined by equation (17). Otherwise, it is obtained through online optimization. As the optimal control surface deflection.

[0120] For a heterogeneous formation flight controller, the UAV v i Control process such as Figure 4 As shown: a) According to equation (9), the perturbation observer obtains b) Solving equation (11) yields the optimization problem. c) Obtain the optimal control force and torque according to equation (12). d) Obtain the optimal actuator control quantity using the actuator allocation strategy in step 6. And it acts on v i Let k = k + 1 and return to step a. At each sampling time, M UAVs simultaneously repeat the above steps until the formation flight mission ends.

[0121] The present invention also relates to a computer-readable storage medium storing a disturbance-compensated unmanned aerial vehicle (UAV) formation distributed predictive control program, which, when executed by a processor, implements the aforementioned disturbance-compensated UAV formation distributed predictive control method.

[0122] The present invention also relates to a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-described distributed predictive control method for UAV formation based on disturbance compensation.

[0123] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0125] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0127] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0128] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A distributed predictive control method for fixed-wing UAV formation based on disturbance compensation, characterized in that: The method includes the following steps: Step 1: Establish a dynamic model of the heterogeneous fixed-wing UAV; Step 2: Design the communication topology connection conditions in a heterogeneous fixed-wing UAV formation; Step 3: Design a cost function based on the flight objectives of the heterogeneous fixed-wing UAV formation; Step 4: Introduce a fixed-time disturbance observer into the controller of each fixed-wing UAV to compensate for various uncertainties encountered during flight; Step 5: Establish an MPC-constrained optimization problem and perform rolling time-domain optimization to obtain the result acting on the fixed-wing UAV v i The optimal control force and torque; for the fixed-wing UAV v at time k i Construct a constrained optimization problem. ， in, Let k be the optimal control sequence obtained at time k. Let N be the initial state, and N be the MPC prediction time domain. Define the actual action on the drone v at time k. i Controller in heterogeneous formation flight , get drone v i Optimal control force and torque , , , , , and ; Step 6: Design an actuator control quantity allocation strategy that combines offline and online optimization to correspond to the optimal control force and torque.

2. The distributed predictive control method for UAV formation based on disturbance compensation according to claim 1, characterized in that: In step 1, let Let be a state variable, let To control the quantity, Let the three-axis positions of the center of mass of the fixed-wing UAV in the inertial coordinate system be given. For the three-axis velocities of the fixed-wing UAV relative to the body coordinate system. For the rotational motion of a fixed-wing UAV relative to the inertial coordinate system within the body coordinate system, (This refers to the Euler angles.) Let ω be the angular velocity relative to the aircraft's coordinate system, and T be the engine thrust. τ is the resultant external force of the three-axis pneumatic system, and τ is the rotational torque acting on the machine body. Define any fixed-wing UAV d represents various uncertainties in formation flying, f c (·) represents the model function; Let there be M heterogeneous fixed-wing UAVs. The discrete dynamics system of the i-th fixed-wing UAV at time k+1 is: , where f i (·) is the model function describing the discrete system corresponding to the i-th fixed-wing UAV.

3. The distributed predictive control method for UAV formation based on disturbance compensation according to claim 2, characterized in that: Set the state and input constraints for the i-th fixed-wing UAV in a heterogeneous fixed-wing UAV configuration. , where x i,min x i,max and u i,min u i,max These are the lower and upper limits of the state variables and the lower and upper limits of the control variables, respectively.

4. The distributed predictive control method for UAV formation based on disturbance compensation according to claim 1, characterized in that: In step 2, the communication topology connection conditions are as follows: ， Where r max This indicates the maximum permissible communication distance. , To be able to communicate with the i-th fixed-wing UAV v i A collection of neighboring fixed-wing drones that exchange information .

5. The distributed predictive control method for UAV formation based on disturbance compensation according to claim 2, characterized in that: In step 3, the flight target of the heterogeneous fixed-wing UAV formation is v for each fixed-wing UAV. i Track its corresponding expected trajectory and maintain a constant speed during formation flight. and stable attitude Meanwhile, fixed-wing UAVs with undirected communication topology v i and v j The specified geometric distance s is maintained between the formations according to the pre-defined offline formation structure. ij , i = 1,2,… ,M.

6. The distributed predictive control method for UAV formation based on disturbance compensation according to claim 2, characterized in that: In step 4, the observer is, ， in, , For the observer state, T is the disturbance estimate. s p is the sampling time. d To adjust the parameter, λ 1,i , λ 2,i and λ 3,i For the weighting coefficients, under the fixed-time perturbation observer estimation, the perturbation estimation error will converge to 0 within a fixed time T0.

7. The distributed predictive control method for UAV formation based on disturbance compensation according to claim 6, characterized in that: , λ 2,i > 0, ,in, d i It is a collection of various uncertain factors. and All are positive constants.

8. The distributed predictive control method for UAV formation based on disturbance compensation according to claim 2, characterized in that: k-moment drone v i Optimal actuator control quantity satisfy ; Thrust at time k T max,i For v i The maximum thrust that the engine can provide, and the optimal throttle opening at time k. ; by To obtain the optimal control surface deflection, the following conditions must be met: ， in, , and v i Elevator, rudder and ailerons, , , , , and For v i The constraint boundaries of each implementing agency.

9. The distributed predictive control method for UAV formation based on disturbance compensation according to claim 8, characterized in that: In step 6, for L i D i Y i , , and At certain intervals μ i Values ​​are taken within the constraints, and all values ​​are processed according to {L, D, Y, , , The groups are arranged as offline datasets. ; To each The optimal control surface deflection is obtained by constraining and optimizing the data within the system. As offline callable data; If the optimal control force and torque obtained at time k during heterogeneous formation flight are... The difference between the norm of the search result and one of the data sets is less than σ. i Then directly call the corresponding This serves as the optimal control surface deflection at the current moment; otherwise, online optimization is performed to obtain... As the optimal control surface deflection.

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