Multi-missile cooperative anti-saturation control method based on autonomous obstacle avoidance and network connectivity maintenance

Abstract

The invention relates to the field of aircraft guidance control, and discloses a multi-missile cooperative anti-saturation control method based on autonomous obstacle avoidance and network connectivity maintenance, comprising the following steps: step S1, constructing a missile group three-dimensional kinematics model based on multiple distance constraint conditions including communication distance, safety distance and collision distance; s2, based on input saturation model conversion of a bounded function, constructing a kinetic model with output constraints by adopting a variable conversion idea, introducing a novel system state, and performing dimension expansion processing on an original nonlinear model to obtain a novel model with anti-saturation characteristics; s3, multi-missile autonomous obstacle avoidance control law design based on an artificial potential field function is carried out, the missile group meets the constraint condition that external obstacle avoidance, internal collision avoidance and network connectivity are kept equidistant, and the autonomous control ability and flight safety of the missile group are remarkably improved while system input saturation is relieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of aircraft guidance control, in particular to a multi-missile cooperative anti-windup control method based on autonomous obstacle avoidance and network connectivity maintenance. BACKGROUND

[0002] Precise guidance weapons shoulder the important mission of long-range damage and pinpoint removal in modern war, reshape today's political pattern and international relations. Future battlefield will present a new form of complex battlefield under system confrontation. Modern war has entered the era of cooperative confrontation with network platform as the core. As an important part of network nodes, weapons and equipment such as missiles improve the comprehensive use level of weapons and equipment such as missiles, which plays an important role in the success or failure of the war. Multi-missile, multi-platform cooperative operation is the direction of future battlefield development. Multiple aircrafts cooperate to perform flight tasks, which can be equivalent to a multi-agent system, which is composed of multiple intelligent agents with autonomous decision-making ability. Each intelligent agent has the ability to interact with surrounding intelligent agents and make control decisions based on the information collected. Compared with single-agent systems, multi-agent systems have the advantage of group superiority, and can perform complex tasks that a single agent cannot complete through the mutual cooperation between intelligent agents. In 1987, Reynolds first introduced the concept of flocking into the computer field based on the observation of biological colonies and proposed the classic Flocking model. The Flocking model and a series of subsequent bionic flocking models have become the classic control model of unmanned system flocking.

[0003] Modern battlefield environments are highly dynamic, with the constant threat of enemy radar detection and anti-missile units. Therefore, the design of cooperative control algorithms for aircraft must consider the impact of external threat zones. Missile swarm collision avoidance faces challenges from multiple levels, including high environmental dynamism and nonlinearity. Research on missile swarm collision avoidance methods involves multiple fields such as perception, communication, and cooperative control of unmanned systems. First, research on missile swarm collision avoidance helps deepen the understanding of swarm intelligence behavior. By studying the cooperative behavior and decision-making strategies of missile swarms in collision avoidance missions, we can better understand the mechanisms and laws of swarm intelligence behavior, which is of great significance for research and theoretical modeling in the field of swarm intelligence. Autonomous missile swarm collision avoidance requires the development of efficient cooperative control algorithms for multiple unmanned systems. By exploring problems such as communication, path planning, coordination, and conflict resolution among missile swarms, cooperative collision avoidance in complex environments can be achieved, improving the cooperative penetration and strike capabilities of multiple missiles. Most existing literature employs intelligent methods or artificial potential field methods to study autonomous collision avoidance problems among multiple missiles. Intelligent methods (such as pigeon swarm algorithms and multi-agent reinforcement learning algorithms) use model iteration and agent training to obtain active obstacle avoidance strategies, exhibiting strong model generalization capabilities; however, their control accuracy often fails to meet practical engineering requirements. Artificial potential field methods are commonly used in existing literature, enabling safe distance constraints between aircraft and obstacles. However, current literature typically only guarantees collision avoidance between aircraft and obstacles, failing to prevent internal collisions among missile swarm members. Furthermore, considering the limited communication distance between missile swarm members, exceeding communication distance constraints may trigger a chain reaction effect in the network, leading to the failure of the coordinated flight mission. Existing literature lacks research on control algorithms that simultaneously consider external obstacle avoidance, internal collision avoidance, and network connectivity maintenance within the missile swarm.

[0004] The cooperative motion control of the missile group plays an important role in the middle flight of the missile group as an important part of realizing remote cooperative penetration and attack. Due to the limitation of the physical properties of the actuator, there is a contradictory relationship between the response speed of the missile group to the planned trajectory and the input saturation. In order to ensure that the actuator can respond to the cooperative guidance instruction, the system input needs to meet certain control quantity constraints. The control quantity constraints of the missile group mainly include engine thrust and control surface constraints. There are generally two types of anti-saturation design methods: direct method and compensation method. The basic idea of the direct method is to consider the control quantity saturation and design a bounded control signal to meet the performance of the control system. The compensation method is also called the anti-saturation method, and the basic idea is to ignore the control quantity saturation, introduce the difference between the input and output of the actuator, and then design a controller to compensate for the saturation effect. The control quantity saturation problem of the cooperative flight of the missile group is also basically carried out according to these two ideas. Some scholars designed a model predictive controller for the stable tracking control of an elastic hypersonic vehicle under the conditions of rudder deflection and state constraints. However, the model predictive control method depends on the real-time rolling optimization solution, and there are still problems such as real-time optimization solution and determination of the prediction time step in the application of the cooperative flight control of the missile group. Some scholars introduced hyperbolic tangent function and Nussbaum function to process the control quantity, reduced the demand for the control quantity, and then designed a disturbance observer to compensate for the external disturbance, so as to realize the robust adaptive control of the aircraft while meeting the control quantity constraint condition. Some scholars designed an auxiliary system to alleviate the input saturation phenomenon, but the above methods all belong to passive anti-saturation methods after the saturation phenomenon occurs. Although some literature proposes a saturation sliding surface method to limit the amplitude of the input signal and realize active anti-saturation control of the system, but this kind of method can only realize the asymptotic convergence of the system error. In order to alleviate the contradiction between input saturation and fast convergence of system error, further research on new anti-saturation control method is needed, and based on this, the present application proposes a multi-missile cooperative anti-saturation control method based on autonomous obstacle avoidance and network connectivity maintenance. SUMMARY

[0005] The present application aims to provide a multi-missile cooperative anti-saturation control method based on autonomous obstacle avoidance and network connectivity maintenance to solve the problems in the background art.

[0006] To achieve the above object, the present application provides the following technical scheme: a multi-missile cooperative anti-saturation control method based on autonomous obstacle avoidance and network connectivity maintenance, comprising the following steps:

[0007] Step S1, based on the three-dimensional aircraft dynamics model, a control-oriented aircraft model is established, and in the aircraft model construction, the missile group has an undirected communication topology structure, based on various distance constraints including communication distance, safety distance, collision distance, a three-dimensional kinematics model of the missile group is constructed;

[0008] Step S2, based on the input saturation model conversion of bounded function, the idea of variable conversion is adopted, based on the three-dimensional kinematics model of the missile group in step S1, a dynamics model with output constraint is constructed, and a new type of system state is introduced, the original nonlinear model is expanded to obtain a new model with anti-saturation characteristics;

[0009] Step S3, based on the artificial potential function of multi-missile autonomous obstacle avoidance control law design, the external obstacles are equivalent to spherical obstacles, and the relative distance between the aircraft and the obstacles and the aircraft and the aircraft is taken as the independent variable, a variety of artificial potential functions are constructed to meet the distance constraints of external obstacle avoidance, internal collision avoidance and network connectivity maintenance of the missile group.

[0010] Preferably, the specific process of step S1 is that a multi-missile system composed of a group of aircrafts, the aircrafts dynamics model is

[0011] (1)

[0012] wherein , , are the three-dimensional positions of the aircrafts , is the mass, , , are the velocity, trajectory inclination angle and deflection angle, , , and are the aerodynamic drag, lift, lateral force and engine thrust, , , are the flight attack angle, sideslip angle and inclination angle;

[0013] Let , be the position vector and velocity vector of the members , be the control force vector, then

[0014] .

[0015] Preferably, step S1 is in the form Based on the above, the following control-oriented dynamic model is obtained

[0016]

[0017] where

[0018]

[0019]

[0020] where are external disturbances to the system, the threat zones are modeled as a set of hemispherical obstacles with radius , denoted by , where is the number of obstacles, the perception radius of the vehicle, i.e., the communication distance, is , the minimum safe distance between members is , the position of the th threat zone is , the neighbor node set of member , the threat zone node set

[0021]

[0022] The mathematical description of the problem of multi-projectile autonomous obstacle avoidance and cooperative motion control with network connectivity preservation is

[0023]

[0024] where is the time constant, and are the position and velocity biases of member relative to member .

[0025] Preferably, the step S2 is specifically, for the bounded variable , the following input transformation function is designed

[0026]

[0027] where are the variables​​​​​​​​ The maximum and minimum of the function with the following properties

[0028]

[0029] The following input is obtained from the formula The relationship of the variable

[0030]

[0031] The derivative of the formula is

[0032]

[0033] On the basis of the formula , the variable is defined to ensure that the variable and satisfy the following one-to-one correspondence

[0034]

[0035] Where , .

[0036] Preferably: the step S2 converts the input saturation problem into an input unconstrained problem based on the formula and defines the following variable

[0037]

[0038]

[0039] Where , .

[0040] Considering the undirected communication topology case, the following members are defined The position consistency error and the speed consistency error of the variable

[0041]

[0042] Where , , , .​​

[0043] Model-based and formula , get the following consistency position error system

[0044]

[0045] wherein , , , .

[0046] For variable , define:

[0047]

[0048]

[0049]

[0050] .

[0051] Preferably: the step S3 is specifically, based on the member Autonomous obstacle avoidance of obstacles , design the following potential function

[0052]

[0053] wherein , is the relative distance between the agent And threat area , is the radius of the threat area, is the distance at which the obstacle potential field function begins to act, and the following potential function is constructed

[0054]

[0055] wherein is the distance between the member And , , ;

[0056] Based on the communication distance constraint condition that the member And member , design the following potential function

[0057]

[0058] in , ;

[0059] For members Design the following fixed-time convergent nonsingular sliding surface.

[0060]

[0061] in And there are

[0062]

[0063] in For odd numbers greater than 0, satisfying , , ,satisfy .

[0064] For sliding surfaces Differentiation has

[0065]

[0066] In the formula And there are

[0067] .

[0068] Preferably, step S3 introduces three types of potential function gradient information into the control law, and designs the following intermediate virtual control instructions.

[0069]

[0070]

[0071]

[0072] in express For variables The partial derivatives, For odd numbers greater than 0, satisfying , , ,parameter . For the model Medium disturbance The estimated value of the disturbance is generated by a disturbance observer as follows

[0073]

[0074] In the formula , , , ;

[0075] The tracking of the virtual instruction The sliding surface is designed , and

[0076]

[0077] The following cooperative obstacle avoidance control law is designed for the system

[0078]

[0079] Wherein , the derivative of the variable Is estimated by the following TD differentiator:

[0080] In the formula

[0081] , Is a constant to be designed, And Respectively, the estimated values of And .

[0082] Compared with the prior art, the present application has the following beneficial effects:

[0083] 1) A new input constraint anti-saturation system is designed, compared with the traditional anti-saturation control method based on auxiliary system, the present application method reduces the risk of system input saturation, and also effectively saves the system input energy consumption, and improves the flight stability of the system;

[0084] 2) Compared with the prior art control method which can only realize autonomous obstacle avoidance, the present application designs multiple potential field functions, realizes multiple distance constraint conditions of the missile group in complex flight environment, such as external obstacle avoidance, internal collision avoidance, network connectivity maintenance, and expands the application range of the algorithm;

[0085] ​3) The combination of the sliding mode nonlinear control theory and the fixed convergence theorem is used to design the multi-missile autonomous obstacle avoidance and cooperative control method, which increases the system robustness, accelerates the convergence speed of the system error, and effectively improves the response speed and autonomous control ability of the missile group in the middle flight. BRIEF DESCRIPTION OF DRAWINGS

[0086] Figure 1 is a flow chart of the method of the present application

[0087] Figure 2 is a communication topology diagram of the missile group of the present application

[0088] Figure 3 is a three-dimensional flight trajectory diagram of the missile group

[0089] Figure 4 is a schematic diagram of the distance between the aircraft and the obstacle

[0090] Figure 5 is a schematic diagram of the distance between the aircraft and the aircraft

[0091] Figure 6 is the position consistency error of the missile group graph

[0092] Figure 7 is the position consistency error of the missile group graph

[0093] Figure 8 is the position consistency error of the missile group graph

[0094] Figure 9 is the axial control input Fx curve diagram of the missile group

[0095] Figure 10 is the normal control input Fy curve diagram of the missile group

[0096] Figure 11 is the normal control input Fz curve diagram of the missile group. DETAILED DESCRIPTION

[0097] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0098] EMBODIMENT

[0099] Please refer to Figures 1-11The illustrated method for multi-launch cooperative anti-saturation control based on autonomous obstacle avoidance and network connectivity maintenance includes the following steps:

[0100] Step S1: Based on the three-dimensional aircraft dynamics model, establish a control-oriented aircraft model. In the construction of the aircraft model, the missile group has an undirected communication topology. Based on various distance constraints, including communication distance, safety distance, and collision distance, construct a three-dimensional kinematic model of the missile group.

[0101] Step S2: Based on the input saturation model transformation of bounded functions, the idea of ​​variable transformation is adopted. Based on the three-dimensional kinematic model of the projectile group in step S1, a dynamic model with output constraints is constructed, and a new system state is introduced to expand the dimension of the original nonlinear model, resulting in a new model with anti-saturation characteristics.

[0102] Step S3: Design a multi-missile autonomous obstacle avoidance control law based on artificial potential field functions. External obstacles are equivalent to spherical obstacles. The relative distances between the aircraft and obstacles, and between aircraft, are used as independent variables to construct various artificial potential field functions to satisfy the equal distance constraints of external obstacle avoidance, internal collision avoidance, and network connectivity maintenance of the missile swarm.

[0103] In this embodiment, step S1 is to establish a three-dimensional kinematic model of the missile swarm: During the mid-flight phase of a missile swarm carrying out a coordinated penetration or strike mission, it often needs to pass through dangerous areas such as enemy radar detection and anti-missile units. In order to improve the flight safety of the missile swarm, it is necessary to carry out research on a cooperative control algorithm with autonomous obstacle avoidance function.

[0104] Consider a set of A multi-missile system consisting of multiple aircraft, aircraft The dynamic model is

[0105]

[0106] in , , aircraft The three-dimensional position, For quality, , , For velocity, trajectory inclination angle, and deflection angle, , , and These are the aerodynamic drag, lift, lateral force, and engine thrust, respectively. , , These are the angle of attack, sideslip angle, and bank angle.

[0107] Further, let , be the position vector and velocity vector of member , be the control force vector, and the expression is

[0108]

[0109] The definitions of the variables in the above formula are the same as in formula .

[0110] Based on formula , the following control-oriented dynamic model is obtained

[0111]

[0112] In the formula, t is time, and

[0113] , ,

[0114]

[0115] where , and are external disturbances suffered by the system, is the mass, , , is the velocity, ballistic inclination angle, and deflection angle.

[0116] In this embodiment, without loss of generality, the threat area is modeled as a hemispherical obstacle with a radius of , and is represented by the set , where is the number of obstacles, the perception radius (communication distance) of the aircraft is , the minimum safe distance between members is , the position of the th threat area is , is the position vector of member , then the neighbor node set of member and the threat area node set are defined as

[0117]

[0118] Given constraints on spatial configuration, communication range, internal collision avoidance, external obstacle avoidance, and input saturation, the mathematical description of the multi-missile autonomous obstacle avoidance and cooperative motion control problem with network connectivity maintenance is as follows:

[0119]

[0120] In the formula It is a time constant. and For members Relative to members Position and velocity biases.

[0121] In step S2 of this embodiment, the input saturation model transformation based on bounded functions is performed: In order to solve the system input saturation problem, for bounded control inputs... Design the following input transformation function.

[0122]

[0123] in , Variables The function has the following properties: (finds) the maximum and minimum values ​​of the given information.

[0124]

[0125] From the formula The following input was obtained With variables Relationship

[0126]

[0127] Pair Differentiate, we have

[0128]

[0129] in for The derivative of for The derivative of for The derivative of, in the equation Define variables based on this. Ensure variables and Satisfying the following one-to-one correspondence

[0130]

[0131] wherein , .

[0132] Further, the input saturation problem is converted into an input unconstrained problem by using the formula and the following variables are defined

[0133]

[0134]

[0135] wherein is the natural base number; , .

[0136] Further, in the case of undirected communication topology, the following members are defined the position consistency error and the velocity consistency error variable

[0137]

[0138] wherein , , , .

[0139] Based on the model and the formula the following consistency position error system can be obtained

[0140]

[0141] wherein , , , .

[0142] In this embodiment, for the convenience of designing the cooperative control law of the projectile group, the variable is defined as follows:

[0143]

[0144]

[0145]

[0146]

[0147] where

[0148]

[0149] Further, Lemma 1: for nonlinear system , assuming that there is a Lyapunov function , parameter satisfies the condition , , , so that the following formula is established

[0150]

[0151] The system is called fixed time stable, and the convergence residual satisfies

[0152]

[0153] where , the convergence time satisfies

[0154]

[0155] In this embodiment, step S3, the multi-missile autonomous obstacle avoidance control law design based on artificial potential field function: the potential field method is a control method for simulating the potential field force formed by electric charges in space, which can generate attractive or repulsive force on space particles, and the artificial potential field method is one of the commonly used methods for realizing the distance constraint control of spacecraft. The present application adopts this method to design the autonomous obstacle avoidance algorithm of the missile group, so that the missile group satisfies multiple distance constraint conditions.

[0156] In order to realize the autonomous obstacle avoidance of the member to the obstacle , the following potential function is designed

[0157]

[0158] where , is the relative distance between the intelligent agent and the threat area , is the radius of the threat area, is the distance at which the obstacle potential field function starts to act.

[0159] In order to avoid the member and members Internal collision occurs, and the potential function is constructed as follows

[0160]

[0161] where is the distance between members and , .

[0162] Further, in order to realize the members and members satisfy the communication distance constraint condition, the following potential function is designed

[0163]

[0164] where , .

[0165] Further, by using potential function, sliding mode control and disturbance observer, etc. Method, design multi-missile cooperative obstacle avoidance control law, for the system , in order to realize the position and velocity consistency error fast convergence, for members the following fixed time convergence nonsingular sliding mode surface is designed

[0166]

[0167] where , and

[0168]

[0169] where is an odd number greater than 0, satisfying , , satisfy , indicates the position consistency error of the ith member in the jth degree of freedom.

[0170] The derivative of the sliding mode surface is

[0171]

[0172] where , and

[0173] ​

[0174] To ensure that the missile swarm satisfies multiple range constraints, three types of potential function gradient information are introduced into the control law, and the following intermediate virtual control command is designed.

[0175]

[0176]

[0177]

[0178] in express For variables The partial derivatives, For odd numbers greater than 0, satisfying , , , The control gain matrix is ​​positive definite. For virtual instruction gain, For the nominal dynamic model, For the acceleration of neighbor j, parameters , For the model medium disturbance The estimated value is generated by the following perturbation observer.

[0179]

[0180] In the formula , , , .

[0181] In order to realize virtual instructions Tracking, designing sliding surfaces And thus

[0182]

[0183] in For dynamic decoupling matrix, These are unmodeled dynamic and disturbance terms, including uncompensated terms such as aerodynamic disturbances. This is for controlling the efficiency matrix.

[0184] For the system The following cooperative obstacle avoidance control law is designed.

[0185]

[0186] where , the variable is the derivative of is estimated by a TD differentiator of the form

[0187]

[0188] where , is the constant to be designed, are the estimated values of and respectively.

[0189] In this embodiment, for the above-mentioned cooperative control law, the following theorem is established:

[0190] Theorem 1: Consider a group of multi- missile systems with undirected communication topology , under the conditions of spatial configuration, input saturation, internal collision avoidance, external obstacle avoidance and communication distance constraints, design the sliding surface as shown in the formula , the first-order bounded system as shown in the formula , the disturbance observer as shown in the formula , and the control law as shown in the formula , then the following conclusions are established: (i) There will be no collision between members and members, and between members and obstacles, and the connectivity of the missile group network is always maintained;

[0191] (ii) The sliding surface

[0192] , is bounded and converges in actual fixed time; (iii) After the sliding surface

[0193] converges to the neighborhood of 0, the consistency error and converge to the neighborhood of 0 within a fixed time. Proof: Construct the following Lyapunov function

[0194]

[0195]

[0196] Take the derivative of along the system trajectory, and substitute the formula into ​

[0197]

[0198] where is the adjacency matrix weight, is the leader connection weight, is the control allocation weight, is the actual disturbance of member i, is the estimated value of the disturbance observer. is the repulsion potential function gradient of member i to the obstacle, is the anti-collision potential function gradient of member i to the neighbors, is the formation keeping potential function gradient of member i to the neighbors.

[0199] Further simplification has

[0200]

[0201] Note that by choosing reasonable parameters for the above equation, the disturbance estimation error can be made small enough, i.e. there exists a time constant , satisfying , then the above equation can be simplified as

[0202]

[0203] Since , , then asymptotically converges to the origin, since is bounded, then the potential function is also bounded, which means that the distance between member and member does not exceed the maximum communication distance , and there is no collision between member and member , and between member and the threat zone , and the conclusion (i) is proved.

[0204] Since the potential function is continuous and bounded, its gradient is also bounded, then there exists a constant satisfying

[0205]

[0206] Since the NDO disturbance estimation error is bounded, then there exists a constant satisfying , and let the maximum speed of the aircraft be Construct Lyapunov function , we have Taking derivative along the system trajectory, we have

[0207]

[0208] Since , , , we have

[0209]

[0210] Substitute the above equation into equation , we have

[0211]

[0212] where , , ,

[0213] , .

[0214] By choosing appropriate controller parameters, we have , , according to Lemma 1, we have converges to a neighborhood of 0 within fixed time , the convergence time satisfies

[0215]

[0216] where , i.e., the sliding surface , is bounded and converges to 0 in actual fixed time, and conclusion (ii) is proved.

[0217] When the sliding surface converges to a neighborhood of 0, we assume , according to equation we have

[0218]

[0219] where .

[0220] When , design Lyapunov function , and its derivative is

[0221]

[0222] According to Lemma 1, converges to the region in fixed time , and the convergence time satisfies

[0223]

[0224] When , design Lyapunov function , and its derivative is

[0225]

[0226] Let , then

[0227]

[0228] Integrate both ends of the above formula, the convergence time of

[0229] (50)

[0230] Finally, will converge to 0 in fixed time , where

[0231]

[0232] Then conclusion (iii) is established, and the proof is complete.

[0233] Further, algorithm simulation verification is carried out on the above method: in order to verify the effectiveness of the algorithm proposed in the application, a multi-missile system composed of 4 aircrafts is considered, and the communication topology structure of the missile group is given. In order to verify the cooperative obstacle avoidance effect of the algorithm, a hemisphere obstacle with a radius of 20km is set at the positions of [100, 0, 15] km and [170, 0, -28] km, the aircraft perception radius is set to 20km, the minimum safety distance between members is set to 2km, two obstacles are set in the simulation, and , respectively represent the distances of members from obstacle 1 and obstacle 2, represent the distances of members from members .

[0234] The initial states of the missile group are shown in Table 1, and the maximum axial and normal overloads of the vehicles are set to 5g and 10g, respectively. The reference trajectories of the 4 vehicles are generated by a virtual leader missile (denoted by M0), which cruises at 3Ma at the altitude of 10km, and the position offset of the missiles relative to the virtual leader missile is set to

[0235]

[0236] Table 1 Initial states of the missile group simulation

[0237] The designed algorithm parameters are set as follows: potential function parameters 40km, 2.5km, 3km, 10km, 18km, 100km, control law parameters are set as The potential function gain coefficient is

[0238] It should be noted that the relational terms such as first and second and the like are used herein solely to distinguish one from another entity or action, without necessarily requiring or implying that these entities or actions are in any way mutually exclusive, unless the context clearly requires such an exclusivity. Moreover, the terms "comprising", "including" or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article or apparatus.

[0239] Although the embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.​​​​​​​​​​​​​​​​

Claims

1. A multi-missile cooperative anti-windup control method based on autonomous obstacle avoidance and network connectivity maintenance, characterized in that, Comprise the following steps: Step S1, based on the three-dimensional aircraft dynamics model, the aircraft model is established, and in the aircraft model construction, the missile group has the undirected communication topology structure, based on the communication distance, the safety distance, the collision distance in the multiple distance constraint conditions, the missile group three-dimensional kinematics model is constructed; Step S2, based on the input saturation model conversion of bounded function, the idea of variable conversion is adopted, the missile group three-dimensional kinematics model based on step S1 is constructed with output constraint dynamics model, and new type system state is introduced, the original nonlinear model is expanded dimension processing, and new type model with anti-saturation characteristic is obtained; Step S3, based on the multi-missile autonomous obstacle avoidance control law design of artificial potential function, the external obstacles are equivalent to spherical obstacles, the relative distance between aircraft and obstacles, aircraft and aircraft is taken as the independent variable, and multiple artificial potential functions are constructed, so that the missile group satisfies the distance constraint conditions such as external obstacle avoidance, internal collision avoidance and network connectivity maintenance.

2. The multi-missile cooperative anti-windup control method based on autonomous obstacle avoidance and network connectivity maintenance according to claim 1, characterized in that: The step S1 is specifically performed as follows: a plurality of aircrafts are selected from a group of aircrafts The multi-missile system is composed of a plurality of aircrafts The dynamic model is （1）； wherein , , are respectively the three-dimensional position, of the aircraft, is the mass, , , are respectively the speed, the ballistic angle of inclination and the angle of declination, , , and are respectively the aerodynamic drag, the lift, the side force and the engine thrust, , , are the angle of attack, the angle of sideslip and the angle of bank; make , members respectively Position vector and velocity vector, If we consider the control force vector, then we have: （2）。 3. The multi-missile cooperative anti-windup control method based on autonomous obstacle avoidance and network connectivity maintenance according to claim 2, characterized in that: The step S1 is based on the formula, and the following facing control dynamics model is obtained （3）； In the formula , , ， where , and are external disturbances to the system, the threat zones are modeled as a set of hemispherical obstacles with radius , denoted by , where is the number of obstacles, the sensing radius of the aircraft, i.e., the communication distance, is , the minimum safe distance between members is , the position of the ththreat zone is , is the position vector of member , then the neighbor set of member , the threat zone node set are defined as​ （4）； The mathematical description of the multi-missile autonomous obstacle avoidance and cooperative motion control problem with network connectivity maintenance is （5）； wherein is a time constant, and is a member position and velocity offsets relative to a member of the group.

4. The multi-missile cooperative anti-windup control method based on autonomous obstacle avoidance and network connectivity maintenance according to claim 3, characterized in that: The step S2 is specifically, for bounded variables The input transformation function is designed as follows （6）； where , are the maximum and minimum values of the variable , respectively, and the function has the following properties （7）； The following input is obtained from the formula in relation to the variable​​ （8）； For the formula derivation, there are （9）； On the basis of the formula , define the variable , ensure that the variable and satisfy the following one-to-one correspondence （10）； wherein , .

5. The multi-missile cooperative anti-windup control method based on autonomous obstacle avoidance and network connectivity maintenance according to claim 4, characterized in that: The step S2 is based on the input saturation problem to be converted into the input non-constraint problem, and the following variable is defined （11）； （12）； wherein , ; Considering the undirected communication topology case, define the following members Position consistency error and velocity consistency error Variables （13）； wherein , , , ; Model-based and formula resulting in the following consistent position error system （14）； wherein , , , ; For the variable , define: （15）； （16）； （17）； （18）。 6. The multi-missile cooperative anti-windup control method based on autonomous obstacle avoidance and network connectivity maintenance according to claim 5, characterized in that: The step S3 is specifically, based on the member Autonomous obstacle avoidance for obstacles A potential function is designed as follows （22）； in , It is an intelligent agent and threat zone The relative distance, The radius of the threat zone, To determine the distance at which the obstacle's potential field function begins to apply, construct the following potential function. （23）； wherein is a member of and the distance, , ; Based on the member And the member Satisfy the communication distance constraint condition, design as follows potential function （24）； wherein , ; For members A fixed-time convergent nonsingular sliding surface is designed as follows （25）； wherein , and have （26）； wherein is an odd integer greater than 0 satisfying , , satisfies . To the sliding surface Derivation has （27）； In the formulae , and has （28）。 7. The multi-missile cooperative anti-windup control method based on autonomous obstacle avoidance and network connectivity maintenance according to claim 6, characterized in that: The step S3 introduces three potential function gradient information in the control law, and is designed as follows intermediate virtual control instruction （29）； （30）； （31）； wherein represents the partial derivative of the variable with respect to the parameter , , , the parameter ; is the estimate of the disturbance in the model generated by the disturbance observer （32）； In the formulae , , , ; Tracking of virtual instructions Design of sliding surface and further （33）； For the system The cooperative obstacle avoidance control law is designed as follows （34）； wherein , the variable derivative of is estimated using a TD differentiator of the form （35）； wherein , is a constant to be designed, are respectively and estimated values.

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