CN112650290B - High-rise fire unmanned aerial vehicle formation optimization control method and system with disturbance compensation - Google Patents
High-rise fire unmanned aerial vehicle formation optimization control method and system with disturbance compensation Download PDFInfo
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Abstract
The utility model discloses a high-rise fire control unmanned aerial vehicle formation optimization control method and system with disturbance compensation, including: collecting operation state information of unmanned aerial vehicle formation; and inputting the acquired running state information of the unmanned aerial vehicle formation into an unmanned aerial vehicle formation optimal control model to acquire a cooperative control strategy of the unmanned aerial vehicle formation, wherein the unmanned aerial vehicle formation optimal control model comprises a distributed Hamiltonian-Jacobian-Belman equation under no disturbance and a disturbance observer model, the distributed Hamiltonian-Jacobian-Belman equation under no disturbance aims at the minimum performance index, the optimal cooperative control strategy under no disturbance is obtained by solving, the external disturbance is estimated through the disturbance observer model, and the cooperative control strategy of the unmanned aerial vehicle formation is acquired through the optimal cooperative control strategy under no disturbance and the estimated external disturbance. The performance index is minimized while the formation of the unmanned aerial vehicle is maintained.
Description
Technical Field
The application relates to the technical field of artificial intelligence and control, in particular to a high-rise firefighting unmanned aerial vehicle formation optimization control method and system with disturbance compensation.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Along with the continuous expansion of urban construction scale, how to extinguish fire in high-rise buildings and escape people in complex and changeable fire scene conditions is attracting more and more attention. The unmanned aerial vehicle is used for on-site fire investigation and personnel escape guidance, so that the unmanned aerial vehicle becomes an effective means. However, high-rise buildings often have complex building structures, rescue tasks are heavy, and the requirement of actual tasks cannot be met by using one unmanned aerial vehicle alone. Compared with a single unmanned aerial vehicle, the unmanned aerial vehicle cluster has the characteristics of cooperative control, capability complementation and the like, and the execution efficiency of tasks is greatly improved. Therefore, cooperative control of multiple unmanned aerial vehicle systems is a research hotspot in the field of unmanned aerial vehicles today. While multi-drone formation control is the most attractive direction of research among them.
How to obtain the optimal cooperative control law of unmanned aerial vehicle cluster formation, so that the unmanned aerial vehicle cluster can realize formation and simultaneously minimize performance indexes, and the unmanned aerial vehicle cluster formation method is a technical problem which is not solved at present.
Disclosure of Invention
In order to solve the problems, the method and the system for optimizing and controlling the formation of the high-rise fire-fighting unmanned aerial vehicle with disturbance compensation are provided, firstly, the optimal cooperative control strategy under the condition of no disturbance is obtained by taking the minimum performance index as a target, the external disturbance is estimated, the cooperative control strategy of the formation of the unmanned aerial vehicle is obtained through the optimal cooperative control strategy under the condition of no disturbance and the estimated external disturbance, and the minimum performance index is realized while the formation of the unmanned aerial vehicle is realized.
In order to achieve the above purpose, the present disclosure adopts the following technical scheme:
in a first aspect, a method for optimizing control of a high-rise unmanned aerial vehicle formation with disturbance compensation is provided, including:
collecting operation state information of unmanned aerial vehicle formation;
and inputting the acquired running state information of the unmanned aerial vehicle formation into an unmanned aerial vehicle formation optimal control model to acquire a cooperative control strategy of the unmanned aerial vehicle formation, wherein the unmanned aerial vehicle formation optimal control model comprises a distributed Hamiltonian-Jacobian-Belman equation under no disturbance and a disturbance observer model, the distributed Hamiltonian-Jacobian-Belman equation under no disturbance aims at the minimum performance index, the optimal cooperative control strategy under no disturbance is obtained by solving, the external disturbance is estimated through the disturbance observer model, and the cooperative control strategy of the unmanned aerial vehicle formation is acquired through the optimal cooperative control strategy under no disturbance and the estimated external disturbance.
In a second aspect, a high-rise unmanned fire-fighting vehicle formation optimization control system with disturbance compensation is provided, including:
the data acquisition module is used for acquiring the running state information of the unmanned aerial vehicle formation;
the cooperative control strategy generation module is used for inputting the acquired operation state information of the unmanned aerial vehicle formation into an unmanned aerial vehicle formation optimization control model to acquire a cooperative control strategy of the unmanned aerial vehicle formation, wherein the unmanned aerial vehicle formation optimization control model comprises a distributed Hamiltonian-Jacobian-Belman equation under no disturbance and a disturbance observer model, the distributed Hamiltonian-Jacobian-Belman equation under no disturbance aims at the minimum performance index, the optimal cooperative control strategy under the condition of no disturbance is obtained by solving, the external disturbance is estimated through the disturbance observer model, and the cooperative control strategy of the unmanned aerial vehicle formation is obtained through the optimal cooperative control strategy under the condition of no disturbance and the estimated external disturbance.
In a third aspect, an electronic device is provided, including a memory and a processor, and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps described in the method for optimizing control of a high-rise fire unmanned aerial vehicle formation with disturbance compensation.
In a fourth aspect, a computer readable storage medium is provided for storing computer instructions that, when executed by a processor, perform the steps described in the method for optimizing control of a high-rise unmanned aerial vehicle formation with disturbance compensation.
Compared with the prior art, the beneficial effects of the present disclosure are:
1. according to the method, firstly, the optimal cooperative control strategy under the condition of no disturbance is obtained by taking the minimum performance index as a target, external disturbance is estimated, and the cooperative control strategy of unmanned aerial vehicle formation is obtained through the optimal cooperative control strategy under the condition of no disturbance and the estimated external disturbance, and the control strategy achieves the formation of unmanned aerial vehicle formation and simultaneously enables the performance index to be minimum.
2. The method and the device ensure that the unmanned aerial vehicle cluster is stable in consistency, the optimal control problem is also considered, the control precision and the control energy consumption can be balanced at the same time, and the method and the device have practical value.
3. The method adopts the disturbance observer to estimate external disturbance, realizes accurate compensation of the external disturbance, and can better improve control precision.
4. The method and the system adopt an evaluation network and a weight updating mode of an execution network, are simpler, overcome the limitation that most of optimal control methods based on reinforcement learning are required to meet continuous excitation conditions, and are easier to apply to engineering practice.
Additional aspects of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application.
Fig. 1 is a horizontal structural view of a tie machine-server cooperative formation involved in embodiment 1 of the present disclosure;
fig. 2 is a V-shaped formation and communication topology diagram of a drone cluster involved in embodiment 1 of the present disclosure;
fig. 3 is a formation trace diagram of a cluster of unmanned aerial vehicles involved in embodiment 1 of the present disclosure;
fig. 4 is an estimation error curve of the external disturbance involved in embodiment 1 of the present disclosure.
The specific embodiment is as follows:
the disclosure is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
In the present disclosure, terms such as "upper", "lower", "left", "right", "front", "rear", "vertical", "horizontal", "side", "bottom", and the like indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, are merely relational terms determined for convenience in describing structural relationships of the various components or elements of the present disclosure, and do not denote any one of the components or elements of the present disclosure, and are not to be construed as limiting the present disclosure.
In the present disclosure, terms such as "fixedly coupled," "connected," and the like are to be construed broadly and refer to either a fixed connection or an integral or removable connection; can be directly connected or indirectly connected through an intermediate medium. The specific meaning of the terms in the disclosure may be determined according to circumstances, and should not be interpreted as limiting the disclosure, for relevant scientific research or a person skilled in the art.
Example 1
In recent years, when solving unmanned aerial vehicle cluster formation control, it is always desirable to design an optimal cooperative control law so as to minimize performance indexes while the unmanned aerial vehicle cluster realizes formation. In a physical sense, optimal cooperative control considers how each drone reaches a formation state with minimal energy. From a mathematical point of view, the optimal control problem eventually translates into solving the hamilton-jacobian-bellman equation, which is a sufficient condition for the existence of an optimal solution. However, for nonlinear systems such as unmanned aerial vehicles, the hamilton-jacobian-bellman equation is effectively a nonlinear partial differential equation that is generally difficult to solve mathematically. The reinforcement learning method provides an effective method for solving the nonlinear partial differential equation.
In a complex fire scene environment, external disturbance suffered by the unmanned aerial vehicle is more severe than that in a normal environment, so that the performance of the whole unmanned aerial vehicle cluster is often reduced, the system stability is affected, and unpredictable loss is caused. Therefore, it is particularly important to improve the external disturbance resistance of the unmanned aerial vehicle cluster. Estimating unknown external disturbances suffered by a system using disturbance observers has become a common approach in the control arts. The disturbance observer is combined to construct the anti-disturbance compensation controller to compensate the influence of external disturbance on the system, so that the system is not only feasible in theory, but also easier to realize in practice.
In this embodiment, a high-rise fire unmanned aerial vehicle formation optimization control method with disturbance compensation is disclosed, including:
collecting operation state information of unmanned aerial vehicle formation;
and inputting the acquired running state information of the unmanned aerial vehicle formation into an unmanned aerial vehicle formation optimal control model to acquire a cooperative control strategy of the unmanned aerial vehicle formation, wherein the unmanned aerial vehicle formation optimal control model comprises a distributed Hamiltonian-Jacobian-Belman equation under no disturbance and a disturbance observer model, the distributed Hamiltonian-Jacobian-Belman equation under no disturbance aims at the minimum performance index, the optimal cooperative control strategy under no disturbance is obtained by solving, the external disturbance is estimated through the disturbance observer model, and the cooperative control strategy of the unmanned aerial vehicle formation is acquired through the optimal cooperative control strategy under no disturbance and the estimated external disturbance.
Further, the construction process of the distributed Hamiltonian-Jacobian-Belman equation under no disturbance is as follows:
establishing a mathematical model of an unmanned aerial vehicle nominal system under the condition of no disturbance for each unmanned aerial vehicle in unmanned aerial vehicle formation;
determining a communication topological structure of the unmanned aerial vehicle in unmanned aerial vehicle formation;
constructing a cooperative consistency error function of the plane in unmanned aerial vehicle formation through a nominal system mathematical model of the unmanned aerial vehicle and a communication topological structure of the unmanned aerial vehicle;
defining a performance index function of the plane by a collaborative consistency error function of the plane, and defining an optimal performance index function by taking the minimum performance index as a target;
defining a system Hamiltonian function through a performance index function of a wing plane to obtain a distributed optimal cooperative control law;
and constructing a distributed Hamiltonian-Jacobian-Bellman equation under no disturbance through the optimal performance index function and the distributed optimal cooperative control law.
Further, the performance index function of the wing plane comprehensively considers the system consistency error of the wing plane and the control input of the wing plane.
Further, a reinforcement learning method of an execution-evaluation dual-network structure is adopted to solve a distributed Hamiltonian-Jacobian-Bellman equation under the condition of no disturbance, and an optimal cooperative control strategy under the condition of no disturbance is obtained.
Furthermore, according to the best principle of Bellman, the Hamiltonian is solved, and the distributed best cooperative control law is obtained.
Further, a directed graph is used to describe the communication topology between the drones.
Further, the operation state information of the unmanned aerial vehicle formation includes a position, a speed, a heading angle and a yaw rate of the unmanned aerial vehicle.
The construction process of the distributed Hamiltonian-Jacobian-Bellman equation without disturbance is described in detail, and the construction process comprises the following steps:
s1, establishing a mathematical model of a nominal system of the high-rise firefighting four-rotor unmanned aerial vehicle under the condition of no disturbance;
s2, determining a communication topological structure among all unmanned aerial vehicles in unmanned aerial vehicle formation;
s3, defining proper cooperative consistency errors and performance index functions aiming at the i-th plane nominal system;
and S4, deducing a distributed Hamiltonian-Jacobian-Belman equation.
Step S1, establishing a mathematical model of a nominal system of the high-rise firefighting four-rotor unmanned aerial vehicle under the condition of no disturbance, wherein the mathematical model is specifically as follows:
first, to simplify the presentation of the problem, attention is paid only to the queuing behavior of four-rotor unmanned aerial vehicles, assuming that each unmanned aerial vehicle is equipped with a throttle, a rudder and a homoautopilot. Complex aerodynamic characteristics of the aircraft can be omitted, and the unmanned aerial vehicle model is simplified. Assuming that each unmanned aerial vehicle is on the same horizontal plane, the simplified mathematical model of the single unmanned aerial vehicle is obtained as follows:
wherein x is i ，y i Representing the position in the east and north directions of the established coordinate system. v i ，θ i ，ω i Speed, heading angle and yaw rate, respectively. u (u) i,1 ，u i,2 Is a control input. d, d i,1 ，d i,2 Is an external disturbance suffered by the system.
Let X i ＝[x i ,y i ,θ i ,v i ,ω i ] T ,f(X i )＝[v i cosθ i ,v i sinθ i ,ω i ,0,0] T ,
g(X i )＝[0,0；0,0；0,0；1,0；0,1],u i ＝[u i,1 ,u i,2 ] T ,d i ＝[d i,1 ,d i,2 ] T The mathematical model can be further reduced to:
neglecting external disturbance, the nominal system mathematical model of the unmanned aerial vehicle under the condition of no disturbance is described as follows:
step S2, determining the communication topological structure among the unmanned aerial vehicles in the unmanned aerial vehicle formation as follows:
describing a communication connection relationship between unmanned aerial vehicles in formation by using a directed graph G= { V, E, A };
where v= {0,1,..n } represents the set of nodes in graph G,represents a set of directed edges in the graph, a= [ a ] ij ]∈R n×n A weight matrix representing a directed graph G;
if the plane i can receive information from plane j, then a ij =1 (i+.j), otherwise, a ij =0; defining the neighbor node of node i as N i = { j E v| (i, j) E, i+.j }, the ingress matrix D is d=diag { h } 1 ,...h N }, whereinSuppose a ii =0, directed graph is a strict join; the connection between the assistant i and the collar is represented as a diagonal matrix b=diag { B 1 ,...,b N If the bureau i is able to receive information from the leader, b i =1, otherwise b i ＝0。
Step S3, defining a proper cooperative consistency error function and a performance index function specifically as follows for an ith plane nominal system:
definition of the coordinated coherence error delta of the plane i i The method comprises the following steps:
wherein, kappa ij ∈R n Representing predefined formation status information between the plane i and the plane j. Kappa (kappa) i0 ∈R n Representing pre-defined formation status information between the leader i and the leader. Two pairs ofAll are constants. Then a consistency error dynamic systemThe method comprises the following steps:
wherein,
defining a performance index function J of a plane i i (δ i (t)) is:
the performance index function comprehensively considers the consistency error delta i And control inputs of the plane i by finding the optimal control strategy u i (δ i ) The minimization of the performance index function is realized, and the consistent stability of the unmanned aerial vehicle cluster under the condition of no disturbance can be ensured.
Step S4 derives the distributed hamilton-jacobian-bellman equation as follows:
defining optimal performance index function with minimum performance index as targetThe method comprises the following steps:
defining a synergistic hamiltonian H i (δ i ,u i ) The method comprises the following steps:
according to BellmanPrinciple of optimality, byCan obtain distributed optimal cooperative control law>The following are provided:
the corresponding distributed hamilton-jacobian-bellman equation is then:
the distributed hamilton-jacobian-bellman equation is in fact a nonlinear partial differential equation, whose analytical solution is difficult to obtain. The reinforcement learning method of the execution-evaluation dual-network structure provides an effective means for overcoming the difficulty.
The execution-evaluation dual-network structure is adopted to approximate the optimal cooperative control law and the performance index function respectively, and the distributed Hamiltonian-Jacobian-Bellman equation is solved to obtain the optimal cooperative control strategy under the condition of no disturbance, and the method comprises the following steps:
can be decomposed into two parts
γ i >0 is a design constant that is set to be equal to the design constant,then
The evaluation network is used to approximate the performance index function:
is to evaluate the network weight,/-, for>Is an excitation function. The evaluation network weight update law is designed as follows:
wherein, kappa ci >0 is a design parameter.
Executing a network to approximate optimal control inputs
To perform network weights. The execution network weight update law is:
wherein, kappa ai >0 is a design parameter.
Solving the obtained optimal control inputIs the optimal cooperative control strategy under the condition of no disturbance.
The disturbance observer is designed to estimate the external disturbance, and thus the disturbance compensator is obtained to cancel the influence of the external disturbance as follows:
the design of the distributed cooperative control law is carried out under the condition of no external disturbance, and then the distributed optimal cooperative robust control law based on disturbance compensation of a disturbance observer is designed to ensure the stability of the unmanned aerial vehicle cluster under the condition of suffering from the external disturbance. The disturbance observer is designed as follows:
wherein,is an estimated value of external disturbance, Z i Is an auxiliary variable, K 0 >0 is the disturbance observer gain matrix.
Thus, the distributed optimal cooperative robust control law concrete form u i The following are provided:
solved u i And a cooperative control strategy for unmanned aerial vehicle formation.
In order to confirm the effectiveness of this example, simulation experiments were performed as follows:
in the simulation experiment, the control target is to design a cooperative control strategy of unmanned aerial vehicle formation, namely an optimal distributed cooperative control law of unmanned aerial vehicle formation, so that a bureau can track a lead machine in a certain formation. The control inputs of the collar machine adopted in the example are as follows:
wherein the time constant τ v ＝3，τ ω ＝2，v com =10m/s sumThe initial value of the system state is selected as Formation information k 10 ＝[-3,3,0,0,0]，κ 21 ＝[-3,3,0,0,0]，κ 30 ＝[-3,-3,0,0,0]，κ 43 ＝[-3,-3,0,0,0]. External disturbance of d 1 ＝[sin(t)；cos(t)]，d 2 ＝[2sin(t)；2cos(t)]，d 3 ＝[1.5sin(t)；1.5cos(t)]，d 4 ＝[2.5sin(t)；2.5cos(t)]. Design parameter gamma 1 ＝γ 2 ＝γ 3 ＝γ 4 ＝40，κ c1 ＝κ c2 ＝κ c3 ＝κ c4 ＝1，κ a1 ＝κ a2 ＝κ a3 ＝κ a4 ＝0.6。
Analysis of results:
selecting a Liapunov function:
deriving available->According to the lisapunov stability theorem, the parameter gamma is adjusted i ，κ ci ，κ ai Can make the system allThe signals are semi-globally consistent and bounded, and the unmanned aerial vehicle cluster consistency error can be converged into a neighborhood centered on the origin.
From fig. 3 it can be seen that the bureau can track the track of the upper leader very well in case of external disturbance and can maintain the preset formation shape. As can be seen from fig. 4, the disturbance observer is able to estimate the external disturbance of the system well, the estimation error being consistent and eventually bounded.
The reinforcement learning method based on the execution-evaluation dual-network structure realizes the formation optimization control of the high-rise fire unmanned aerial vehicle under the directed communication network, and considers the influence of external disturbance. Based on the co-operation mode of the lead-the-plane, the communication topological structure and the formation information shown in fig. 1 and the communication topological structure and the formation information shown in fig. 2, an unmanned plane cluster co-operation consistency error dynamic equation is established. And selecting a proper performance index function, so that the scheme can comprehensively consider the balance between control precision and control energy consumption. Based on the reinforcement learning idea, the network approximation control input is executed by adopting the evaluation network approximation performance index function, and a simplified weight updating law is designed, so that the limitation that the continuous excitation condition must be met is overcome. Based on a disturbance observer, a compensation controller is provided to achieve the purpose of counteracting the influence of external disturbance. And finally, applying the control method of the cooperative distributed optimal control and disturbance compensation control under the condition of no disturbance to the formation optimal control of the high-rise unmanned aerial vehicle, and verifying the effectiveness of the method.
The embodiment discloses a high-rise fire-fighting unmanned aerial vehicle formation optimization control method with disturbance compensation, and a distributed self-adaptive optimal cooperative robust control scheme is designed aiming at unmanned aerial vehicle formation in a tractor-plane cooperative mode and considering the situation that unmanned aerial vehicles suffer external disturbance. The proposed controller can be divided into two parts: (1) an optimal cooperative control strategy in the absence of disturbances; (2) a disturbance compensator. Firstly, solving a distributed Hamiltonian-Jacobian-Bellman equation by adopting a reinforcement learning method of an execution-evaluation dual-network structure aiming at each bureau, and further obtaining a distributed optimal cooperative control strategy. Subsequently, a disturbance compensator is derived based on the disturbance observer to eliminate the influence of external disturbance. The whole controller not only ensures that all signals in the unmanned aerial vehicle system are finally consistent and bounded, but also ensures that the cooperative cost function is minimized.
The method and the device for controlling the unmanned aerial vehicle cluster have the advantages that on the basis of guaranteeing that the unmanned aerial vehicle cluster keeps consistent and stable, the optimal control problem is also considered, the control precision and the control energy consumption can be balanced at the same time, and the method and the device have practical values.
The disturbance observer is adopted to estimate external disturbance, so that accurate compensation of the external disturbance is realized, and the control precision can be better improved.
The evaluation network and the weight updating mode of the execution network are simpler, and the limitation that the continuous excitation condition is met necessary for most of the optimal control methods based on reinforcement learning is overcome, so that the method is easier to apply to engineering practice.
Example 2
In this embodiment, a high-rise fire unmanned aerial vehicle formation optimization control system with disturbance compensation is disclosed, comprising:
the data acquisition module is used for acquiring the running state information of the unmanned aerial vehicle formation;
the cooperative control strategy generation module is used for inputting the acquired operation state information of the unmanned aerial vehicle formation into an unmanned aerial vehicle formation optimization control model to acquire a cooperative control strategy of the unmanned aerial vehicle formation, wherein the unmanned aerial vehicle formation optimization control model comprises a distributed Hamiltonian-Jacobian-Belman equation under no disturbance and a disturbance observer model, the distributed Hamiltonian-Jacobian-Belman equation under no disturbance aims at the minimum performance index, the optimal cooperative control strategy under the condition of no disturbance is obtained by solving, the external disturbance is estimated through the disturbance observer model, and the cooperative control strategy of the unmanned aerial vehicle formation is obtained through the optimal cooperative control strategy under the condition of no disturbance and the estimated external disturbance.
Example 3
In this embodiment, an electronic device is disclosed that includes a memory and a processor, and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps described in the method for optimizing control of a high-rise fire unmanned aerial vehicle formation with disturbance compensation disclosed in embodiment 1.
Example 4
In this embodiment, a computer readable storage medium is disclosed for storing computer instructions that, when executed by a processor, perform the steps described in the high-rise fire unmanned aerial vehicle formation optimization control method with disturbance compensation disclosed in embodiment 1.
The above is only a preferred embodiment of the present application, and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may 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, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may 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, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present application and not for limiting the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the application without departing from the spirit and scope of the application, which is intended to be covered by the claims.
Claims (9)
1. The high-rise unmanned aerial vehicle formation optimization control method with disturbance compensation is characterized by comprising the following steps of:
collecting operation state information of unmanned aerial vehicle formation;
inputting the acquired running state information of the unmanned aerial vehicle formation into an unmanned aerial vehicle formation optimal control model to acquire a cooperative control strategy of the unmanned aerial vehicle formation, wherein the unmanned aerial vehicle formation optimal control model comprises a distributed Hamiltonian-Jacobian-Belman equation under no disturbance and a disturbance observer model, the distributed Hamiltonian-Jacobian-Belman equation under no disturbance aims at the minimum performance index, the optimal cooperative control strategy under no disturbance is obtained by solving, the external disturbance is estimated through the disturbance observer model, and the cooperative control strategy of the unmanned aerial vehicle formation is obtained by the optimal cooperative control strategy under no disturbance and the estimated external disturbance, wherein the distributed Hamiltonian-Jacobian equation under no disturbance is solved by adopting a reinforcement learning method for executing and evaluating a double-network structure to obtain the optimal cooperative control strategy under no disturbance, and the method is as follows:
is decomposed into two parts:
wherein, gamma i >0 is a design constant, ">δ i For synergistic consistency error, ++>a ij Indicating whether or not the plane i can receive information from plane j, b i Indicating whether the bureau i can receive information from the leader->Is an optimal performance index function;
the optimal cooperative control law isWherein the system state value X i ＝[x i ,y i ,θ i ,v i ,ω i ] T ，x i ，y i Representing the positions of the established coordinate system in the east and north directions; v i ，θ i ，ω i Speed, heading angle and yaw rate, respectively; g T (X i ) Representing a transpose of the system control gain matrix;
the evaluation network is used to approximate the performance index function:wherein (1)>Is to evaluate the network weight,/-, for>For the excitation function +.>Delta as an approximately optimal performance index function i (t) represents a collaborative consistency error function;
the evaluation network weight update law is designed as follows:
wherein, kappa ci >0 is a design parameter;
executing a network to approximate optimal control inputs
Wherein (1)>To perform network weights;
the execution network weight update law is:
wherein, kappa ai >0 is a design parameter;
solving the obtained optimal control inputIs the optimal cooperative control strategy under the condition of no disturbance.
2. The method for optimizing control of high-rise unmanned aerial vehicle formation with disturbance compensation according to claim 1, wherein the construction process of the distributed hamilton-jacobian-bellman equation without disturbance is as follows:
establishing a mathematical model of an unmanned aerial vehicle nominal system under the condition of no disturbance for each unmanned aerial vehicle in unmanned aerial vehicle formation;
determining a communication topological structure of the unmanned aerial vehicle in unmanned aerial vehicle formation;
constructing a performance index function of the unmanned aerial vehicle in unmanned aerial vehicle formation through a nominal system mathematical model of the unmanned aerial vehicle and a communication topological structure of the unmanned aerial vehicle;
and constructing a distributed Hamiltonian-Jacobian-Bellman equation under no disturbance through a performance index function of the plane.
3. The method for optimizing control of a high-rise fire-fighting unmanned aerial vehicle formation with disturbance compensation according to claim 2, wherein the performance index function of the plane comprehensively considers the system consistency error of the plane and the control input of the plane.
4. The method for optimizing control of high-rise unmanned aerial vehicle formation with disturbance compensation according to claim 2, wherein the Hamiltonian function is solved according to the principle of optimality of Belman to obtain a distributed optimal cooperative control law.
5. The method for optimized control of high-rise fire unmanned aerial vehicle formation with disturbance compensation according to claim 1, wherein the communication topology between unmanned aerial vehicles is described by using a directed graph.
6. The method for optimized control of high-rise fire unmanned aerial vehicle formation with disturbance compensation according to claim 1, wherein the operation state information of unmanned aerial vehicle formation includes position, speed, heading angle and yaw rate of unmanned aerial vehicle.
7. High-rise fire control unmanned aerial vehicle formation optimal control system with disturbance compensation, its characterized in that includes:
the data acquisition module is used for acquiring the running state information of the unmanned aerial vehicle formation;
the cooperative control strategy generation module is used for inputting the acquired operation state information of the unmanned aerial vehicle formation into an unmanned aerial vehicle formation optimization control model to acquire a cooperative control strategy of the unmanned aerial vehicle formation, wherein the unmanned aerial vehicle formation optimization control model comprises a distributed Hamiltonian-Jacobian-Belman equation under no disturbance and a disturbance observer model, the distributed Hamiltonian-Jacobian-Belman equation under no disturbance aims at the minimum performance index, the optimal cooperative control strategy under the condition of no disturbance is obtained by solving, the external disturbance is estimated through the disturbance observer model, and the cooperative control strategy of the unmanned aerial vehicle formation is obtained through the optimal cooperative control strategy under the condition of no disturbance and the estimated external disturbance, and the cooperative control strategy of the unmanned aerial vehicle formation is specifically:is decomposed into two parts:
wherein, gamma i >0 is a design constant, ">δ i For synergistic consistency error, ++>a ij Indicating whether or not the plane i can receive information from plane j, b i Indicating whether the bureau i can receive information from the leader->Is an optimal performance index function;
the optimal cooperative control law isWherein the system state value X i ＝[x i ,y i ,θ i ,v i ,ω i ] T ，x i ，y i Representing the positions of the established coordinate system in the east and north directions; v i ，θ i ，ω i Speed, heading angle and yaw rate, respectively; g T (X i ) Representing a transpose of the system control gain matrix;
the evaluation network is used to approximate the performance index function:wherein (1)>Is to evaluate the network weight,/-, for>Is an excitation function; />Delta as an approximately optimal performance index function i (t) represents a collaborative consistency error function;
the evaluation network weight update law is designed as follows:
wherein, kappa ci >0 is a design parameter;
executing a network to approximate optimal control inputs
Wherein (1)>To perform network weights;
the execution network weight update law is:
wherein, kappa ai >0 is a design parameter;
solving the obtained optimal control inputIs the optimal cooperative control strategy under the condition of no disturbance.
8. An electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps of the high-rise fire unmanned aerial vehicle formation optimization control method with disturbance compensation of any one of claims 1-6.
9. A computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the high-rise unmanned fire aircraft formation optimization control method with disturbance compensation of any one of claims 1-6.
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