Adaptive tracking control method, system and device for drone clusters under limited communication

Abstract

The present application relates to a method, system and device for adaptive tracking control of a drone cluster under communication constraints with precise control. The method uses a quantizer to quantize the control input instructions related to the desired flight trajectory, including speed, pitch angle and yaw angle, to obtain a control input quantization signal. The current error data is obtained according to the control input quantization signal and the current real-time flight data. An adaptive controller designed based on error dynamics and adaptive backstepping method is used to obtain linear velocity quantization instructions and angular velocity quantization instructions according to the current error data. The drone autopilot model is used to adjust the drone attitude and speed according to the linear velocity quantization instructions and angular velocity quantization instructions to achieve adaptive control of the drone aircraft trajectory. The method can achieve effective and accurate tracking control of each drone in the context of a large number of drone clusters with massive interactive data.

Description

Technical Field

[0001] The present application relates to the field of drone control technology, and in particular to a method, system and device for adaptive tracking control of a drone cluster under limited communication. Background Art

[0002] Unmanned aerial vehicle (UAV) clusters have unique advantages such as no blind spots, zero casualties, all-day and all-weather, as well as time-space consistency, information consistency, and spatial situation consistency. In the cross-domain collaborative operation of UAV clusters, due to the communication networking composed of multiple UAVs, the massive data interaction between networking nodes and nodes and ground stations, and in order to ensure real-time performance, communication overload is very likely to occur, affecting the timeliness of cluster tracking.

[0003] However, quantifying the massive amount of interactive data to meet the requirements of real-time communication will help the swarm formation to quickly track time-sensitive non-cooperative targets. With the rapid increase in the scale of drones and the number of time-sensitive targets, the behavioral strategy dimensions and state combinations of the group are increasing exponentially, which has a particularly prominent impact on the information quantification processing capabilities and efficiency of swarm communication networks. This is one of the key technologies that urgently need to be solved. Summary of the invention

[0004] Based on this, it is necessary to provide a method, system and equipment for adaptive tracking control of drone clusters under communication constraints, which can perform precise control in the context of massive interactive data, in response to the above technical problems.

[0005] A method for adaptive tracking control of a drone cluster under communication constraints, the method being implemented in a drone cluster, the drone cluster comprising a plurality of drones communicating with each other, the method being implemented in each of the drones, comprising:

[0006] Obtaining control input instructions related to a desired flight trajectory, wherein the control input instructions include speed, pitch angle, and yaw angle;

[0007] quantizing the control input command using a quantizer to obtain a control input quantized signal;

[0008] Obtaining current error data according to the control input quantized signal and current real-time flight data, and using an adaptive controller to obtain a linear velocity quantized instruction and an angular velocity quantized instruction according to the current error data, wherein the adaptive controller is designed based on error dynamics and an adaptive backstepping method;

[0009] The UAV autopilot model is used to adjust the attitude and speed of the UAV according to the linear velocity quantization instruction and the angular velocity quantization instruction, so as to realize adaptive control of the trajectory of the UAV aircraft.

[0010] In one embodiment, the control input instruction is time series data including a plurality of consecutive moments, each moment including a corresponding speed expected value, a pitch angle expected value and a yaw angle expected value.

[0011] In one embodiment, when the current error data is obtained according to the control input quantization signal and the current flight real-time data:

[0012] The speed value, pitch angle value and yaw angle value collected in real time at the current moment, and the expected speed value, pitch angle expected value and yaw angle expected value at the corresponding moment of the control input quantization signal are used for calculation to obtain the speed error value, pitch angle error value and yaw angle error value respectively.

[0013] In one of the embodiments, the quantizer is a hysteresis quantizer.

[0014] In one of the embodiments, the UAV autopilot model is based on a class of high-order nonlinear systems with some unknown parameters and is constructed using quantitative theory.

[0015] In one embodiment, the drone autopilot model is represented as:

[0016] ;

[0017] In the above formula, , as well as represents the linear velocity quantization instruction and the angular velocity quantization instruction, , as well as represents an unknown positive constant, , as well as Indicates the speed, pitch angle and yaw angle of each aircraft, which are the actual control values ​​of the drone at the current moment. , as well as The acceleration, pitch rate, and yaw rate of each drone.

[0018] In one of the embodiments, in the adaptive controller, the adaptive control law is expressed as:

[0019] ;

[0020] In the above formula, Indicates Drone speed control amount, Indicates The pitch rate control value of the drone, Indicates The yaw rate control value of the drone, Represents unknown parameters, represents the estimated value of the unknown parameter, , , Respectively represent The speed error, pitch rate error and yaw rate error of the drone, , and Both represent intermediate quantities. and Denotes unknown positive parameter, subscript Indicates UAV, subscript 1, 2, 3 refer to , , Related parameters.

[0021] The present application also provides an adaptive tracking control system for a cluster of unmanned aerial vehicles under communication constraints. The system includes a plurality of unmanned aerial vehicles that communicate with each other, and the above-mentioned adaptive tracking control method for a cluster of unmanned aerial vehicles under communication constraints is implemented in each unmanned aerial vehicle.

[0022] The present application also provides a UAV cluster adaptive tracking control device under communication restriction, the device comprising:

[0023] An expected data acquisition module is used to acquire control input instructions related to the expected flight trajectory, wherein the control input instructions include speed, pitch angle and yaw angle;

[0024] A data quantization module, used for quantizing the control input instruction by using a quantizer to obtain a control input quantization signal;

[0025] A real-time control instruction obtaining module, used to obtain current error data according to the control input quantized signal and current flight real-time data, and to obtain linear velocity quantized instructions and angular velocity quantized instructions according to the current error data using an adaptive controller, wherein the adaptive controller is designed based on error dynamics and an adaptive backstepping method;

[0026] The adaptive control module uses the UAV autopilot model to adjust the UAV attitude and speed according to the linear velocity quantization instruction and the angular velocity quantization instruction, so as to realize adaptive control of the UAV aircraft trajectory.

[0027] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0028] Obtaining control input instructions related to a desired flight trajectory, wherein the control input instructions include speed, pitch angle, and yaw angle;

[0029] quantizing the control input command using a quantizer to obtain a control input quantized signal;

[0030] Obtaining current error data according to the control input quantized signal and current real-time flight data, and using an adaptive controller to obtain a linear velocity quantized instruction and an angular velocity quantized instruction according to the current error data, wherein the adaptive controller is designed based on error dynamics and an adaptive backstepping method;

[0031] The UAV autopilot model is used to adjust the attitude and speed of the UAV according to the linear velocity quantization instruction and the angular velocity quantization instruction, so as to realize adaptive control of the trajectory of the UAV aircraft.

[0032] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0033] Obtaining control input instructions related to a desired flight trajectory, wherein the control input instructions include speed, pitch angle, and yaw angle;

[0034] quantizing the control input command using a quantizer to obtain a control input quantized signal;

[0035] Obtaining current error data according to the control input quantized signal and current real-time flight data, and using an adaptive controller to obtain a linear velocity quantized instruction and an angular velocity quantized instruction according to the current error data, wherein the adaptive controller is designed based on error dynamics and an adaptive backstepping method;

[0036] The UAV autopilot model is used to adjust the attitude and speed of the UAV according to the linear velocity quantization instruction and the angular velocity quantization instruction, so as to realize adaptive control of the trajectory of the UAV aircraft.

[0037] The above-mentioned adaptive tracking control method, system and device for drone clusters under limited communication, quantizes the control input instructions related to the desired flight trajectory, including speed, pitch angle and yaw angle, by using a quantizer to obtain a control input quantization signal, obtains the current error data according to the control input quantization signal and the current real-time flight data, uses an adaptive controller designed based on error dynamics and adaptive backstepping method, obtains linear velocity quantization instructions and angular velocity quantization instructions according to the current error data, and uses a drone autopilot model to adjust the drone attitude and speed according to the linear velocity quantization instructions and angular velocity quantization instructions, so as to realize adaptive control of the drone aircraft trajectory. This method can realize effective and accurate tracking control of each drone in the context of large-scale drone clusters with massive interactive data. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of a flow chart of a method for adaptive tracking control of a drone cluster under limited communication in one embodiment;

[0039] Figure 2 A schematic diagram of a cluster formation tracking control structure in one embodiment;

[0040] Figure 3 This is a schematic diagram of the "leader-wingman" structure of a drone formation in an experiment;

[0041] Figure 4 A schematic diagram of the reconstructed path of a multi-UAV formation in an experiment;

[0042] Figure 5 This is a schematic diagram of the motion posture curve of a drone in an experiment;

[0043] Figure 6 This is a schematic diagram of the angular velocity change curve of a drone in an experiment;

[0044] Figure 7 A schematic diagram of the speed curves of a drone in three directions in an experiment;

[0045] Figure 8 This is a schematic diagram of the speed tracking error curve of the UAV in three directions in an experiment;

[0046] Fig. 9 A schematic diagram of a quantized instruction angle curve in an experiment;

[0047] Fig.10 This is a schematic diagram of the pitch rate change curve of the UAV under the action of three different methods in an experiment;

[0048] Fig.11 This is a schematic diagram of the tracking error curve of a drone under three different methods in an experiment;

[0049] Fig.12 This is a schematic diagram of the pitch angle change curve of the drone before and after signal quantization in an experiment;

[0050] Fig.13 It is a structural block diagram of a UAV cluster adaptive tracking control device under communication limitation in one embodiment;

[0051] Fig.14 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0053] In view of the existing UAV cluster control technology, the timeliness and security of autonomous collaborative tracking of UAV clusters under large-scale communication networking have not been considered. In this application, Figure 1 As shown, an adaptive cooperative tracking control strategy for cluster formation system design with communication quantization is provided to realize a real-time tracking adaptive tracking control method for a cluster of unmanned aerial vehicles under communication constraints. The method is implemented in a cluster of multiple unmanned aerial vehicles that communicate with each other. The method is implemented in each unmanned aerial vehicle and specifically includes the following steps:

[0054] Step S100, obtaining control input instructions related to the desired flight trajectory, the control input instructions including speed, pitch angle and yaw angle.

[0055] Step S110: quantize the control input instruction using a quantizer to obtain a control input quantized signal.

[0056] Step S120, obtaining current error data according to the control input quantization signal and the current real-time flight data, and using an adaptive controller to obtain linear velocity quantization instructions and angular velocity quantization instructions according to the current error data, wherein the adaptive controller is designed based on error dynamics and an adaptive backstepping method.

[0057] Step S130, using the UAV autopilot model to adjust the UAV attitude and speed according to the linear velocity quantization instruction and the angular velocity quantization instruction, so as to achieve adaptive control of the UAV aircraft trajectory.

[0058] Considering that the UAV cluster has a large signal delay due to communication overload during the mission, and the UAV is a high-order system with complex dynamic performance and cannot accurately obtain information. The application of traditional adaptive control on UAV clusters is not very ideal. Usually, the mathematical model of the cluster is not very accurate and cannot guarantee the convergence and stability of the system. Therefore, adaptive control based on the inversion method is proposed in this method.

[0059] Furthermore, considering that drone clusters need to interact and share information during mission execution, and are affected by internal and external factors, the amount of data in the communication network has increased dramatically, making it very important for drones to communicate with each other and with ground stations. Due to the limitation of network bandwidth and the widespread use of digital sensors, the signals in the system need to be quantized before being sent through the network to meet the preset performance requirements of the drone cluster.

[0060] First, the UAV swarm in this method performs missions in a distributed “leader-wingman” formation flight, and its kinematic model is expressed as:

[0061] (1)

[0062] In formula (1), Indicates The location of the drone, Indicates the speed of the drone, , , Respectively represent the speed, pitch angle and yaw angle of the drone. Indicates the pitch rate of the drone, Indicates the pitch rate of the drone, Indicates the yaw rate of the drone, Indicates the yaw rate of the drone, subscript Indicates the first A drone.

[0063] Expressing formula (1) in matrix form, we can obtain:

[0064] (2)

[0065] Furthermore, the speed control law, pitch angle and yaw angle control law of each drone in the drone formation can ensure that the drone flies at a preset speed, pitch angle and yaw angle.

[0066] Quantify the control inputs of the UAV motion model, including quantized velocity , pitch angle and yaw angle , combined with practical problems, from the perspective of convenient solution, based on a class of high-order nonlinear systems with some unknown parameters, the autopilot model is constructed using quantitative theory, that is, the autopilot model used in the above step S130, as shown below:

[0067] (3)

[0068] In formula (3), , as well as They represent linear velocity quantization instructions and angular velocity quantization instructions respectively. , as well as represents an unknown positive constant, , as well as Indicates the speed, pitch angle and yaw angle of each drone, which is the actual control value of the drone at the current moment. , as well as The acceleration, pitch rate, and yaw rate of each drone.

[0069] Based on the UAV cluster and single-unit autopilot model, a cooperative tracking control law under communication overload is designed to achieve the purpose of cooperative tracking of time-sensitive cooperation targets and maintaining formation stability under limited cluster communication.

[0070] In step S100, the expected flight trajectory is the flight trajectory of the UAV planned in a complex mission and dynamic environment.

[0071] In the process of interaction and sharing of communication data between drone clusters, the demand for bandwidth between drones can be effectively reduced by quantizing the input signal. At the same time, in order to avoid the occurrence of chattering, in this method, that is, in step S110, a hysteresis quantizer is used, which is expressed as:

[0072] (4)

[0073] In formula (5), Indicates Control input for drones, Indicates the minimum value of the drone input signal, represents the quantized density, , represents the control input, represents the quantized input, represents the quantization parameter, , represents the rate of the control quantity, Indicates time.

[0074] In actual engineering, the dynamic communication networking of drone clusters is very critical to the success of mission execution. In order to solve practical needs, the scientific problems extracted from it are refined and studied, so the nonlinear communication overload signal is quantified.

[0075] Hysteresis Quantizer Decomposition into linear terms and nonlinear terms , as shown below:

[0076] (5)

[0077] In order to facilitate the design of control law under communication constraints, the nonlinear signal is decoupled, the nonlinear term is supplemented with control, and the control law is designed for the linear term.

[0078] For any constant , there is an inequality Established, of which .

[0079] In the process of control law design, the nonlinear terms are mathematically processed using this lemma to obtain the control law under communication constraints.

[0080] Assuming a nonlinear system , if there exists a differentiable continuous function : satisfies bounded positive definite, and , and meet ,in , , , then the system has semi-global practical finite-time stability. satisfy ,in is a parameter to be determined. When the system status is always within an arbitrary small neighborhood containing the origin, that is:

[0081] (6)

[0082] Next, when designing the adaptive controller in step S120, this paper considers the influence of input quantization on the UAV cluster tracking effect, and designs the control law for the standard UAV autopilot model. Since the UAV cluster system is a high-order, dynamic and uncertain complex system, the adaptive control response speed and robustness of the UAV cluster tracking control under limited communication are not high, and adaptive control usually requires the identification of dynamic characteristic parameters, and then for the mathematical model of the UAV cluster, the convergence and stability of the system are often not guaranteed. Therefore, an adaptive backstepping method is proposed to make up for the fact that adaptive control does not meet the needs of cluster control, thereby ensuring the convergence and stability of the system. The structure of its cluster formation tracking control is as follows: Figure 2 shown.

[0083] Combination Figure 2 , the steps in this method are described. In step S100, the control input command obtained by each drone includes time series data of multiple consecutive moments, each moment including the corresponding speed expectation value, pitch angle expectation value and yaw angle expectation value. , elevation angular rate Yaw rate Then in step S110, the control input instruction is quantized by the hysteresis quantizer mentioned above to obtain the control input quantization signal, that is, the speed quantization value , Quantized value of the elevation angular rate The yaw rate is quantized .

[0084] Next, when the current error data is obtained according to the control input quantization signal and the current real-time flight data: the speed value, pitch angle value and yaw angle value collected in real time at the current moment, and the speed expectation value at the corresponding moment of the control input quantization signal are used. , the expected value of the pitch angular rate is And the expected value of the yaw rate is Calculate and get the speed error values ​​respectively , Pitch angle rate error value And the yaw rate error .

[0085] In this embodiment, an adaptive controller is designed based on error dynamics and adaptive backstepping method. The process is as follows:

[0086] The design of adaptive quantized control law includes two steps: , as well as and the final control signal , as well as .

[0087] Define the tracking error as follows:

[0088] (7)

[0089] In formula (7): Respectively represent The desired formation position of the drones, , , Respectively represent The position error of the UAV, represents the reference position of the drone, represents the desired position of the drone, , Respectively represent the expected pitch angle and expected yaw angle of each drone, , They represent the reference pitch angle and reference yaw angle of the UAV respectively.

[0090] Taking the time derivative of formula (7), we can get:

[0091] (8)

[0092] In formula (8), the points above each parameter represent the corresponding first-order derivative.

[0093] Construct the Lyapunov function as follows:

[0094] (9)

[0095] The time derivative of formula (9) is as follows:

[0096] (10)

[0097] Substituting formula (7) and formula (8) into formula (9) and formula (10) to simplify the equation, we can obtain:

[0098] (11)

[0099] In formula (11), Indicates the intermediate quantity, and Represents different functions, It means partial derivative.

[0100] From formula (11), we can get the virtual control signal , as well as , are virtual control signals representing the speed, pitch rate, and yaw rate of the drone, as shown below:

[0101] (12)

[0102] (13)

[0103] (14)

[0104] In formula (12) to formula (14), represents the inverse sign, , as well as All are normal numbers.

[0105] Based on this, considering The derivative of is as follows:

[0106] (15)

[0107] In formula (15):

[0108] ;

[0109] ;

[0110] ;

[0111] Next, define the error variable as follows:

[0112] (16)

[0113] Taking the derivative of formula (16), we can get:

[0114] (17)

[0115] Substituting formula (16) into formula (17), we can obtain:

[0116] (18)

[0117] Construct the Lyapunov function as follows:

[0118] (19)

[0119] In formula (19): , as well as The parameters are , as well as The estimation error of , , represents an unknown constant.

[0120] Taking the derivative of formula (19), we can get:

[0121] (20)

[0122] From Young's inequality, we can get formula (21) to formula (23):

[0123] (twenty one)

[0124] (twenty two)

[0125] (twenty three)

[0126] In formulas (21) to (23), Indicates the intermediate quantity, Indicates the minimum control amount of the drone,

[0127] The following control law is defined as follows:

[0128] , , (twenty four)

[0129] The designed parameter adaptive update law is shown in formula (25):

[0130] (25)

[0131] In formula (25), is a projection operator, ensuring the parameters , as well as Both are greater than zero.

[0132] Combining formula (21) to formula (25), we can get:

[0133] (26)

[0134] In formula (26): All are normal numbers.

[0135] The final control law, i.e., the adaptive controller, can be obtained from formula (26), as shown in formula (27):

[0136] (27)

[0137] in, ;

[0138] ;

[0139] ;

[0140] In formula (27), Indicates Drone speed control amount, Indicates The pitch rate control value of the drone, Indicates The yaw rate control value of the drone, Represents unknown parameters, represents the estimated value of the unknown parameter, , , Respectively represent The speed error, pitch rate error and yaw rate error of the drone, , and Both represent intermediate quantities. and Indicates unknown parameters, subscript Indicates UAV, subscript 1, 2, 3 refer to , , Related parameters.

[0141] The stability of the adaptive controller shown in formula (27) is further analyzed.

[0142] For the time-varying reference trajectory And its corresponding first-order derivative And the second-order derivative are all bounded, then for the quantization parameter In general, all system state changes in the world are uniformly bounded.

[0143] Therefore, by designing reasonable parameters, the tracking error of the UAV formation can be converged to a small neighborhood near the origin. The proof process is as follows:

[0144] Based on Young's inequality, the following inequality transformation can be performed, as shown in formula (28):

[0145] (28)

[0146] From formula (28), we can get:

[0147] (29)

[0148] According to Young's inequality, we can get:

[0149] (30)

[0150] According to formula (26) to formula (30), we can get:

[0151] (31)

[0152] In formula (31):

[0153] ;

[0154] ;

[0155] The algebraic solution of formula (31) satisfies the following inequality:

[0156] , (32)

[0157] Therefore, we can get , , are all bounded.

[0158] At the same time, the following relationship can be obtained, as shown below:

[0159] (33)

[0160] From formula (33), it is easy to know that , , , as well as are all bounded, so the designed control law can make the formation system converge to a small neighborhood near the origin during the target tracking process. At the same time, from formula (31), we can get:

[0161] (34)

[0162] Furthermore, in this paper, the effectiveness of this method is demonstrated through experimental simulation data.

[0163] Initial condition setting: Consider formula (2) NThe system consists of a UAV system model, a hysteresis quantizer in formula (4), an actual control law in formula (27), and a parameter adaptive update law in formula (25). In the simulation experiment, 13 UAVs form a "leader-wingman" formation structure. Each UAV is numbered UAV#1, UAV#2, UAV#3, UAV#4, UAV#5, UAV#6, UAV#7, UAV#8, UAV#9, UAV#10, UAV#11, UAV#12, and UAV#13, among which UAV#1 is the leader and the other 12 are wingmen. The schematic diagram of the "leader-wingman" structure of the UAV formation is shown in the figure below. Figure 3 shown.

[0164] Semi-physical simulation was used for the experiment, and its main parameters were: wingspan c =2.5 m, chord length d =3 m, weight m=10kg, cruising speed v =3 m / s, the input parameters of the model need to be provided , and output parameters , and .

[0165] The motion characteristics of the UAV are considered, and the model of formula (2) and the autopilot model of formula (3) are used. The designed quantitative adaptive control law is applied to the multi-UAV formation, so that the tracking error can be quickly converged. At the same time, the initial position of the UAV is , the desired formation position and the nonlinear disturbance term of the quantized signal in the standard autopilot model The relevant parameters of the autopilot model are set as shown in Table 1.

[0166] Table 1 Autopilot model related parameters

[0167]

[0168] At the same time, the unknown parameters in the autonomous driving model , the initial speed of each drone is and , the desired reference trajectory is set to ,as well as .

[0169] Experimental results:

[0170] Tracking characteristic analysis,In order to verify the dynamic characteristics of the designed quantitative adaptive time-varying tracking control law in the multi-UAV formation tracking process, the above initial conditions are used to carry out simulation experiments, and the results are as follows Figures 4 to 9 shown.

[0171] Figure 4 The projection curve of the multi-UAV formation reconstructed path on the horizontal plane is given. Figure 4 It can be seen that the 13 UAVs fly at different speeds and azimuths from different starting points, and at the same time follow a time-varying reference trajectory ( , ) tracks the desired designated position, undergoes cluster assembly, formation maintenance, and then forms a designated formation for flight.

[0172] Figure 5 The motion posture diagram of the UAV (UAV#6) is given. It can be seen from the figure that the UAV tracks the desired specified position according to the time-varying reference trajectory. At the initial moment, the yaw angle is close to or consistent with the trend of the reference trajectory. Then, under the action of the proposed control method, the UAV adjusts its posture and position in real time to keep consistent with the changing trend of the reference trajectory. This allows the UAV to quickly track the desired specified position. At the same time, when the UAV is about to approach the desired specified position, the change in the yaw angle is small.

[0173] Depend on Figure 6 and Figure 7 It can be seen that with the change of time, the angular rate, speed in three directions and tracking error of the UAV change from a small change at the beginning, to a slight change in the middle stage, until it reaches a stable state. During the assembly process and formation maintenance process, any two UAVs in the formation need to maintain continuous and stable communication. As the size and number of clusters increase, communication overload will cause delays or packet loss in information transmission between UAV clusters. In the "leader-wingman" mode, the wingman is more affected. For this reason, the curve will show the above-mentioned change trend. Under the action of the designed quantitative controller, the UAV will show a larger change. By Figure 8 and Fig. 9 It can be seen that during the tracking process, the UAV cluster quantifies the linear velocity and angular velocity signals, which enables effective information interaction and sharing between UAVs, thereby improving the tracking accuracy and facilitating the commander's decision-making.

[0174] Tracking effect analysis,Using the same initial conditions, the same simulation environment and parameters, the method proposed in this paper is compared with sliding mode control and dynamic surface control. At the same time, the effect of the UAV before and after quantification is compared and analyzed. Figures 10 to 12 shown.

[0175] Depend on Fig.10 It can be seen that with the change of time, the proposed method can make the pitch rate of the UAV change less after quantization, and the communication between UAVs is relatively good, thus maintaining the formation of UAVs well.

[0176] Depend on Fig.11 It can be seen that, as time changes, compared with sliding mode control (SMC) and dynamic surface control (DSC), the proposed method can make the tracking error of the UAV change less and quickly stabilize the area. That is, the UAV quickly approaches the desired position while maintaining a stable formation flight.

[0177] Depend on Fig.12 It can be seen that in the process of information interaction and sharing of drone clusters, there may be phenomena such as signal loss due to communication overload between drones. Therefore, quantized signals are easier to communicate, thereby ensuring that drones can communicate normally and achieve preset performance.

[0178] In the above-mentioned adaptive tracking control method of UAV cluster under communication constraints, this method quantitatively controls the communication networking of the UAV cluster, thereby maintaining the timeliness and stability of the collaborative tracking of the UAV cluster, setting a time-varying reference trajectory, and under the action of the designed adaptive collaborative control law, the UAV cluster autonomously and collaboratively tracks the expected specified position with the time-varying reference trajectory, thereby realizing the real-time collaborative tracking of multiple agents under communication constraints.

[0179] This method designs a quantized adaptive cluster formation time-varying tracking control law for a type of unmanned cluster formation system with limited communication, which is used to reduce the communication load between UAVs and improve efficiency. At the same time, it tracks the expected designated position according to the time-varying reference trajectory to form a "triangle" formation, and then maintains the formation to perform combat missions.

[0180] In actual engineering, as the scale of UAVs increases, the control of formation changes between UAVs is very important. The communication between UAVs needs to maintain real-time and effectiveness to provide accurate position information and attitude information. For this reason, the control strategy proposed in this paper is compared and analyzed with the existing modern control theory methods (sliding mode control, dynamic surface control). In terms of performance indicators, they are relatively superior. In addition, stability theory and semi-physical simulation are used to verify that the proposed method is effective and reasonable.

[0181] In addition, the proposed method can not only realize the UAV cluster tracking the desired specified formation, but also maintain the desired formation flight. At the same time, the UAV formation system can still quickly and stably track the target under the condition of quantitative input.

[0182] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0183] In one embodiment, a drone cluster system is also provided, which includes a plurality of drones communicating with each other, and the above-mentioned drone cluster adaptive tracking control method under limited communication is implemented in each drone.

[0184] In one embodiment, Fig.13 As shown, a UAV cluster adaptive tracking control device under communication restriction is provided, comprising: an expected data acquisition module 200, a data quantization module 210, a real-time control instruction acquisition module 220 and an adaptive control module 230, wherein:

[0185] The expected data acquisition module 200 is used to acquire control input instructions related to the expected flight trajectory, wherein the control input instructions include speed, pitch angle and yaw angle;

[0186] A data quantization module 210, configured to quantize the control input instruction using a quantizer to obtain a control input quantization signal;

[0187] A real-time control instruction obtaining module 220 is used to obtain current error data according to the control input quantized signal and the current flight real-time data, and to obtain a linear velocity quantized instruction and an angular velocity quantized instruction according to the current error data using an adaptive controller, wherein the adaptive controller is designed based on error dynamics and an adaptive backstepping method;

[0188] The adaptive control module 230 uses the UAV autopilot model to adjust the UAV attitude and speed according to the linear velocity quantization instruction and the angular velocity quantization instruction to achieve adaptive control of the UAV aircraft trajectory.

[0189] For the specific limitations of the adaptive tracking control device for a swarm of unmanned aerial vehicles under communication constraints, please refer to the limitations of the adaptive tracking control method for a swarm of unmanned aerial vehicles under communication constraints mentioned above, which will not be repeated here. Each module in the above-mentioned adaptive tracking control device for a swarm of unmanned aerial vehicles under communication constraints can be implemented in whole or in part through software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0190] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig.14 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for adaptive tracking control of a drone cluster under communication constraints is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0191] Those skilled in the art will understand that Fig.14 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0192] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0193] Obtaining control input instructions related to a desired flight trajectory, wherein the control input instructions include speed, pitch angle, and yaw angle;

[0194] quantizing the control input command using a quantizer to obtain a control input quantized signal;

[0195] Obtaining current error data according to the control input quantized signal and current real-time flight data, and using an adaptive controller to obtain a linear velocity quantized instruction and an angular velocity quantized instruction according to the current error data, wherein the adaptive controller is designed based on error dynamics and an adaptive backstepping method;

[0196] The UAV autopilot model is used to adjust the attitude and speed of the UAV according to the linear velocity quantization instruction and the angular velocity quantization instruction, so as to realize adaptive control of the trajectory of the UAV aircraft.

[0197] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0198] Obtaining control input instructions related to a desired flight trajectory, wherein the control input instructions include speed, pitch angle, and yaw angle;

[0199] quantizing the control input command using a quantizer to obtain a control input quantized signal;

[0200] Obtaining current error data according to the control input quantized signal and current real-time flight data, and using an adaptive controller to obtain a linear velocity quantized instruction and an angular velocity quantized instruction according to the current error data, wherein the adaptive controller is designed based on error dynamics and an adaptive backstepping method;

[0201] The UAV autopilot model is used to adjust the attitude and speed of the UAV according to the linear velocity quantization instruction and the angular velocity quantization instruction, so as to realize adaptive control of the trajectory of the UAV aircraft.

[0202] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0203] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0204] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for adaptive tracking control of drone clusters under limited communication, characterized in that: The method is implemented in a drone cluster, the drone cluster includes a plurality of drones communicating with each other, and the method is implemented in each of the drones, including: Obtaining control input instructions related to a desired flight trajectory, wherein the control input instructions include speed, pitch angle, and yaw angle; quantizing the control input command using a quantizer to obtain a control input quantized signal; According to the control input quantized signal and the current real-time flight data, current error data is obtained, and an adaptive controller is used to obtain a linear velocity quantized instruction and an angular velocity quantized instruction according to the current error data, wherein the adaptive controller is designed based on error dynamics and an adaptive backstepping method, wherein in the adaptive controller, the adaptive control law is expressed as: ； In the above formula, Indicates Drone speed control amount, Indicates The pitch rate control value of the drone, Indicates The yaw rate control value of the drone, Represents unknown parameters, represents the estimated value of the unknown parameter, , , Respectively represent The speed error, pitch rate error and yaw rate error of the drone, , and Both represent intermediate quantities. and Denotes unknown positive parameter, subscript Indicates UAV, subscript 1, 2, 3 refer to , , Related parameters; The UAV autopilot model is used to adjust the attitude and speed of the UAV according to the linear velocity quantization instruction and the angular velocity quantization instruction, so as to realize adaptive control of the trajectory of the UAV aircraft.

2. The adaptive tracking control method for drone swarm under limited communication according to claim 1 is characterized in that: The control input instruction is time series data including a plurality of consecutive moments, and each moment includes a corresponding speed expected value, a pitch angle expected value, and a yaw angle expected value.

3. The adaptive tracking control method for drone swarm under limited communication according to claim 2 is characterized in that: When the current error data is obtained according to the control input quantization signal and the current flight real-time data: The speed value, pitch angle value and yaw angle value collected in real time at the current moment, and the expected speed value, pitch angle expected value and yaw angle expected value at the corresponding moment of the control input quantization signal are used for calculation to obtain the speed error value, pitch angle error value and yaw angle error value respectively.

4. The adaptive tracking control method for drone swarm under limited communication according to claim 3 is characterized in that: The quantizer is a hysteresis quantizer.

5. The adaptive tracking control method for drone swarm under limited communication according to claim 4 is characterized in that: The UAV autopilot model is based on a class of high-order nonlinear systems with some unknown parameters and is constructed using quantitative theory.

6. The method for adaptive tracking control of drone swarms under limited communication according to claim 5 is characterized in that: The UAV autopilot model is expressed as: In the above formula, , as well as represents the linear velocity quantization instruction and the angular velocity quantization instruction, , as well as represents an unknown positive constant, , as well as Indicates the speed, pitch angle and yaw angle of each aircraft, which are the actual control values ​​of the drone at the current moment. , as well as Represents the acceleration, pitch rate, and yaw rate of each drone.

7. An adaptive tracking control system for drone swarms under limited communication, characterized in that: The system includes a plurality of mutually communicating UAVs, and the UAV cluster adaptive tracking control method under communication restriction described in any one of claims 1 to 6 is implemented in each UAV.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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