A multi-time scale dual-channel based unmanned aerial vehicle communication and synchronization control method

Abstract

This invention discloses a method for UAV communication and synchronization control based on a multi-timescale dual-channel architecture, comprising the following steps: S1, establishing a nonlinear singular perturbation system model for each UAV node; S2, configuring dual channels for each UAV node; S3, selecting to transmit either the original measurement value or the differential value; S4, generating quantized symbols and transmitting them to a remote controller via the corresponding channel; S5, decoding the received quantized symbols to obtain the reconstructed measurement value; S6, solving for the controller gain that meets stability requirements; S7, designing a controller based on the reconstructed measurement value and the controller gain, generating control inputs, and applying them to the UAV nodes to achieve high-precision synchronization control of the UAV system network nodes. This invention achieves high-precision synchronization control of UAV system network nodes and ensures system stability under conditions of limited total bit rate through dual-channel event-triggered differential transmission and dynamic bit allocation.

Description

Technical Field

[0001] This invention relates to the field of automatic control technology, specifically to a method for communication and synchronization control of unmanned aerial vehicles (UAVs) based on a multi-timescale dual-channel architecture. Background Technology

[0002] Unmanned aerial vehicle (UAV) systems are increasingly widely used in modern technology, from logistics and delivery to environmental monitoring, from agricultural plant protection to emergency rescue. Due to their flexibility and efficiency, UAVs are becoming indispensable tools. However, as UAV application scenarios become more complex and mission requirements more diverse, collaborative operation between UAV systems is becoming increasingly important. In this context, collaborative operation of multiple UAV systems requires precise synchronization of status information and efficient communication mechanisms to ensure smooth mission execution and stable system operation.

[0003] However, existing communication and control methods face numerous challenges in meeting these requirements. Due to limited communication bandwidth, network latency, and data loss, traditional communication and control methods struggle to meet the demands of high-precision synchronization. Furthermore, the dynamic characteristics of UAV systems often exhibit differences between fast and slow states, such as fast-responding propulsion systems and slowly changing navigation systems. This multi-timescale characteristic can be modeled using singular perturbation systems (SPS), introducing singular perturbation parameters to differentiate between fast and slow dynamics.

[0004] While some existing research focuses on the synchronization control of singularly perturbated systems, most methods fail to fully utilize the bandwidth resources of communication networks and the characteristics of singularly perturbated systems. Existing methods lack effective transmission mechanisms and bit allocation strategies when handling UAV state synchronization under bandwidth constraints, resulting in low communication efficiency. Furthermore, existing research fails to adequately consider the redundancy of slow-state data when dealing with fast and slow states, leading to wasted resources during slow-state updates, increased data quantization errors, and reduced synchronization control accuracy. Moreover, existing technologies also fail to provide efficient communication support for the high-frequency update requirements of fast states, further exacerbating the overall communication inefficiency.

[0005] Therefore, when facing the need for synchronous control of fast and slow states in UAV systems, existing technologies not only have significant shortcomings in communication efficiency, but also face many challenges in terms of synchronization accuracy and resource utilization. Summary of the Invention

[0006] To overcome the shortcomings of existing technologies, the present invention aims to provide a UAV communication and synchronization control method based on multi-timescale dual-channel. Under the condition of limited total bit rate, it achieves high-precision synchronization control of UAV system network nodes and ensures system stability through dual-channel event-triggered differential transmission and dynamic bit allocation.

[0007] To achieve the objective of this invention, the following solution is adopted: A method for UAV communication and synchronization control based on multi-timescale dual-channel includes the following steps: S1. Establish a nonlinear singular perturbation system model for UAV nodes. The nonlinear singular perturbation system model decomposes the state vector of the UAV into a slow state vector and a fast state vector, and introduces singular perturbation parameters to describe the time scale separation characteristics of the slow state vector and the fast state vector. S2. Configure dual channels for each UAV node, including a slow channel for transmitting the slow state vector and a fast channel for transmitting the fast state vector, and dynamically allocate bit rates for the slow channel and the fast channel under the condition of limited total bit rate; S3. Based on the difference between the current measurement value of the slow state vector and the fast state vector and the measurement value at the previous transmission moment, design event triggering conditions respectively, and select to transmit the original measurement value or the difference value according to the satisfaction of the event triggering conditions. S4. Based on the dynamically allocated bit rate, a high-dimensional uniform quantizer is used to quantize and encode the selected original measurement value or the differential value to generate quantized code elements, and then transmits them to the remote controller through the corresponding channel. S5. At the remote controller, the received quantized code is decoded to obtain the reconstructed measurement value; S6. By introducing the Lyapunov function based on singular perturbation parameters, solve the sufficient linear matrix inequality condition for the exponential final stability of the synchronization control system of the UAV node, and solve the controller gain that satisfies the stability requirements based on the matrix inequality condition. S7. On the remote controller, a controller is designed based on the reconstructed measurement value and the controller gain to generate control inputs, which are applied to the UAV node to achieve high-precision synchronous control of the UAV system network node.

[0008] Further, in step S3, based on whether the event triggering condition is met, the transmission of the original measurement value or the differential value is selected, specifically including: For the slow state vector, when the norm of the difference value of the slow state vector is less than or equal to the first trigger threshold, it is determined that the first event trigger condition is met, and the difference value of the slow state vector is selected for transmission; otherwise, it is determined that the first event trigger condition is not met, and the original measurement value of the slow state vector is selected for transmission. For the fast state vector, when the norm of the difference value of the fast state vector is less than or equal to the second trigger threshold, it is determined that the second event trigger condition is met, and the difference value of the fast state vector is selected for transmission; otherwise, it is determined that the second event trigger condition is not met, and the original measurement value of the fast state vector is selected for transmission. Wherein, the first trigger threshold is greater than the second trigger threshold.

[0009] Further, in step S2, dynamically allocating bit rates for the slow channel and the fast channel specifically includes: The transmission demand ratio is calculated based on the ratio of the difference norm of the slow state vector to the first trigger threshold, and the ratio of the difference norm of the fast state vector to the second trigger threshold. Based on the transmission demand ratio, the first bit rate allocated to the slow channel and the second bit rate allocated to the fast channel are dynamically adjusted. When the transmission demand ratio is less than 1, it indicates that the change in the fast state vector is greater than the change in the slow state vector, and a higher bit rate is allocated to the fast channel.

[0010] Further, in step S5, at the remote controller, the received quantized symbols are decoded to obtain the reconstructed measurement values, specifically including: If the received quantized code is obtained by quantizing and encoding the original measurement value, then the quantized code is directly decoded to obtain the reconstructed measurement value at the current moment, and the reconstructed measurement value is stored as a new reference value. If the received quantized code is obtained by quantization encoding of the differential value, the quantized code is decoded to obtain the reconstructed differential value, and the reconstructed differential value is added to the reference value of the previous time to obtain the reconstructed measurement value of the current time.

[0011] Further, in step S4, a high-dimensional uniform quantizer is used to quantize and encode the selected transmitted original measurement value or the difference value, specifically including: The number of quantization levels is determined based on the bit rate currently allocated to the slow channel or the fast channel. The dynamic range of the original measurement value or the difference value is divided into multiple uniform sub-intervals corresponding to the number of quantization levels, and each sub-interval corresponds to a quantization symbol. Determine the sub-interval into which the original measurement value or the difference value falls as the value to be quantized, and output the quantized code corresponding to the sub-interval.

[0012] Furthermore, the dynamic range includes: When transmitting raw measurements, its dynamic range is determined by the maximum possible amplitude of the raw measurements. When transmitting differential values, their dynamic range is determined by the maximum possible amplitude of the differential values, and the dynamic range of the differential values ​​is smaller than the dynamic range of the original measurement values.

[0013] Further, in step S7, designing a controller based on the reconstructed measurement values ​​and the controller gain specifically includes: Obtain the target trajectory of the drone node; Calculate the difference between the reconstructed measurement and the measurement of the target trajectory; The difference is multiplied by the controller gain to generate the control input, so that the drone tracks the target trajectory.

[0014] Further, in step S6, by introducing a Lyapunov function based on singular perturbation parameters, the sufficient linear matrix inequality condition for the exponential final stability of the synchronization control system of the UAV node is solved, specifically including: Based on the nonlinear singular perturbation system model and the structure of the controller, a dynamic equation for the synchronization error is established. Construct a Lyapunov function that includes the singular perturbation parameters; Based on the dynamic equation of the synchronization error, the Lyapunov function is differentially analyzed, and the Lipschitz condition for nonlinear functions is applied to derive the linear matrix inequality condition that guarantees the eventual boundedness of the exponent of the synchronization control system.

[0015] Further, in step S6, the controller gain that satisfies the stability requirements is solved based on the matrix inequality conditions, specifically including: The linear matrix inequality conditions are transformed into linear matrix inequalities with respect to the controller gain; The transformed linear matrix inequalities are solved using a solver, and the positive definite matrix in the Lyapunov function and the controller gain are obtained simultaneously.

[0016] Furthermore, the nonlinear singular perturbation system model of the UAV node includes a representation of the coupling relationship between nodes, external disturbances, and quantization errors; in step S6, when solving the sufficient linear matrix inequality condition for the exponential final stability of the synchronous control system of the UAV node, the coupling relationship, external disturbances, and quantization errors are incorporated as bounded terms into the stability analysis of the synchronous control system.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention decomposes the UAV state into slow and fast state vectors and configures independent slow and fast channels for transmission, achieving separate processing of state data with different time scales. Based on this, an event-triggered mechanism based on differential values ​​is introduced, transmitting the original measurement value only when the state change is significant; otherwise, only the differential value is transmitted, effectively reducing data redundancy. Simultaneously, under the condition of limited total bit rate, the bit rate is dynamically allocated to the dual channels, allowing limited bandwidth resources to be rationally allocated according to the actual changing needs of fast and slow states, thereby significantly improving communication efficiency and optimizing bandwidth resource utilization.

[0018] 2. This invention decodes the received quantized symbols at the remote controller end to obtain the reconstructed measurement values, and designs a controller based on these reconstructed measurement values. During the encoding and transmission process, a high-dimensional uniform quantizer is used to quantize and encode the original measurement values ​​or difference values, and quantization errors are reduced through an event-triggered mechanism and a dynamic bit allocation strategy. In particular, differentiated processing mechanisms are designed to address the redundancy problem of slow-state data and the high-frequency update requirements of fast-state data, effectively reducing information loss during data transmission. This provides more accurate state information for high-precision synchronous control, improving the synchronization accuracy and reliability of multi-UAV system collaborative operations.

[0019] 3. This invention introduces a Lyapunov function based on singular perturbation parameters to solve the sufficient linear matrix inequality condition for the exponential final stability of the synchronous control system, and then uses this condition to solve for the controller gain that satisfies the stability requirements. This design ensures that the controller can still guarantee that the synchronization error exponent converges to a bounded range even in the presence of adverse factors such as inter-node coupling, external disturbances, and quantization errors, providing a rigorous theoretical guarantee for the stable operation of the system. Attached Figure Description

[0020] Figure 1 This is a flowchart of a UAV communication and synchronization control method based on multi-timescale dual-channel in an embodiment of the present invention; Figure 2 This is a schematic diagram of the synchronization control model of the unmanned aerial vehicle system in an embodiment of the present invention. Detailed Implementation

[0021] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0022] like Figure 1 As shown, this embodiment of the invention provides a method for UAV communication and synchronization control based on multi-timescale dual-channel, including the following steps: S1. Establish a nonlinear singular perturbation system model for the UAV node. The nonlinear singular perturbation system model decomposes the UAV's state vector into a slow state vector and a fast state vector, and introduces singular perturbation parameters to describe the time-scale separation characteristics of the slow state vector and the fast state vector.

[0023] S2. Configure dual channels for each UAV node, including a slow channel for transmitting the slow state vector and a fast channel for transmitting the fast state vector, and dynamically allocate bit rates for the slow channel and the fast channel under the condition of limited total bit rate.

[0024] S3. Based on the difference between the current measurement value of the slow state vector and the fast state vector and the measurement value at the previous transmission time, design event triggering conditions respectively, and select to transmit the original measurement value or the difference value according to the satisfaction of the event triggering conditions.

[0025] S4. Based on the dynamically allocated bit rate, a high-dimensional uniform quantizer is used to quantize and encode the selected original measurement value or the differential value to generate quantized symbols, which are then transmitted to the remote controller through the corresponding channel.

[0026] S5. At the remote controller, the received quantized code is decoded to obtain the reconstructed measurement value.

[0027] S6. By introducing the Lyapunov function based on singular perturbation parameters, solve the sufficient linear matrix inequality condition for the exponential final stability of the synchronization control system of the UAV node, and solve the controller gain that satisfies the stability requirements based on the matrix inequality condition.

[0028] S7. On the remote controller, a controller is designed based on the reconstructed measurement value and the controller gain to generate control inputs, which are applied to the UAV node to achieve high-precision synchronous control of the UAV system network node.

[0029] This invention presents a multi-timescale dual-channel UAV communication and synchronization control method, primarily used for efficient communication and high-precision synchronization of UAVs with singular perturbation systems. The method utilizes singular perturbation theory to separate fast and slow states. Sensor measurement information is remotely transmitted to the controller via a wireless network through dual channels. Leveraging the characteristics of fast and slow states, an event-triggered differential encoding / decoding protocol is designed, and a high-dimensional uniform quantizer is introduced. Under a limited total bit size, the bit size is dynamically allocated by relatively varying weight parameters to achieve high-precision transmission of measured values. Furthermore, a controller based on the decoder output is designed to achieve high-precision synchronization control of the UAV system network nodes. Additionally, the Lyapunov function based on singular perturbation parameters is used to solve the fully linear matrix inequality (LMI) condition for the exponentially eventually stable system, and the controller gain satisfying the stability condition is obtained.

[0030] The following is a more detailed description of the UAV communication and synchronization control method based on multi-timescale dual channels according to embodiments of the present invention.

[0031] The technical principle of the UAV communication and synchronization control method based on multi-timescale dual-channel according to embodiments of the present invention is as follows: For UAV systems with fast and slow state characteristics, an event-triggered dual-channel transmission mechanism is adopted, which utilizes differential values ​​to efficiently transmit measurement values. Specifically, each UAV node maintains two independent transmission channels: a slow channel for transmitting slow state vectors and a fast channel for transmitting fast state vectors. Slow state vectors change relatively slowly, while fast state vectors have a rapid response to system dynamics. At each time step, the node first calculates the measurement values ​​of the current slow and fast state vectors and the difference between them and the measurement values ​​at their respective previous transmission times. Subsequently, based on preset event triggering conditions, it determines whether to transmit the original measurement value or the differential value: if the differential value exceeds a preset threshold, it indicates a significant state change, and the original measurement value is transmitted to update the state information at the remote controller; if the differential value does not exceed the threshold, the differential value is transmitted to reduce data redundancy. For slow state vectors, due to their slow changes, the threshold may not be exceeded most of the time, thus the differential value is transmitted at a higher frequency; for fast state vectors, due to their rapid changes, the original measurement value may need to be transmitted more frequently to maintain the real-time nature of the information. At the remote controller, upon receiving the differential value, the decoded differential value at that moment is compared with the measured value decoded at the previous moment to update the state estimate. This event-triggered transmission method based on differential values ​​not only reduces the amount of data that needs to be transmitted but also ensures that the original measured value is transmitted only when the state changes significantly, thereby optimizing the use of communication resources and improving the system's synchronization control accuracy. Figure 2 The figure shows a synchronous control model for an unmanned aerial vehicle (UAV) system.

[0032] In this embodiment, the specific details of the singular perturbation system modeling are as follows: Considering the complex network of multi-node UAVs, whose states exhibit multi-timescale characteristics, the following system state equation can be established: in, Indicates the node number. ; This indicates the inclusion of a fast state vector. and slow state vector state vector ,and It is the control input. Singular Perturbation Parameter (SPP) It is a small positive constant used to control the separation of fast and slow time scales. Matrix By submatrix and The composition is an internal coupling matrix used to define the relationships between elements within a node and its neighboring nodes. External interference... Satisfy constraints . It is by and The parameter matrix formed. and It is a known constant matrix. It is a symmetric coupled configuration matrix, where Represents a node Can receive from nodes The data stream, and This indicates that the message cannot be received.

[0033] nonlinear functions and Follow the following Lipschitz conditions: in, and .

[0034] The target trajectory is This can be interpreted as an isolated node in a drone swarm, corresponding to the solution of the following dynamical system: in, It belongs to the known set The initial value.

[0035] The measurement output is given by the following formula: in, in, Depend on and Composition, and and It is a known constant matrix.

[0036] In this embodiment, the encoding and decoding process is triggered by a dual-channel event under bit rate constraints, as detailed below: Assigned to sensor The bit rate is denoted as For nodes Fixed bit budget , measured value It is divided into a slow lane and a fast lane, denoted as follows: and The goal is to maximize utility within a total bit constraint by allocating different numbers of bits to the two channels. The following model can be established: in, Corresponding to the total bit rate obtained by the entire network, and These represent the number of bits allocated to the slow state and the fast state, respectively.

[0037] To improve the efficiency of sensor data transmission in bandwidth-constrained channels, data compression techniques must be deployed. Given the redundancy of sensor data, differential encoding / decoding techniques are introduced to simplify the data and reduce communication overhead. Definition This is the measurement value transmitted at the last non-triggered moment. The event-triggered differential encoding / decoding model is as follows: Based on the differential principle described above, an event-triggered hybrid differential transmission encoding and decoding scheme is designed. Definition This is a binary variable, taking the value 0 or 1. For the slow state, the following relationship holds: in, For trigger coefficient, It is a tiny constant used to prevent the measurement dynamic range from being too small to trigger. This indicates that the trigger condition has been met, and at this time, the differential value is transmitted instead of the original measurement value; This indicates that the original measurement values ​​have been transmitted. This indicates the maximum value of the dynamic range measured in slow conditions.

[0038] Similarly, the following triggering conditions apply to fast states: in, and Similar to the definition of a slow state, the triggering condition for a fast state differs from that of a slow state because the time scale of a fast state is smaller. Therefore, a smaller trigger threshold needs to be designed, and there is no need to set a tiny constant term.

[0039] The above trigger thresholds are respectively denoted as slow state. and fast state Based on the difference and the threshold value, the relative change metric is designed as follows: Therefore, this invention defines a transmission demand ratio to assess the importance of fast and slow states, thereby achieving reasonable and dynamic bit allocation.

[0040] The importance of fast and slow states is due to Value judgment: when When the bit ratio is high, it indicates that the fast state is relatively more important and should be allocated a larger proportion of bits; conversely, it indicates that the slow state has a larger change amplitude and also needs to be focused on. Based on this, design a proportional allocation strategy: In this embodiment, the dynamic quantizer is described in detail below: To address bandwidth limitations, a high-dimensional dynamic uniform quantizer is introduced. Taking the slow state as an example, when... Time (i.e., transmitting the original measurement value) ),set up If it is a positive scalar, then the node The slow state quantization region can be described as: in, yes of -th element. Slow-state quantization level is denoted as At this time, the hyperrectangle Divided evenly into A smaller sub-hyperrectangle, labeled as: Quantification level by Constraints are applied to ensure the uniqueness of sub-hyperrectangle encodings. A proportional allocation strategy is used for nodes. Slow state allocation bit rate Therefore, the maximum quantization level can be expressed as: in, This represents the floor function.

[0041] set up In order to make The established quantized value. Quantized dataset. For a given sub-hypermatrix, its center point can be represented as: in, in, It is a quantization function that satisfies the error inequality: Therefore, set These constitute the code element components in the encoding process.

[0042] because According to the aforementioned event triggering mechanism, the encoder transmits the raw measurement values. Therefore, the decoder output is ,at this time Can be updated to: Therefore, for nodes The deviation between the sensor's raw data source and the digital network data transmitted after quantization encoding and decoding is defined as quantization error. when When the trigger condition is met, the encoder input is the difference between the measured values. After a quantization and decoding process similar to that described above, It can be recovered and reconstructed at the remote decoder. Therefore, based on the measurement value transmitted at the previous time point, the difference obtained from the current quantization, and the differential mechanism, the current measurement value can be reconstructed at the remote decoder, as follows: in, Indicates the most recent condition satisfied before the current time point. At that moment.

[0043] Unlike the transmission of raw measurement values, the dynamic range of differential transmission is smaller. Therefore, when At that time, set If is the upper bound of the dynamic range of the slow-state difference, then it is obvious that... The quantization error can then be expressed as: Similarly, the fast state measurements at the decoder can be directly obtained through... Get (when) (time), then The corresponding update will be made; or via... Get (when) (Time). By combining the fast state event triggering condition and the bit allocation rule, and following the above encoding-decoding and quantization steps, we can obtain: in, and These are the upper limits of the dynamic range for transmitting the original measurement values ​​and the fast state differential measurement values, respectively. Therefore, it can be known that... .

[0044] In summary, at the point in time Taking into account both slow and fast states, the quantized measurement value after encoding and decoding is expressed as follows: Therefore, the quantization error of the measurement value output by the decoder is denoted as: In this embodiment, the design of the controller and the specific details of the synchronization error dynamics are as follows: Let the synchronization error vector be defined by its nodal components as follows: Based on the aforementioned encoding and decoding process, the network controller obtains... At this point, regarding the node... The decoder controller is designed as follows: in, Let be the control gain matrix. Combining the state update equation, the objective equation, and the controller, we can obtain the state synchronization error equation: in, Define the global synchronization error vector as .make Each of them All are row-switching basic matrices. Define the error after transformation. and noticed (Based on the properties of elementary permutation matrices), left-multiply the matrix We can obtain: This can be summarized into the following simplified form: In this embodiment, the specific details of the UAV system stability analysis and controller solution are as follows: The following theorem derives a sufficient condition for the error system to satisfy the eventual boundedness of the exponent.

[0045] Theorem 1: Let scalar Let be the convergence coefficient, and assume the controller gain. Given. Under bit rate constraints, if a positive scalar exists... and positive definite matrix This satisfies the following inequalities: in, Theorem 2: For ,and Let the upper bound be known. Let the convergence coefficients be... and Under bit rate constraints, if a positive scalar exists... Symmetric matrix and invertible matrices , making in, In addition, the gain parameter in the pulse control law It can be derived through the following formula: in, and .

[0046] The UAV communication and synchronization control method based on multi-timescale dual-channel in this invention has the following advantages: This invention effectively reduces data redundancy by using a dual-channel transmission mechanism and an event-triggered differential encoding and decoding protocol, combined with a high-dimensional uniform quantizer. It achieves high-precision transmission of measured values ​​at a limited bit rate, significantly improving communication efficiency and optimizing the utilization of bandwidth resources.

[0047] The controller designed in this invention can fully utilize the fast and slow state characteristics of singular perturbation systems to achieve high-precision synchronous control of UAV system network nodes, effectively improving the performance and reliability of multi-UAV collaborative operations.

[0048] This invention introduces a Lyapunov function based on singular perturbation parameters to solve the sufficient linear matrix inequality (LMI) condition for the exponential final stability of a synchronous control system, and thereby solves for the controller gain that meets the stability requirements, providing a theoretical guarantee for the stable operation of the system.

[0049] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for UAV communication and synchronization control based on multi-timescale dual-channel, characterized in that, Includes the following steps: S1. Establish a nonlinear singular perturbation system model for UAV nodes. The nonlinear singular perturbation system model decomposes the state vector of the UAV into a slow state vector and a fast state vector, and introduces singular perturbation parameters to describe the time scale separation characteristics of the slow state vector and the fast state vector. S2. Configure dual channels for each UAV node, including a slow channel for transmitting the slow state vector and a fast channel for transmitting the fast state vector, and dynamically allocate bit rates for the slow channel and the fast channel under the condition of limited total bit rate; S3. Based on the difference between the current measurement value of the slow state vector and the fast state vector and the measurement value at the previous transmission moment, design event triggering conditions respectively, and select to transmit the original measurement value or the difference value according to the satisfaction of the event triggering conditions. S4. Based on the dynamically allocated bit rate, a high-dimensional uniform quantizer is used to quantize and encode the selected original measurement value or the differential value to generate quantized code elements, and then transmits them to the remote controller through the corresponding channel. S5. At the remote controller, the received quantized code is decoded to obtain the reconstructed measurement value; S6. By introducing the Lyapunov function based on singular perturbation parameters, solve the sufficient linear matrix inequality condition for the exponential final stability of the synchronization control system of the UAV node, and solve the controller gain that satisfies the stability requirements based on the matrix inequality condition. S7. On the remote controller, a controller is designed based on the reconstructed measurement value and the controller gain to generate control inputs, which are applied to the UAV node to achieve high-precision synchronous control of the UAV system network node.

2. The UAV communication and synchronization control method based on multi-timescale dual-channel as described in claim 1, characterized in that, In step S3, based on whether the event triggering condition is met, the original measurement value or the differential value is selected for transmission, specifically including: For the slow state vector, when the norm of the difference value of the slow state vector is less than or equal to the first trigger threshold, it is determined that the first event trigger condition is met, and the difference value of the slow state vector is selected for transmission; otherwise, it is determined that the first event trigger condition is not met, and the original measurement value of the slow state vector is selected for transmission. For the fast state vector, when the norm of the difference value of the fast state vector is less than or equal to the second trigger threshold, it is determined that the second event trigger condition is met, and the difference value of the fast state vector is selected for transmission; otherwise, it is determined that the second event trigger condition is not met, and the original measurement value of the fast state vector is selected for transmission. Wherein, the first trigger threshold is greater than the second trigger threshold.

3. The UAV communication and synchronization control method based on multi-timescale dual-channel as described in claim 2, characterized in that, In step S2, the bit rate is dynamically allocated to the slow channel and the fast channel, specifically including: The transmission demand ratio is calculated based on the ratio of the difference norm of the slow state vector to the first trigger threshold, and the ratio of the difference norm of the fast state vector to the second trigger threshold. Based on the transmission demand ratio, the first bit rate allocated to the slow channel and the second bit rate allocated to the fast channel are dynamically adjusted. When the transmission demand ratio is less than 1, it indicates that the change in the fast state vector is greater than the change in the slow state vector, and a higher bit rate is allocated to the fast channel.

4. The UAV communication and synchronization control method based on multi-timescale dual-channel as described in claim 1, characterized in that, In step S5, at the remote controller, the received quantized symbols are decoded to obtain the reconstructed measurement values, specifically including: If the received quantized code is obtained by quantizing and encoding the original measurement value, then the quantized code is directly decoded to obtain the reconstructed measurement value at the current moment, and the reconstructed measurement value is stored as a new reference value. If the received quantized code is obtained by quantization encoding of the differential value, the quantized code is decoded to obtain the reconstructed differential value, and the reconstructed differential value is added to the reference value of the previous time to obtain the reconstructed measurement value of the current time.

5. The UAV communication and synchronization control method based on multi-timescale dual-channel as described in claim 1, characterized in that, In step S4, a high-dimensional uniform quantizer is used to quantize and encode the selected transmitted original measurement value or the difference value, specifically including: The number of quantization levels is determined based on the bit rate currently allocated to the slow channel or the fast channel. The dynamic range of the original measurement value or the difference value is divided into multiple uniform sub-intervals corresponding to the number of quantization levels, and each sub-interval corresponds to a quantization symbol. Determine the sub-interval into which the original measurement value or the difference value falls as the value to be quantized, and output the quantized code corresponding to the sub-interval.

6. The UAV communication and synchronization control method based on multi-timescale dual-channel as described in claim 5, characterized in that, The dynamic range includes: When transmitting raw measurements, its dynamic range is determined by the maximum possible amplitude of the raw measurements. When transmitting differential values, their dynamic range is determined by the maximum possible amplitude of the differential values, and the dynamic range of the differential values ​​is smaller than the dynamic range of the original measurement values.

7. The UAV communication and synchronization control method based on multi-timescale dual-channel as described in claim 1, characterized in that, In step S7, the controller is designed based on the reconstructed measurement values ​​and the controller gain, specifically including: Obtain the target trajectory of the drone node; Calculate the difference between the reconstructed measurement and the measurement of the target trajectory; The difference is multiplied by the controller gain to generate the control input, so that the drone tracks the target trajectory.

8. The UAV communication and synchronization control method based on multi-timescale dual-channel as described in claim 1, characterized in that, In step S6, by introducing a Lyapunov function based on singular perturbation parameters, the sufficient linear matrix inequality condition for the exponential final stability of the synchronization control system of the UAV node is solved, specifically including: Based on the nonlinear singular perturbation system model and the structure of the controller, a dynamic equation for the synchronization error is established. Construct a Lyapunov function that includes the singular perturbation parameters; Based on the dynamic equation of the synchronization error, the Lyapunov function is differentially analyzed, and the Lipschitz condition for nonlinear functions is applied to derive the linear matrix inequality condition that guarantees the eventual boundedness of the exponent of the synchronization control system.

9. The UAV communication and synchronization control method based on multi-timescale dual-channel as described in claim 8, characterized in that, In step S6, the controller gain that satisfies the stability requirements is solved based on the matrix inequality conditions, specifically including: The linear matrix inequality conditions are transformed into linear matrix inequalities with respect to the controller gain; The transformed linear matrix inequalities are solved using a solver, and the positive definite matrix in the Lyapunov function and the controller gain are obtained simultaneously.

10. The UAV communication and synchronization control method based on multi-timescale dual-channel as described in claim 1, characterized in that, The nonlinear singular perturbation system model of the UAV node includes a representation of the coupling relationship between nodes, external disturbances, and quantization errors. In step S6, when solving the sufficient linear matrix inequality condition for the exponential final stability of the synchronous control system of the UAV node, the coupling relationship, external disturbances, and quantization errors are included as bounded terms in the stability analysis of the synchronous control system.

Images