An intelligent rotor collaborative communication system for a multi-rotor unmanned aerial vehicle

Abstract

An intelligent rotor collaborative communication system for a multi-rotor unmanned aerial vehicle disclosed by the present invention belongs to the field of communication technology, and includes an integrated model of an electric aviation engine and a rotor, a distributed rotor collaborative communication module, a fault monitoring and prediction module, and an interference observation and prediction module; the integrated model of the electric aviation engine and the rotor is used to integrate each rotor and its corresponding electric aviation engine, and describe the influence of communication faults and communication interference acting on the propeller blades; the communication modes between the rotor control systems are converted into an adaptive neural network model through the distributed rotor collaborative communication module, and the communication modes between the rotors are dynamically changed by adjusting the connection weights of the adaptive neural network model to meet the requirements of different flight missions; the fault monitoring and prediction module is used to monitor and predict the communication faults of the rotor collaborative communication system; the interference observation and prediction module is used to observe and predict the communication interference on the rotor collaborative communication system.

Description

Technical Field

[0001] The present invention belongs to the field of communication technologies, and particularly relates to an intelligent rotor collaborative communication system for a multi-rotor unmanned aerial vehicle (UAV). Background Art

[0002] With the proposal of the concept of "low-altitude economy" in the country, UAVs are increasingly widely used in various fields. Scenarios such as logistics transportation, emergency rescue, and manned flight are gradually closely connected to people's daily lives, which has elevated the reliability and safety of UAVs to an unprecedented height. In recent years, UAVs have been widely applied in numerous industries and inevitably frequently face complex environments such as "dangerous", "extreme", "special", and "harsh", and the probability of encountering such environments has increased exponentially. This current situation has posed a huge challenge to the safety and reliability of UAVs.

[0003] Currently, the rotors of existing UAVs are mainly controlled by their respective corresponding electric aviation engines, which have obvious limitations. Specifically, there is a lack of mutual communication mechanism between the rotors, which makes it difficult for them to cooperate when facing "dangerous", "extreme", "special", and "harsh" environments and unable to achieve precise control of the UAV. The current rotor UAVs have not considered the communication function between the rotors in their designs. However, this function is crucial for the safe operation of future UAVs and is very likely to become a key factor in improving the safety performance of UAVs.

[0004] Therefore, in order to solve the problem of the lack of mutual communication mechanism between the rotors of the above-mentioned UAVs, it is extremely urgent to develop an intelligent rotor collaborative communication system for a multi-rotor UAV, which is of great significance for improving the safety, reliability, and precise control ability of UAVs in complex environments. Summary of the Invention

[0005] The present invention aims at the above problems, makes up for the deficiencies of the prior art, and provides an intelligent rotor collaborative communication system for a multi-rotor UAV; the present invention can complete the collaborative communication of multiple rotors, and then realize the collaborative operation of multiple rotors, and can effectively improve the safety and reliability of the operation of the multi-rotor UAV.

[0006] To achieve the above object, the present invention adopts the following technical solutions.

[0007] An intelligent rotor collaborative communication system for a multi-rotor UAV provided by the present invention includes an integrated model of an electric aviation engine and a rotor, a distributed rotor collaborative communication module, a fault monitoring and prediction module, an interference observation and prediction module, and a control module;

[0008] Integrate each rotor with its corresponding electric aviation engine through the electric aviation engine-rotor integrated model, and describe the effects of communication failures and communication interferences on the rotors;

[0009] Convert the communication mode between the rotor control systems into an adaptive neural network model through the distributed rotor cooperative communication module, and dynamically change the communication mode between the rotors by adjusting the connection weights of the adaptive neural network model to meet the requirements of different flight mission;

[0010] The fault monitoring and prediction module monitors the occurrence of communication failures with the aid of a fault monitoring model, classifies the communication failures, and thus characterizes and quantifies the communication failures; the fault monitoring and prediction module predicts the possible occurrence of communication failures with the aid of a fault prediction model, and once they occur, enables the fault monitoring model to characterize and quantify the communication failures;

[0011] The interference observation and prediction module observes the generation of communication interferences with the aid of an interference observer, classifies the communication interferences, and thus characterizes and quantifies the communication interferences; the interference observation and prediction module predicts the possible occurrence of communication interferences with the aid of an interference predictor, and once they occur, enables the interference observer to characterize and quantify the communication interferences;

[0012] Transmit the above-mentioned communication failure and communication interference information to the control module, and the control module processes the communication failures and communication interferences through the control terminal.

[0013] As a preferred embodiment of the present invention, the electric aviation engine-rotor integrated model is established through the following mathematical formula, and the mathematical formula is expressed as follows:

[0014] , ;

[0015] wherein, represents the torque on each rotor, which is the initial control input of the electric aviation engine-rotor integrated model; is the friction coefficient; is the electrical angular velocity; is the moment of inertia; is the disturbing torque caused by communication failures; is the disturbing torque caused by communication interferences.

[0016] As another preferred embodiment of the present invention, the adaptive neural network model is specifically expressed as the following formula:

[0017] ;

[0018] wherein, is the output of the adaptive neural network model; , is the input of the adaptive neural network model and represents each rotor control system, ; is the connection weight; n is the number of rotor control systems; Using the BP algorithm or the RLS algorithm as the learning algorithm of the adaptive neural network model to optimize the connection weight. When the connection weight , there is no communication between any two rotor control systems.

[0019] As another preferred embodiment of the present invention, the fault monitoring and prediction module uses the Lagrange network as a fault monitoring model, marks the no-fault, network packet loss, and data out-of-order faults as 0, -1, 1, converts them into a three-classification problem, and uses the monitoring data as the input of the fault monitoring model, and uses the network packet loss and data out-of-order faults as the output of the fault monitoring model to train the network to complete the characterization and quantization of communication faults.

[0020] As another preferred embodiment of the present invention, the fault monitoring and prediction module also uses the Lagrange network as a fault prediction model. The fault prediction model uses the historical data of the two types of faults of network packet loss and data out-of-order for fault prediction, explores the data change law, and intervenes in the upcoming faults in advance.

[0021] As another preferred embodiment of the present invention, the interference observation and prediction module uses the Lagrange network as an interference observer, marks the no-interference, electromagnetic interference, and transmission interference as 0, -1, 1, converts them into a three-classification problem, and completes the characterization and quantization of interference through the training of the observation data.

[0022] As another preferred embodiment of the present invention, the interference observation and prediction module also uses the Lagrange network as an interference predictor. The interference predictor uses the historical data of the two types of interference of electromagnetic interference and transmission interference for interference prediction, explores the data change law, and intervenes in the upcoming interference in advance.

[0023] As another preferred embodiment of the present invention, the Lagrange network is used as an approximation model and a classification model, and its expression is as follows:

[0024] ;

[0025] Among them, is the output of the Lagrange network; is the weight of the Lagrange network; is the input of the Lagrange network; is the corresponding output; is the coefficient.

[0026] As another preferred embodiment of the present invention, the Lagrange network optimizes its parameters using any one of the learning algorithms of recursive least squares algorithm, backpropagation algorithm, and wake-sleep algorithm.

[0027] As another preferred embodiment of the present invention, the control module processes communication faults and communication interferences between the rotors using the control input of the updated integrated model of the electric aviation engine and the rotor. The control input of the updated integrated model of the electric aviation engine and the rotor is expressed as:

[0028] ;

[0029] where, is the initial control input of the integrated model of the electric aviation engine and the rotor, is the estimated value of the disturbing torque caused by communication faults estimate, is the estimated value of the disturbing torque caused by communication interference estimate.

[0030] Beneficial effects of the present invention: The intelligent rotor collaborative communication system of a multi-rotor unmanned aerial vehicle provided by the present invention combines an integrated model of an electric aviation engine and a rotor, a distributed rotor collaborative communication module, a fault monitoring and prediction module, an interference observation and prediction module, and a control module to complete the collaborative communication between the multi-rotors of the unmanned aerial vehicle, and further realize the collaborative operation of the multi-rotors, effectively improving the safety and reliability of the application of the unmanned aerial vehicle. It can monitor and predict the communication faults of the rotor collaborative communication system itself of the present invention, and can observe and predict the communication interference of the rotor collaborative communication system itself of the present invention.

[0031] In summary, the intelligent rotor collaborative communication system of the multi-rotor unmanned aerial vehicle of the present invention has the capabilities of high reliability, strong safety, strong autonomy, and high intelligence, improving the intelligence, safety, and reliability of the multi-rotor unmanned aerial vehicle. Description of the Drawings

[0032] Figure 1 is a communication mode diagram between the rotor control systems of the intelligent rotor collaborative communication system of a quad-rotor unmanned aerial vehicle taking the quad-rotor as an example of the present invention.

[0033] Figure 2 is a schematic structural diagram of the adaptive neural network model of the intelligent rotor collaborative communication system of a multi-rotor unmanned aerial vehicle of the present invention.

[0034] Markings in the figure: 1 is the No. 1 rotor control system, 2 is the No. 2 rotor control system, 3 is the No. 3 rotor control system, and 4 is the No. 4 rotor control system. Detailed Embodiments

[0035] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0036] Combined with Figure 1 and Figure 2 As shown, an intelligent rotor collaborative communication system for a multi-rotor unmanned aerial vehicle provided by an embodiment of the present invention includes an integrated model of an electric aviation engine and a rotor, a distributed rotor collaborative communication module, a fault monitoring and prediction module, an interference observation and prediction module, and a control module; through the integrated model of the electric aviation engine and the rotor, each rotor and its corresponding electric aviation engine are integrated, and the effects of communication faults and communication interferences acting on the rotor are described; through the distributed rotor collaborative communication module, the communication mode between each rotor control system is converted into an adaptive neural network model, and the communication mode between each rotor is dynamically changed by adjusting the connection weights of the adaptive neural network model to adapt to different flight mission requirements; the fault monitoring and prediction module monitors the occurrence of communication faults with the help of a fault monitoring model, classifies the communication faults, and thus characterizes and quantifies the communication faults; the fault monitoring and prediction module predicts the possible occurrence of communication faults with the help of a fault prediction model, and once it occurs, enables the fault monitoring model to characterize and quantify the communication faults; the interference observation and prediction module observes the generation of communication interferences with the help of an interference observer, classifies the communication interferences, and thus characterizes and quantifies the communication interferences; the interference observation and prediction module predicts the possible occurrence of communication interferences with the help of an interference predictor, and once it occurs, enables the interference observer to characterize and quantify the communication interferences; the above communication fault and communication interference information are transmitted to the control module, and the control module processes the communication faults and communication interferences through the control end.

[0037] Among them, Figure 1 is a communication mode diagram between the rotor control systems of an intelligent rotor collaborative communication system for a quad-rotor unmanned aerial vehicle taking a quad-rotor as an example, Figure 1 showing the communication connection modes of any two rotor control system combinations among the No. 1 rotor control system 1, the No. 2 rotor control system 2, the No. 3 rotor control system 3, and the No. 4 rotor control system 4. It can be seen from Figure 1 that there are a total of 6 communication connection modes, which are the communication connection mode between the No. 1 rotor control system 1 and the No. 2 rotor control system 2 w 1, the communication connection mode between the No. 2 rotor control system 2 and the No. 4 rotor control system 4 w 2, the communication connection mode between the No. 3 rotor control system 3 and the No. 4 rotor control system 4 w3. Communication connection mode between the No. 1 rotor control system 1 and the No. 3 rotor control system 3 w 4. Communication connection mode between the No. 2 rotor control system 2 and the No. 3 rotor control system 3 w 5. Communication connection mode between the No. 1 rotor control system 1 and the No. 4 rotor control system 4 w 6.

[0038] Specifically, the integrated model of the electric aviation engine and the rotor is used to integrate each rotor and its corresponding electric aviation engine, and describe the influence of communication faults and communication interference on the rotor; the distributed rotor cooperative communication module includes multiple rotor control systems, and the rotor control systems communicate with each other to form an adaptive neural network model; among them, whether any two rotor control systems communicate is determined by the connection weight of the adaptive neural network model; the fault monitoring and prediction module is used to monitor and predict the communication faults of the rotor cooperative communication system, and the communication faults include phenomena such as network packet loss and data disorder; the interference observation and prediction module is used to observe and predict the communication interference on the rotor cooperative communication system, and the communication interference includes electromagnetic interference and transmission interference; the control module processes communication faults and communication interference through the control end.

[0039] Specifically, the integrated model of the electric aviation engine and the rotor is established through the following mathematical formula, and the mathematical formula is expressed as follows:

[0040] , ;

[0041] Among them, represents the torque on each rotor, which is the initial control input of the integrated model of the electric aviation engine and the rotor; is the friction coefficient; is the electrical angular velocity; is the moment of inertia; is the disturbing torque caused by communication faults; is the disturbing torque caused by communication interference.

[0042] As Figure 2 shown in the structural schematic diagram of the adaptive neural network model, the adaptive neural network model is specifically expressed as the following formula:

[0043] ;

[0044] Among them, is the output of the adaptive neural network model; , are the inputs of the adaptive neural network model and represent each rotor control system, ; is the connection weight; n is the number of rotor control systems; Using the BP algorithm or the RLS algorithm as the learning algorithm of the adaptive neural network model to optimize the connection weight. When the connection weight , there is no communication between any two rotor control systems. Specifically, taking a quadrotor as an example, the adaptive neural network model can be specifically expressed as the following formula:

[0045] .

[0046] Specifically, the fault monitoring and prediction module uses the Lagrange network as the fault monitoring model, marks the no-fault, network packet loss, and data out-of-order faults as 0, -1, 1, converts them into a three-classification problem, and uses the monitoring data as the input of the fault monitoring model and the network packet loss and data out-of-order faults as the output of the fault monitoring model to train the network to complete the characterization and quantification of communication faults; among them, the establishment of the fault monitoring model is as follows: Using the Lagrange network as the fault monitoring model, the input layer of the fault monitoring model is the network transmission data between each rotor control system, and the output layer of the fault monitoring model is the no-fault data and the faulty data respectively. Mark the no-fault data as "0", mark the network packet loss fault as "-1", and mark the data out-of-order fault as "1" to train the network.

[0047] Specifically, the fault monitoring and prediction module also uses the Lagrange network as the fault prediction model. The fault prediction model uses the historical data of the two types of faults of network packet loss and data out-of-order to predict faults, explores the data change law, and intervenes in the upcoming faults in advance; among them, the establishment of the fault prediction model is as follows: Using the Lagrange network as the fault prediction model, the input layer of the fault prediction model adopts the historical data of the network packet loss and data out-of-order faults, and the output layer of the fault prediction model is the prediction data of the two types of faults to train the network.

[0048] Specifically, the interference observation and prediction module uses the Lagrange network as the interference observer, marks the no-interference, electromagnetic interference, and transmission interference as 0, -1, 1, converts them into a three-classification problem, and completes the characterization and quantification of interference through the training of the observation data; among them, the establishment of the interference observer is as follows: Using the Lagrange network as the interference observer, the input layer of the interference observer is the network transmission data between each rotor control system, and the output layer of the interference observer is the no-interference data and the faulty data respectively. Mark the no-interference data as "0", mark the electromagnetic interference as "-1", and mark the transmission interference as "1" to train the network.

[0049] Specifically, the interference observation and prediction module also uses the Lagrange network as an interference predictor. The interference predictor uses historical data of two types of interference, namely electromagnetic interference and transmission interference, to predict interference, explores the data change law, and intervenes in the upcoming interference in advance. The establishment of the interference predictor is as follows: Using the Lagrange network as the interference predictor, the input layer of the interference predictor adopts the historical data of two types of interference, namely electromagnetic interference and transmission interference, and the output layer of the interference predictor is the prediction data of the two types of interference, and the network is trained.

[0050] Specifically, the Lagrange network is used as an approximation model and a classification model. The Lagrange network has strong nonlinear approximation ability, fast convergence, strong generalization ability, has "self-learning ability", robustness and anti-interference performance, and its expression is as follows:

[0051] ;

[0052] Among them, is the output of the Lagrange network; is the weight of the Lagrange network; is the input of the Lagrange network; is the corresponding output; is the coefficient.

[0053] Specifically, the Lagrange network optimizes its parameters using any one of the recursive least squares algorithm, backpropagation algorithm, and wake-sleep algorithm; the learning algorithm for optimizing the parameters of the Lagrange network can also use other algorithms that can meet the optimization function, not limited to the above several learning algorithms.

[0054] Specifically, the control module uses the control input of the updated integrated model of the electric aircraft engine and rotor to handle the communication faults and communication interferences between the rotors. The control input of the updated integrated model of the electric aircraft engine and rotor is expressed as:

[0055] ;

[0056] Among them, is the initial control input of the integrated model of the electric aircraft engine and rotor, is the estimated value of the disturbing torque caused by communication faults , is the estimated value of the disturbing torque caused by communication interference .

[0057] Based on the above technical solutions, the following advantages of the present invention are summarized: (1) The intelligent rotor collaborative communication system of the multi-rotor UAV of the present invention is constituted by the combination of the electric aviation engine and rotor integration model, the distributed rotor collaborative communication module, the fault monitoring and prediction module, the interference observation and prediction module, and the control module, which has the capabilities of high reliability, strong security, strong autonomy, and high intelligence, improving the intelligence, safety, and reliability of the multi-rotor UAV.

[0058] (2) The present invention adopts the distributed rotor collaborative communication module to realize the mutual communication connection between each rotor control system, and transforms the multi-rotor communication problem into an adaptive neural network model. Whether any two rotor control systems communicate is determined by the connection weight; in this way, the problem that the rotor control system is not easy to control in the "extremely dangerous and special" situation can be solved collaboratively.

[0059] (3) The fault monitoring and prediction module of the present invention can be used to monitor and predict the communication faults of the rotor collaborative communication system; the fault monitoring and prediction module uses the Lagrange network as the fault monitoring model, takes the monitoring data as the input of the fault monitoring model, takes the network packet loss and data disorder faults as the output of the fault monitoring model, trains the network, and completes the characterization and quantification of the communication faults; the fault monitoring and prediction module uses the Lagrange network as the fault prediction model, and the fault prediction model uses the historical data of the two types of faults of network packet loss and data disorder to predict faults, explores the data change law, and intervenes in the upcoming faults in advance.

[0060] (4) The interference observation and prediction module of the present invention can be used to observe and predict the communication interference to the rotor collaborative communication system, where the interference includes electromagnetic interference and transmission interference; the interference observation and prediction module uses the Lagrange network as the interference observer, the input layer of the interference observer is the network transmission data between each rotor control system, the output layers of the interference observer are the interference-free data and the faulty data respectively, and through the training of the observed data, the characterization and quantification of the interference are completed; the interference observation and prediction module uses the Lagrange network as the interference predictor, and the interference predictor uses the historical data of the two types of interference of electromagnetic interference and transmission interference to predict interference, explores the data change law, and intervenes in the upcoming interference in advance.

[0061] (5) The Lagrange network used in the present invention has powerful non-linear approximation, fast convergence, strong generalization ability, has "self-learning ability", robustness, and anti-interference performance, and can be preferably applied as an approximation model and a classification model.

[0062] It is understandable that the above specific description of the present invention is only for the purpose of illustrating the present invention and is not limited to the technical solutions described in the embodiments of the present invention. Those of ordinary skill in the art should understand that the present invention can still be modified or equivalently replaced to achieve the same technical effects; as long as the usage requirements are met, they are all within the protection scope of the present invention.

Claims

1. An intelligent rotor cooperative communication system for a multi-rotor UAV, characterized in that: It includes an electric aviation engine and rotor integrated model, a distributed rotor collaborative communication module, a fault monitoring and prediction module, an interference observation and prediction module, and a control module; Integrate each rotor with its corresponding electric aircraft engine through the electric aircraft engine and rotor integrated model, and describe the impact of communication failure and communication interference on the rotor; The distributed rotor cooperative communication module converts the communication mode between the rotor control systems into an adaptive neural network model, and dynamically changes the communication mode between the rotors by adjusting the connection weights of the adaptive neural network model to adapt to different flight mission requirements; The fault monitoring and prediction module monitors the occurrence of communication faults with the help of a fault monitoring model, classifies the communication faults, and thus characterizes and quantifies the communication faults; the fault monitoring and prediction module predicts the possibility of the communication fault with the help of a fault prediction model, and once it occurs, activates the fault monitoring model to characterize and quantify the communication fault; The interference observation and prediction module observes the generation of communication interference with the help of the interference observer, classifies the communication interference, and thus characterizes and quantifies the communication interference; the interference observation and prediction module predicts the possible occurrence of communication interference with the help of the interference predictor, and once it occurs, activates the interference observer to characterize and quantify the communication interference; The communication failure and communication interference information is transmitted to the control module, and the control module processes the communication failure and communication interference through the control terminal; The electric aircraft engine and rotor integration model is established by the following mathematical formula, which is expressed as follows: ； ； in, represents the torque on each rotor, which is the initial control input of the electric aircraft engine and rotor integration model; is the friction coefficient; is the electrical angular velocity; is the moment of inertia; is the disturbance torque caused by communication failure; is the disturbance torque caused by communication interference; The adaptive neural network model is specifically expressed as follows: ； in, Output of the adaptive neural network model; , is the input of the adaptive neural network model and represents each rotor control system, ; is the connection weight; n is the number of rotor control systems; BP algorithm or RLS algorithm is used as the learning algorithm of the adaptive neural network model to optimize the connection weight. , there is no communication between any two rotor control systems.

2. The intelligent rotor cooperative communication system for a multi-rotor UAV according to claim 1, characterized in that: The fault monitoring and prediction module uses the Lagrange network as a fault monitoring model, marks no fault, network packet loss and data disorder fault as 0, -1, and 1, and converts it into a three-classification problem. The monitoring data is used as the input of the fault monitoring model, and the network packet loss and data disorder fault are used as the output of the fault monitoring model. The network is trained to complete the characterization and quantification of communication faults.

3. The intelligent rotor cooperative communication system for a multi-rotor UAV according to claim 2, characterized in that: The fault monitoring and prediction module also uses the Lagrange network as a fault prediction model. The fault prediction model uses historical data of two types of faults, network packet loss and data disorder, to predict faults, explore data change patterns, and intervene in impending faults in advance.

4. The intelligent rotor cooperative communication system for a multi-rotor UAV according to claim 1, characterized in that: The interference observation and prediction module uses the Lagrange network as an interference observer, marks no interference, electromagnetic interference and transmission interference as 0, -1, 1, converts it into a three-classification problem, and completes the characterization and quantification of interference by training the observed data.

5. The intelligent rotor cooperative communication system for a multi-rotor UAV according to claim 4, characterized in that: The interference observation and prediction module also uses the Lagrange network as an interference predictor. The interference predictor uses historical data of electromagnetic interference and transmission interference to predict interference, explore data change rules, and intervene in the impending interference in advance.

6. An intelligent rotor cooperative communication system for a multi-rotor UAV according to any one of claims 2 to 5, characterized in that: The Lagrange network is used as an approximation model and a classification model, and its expression is as follows: ； in, Output for Lagrange network; is the Lagrange network weight; Input for Lagrange network; for The corresponding output: is the coefficient.

7. The intelligent rotor cooperative communication system for a multi-rotor UAV according to claim 6, characterized in that: The Lagrange network optimizes its parameters using any one of the learning algorithms of the recursive least squares algorithm, the back propagation algorithm, and the wake-sleep algorithm.

8. According to the intelligent rotor cooperative communication system of a multi-rotor UAV in claim 1, the control module uses the control input of the updated electric aircraft engine and rotor integration model to process the communication failure and communication interference between the rotors, and the control input of the updated electric aircraft engine and rotor integration model is expressed as: ； in, is the initial control input for the electric aero engine and rotor integrated model, The disturbance torque caused by communication failure Estimated value, The disturbance torque caused by communication interference Estimated value.

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