Multi-unmanned helicopter suspended load system based on cooperative control

Abstract

According to the multi-unmanned helicopter hoisting system based on cooperative control, the safety, the stability and the operation efficiency of multi-helicopter cooperative hoisting are improved. Firstly, dynamics and kinematics constraints are fused, and a distributed control strategy is adopted, so that multiple unmanned helicopters can realize accurate cooperation according to a real-time state; thirdly, arranging an attitude sensor and an acceleration sensor on the suspended object, and combining a feedback control algorithm to effectively inhibit swinging of the object; and finally, the system adopts a self-adaptive communication protocol, a redundant link and a hierarchical fault-tolerant mechanism, so that the reliability of information transmission and the self-adaptive capability after a fault are enhanced. According to the invention, through multi-source environment perception and adaptive flight adjustment, efficient and safe operation of a suspended load task under a complex environment condition is realized, and the environment adaptability and robustness of an unmanned helicopter cooperative suspended load system are significantly improved.

Description

Technical Field

[0001] The present invention relates to the field of multi-unmanned helicopter hoisting, and in particular to a multi-unmanned helicopter hoisting system based on coordinated control. Background Art

[0002] In many fields such as modern engineering construction, emergency rescue, and logistics and transportation, it is often necessary to transport large single objects. Traditional transportation methods mainly rely on large cranes, freight vehicles and other equipment, but these equipment have certain limitations. For example, the operating range of the crane is limited by its arm length and installation position, and it is difficult to carry out work in some areas with complex terrain and limited space; freight vehicles require good road conditions. For places with inconvenient transportation such as remote mountainous areas and disaster-stricken areas, their transportation capacity is greatly reduced.

[0003] As an aircraft with the characteristics of vertical take-off and landing and flexible maneuverability, unmanned helicopters have great application potential in the field of transportation. However, the lifting capacity of a single unmanned helicopter is limited. For heavy objects, a single unmanned helicopter is often unable to complete the lifting task. Therefore, the use of two or more unmanned helicopters to coordinate lifting has become an effective way to solve the problem of transporting heavy objects.

[0004] At present, the practical application of multi-unmanned helicopter collaborative lifting technology still faces many challenges. First, the collaborative control between multiple unmanned helicopters is a key issue. Due to the differences in flight performance, load conditions, etc. of each unmanned helicopter, how to ensure that they can fly in a coordinated manner and avoid mutual interference and collision is the prerequisite for achieving safe and efficient lifting. Secondly, during the lifting process, the swing and posture change of the object will affect the flight stability of the unmanned helicopter. If the swing of the object cannot be effectively controlled, it may cause the unmanned helicopter to lose balance or even crash. In addition, the communication and information transmission of the multi-unmanned helicopter collaborative lifting system are also crucial. Accurate and timely information exchange is the basis for achieving collaborative control, but in a complex electromagnetic environment, the reliability and stability of communication are often difficult to guarantee.

[0005] In addition, the existing multi-unmanned helicopter collaborative lifting technology is not capable of coping with complex environments and emergencies. For example, in severe weather conditions such as strong winds and heavy rain, the flight performance of unmanned helicopters will be seriously affected. How to adjust the collaborative strategy to ensure the smooth progress of the lifting mission is an urgent problem to be solved. At the same time, when an unmanned helicopter fails, how to quickly adjust the task allocation of other helicopters to ensure the safe lifting of objects is also a topic that requires in-depth research. Summary of the Invention

[0006] In order to solve the technical problems mentioned in the current background technology, the present invention proposes a multi-unmanned helicopter lifting system based on cooperative control.

[0007] To this end, the technical solution adopted in the present invention is as follows:

[0008] 1. A multi-unmanned helicopter lifting system based on cooperative control, characterized in that the system includes:

[0009] The modeling and control module collects raw data, including flight parameters and load conditions of multiple unmanned helicopters, as well as basic information about the objects being hoisted, and establishes a collaborative control model based on dynamics and kinematics. Each unmanned helicopter is equipped with a controller that interacts with each other and performs collaborative control via a wireless communication network. Based on a distributed control strategy using a consensus algorithm, control inputs are generated for each unmanned helicopter, and the controller adjusts the flight parameters based on the control inputs.

[0010] A swing and feedback module, wherein the suspended object is equipped with a sensor, which collects status information of the suspended object in real time and transmits it to the controller. Based on the status information, the controller adjusts the flight parameters of the unmanned helicopter through a feedback control algorithm;

[0011] The communication and fault-tolerance module uses an adaptive communication protocol and redundant communication links to encrypt and error-correct communication data, including communication between controllers and between controllers and sensors. It also uses a fault diagnosis mechanism to detect the status of the unmanned helicopter in real time and generate fault-tolerant control instructions based on the detection results.

[0012] The environmental perception and adaptive adjustment module deploys multiple types of environmental sensors on the unmanned helicopter to obtain flight environment data in real time and adjusts the collaborative control model according to the flight environment data.

[0013] Furthermore, in the process of establishing the collaborative control model, the unmanned helicopter is a rigid body, the suspended object is a rigid body and the center of mass position is known.

[0014] Furthermore, during the use of the distributed control strategy, the unmanned helicopters exchange flight parameters and load conditions through a wireless communication network.

[0015] The topological structure of the wireless communication network is represented by a graph structure, and the graph structure includes graph nodes and graph edges. The graph nodes represent unmanned helicopters, and the graph edges represent communication connections between unmanned helicopters.

[0016] Furthermore, the sensors configured for the suspended object include a posture sensor and an acceleration sensor, and the sampling frequency is f s Collect status information of hoisted objects.

[0017] The state information is transmitted to the controller via a wireless communication network, and a swing model of the suspended object is established. According to the swing model, the flight parameters of the unmanned helicopter are adjusted through a feedback control algorithm to suppress the swing of the suspended object.

[0018] Furthermore, the adaptive communication protocol adjusts the transmission rate of the wireless communication network according to the channel quality indicator, which is expressed as:

[0019]

[0020] Among them, R1>R2>…>R n is the transmission rate level, γ1>γ2>…>γ l-1 is the division threshold of the channel quality indicator.

[0021] Furthermore, the fault diagnosis mechanism adopts a layered architecture, including an acquisition layer, a processing layer, and a fault diagnosis decision layer.

[0022] The fault diagnosis decision layer identifies the fault type of the unmanned helicopter according to a method based on a support vector machine (SVM), solves the problem according to the optimization formula of the support vector machine, finds the optimal hyperplane, identifies the fault category, and generates the fault-tolerant control instructions. According to the fault-tolerant control instructions, the task allocation of the unmanned helicopter is adjusted.

[0023] Furthermore, the multi-type environmental sensors include meteorological sensors and lidars. The meteorological sensors collect meteorological data information, and the lidars collect obstacle data information. The Kalman filter algorithm is used to fuse the meteorological data information and the obstacle data information to obtain the flight environment data.

[0024] Compared with the prior art, the advantages of the present invention are:

[0025] 1. Precise collaborative control. This invention establishes a multi-unmanned helicopter collaborative control model that integrates dynamic and kinematic constraints and introduces a distributed control strategy, enabling each unmanned helicopter to coordinate and adjust flight parameters according to real-time status, thereby improving the collaborative control accuracy and operational stability of the overall system.

[0026] 2. Suppressing the swing of the hoisted object. The present invention installs attitude and acceleration sensors on the hoisted object, combined with a feedback-based control algorithm, which can sense and suppress the swing of the hoisted object in real time, thereby improving the safety of the hoisting operation and the accuracy of object transportation.

[0027] 3. Communication optimization: The present invention adopts an adaptive communication protocol and redundant communication links to encrypt and error-correct data, significantly improving the reliability of information exchange between multiple unmanned helicopters. At the same time, it introduces a layered architecture fault diagnosis system to achieve rapid diagnosis of single-machine faults and adaptive task reallocation, enhancing the robustness and fault tolerance of the invention.

[0028] 4. Environmental Perception: The system uses multiple types of sensors to perceive the flight environment in real time, and uses Kalman filtering for information fusion to adjust the collaborative control strategy, giving the unmanned helicopter's lifting operations excellent environmental adaptability and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0030] Figure 1 This is a flow chart of the multi-unmanned helicopter hoisting system of the present invention;

[0031] Figure 2 Constructing a flow chart for the collaborative control model of the present invention;

[0032] Figure 3 This is a flow chart of the swing and feedback module of the present invention. DETAILED DESCRIPTION

[0033] To achieve the above objectives, the present invention is implemented through the following technical solutions: the present invention provides a multi-unmanned helicopter lifting system based on cooperative control, please refer to Figures 1 to 3 , the system comprises:

[0034] M1, modeling and control module, collects raw data including flight parameters and load conditions of multiple unmanned helicopters, as well as basic information of suspended objects, and establishes a collaborative control model based on dynamics and kinematics; each unmanned helicopter is equipped with a controller, which conducts information exchange and collaborative control through a wireless communication network; based on the distributed control strategy of the consistency algorithm, it generates control inputs for each unmanned helicopter, and the controller adjusts the flight parameters according to the control inputs.

[0035] The unmanned helicopter is a rigid body, and the elastic deformation of its own structure is ignored; the suspended object is a rigid body, and the position of the center of mass is known; the flight environment is an ideal Newtonian mechanics environment, and the relativistic effect is not considered.

[0036] The power M of the i-th unmanned helicopter is obtained by the dynamic formula i, and the power N of the hoisted object, where i = 1, 2, ..., n, and n is the number of unmanned helicopters;

[0037] The kinematic constraint equations between the unmanned helicopter and the suspended object are obtained through the kinematic formula, including the velocity constraint equation and the acceleration constraint equation.

[0038] The velocity constraint equation is:

[0039] (Vv i ) T u i =0

[0040] Where V is the velocity vector of the suspended object; v i is the velocity vector of the i-th unmanned helicopter; u i is the direction vector of the sling between the i-th unmanned helicopter and the suspended object;

[0041] The acceleration constraint equation is:

[0042]

[0043] Where A is the acceleration vector of the suspended object; a i is the acceleration vector of the i-th unmanned helicopter; t is the time t;

[0044] By integrating dynamics and kinematics, a collaborative control model is obtained. Through the model, the motion trajectory and force conditions of the unmanned helicopter under different flight states can be predicted. In this embodiment, given the initial flight parameters, load conditions and basic information of the suspended object of the unmanned helicopter, the collaborative control model is solved using numerical methods to obtain the changes in the motion state of the unmanned helicopter and the suspended object over time.

[0045] The controller of each unmanned helicopter periodically sends and receives status information through the wireless communication network. In this embodiment, within the communication period T, the i-th helicopter sends status information to the neighboring helicopter and receives status information from the neighboring helicopter.

[0046] In order to improve the accuracy of cooperative control, a prediction and correction mechanism is adopted. In each control cycle, the controller of the unmanned helicopter predicts the state information of the neighboring helicopter at the current moment based on the state information of the neighboring helicopter received in the previous cycle, and then calculates the control input.

[0047] The control input U of each unmanned helicopter is generated by the collaborative control model and flight parameters according to the consistency algorithm of the distributed control strategy. The control input of each unmanned helicopter depends on the difference between the state information of its neighboring helicopters and its own state information.

[0048] Through distributed control strategies, each unmanned helicopter can autonomously adjust its flight parameters based on its own status information and the status information of its neighboring helicopters, achieving coordination among multiple unmanned helicopters, avoiding mutual interference and collision, and improving the safety and efficiency of collaborative lifting of multiple unmanned helicopters.

[0049] M2, the swing and feedback module, is equipped with sensors for the hoisted objects. The sensors collect the status information of the hoisted objects in real time and transmit it to the controller. Based on the status information, the controller adjusts the flight parameters of the unmanned helicopter through the feedback control algorithm.

[0050] The status information of the suspended object includes posture information and acceleration. The posture information includes swing amplitude and swing frequency, which are collected by posture sensors and acceleration sensors. In order to comprehensively and accurately monitor the swing of the suspended object, the sensors should be installed at key positions of the object. In this embodiment, for suspended objects with regular shapes, they can be installed near the center of mass; for irregular suspended objects, sensors can be installed at multiple feature points.

[0051] The sensor samples at a frequency f s The status information is collected and transmitted to the controller of the unmanned helicopter via a wireless communication network.

[0052] According to the principles of Newtonian mechanics, a swing model of a suspended object is established. In this embodiment, the suspended object is a rigid body, and its swing in three-dimensional space is expressed by the Euler equation. A coordinate system is established with the center of mass of the suspended object as the origin. The relationship between the angular momentum H and the angular velocity ω of the suspended object is H=Iω, where I is the inertia vector of the suspended object. According to the Euler equation, the angular acceleration W of the suspended object is expressed as:

[0053] W=I -1 (M-ω×H)

[0054] Where M is the external torque acting on the suspended object;

[0055] The external moment M is expressed as:

[0056] M=r×F

[0057] Among them, F is the pulling force of the unmanned helicopter on the suspended object; r is the position vector of the pulling force application point relative to the center of mass of the suspended object.

[0058] Based on the established swing model, the flight parameters of the unmanned helicopter are adjusted through the feedback control algorithm to suppress the swing of the suspended object. The PID controller is used to calculate the adjustment value. The formula is:

[0059] X=K p e θ +K i ∫e θ dt+Kd e ω

[0060] Where X is the output of the PID controller; K p , K i and K d are proportional, integral and differential gains respectively; e θ is the attitude error of the hoisted object; e ω is the angular velocity error of the suspended object;

[0061] According to the output of the PID controller, the flight parameters of the unmanned helicopter are adjusted. Specifically, by changing the rotor speed and tilt angle of the unmanned helicopter, the magnitude and direction of the pulling force are changed, thereby generating a suitable external torque to suppress the swing of the suspended object.

[0062] In order to improve the performance of the feedback control algorithm under different working conditions, an adaptive adjustment mechanism is introduced to adjust the gain of the PID controller in real time according to the attitude information of the suspended object and the changes in the flight environment;

[0063] In this embodiment, when the swing amplitude of the suspended object is large, the proportional gain K is increased. p To speed up the response; when the swing frequency is high, increase the differential gain K d To enhance the ability to suppress rapid changes.

[0064] M3, the communication and fault-tolerant module, uses adaptive communication protocols and redundant communication links to encrypt and correct communication data. The communication data includes communication between controllers and communication between controllers and sensors. Through the fault diagnosis mechanism, the status of the unmanned helicopter is detected in real time, and fault-tolerant control instructions are generated based on the detection results.

[0065] During the collaborative lifting process of multiple unmanned helicopters, the communication environment is complex and changeable. Different flight phases and scenarios have different communication requirements. Adaptive communication protocols are used to improve communication quality in various situations.

[0066] According to the channel quality index γ, the transmission rate R is adjusted to improve the communication quality. When γ is high, it means that the channel quality is good. Increasing the transmission rate R increases the amount of information communicated. When γ is low, reducing the transmission rate R ensures the reliability of communication. The specific adjustment strategy is as follows:

[0067]

[0068] Among them, R1>R2>…>R n For different transmission rate levels, γ1>γ2>…>γ l-1 is the channel quality classification threshold.

[0069] In order to improve the reliability of communication and prevent communication interruption due to a single link failure, redundant communication links are constructed. The reliability P of communication is expressed as:

[0070]

[0071] Where m is the number of communication links; p a is the reliability of the ath communication link; by increasing the number of links m or improving the reliability of each link p a , which can effectively improve the reliability of communication;

[0072] In this embodiment, different communication technologies may be used to construct redundant communication links, including using both wireless local area network (WLAN) and cellular networks. When a link fails, it automatically switches to other available links to ensure continuity of communication.

[0073] Encryption processing is to use encryption algorithms to process communication data in complex electromagnetic environments, where communication data is susceptible to eavesdropping and interference. In order to ensure data security, encryption algorithms are used to process communication data.

[0074] In this embodiment, a symmetric encryption algorithm in the encryption algorithm is used to encrypt the communication data, and the communication data is encrypted by an encryption key and an encryption function. At the receiving end of the encrypted communication data, the encrypted communication data is decrypted using a decryption function and the same encryption key. In order to further improve security, the encryption key can be updated periodically, and the frequency of the update is determined according to the security level of the communication environment.

[0075] During the communication process, data loss or errors may occur. To ensure data accuracy, error correction coding technology is used. In this embodiment, Hamming code is selected as the error correction coding scheme. The coding efficiency of Hamming code is the ratio of the communication data length to the encoded length. Hamming code can detect and correct communication errors. The error correction capability is obtained based on the code distance d. The code distance d is expressed as:

[0076] d=2y+1

[0077] Where y is the number of correctable error bits. By encoding the communication data at the sending end and decoding and error correction at the receiving end, the accuracy of the communication data can be effectively improved.

[0078] The fault diagnosis mechanism adopts a layered architecture. The bottom layer is the acquisition layer, the middle layer is the processing layer, which is responsible for pre-processing the collected raw data, including filtering, feature extraction and other operations. The upper layer is the fault diagnosis decision layer, which makes fault judgment and type identification based on the processed raw data.

[0079] Through noise removal and feature extraction, the real-time feature value is obtained and compared with the feature value in the normal state to determine whether the helicopter has any abnormality. When the real-time feature value exceeds the set threshold of the feature value in the normal state, it is determined that there is a fault;

[0080] Fault type identification is performed according to a method based on support vector machine (SVM). The optimization formula of the support vector machine is solved to find the optimal hyperplane so that samples of different categories can be separated by the maximum interval, thereby achieving accurate identification of the fault type.

[0081] The fault-tolerant control instruction is to adjust the task allocation of the remaining unmanned helicopters according to the load L of the failed unmanned helicopter when the detection result shows that an unmanned helicopter has failed. f and the remaining load capacity of the remaining unmanned helicopters for task allocation;

[0082] According to the sum of the remaining load capacities of the remaining unmanned helicopters, the total remaining load capacity L of the remaining helicopters is obtained. total , is greater than the load of the faulty unmanned helicopter, task allocation is performed, and the allocation formula is:

[0083]

[0084] Where, ΔL i is the load assigned to the i-th unmanned helicopter; is the remaining load capacity of the i-th unmanned helicopter; at the same time, according to the new load distribution, the collaborative control model is readjusted to ensure that multiple unmanned helicopters can continue to fly in a coordinated manner and ensure the safe lifting of the hoisted objects.

[0085] To ensure the real-time performance of fault-tolerant control instructions, a priority-based task scheduling algorithm is adopted, and fault diagnosis and fault-tolerant control instructions are set as high-priority tasks. When a fault is detected, low-priority tasks are immediately suspended and fault-related tasks are prioritized. At the same time, the communication network is optimized to reduce data transmission delays, ensuring that fault information can be transmitted to the controllers of each helicopter in a timely and accurate manner, realizing rapid task adjustment and coordinated control.

[0086] M4, environmental perception and adaptive adjustment module, deploys multiple types of environmental sensors on the unmanned helicopter to obtain flight environment data in real time, and adjusts the collaborative control model based on the flight environment data.

[0087] In order to fully and accurately perceive the flight environment, multiple types of environmental sensors are selected, including meteorological sensors and lidar. Meteorological sensors are used to obtain temperature, air pressure, wind speed and wind direction data information, and lidar is used to detect obstacle data information, including location and distance.

[0088] On the unmanned helicopter, the meteorological sensor is installed on the top of the fuselage to reduce the impact of the fuselage on meteorological data collection. The lidar is installed on the front, sides and bottom of the helicopter to form an all-round obstacle detection network.

[0089] The Kalman filter algorithm is used to analyze the meteorological data information Z o and obstacle data Z p The information is fused to obtain the flight environment data Z, which is expressed as:

[0090] Z o =[T,P,v w ,θ w ] T

[0091] Z p =[d1,d2,…,d p ] T

[0092]

[0093] According to the flight environment data, the cooperative control model is adjusted. In this embodiment, when the wind speed v w When the threshold is exceeded, the pulling force of the unmanned helicopter is increased to ensure the stability of the hoisted object. When the laser radar detects that the obstacle ahead is at a distance of d p When the distance is less than the safe distance, change the flight direction of the unmanned helicopter.

[0094] Under adverse weather conditions, such as strong winds and heavy rain, the coordination strategy between multiple unmanned helicopters needs to be further optimized. Considering the impact of adverse weather on the flight performance of helicopters, an environmental impact factor σ is introduced with a value range of [0,1]. The closer σ is to 0, the more severe the environment.

[0095] According to the environmental influencing factors, the parameters in the collaborative control model are adjusted. At the same time, in fault diagnosis and fault-tolerant control, when the environmental influencing factor σ is large, the sensitivity of fault detection is improved so as to promptly detect possible faults that may occur in helicopters in harsh environments.

[0096] Through the above environmental perception and adaptive control technologies, the collaborative lifting of multiple unmanned helicopters can automatically adjust the control strategy according to changes in the flight environment.

[0097] The present invention proposes a multi-unmanned helicopter hoisting system based on collaborative control. By establishing a collaborative control model that integrates dynamic and kinematic constraints and adopting a distributed control strategy, it achieves efficient coordination between multiple unmanned helicopters and adaptive adjustment of flight parameters. At the same time, it combines attitude and acceleration sensors and feedback control algorithms to effectively suppress object swinging. It cooperates with adaptive communication protocols, redundant links and a layered fault-tolerant architecture to improve the reliability of information transmission and the fault tolerance of the system. And through a variety of environmental sensors and information fusion algorithms, it realizes real-time perception of complex environments and adaptive flight adjustment.

[0098] In summary, the present invention significantly improves the safety, stability and operational efficiency of collaborative lifting by multiple unmanned helicopters, has excellent environmental adaptability and robustness, and can reliably and efficiently complete large-object lifting tasks in complex scenarios.

[0099] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A multi-unmanned helicopter lifting system based on cooperative control, characterized in that: The system includes: The modeling and control module collects raw data, including flight parameters and load conditions of multiple unmanned helicopters, as well as basic information about the objects being hoisted, and establishes a collaborative control model based on dynamics and kinematics. Each unmanned helicopter is equipped with a controller that interacts with each other and performs collaborative control via a wireless communication network. Based on the distributed control strategy of the consensus algorithm and the collaborative control model and raw data, control inputs are generated for each unmanned helicopter. The controller adjusts the flight parameters based on the control inputs. A swing and feedback module, wherein the suspended object is equipped with a sensor, which collects status information of the suspended object in real time and transmits it to the controller. Based on the status information, the controller adjusts the flight parameters of the unmanned helicopter through a feedback control algorithm; The communication and fault-tolerance module uses an adaptive communication protocol and redundant communication links to encrypt and error-correct communication data, including communication between controllers and between controllers and sensors. It also uses a fault diagnosis mechanism to detect the status of the unmanned helicopter in real time and generate fault-tolerant control instructions based on the detection results. The environmental perception and adaptive adjustment module deploys multiple types of environmental sensors on the unmanned helicopter to obtain flight environment data in real time and adjusts the collaborative control model according to the flight environment data.

2. The multi-unmanned helicopter lifting system based on cooperative control according to claim 1 is characterized in that: During the establishment of the collaborative control model, the unmanned helicopter is a rigid body, and the suspended object is a rigid body with a known center of mass.

3. The multi-unmanned helicopter lifting system based on cooperative control according to claim 1 is characterized in that: During the use of the distributed control strategy, the unmanned helicopters exchange flight parameters and load conditions through the wireless communication network. The topological structure of the wireless communication network is represented by a graph structure, and the graph structure includes graph nodes and graph edges. The graph nodes represent unmanned helicopters, and the graph edges represent communication connections between unmanned helicopters.

4. The multi-unmanned helicopter lifting system based on cooperative control according to claim 1 is characterized in that: The sensors configured for the suspended object include a posture sensor and an acceleration sensor, and the sampling frequency is f s Collect status information of hoisted objects. The state information is transmitted to the controller via a wireless communication network, and a swing model of the suspended object is established. According to the swing model, the flight parameters of the unmanned helicopter are adjusted through a feedback control algorithm to suppress the swing of the suspended object.

5. The multi-unmanned helicopter lifting system based on cooperative control according to claim 1 is characterized in that: The adaptive communication protocol adjusts the transmission rate of the wireless communication network according to the channel quality indicator, which is expressed as: Among them, R1>R2>…>R n is the transmission rate level, γ1>γ2>…>γ l-1 is the division threshold of the channel quality indicator.

6. The multi-unmanned helicopter lifting system based on cooperative control according to claim 1 is characterized in that: The fault diagnosis mechanism adopts a layered architecture, including the acquisition layer, the processing layer and the fault diagnosis decision layer. The fault diagnosis decision layer identifies the fault type of the unmanned helicopter according to a method based on a support vector machine (SVM), solves the problem according to the optimization formula of the support vector machine, finds the optimal hyperplane, identifies the fault category, and generates the fault-tolerant control instructions. According to the fault-tolerant control instructions, the task allocation of the unmanned helicopter is adjusted.

7. The multi-unmanned helicopter lifting system based on cooperative control according to claim 1 is characterized in that: The multi-type environmental sensors include meteorological sensors and laser radars. The meteorological sensors collect meteorological data information, and the laser radars collect obstacle data information. The Kalman filter algorithm is used to fuse the meteorological data information and the obstacle data information to obtain the flight environment data.

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