Aerostat control method and system

Abstract

The invention relates to the technical field of aerostat control, and discloses an aerostat control method and system, and the method comprises the steps: S1, collecting the internal state data, external environment data and environment parameters of an aerostat through a multi-source sensor; s2, processing the data acquired in the S1, and constructing a state and disturbance observer to obtain an aerostat state estimation value and an environmental wind disturbance estimation value; s3, based on the state estimation value and the wind disturbance estimation value of the aerostat, a self-adaptive control algorithm is adopted, a control instruction used for controlling the height and the horizontal position of the aerostat is generated, and the control instruction comprises a feedforward component used for actively compensating wind disturbance; and S4, distributing the control instruction to a channel power system of the aerostat and executing the control instruction. An integrated control architecture with state estimation, disturbance observation and adaptive control capabilities is constructed, three technical bottlenecks of weak control robustness, poor anti-interference capability and low intelligent level of a traditional aerostat in outdoor application are effectively solved, and the positioning precision and stability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of aerostat control, and more particularly to a control method and system for an aerostat. Background Art

[0002] Aerostats (such as airships and tethered balloons) have broad application prospects in aerial photography, monitoring, communication relay, logistics and transportation due to their advantages such as long flight time, low energy consumption and large payload. Traditional aerostats are usually filled with lighter-than-air gases such as helium to achieve buoyancy. However, existing technologies have obvious drawbacks:

[0003] 1) Poor altitude control accuracy: Aerostats are filled with helium, and their buoyancy is significantly affected by changes in ambient temperature and air pressure. Traditional methods rely solely on simple constant-speed control of ascent and descent, which cannot compensate for these environmental disturbances, resulting in unstable altitude maintenance and prone to drift.

[0004] 2) Weak Position Control Robustness: In outdoor environments, aerostats are highly susceptible to wind disturbances. Traditional open-loop or simple PID control algorithms struggle to withstand these constant and variable disturbances, resulting in low positioning accuracy, large trajectory fluctuations when following the target, and high energy consumption.

[0005] 3) Low level of intelligence: It lacks the ability to perceive and integrate its own status (such as posture, residual buoyancy) and environmental status (such as wind force) and make decisions, and cannot achieve true "automatic" following or fixed-point hovering.

[0006] Therefore, there is an urgent need for a method that can intelligently perceive the environment and adaptively generate high-precision control instructions to solve the above problems. Summary of the Invention

[0007] In view of this, the present invention provides a control method and system for an aerostat, which, by fusing multi-source sensor data, estimates the state of the aerostat and environmental disturbances in real time, and uses an adaptive control algorithm to dynamically generate accurate height and position control instructions, thereby achieving stable tracking of the target point and high-precision hovering.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A method for controlling an aerostat, comprising:

[0010] S1: Collect the internal state data, external environment data and environmental parameters of the aerostat through multi-source sensors;

[0011] S2: Process the data collected by S1, build a state and disturbance observer, and obtain the estimated value of the aerostat state and the estimated value of the ambient wind disturbance;

[0012] S3: Based on the estimated value of the aerostat state and the estimated value of wind disturbance, an adaptive control algorithm is used to generate control instructions for controlling the height and horizontal position of the aerostat, wherein the control instructions include a feedforward component for actively compensating for wind disturbance;

[0013] S4: Allocate the control instructions to the channel power system of the aerostat and execute them.

[0014] Preferably, the internal state data includes: monitoring the airbag pressure through an internal air pressure sensor; obtaining three-axis acceleration, three-axis angular velocity and three-axis attitude angle through an inertial measurement unit; and obtaining the motor speed, current and voltage through a power system state monitoring module;

[0015] External environment data includes: longitude, latitude and altitude obtained through the Beidou positioning module; absolute altitude obtained through the barometer; and relative ground height obtained through the ultrasonic / laser ranging module;

[0016] Environmental parameters include: ambient temperature and humidity obtained through temperature and humidity sensors.

[0017] Preferably, S2 specifically includes:

[0018] The Kalman filter algorithm is used to fuse the altitude, absolute altitude and relative ground altitude, and the absolute altitude is compensated and corrected by the ambient temperature and humidity to generate the optimal altitude estimate;

[0019] The three-axis attitude angle of the aerostat is updated in real time based on the three-axis angular velocity and the three-axis attitude angle. The three-axis acceleration data is subjected to coordinate transformation and integration processing based on the updated three-axis attitude angle to obtain attitude and angular velocity status information with high dynamic response;

[0020] By combining the motor speed, current and voltage with the motor-propeller thrust model, the actual thrust magnitude and direction currently generated by each channel are inferred, and then the total control thrust of the aerostat is synthesized;

[0021] An extended Kalman filter based on the aerostat dynamics model is constructed. The optimal altitude estimate, the total control thrust of the aerostat, the attitude and angular velocity state information, and the horizontal position and velocity information obtained after coordinate conversion of the longitude and latitude data are taken as input. Combined with the state estimate at the previous moment, the theoretical state vector at the current moment is predicted. The residual of the theoretical state vector is compared with the actual operating state to obtain the estimated value of the aerostat state and the estimated value of the environmental wind disturbance.

[0022] Preferably, S3 specifically includes:

[0023] Get the expected height of the current control cycle, compare it with the optimal height estimate, and get the height deviation;

[0024] The altitude deviation is input into the adaptive PID controller to obtain the PID feedback control output, which is then combined with the vertical wind disturbance component in the estimated value of the ambient wind disturbance to synthesize the altitude control command.

[0025] Obtain the expected horizontal position according to the mission objective, compare it with the horizontal position estimate in the aerostat state estimate, and obtain the position deviation;

[0026] The position deviation is input into the trajectory tracking controller to generate a basic feedback control thrust command for tracking the target, and the horizontal wind disturbance component in the estimated value of the ambient wind disturbance is combined to obtain the horizontal total control command;

[0027] A control command for controlling the height and horizontal position of the aerostat is generated based on the height control command and the horizontal direction total control command.

[0028] Preferably, the proportional, integral and differential parameters in the adaptive PID controller are adaptively adjusted according to the collected temperature and air pressure.

[0029] A control system for an aerostat, comprising:

[0030] Data acquisition unit: collects the internal state data, external environment data and environmental parameters of the aerostat through multi-source sensors;

[0031] State and disturbance observer construction unit: processes the data collected by S1, constructs the state and disturbance observer, and obtains the estimated value of the aerostat state and the estimated value of the ambient wind disturbance;

[0032] An instruction generation unit generates control instructions for controlling the height and horizontal position of the aerostat based on the estimated value of the aerostat state and the estimated value of wind disturbance, using an adaptive control algorithm, wherein the control instructions include a feedforward component for actively compensating for wind disturbance;

[0033] Distribution unit: distributes control instructions to the channel power system of the aerostat and executes them.

[0034] Preferably, the internal state data includes: monitoring the airbag pressure through an internal air pressure sensor; obtaining three-axis acceleration, three-axis angular velocity and three-axis attitude angle through an inertial measurement unit; and obtaining the motor speed, current and voltage through a power system state monitoring module;

[0035] External environment data includes: longitude, latitude and altitude obtained through the Beidou positioning module; absolute altitude obtained through the barometer; and relative ground height obtained through the ultrasonic / laser ranging module;

[0036] Environmental parameters include: ambient temperature and humidity obtained through temperature and humidity sensors.

[0037] Preferably, the state and disturbance observer construction unit specifically includes:

[0038] The Kalman filter algorithm is used to fuse the altitude, absolute altitude and relative ground altitude, and the absolute altitude is compensated and corrected by the ambient temperature and humidity to generate the optimal altitude estimate;

[0039] The three-axis attitude angle of the aerostat is updated in real time based on the three-axis angular velocity and the three-axis attitude angle. The three-axis acceleration data is subjected to coordinate transformation and integration processing based on the updated three-axis attitude angle to obtain attitude and angular velocity status information with high dynamic response;

[0040] By combining the motor speed, current and voltage with the motor-propeller thrust model, the actual thrust magnitude and direction currently generated by each channel are inferred, and then the total control thrust of the aerostat is synthesized;

[0041] An extended Kalman filter based on the aerostat dynamics model is constructed. The optimal altitude estimate, the total control thrust of the aerostat, the attitude and angular velocity state information, and the horizontal position and velocity information obtained after coordinate conversion of the longitude and latitude data are taken as input. Combined with the state estimate at the previous moment, the theoretical state vector at the current moment is predicted. The residual of the theoretical state vector is compared with the actual operating state to obtain the estimated value of the aerostat state and the estimated value of the environmental wind disturbance.

[0042] Preferably, the instruction generation unit specifically includes:

[0043] Get the expected height of the current control cycle, compare it with the optimal height estimate, and get the height deviation;

[0044] The altitude deviation is input into the adaptive PID controller to obtain the PID feedback control output, which is then combined with the vertical wind disturbance component in the estimated value of the ambient wind disturbance to synthesize the altitude control command.

[0045] Obtain the expected horizontal position according to the mission objective, compare it with the horizontal position estimate in the aerostat state estimate, and obtain the position deviation;

[0046] The position deviation is input into the trajectory tracking controller to generate a basic feedback control thrust command for tracking the target, and the horizontal wind disturbance component in the estimated value of the ambient wind disturbance is combined to obtain the horizontal total control command;

[0047] A control command for controlling the height and horizontal position of the aerostat is generated based on the height control command and the horizontal direction total control command.

[0048] Preferably, the proportional, integral and differential parameters in the adaptive PID controller are adaptively adjusted according to the collected temperature and air pressure.

[0049] It can be seen from the above technical solutions that compared with the existing technology, the present invention discloses a control method and system for an aerostat, constructs an integrated control architecture with state estimation, disturbance observation, and adaptive control capabilities, effectively solves the three major technical bottlenecks of traditional aerostats in outdoor applications: "weak control robustness, poor anti-interference ability, and low intelligence level", and improves positioning accuracy and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0051] Figure 1 This is a flow chart of a control method for an aerostat provided by the present invention.

[0052] Figure 2 The control instruction generation flow chart provided by the present invention.

[0053] Figure 3 This is a principle block diagram of a control system of an aerostat provided by the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0055] The embodiment of the present invention discloses a method for controlling an aerostat. Figure 1 Shown, including:

[0056] S1: Collect the internal state data, external environment data and environmental parameters of the aerostat through multi-source sensors;

[0057] S2: Process the data collected by S1, build a state and disturbance observer, and obtain the estimated value of the aerostat state and the estimated value of the ambient wind disturbance;

[0058] S3: Based on the estimated value of the aerostat state and the estimated value of wind disturbance, an adaptive control algorithm is used to generate control instructions for controlling the height and horizontal position of the aerostat, wherein the control instructions include a feedforward component for actively compensating for wind disturbance;

[0059] S4: Allocate the control instructions to the channel power system of the aerostat and execute them.

[0060] In this embodiment, the internal state data includes: monitoring the airbag pressure through the internal air pressure sensor; obtaining three-axis acceleration, three-axis angular velocity and three-axis attitude angle through the inertial measurement unit, wherein the three-axis attitude angle (pitch, roll, yaw) is used to determine the spatial orientation of the aerostat and support the attitude solution of the dynamic model; and obtaining the motor speed, current and voltage through the power system state monitoring module;

[0061] External environment data includes: longitude, latitude and altitude obtained through the Beidou positioning module; absolute altitude obtained through the barometer; and relative ground height obtained through the ultrasonic / laser ranging module;

[0062] Environmental parameters include: ambient temperature and humidity obtained through temperature and humidity sensors.

[0063] In this embodiment, S2 specifically includes:

[0064] S2.1: The Kalman filter algorithm is used to perform data fusion processing on the altitude obtained by the Beidou positioning module in S1, the absolute altitude obtained by the barometer, and the relative ground altitude obtained by the ultrasonic / laser ranging module. The barometric altitude is compensated and corrected in combination with the ambient temperature and humidity obtained by the temperature and humidity sensor in S1 to generate an optimal altitude estimate that is noise-resistant, low-latency, and high-precision. Specifically, the filter dynamically allocates weights based on the noise characteristics and applicable range of each sensor: in the high-altitude stage (>30 meters), Beidou and barometer are mainly used, ultrasonic / laser ranging is invalid, and the filter suppresses air pressure drift; in the medium and low-altitude stage (<30 meters), the weight of ultrasonic / laser ranging is greatly enhanced, and its high-precision characteristics are used to correct barometer drift and Beidou jitter.

[0065] Based on the three-axis angular velocity and three-axis attitude angle obtained by the inertial measurement unit in S1, combined with the dynamic response characteristics of the gyroscope and accelerometer, a complementary filtering or attitude solution algorithm is used to update the three-axis attitude angle of the aerostat in real time. Based on the three-axis attitude angle, the three-axis acceleration data is subjected to coordinate transformation and integration processing. After the transformation, the true east-west, north-south and vertical acceleration components can be separated to obtain highly dynamic response attitude and angular velocity status information;

[0066] Based on the motor speed, current and voltage obtained by the power system status monitoring module in S1, combined with the pre-calibrated motor-propeller thrust model, the actual thrust magnitude and direction generated by each channel power unit are inferred, and the total control thrust vector is further synthesized according to the geometric relationship of the power layout of the aerostat;

[0067] S2.2: Construct an extended Kalman filter based on the aerostat's six-degree-of-freedom dynamic model. This filter uses the optimal altitude estimate, attitude and angular velocity state information, the total control thrust vector, and the horizontal position and velocity information obtained by coordinate conversion from the longitude and latitude data obtained by the Beidou positioning module in S1 (converting the longitude and latitude data to the easting and northing positions in the Northeastern Universe (ENU) coordinate system; obtaining the easting and northing velocities through position differentiation or Beidou's built-in velocity solution). These are all used as measurement inputs for the observer.

[0068] Inside the observer, the theoretical motion state of the aerostat under the current control input is calculated based on the dynamic model. The residual of this theoretical state is compared with the actual motion state. The residual information is dynamically estimated using a filtering algorithm. The external disturbance components that cannot be explained by the control input are separated online in real time, and the estimated value of the ambient wind disturbance acting on the aerostat body is output, including the wind disturbance components in the horizontal and vertical directions.

[0069] The theoretical state is compared with the actual motion state by residual, and the residual information is dynamically estimated using a filtering algorithm, specifically including:

[0070] The theoretical state vector at the current moment includes: theoretical three-dimensional position (east, north, and celestial); theoretical three-dimensional velocity; theoretical acceleration; and theoretical attitude angle.

[0071] Compare the celestial position in the theoretical three-dimensional position with the best altitude estimate;

[0072] Compare the theoretical acceleration with the acceleration of the three-axis acceleration in the body coordinate system in S2.1 after converting it to the navigation coordinate system after the three-axis attitude angle compensation;

[0073] Compare the horizontal position and theoretical three-dimensional speed of the theoretical three-dimensional position with the horizontal position and speed information obtained after coordinate conversion of the longitude and latitude data obtained by the Beidou positioning module in S1;

[0074] The residual is generated through the above comparison, which drives the filter to complete the state correction and further separate the influence of external wind disturbance.

[0075] S2.3: The fusion results from S2.1 to S2.2 are integrated to form a complete state estimate of the aerostat, including three-dimensional position (longitude, latitude, and optimal altitude estimate), three-dimensional velocity, three-axis attitude angle, and three-axis angular velocity. Together with the ambient wind disturbance estimate output by S2.2, these together constitute the final output of the state and disturbance observer.

[0076] This step achieves real-time estimation of the airship's high-precision motion state through multi-source data fusion and a model-driven observation mechanism. On this basis, it completes the online identification of the key external disturbance - wind disturbance, providing a reliable basis for the introduction of feedforward compensation control in S3, and significantly improving the system's perception capability and control robustness in complex environments.

[0077] In this embodiment, if Figure 2 As shown, S3 specifically includes:

[0078] Get the expected height of the current control cycle, compare it with the optimal height estimate, and get the height deviation: e h =h ref -h est , where e h Indicates height deviation, h ref Indicates the desired height, h est represents the best height estimate;

[0079] The altitude deviation is input into the adaptive PID controller to obtain the PID feedback control output, and the vertical wind disturbance component in the estimated value of the ambient wind disturbance is combined to synthesize the altitude control command, which is used to drive the vertical thrust channel to achieve high-precision, anti-disturbance altitude tracking. Among them, the vertical wind disturbance component in the estimated value of the ambient wind disturbance is used as wind disturbance feedforward compensation and superimposed on the control output end to offset the wind disturbance before it causes altitude deviation; the altitude control command calculation formula is:

[0080]

[0081] in, Indicates the height control command in Newtons, f wz represents the vertical wind disturbance component, and m represents the mass of the aerostat.

[0082] The expected horizontal position is obtained according to the mission objective, and compared with the estimated horizontal position in the aerostat state estimate to obtain the position deviation: e xy =p ref -p est , where e xy Indicates position deviation, p ref represents the desired horizontal position, p est Indicates the estimated horizontal position of the aerostat state.

[0083] The position deviation is input to the trajectory tracking controller to generate the basic feedback control thrust command for tracking the target The horizontal direction wind disturbance component in the estimated value of the ambient wind disturbance is combined to obtain the horizontal direction total control instruction; wherein, the horizontal direction wind disturbance component f in the estimated value of the ambient wind disturbance is wxyAs the feedforward input, it is reversely mapped into the wind resistance compensation thrust that the power system needs to provide: Then get the horizontal direction total control instruction This composite control strategy not only ensures rapid response to the target, but also significantly suppresses drift and oscillation caused by wind disturbance.

[0084] A control command for controlling the height and horizontal position of the aerostat is generated based on the height control command and the horizontal direction total control command.

[0085] Among them, the proportional (Kp), integral (Ki), and differential (Kd) parameters of the adaptive PID controller are not fixed, but are dynamically adjusted according to the ambient temperature and air pressure data collected in S1. For example:

[0086] Temperature rises → helium buoyancy increases → Kp automatically decreases to prevent overshoot;

[0087] Air pressure drops → air becomes thinner → Ki is automatically reduced to prevent integral saturation;

[0088] Rapid temperature change → automatically increase Kd and enhance damping;

[0089] Achieve adaptive compensation for buoyancy fluctuations caused by environmental changes to ensure stable control performance.

[0090] The present invention directly adds the estimated wind disturbance force as a feedforward item to the control instruction, and the control logic changes from "passive response" to "active resistance", which improves the robustness of the system. In addition, the present invention does not rely on direct measurement values ​​of external wind speed sensors, but realizes "soft measurement" of wind disturbance through system behavior inversion.

[0091] This embodiment provides a control system for an aerostat, such as Figure 3 Shown, including:

[0092] Data acquisition unit: collects the internal state data, external environment data and environmental parameters of the aerostat through multi-source sensors;

[0093] State and disturbance observer construction unit: processes the data collected by S1, constructs the state and disturbance observer, and obtains the estimated value of the aerostat state and the estimated value of the ambient wind disturbance;

[0094] An instruction generation unit generates control instructions for controlling the height and horizontal position of the aerostat based on the estimated value of the aerostat state and the estimated value of wind disturbance, using an adaptive control algorithm, wherein the control instructions include a feedforward component for actively compensating for wind disturbance;

[0095] Distribution unit: distributes control instructions to the channel power system of the aerostat and executes them.

[0096] The specific implementation process and effects of the system of the present invention are consistent with the method part, which will not be repeated here. Please refer to the description of the method part.

[0097] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0098] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling an aerostat, characterized in that: include: S1: Collect the internal state data, external environment data and environmental parameters of the aerostat through multi-source sensors; S2: Process the data collected by S1, build a state and disturbance observer, and obtain the estimated value of the aerostat state and the estimated value of the ambient wind disturbance; S3: Based on the estimated value of the aerostat state and the estimated value of wind disturbance, an adaptive control algorithm is used to generate control instructions for controlling the height and horizontal position of the aerostat, wherein the control instructions include a feedforward component for actively compensating for wind disturbance; S4: Allocate the control instructions to the channel power system of the aerostat and execute them.

2. The method for controlling an aerostat according to claim 1, wherein: Internal status data includes: monitoring of airbag pressure through internal air pressure sensors; obtaining three-axis acceleration, three-axis angular velocity, and three-axis attitude angle through the inertial measurement unit; and obtaining motor speed, current, and voltage through the power system status monitoring module; External environment data includes: longitude, latitude and altitude obtained through the Beidou positioning module; absolute altitude obtained through the barometer; and relative ground height obtained through the ultrasonic / laser ranging module; Environmental parameters include: ambient temperature and humidity obtained through temperature and humidity sensors.

3. The method for controlling an aerostat according to claim 2, wherein: S2 specifically includes: The Kalman filter algorithm is used to fuse the altitude, absolute altitude and relative ground altitude, and the absolute altitude is compensated and corrected by the ambient temperature and humidity to generate the optimal altitude estimate; The three-axis attitude angle of the aerostat is updated in real time based on the three-axis angular velocity and the three-axis attitude angle. The three-axis acceleration data is subjected to coordinate transformation and integration processing based on the updated three-axis attitude angle to obtain attitude and angular velocity status information with high dynamic response; By combining the motor speed, current and voltage with the motor-propeller thrust model, the actual thrust magnitude and direction currently generated by each channel are inferred, and then the total control thrust of the aerostat is synthesized; An extended Kalman filter based on the aerostat dynamics model is constructed. The optimal altitude estimate, the total control thrust of the aerostat, the attitude and angular velocity state information, and the horizontal position and velocity information obtained after coordinate conversion of the longitude and latitude data are taken as input. Combined with the state estimate at the previous moment, the theoretical state vector at the current moment is predicted. The residual of the theoretical state vector is compared with the actual operating state to obtain the estimated value of the aerostat state and the estimated value of the environmental wind disturbance.

4. The method for controlling an aerostat according to claim 3, wherein: S3 specifically includes: Get the expected height of the current control cycle, compare it with the optimal height estimate, and get the height deviation; The altitude deviation is input into the adaptive PID controller to obtain the PID feedback control output, which is then combined with the vertical wind disturbance component in the estimated value of the ambient wind disturbance to synthesize the altitude control command. Obtain the expected horizontal position according to the mission objective, compare it with the horizontal position estimate in the aerostat state estimate, and obtain the position deviation; The position deviation is input into the trajectory tracking controller to generate a basic feedback control thrust command for tracking the target, and the horizontal wind disturbance component in the estimated value of the ambient wind disturbance is combined to obtain the horizontal total control command; A control command for controlling the height and horizontal position of the aerostat is generated based on the height control command and the horizontal direction total control command.

5. The method for controlling an aerostat according to claim 4, wherein: The proportional, integral and differential parameters in the adaptive PID controller are adaptively adjusted according to the collected temperature and pressure.

6. A control system for an aerostat, characterized in that: include: Data acquisition unit: collects the internal state data, external environment data and environmental parameters of the aerostat through multi-source sensors; State and disturbance observer construction unit: processes the data collected by S1, constructs the state and disturbance observer, and obtains the estimated value of the aerostat state and the estimated value of the ambient wind disturbance; An instruction generation unit generates control instructions for controlling the height and horizontal position of the aerostat based on the estimated value of the aerostat state and the estimated value of wind disturbance, using an adaptive control algorithm, wherein the control instructions include a feedforward component for actively compensating for wind disturbance; Distribution unit: distributes control instructions to the channel power system of the aerostat and executes them.

7. The control system of an aerostat according to claim 6, characterized in that: Internal status data includes: monitoring of airbag pressure through internal air pressure sensors; obtaining three-axis acceleration, three-axis angular velocity, and three-axis attitude angle through the inertial measurement unit; and obtaining motor speed, current, and voltage through the power system status monitoring module; External environment data includes: longitude, latitude and altitude obtained through the Beidou positioning module; absolute altitude obtained through the barometer; and relative ground height obtained through the ultrasonic / laser ranging module; Environmental parameters include: ambient temperature and humidity obtained through temperature and humidity sensors.

8. The control system of an aerostat according to claim 7, characterized in that: The state and disturbance observer building blocks specifically include: The Kalman filter algorithm is used to fuse the altitude, absolute altitude and relative ground altitude, and the absolute altitude is compensated and corrected by the ambient temperature and humidity to generate the optimal altitude estimate; The three-axis attitude angle of the aerostat is updated in real time based on the three-axis angular velocity and the three-axis attitude angle. The three-axis acceleration data is subjected to coordinate transformation and integration processing based on the updated three-axis attitude angle to obtain attitude and angular velocity status information with high dynamic response; By combining the motor speed, current and voltage with the motor-propeller thrust model, the actual thrust magnitude and direction currently generated by each channel are inferred, and then the total control thrust of the aerostat is synthesized; An extended Kalman filter based on the aerostat dynamics model is constructed. The optimal altitude estimate, the total control thrust of the aerostat, the attitude and angular velocity state information, and the horizontal position and velocity information obtained after coordinate conversion of the longitude and latitude data are taken as input. Combined with the state estimate at the previous moment, the theoretical state vector at the current moment is predicted. The residual of the theoretical state vector is compared with the actual operating state to obtain the estimated value of the aerostat state and the estimated value of the environmental wind disturbance.

9. The control system of an aerostat according to claim 8, characterized in that: The instruction generation unit specifically includes: Get the expected height of the current control cycle, compare it with the optimal height estimate, and get the height deviation; The altitude deviation is input into the adaptive PID controller to obtain the PID feedback control output, which is then combined with the vertical wind disturbance component in the estimated value of the ambient wind disturbance to synthesize the altitude control command. Obtain the expected horizontal position according to the mission objective, compare it with the horizontal position estimate in the aerostat state estimate, and obtain the position deviation; The position deviation is input into the trajectory tracking controller to generate a basic feedback control thrust command for tracking the target, and the horizontal wind disturbance component in the estimated value of the ambient wind disturbance is combined to obtain the horizontal total control command; A control command for controlling the height and horizontal position of the aerostat is generated based on the height control command and the horizontal direction total control command.

10. The aerostat control system according to claim 9, characterized in that: The proportional, integral and differential parameters in the adaptive PID controller are adaptively adjusted according to the collected temperature and pressure.

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