Control method and system for an aerostat

Abstract

The application relates to the technical field of airship control, and discloses an airship control method and system, which comprises the following steps: S1, collecting internal state data, external environment data and environment parameters of the airship through a multi-source sensor; S2, processing the data collected in S1, constructing a state and disturbance observer, and obtaining an airship state estimation value and an environment wind disturbance estimation value; S3, based on the airship state estimation value and the wind disturbance estimation value, using an adaptive control algorithm to generate a control instruction for controlling the height and horizontal position of the airship, wherein the control instruction contains a feedforward component for actively compensating wind disturbance; and S4, distributing the control instruction to the channel power system of the airship and executing the control instruction. An integrated control architecture with state estimation, disturbance observation and adaptive control capabilities is constructed, three technical bottlenecks of traditional airships in outdoor applications, i.e., weak control robustness, poor anti-disturbance capability and low intelligent level, are effectively solved, and the positioning accuracy and stability are improved.

Description

Technical Field

[0001] This invention relates to the field of airship control technology, and more specifically to an airship control method and system. Background Technology

[0002] Aerial vehicles (such as airships and tethered balloons) have broad application prospects in aerial photography, monitoring, communication relay, and logistics transportation due to their advantages such as long flight time, low energy consumption, and large payload. Traditional aerial vehicles typically achieve levitation by filling them with gases lighter than air, such as helium. However, existing technologies have significant drawbacks:

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

[0004] 2) Weak robustness of position control: In outdoor environments, airships are highly susceptible to wind interference. Traditional open-loop or simple PID control algorithms are unable to resist such continuous 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 state (such as attitude and remaining buoyancy) and environmental state (such as wind force), and cannot achieve true "automatic" following or fixed-point hovering.

[0006] Therefore, there is an urgent need for a method that can intelligently sense the environment and adaptively generate high-precision control commands 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 airship, which integrates multi-source sensor data to estimate the state of the airship and environmental disturbances in real time, and uses an adaptive control algorithm to dynamically generate precise altitude and position control commands, thereby achieving stable tracking and high-precision hovering of the target point.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A method for controlling an airship includes:

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

[0011] S2: Process the data collected in S1, construct a state and disturbance observer, and obtain the estimated values ​​of the airship state and the environmental wind disturbance.

[0012] S3: Based on the airship state estimate and wind disturbance estimate, an adaptive control algorithm is used to generate control commands for controlling the airship's altitude and horizontal position. The control commands include feedforward components for actively compensating for wind disturbance.

[0013] S4: Distribute control commands to the airship's channel propulsion system and execute them.

[0014] Preferably, the internal status data includes: monitoring airbag pressure via an internal pressure sensor; acquiring triaxial acceleration, triaxial angular velocity, and triaxial attitude angle via an inertial measurement unit; and acquiring motor speed, current, and voltage via a power system status monitoring module.

[0015] External environmental data includes: latitude, longitude, and altitude obtained through the BeiDou positioning module; absolute altitude obtained through the barometer; and relative altitude 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 altitude, absolute altitude and relative ground altitude, and the absolute altitude is compensated and corrected by ambient temperature and humidity to generate the optimal altitude estimate.

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

[0020] By using motor speed, current and voltage, and combining them with the motor-propeller thrust model, the actual magnitude and direction of the thrust generated by each channel can be deduced, and then the total control thrust of the airship can be synthesized.

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

[0022] Preferably, S3 specifically includes:

[0023] Obtain the expected height for the current control cycle and compare it with the optimal height estimate to obtain the height deviation;

[0024] The height deviation is input into the adaptive PID controller to obtain the PID feedback control output, and combined with the vertical wind disturbance component in the estimated value of the environmental wind disturbance, a height control command is synthesized.

[0025] The desired horizontal position is obtained based on the mission objective, and compared with the horizontal position estimate in the airship state estimate to obtain the position deviation.

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

[0027] Control commands for controlling the altitude and horizontal position of the airship are generated based on the altitude control command and the horizontal general control command.

[0028] Preferably, the proportional, integral, and derivative parameters in the adaptive PID controller are adaptively adjusted based on the collected temperature and air pressure.

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

[0030] Data acquisition unit: Collects internal status data, external environmental data, and environmental parameters of the airship through multi-source sensors;

[0031] State and disturbance observer construction unit: Processes the data collected by S1 to construct the state and disturbance observer, and obtains the estimated values ​​of the airship state and the estimated values ​​of the environmental wind disturbance;

[0032] Command generation unit: Based on the airship state estimate and wind disturbance estimate, an adaptive control algorithm is used to generate control commands for controlling the airship's altitude and horizontal position, wherein the control commands include feedforward components for actively compensating for wind disturbance;

[0033] Distribution unit: Distributes control commands to the airship's channel propulsion system and executes them.

[0034] Preferably, the internal status data includes: monitoring airbag pressure via an internal pressure sensor; acquiring triaxial acceleration, triaxial angular velocity, and triaxial attitude angle via an inertial measurement unit; and acquiring motor speed, current, and voltage via a power system status monitoring module.

[0035] External environmental data includes: latitude, longitude, and altitude obtained through the BeiDou positioning module; absolute altitude obtained through the barometer; and relative altitude 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 building unit specifically includes:

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

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

[0040] By using motor speed, current and voltage, and combining them with the motor-propeller thrust model, the actual magnitude and direction of the thrust generated by each channel can be deduced, and then the total control thrust of the airship can be synthesized.

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

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

[0043] Obtain the expected height for the current control cycle and compare it with the optimal height estimate to obtain the height deviation;

[0044] The height deviation is input into the adaptive PID controller to obtain the PID feedback control output, and combined with the vertical wind disturbance component in the estimated value of the environmental wind disturbance, a height control command is synthesized.

[0045] The desired horizontal position is obtained based on the mission objective, and compared with the horizontal position estimate in the airship state estimate to obtain the position deviation.

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

[0047] Control commands for controlling the altitude and horizontal position of the airship are generated based on the altitude control command and the horizontal general control command.

[0048] Preferably, the proportional, integral, and derivative parameters in the adaptive PID controller are adaptively adjusted based on the collected temperature and air pressure.

[0049] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a control method and system for an airship, and constructs an integrated control architecture with state estimation, disturbance observation and adaptive control capabilities. It effectively solves the three major technical bottlenecks of traditional airships in outdoor applications: "weak control robustness, poor anti-disturbance capability and low level of intelligence", and improves positioning accuracy and stability. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0051] Figure 1 A flowchart of a control method for an airship provided by the present invention.

[0052] Figure 2 A flowchart for generating control instructions provided by the present invention.

[0053] Figure 3 This invention provides a schematic diagram of the control system principle for an airship. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] This invention discloses a control method for an airship, such as... Figure 1 As shown, it includes:

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

[0057] S2: Process the data collected in S1, construct a state and disturbance observer, and obtain the estimated values ​​of the airship state and the environmental wind disturbance.

[0058] S3: Based on the airship state estimate and wind disturbance estimate, an adaptive control algorithm is used to generate control commands for controlling the airship's altitude and horizontal position. The control commands include feedforward components for actively compensating for wind disturbance.

[0059] S4: Distribute control commands to the airship's channel propulsion system and execute them.

[0060] In this embodiment, the internal state data includes: monitoring airbag pressure through an internal air pressure sensor; acquiring triaxial acceleration, triaxial angular velocity, and triaxial attitude angles through an inertial measurement unit, wherein the triaxial attitude angles (pitch, roll, yaw) are used to determine the spatial orientation of the airship and support the attitude calculation of the dynamic model; and acquiring motor speed, current, and voltage through a power system state monitoring module.

[0061] External environmental data includes: latitude, longitude, and altitude obtained through the BeiDou positioning module; absolute altitude obtained through the barometer; and relative altitude 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 fuse the altitude obtained by the Beidou positioning module, the absolute altitude obtained by the barometer, and the relative ground altitude obtained by the ultrasonic / laser ranging module in S1. Combined with the ambient temperature and humidity obtained by the temperature and humidity sensor in S1, the barometric altitude is compensated and corrected to generate an optimal altitude estimate that is noise-resistant, low-latency, and high-precision. Specifically, the filter dynamically allocates weights according to the noise characteristics and applicable range of each sensor: in the high-altitude stage (>30 meters), Beidou and barometer are the main sensors, ultrasonic / laser ranging is ineffective, and the filter suppresses barometric pressure drift; in the mid-to-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 fluctuations.

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

[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 deduced, and the total control thrust vector is further synthesized according to the dynamic layout geometry of the airship.

[0067] S2.2: Construct an extended Kalman filter based on the six-degree-of-freedom dynamic model of the airship. The optimal altitude estimate, attitude and angular velocity state information, total control thrust vector, and horizontal position and velocity information obtained from the latitude and longitude data acquired by the Beidou positioning module in S1 after coordinate transformation (converting latitude and longitude to east and north positions in the ENU coordinate system; obtaining east and north velocities through position differentiation or Beidou built-in velocity calculation) are used together as the measurement input of the observer.

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

[0069] This involves comparing the theoretical state with the actual motion state using residuals, and then dynamically estimating the residual information using a filtering algorithm. Specifically, this includes:

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

[0071] Compare the celestial position in the theoretical three-dimensional position with the optimal height estimate;

[0072] The theoretical acceleration is compared with the acceleration in the navigation coordinate system after three-axis attitude angle compensation in the body coordinate system in S2.1.

[0073] The horizontal position and theoretical three-dimensional velocity of the theoretical three-dimensional position are compared with the horizontal position and velocity information obtained by coordinate transformation of the latitude and longitude data obtained by the Beidou positioning module in S1.

[0074] The above comparison generates residuals, which drive the filter to complete state correction and further separate the effects of external wind disturbances.

[0075] S2.3: Integrating the fusion results of S2.1 to S2.2, a complete airship state estimate is formed, 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 environmental wind disturbance estimate output from S2.2, they constitute the final output of the state and disturbance observer.

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

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

[0078] Obtain the desired altitude for the current control cycle and compare it with the optimal altitude estimate to obtain the altitude deviation: e h =h ref -h est , where e h Indicates altitude deviation, h ref h represents the desired height. est This represents the optimal height estimate;

[0079] The altitude deviation is input into the adaptive PID controller to obtain the PID feedback control output. This output, combined with the vertical wind disturbance component from the estimated environmental wind disturbance, is used to synthesize an altitude control command. This command drives the vertical thrust channel, achieving high-precision, disturbance-resistant altitude tracking. The vertical wind disturbance component from the estimated environmental wind disturbance serves as wind disturbance feedforward compensation, superimposed on the control output to cancel out the altitude deviation before it is caused by wind disturbance. The altitude control command calculation formula is as follows:

[0080]

[0081] in, This indicates an altitude control command, measured in Newtons (f). wz denoted by , m represents the vertical wind disturbance component, and m represents the mass of the airship.

[0082] The desired horizontal position is obtained based on the mission objective and compared with the horizontal position estimate in the airship state estimate to obtain the position deviation: e xy =p ref -p est , where e xy p represents the positional deviation. ref p represents the desired horizontal position. est This represents the horizontal position estimate in the airship state estimate.

[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 control command is obtained by combining the horizontal wind disturbance component in the estimated value of the environmental wind disturbance; wherein, the horizontal wind disturbance component f in the estimated value of the environmental wind disturbance is... wxyAs a feedforward input, it is mapped inversely to the wind-compensating thrust that the power system needs to provide: This leads to the overall horizontal control command. This composite control strategy ensures both rapid response to the target and significant suppression of drift and oscillation caused by wind disturbance.

[0084] Control commands for controlling the altitude and horizontal position of the airship are generated based on the altitude control command and the horizontal general control command.

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

[0086] Temperature increases → helium buoyancy increases → Kp is automatically reduced to prevent overshoot;

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

[0088] Rapid temperature changes → automatically increase Kd to enhance damping;

[0089] It achieves adaptive compensation for buoyancy fluctuations caused by environmental changes, ensuring stable control performance.

[0090] This invention directly incorporates the estimated wind disturbance force as a feedforward term into the control command, changing the control logic from "passive response" to "active resistance," thereby improving the system's robustness. Furthermore, this invention does not rely on direct measurements from external wind speed sensors but achieves "soft measurement" of wind disturbance through system behavior inversion.

[0091] This embodiment provides a control system for an airship, such as Figure 3 As shown, it includes:

[0092] Data acquisition unit: Collects internal status data, external environmental data, and environmental parameters of the airship through multi-source sensors;

[0093] State and disturbance observer construction unit: Processes the data collected by S1 to construct the state and disturbance observer, and obtains the estimated values ​​of the airship state and the estimated values ​​of the environmental wind disturbance;

[0094] Command generation unit: Based on the airship state estimate and wind disturbance estimate, an adaptive control algorithm is used to generate control commands for controlling the airship's altitude and horizontal position, wherein the control commands include feedforward components for actively compensating for wind disturbance;

[0095] Distribution unit: Distributes control commands to the airship's channel propulsion system and executes them.

[0096] The specific implementation process and effects of the system of this invention are the same as those in the method section, and will not be repeated here. Please refer to the description in the method section.

[0097] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0098] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those 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 invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A control method of an aerostat, characterized by, Comprise: S1: Collect internal state data, external environment data and environmental parameters of the aerostat through multi-source sensors; S2: Process the data collected in S1, construct a state and disturbance observer, and obtain the state estimation value of the aerostat and the estimation value of the environmental wind disturbance; S3: Based on the state estimation value of the aerostat and the wind disturbance estimation value, 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 wind disturbance; S4: Distribute the control instructions to the channel power system of the aerostat and execute them; 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; and obtaining motor speed, current and voltage through the power system state monitoring module; The external environment data includes: obtaining latitude, longitude and altitude through the Beidou positioning module; obtaining absolute height through the barometer; and obtaining relative ground height through the ultrasonic / laser ranging module; The environmental parameters include: obtaining environmental temperature and humidity through the temperature and humidity sensor; S2 specifically includes: The Kalman filtering algorithm is used to fuse the altitude, absolute height and relative ground height, and the absolute height is compensated and corrected by the environmental temperature and humidity to generate the optimal height estimation value; Based on the three-axis angular velocity and three-axis attitude angle, the three-axis attitude angle of the aerostat is updated in real time, and based on the updated three-axis attitude angle, the three-axis acceleration data is processed by coordinate transformation and integration to obtain high dynamic response attitude and angular velocity state information; The actual thrust size and direction generated by each channel are back calculated through the motor speed, current and voltage combined with the motor-propeller thrust model, and then the total control thrust of the aerostat is synthesized; An extended Kalman filter based on the aerostat dynamics model is constructed, and the optimal height estimation value, the total control thrust of the aerostat, the attitude and angular velocity state information, and the horizontal position and speed information obtained by coordinate conversion of the latitude and longitude data are used as inputs, combined with the state estimation value at the last time, to predict the theoretical state vector at the current time, and the residual error between the theoretical state vector and the actual running state is compared to obtain the state estimation value of the aerostat and the estimation value of the environmental wind disturbance; S3 specifically includes: The expected height of the current control period is compared with the optimal height estimation value to obtain the height deviation; The height deviation is input into the adaptive PID controller to obtain the PID feedback control output, and combined with the vertical wind disturbance component in the estimation value of the environmental wind disturbance, the height control instruction is synthesized; The expected horizontal position is obtained according to the task target, and compared with the horizontal position estimation value in the state estimation value of the aerostat to obtain the position deviation; The position deviation is input into the trajectory tracking controller to generate the basic feedback control thrust instruction for tracking the target, and combined with the horizontal wind disturbance component in the estimation value of the environmental wind disturbance to obtain the total control instruction in the horizontal direction; The control instructions for controlling the height and horizontal position of the aerostat are generated based on the height control instruction and the total control instruction in the horizontal direction.

2. A control method for a floatation device according to claim 1, wherein The proportional, integral and differential parameters in the adaptive PID controller are adaptively adjusted according to the collected temperature and air pressure.

3. A control system for an aerostat, characterized by, The method comprises the following steps: a data acquisition unit: collecting internal state data, external environment data and environmental parameters of the aerostat through multi-source sensors; a state and disturbance observer construction unit: processing the data collected by S1, constructing a state and disturbance observer, and obtaining state estimation values of the aerostat and estimation values of environmental wind disturbance; an instruction generation unit: based on the state estimation values of the aerostat and the wind disturbance estimation values, 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 wind disturbance; a distribution unit: distributing the control instructions to the channel power system of the aerostat and executing them; 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 motor speed, current and voltage through a power system state monitoring module; the external environment data includes: obtaining longitude, latitude and altitude through a Beidou positioning module; obtaining absolute height through a barometer; and obtaining relative ground height through an ultrasonic / laser ranging module; the environmental parameters include: obtaining environmental temperature and humidity through a temperature and humidity sensor; the state and disturbance observer construction unit specifically comprises: using Kalman filtering algorithm to fuse altitude, absolute height and relative ground height, and compensating and correcting the absolute height through environmental temperature and humidity to generate optimal height estimation values; updating the three-axis attitude angle of the aerostat in real time based on the three-axis angular velocity and the three-axis attitude angle, and based on the updated three-axis attitude angle, performing coordinate transformation and integral processing on the three-axis acceleration data to obtain high dynamic response attitude and angular velocity state information; by combining the motor speed, current and voltage with the motor-propeller thrust model, the actual thrust size and direction generated by each channel are back calculated, 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 height estimation values, the total control thrust of the aerostat, the attitude and angular velocity state information, and the horizontal position and speed information obtained by coordinate conversion of the latitude and longitude data are taken as inputs, combined with the state estimation values at the last moment, the theoretical state vector at the current moment is predicted, the residual error between the theoretical state vector and the actual running state is compared, and the state estimation values of the aerostat and the estimation values of the environmental wind disturbance are obtained; the instruction generation unit specifically comprises: obtaining the expected height of the current control period, comparing it with the optimal height estimation values, and obtaining the height deviation; inputting the height deviation into the adaptive PID controller to obtain the PID feedback control output, and combining the vertical wind disturbance component in the estimation values of the environmental wind disturbance to synthesize the height control instruction; obtaining the expected horizontal position according to the task target, comparing it with the horizontal position estimation value in the state estimation values of the aerostat, and obtaining the position deviation; inputting the position deviation into the trajectory tracking controller to generate the basic feedback control thrust instruction for tracking the target, and combining the horizontal wind disturbance component in the estimation values of the environmental wind disturbance to obtain the total horizontal control instruction; The control instructions for controlling the altitude and horizontal position of the aerostat are generated based on the altitude control instructions and the horizontal direction total control instructions.

4. A control system for an aerostat according to claim 3, wherein, The proportional, integral and differential parameters in the adaptive PID controller are adaptively adjusted according to the collected temperature and air pressure.

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