UAV fixed-point delivery control system and method based on inertial guidance

Abstract

The present invention discloses a control system and method for fixed-point delivery of unmanned aerial vehicles based on inertial guidance, relates to the field of navigation and control of unmanned aerial vehicles, and is applicable to the inertial-guided dive attitude and fixed-point delivery of unmanned aerial vehicles. On the basis of existing inertial guidance technology, the present invention introduces an adaptive algorithm and a real-time feedback mechanism, accurately monitors the attitude, speed and position of the unmanned aerial vehicle based on the inertial navigation system INS, and dynamically adjusts the flight trajectory and attitude during flight, taking into account the influence of target dynamics and external interference, reducing the interference of these factors on the precise delivery results, and providing a more reliable and efficient fixed-point delivery method, overcoming the influence of external environmental interference on precise delivery, and being widely applicable to the fields of airdropping materials, disaster relief, etc., and being able to ensure that the aircraft maintains high accuracy and stability when performing tasks in complex environments, and having high practical value and commercial potential.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle navigation and control, and in particular to an unmanned aerial vehicle fixed-point delivery control system and method based on inertial guidance. Background Art

[0002] Currently, among precision delivery technologies, drone delivery has been widely used in various aerial precision rescue missions. However, when performing dive delivery missions, existing drones often rely on simple preset trajectories and delivery angle control, resulting in positioning accuracy and delivery effects being limited by factors such as target movement and external interference. Especially in complex and dynamic field rescue environments, drone delivery faces great challenges. In such cases, traditional drones rely on external measurement tools such as GPS to correct the delivery path, but these external tools are easily interfered with or fail under certain environmental conditions, so it is often difficult to ensure high accuracy, real-time performance, and reliability in actual applications.

[0003] When performing fixed-point delivery, traditional drones usually lack independent and accurate inertial navigation support, resulting in limited delivery accuracy, and small errors in the delivery process may be further amplified as the delivery angle changes. Existing drones rely on external auxiliary equipment, such as satellite navigation. Once these devices fail or are subject to other interference, the stability and accuracy of the drone's flight will be seriously affected, resulting in delivery failure or deviation, and the overall performance of the drone may be seriously affected.

[0004] Therefore, the existing positioning delivery system for drones has a certain degree of accuracy reduction and insufficient anti-interference ability when performing fixed-point delivery tasks. These problems will affect the overall performance of the system in situations where rapid response and precise delivery are of great significance, such as emergency rescue tasks. In order to ensure the successful execution of the delivery task, a new drone fixed-point delivery system and method is urgently needed, which can overcome the above limitations and provide a drone fixed-point delivery system with efficient, accurate and reliable dive attitude control and fixed-point delivery capabilities.

[0005] This type of technology can cover a variety of scenarios and situations. Its main purpose is to quickly and accurately deliver the equipment when an emergency, emergency or major public event occurs to ensure disaster relief, public safety and coordinated emergency response. In extreme cases, if the drone's inertial guidance system cannot achieve high-precision, low-error delivery, it will have a serious negative impact on the mission objectives, thereby affecting the timeliness, accuracy and reliability of the rescue mission, and may even affect the smooth progress of the aid operation. Summary of the invention

[0006] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a UAV fixed-point delivery control system based on inertial guidance. The technical problem to be solved is: the inertial guidance system of the traditional UAV mainly performs flight control based on the preset trajectory and delivery angle, and there are large errors when applied to complex fixed-point delivery tasks, which may affect the accuracy and success rate of the task, especially in the rescue environment where the target changes dynamically and the external interference is relatively serious; the purpose of the present invention is to specifically target the fixed-point delivery tasks in the above-mentioned complex rescue environment, and provide a UAV fixed-point delivery control system and method based on inertial guidance. On the basis of the existing inertial guidance technology, the present invention introduces an adaptive algorithm and a real-time feedback mechanism, accurately monitors the attitude, speed and position of the UAV based on the inertial navigation system INS, and dynamically adjusts the flight trajectory and attitude during the flight, taking into account the influence of target dynamics and external interference, reducing the interference of these factors on the precise delivery results, and providing a more reliable and efficient fixed-point delivery method.

[0007] The purpose of the present invention is mainly achieved through the following technical solutions:

[0008] The UAV fixed-point delivery control system based on inertial guidance includes: an inertial navigation module, a target positioning module, a path planning module, a feedback control module and an attitude control module.

[0009] in:

[0010] The inertial navigation module is used to obtain the real-time flight status data of the UAV and send the collected real-time flight status data to the target positioning module, attitude control module, path planning module and feedback control module; the flight status data includes the position, speed and flight attitude of the UAV, and the flight attitude includes heading angle, pitch angle and roll angle data.

[0011] The target positioning module is used to capture the target position and the UAV position. The UAV movement route is obtained according to the UAV position captured during the flight. Combined with the real-time flight status data output by the inertial navigation module, the target positioning module outputs the UAV movement route data. The movement route data includes speed, route, and route. At takeoff, the initial position of the UAV is calibrated. During the delivery mission, the UAV movement route data is compared with the path planning data output by the path planning module. If they are consistent, no processing is performed. If they are inconsistent, the route error between the UAV movement route data and the path planning data is calculated. The route error includes distance error and heading error. It is used to output the target position, the UAV position and the calculated route error, and send them to the path planning module.

[0012] The path planning module is used to receive the real-time flight status data output by the inertial navigation module, as well as the target position, UAV position and calculated route error output by the target positioning module, calculate the path planning data, including the UAV flight planning path and flight planning attitude, adjust the flight path and attitude, and adjust the release time; when the set preparation release distance is reached, a trigger action correction command is generated, and the action correction command is transmitted to the attitude control module; when the completion action correction command is received and the release time is reached, the release action is triggered to release the release object in the set position area; it is used to send the output path planning data to the target positioning module, and transmit the adjusted flight path and attitude to the feedback control module.

[0013] The feedback control module is used to receive the flight status data output by the inertial navigation module and the flight planning attitude output by the path planning module, and compare them. If they are consistent, no processing is done. If they are inconsistent, the difference between the flight status data and the flight planning attitude is calculated, and a signal that dynamic adjustment and correction is required is sent to the attitude control module.

[0014] The attitude control module is used to receive real-time flight status data and determine whether the flight attitude of the flight status is within the set threshold range. If it is within the set threshold range, no processing will be performed. Otherwise, an adaptive control algorithm is used to calculate the attitude adjustment control command of the UAV, and control the angles of the three axes of the UAV to adjust the flight attitude to the set threshold range. The angles of the three axes include pitch angle, roll angle and heading angle; the attitude control module receives the dynamic adjustment and correction signal output by the feedback control module to control the corresponding differences in the angles of the three axes to adjust; the attitude control module receives the action correction command triggered by the path planning module, controls the UAV to execute the set delivery flight speed and flight attitude, and when the set value is reached, completes the action correction command, and returns the completed action correction command to the path planning module.

[0015] In order to further optimize the above technical solution, the inertial navigation module first performs initial calibration when starting to execute the delivery mission, collects real-time data during the delivery mission, and calculates the attitude of the collected data; the inertial navigation module includes multiple inertial navigation systems.

[0016] The initial calibration is that the UAV needs to obtain the initial position and attitude data at this time through the inertial navigation system before taking off.

[0017] The real-time data acquisition is that the inertial navigation system continuously collects the real-time acceleration and angular velocity during the flight, and uses the fusion method of multiple inertial navigation systems to obtain the measurement equation and observation equation, and outputs the real-time flight status data of the drone; the attitude calculation uses the angular velocity signal provided by the gyroscope and the output of the accelerometer to estimate the flight attitude of the drone.

[0018] In order to further optimize the above technical solution, the measurement equation and the observation equation are obtained by adopting the method of fusing multiple inertial navigation systems, which are processed by multiple inertial navigation system data fusion and Kalman filter algorithm.

[0019] The inertial navigation system has hardware errors, which include gyro drift errors and accelerometer errors. The inertial navigation systems are started separately at set earth cycles. The inertial navigation system uses position, velocity, and attitude as observation quantities. Multiple inertial guidance systems are mutually referenced. The data output by other inertial guidance systems are used as observation data for Kalman filtering. After the hardware errors are estimated, each inertial navigation system is compensated, and then the real-time flight status data of the drone obtained after the multi-inertial navigation system data is output and fused.

[0020] In order to further optimize the above technical solution, the measurement equation and observation equation are obtained by fusing multiple inertial navigation systems, which specifically includes:

[0021] In the initial alignment phase of the multi-inertial navigation system, the performance of the gyroscope and accelerometer is combined to merge the errors of the inertial components and the system hardware itself into the hardware error, that is:

[0022] The model of gyroscope drift error is: ,in The variance is Gaussian white noise;

[0023] Modeling Accelerometer Errors ,in The variance is Gaussian white noise.

[0024] Establish multi-inertial navigation system measurement and convert the measurement value of the gyroscope into the data of the object's motion state; for any two inertial navigation systems in the multi-inertial navigation system in the observation equation, establish the multi-inertial navigation system measurement equation: ;

[0025] Take the state variables of the two inertial navigation systems:

[0026] ;

[0027] in,

[0028] is the three-dimensional attitude error angle of the first inertial navigation system, is the three-dimensional attitude error angle of the second inertial navigation system,

[0029] is the three-dimensional velocity error of the first inertial navigation system, is the three-dimensional velocity error of the second inertial navigation system,

[0030] Among them, E, N, and U are three-dimensional directions, namely, east, north, and sky directions;

[0031] are the position and altitude errors of the first inertial navigation system, are the position and altitude errors of the second inertial navigation system,

[0032] is the gyro drift error of the first inertial navigation system, is the gyro drift error of the second inertial navigation system,

[0033] is the accelerometer error of the first inertial navigation system; is the accelerometer error of the second inertial navigation system.

[0034] Noise matrix of inertial navigation system hardware error: ;

[0035] State variable contact matrix ,

[0036] in is a 3x6 zero matrix, is a 6x15 zero matrix, a matrix where all elements are zero.

[0037] It is the state transfer matrix derived from the measurement equation of the multi-inertial navigation system. It maps the state of the previous moment to the state of the current moment. The state includes attitude, speed, position and altitude, gyro drift and acceleration.

[0038] , C is the cosine transformation matrix from the orthogonal coordinate system formed by the input axes of the first inertial navigation system to the orthogonal coordinate system formed by the input axes of the second inertial navigation system, wherein the orthogonal coordinate system formed by the input axes of the inertial navigation system is the platform coordinate system.

[0039] Parameters that control the effect of inertial navigation system hardware errors on the measurement equations ,

[0040] is a 6x6 identity matrix, where all elements on the main diagonal are 1.

[0041] The observation equation is obtained by taking the three-dimensional directions of the geographic coordinate systems of the first inertial navigation system and the second inertial navigation system as the observed quantities:

[0042] ,

[0043] Where V is the disturbance vector of the measurement equation.

[0044] The Kalman filter equation is constructed through the measurement equation and the observation equation. When filtering, it is first discretized and then the standard discrete Kalman filter formula is used.

[0045] In order to further optimize the above technical solution, the target positioning module obtains the real-time position data and motion status of the UAV and the target through radar combined with optical imaging positioning means.

[0046] In order to further optimize the above technical solution, the path planning module will take the speed, attitude and heading angle of the UAV as input according to the current flight status data and movement route data of the UAV, establish a cost function, control the input variables, and output the optimal answer according to the constraints as the path planning data.

[0047] The constraints of the cost function are based on the dynamic constraints of the hardware of the drone, including the maximum tolerable acceleration, heading adjustment limit and dive angle range.

[0048] The optimal answer outputted is obtained by evaluating the error between the current route and the predetermined target through the relative position error between the UAV and the target, the shortest distance of the flight path and the minimum time. The one with the smallest error is the optimal answer.

[0049] In order to further optimize the above technical solution, the path planning module is also used to adjust the delivery timing. The path planning module needs to calculate the delivery task of the drone on the flight planning path according to the current position, speed, target position and real-time path planning data of the drone through a prediction algorithm, arrive at the ready delivery point, adjust the drone's attitude to reach the set attitude, and finally reach the delivery distance threshold for delivery, and output the real-time delivery timing.

[0050] The prepared launch point is a point on the flight planning path where the drone is a set distance away from the target position and reaches the prepared position.

[0051] In order to further optimize the above technical solution, the adaptive control algorithm adopts a PID controller to calculate the attitude adjustment control instructions of the UAV according to the error value of the flight status data and the path planning data, and control the UAV to adjust its attitude.

[0052] The dynamic adjustment is that as the flight progresses, the attitude control module needs to perform real-time adjustment control according to the new error value, that is, dynamic adjustment.

[0053] The PID controller calculates the control input based on the error value. The output of the PID controller is expressed as:

[0054] ;

[0055] in:

[0056] is the error value at the current moment, including the error of pitch angle, roll angle or heading angle;

[0057] is the proportional gain, which determines the intensity of the control system response error;

[0058] is the integral gain, which is used to correct the accumulated error;

[0059] is the differential gain, which is used to predict the changing trend of the error;

[0060] It is the definite integral of the error value from 0 to the current time t;

[0061] is the differential error value at the current moment.

[0062] Proportional control, integral control and differential control are carried out in sequence, as follows:

[0063] First, perform proportional control and adjust the attitude of the drone according to the current error value; the larger the error, the greater the control output.

[0064] Integral control is then performed to eliminate steady-state errors by accumulating errors, where the steady-state errors include external disturbances and drone hardware parameters.

[0065] Finally, differential control is performed, and adjustments are made based on the error change rate to improve stability.

[0066] According to the real-time errors of the UAV, including heading error and distance error, the output of the current controller is calculated; then the PID gain is dynamically adjusted according to the rate of change and accumulation of the errors, and the gain adjustment method is adopted to dynamically increase or decrease the PID gain according to the flight speed of the UAV.

[0067] This solution also includes a UAV fixed-point delivery control method based on an inertial navigation module. This method is implemented by a UAV fixed-point delivery control system based on an inertial navigation module, and the method includes the following steps:

[0068] Step S1, when taking off, perform initialization settings; when taking off, determine the target location and the position of the drone, calibrate the initial position of the drone, set the initial planned route to perform the delivery task; initialize the movement route data and flight status data of the drone.

[0069] Step S2: When taking off, according to the preset initial planned route, by comparing the current position of the drone with the initial planned route, the current drone movement route data and the error of the initial planned route are obtained, and the route is optimized in real time in combination with the dynamic changes of the target to obtain the path planning data.

[0070] Step S3: During the flight, the UAV flies according to the path planning data; the inertial navigation module is calculated, and the flight status data of the UAV is calculated in real time by using the inertial navigation module, and the changes in position, speed and flight attitude are output; the target positioning module continuously updates the position of the UAV, outputs the motion route data and the gap between the UAV position and the target position, and the path planning module updates the path planning data in real time.

[0071] Step S4: adjust the dive attitude. According to the current flight path, i.e., the path planning data and the error, an adaptive control algorithm is used to adjust the dive angle and dive attitude of the UAV. Based on the flight status data and the target position error, the attitude of the UAV is controlled to ensure that the drop can be released at the optimal angle and timing.

[0072] Step S5: Control the fixed-point delivery. According to the real-time flight status data, when approaching the target area, trigger the action correction command to accurately adjust the speed and flight attitude of the drone at this time. After reaching the set value, trigger the delivery action.

[0073] Step S6: Determine the timing of delivery. Based on the relative position and speed between the UAV and the target and the dynamic changes of the target, the best delivery timing is predicted and the delivery is performed to ensure that the delivery object is accurately released in the target area.

[0074] In summary, the present invention has the following beneficial effects compared with the prior art:

[0075] Independent inertial navigation support: The present invention does not rely on external measurement tools, such as GPS, but completely realizes precise flight attitude control and target positioning through inertial navigation modules and adaptive algorithms, and has stronger anti-interference ability.

[0076] Dynamic dive attitude adjustment: The dynamic dive attitude adjustment method proposed in the present invention can accurately adjust the dive angle and trajectory of the UAV according to the real-time position of the target and the motion state of the aircraft, thereby improving the delivery accuracy.

[0077] Real-time fixed-point delivery control: The fixed-point delivery method of the present invention combines inertial navigation and real-time feedback mechanism, which can continuously optimize the delivery timing and trajectory during the flight to ensure that the delivery object accurately hits the target.

[0078] Efficient target prediction and error correction mechanism: Based on the real-time flight status and target movement, the present invention adopts a prediction algorithm to dynamically adjust the release timing, reduce errors, and improve the accuracy of release.

[0079] The inertial-guided UAV fixed-point delivery control system and method of the present invention can effectively improve delivery accuracy, overcome the influence of external environmental interference on precise delivery, and is widely applicable to the fields of airdrop materials, disaster relief, etc. Through innovative dynamic adjustment and adaptive algorithms, the present invention can ensure that the aircraft maintains high accuracy and stability when performing tasks in complex environments, and has high practical value and commercial potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0081] Figure 1 This is a flow chart of the UAV fixed-point delivery control system based on inertial guidance;

[0082] Figure 2 It is a step diagram of the UAV fixed-point delivery control method based on inertial guidance;

[0083] Figure 3 Flowchart for attitude calculation of the inertial navigation module of the UAV fixed-point delivery control system based on inertial guidance. DETAILED DESCRIPTION

[0084] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0085] The traditional inertial guidance system mainly controls flight based on preset trajectories and delivery angles. When used in complex fixed-point delivery tasks, there are large errors, which may affect the accuracy and success rate of the tasks, especially in field rescue environments with dynamic changes in targets and uncertain external interference. Accurate delivery faces great challenges. Existing technologies often rely on external positioning tools, such as GPS, radar, etc., to correct paths. However, in strong interference environments, the reliability of these external positioning tools is reduced, resulting in errors in the delivery process that cannot be effectively corrected. Therefore, how to ensure that drones can autonomously and accurately complete fixed-point delivery tasks without relying on external positioning tools has become a technical problem that needs to be solved urgently. In view of this, the present invention provides the following embodiments to solve the above-mentioned technical problems.

[0086] like Figure 1The UAV fixed-point delivery control system based on inertial guidance is characterized by comprising: an inertial navigation module, a target positioning module, a path planning module, a feedback control module and an attitude control module. Among them:

[0087] The inertial navigation module is used to obtain the real-time flight status data of the UAV and send the collected real-time flight status data to the target positioning module, attitude control module, path planning module and feedback control module; the flight status data includes the position, speed and flight attitude of the UAV, and the flight attitude includes heading angle, pitch angle and roll angle data; the flight status data provided by the inertial navigation module is the basis of the entire system, ensuring that the system can operate accurately even without external positioning data.

[0088] The target positioning module is used to capture the target position and the position of the UAV. The UAV movement route is obtained according to the UAV position captured during the flight, and combined with the real-time flight status data output by the inertial navigation module, the target positioning module outputs the UAV movement route data, and the movement route data includes speed, route, and route; at takeoff, the initial position of the UAV is calibrated, and during flight, the real-time flight status data output by the inertial navigation module is combined to mark the flight status data at each moment on the UAV movement route to obtain the UAV movement route data; during the execution of the delivery mission, the UAV movement route data is compared with the path planning data output by the path planning module. If they are consistent, no processing is performed. If they are inconsistent, the route error between the UAV movement route data and the path planning data is calculated, and the route error includes distance error and heading error.

[0089] The target positioning module is used to output the target position, the drone position and the calculated route error, and send them to the path planning module. The target position is the specific delivery point of the flight mission, with the delivery point as the center and the set distance as the radius. The area within this range is called the target position.

[0090] The path planning module is used to receive the real-time flight status data output by the inertial navigation module, as well as the target position, UAV position and calculated route error output by the target positioning module, calculate the path planning data, including the UAV flight planning path and flight planning attitude, adjust the flight path and attitude, and adjust the release timing; when the set preparation release distance is reached, a trigger action correction command is generated, and the action correction command is transmitted to the attitude control module. When the completion action correction command is received and the release timing is reached, the release action is triggered, so that the release object is released in the set position area, so that the release object can hit the target in a parabola.

[0091] The path planning module is used to send the output path planning data to the target positioning module to facilitate the calculation of the heading error of the UAV movement; and transmit the adjusted flight path and attitude to the feedback control module, so that after the feedback control module calculates the difference, the attitude module adjusts the control of the UAV accordingly, and changes the flight path by changing the flight attitude of the UAV.

[0092] The feedback control module is used to receive the flight status data output by the inertial navigation module and compare it with the flight planning attitude output by the path planning module. If they are consistent, no processing is done. If they are inconsistent, the difference between the flight status data and the flight planning attitude is calculated, and a signal that dynamic adjustment and correction is required is sent to the attitude control module.

[0093] The attitude control module is used to receive real-time flight status data and determine whether the flight attitude of the flight status is within the set threshold range. If it is within the set threshold range, no processing will be performed. Otherwise, an adaptive control algorithm is used to calculate the attitude adjustment control command of the UAV, and control the angles of the three axes of the UAV to adjust the flight attitude to the set threshold range. The angles of the three axes include pitch angle, roll angle and heading angle; the attitude control module receives the dynamic adjustment and correction signal output by the feedback control module to control the corresponding differences in the angles of the three axes to adjust; the attitude control module receives the action correction command triggered by the path planning module, controls the UAV to execute the set delivery flight speed and flight attitude, and when the set value is reached, completes the action correction command, and returns the completed action correction command to the path planning module.

[0094] like Figure 2 The UAV fixed-point delivery control method based on the inertial navigation module can be applied to the above system. The specific steps are as follows:

[0095] Step S1, when taking off, perform initialization settings; when taking off, determine the target position and drone position of the delivery, calibrate the initial position of the drone according to the drone position of the inertial navigation module and the drone position before takeoff of the target positioning module. In this embodiment, the straight line of the target position and the drone position can be directly set as the initial planned route to perform the delivery task; initialize the movement route data and flight status data of the drone.

[0096] Step S2: When taking off, according to the preset initial planned route, by comparing the current position of the drone with the initial planned route, the current drone movement route data and the error of the initial planned route are obtained, and the route is optimized in real time in combination with the dynamic changes of the environment and the target to obtain the path planning data.

[0097] Step S3: During the flight, the UAV flies according to the path planning data; the inertial navigation module is calculated, and the inertial navigation module INS system is used to calculate the UAV flight status data in real time, and output the changes in position, speed and flight attitude; the target positioning module continuously updates the position of the UAV, outputs the movement route data and the gap between the UAV position and the target position, and the path planning module updates the path planning data in real time.

[0098] Step S4: adjust the dive attitude. According to the current flight path, i.e., the path planning data and the error, an adaptive control algorithm is used to adjust the dive angle and dive attitude of the UAV. Based on the flight status data and the target position error, the roll angle, pitch angle, and yaw angle of the UAV are controlled to ensure that the drop can be released at the optimal angle and timing.

[0099] Step S5, control the fixed-point delivery. According to the real-time flight status data, when approaching the target area, trigger the action correction command to accurately adjust the speed and flight attitude of the UAV at this time. After reaching the set value, trigger the delivery action; this process is dynamically adjusted through the attitude control module to ensure that the speed and flight attitude of the UAV do not interfere with the delivery accuracy during delivery.

[0100] Step S6: Determine the timing of delivery. Based on the relative position and speed between the UAV and the target and the dynamic changes of the target, the best delivery timing is predicted and the delivery is performed to ensure that the delivery object is accurately released in the target area to ensure that the delivery object accurately hits the target position.

[0101] The inertial navigation module begins to provide the flight status data of the drone. The data provided by the inertial navigation module is the basis of the entire system and ensures the accurate operation of the system.

[0102] When the inertial navigation module starts to execute the delivery mission, it first performs initial calibration, collects real-time data during the delivery mission, and performs attitude calculation on the collected data; in the present invention, the inertial navigation module includes multiple inertial navigation systems, each of which is independent of each other and collects real-time flight status data of the drone.

[0103] Initial calibration is the high-precision calibration that the drone needs to perform through the inertial navigation system before takeoff to obtain the initial position and attitude data at this time.

[0104] like Figure 3 Real-time data acquisition means that the inertial navigation system continuously collects the real-time acceleration and angular velocity during the flight, obtains the measurement equation and observation equation by fusing multiple inertial navigation systems, and outputs the real-time flight status data observed by the UAV; the inertial navigation system is the INS system.

[0105] Attitude calculation, uses the angular velocity signal provided by the gyroscope and the output of the accelerometer to estimate the flight attitude of the drone, namely the pitch, roll and heading angles.

[0106] The inertial navigation module provides the motion status of the UAV, and the target positioning module provides the position data of the UAV and the target through radar. The status data of the inertial navigation module and the target positioning module need to be synchronized in real time for subsequent error calculation.

[0107] The inertial navigation module provides real-time attitude data, and the attitude control module adjusts the attitude of the drone according to the error.

[0108] The inertial navigation module provides real-time data, and the feedback control module is used to correct errors during the delivery process to ensure the accurate release of the delivery object.

[0109] The measurement equation and observation equation are obtained by adopting the fusion method of multiple inertial navigation systems, which are processed by multi-inertial navigation system data fusion and Kalman filter algorithm.

[0110] Because the inertial navigation system will be affected by the positive and negative effects of the north and celestial gyro drifts within its own inertial navigation system, and the longitude, latitude, and heading are also affected by the initial heading angle error and gyro drift, so multiple inertial navigation systems are set up for data fusion and then processed by the Kalman filter algorithm of the Kalman filter to estimate the inertial navigation system error. The inertial navigation system has hardware errors, which include gyro drift errors and accelerometer errors, and produce attitude errors, velocity errors, and position errors. The inertial navigation systems are started separately at set earth cycles. The inertial navigation system uses position, velocity, and attitude as observation quantities. Multiple inertial guidance systems are mutually referenced, and the data output by other inertial guidance systems is used as observation data for Kalman filtering. After estimating the hardware error, each inertial navigation system is compensated, and then the real-time flight status data of the drone obtained after the multi-inertial navigation system data is output.

[0111] If only a single inertial guidance system is used, there may be uncertain external interference in the field environment, which may cause large errors in the real-time data collected by the aircraft or even make it impossible to collect the data; while multiple inertial guidance systems serve as references to each other to avoid errors or abnormal positioning due to external interference.

[0112] The measurement equation and observation equation are obtained by fusion of multiple inertial navigation systems, including:

[0113] In the initial alignment phase of the multi-inertial navigation system, the performance of the gyroscope and accelerometer is combined to merge the errors of the inertial components and the system hardware itself into the hardware error, that is:

[0114] The model of gyroscope drift error is: ,in The variance is Gaussian white noise;

[0115] The accelerometer error model is ,in The variance is Gaussian white noise;

[0116] Gaussian white noise refers to a random process, the variance matrix of the noise signal is expressed as Q, and the noise signal obeys Gaussian distribution.

[0117] Therefore, the measurement and observation equations of multiple inertial navigation systems can be established, and the gyroscope measurement values ​​in the inertial navigation system are the data of the state variables of the object's motion.

[0118] Among the observation equations, the acceleration observation equation decomposes the acceleration into components on three axes and takes the noise term into consideration; the angular velocity observation equation measures the rate of change of angular velocity through a gyroscope and converts it into linear velocity relative to the center of the earth.

[0119] For any two inertial navigation systems in a multi-inertial navigation system, the multi-inertial navigation system measurement equation is established: ,

[0120] Take the state variables of the two inertial navigation systems:

[0121] ;

[0122] in,

[0123] is the three-dimensional attitude error angle of the first inertial navigation system, is the three-dimensional attitude error angle of the second inertial navigation system,

[0124] is the three-dimensional velocity error of the first inertial navigation system, is the three-dimensional velocity error of the second inertial navigation system,

[0125] Among them, E, N, and U are three-dimensional directions, namely, east, north, and sky directions;

[0126] are the position and altitude errors of the first inertial navigation system, are the position and altitude errors of the second inertial navigation system,

[0127] is the gyro drift error of the first inertial navigation system, is the gyro drift error of the second inertial navigation system,

[0128] is the accelerometer error of the first inertial navigation system, is the accelerometer error of the second inertial navigation system.

[0129] Noise matrix of inertial navigation system hardware error:

[0130] ;

[0131] State variable contact matrix: ,

[0132] in is a 3x6 zero matrix, is a 6x15 zero matrix, a matrix where all elements are zero.

[0133] It is the state transfer matrix obtained by derivation of the measurement equation of the multi-inertial navigation system.

[0134] , C is the cosine transformation matrix from the orthogonal coordinate system formed by the input axes of the first inertial navigation system to the orthogonal coordinate system formed by the input axes of the second inertial navigation system, wherein the orthogonal coordinate system formed by the input axes of the inertial navigation system is the platform coordinate system.

[0135] The platform coordinate system is a coordinate system that uses the object itself as a reference. It is used to describe the position and direction inside or relative to an object. It consists of three mutually perpendicular axes that are fixed on the object and move with the movement of the object, similar to the X, Y, and Z axes. Here E, N, and U are the three-dimensional east, north, and sky directions, which also change with the movement of the inertial navigation system.

[0136] The origin of the platform coordinate system is usually located at the center of mass of the drone. The OX axis is located in the drone reference plane, which is also the east direction, parallel to the fuselage axis and pointing to the front of the drone; the OY axis is perpendicular to the drone reference plane and points to the right of the drone, which is also the north direction; the OZ axis is perpendicular to the XOY plane in the reference plane, pointing to the top of the aircraft, which is also the sky direction.

[0137] Parameters that control the effect of inertial navigation system hardware errors on the measurement equations ,

[0138] is a 6x6 identity matrix, where all elements on the main diagonal are 1.

[0139] The observation equation is obtained by taking the three-dimensional directions of the geographic coordinate systems of the first inertial navigation system and the second inertial navigation system as the observed quantities:

[0140] ,

[0141] Where V is the interference vector of the measurement equation;

[0142] Among them, the geographic coordinate system is a reference system used to determine the location on the surface of the earth, based on the shape of the earth, using longitude and latitude to express the location;

[0143] The three-dimensional directions of the geographic coordinate system include the two-dimensional plane coordinates of longitude and latitude, as well as the three-dimensional directional coordinate of altitude. The three-dimensional directions of the geographic coordinate system are the vertical altitude direction, the east-west longitude direction, and the north-south latitude direction.

[0144] The Kalman filter equation is constructed through the measurement equation and the observation equation. Since the measurement equation and the observation equation for the initial Kalman filter are in continuous form, they are first discretized when filtering, and then the standard discrete Kalman filter formula is used.

[0145] Kalman filtering is an optimal estimation theory, which makes a modified estimate of the values ​​obtained from the measurement and observation equations. Because in practical applications, computers can only process discrete data and discrete time points, discretization is performed. Discretization is the process of converting a continuous-time system into a discrete-time system. In the initial alignment of the inertial navigation system, the standard discrete Kalman filtering formula is applied after discretizing the continuous measurement and observation equations. Because of the fusion of sensor data, the flight status data of the inertial navigation system is calculated and the accuracy of the status data is improved.

[0146] Target data collection is to obtain the real-time position and motion status of the UAV and the target through radar combined with optical imaging positioning means, and obtain the UAV's motion route data through the UAV's motion trajectory.

[0147] The target positioning module will obtain the target's position and movement route data, compare it with the data from the inertial navigation module, and calculate the route error between the drone and the target in real time.

[0148] During the delivery mission, the target positioning module collects target data in real time, calculates the route error of the collected data, and dynamically updates the target; the error calculation is based on the current position and speed of the drone provided by the inertial navigation module and the real-time state of the target, and calculates the error between the two; the dynamic update includes the target positioning module updating the position and movement of the target in real time, and continuously calculating the error to provide a reference for subsequent control.

[0149] The target positioning module provides the error data between the UAV and the target, including distance error and heading error. The attitude control module uses these error values ​​to adjust the attitude of the UAV.

[0150] The target positioning module provides the dynamic position and route of the target for path planning. The optimization algorithm of the path planning module needs to adjust the flight path based on these data.

[0151] During the delivery mission, the path planning module calculates the route in real time. When the UAV approaches the delivery point, the path planning module uses an optimization algorithm to optimize the speed, posture, and route of the UAV in real time, and determines the delivery timing to ensure that the delivery object can be delivered to the predetermined location at the best time.

[0152] The path planning module will use the optimal control algorithm to optimize the current flight status data and movement route data of the UAV, combined with the real-time target position and UAV position, and calculate the optimal flight route as the path planning data. It will adjust the speed, dive angle, and heading parameters of the UAV to ensure that the UAV can accurately reach the delivery location.

[0153] The optimal control algorithm is a process of solving the optimal answer by controlling the input. The speed, attitude and heading angle of the drone are used as input to establish a cost function. The goal of the cost function is that the drone can adjust the flight path according to the current target position and the drone position. By controlling the input variables of the cost function, the optimal answer is output according to the constraints as the path planning data. The variables controlled in the function include speed, attitude, and heading angle.

[0154] The speed adjusts the speed of the drone according to the target location and the drone's position to ensure that the drone can reach the target location on time; the dive angle controls the drone's descent path to ensure that the drone can accurately aim at the delivery point; the heading angle is used to adjust the drone's heading so that it always faces the target area.

[0155] The constraints of the cost function are usually based on the dynamic constraints of the drone's hardware itself, including the maximum tolerable acceleration, heading adjustment limits, and dive angle range.

[0156] The optimal answer is output by evaluating the error between the current route and the predetermined target through the relative position error between the UAV and the target, the shortest distance of the flight path, and the minimum time. The one with the smallest error is the optimal answer.

[0157] The path planning module is also used to adjust the timing of delivery. The path planning module calculates the best time to release the object, ensuring that the object is released at the best time and location, thereby improving delivery accuracy.

[0158] The timing of delivery is another key part of the path planning module. To ensure that the delivery object can be released at the best time and location, the path planning module needs to calculate when the drone reaches the ready delivery distance on the flight planning path to perform the delivery task based on the drone's current location, speed, target location and real-time path planning data. After reaching the ready delivery distance, the drone's attitude is adjusted. When the set attitude is reached and the delivery point and target location are within the delivery distance threshold, the delivery timing is output and the delivery is carried out.

[0159] The preparation launch point is the point on the planned flight path where the drone is at a set distance from the target position. When it reaches the preparation position, the drone adjusts its attitude so that when the drone drops objects, it is based on mission requirements and has certain specific conditions, such as flight altitude, speed, angle, and even the state when it reaches a certain position. These conditions can be programmed directly in the drone's system.

[0160] The release timing of the object is calculated based on the target's movement path and the current position of the drone, and the time and place where the object should be released are calculated. If the target drop point begins to accelerate or change direction at a certain moment, the optimization algorithm needs to adjust the release timing in real time to ensure that the object can be accurately dropped to the target location. And by cooperating with the feedback control module, the optimization algorithm can adjust the release timing according to real-time feedback data, thereby improving the accuracy of the release.

[0161] The path planning module optimizes the flight path based on the UAV status data provided by INS.

[0162] The path planning module adjusts the flight path according to the target's motion path data, position and the error between the UAV and the target provided by the target positioning module.

[0163] The path planning module provides the feedback control module with the release timing and path data, and the feedback control module will make the final attitude and flight path adjustments based on the optimization results.

[0164] The drone's status data and target positioning data are used to optimize the drone's speed, dive angle, heading and other parameters. Through this optimization process, the drone can accurately calculate and adjust its flight path to ensure that the object is dropped to the predetermined location at the best time. At the same time, the feedback control module further adjusts the drone's attitude and flight path based on the optimization results of the path planning module to achieve the final delivery accuracy.

[0165] The attitude control module performs error feedback based on the error values ​​of the flight status data and the path planning data, uses an adaptive control algorithm to adjust the attitude of the UAV, and makes dynamic adjustments in real time during the flight to adjust the flight attitude of the UAV, namely the pitch angle, roll angle and heading angle, to ensure that the UAV maintains the correct dive angle and heading during the flight and avoids deviation from the predetermined route.

[0166] The purpose of the present invention is to dynamically adjust the attitude of the UAV by using an adaptive control algorithm so as to respond to error changes in real time during flight and keep the UAV flying toward the target and along a predetermined flight planning path.

[0167] When there is a distance error and heading error between the target and the UAV, the error feedback calculates the pitch angle, roll angle, and heading angle that the UAV needs to adjust based on the error data provided by the target positioning module so that the UAV always faces the target.

[0168] Based on the data output by the inertial navigation module and the data output by the target positioning module, the error between the target and the UAV is calculated, and the attitude angle that needs to be adjusted is calculated.

[0169] The inertial navigation module provides the current attitude information of the drone, such as pitch angle, roll angle and heading angle.

[0170] The target positioning module provides the error between the target position and the drone, including distance error, heading error, etc.

[0171] Among them, the heading error can be compared with the current posture of the drone based on the target coordinates provided by the target positioning module to calculate the error between the target direction and the current posture, that is, the difference between the pitch, roll and heading angles.

[0172] The distance error is based on the current position of the UAV and the target position, and the distance error between the UAV and the target is calculated.

[0173] The control system needs to perform feedback calculations on these error values ​​and output corresponding control instructions to adjust the drone's attitude so that the drone always faces the target.

[0174] The adaptive control algorithm uses a PID controller to calculate the attitude adjustment control instructions of the UAV based on the error values ​​of the flight status data and the path planning data. The instructions are used to control the angles of three axes, including the pitch angle, roll angle and heading angle, and control the UAV's servos and control surfaces to adjust the attitude to the required attitude.

[0175] Dynamic adjustment means that as the flight progresses, the target error continues to change. The attitude control module needs to make real-time adjustments based on the new error value, that is, dynamic adjustment. The dynamic adjustment strategy ensures that the drone always faces the target and maintains a suitable dive angle.

[0176] PID controller, or proportional-integral-differential controller, is a commonly used feedback control algorithm that can be used to calculate the attitude adjustment control instructions of drones. The PID controller calculates the control input based on the error value and continuously adjusts the attitude of the drone to achieve the desired goal.

[0177] The PID controller calculates the control input based on the error value. The output of the PID controller is expressed as:

[0178] ;

[0179] in:

[0180] is the error value at the current moment, including the error of pitch angle, roll angle or heading angle;

[0181] is the proportional gain, which determines the intensity of the control system response error;

[0182] is the integral gain, which is used to correct the accumulated error;

[0183] is the differential gain, which is used to predict the changing trend of the error;

[0184] It is the definite integral of the error value from 0 to the current time t;

[0185] is the differential error value at the current moment.

[0186] Proportional control, integral control and differential control are carried out in sequence, as follows:

[0187] Proportional control: Proportional control is performed first, and the attitude of the drone is adjusted according to the current error value. The larger the error, the greater the control output. Proportional control can respond to errors quickly, but it may cause system oscillation or deviation.

[0188] Integral control: Integral control is then performed to eliminate steady-state errors by accumulating errors. Since the errors of the drone may persist due to external disturbances or changes in system parameters, the integral term can compensate for these persistent errors and help the system accurately align with the target.

[0189] Differential control: Finally, differential control is performed, and adjustments are made based on the error change rate. By predicting the error change trend, differential control can improve the stability of system response and reduce oscillation.

[0190] In the actual flight process, in order to cope with the dynamic change of errors during flight, an adaptive control algorithm is used to adjust the parameters of the PID controller. This means that the gains Kp, Ki, and Kd of the PID controller are dynamically adjusted rather than fixed. This method can adjust the controller parameters according to the real-time error during flight, ensuring that the drone can optimally adjust its attitude under different flight conditions.

[0191] First, according to the real-time errors of the UAV, including heading error and distance error, the output of the current controller is calculated; then, according to the rate of change and accumulation of the errors, the PID gain is dynamically adjusted, and the gain adjustment method is adopted to dynamically increase or decrease the PID gain according to the flight speed of the UAV.

[0192] During flight, the attitude adjustment of the drone is not static. As the target position and the drone change dynamically, the target error will also be continuously updated. Therefore, the attitude control module needs to have a real-time feedback mechanism to continuously adjust the control output according to the new error data.

[0193] Whenever the target position or the state of the UAV changes, the control system needs to recalculate the error value and update the control instructions, and the output instructions of the controller need to be fed back to the UAV's actuators, servos and control surfaces in real time to ensure that the UAV can respond immediately to new attitude requirements.

[0194] The feedback control module is used to prepare for and dynamically correct the delivery and execute the delivery in the delivery-approaching stage of the delivery process based on real-time flight status data and target position differences, to ensure that the delivery object can accurately hit the target.

[0195] When the delivery preparation is close to the delivery point, the feedback control module will continuously monitor the error between the UAV and the target; based on the path planning data, the delivery timing is determined.

[0196] Dynamic correction means that when there is a deviation between the UAV and the target, the feedback control module will respond quickly and adjust the UAV's attitude and flight path to ensure that the dropped objects can accurately hit the target.

[0197] When the drone reaches the predetermined delivery location, the feedback control module triggers the delivery operation. At this time, the delivery speed, angle and other parameters are corrected according to the real-time data to ensure that the delivery object is released along the predetermined route.

[0198] The feedback control module relies on the real-time data provided by the inertial navigation module to correct the attitude and motion state of the drone.

[0199] The target positioning module monitors the error between the UAV and the target in real time, and feeds back to the feedback control module to ensure that the UAV maintains the correct attitude and flight path.

[0200] Based on the error feedback, the feedback control module will issue new attitude adjustment control instructions to adjust the attitude of the UAV through the attitude control module.

[0201] Through the present invention, the UAV can independently complete high-precision delivery tasks, avoid dependence on external measurement tools, enhance the system's anti-interference ability and adaptability, ensure that the delivery objects can be accurately released at the best time and the best position, and greatly improve the success rate of precision airdrop missions.

[0202] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. UAV fixed-point delivery control system based on inertial guidance, characterized in that: include: Inertial navigation module, target positioning module, path planning module, feedback control module and attitude control module; among which: An inertial navigation module is used to obtain real-time flight status data of the UAV and send the collected real-time flight status data to a target positioning module, an attitude control module, a path planning module and a feedback control module; the flight status data includes the position, speed and flight attitude of the UAV, and the flight attitude includes heading angle, pitch angle and roll angle data; The target positioning module is used to capture the target position and the position of the UAV. The UAV movement route is obtained according to the UAV position captured during the flight. In combination with the real-time flight status data output by the inertial navigation module, the target positioning module outputs the UAV movement route data, which includes speed, route, and route. At takeoff, the initial position of the UAV is calibrated. During the delivery mission, the UAV movement route data is compared with the path planning data output by the path planning module. If they are consistent, no processing is performed. If they are inconsistent, the route error between the UAV movement route data and the path planning data is calculated. The route error includes distance error and heading error. It is used to output the target position, the UAV position and the calculated route error, and send them to the path planning module. The path planning module is used to receive the real-time flight status data output by the inertial navigation module, as well as the target position, the UAV position and the calculated route error output by the target positioning module, calculate the path planning data, including the UAV flight planning path and flight planning attitude, adjust the flight path and attitude, and adjust the release time; when the set preparation release distance is reached, a trigger action correction command is generated, and the action correction command is transmitted to the attitude control module; when the completion action correction command is received and the release time is reached, the release action is triggered to release the release object in the set position area; it is used to send the output path planning data to the target positioning module, and transmit the adjusted flight path and attitude to the feedback control module; The feedback control module is used to receive the flight status data output by the inertial navigation module and the flight planning attitude output by the path planning module, and compare them. If they are consistent, no processing is performed. If they are inconsistent, the difference between the flight status data and the flight planning attitude is calculated, and a signal that dynamic adjustment and correction is required is sent to the attitude control module; The attitude control module is used to receive real-time flight status data and determine whether the flight attitude of the flight status is within the set threshold range. If it is within the set threshold range, no processing is performed. Otherwise, an adaptive control algorithm is used to calculate the attitude adjustment control command of the UAV, and the angles of the three axes of the UAV are controlled to adjust the flight attitude to the set threshold range. The angles of the three axes include the pitch angle, the roll angle and the heading angle. The attitude control module receives the dynamic adjustment and correction signal output by the feedback control module to control the angles of the three axes to adjust the corresponding differences. The attitude control module receives the action correction command triggered by the path planning module, controls the UAV to execute the set delivery flight speed and flight attitude, and when the set value is reached, the action correction command is completed, and the completed action correction command is returned to the path planning module. When the inertial navigation module starts to perform a delivery mission, it first performs an initial calibration, collects real-time data during the delivery mission, and performs attitude calculation on the collected data; the inertial navigation module includes a plurality of inertial navigation systems; The initial calibration is that the UAV needs to obtain the initial position and attitude data at this time through the inertial navigation system before taking off; The real-time data acquisition is that the inertial navigation system continuously collects the real-time acceleration and angular velocity during the flight, adopts the method of fusion of multiple inertial navigation systems to obtain the measurement equation and observation equation, and outputs the real-time flight status data observed by the drone; the attitude calculation uses the angular velocity signal provided by the gyroscope and the output of the accelerometer to estimate the flight attitude of the drone; The method of using multiple inertial navigation systems to fuse to obtain the measurement equation and observation equation specifically includes: In the initial alignment phase of the multi-inertial navigation system, the performance of the gyroscope and accelerometer is combined to merge the errors of the inertial components and the system hardware itself into the hardware error, that is: The model of gyroscope drift error is: ,in The variance is Gaussian white noise; The accelerometer error model is ,in The variance is Gaussian white noise; Establish multi-inertial navigation system measurement and convert the measurement value of the gyroscope into the data of the object's motion state; for any two inertial navigation systems in the multi-inertial navigation system in the observation equation, establish the multi-inertial navigation system measurement equation: , Take the state variables of the two inertial navigation systems: ； in, is the three-dimensional attitude error angle of the first inertial navigation system, is the three-dimensional attitude error angle of the second inertial navigation system, is the three-dimensional velocity error of the first inertial navigation system, is the three-dimensional velocity error of the second inertial navigation system, Among them, E, N, and U are three-dimensional directions, namely, east, north, and sky directions; are the position and altitude errors of the first inertial navigation system, are the position and altitude errors of the second inertial navigation system, is the gyro drift error of the first inertial navigation system, is the gyro drift error of the second inertial navigation system, is the accelerometer error of the first inertial navigation system, is the accelerometer error of the second inertial navigation system; Noise matrix of inertial navigation system hardware errors ; State variable connection matrix , in is a 3x6 zero matrix, is a 6x15 zero matrix, a matrix where all elements are 0. is the state transfer matrix derived from the multi-inertial navigation system measurement equation, , C is the cosine transformation matrix from the orthogonal coordinate system formed by the input axis of the first inertial navigation system to the orthogonal coordinate system formed by the input axis of the second inertial navigation system, where the orthogonal coordinate system formed by the input axis of the inertial navigation system is the platform coordinate system, Parameters that control the effect of inertial navigation system hardware errors on the measurement equations ; is a 6x6 identity matrix, where all elements on the main diagonal are 1. The observation equation is obtained by taking the three-dimensional directions of the geographic coordinate systems of the first inertial navigation system and the second inertial navigation system as the observed quantities: ； Where V is the interference vector of the measurement equation; The Kalman filter equation is constructed through the measurement equation and the observation equation. When filtering, it is first discretized and then the standard discrete Kalman filter formula is used.

2. The UAV fixed-point delivery control system based on inertial guidance according to claim 1 is characterized in that: The measurement equation and the observation equation are obtained by adopting the method of fusion of multiple inertial navigation systems, which are processed by multi-inertial navigation system data fusion and Kalman filter algorithm; The inertial navigation system has hardware errors, which include gyro drift errors and accelerometer errors. The inertial navigation systems are started separately at set earth cycles. The inertial navigation system uses position, velocity, and attitude as observation quantities. Multiple inertial guidance systems are mutually referenced. The data output by other inertial guidance systems are used as observation data for Kalman filtering. After the hardware errors are estimated, each inertial navigation system is compensated, and then the real-time flight status data of the drone obtained after the multi-inertial navigation system data is output and fused.

3. The UAV fixed-point delivery control system based on inertial guidance according to claim 1 is characterized in that: The target positioning module obtains the real-time position data and motion status of the UAV and the target through a positioning method combining radar and optical imaging.

4. The UAV fixed-point delivery control system based on inertial guidance according to claim 1 is characterized in that: The path planning module will use the speed, attitude and heading angle of the drone as input variables according to the current flight status data and movement route data of the drone, establish a cost function, control the input variables, and output the optimal answer according to the constraints as the path planning data; The constraints of the cost function are based on the dynamic constraints of the hardware of the drone, including the maximum tolerable acceleration, heading adjustment limit and dive angle range; The optimal answer outputted is obtained by evaluating the error between the current route and the predetermined target through the relative position error between the UAV and the target, the shortest distance of the flight path and the minimum time. The one with the smallest error is the optimal answer.

5. The UAV fixed-point delivery control system based on inertial guidance according to claim 1 is characterized in that: The path planning module is also used to adjust the release timing. The path planning module needs to calculate the UAV's performance of the release task on the flight planning path according to the UAV's current position, speed, target position and real-time path planning data through a prediction algorithm, arrive at the ready release point, adjust the UAV's attitude to reach the set attitude, and finally reach the release distance threshold for release, and output the real-time release timing; The prepared launch point is a point on the flight planning path where the drone is a set distance away from the target position and reaches the prepared position.

6. The UAV fixed-point delivery control system based on inertial guidance according to claim 1 is characterized in that: The adaptive control algorithm uses a PID controller to calculate the attitude adjustment control instructions of the drone based on the error value of the flight status data and the path planning data; The dynamic adjustment is that as the flight progresses, the attitude control module needs to make real-time adjustment control according to the new error value, i.e. dynamic adjustment; The PID controller calculates the control input based on the error value. The output of the PID controller is expressed as: ； in: is the error value at the current moment, including the error of pitch angle, roll angle or heading angle; is the proportional gain, which determines the intensity of the control system's response to the error; is the integral gain, which is used to correct the accumulated error; is the differential gain, which is used to predict the changing trend of the error; It is the definite integral of the error value from 0 to the current time t; is the differential of the error value at the current moment; Proportional control, integral control and differential control are carried out in sequence, as follows: First, perform proportional control and adjust the attitude of the drone according to the current error value; The larger the error, the larger the control output; Then, integral control is performed to eliminate steady-state errors by accumulating errors, wherein the steady-state errors include external disturbances and drone hardware parameters; Finally, differential control is performed, and adjustments are made based on the error change rate to improve stability; According to the real-time errors of the UAV, including heading error and distance error, the output of the current controller is calculated; then the PID gain is dynamically adjusted according to the rate of change and accumulation of the errors, and the gain adjustment method is adopted to dynamically increase or decrease the PID gain according to the flight speed of the UAV.

7. The method for controlling the fixed-point delivery of unmanned aerial vehicles based on inertial guidance is characterized in that: The method is implemented based on the inertial guidance based UAV fixed-point delivery control system according to any one of claims 1 to 6, and comprises the following steps: Step S1: Initialize the settings when taking off; determine the target location and the position of the drone when taking off, calibrate the initial position of the drone, set the initial planned route to perform the delivery task; initialize the movement route data and flight status data of the drone; Step S2: when taking off, according to the preset initial planned route, by comparing the current position of the drone with the initial planned route, obtaining the current drone movement route data and the error of the initial planned route, and optimizing the route in real time in combination with the dynamic change of the target to obtain the path planning data; Step S3: During the flight, the UAV flies according to the path planning data; the inertial navigation module is used to calculate the flight status data of the UAV in real time, and the changes in position, speed and flight attitude are output; the target positioning module continuously updates the position of the UAV, outputs the movement route data and the difference between the UAV position and the target position, and the path planning module updates the path planning data in real time; Step S4: perform dive attitude adjustment. According to the current flight path, i.e., path planning data and error, an adaptive control algorithm is used to adjust the dive angle and dive attitude of the drone. Based on the flight status data and target position error, the attitude of the drone is controlled to ensure that the drop can be released at the best angle and timing. Step S5: Control the fixed-point delivery. According to the real-time flight status data, when approaching the target area, trigger the action correction command to accurately adjust the speed and flight attitude of the drone at this time. When the set value is reached, trigger the delivery action; Step S6: Determine the timing of delivery. Based on the relative position and speed between the UAV and the target and the dynamic changes of the target, the best delivery timing is predicted and the delivery is performed to ensure that the delivery object is accurately released in the target area.

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