CN114217632A - Adaptive fault-tolerant unmanned aerial vehicle tracking and cruising system and method - Google Patents
Adaptive fault-tolerant unmanned aerial vehicle tracking and cruising system and method Download PDFInfo
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Abstract
The invention provides a tracking and cruising system and a tracking and cruising method of a self-adaptive fault-tolerant unmanned aerial vehicle, wherein the tracking and cruising system comprises an unmanned aerial vehicle flight parameter acquisition module, a self-adaptive alignment calculation module, an early warning and obstacle avoidance control module and a wireless power supply module; the self-adaptive alignment calculation module comprises an upper layer control module and a bottom layer control module, the bottom layer control module comprises a position control module, a tension and attitude distribution module, an attitude control module and an accelerator control module, and the tension and attitude distribution module controls the position control module, the attitude control module and the accelerator control module to achieve the purpose of achieving the expected cruising route with the minimum inclination angle and power. The invention can monitor the three-dimensional attitude speed, position coordinates and three-dimensional stress conditions of the unmanned aerial vehicle in the flight process in real time, optimize iteration by adopting a random forest method, avoid deviation in the calculation process, carry out active fault-tolerant control through the early warning obstacle avoidance control module, and ensure that the unmanned aerial vehicle can be corrected in real time to deviate from a route so as to cruise in a preset target cruise route.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to a tracking and cruising system and method of a self-adaptive fault-tolerant unmanned aerial vehicle.
Background
With the development of drone technology, drones are increasingly used to perform tasks that cruise in the air. Meanwhile, the task environment of the unmanned aerial vehicle is more and more complex, so that the unmanned aerial vehicle is time-consuming and labor-consuming by relying on the traditional manual flight path planning method and is difficult to meet the task requirement. The quad-rotor unmanned aerial vehicle is a robot system which acquires external information in real time through an airborne sensor, uploads the state of the quad-rotor unmanned aerial vehicle and completes work tasks in an autonomous or manual remote control mode. Compare traditional wheeled unmanned systems, four rotor unmanned aerial vehicle can take off and land perpendicularly, and the fixed point is hovered, carries on different operation tools in different topography and landform areas, and is wide in field application prospect such as disaster search and rescue, life service.
In the prior art, as disclosed in chinese patent application 202010801338.2, an unmanned aerial vehicle cruise and tracking system and method based on deep learning technology, the main technical scheme is to adopt an image real-time acquisition module to monitor whether there are dangerous articles, dangerous behaviors and dangerous personnel enter the field of real-time monitoring of an unmanned aerial vehicle, guarantee automatic tracking flight of the unmanned aerial vehicle through a video acquisition module, a personnel tracking module, a laser ranging module, a flight control module and the like, and enable the dangerous personnel to be located in the center of a picture, meanwhile, a ground command station can also send control instructions (stop tracking, switch tracking targets and the like) to the unmanned aerial vehicle, the control mode is more flexible, and the tracking efficiency is further improved. However, in the monitoring process of the unmanned aerial vehicle, the unmanned aerial vehicle may deviate from a predetermined target area to be monitored in real time, so that the real-time monitoring result is missed or dangerous articles, dangerous behaviors, dangerous personnel cannot enter the unmanned aerial vehicle, and the like cannot be found. Therefore, a control system and a method capable of correcting the deviation of the unmanned aerial vehicle from the route in real time to enable the unmanned aerial vehicle to cruise in a preset target cruise route are urgently needed.
Disclosure of Invention
The present invention addresses the above-mentioned shortcomingsAnd the method can monitor the three-dimensional attitude speed, position coordinates and three-dimensional stress conditions of the unmanned aerial vehicle in the flight process in real time, adopts a random forest optimization algorithm for continuous iteration, and avoids the pitch angle speed of the unmanned aerial vehicle at the t + delta t moment obtained in the calculation process
Course angular velocity
And roll angular velocity
And the self-adaptive fault-tolerant unmanned aerial vehicle tracking and cruising system and method have the advantages that the phenomenon of deviation is generated, the nonlinearity of the system is processed by adopting a Takagi-Sugeno model through the early warning and obstacle avoidance control module, the active fault-tolerant control is carried out, and the unmanned aerial vehicle can be corrected in real time to deviate from a route so as to cruise in a preset target cruising route.
The invention provides the following technical scheme: the system is used for monitoring the flight path of the unmanned aerial vehicle in real time and carrying out self-fault-tolerant early warning and flight control on the flight path, and is characterized by comprising an unmanned aerial vehicle flight parameter acquisition module, a self-adaptive alignment calculation module, an early warning and obstacle avoidance control module and a wireless power supply module; the self-adaptive alignment calculation module comprises an upper layer control module and a bottom layer control module, the bottom layer control module comprises a position control module, a tension and attitude distribution module, an attitude control module and an accelerator control module, and the tension and attitude distribution module controls the position control module, the attitude control module and the accelerator control module and is used for achieving the purpose of achieving the expected cruising route with the minimum inclination angle and power;
the unmanned aerial vehicle flight parameter acquisition module acquires the parameters of the relevant attitude of the unmanned aerial vehicle at the moment t: unmanned aerial vehicle pitch angle speed
Course angular velocity
Roll angular velocity
Acceleration of pitch angle
Angular acceleration of course
Acceleration of roll angle
And the mechanical parameters controlled by the unmanned aerial vehicle at the time t are as follows: the method comprises the following steps that a first power moment A (t) in the x-axis direction, a second power moment B (t) in the y-axis direction, a third power moment C (t) in the z-axis direction parallel to the north pole of the earth and a received resistance moment M (t) are given to the unmanned aerial vehicle by an accelerator;
the upper control module trains and optimizes data acquired by the unmanned aerial vehicle flight parameter acquisition module by adopting a random forest algorithm, and the data are used for air route planning, task planning and path planning;
and the wireless power supply module is used for tracking and cruising wireless power supply for the unmanned aerial vehicle.
Further, the early warning and obstacle avoidance control module processes nonlinearity of the system by adopting a Takagi-Sugeno model to carry out active fault-tolerant control.
Further, the attitude controller is a sliding mode controller.
Further, the flight parameter acquisition module comprises an unmanned aerial vehicle flight three-dimensional speed acquisition module, an unmanned aerial vehicle flight three-dimensional attitude acquisition module, an unmanned aerial vehicle three-dimensional position acquisition module and an unmanned aerial vehicle flight three-dimensional moment acquisition module.
Further, the wireless power supply module supplies power to the battery in an inductive manner.
Further, the wireless power supply module comprises a direct current voltage source, a transmitting circuit module and a transmitting coil which are arranged at a transmitting end, and a receiving coil, an AC-DC converter and a DC-DC converter which are arranged on the unmanned aerial vehicle.
Further, the output power of the direct current voltage source is 25W.
The invention also provides a tracking and cruising method of the self-adaptive fault-tolerant unmanned aerial vehicle of the system, which comprises the following steps:
s1: the unmanned aerial vehicle flight parameter acquisition module acquires the parameters of the relevant attitude of the unmanned aerial vehicle at the moment t: unmanned aerial vehicle pitch angle speed
Course angular velocity
Roll angular velocity
Acceleration of pitch angle
Angular acceleration of course
Acceleration of roll angle
And the mechanical parameters controlled by the unmanned aerial vehicle at the time t are as follows: the method comprises the following steps that a first power moment A (t) in the x-axis direction, a second power moment B (t) in the y-axis direction, a third power moment C (t) in the z-axis direction parallel to the north pole of the earth and a received resistance moment M (t) are given to the unmanned aerial vehicle by an accelerator;
s2: transmitting the acquired data to an upper control module, wherein the upper control module adopts a random forest algorithm to train and optimize the data acquired by the unmanned aerial vehicle flight parameter acquisition module for air route planning, task planning and path planning;
s3: the upper-layer control module sends the planned route, tasks and path diameters to the bottom-layer control module, the tension and attitude distribution module distributes tension instructions to be executed after planning to the throttle control module, and the attitude instructions are respectively distributed to the attitude control module and the position control module and used for controlling the unmanned aerial vehicle to reach the expected cruising route at the minimum inclination angle and power;
s4: and the early warning obstacle avoidance control module processes the nonlinearity of the system by adopting a Takagi-Sugeno model to carry out active fault-tolerant control, if the nonlinear nonlinearity exceeds a tension and attitude instruction threshold value, an alarm is given to the upper control module, the upper control module carries out the air route planning, task planning and path planning of the steps S2-S4 again, and if the nonlinear nonlinearity does not exceed the tension and attitude instruction threshold value, the self-adaptive fault-tolerant control is completed.
Further, the step of S2 includes the steps of:
s21: the upper layer control module constructs a flight arrival position [ x 'y' z 'of the unmanned aerial vehicle flight within a delta t time interval']TCalculating a model:
wherein x is a flight coordinate value of the unmanned aerial vehicle in the x-axis direction at the time t, y is a flight coordinate value of the unmanned aerial vehicle in the y-axis direction at the time t, and z is a flight coordinate value of the unmanned aerial vehicle in the z-axis direction at the time t;
the roll angular velocity of the drone at time t + deltat,
the pitch angle rate of the drone at time t + deltat,
the course angular velocity of the unmanned aerial vehicle at the time t + delta t; the flight coordinate value of the unmanned aerial vehicle at the time of t + delta t in the x-axis direction of x ', the flight coordinate value of the unmanned aerial vehicle at the time of t + delta t in the y-axis direction of y ', and the flight coordinate value of the unmanned aerial vehicle at the time of t + delta t in the z-axis direction of z ';
s22: the upper layer control moduleAcceleration of pitch angle acquired according to time t
Angular acceleration of course
Acceleration of roll angle
Calculating the pitch angle speed of the unmanned aerial vehicle at the t + delta t moment in the S21 model
Course angular velocity
And roll angular velocity
Wherein m is a time component, and m belongs to [ t, t + delta t ];
s23: the upper-layer control module controls a first power moment A (t) in the x-axis direction, a second power moment B (t) in the y-axis direction, a third power moment C (t) in the z-axis direction parallel to the earth north pole and the applied resistance moment M (t) given to the unmanned aerial vehicle by the accelerator in the delta t time interval to continuously iterate and optimize the variation error of the first power moment A (t), the second power moment B (t) in the y-axis direction, the third power moment C (t) in the z-axis direction parallel to the earth north pole and the applied resistance moment M (t) along a preset flight path so as to ensure that the unmanned aerial vehicle at the t + delta t moment finally obtained in the step S22Pitch angular velocity
Course angular velocity
And roll angular velocity
No deviation is generated;
s24: the last step of each iteration is to determine whether a deviation threshold value is exceeded, if not, the step of S3 is continued, and if so, the steps of S21-S23 are repeated.
The invention has the beneficial effects that:
1. the adaptive fault-tolerant unmanned aerial vehicle tracking cruise system and method provided by the invention can monitor the three-dimensional attitude speed, position coordinates and three-dimensional stress conditions of the unmanned aerial vehicle in the flight process in real time, and adopt the random forest optimization method to iterate continuously, thereby avoiding the pitch angle speed of the unmanned aerial vehicle at the t + delta t moment obtained in the calculation process
Course angular velocity
And roll angular velocity
And (3) generating a deviation phenomenon, processing nonlinearity of the system by adopting a Takagi-Sugeno model through an early warning obstacle avoidance control module, performing active fault-tolerant control, if the nonlinear deviation phenomenon exceeds a tension and attitude instruction threshold value, giving an alarm to an upper control module, and performing the air route planning, task planning and path planning of the steps S2-S4 again by the upper control module to ensure that the unmanned aerial vehicle can be corrected in real time to cruise in a preset target cruise route
2. The self-adaptive fault-tolerant unmanned aerial vehicle tracking and cruising system verifies the effectiveness of the unmanned aerial vehicle fault-tolerant path tracking control algorithm based on the random forest optimization method through Matlab \ Simulink simulation, and the simulation result shows that the algorithm can still ensure the robustness of unmanned aerial vehicle tracking control under the condition of external uncertain disturbance in a complex environment.
3. According to the self-adaptive fault-tolerant unmanned aerial vehicle tracking cruise system and the method, the translation and attitude control is realized through continuous iteration of the upper control module, the effect of stable flight can be achieved, the improved self-adaptive inverse controller can effectively eliminate static errors, and the self-adaptive fault-tolerant unmanned aerial vehicle tracking cruise system has good interference suppression capability.
4. The invention provides a self-adaptive fault-tolerant unmanned aerial vehicle tracking cruise system and a method, which adopt an upper control module to construct the relationship between the total upward thrust, the roll moment, the pitching moment and the yawing moment of a four-shaft aircraft and the four rotating speeds of a rotor wing, utilize the relationship to construct the relationship between the total upward thrust, the roll moment, the pitching moment and the yawing moment and the four rotating speeds of the rotor wing, finally distribute a planned tension instruction to be executed to a throttle control module through a tension and attitude distribution module, distribute the attitude instruction to the attitude control module and a position control module respectively for controlling the unmanned aerial vehicle to achieve the expected cruise route with the minimum inclination angle and power, can more accurately control the unmanned aerial vehicle to track and cruise on the self-adaptive fault-tolerant cruise route, and avoid the defects of overlong calculation and execution time caused by the instruction being issued and executed through the same module, the self-adaptive fault-tolerant unmanned aerial vehicle tracking cruise system and method provided by the invention can be realized without a highly configured chip.
Drawings
The invention will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings. Wherein:
fig. 1 is an overall schematic diagram of an adaptive fault-tolerant unmanned aerial vehicle tracking cruise system provided by the invention;
fig. 2 is a schematic structural diagram of a wireless power supply module in the adaptive fault-tolerant unmanned aerial vehicle tracking cruise system provided by the invention;
FIG. 3 is a schematic flow chart of a tracking and cruising method of an adaptive fault-tolerant unmanned aerial vehicle provided by the invention;
fig. 4 is a schematic flow diagram of an upper control module in the tracking and cruising method of the adaptive fault-tolerant unmanned aerial vehicle, which is provided by the invention, training and optimizing data by adopting a random forest algorithm.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1, the adaptive fault-tolerant unmanned aerial vehicle tracking and cruising system provided by the invention is used for monitoring the flight path of an unmanned aerial vehicle in real time and performing self-fault-tolerant early warning and flight control on the flight path, and is characterized by comprising an unmanned aerial vehicle flight parameter acquisition module, an adaptive alignment calculation module, an early warning and obstacle avoidance control module and a wireless power supply module; the self-adaptive alignment calculation module comprises an upper layer control module and a bottom layer control module, the bottom layer control module comprises a position control module, a tension and attitude distribution module, an attitude control module and an accelerator control module, and the tension and attitude distribution module controls the position control module, the attitude control module and the accelerator control module and is used for achieving the purpose of achieving the expected cruising route with the minimum inclination angle and power;
the unmanned aerial vehicle flight parameter acquisition module acquires the parameters of the relevant attitude of the unmanned aerial vehicle at the moment t: unmanned aerial vehicle pitch angle speed
Course angular velocity
Roll angular velocity
Acceleration of pitch angle
Angular acceleration of course
Acceleration of roll angle
And the mechanical parameters controlled by the unmanned aerial vehicle at the time t are as follows: the method comprises the following steps that a first power moment A (t) in the x-axis direction, a second power moment B (t) in the y-axis direction, a third power moment C (t) in the z-axis direction parallel to the north pole of the earth and a received resistance moment M (t) are given to the unmanned aerial vehicle by an accelerator;
the upper control module trains and optimizes data acquired by the unmanned aerial vehicle flight parameter acquisition module by adopting a random forest algorithm, and the data are used for air route planning, task planning and path planning;
and the wireless power supply module is used for tracking and cruising wireless power supply for the unmanned aerial vehicle.
And the early warning and obstacle avoidance control module processes the nonlinearity of the system by adopting a Takagi-Sugeno model to carry out active fault-tolerant control.
Example 2
On the basis of embodiment 1, the attitude controller that this embodiment adopted is the sliding mode controller, flight parameter collection module includes unmanned aerial vehicle flight three-dimensional speed collection module, unmanned aerial vehicle flight three-dimensional attitude collection module, three-dimensional speed collection module is used for gathering unmanned aerial vehicle at the x axle, the real-time flying speed and the real-time flight acceleration of y axle and z axle, unmanned aerial vehicle flight three-dimensional attitude collection module is used for gathering unmanned aerial vehicle at the x axle, the real-time flight acceleration of y axle and z axle, unmanned aerial vehicle three-dimensional position collection module is used for gathering unmanned aerial vehicle at the x axle, the real-time flight position coordinate data information of y axle and z axle, unmanned aerial vehicle three-dimensional moment collection module is used for gathering unmanned aerial vehicle at the x axle, the driving force moment data that the throttle that receives during the real-time flight of y axle and z axle.
Example 3
On the basis of embodiment 1, the wireless power supply module provided by this embodiment is a battery induction type wireless power supply, and as shown in fig. 2, the wireless power supply module includes a direct current voltage source, a transmitting circuit module, a transmitting coil, a receiving coil, an AC-DC converter, and a DC-DC converter, which are disposed on the unmanned aerial vehicle.
Example 4
In addition to example 3, the output power of the dc voltage source was 25W.
Example 5
The invention also provides a tracking and cruising method of the self-adaptive fault-tolerant unmanned aerial vehicle of any system provided by the embodiments 1 to 5, as shown in fig. 3, comprising the following steps:
s1: the unmanned aerial vehicle flight parameter acquisition module acquires the parameters of the relevant attitude of the unmanned aerial vehicle at the moment t: unmanned aerial vehicle pitch angle speed
Course angular velocity
Roll angular velocity
Acceleration of pitch angle
Angular acceleration of course
Acceleration of roll angle
And the mechanical parameters controlled by the unmanned aerial vehicle at the time t are as follows: the method comprises the following steps that a first power moment A (t) in the x-axis direction, a second power moment B (t) in the y-axis direction, a third power moment C (t) in the z-axis direction parallel to the north pole of the earth and a received resistance moment M (t) are given to the unmanned aerial vehicle by an accelerator;
s2: transmitting the acquired data to an upper control module, wherein the upper control module adopts a random forest algorithm to train and optimize the data acquired by the unmanned aerial vehicle flight parameter acquisition module for air route planning, task planning and path planning;
s3: the upper-layer control module sends the planned route, tasks and path diameters to the bottom-layer control module, the tension and attitude distribution module distributes tension instructions to be executed after planning to the throttle control module, and the attitude instructions are respectively distributed to the attitude control module and the position control module and used for controlling the unmanned aerial vehicle to reach the expected cruising route at the minimum inclination angle and power;
s4: the early warning obstacle avoidance control module adopts a Takagi-Sugeno model to process the nonlinearity of the system, and active fault-tolerant control is carried out; and if the pulling force and attitude instruction threshold is exceeded, alarming to the upper control module, carrying out the air route planning, the task planning and the path planning in the steps S2-S4 again by the upper control module, and if the pulling force and attitude instruction threshold is not exceeded, completing the self-adaptive fault tolerance.
The self-adaptive fault-tolerant unmanned aerial vehicle tracking and cruising system verifies the effectiveness of the unmanned aerial vehicle fault-tolerant path tracking control algorithm based on the random forest optimization method through Matlab \ Simulink simulation, and the simulation result shows that the algorithm can still ensure the robustness of unmanned aerial vehicle tracking control under the condition of external uncertain disturbance in a complex environment.
Example 6
On the basis of embodiment 5, as shown in fig. 4, the step of S2 includes the following steps:
s21: the upper layer control module constructs a flight arrival position [ x 'y' z 'of the unmanned aerial vehicle flight within a delta t time interval']TCalculating a model:
wherein x is the flight coordinate value of the unmanned aerial vehicle in the x-axis direction at the moment t, y is the flight coordinate value of the unmanned aerial vehicle in the y-axis direction at the moment t, and z is the flight coordinate value of the unmanned aerial vehicle in the z-axis direction at the moment tCoordinate values;
the roll angular velocity of the drone at time t + deltat,
the pitch angle rate of the drone at time t + deltat,
the course angular velocity of the unmanned aerial vehicle at the time t + delta t; the flight coordinate value of the unmanned aerial vehicle at the time of t + delta t in the x-axis direction of x ', the flight coordinate value of the unmanned aerial vehicle at the time of t + delta t in the y-axis direction of y ', and the flight coordinate value of the unmanned aerial vehicle at the time of t + delta t in the z-axis direction of z ';
s22: the upper control module is used for acquiring pitch angle acceleration according to the time t
Angular acceleration of course
Acceleration of roll angle
Calculating the pitch angle speed of the unmanned aerial vehicle at the t + delta t moment in the S21 model
Course angular velocity
And roll angular velocity
Wherein m is a time component, and m belongs to [ t, t + delta t ];
s23: the upper-layer control module controls a first power moment A (t) in the x-axis direction, a second power moment B (t) in the y-axis direction, a third power moment C (t) in the z-axis direction parallel to the earth north pole and the applied resistance moment M (t) given to the unmanned aerial vehicle by the accelerator in the delta t time interval to continuously iterate and optimize the variation error of the first power moment A (t), the second power moment B (t) in the y-axis direction, the third power moment C (t) in the z-axis direction parallel to the earth north pole and the applied resistance moment M (t) along a preset flight path so as to ensure that the pitch angle speed of the unmanned aerial vehicle at the t + delta t moment finally obtained in the step S22
Course angular velocity
And roll angular velocity
No deviation is generated;
s24: the last step of each iteration is to determine whether a deviation threshold value is exceeded, if not, the step of S3 is continued, and if so, the steps of S21-S23 are repeated.
According to the self-adaptive fault-tolerant unmanned aerial vehicle tracking cruise system and the method, the translation and attitude control is realized through continuous iteration of the upper control module, the effect of stable flight can be achieved, the improved self-adaptive inverse controller can effectively eliminate static errors, and the self-adaptive fault-tolerant unmanned aerial vehicle tracking cruise system has good interference suppression capability; and the early warning obstacle avoidance control module adopts a Takagi-Sugeno model to process the nonlinearity of the system, active fault-tolerant control is carried out, and the unmanned aerial vehicle can be corrected in real time to cruise in a preset target cruise route.
While the invention has been described with reference to a preferred embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the technical features mentioned in the embodiments can be combined in any way as long as there is no structural conflict. It is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (9)
1. The system is used for monitoring the flight path of the unmanned aerial vehicle in real time and carrying out self-fault-tolerant early warning and flight control on the flight path, and is characterized by comprising an unmanned aerial vehicle flight parameter acquisition module, a self-adaptive alignment calculation module, an early warning and obstacle avoidance control module and a wireless power supply module; the self-adaptive alignment calculation module comprises an upper layer control module and a bottom layer control module, the bottom layer control module comprises a position control module, a tension and attitude distribution module, an attitude control module and an accelerator control module, and the tension and attitude distribution module controls the position control module, the attitude control module and the accelerator control module and is used for achieving the purpose of achieving the expected cruising route with the minimum inclination angle and power;
the unmanned aerial vehicle flight parameter acquisition module acquires the parameters of the relevant attitude of the unmanned aerial vehicle at the moment t: unmanned aerial vehicle pitch angle speed
Course angular velocity
Roll angular velocity
Acceleration of pitch angle
Angular acceleration of course
Acceleration of roll angle
And the mechanical parameters controlled by the unmanned aerial vehicle at the time t are as follows: the method comprises the following steps that a first power moment A (t) in the x-axis direction, a second power moment B (t) in the y-axis direction, a third power moment C (t) in the z-axis direction parallel to the north pole of the earth and a received resistance moment M (t) are given to the unmanned aerial vehicle by an accelerator;
the upper control module trains and optimizes data acquired by the unmanned aerial vehicle flight parameter acquisition module by adopting a random forest algorithm, and the data are used for air route planning, task planning and path planning;
and the wireless power supply module is used for tracking and cruising wireless power supply for the unmanned aerial vehicle.
2. The adaptive fault-tolerant unmanned aerial vehicle tracking cruise system according to claim 1, wherein the early warning obstacle avoidance control module processes nonlinearity of the system by using a Takagi-Sugeno model to perform active fault-tolerant control.
3. The adaptive fault-tolerant unmanned aerial vehicle tracks cruise system of claim 1, wherein the attitude controller is a sliding mode controller.
4. The adaptive fault-tolerant unmanned aerial vehicle tracking cruise system according to claim 1, wherein the flight parameter acquisition module comprises an unmanned aerial vehicle flight three-dimensional speed acquisition module, an unmanned aerial vehicle flight three-dimensional attitude acquisition module, an unmanned aerial vehicle three-dimensional position acquisition module, and an unmanned aerial vehicle flight three-dimensional moment acquisition module.
5. The adaptive fault-tolerant unmanned aerial vehicle tracking cruise system of claim 1, wherein the wireless power module is battery-inductively wirelessly powered.
6. The adaptive fault-tolerant unmanned aerial vehicle tracking cruise system according to claim 5, wherein the wireless power supply module comprises a direct current voltage source arranged at a transmitting end, a transmitting circuit module, a transmitting coil, and a receiving coil, an AC-DC converter and a DC-DC converter arranged on the unmanned aerial vehicle.
7. The adaptive fault tolerant unmanned aerial vehicle tracking cruise system of claim 6, wherein the output power of the DC voltage source is 25W.
8. An adaptive fault-tolerant unmanned aerial vehicle tracking cruise method according to the system of any one of claims 1-7, characterized by comprising the following steps:
s1: the unmanned aerial vehicle flight parameter acquisition module acquires the parameters of the relevant attitude of the unmanned aerial vehicle at the moment t: unmanned aerial vehicle pitch angle speed
Course angular velocity
Roll angular velocity
Acceleration of pitch angle
Angular acceleration of course
Acceleration of roll angle
And the mechanical parameters controlled by the unmanned aerial vehicle at the time t are as follows: the method comprises the following steps that a first power moment A (t) in the x-axis direction, a second power moment B (t) in the y-axis direction, a third power moment C (t) in the z-axis direction parallel to the north pole of the earth and a received resistance moment M (t) are given to the unmanned aerial vehicle by an accelerator;
s2: transmitting the acquired data to an upper control module, wherein the upper control module adopts a random forest algorithm to train and optimize the data acquired by the unmanned aerial vehicle flight parameter acquisition module for air route planning, task planning and path planning;
s3: the upper-layer control module sends the planned route, tasks and path diameters to the bottom-layer control module, the tension and attitude distribution module distributes tension instructions to be executed after planning to the throttle control module, and the attitude instructions are respectively distributed to the attitude control module and the position control module and used for controlling the unmanned aerial vehicle to reach the expected cruising route at the minimum inclination angle and power;
s4: the early warning obstacle avoidance control module adopts a Takagi-Sugeno model to process the nonlinearity of the system, and active fault-tolerant control is carried out; and if the pulling force and attitude instruction threshold is exceeded, alarming to the upper control module, carrying out the air route planning, the task planning and the path planning in the steps S2-S4 again by the upper control module, and if the pulling force and attitude instruction threshold is not exceeded, completing the self-adaptive fault tolerance.
9. The adaptive fault-tolerant unmanned aerial vehicle tracking cruise method according to claim 8, wherein the step S2 comprises the steps of:
s21: the upper layer control module constructs a flight arrival position [ x 'y' z 'of the unmanned aerial vehicle flight within a delta t time interval']TCalculating a model:
wherein x is a flight coordinate value of the unmanned aerial vehicle in the x-axis direction at the time t, y is a flight coordinate value of the unmanned aerial vehicle in the y-axis direction at the time t, and z is a flight coordinate value of the unmanned aerial vehicle in the z-axis direction at the time t;
for the crossarm of the drone at time t + Δ tThe speed of the rolling angle is controlled by the speed of the rolling angle,
the pitch angle rate of the drone at time t + deltat,
the course angular velocity of the unmanned aerial vehicle at the time t + delta t; the flight coordinate value of the unmanned aerial vehicle at the time of t + delta t in the x-axis direction of x ', the flight coordinate value of the unmanned aerial vehicle at the time of t + delta t in the y-axis direction of y ', and the flight coordinate value of the unmanned aerial vehicle at the time of t + delta t in the z-axis direction of z ';
s22: the upper control module is used for acquiring pitch angle acceleration according to the time t
Angular acceleration of course
Acceleration of roll angle
Calculating the pitch angle speed of the unmanned aerial vehicle at the t + delta t moment in the S21 model
Course angular velocity
And roll angular velocity
Wherein m is a time component, and m belongs to [ t, t + delta t ];
s23: the upper-layer control module controls a first power moment A (t) in the x-axis direction, a second power moment B (t) in the y-axis direction, a third power moment C (t) in the z-axis direction parallel to the earth north pole and the applied resistance moment M (t) given to the unmanned aerial vehicle by the accelerator in the delta t time interval to continuously iterate and optimize the variation error of the first power moment A (t), the second power moment B (t) in the y-axis direction, the third power moment C (t) in the z-axis direction parallel to the earth north pole and the applied resistance moment M (t) along a preset flight path so as to ensure that the pitch angle speed of the unmanned aerial vehicle at the t + delta t moment finally obtained in the step S22
Course angular velocity
And roll angular velocity
No deviation is generated;
s24: the last step of each iteration is to determine whether a deviation threshold value is exceeded, if not, the step of S3 is continued, and if so, the steps of S21-S23 are repeated.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114924581A (en) * | 2022-07-21 | 2022-08-19 | 成都飞机工业（集团）有限责任公司 | Method for judging failure of pitch angle of single-redundancy unmanned aerial vehicle |
| CN117389320A (en) * | 2023-12-07 | 2024-01-12 | 陕西德鑫智能科技有限公司 | Unmanned aerial vehicle cruise control method and system |
| CN117710844A (en) * | 2024-02-05 | 2024-03-15 | 四川铁投泰景置业有限公司 | Building safety monitoring method based on unmanned aerial vehicle |
| CN118151675A (en) * | 2024-05-09 | 2024-06-07 | 南京新航线无人机科技有限公司 | Unmanned aerial vehicle attitude control method and system for complex scene cruising |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107688354A (en) * | 2017-10-30 | 2018-02-13 | 北京博鹰通航科技有限公司 | The UAS and its control method of a kind of autonomous flight |
| US20190033892A1 (en) * | 2017-07-27 | 2019-01-31 | Intel Corporation | Trajectory tracking controllers for rotorcraft unmanned aerial vehicles (uavs) |
| CN109947126A (en) * | 2019-03-07 | 2019-06-28 | 中国科学院深圳先进技术研究院 | Control method, device, equipment and the readable medium of quadrotor drone |
| US20200310448A1 (en) * | 2019-03-26 | 2020-10-01 | GM Global Technology Operations LLC | Behavioral path-planning for a vehicle |
| CN112180960A (en) * | 2020-09-29 | 2021-01-05 | 西北工业大学 | Unmanned aerial vehicle fault-tolerant flight method and flight system for actuator faults |
| CN113268074A (en) * | 2021-06-07 | 2021-08-17 | 哈尔滨工程大学 | Unmanned aerial vehicle flight path planning method based on joint optimization |
-
2021
- 2021-12-03 CN CN202111463965.0A patent/CN114217632B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190033892A1 (en) * | 2017-07-27 | 2019-01-31 | Intel Corporation | Trajectory tracking controllers for rotorcraft unmanned aerial vehicles (uavs) |
| CN107688354A (en) * | 2017-10-30 | 2018-02-13 | 北京博鹰通航科技有限公司 | The UAS and its control method of a kind of autonomous flight |
| CN109947126A (en) * | 2019-03-07 | 2019-06-28 | 中国科学院深圳先进技术研究院 | Control method, device, equipment and the readable medium of quadrotor drone |
| US20200310448A1 (en) * | 2019-03-26 | 2020-10-01 | GM Global Technology Operations LLC | Behavioral path-planning for a vehicle |
| CN112180960A (en) * | 2020-09-29 | 2021-01-05 | 西北工业大学 | Unmanned aerial vehicle fault-tolerant flight method and flight system for actuator faults |
| CN113268074A (en) * | 2021-06-07 | 2021-08-17 | 哈尔滨工程大学 | Unmanned aerial vehicle flight path planning method based on joint optimization |
Non-Patent Citations (3)
| Title |
|---|
| XIE， NINGGUANG: "Personalized route planning of rural ecotourism using mobile computing and random forest algorithm", WIRELESS COMMUNICATIONS & MOBILE COMPUTING * |
| 丁力;柴华伟;李兴成;: "基于航迹规划的四旋翼飞行器轨迹跟踪控制", 电光与控制, no. 11 * |
| 赵辉: "电力巡检四旋翼无人机自主控制系统设计", 中国优秀硕士学位论文全文数据库工程科技Ⅱ辑, pages 042 - 2258 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114924581A (en) * | 2022-07-21 | 2022-08-19 | 成都飞机工业（集团）有限责任公司 | Method for judging failure of pitch angle of single-redundancy unmanned aerial vehicle |
| CN117389320A (en) * | 2023-12-07 | 2024-01-12 | 陕西德鑫智能科技有限公司 | Unmanned aerial vehicle cruise control method and system |
| CN117389320B (en) * | 2023-12-07 | 2024-03-08 | 陕西德鑫智能科技有限公司 | Unmanned aerial vehicle cruise control method and system |
| CN117710844A (en) * | 2024-02-05 | 2024-03-15 | 四川铁投泰景置业有限公司 | Building safety monitoring method based on unmanned aerial vehicle |
| CN117710844B (en) * | 2024-02-05 | 2024-04-16 | 四川铁投泰景置业有限公司 | Building safety monitoring method based on unmanned aerial vehicle |
| CN118151675A (en) * | 2024-05-09 | 2024-06-07 | 南京新航线无人机科技有限公司 | Unmanned aerial vehicle attitude control method and system for complex scene cruising |
| CN118151675B (en) * | 2024-05-09 | 2024-09-10 | 南京新航线无人机科技有限公司 | Unmanned aerial vehicle attitude control method and system for complex scene cruising |
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