CN112678204B - Health monitoring method for unmanned aerial vehicle power system hardware - Google Patents
Health monitoring method for unmanned aerial vehicle power system hardware Download PDFInfo
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Abstract
The invention discloses a health monitoring method for unmanned aerial vehicle power system hardware, which comprises the following steps: starting the unmanned aerial vehicle, performing self-checking on the health monitoring module, if the self-checking of the health monitoring module is successful, entering the step S2, and otherwise, performing troubleshooting on the health monitoring module; s2: after the self-checking is successful, the health monitoring module detects the motor speed and the battery output of the unmanned aerial vehicle during standard operation to obtain real-time motor speed data, real-time battery output data and the like. The invention can detect the health state of the hardware of the unmanned aerial vehicle power system in the flight state, can detect the hardware through some simple conventional actions, compares the obtained detection data with the existing standard efficiency parameters, and accurately and directly obtains the current health states of the battery and the motor. The method is simple and convenient to operate during detection, can be used for performing dynamic physical hardware health detection on the unmanned aerial vehicle in the flying state, and can be used for more accurately detecting the health states of the battery and the motor of the unmanned aerial vehicle during flying.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle power testing, and particularly relates to a health monitoring method for unmanned aerial vehicle power system hardware.
Background
At present, along with the evolution of unmanned aerial vehicle intellectuality and modularization, unmanned aerial vehicle begins to receive every industry's favor, and unmanned aerial vehicle's complexity and fineness are also higher and higher, and health detection to the unmanned aerial vehicle hardware becomes very important, especially is directed at the detection to unmanned aerial vehicle driving system hardware. The power system is a core part for judging whether the unmanned aerial vehicle can stably fly, the hardware of the unmanned aerial vehicle can be statically detected before takeoff by the conventional detection method, but the hardware state of the unmanned aerial vehicle in the flying state cannot be detected; in addition, in the detection method in the prior art, after the unmanned aerial vehicle is started, the unmanned aerial vehicle hovers for a certain period of time, whether each index is normal or not is monitored, the detection is only based on detection in a flight state, and comprehensiveness and detection result accuracy are lacked.
When unmanned aerial vehicle flies under friendly environment, some problems can't show through the visual appearance, and under extreme environment, the problem just exposes nothing, but at daily actual process, unmanned aerial vehicle can't accomplish to carry out health detection constantly, especially in flight process, can't judge the health condition on the physical hardware, or cause the unmanned aerial vehicle task failure and cause uncontrollable loss.
Therefore, a health monitoring method for the hardware of the unmanned aerial vehicle power system needs to be designed at the present stage to solve the above problems. It is to be understood that the present invention is directed to a health monitoring method developed for hidden dangers in the hardware of the unmanned aerial vehicle power system.
Disclosure of Invention
The invention aims to provide a health monitoring method for unmanned aerial vehicle power system hardware, which is used for solving the technical problems in the prior art, such as: the existing detection method can carry out static detection on hardware of the unmanned aerial vehicle before takeoff, but cannot detect the hardware state of the unmanned aerial vehicle in the flight state; in addition, in the detection method in the prior art, after the unmanned aerial vehicle is started, the unmanned aerial vehicle hovers for a certain period of time, whether each index is normal or not is monitored, the detection is only based on detection in a flight state, and comprehensiveness and detection result accuracy are lacked. That is to say, when unmanned aerial vehicle flies under friendly environment, some problems can't appear through directly perceived, and under extreme environment, the problem just exposes nothing, but at daily actual process, unmanned aerial vehicle can't accomplish constantly carries out health detection, especially in flight process, can't judge the health status on the physical hardware, or cause the unmanned aerial vehicle task to fail and cause uncontrollable loss.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a health monitoring method for unmanned aerial vehicle power system hardware comprises the following steps:
s1: the unmanned aerial vehicle is started, self-checking is carried out on the health monitoring module, and the self-checking method comprises the following steps: the system gives a specific signal wave to the health monitoring module, the monitoring module processes the signal wave and gives a feedback value, if the feedback value is within a normal threshold interval, the self-checking is successful, if the self-checking of the health monitoring module is successful, the step S2 is entered, otherwise, the health monitoring module is subjected to troubleshooting; s2: after the self-checking is successful, the health monitoring module monitors the motor speed and the battery output of the unmanned aerial vehicle during standard operation to obtain real-time motor speed data and real-time battery output data;
the real-time battery output data comprises real-time voltage monitoring data, real-time peak discharge current data and real-time continuous discharge current data which are detected by a health monitoring module and correspond to the current unmanned aerial vehicle battery; the real-time motor rotating speed data comprises real-time motor rotating speed peak voltage data, real-time motor current data and real-time motor rotating speed data which are detected by a health monitoring module and correspond to the current unmanned aerial vehicle motor;
s3: comparing and judging real-time motor rotating speed data and real-time battery output data in standard operation with corresponding threshold motor rotating speed data and threshold battery output data in standard operation; and obtaining a comparison judgment result;
s4: mapping the health state of the unmanned aerial vehicle during operation by comparing the judgment results;
in step S3, the comparison and determination specifically include the following steps:
when the unmanned aerial vehicle is a four-axis multi-rotor unmanned aerial vehicle, if the power output of the motor is abnormal;
under the full voltage state, the output percentage of the hovering accelerator is 50%, the rotating speed of the motor is 5500rpm, but a certain motor only has 5200rpm, because of the self-stabilization function of the unmanned aerial vehicle flight control, the body of the unmanned aerial vehicle reaches a stable state, the whole performance of the unmanned aerial vehicle is normal, all the electromechanical, avionic and fuselage structures are subjected to flight tasks after static ground detection, when the unmanned aerial vehicle takes off, the machine body is stable and has no obvious shake, the power output quantity observed by the ground station is normal, at the moment, the voltage output of 4 motors has almost the same difference, that is, the rotation speed of the motor has almost the same difference, the actual situation is that a single motor has abnormal output, due to the self-stabilizing function of the flight control, wherein a single motor is changed to improve the control output, linear data is obtained by calibrating nonlinear data according to the characteristics of an unmanned aerial vehicle power system to carry out data matching, therefore, the health state of the hardware of the unmanned aerial vehicle power system is monitored in the real-time motion data of the unmanned aerial vehicle;
specifically, motor power output values and health threshold values of healthy motors under different voltages and different power output percentages of the healthy motors are obtained through a large number of unmanned aerial vehicle power system operation experiments, the motor power output values and the health threshold values are calibrated to be detectable linear data, and in the task execution process of the unmanned aerial vehicle, real-time motor rotating speed data and real-time battery output data in standard operation are compared and judged with threshold motor rotating speed data and threshold battery output data in corresponding standard operation respectively by issuing some action instructions to the unmanned aerial vehicle after takeoff; and obtaining a comparison judgment result; if the motor exceeds/is lower than the motor power output value and the health threshold value of the unmanned aerial vehicle under the action of the voltage and the output percentage, the motor is judged to be in danger; the operation experiment of the motor power system with problems on each shaft and the linear data thereof are calibrated, so that the specific motor or motors can be intuitively obtained to output the health state.
Further, in step S1, the health monitoring module specifically performs troubleshooting as follows:
and 4, judging whether the change value delta H of the operation data of the unmanned aerial vehicle is smaller than or equal to a set threshold value, and determining that the health monitoring module breaks down when the change value delta H of the operation data of the unmanned aerial vehicle is smaller than or equal to the threshold value.
Further, in step S3, the comparison and determination specifically include the following:
comparing and judging the real-time voltage monitoring data and the threshold voltage monitoring data, and if the real-time voltage monitoring data and the threshold voltage monitoring data are not matched, performing voltage monitoring abnormity alarm;
comparing and judging the real-time peak value discharge current data and the threshold value peak value discharge current data, and if the real-time peak value discharge current data and the threshold value peak value discharge current data are not matched, performing abnormal alarm on the peak value discharge current;
comparing and judging the real-time continuous discharge current data with threshold continuous discharge current data, and if the real-time continuous discharge current data and the threshold continuous discharge current data are not matched, alarming the abnormity of the continuous discharge current;
comparing and judging the real-time motor rotating speed peak voltage data with threshold motor rotating speed peak voltage data, and if the real-time motor rotating speed peak voltage data and the threshold motor rotating speed peak voltage data are not matched, alarming the abnormal motor rotating speed peak voltage;
comparing and judging the real-time motor current data with threshold motor current data, and if the real-time motor current data and the threshold motor current data are not matched, alarming the motor current abnormity;
and comparing and judging the real-time motor rotating speed data and the threshold motor rotating speed data, and if the real-time motor rotating speed data and the threshold motor rotating speed data are not matched, alarming the abnormal motor rotating speed.
Compared with the prior art, the invention has the beneficial effects that:
the method has the innovation point that the method can detect the health state of the hardware of the unmanned aerial vehicle power system in the flying state, can detect the hardware through some simple conventional actions (such as lifting, rolling, pitching, frog jumping, cruising and the like), and compares the obtained detection data with the existing standard efficiency parameters to accurately and directly obtain the health states of the current battery and the motor. The method is simple and convenient to operate during detection, can be used for performing dynamic physical hardware health detection on the unmanned aerial vehicle in the flying state, and can be used for more accurately detecting the health states of the battery and the motor of the unmanned aerial vehicle during flying. According to unmanned aerial vehicle's flight characteristic, make unmanned aerial vehicle fly the experiment under normal environment, complex environment and special environment, reach four-axis four-oar multi-rotor unmanned aerial vehicle, four-axis eight-oar multi-rotor unmanned aerial vehicle, six-axis twelve-oar multi-rotor unmanned aerial vehicle driving system's headroom threshold value to motor voltage output, motor current output, motor speed output, the gesture change state under the different headroom carry out the calibration. The unmanned aerial vehicle is at a lower height after flying off the ground, the unmanned aerial vehicle is controlled to perform quick maneuvering actions (pitching, rolling and yawing) through an automatic detection program, control response data and calibration data of the unmanned aerial vehicle are compared, and the health state of a power system is automatically detected, so that the health state criterion is quickly given. The experience requirement on operators is reduced, and the unmanned aerial vehicle system is more beneficial to realizing full-autonomous flight control.
Drawings
FIG. 1 is a schematic flow chart of steps of an embodiment of the present invention.
Fig. 2 is a schematic diagram of a four-axis multi-rotor drone according to a specific embodiment of the invention.
FIG. 3 is a schematic representation of wind and no wind conditions according to an embodiment of the present invention.
Fig. 4 is a schematic diagram illustrating that the throttle input ratio gradually increases from 70% to 80% when the drone advances horizontally according to the particular embodiment of the present invention.
Fig. 5 is a diagram illustrating the rotation speed values of 4 motors according to an embodiment of the present invention.
Fig. 6 is a schematic diagram of 4 steps occurring in the rotation speed values of 4 motors according to the embodiment of the present invention.
Fig. 7 is a schematic rotational speed diagram of a four-axis eight-paddle multi-rotor drone according to an embodiment of the invention.
Fig. 8 is a schematic diagram of a four-axis eight-paddle multi-rotor drone according to an embodiment of the invention.
Fig. 9 is a schematic view of a normal hovering condition of the four-axis eight-paddle multi-rotor drone according to the embodiment of the present invention.
Fig. 10 is a schematic view of a four-axis eight-paddle multi-rotor drone of an embodiment of the present invention in a normal forward flight situation.
Fig. 11 is a schematic diagram of a normal hovering abnormal condition of the four-axis eight-paddle multi-rotor drone according to the embodiment of the present invention.
Fig. 12 is a schematic diagram of an abnormal forward flight condition of the four-axis eight-paddle multi-rotor drone according to the specific embodiment of the invention.
Fig. 13 is a schematic diagram of a case where the power output of a motor is abnormal and the unmanned aerial vehicle shows normal operation according to the embodiment of the present invention.
FIG. 14 is a diagram illustrating normal hover and high power anomaly, in accordance with an embodiment of the present invention.
FIG. 15 is a graphical illustration of the greater deflection rate deviation or overall decrease in accordance with an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to fig. 1 to 15 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 (b):
as shown in fig. 1, therefore, a health monitoring method for hardware of an unmanned aerial vehicle power system is provided, which includes the following steps:
s1: starting the unmanned aerial vehicle, performing self-checking on the health monitoring module, if the self-checking of the health monitoring module is successful, entering the step S2, and otherwise, performing troubleshooting on the health monitoring module;
s2: after the self-checking is successful, the health monitoring module detects the motor speed and the battery output of the unmanned aerial vehicle during standard operation to obtain real-time motor speed data and real-time battery output data;
the real-time battery output data comprises real-time voltage monitoring data, real-time peak discharge current data and real-time continuous discharge current data which are detected by a health monitoring module and correspond to the current unmanned aerial vehicle battery; the real-time motor rotating speed data comprises real-time motor rotating speed peak voltage data, real-time motor current data and real-time motor rotating speed data which are detected by a health monitoring module and correspond to the current unmanned aerial vehicle motor;
s3: comparing and judging real-time motor rotating speed data and real-time battery output data in standard operation with corresponding threshold motor rotating speed data and threshold battery output data in standard operation; and obtaining a comparison judgment result;
s4: and mapping the health state of the unmanned aerial vehicle during operation by comparing the judgment result.
In the scheme, the method can detect the health state of the hardware of the unmanned aerial vehicle power system in the flight state, can detect the health state through some simple conventional actions (such as lateral flying, vertical lifting, linear crossing, pitching and the like), and compares the obtained detection data with the existing standard efficiency parameters to accurately and directly know the current health states of the battery and the motor. The method is simple and convenient to operate during detection, can be used for performing dynamic physical hardware health detection on the unmanned aerial vehicle in the flying state, and can be used for more accurately detecting the health states of the battery and the motor of the unmanned aerial vehicle during flying.
Further, in step S1, the health monitoring module specifically performs troubleshooting as follows:
and 4, judging whether the change value delta H of the operation data of the unmanned aerial vehicle is smaller than or equal to a set threshold value, and determining that the health monitoring module breaks down when the change value delta H of the operation data of the unmanned aerial vehicle is smaller than or equal to the threshold value.
In the scheme, when the health monitoring module fails, the alarm is stopped. The invention can quickly detect that the health monitoring module has faults and is beneficial to saving the troubleshooting time of workers.
Further, in step S3, the comparison and determination specifically include the following:
comparing and judging the real-time voltage monitoring data and the threshold voltage monitoring data, and if the real-time voltage monitoring data and the threshold voltage monitoring data are not matched, performing voltage monitoring abnormity alarm;
comparing and judging the real-time peak value discharge current data and the threshold value peak value discharge current data, and if the real-time peak value discharge current data and the threshold value peak value discharge current data are not matched, performing abnormal alarm on the peak value discharge current;
comparing and judging the real-time continuous discharge current data with threshold continuous discharge current data, and if the real-time continuous discharge current data and the threshold continuous discharge current data are not matched, alarming the abnormity of the continuous discharge current;
comparing and judging the real-time motor rotating speed peak voltage data with threshold motor rotating speed peak voltage data, and if the real-time motor rotating speed peak voltage data and the threshold motor rotating speed peak voltage data are not matched, alarming the abnormal motor rotating speed peak voltage;
comparing and judging the real-time motor current data with threshold motor current data, and if the real-time motor current data and the threshold motor current data are not matched, alarming the motor current abnormity;
and comparing and judging the real-time motor rotating speed data and the threshold motor rotating speed data, and if the real-time motor rotating speed data and the threshold motor rotating speed data are not matched, alarming the abnormal motor rotating speed.
Flight control (flight control system) can carry out health monitoring to inside components and parts of unmanned aerial vehicle and most live equipment, but can't accomplish the real-time health monitoring to physical hardware, like screw (deformation, crack), physical hardware such as organism structure (not hard up), need additionally carry out special ground test alone, no matter be time cost or economic cost all very high, this method can carry out dynamic health detection to the unmanned aerial vehicle physical hardware that is in flight state, compare in ground detection, possess following advantage: firstly, the health state of material resource hardware can be fed back in real time when the unmanned aerial vehicle executes tasks each time; secondly, the operation is simple, and the health detection can be completed only by a plurality of simple conventional actions; the unmanned aerial vehicle life-span is harmless, and motor and battery have life, if unmanned aerial vehicle carries out the health detection of physics hardware outside at every execution task forehead, that unmanned aerial vehicle's flight life directly reduces half.
A hardware health monitoring method, the method comprising a hardware health monitoring module: the module can monitor real-time output data of the battery (namely real-time voltage monitoring data, real-time peak discharge current data and real-time continuous discharge current data corresponding to each battery of the current unmanned aerial vehicle); the module can monitor real-time rotating speed data of the motor (namely real-time motor rotating speed peak voltage data, real-time motor current data and real-time motor rotating speed data corresponding to each motor of the existing unmanned aerial vehicle).
Example one:
taking a four-axis multi-rotor unmanned aerial vehicle as an example (fig. 2), under an ideal state (i.e., without considering environmental factors and without considering battery voltage reduction), a 50% throttle input ratio is given, so that the unmanned aerial vehicle takes off and is in a hovering state (i.e., lift force = gravity), the voltage output of 4 motors is supposed to have almost the same difference, i.e., the motor rotation speed has almost the same difference, and when the unmanned aerial vehicle is in the hovering state, the rotation speed of each motor is 5000 revolutions. During actual flight, in order to enable the unmanned aerial vehicle to reach a self-stabilization state, the flight control can compensate the rotating speed of each motor according to actual conditions, so that the total lift force of the unmanned aerial vehicle is stable and unchanged, the fluctuation of the rotating speed of the motors is +/-500 revolutions, namely the rotating speed range of 4 motors is 4500r-5500 r.
Actual flight, when unmanned aerial vehicle suspends, the rotational speed of 4 motors is 5000r, there is a gust of wind this moment, blow down from unmanned aerial vehicle's top, flight control is in order to guarantee that unmanned aerial vehicle does not fall the height, can give a rotational speed compensation of every motor, 4 motor rotational speeds can promote 5500r promptly, make lift = gravity + wind-force, but actually embody, unmanned aerial vehicle still is the state of hovering, and is similar with the appearance under the no wind state (fig. 3).
Example two:
in actual flight, when the unmanned aerial vehicle horizontally moves forward, the throttle input ratio gradually rises to 80% from 70% (fig. 4), no abnormity is reported in flight control (aiming at monitoring of components, sensors and part of electrified equipment), artificial observation is carried out, the unmanned aerial vehicle is in a normal operation state, but the rotating speed values of 4 motors (fig. 5) measured by a hardware health monitoring module can find that 4 steps (namely, the motor is abnormally increased and the motor is abnormally reduced) appear in the rotating speed values of the 4 motors in the horizontal forward process of the unmanned aerial vehicle (fig. 6), the motor is finally (namely, the motor even reaches 100% rotating speed, and the rotating speed threshold value of the motor in normal operation is far exceeded, so that the unmanned aerial vehicle is judged to be in a healthy abnormal state at all times. Further, it is possible to further determine which part of the hardware is abnormal based on other abnormal data (data of Δ H ═ H2-H1| > threshold).
Example three:
taking a four-axis eight-propeller multi-rotor unmanned aerial vehicle as an example (fig. 8), when the unmanned aerial vehicle is in a hovering state (the throttle input is 50% and the throttle input is 70%), the rotating speeds of 8 motors fluctuate within an interval (the fluctuation range is assumed to be +/-5), the fluctuation is normal data within the interval, when the unmanned aerial vehicle horizontally moves forward, the rotating speed of the No. 1-4 motor is reduced, the rotating speed of the No. 5-8 motor is increased, the attitude of the unmanned aerial vehicle is inclined forwards, and therefore the unmanned aerial vehicle has a forward component force in the horizontal direction. The fluctuation range is shown in fig. 9 and 10.
Next, we will take the damaged number 1 paddle as an example, and see the rotation speed of the motor (fig. 7), as shown in fig. 11 and fig. 12. The No. 1 propeller is damaged, the No. 2 propeller needs to provide more thrust to make up for the thrust lost by the No. 1 propeller, and when the conventional maneuvering (lifting, rolling, pitching, frog jumping and cruising, and the forward movement belongs to one of cruising) is carried out, the thrust of other seven propellers is obviously abnormal and normal values, so that the abnormality of the No. 1 propeller can be judged.
Example four:
the motor power output is abnormal, and it should be mentioned that the power output is abnormal, and the motor is not failed or damaged, but does not conform to the due power output value. For example, taking a four-axis four-paddle multi-rotor unmanned aerial vehicle as an example, under a full voltage state, the output percentage of a hovering throttle is 50%, the rotating speed of a motor should reach 5500rpm, but a certain motor only has 5200rpm, because of the self-stabilization function of unmanned aerial vehicle flight control, a flight control system can improve the throttle control input amount with lower rotating speed (for example, the throttle control input amount can be improved to more than 70%), so as to improve the rotating speed of a motor, so that the body reaches a stable state, the integral performance of the unmanned aerial vehicle is still normal, and because a general controller is a real average throttle, the problem cannot be found through a ground station during static ground detection and flight, as shown in fig. 13. For example, the screws of the automobile are loosened, the automobile cannot be shown in a short time, and the self-detection system of the automobile cannot detect the loosening condition of a certain screw. Under this same condition, unmanned aerial vehicle can be in the short time friendly environment normal flight down, but long continuation of the journey or flight under the special environment will go wrong, for example, the not hard up car of screw can normally travel on steady road, but travels on the road of pothole, and not hard up car automobile body is more unstable than normal car automobile body, if this screw is certain key part on the car, can make the automobile body scatter the frame even.
If the driving system hardware is unhealthy, single motor output abnormal condition because unmanned aerial vehicle flies to control has self-adjusting function, under friendly environment, normal short-time flight in-process, and obvious performance such as fuselage unstability can not appear in unmanned aerial vehicle, nevertheless under extreme environment or uncontrollable factor, the loss that can not predict is caused because single motor output unstability appears unusually even the crash. And current technique except the initial detection on quiet ground, the unmanned aerial vehicle health status that the performance that more is gone on through unmanned aerial vehicle observation at flight in-process people eye judges, and at unmanned aerial vehicle flight in-process, ground station monitoring also only to components and parts, can't do the monitoring to the driving system hardware.
Use many rotor unmanned aerial vehicle of four-axis as an example, each is electromechanical, the avionics, the fuselage structure all detects the back through quiet ground, carry out the flight task, unmanned aerial vehicle takes off, the steady no obvious shake of fuselage, the power output volume that the ground station observed is normal, at this moment, the voltage output reason of 4 motors should differ a little, the motor speed differs a little promptly, but actual conditions is, the unusual phenomenon of output appears in certain single motor, because the autostability function of flight control, one of them motor has improved control output, unmanned aerial vehicle's stability has been ensured, and this phenomenon can't follow and observe on people's eye and the ground station, this phenomenon is more obvious on many rotor unmanned aerial vehicle of more wings. This many rotor unmanned aerial vehicle of four-axis finishes steady flight, when beginning to carry out special task, single motor output causes power system unusually that the sufficient power that can't provide unmanned aerial vehicle leads to the crash.
Based on the characteristic of the unmanned aerial vehicle power system, the linear data are obtained by calibrating the nonlinear data to perform data matching, so that the health state of hardware of the unmanned aerial vehicle power system can be monitored in the real-time motion data of the unmanned aerial vehicle.
Through a large number of unmanned aerial vehicle power system operation experiments, motor power output values and health threshold values of healthy motors when the unmanned aerial vehicle performs different actions under different voltages and different power output percentages are obtained and are calibrated to detectable linear data, and in the process of executing tasks by the unmanned aerial vehicle, the unmanned aerial vehicle after taking off issues some action instructions (actions such as turning, emergency braking, repeated lifting, S winding and the like), so that real-time motor rotating speed data and real-time battery output data during standard operation are respectively compared and judged with corresponding threshold motor rotating speed data and threshold battery output data during standard operation; and obtaining a comparison judgment result; if the motor exceeds/is lower than the motor power output value and the health threshold value of the unmanned aerial vehicle under the action of the voltage and the output percentage, the motor is judged to be dangerous. Furthermore, motor power system operation experiments and linear data of each shaft with problems are calibrated, so that the motor or the motors can be intuitively output to a healthy state, and the flight risk of the unmanned aerial vehicle is reduced.
Example five:
the motor high power state is abnormal. When the unmanned aerial vehicle is in power output, a certain output margin can be reserved for flight control, adjustment and allocation to handle emergent emergency situations, for example, a four-shaft four-paddle multi-rotor unmanned aerial vehicle is taken as an example, under the condition of full voltage, the output percentage of an accelerator is 100%, the rotating speed of each motor is 8000rpm, the accelerator required in a hovering state is 50%, the rotating speed of each motor is 5500rpm, the extra 2500rpm is called as the power output margin of each motor, the margin is used for ensuring that the flight control is under the premise of implementing the flight command of the unmanned aerial vehicle, certain extra power can deal with the emergent situations, and under the condition, if the motor is blown by wind, the motor can have the power margin to perform wind-resistant action, or can realize self-adjustment and stability after being hit by a certain object.
If the hardware of the power system is not healthy, the problem of insufficient power output allowance of one or more motors may occur, that is, in a hovering state, the input control and actual output of the motors are in a normal state, but in a high-power input state (for example, over 90% of accelerator), under the same voltage condition, the rotating speed of each motor is far smaller than a normal value, in the four-shaft four-propeller multi-rotor unmanned aerial vehicle, under the full-voltage condition, when the actual power output percentage is 100%, the rotating speed of each motor can only reach 7000rpm or even less, so that the unmanned aerial vehicle lacks the self-stability regulation capability, cannot be effectively controlled during maneuvering action, and even endangers the safety of the unmanned aerial vehicle. As in fig. 14.
Taking a certain flight example of a four-axis four-propeller multi-rotor unmanned aerial vehicle as an example, the unmanned aerial vehicle normally takes off and flies in a windless environment, all parameters are well represented, realized action instructions can be carried out, the behavior of the unmanned aerial vehicle body is well observed by human eyes, and the ground station does not show any abnormity. Because the unmanned aerial vehicle adopts the symmetrical design, the maneuvering capabilities to the left and the right should be basically the same, but under the condition of high power output failure of a single motor, the difference of the maneuvering capabilities of the unmanned aerial vehicle to the left and the right is larger. For example, the motor on the left side has a high-power output fault, the left maneuvering capability of the unmanned aerial vehicle is not affected, but normal right maneuvering cannot be implemented. The forward and backward maneuvers are similar.
In the prior art, most of the power consumption of the unmanned aerial vehicle and the loss of the battery voltage are measured, the power margin is artificially estimated, and real-time monitoring cannot be achieved. The operation of general unmanned aerial vehicle carries out the horizontal direction for unmanned aerial vehicle operator manual operation unmanned aerial vehicle and moves, judges unmanned aerial vehicle's health status through unmanned aerial vehicle's horizontal motion response. The requirement of this kind of mode to unmanned aerial vehicle operator is higher, generally can only do less input inspection, can't carry out boundary state inspection. In addition, the unmanned aerial vehicle operated manually is very easy to maneuver in the horizontal direction, the required take-off and landing range is wider, the rapid detection cannot be realized, a large amount of energy can be consumed, and the flying time is shortened.
In experimental research many times, we find that the high power state of unmanned aerial vehicle power is relevant with unmanned aerial vehicle self control response rate, namely, give unmanned aerial vehicle an attitude change instruction, the rate of deflection of unmanned aerial vehicle organism can be influenced because of the power headroom. For example, if the power margin of the unmanned aerial vehicle system is sufficient, the flight control is deflected at a set attitude deflection rate, but if the power margin of the unmanned aerial vehicle system is insufficient, the deflection rate is slightly decreased.
A normal multi-rotor drone, with a power margin of at least 10% of its total power, has default settings for the Yaw rate of flight control (Roll: P = 0.135I = 0.09D = 0.0036F =20.00, Pitch: P = 0.135I = 0.09D = 0.0036F =20.00, and Yaw: P = 0.180I = 0.18D = 0.00F = 5.00). In practical experiments, under the condition of full voltage, the roll rocker is fully driven, the average rate of the left deflection (roll) and the right deflection (roll) of the airplane is measured to be about 30 degrees/s, under the condition of full voltage, the roll rocker is fully driven, the average rate of the left deflection (roll) of the airplane is measured to be about 30 degrees/s, the average rate of the right deflection (roll) is about 15 degrees/s, and the motion response rates of the left side and the right side deviate by 15 degrees/s. During actual flight, the maximum horizontal flying speed of the unmanned aerial vehicle to the left is much higher than the horizontal flying speed to the right (no wind condition). Further, the more the margin is insufficient, the larger the deflection rate deviation or the lower the entire deviation. As in fig. 15.
Based on the above, based on the flight characteristics of the unmanned aerial vehicle, the unmanned aerial vehicle is subjected to flight experiments in normal environment, complex environment and special environment, the power headroom thresholds of the four-axis four-propeller multi-rotor unmanned aerial vehicle, the four-axis eight-propeller multi-rotor unmanned aerial vehicle and the six-axis twelve-propeller multi-rotor unmanned aerial vehicle power system are obtained, and motor voltage output, motor current output, motor speed output and attitude change states under different power headroom are calibrated. The unmanned aerial vehicle is at a lower height after flying off the ground, the unmanned aerial vehicle is controlled to perform quick maneuvering actions (pitching, rolling and yawing) through an automatic detection program, control response data and calibration data of the unmanned aerial vehicle are compared, and the health state of a power system is automatically detected, so that the health state criterion is quickly given. The experience requirement on operators is reduced, and the unmanned aerial vehicle system is more beneficial to realizing full-autonomous flight control.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.
Claims (3)
1. A health monitoring method for unmanned aerial vehicle power system hardware is characterized by comprising the following steps:
s1: starting the unmanned aerial vehicle, performing self-checking on the health monitoring module, if the self-checking of the health monitoring module is successful, entering the step S2, and otherwise, performing troubleshooting on the health monitoring module;
s2: after the self-checking is successful, the health monitoring module detects the motor speed and the battery output of the unmanned aerial vehicle during standard operation to obtain real-time motor speed data and real-time battery output data;
the real-time battery output data comprises real-time voltage monitoring data, real-time peak discharge current data and real-time continuous discharge current data which are detected by a health monitoring module and correspond to the current unmanned aerial vehicle battery; the real-time motor rotating speed data comprises real-time motor rotating speed peak voltage data, real-time motor current data and real-time motor rotating speed data which are detected by a health monitoring module and correspond to the current unmanned aerial vehicle motor;
s3: comparing and judging real-time motor rotating speed data and real-time battery output data in standard operation with corresponding threshold motor rotating speed data and threshold battery output data in standard operation; and obtaining a comparison judgment result;
s4: mapping the health state of the unmanned aerial vehicle during operation by comparing the judgment results;
in step S3, the comparison and determination specifically include the following steps:
the unmanned aerial vehicle is a four-axis multi-rotor unmanned aerial vehicle, and if the power output of the motor is abnormal;
under the full voltage state, the output percentage of the hovering accelerator is 50%, the rotating speed of the motor is 5500rpm, but a certain motor only has 5200rpm, because of the self-stabilization function of the unmanned aerial vehicle flight control, the body of the unmanned aerial vehicle reaches a stable state, the whole performance of the unmanned aerial vehicle is normal, all the electromechanical, avionic and fuselage structures are subjected to flight tasks after static ground detection, when the unmanned aerial vehicle takes off, the machine body is stable and has no obvious shake, the power output quantity observed by the ground station is normal, at the moment, the voltage output of 4 motors has almost the same difference, that is, the rotation speed of the motor has almost the same difference, the actual situation is that a single motor has abnormal output, due to the self-stabilizing function of the flight control, wherein a single motor is changed to improve the control output, linear data is obtained by calibrating nonlinear data according to the characteristics of an unmanned aerial vehicle power system to carry out data matching, therefore, the health state of the hardware of the unmanned aerial vehicle power system is monitored in the real-time motion data of the unmanned aerial vehicle;
specifically, motor power output values and health threshold values of healthy motors under different voltages and different power output percentages of the healthy motors are obtained through a large number of unmanned aerial vehicle power system operation experiments, the motor power output values and the health threshold values are calibrated to be detectable linear data, and in the task execution process of the unmanned aerial vehicle, real-time motor rotating speed data and real-time battery output data in standard operation are compared and judged with threshold motor rotating speed data and threshold battery output data in corresponding standard operation respectively by issuing some action instructions to the unmanned aerial vehicle after takeoff; and obtaining a comparison judgment result; if the motor exceeds or is lower than the motor power output value and the health threshold value of the unmanned aerial vehicle under the action of the voltage and the output percentage, the motor is judged to be dangerous; the operation experiment of the motor power system with problems on each shaft and the linear data thereof are calibrated, so that the specific motor or motors can be intuitively obtained to output the health state.
2. The health monitoring method for the hardware of the Unmanned Aerial Vehicle (UAV) as claimed in claim 1, wherein in step S1, the health monitoring module is configured to perform troubleshooting as follows:
step 1, when an operation signal starts, acquiring the number of pulse signals of a health monitoring module at the initial time, recording the number of the pulse signals as A1, and calculating the current unmanned aerial vehicle operation data H1 according to the number of the pulse signals A1;
step 2, when the acquisition time period comes, acquiring the number of pulse signals of the current health monitoring module, recording the number of the pulse signals as A2, and calculating the current unmanned aerial vehicle operation data H2 according to the number of the pulse signals A2;
step 3, calculating a change value delta H of the operation data of the unmanned aerial vehicle in a time period according to the operation data of the unmanned aerial vehicle obtained twice in the adjacent time, wherein the delta H is | H2-H1 |;
and 4, judging whether the change value delta H of the operation data of the unmanned aerial vehicle is smaller than or equal to a set threshold value, and determining that the health monitoring module breaks down when the change value delta H of the operation data of the unmanned aerial vehicle is smaller than or equal to the threshold value.
3. The method as claimed in claim 1, wherein in step S3, the comparison and determination is as follows:
comparing and judging the real-time voltage monitoring data and the threshold voltage monitoring data, and if the real-time voltage monitoring data and the threshold voltage monitoring data are not matched, performing voltage monitoring abnormity alarm;
comparing and judging the real-time peak value discharge current data and the threshold value peak value discharge current data, and if the real-time peak value discharge current data and the threshold value peak value discharge current data are not matched, performing abnormal alarm on the peak value discharge current;
comparing and judging the real-time continuous discharge current data with threshold continuous discharge current data, and if the real-time continuous discharge current data and the threshold continuous discharge current data are not matched, alarming the abnormity of the continuous discharge current;
comparing and judging the real-time motor rotating speed peak voltage data with threshold motor rotating speed peak voltage data, and if the real-time motor rotating speed peak voltage data and the threshold motor rotating speed peak voltage data are not matched, alarming the abnormal motor rotating speed peak voltage;
comparing and judging the real-time motor current data with threshold motor current data, and if the real-time motor current data and the threshold motor current data are not matched, alarming the motor current abnormity;
and comparing and judging the real-time motor rotating speed data and the threshold motor rotating speed data, and if the real-time motor rotating speed data and the threshold motor rotating speed data are not matched, alarming the abnormal motor rotating speed.
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