Wide-range efficient low-noise control method based on structural electrical control collaborative matching

Abstract

The present application belongs to the field of structure design control, and relates to a wide-range efficient low-noise control method based on structure electrical control collaborative matching. The method comprises: constructing a multi-rotor unmanned aerial vehicle maneuvering model; constructing four maneuvering models of propeller model, motor model, electronic speed controller model and battery model according to environmental information, propeller parameters, motor parameters, electronic speed controller parameters and battery parameters; correcting the maneuvering model; modeling analysis and correcting the battery model in the maneuvering model according to the influence of different temperatures on the battery performance, and then iteratively correcting the battery parameters to realize electrical control collaborative matching. The method provided by the present application updates the multi-rotor unmanned aerial vehicle maneuvering model according to environmental factors, iteratively corrects different propeller and battery parameters to realize electrical control collaborative matching, and finally realizes robust application at a wide-range altitude of 0-5000 meters.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of structural design and control, and relates to a wide-range efficient low-noise control method based on structural electrical control collaborative matching. BACKGROUND

[0002] Conventional multi-rotor unmanned aerial vehicles all apply motor and constant moment propeller power devices, and the flight and control of the unmanned aerial vehicle are realized by the change of the vertical pulling force of the uniformly distributed propeller, and the change of the pulling force of the propeller completely depends on the change of the rotating speed of the propeller. In order to realize the stable hovering and flight of the unmanned aerial vehicle, the maximum pulling force provided by the propeller obviously needs to be greater than the gravity of the unmanned aerial vehicle. The pulling force of the propeller is affected by the motor power or torque, rotating speed, propeller moment, surrounding air density, temperature and the like.

[0003] Therefore, in order to realize the efficient work of the unmanned aerial vehicle on the plain and plateau, various situations need to be comprehensively considered. Domestic unmanned aerial vehicle design such as Tian Weijun et al. designs the structure of a four-rotor unmanned aerial vehicle in combination with vibration characteristics. Shi Weixing et al. analyzes the calculation method of the plateau characteristics and wind resistance characteristics of the multi-rotor unmanned aerial vehicle. However, only high-altitude application is considered in the design, and the wide-range design is not realized through structural electrical control collaborative matching. SUMMARY

[0004] In the prior art, when designing a rotor unmanned aerial vehicle, only the normal temperature and pressure state is considered, which causes poor system working state at high altitude and low temperature. The application provides a wide-range efficient low-noise control method based on structural electrical control collaborative matching, which realizes the wide-range low-noise control of the unmanned aerial vehicle by designing and correcting the battery model in the unmanned aerial vehicle motion model and then iteratively correcting the battery parameters to realize the electrical control collaborative matching.

[0005] The application is realized by the following technical solutions:

[0006] A wide-range efficient low-noise control method based on structural electrical control collaborative matching, the method comprising:

[0007] Constructing a multi-rotor unmanned aerial vehicle motion model: according to the environmental information, propeller parameters, motor parameters, electronic speed regulator parameters and battery parameters, four motion models of the propeller model, motor model, electronic speed regulator model and battery model are constructed;

[0008] Correcting the motion model: the effective discharge current of the battery at the current time is predicted by using an LSTM network structure, the battery model in the motion model is corrected, and then the battery parameters are iteratively corrected to realize the electrical control collaborative matching.

[0009] Further, the construction method of the propeller model is:

[0010] The multi-rotor adopts a constant pitch propeller, and the calculation formula of the pulling force T and the torque M of the propeller is expressed as:

[0011]

[0012]

[0013] Wherein, N is the propeller speed, D P is the propeller diameter, p is the air density of the flight environment, C T and C M are the dimensionless pulling force coefficient and torque coefficient, respectively.

[0014] Wherein, the air density p is calculated by reading the air pressure P α and the temperature T α of the temperature sensor, and then using the formula:

[0015] p = 1.293 x (P a / 101.325kPa) x (273.15 / (T a +273.15)).

[0016] Further, due to the noise reduction limit, the propeller rotation frequency F 桨 satisfies:

[0017] F 桨 =N x n 磁 / 60≠F 机

[0018] Wherein, F 桨 represents the propeller frequency, F 机 represents the airborne resonant frequency, and n 磁 represents the number of clicks of the magnetic pole pair.

[0019] Further, the motor model is constructed by the method comprising:

[0020] The motor used by the multi-rotor unmanned aerial vehicle is a brushless DC motor, and according to the motor theory, the motor model is expressed as:

[0021]

[0022] In the formula, U m is the equivalent voltage of the motor; I m is the equivalent current; M is the motor load torque equal to the propeller torque; N is the motor speed equal to the propeller speed; K V0 is the nominal no-load KV value; I m0 is the nominal no-load current; U m0 is the nominal no-load voltage; and R m is the motor internal resistance.

[0023] Further, the construction method of the electronic speed regulator model is:

[0024] The input oil door instruction σ of the electric governor and the input current I are calculated e And the input voltage U e :

[0025]

[0026] I e = σI m ;

[0027] U e = U b -I b R b

[0028] In the formula, σ is the input oil door instruction of the electric governor, the value range is 0-1; I e represents the input current; U e represents the input voltage; U m and I m are the equivalent voltage and current of the motor obtained by using the motor model; I b represents the battery current, R b is the internal resistance of the battery, U b is the battery voltage; R e is the input resistance.

[0029] Further, the construction method of the battery model is:

[0030] The battery current I b is used to solve the endurance time T b of the multi-rotor, and the maximum discharge current limit of the battery is met; the battery modeling simplifies the actual discharge of the battery, the voltage remains unchanged during the discharge process, and the remaining capacity of the battery changes linearly. The specific battery model is as follows:

[0031]

[0032]

[0033] Where C b is the current battery capacity; C min is the minimum remaining capacity of the battery; I b is the battery current; K b is the maximum discharge rate of the battery.

[0034] Further, the battery model in the correction maneuver model is predicted by using an LSTM network structure:

[0035] When training the network, the data of the previous k time nodes {tn-k+1 ,t n-k+2 ,...,t n} as input, the next node t n+1 The data is used as the output; the data input for each node includes the real-time ambient temperature T. real Current battery capacity C b The battery internal resistance R at the current moment b Motor speed N and discharge current i;

[0036] Furthermore, using the trained LSTM network, with the data from the k previous time points {t} at the current time, n-k+1 ,t n-k+2 ,...,t n Using} as input, predict the node data t at the current time. n+1 ;

[0037] The discharge current *i* in the current node data is predicted by inputting the node data of the first *k* nodes. This *i* is then used as the effective discharge current of the battery during operation. The effective discharge current of the battery during operation is then substituted into the battery model, replacing the battery current *I*. b This allows for the correction of the battery model.

[0038] Furthermore, the time interval between two adjacent nodes is 1 second.

[0039] Furthermore, we take k=10, that is, by inputting the node data of the 10 nodes before the current moment, we predict the discharge current i at the current moment, which is used as the effective discharge current of the battery during operation.

[0040] Beneficial technical effects of the present invention:

[0041] The present invention provides a wide-range, high-efficiency, and low-noise control method based on structural electrical control cooperative matching.

[0042] This is an environment-based energy optimization method for UAV power units. It uses an LSTM network to update the battery model in the multi-rotor UAV maneuvering model according to environmental factors, and then iterates different propeller and battery parameters to achieve coordinated matching of electrical control, ultimately achieving robust application in a wide altitude range of 0-5000 meters. Furthermore, by controlling the engine speed and avoiding the resonant frequency of the airframe, the overall noise is reduced. Attached Figure Description

[0043] Fig. 1 This is a schematic diagram of a wide-range, high-efficiency, low-noise control method based on structural electrical control cooperative matching in an embodiment of the present invention;

[0044] Fig. 2 This is a schematic diagram illustrating the process of constructing a multi-rotor UAV maneuvering model in an embodiment of the present invention; Detailed Implementation

[0045] To further clarify the technical means and effects taken by the present application to achieve the predetermined inventive purpose, the specific embodiments, structures, features and effects thereof according to the present application are described in detail below in conjunction with the drawings and preferred embodiments.

[0046] A wide-range efficient low-noise control method based on structural electrical control collaborative matching, as shown in Figs. 1-2 The method comprises the following steps:

[0047] Constructing a multi-rotor unmanned aerial vehicle maneuver model: according to environmental information, propeller parameters, motor parameters, electronic speed controller parameters and battery parameters, four maneuver models of propeller model, motor model, electronic speed controller model and battery model are constructed;

[0048] Specifically, the construction of the multi-rotor unmanned aerial vehicle maneuver model is based on blade element theory, air propeller theory and motor theory, and the multi-rotor unmanned aerial vehicle propeller, motor, electronic speed controller and battery are modeled, and the modeling flowchart is shown in 2: the environmental parameters are obtained through the barometer and temperature sensor to estimate the air density, and the propeller model is obtained according to the air density and propeller parameters; the motor model, the electronic speed controller model and the battery model are constructed according to the motor parameters, the electronic speed controller parameters and the battery parameters respectively;

[0049] Correcting the maneuver model: the effective discharge current of the battery at the current time is predicted by using the LSTM network structure, the battery model in the maneuver model is corrected, and the electrical control collaborative matching is realized by iterating the battery parameters.

[0050] In the embodiment, the construction method of the propeller model is as follows:

[0051] The multi-rotor adopts a constant pitch propeller, and the calculation formula of the propeller tension T and torque M is represented as:

[0052]

[0053]

[0054] Wherein, N is the propeller speed, D P is the propeller diameter, ρ is the air density of the flight environment, C T and C M are the dimensionless tension coefficient and torque coefficient respectively;

[0055] Wherein, the air density ρ is calculated by the formula after reading the air pressure P α and temperature T α by the barometer and temperature sensor respectively:

[0056] ρ=1.293×(P a / 101.325 kPa) x (273.15 / (T a + 273.15)).

[0057] Due to the noise reduction limit, the propeller rotation frequency F 桨 satisfies:

[0058] F 桨 = N x n 磁 / 60 ≠ F 机

[0059] wherein F 桨 represents the propeller frequency, F 机 represents the airborne resonant frequency, n 磁 represents the number of clicks of the magnetic pole. By controlling the engine speed, the overall noise reduction is achieved by avoiding the body resonant frequency.

[0060] In the embodiment, the construction method of the motor model is:

[0061] The motor used by the multi-rotor unmanned aerial vehicle is a brushless direct current motor. According to the motor theory, the motor model is represented as:

[0062]

[0063] In the formula, U m is the equivalent voltage of the motor; I m is the equivalent current; M is the motor load torque equal to the propeller torque; N is the motor speed equal to the propeller speed; K V0 is the nominal no-load KV value; I m0 is the nominal no-load current; U m0 is the nominal no-load voltage; and R m is the internal resistance of the motor.

[0064] In the embodiment, the construction method of the electronic speed controller model is:

[0065] For a brushless motor, the actual process is that the current direct current voltage is modulated by the electronic speed controller (referred to as the electric governor) to become a three-phase alternating current signal input to the brushless motor, so that the armature generates an alternating magnetic field to drive the rotor to rotate. The speed range of the brushless motor under the electric governor modulation mainly depends on the motor load torque and the battery voltage.

[0066] The construction method of the electronic speed controller model is:

[0067] The input throttle command σ, input current I e and input voltage U e of the electric governor are calculated:

[0068]

[0069] I e = σI m ;

[0070] U e = U b -I b R b

[0071] wherein, σ is the electric throttle input command, the value range is 0~1; I e represents the input current; U e represents the input voltage; U m and I m are the equivalent voltage and equivalent current of the motor obtained by using the motor model; I b represents the battery current, R b is the battery internal resistance, U b is the battery voltage; R e is the input resistance.

[0072] In the embodiment, the construction method of the battery model is:

[0073] The battery current I b is used to solve the endurance time T b of the multi-rotor, and the maximum discharge current limit of the battery is met; the battery modeling simplifies the actual discharge of the battery, the voltage remains unchanged during the discharge process, and the remaining capacity of the battery changes linearly, and the specific battery model is as follows:

[0074]

[0075]

[0076] wherein, C b is the current battery capacity; C min is the minimum remaining capacity of the battery; I b is the battery current; K b is the maximum discharge rate of the battery.

[0077] In the embodiment, the modified wide-area unmanned aerial vehicle maneuvering model is specifically:

[0078] The battery model in the modified maneuvering model uses an LSTM network structure for prediction:

[0079] When training the network, the data of the previous k time nodes {t n-k+1 ,t n-k+2 ,...,t n} are used as input, and the data of the next node t n+1 is used as output; each node data input includes real-time environmental temperature T real, current battery capacity C b , current time battery resistance R b , motor speed N and discharge current i;

[0080] The trained LSTM network is used to predict the current node data t n-k+1 , t n-k+2 ,...,t n} as input, and the current node data t n+1 ;

[0081] The discharge current i in the current node data is predicted by inputting the node data of the previous k nodes, as the effective discharge current of the working battery;

[0082] In this embodiment, the time interval between adjacent two nodes is 1s. In the experiment, k=10 is taken, that is, by inputting the node data of the previous 10 nodes of the current time, the discharge current i of the current time is predicted, as the effective discharge current of the working battery, and the effective discharge current of the working battery is brought into the battery model to replace the battery current I b , the correction of the battery model is realized. Finally, the robust application and low noise control of the unmanned aerial vehicle in the wide range of 0-5000 meters are realized.

[0083] The above is only a preferred embodiment of the present application, not any form of limitation on the present application, although the present application has been disclosed as above with a preferred embodiment, however, it is not intended to limit the present application, any person skilled in the art, without departing from the scope of the present application, can make some changes or modifications of the above disclosed technical content as equivalent embodiments, but as long as it does not deviate from the technical solution of the present application, according to the technical essence of the present application, any modification, equivalent change and modification of the above embodiments, all still belong to the scope of the present application technical solution.

Claims

1. A wide-range, high-efficiency, low-noise control method based on structural electrical control cooperative matching, characterized in that, The method includes: Constructing multi-rotor UAV maneuver models: Based on environmental information, propeller parameters, motor parameters, electronic speed controller parameters, and battery parameters, construct four maneuver models: propeller model, motor model, electronic speed controller model, and battery model. Correcting the maneuver model: Using an LSTM network structure to predict the effective discharge current of the battery at the current moment, correcting the battery model in the maneuver model, and then iterating the battery parameters to achieve coordinated matching of electrical control; The battery model in the modified maneuver model uses an LSTM network structure for prediction. When training the network, the data from the previous k time points {t} n-k+1 ,t n-k+2 ,...,t n } as input, the next node t n+1 The data is used as the output; the data input for each node includes the real-time ambient temperature T. real Current battery capacity C b The battery internal resistance R at the current moment b Motor speed N and discharge current i; Using a trained LSTM network, with data from the k previous time points {t} at the current time... n-k+1 ,t n-k+2 ,...,t n Using} as input, predict the node data t at the current time. n+1 ; The discharge current *i* in the current node data is predicted by inputting the node data of the first *k* nodes. This *i* is then used as the effective discharge current of the battery during operation. The effective discharge current of the battery during operation is then substituted into the battery model, replacing the battery current *I*. b This allows for the correction of the battery model.

2. The wide-range, high-efficiency, low-noise control method based on structural electrical control cooperative matching according to claim 1, characterized in that, The method for constructing the propeller model is as follows: Multi-rotor rotors use fixed-pitch propellers. The formulas for calculating the propeller thrust T and torque M are as follows: Where N is the propeller speed, and D P Where C is the propeller diameter, ρ is the air density of the flight environment, and C is the density of the air in the flight environment. T and C M These are the dimensionless tensile force coefficient and torque coefficient, respectively; The air density ρ is measured by reading the air pressure P using a barometric altimeter. α Temperature sensor reads temperature T α The following calculation was then performed using a formula: ρ=1.293×(P a / 101.325kPa)×(273.15 / (T a +273.15)).

3. The wide-range, high-efficiency, low-noise control method based on structural electrical control cooperative matching according to claim 1, characterized in that, Due to noise reduction limitations, the propeller rotation frequency F 桨 satisfy: F 桨 =N×n 磁 / 60≠F 机 Among them, F 桨 F represents the propeller frequency. 机 Indicates the airborne resonant frequency, n 磁 This indicates the number of poles of the clicked magnetic pair.

4. The wide-range, high-efficiency, low-noise control method based on structural electrical control cooperative matching according to claim 1, characterized in that, The method for constructing the motor model is as follows: The multi-rotor drone uses a brushless DC motor. According to electrical machinery theory, the motor model is represented as follows: In the formula, U m I is the equivalent voltage of the motor; m The equivalent current is M; the motor load torque is equal to the propeller torque; N is the motor speed, equal to the propeller speed; K V0 The nominal no-load kV value; I m0 Nominal no-load current; U m0 Nominal open-circuit voltage; R m This is the internal resistance of the motor.

5. The wide-range, high-efficiency, low-noise control method based on structural electrical control cooperative matching according to claim 4, characterized in that, The method for constructing the electronic speed governor model is as follows: Calculate and obtain the ESC input throttle command σ and input current I. e and input voltage U e : In the formula, σ represents the electronic speed controller input throttle command, with a value range of 0 to 1; I e Indicates the input current; U e Indicates input voltage; U m and I m These are the equivalent voltage and equivalent current of the motor obtained using the motor model, respectively; I b R represents the battery current. b U is the internal resistance of the battery. b Battery voltage; R e It is the input resistance.

6. The wide-range, high-efficiency, low-noise control method based on structural electrical control cooperative matching according to claim 1, characterized in that, The method for constructing the battery model is as follows: Using battery current I b To solve for the flight time T of the multirotor b And it meets the maximum discharge current limit of the battery; the battery model simplifies the actual discharge of the battery, the voltage remains constant during the discharge process, and the remaining capacity of the battery changes linearly. The specific battery model is as follows: Among them, C b This is the current battery capacity; C min It is the minimum remaining capacity of the battery; I b Battery current; K b It is the battery's maximum discharge rate.

7. The wide-range, high-efficiency, low-noise control method based on structural electrical control cooperative matching according to claim 1, characterized in that, The time interval between two adjacent nodes is 1 second.

8. The wide-range, high-efficiency, low-noise control method based on structural electrical control cooperative matching according to claim 1, characterized in that, In the experiment, k=10, that is, by inputting the node data of the 10 nodes before the current moment, the discharge current i at the current moment is predicted, which is used as the effective discharge current of the battery during operation.

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