Hovering type wireless charging control method for electric power inspection unmanned aerial vehicle

Abstract

The invention provides a hovering type wireless charging control method for an electric power inspection unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicles. According to the technical scheme, firstly, modeling is conducted on a multi-transmitting-stage magnetic coupling type wireless electric energy transmission system, then directional constant-voltage output parameters of a wireless charging system in a coil offset state are determined, and control parameters of the wireless electric energy transmission system of the unmanned aerial vehicle are determined; on the basis, modeling is carried out for unmanned aerial vehicle power inspection power consumption and the charge state, and multi-source disturbance suppression parameters of the unmanned aerial vehicle and attitude track full-time-domain preset time control parameters of the unmanned aerial vehicle are determined. According to the method, a multi-transmitting-stage MCR-WPT system is adopted, a directional constant voltage output model is constructed, meanwhile, a charging system control strategy is optimized, and a wireless charging scheme based on an LCC-S type compensation network is designed. According to the invention, the transmission efficiency and stability of the wireless charging system of the electric power inspection unmanned aerial vehicle are improved, and the hovering wireless charging control precision of the electric power inspection unmanned aerial vehicle is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicles, and in particular relates to a hovering wireless charging control method for an electric power inspection unmanned aerial vehicle. Background Art

[0002] When drones perform various inspection tasks, range and hovering time are important indicators for judging their ability to complete tasks. However, due to related technical bottlenecks represented by battery energy storage and aerodynamics. It is difficult for inspection drones to further increase their range by increasing energy density and reducing unit energy consumption. At present, the flight time of power inspection drones does not exceed 30 minutes, and the flight distance is often within 5km. The range constraint limits the further expansion of drone inspection scenarios. To solve the above problems, it is usually necessary to set up a "drone field" on the inspection path to effectively increase the range of drones. However, the most common way to extend the life of power inspection drones is still to manually replace batteries. When facing more complex drone application scenarios in the future and a higher density of drones in the region, this method will often greatly increase the investment of power grid operators in terms of time, manpower, management, and cost.

[0003] Therefore, in order to give full play to the potential of flexible and rapid power inspection by drones and promote the safe and efficient operation of wide-area power equipment in Qinghai, considering the requirements of fast charging speed, low construction cost, and adaptability to various types of inspection drones, it is urgent to carry out research on hovering magnetically coupled wireless power transmission technology for drones, while considering the whole process of hovering charging, and further construct a drone charging process control, wireless charging power transmission and communication technology system. Summary of the invention

[0004] The technical problem to be solved by the present invention is: how to further improve the control accuracy of hovering wireless charging of power inspection drones and improve the charging efficiency.

[0005] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0006] A hovering wireless charging control method for a power inspection UAV includes the following steps: 1) modeling a multi-transmitter magnetic coupling wireless power transmission system; 2) determining the directional constant voltage output parameters of the wireless charging system under the coil offset state; 3) determining the control parameters of the wireless power transmission system for the power inspection UAV; 4) modeling the power inspection power consumption and charge state of the UAV for wireless charging; 5) determining the multi-source disturbance suppression parameters of the power inspection UAV; and 6) determining the full-time domain preset time control parameters of the attitude trajectory of the hovering wireless charging UAV.

[0007] Preferably, step 1) includes: selecting a series resonance mode to construct an MCR-WPT system; modeling a single-transmitter MCR-WPT system to obtain parameter characteristics that affect output power and transmission efficiency, and then obtaining parameter characteristics of the single-transmitter in the optimal state; based on the single-transmitter MCR-WPT system, adding a transmitter with the same parameters to generate a multi-transmitter MCR-WPT system; using a quantitative analysis method to determine the influence of the input voltage amplitude and phase difference on the output power and transmission efficiency, and using the same method to determine the influence of the operating frequency, coupling coefficient and load resistance on the system transmission performance, and thereby determine the optimal state parameter characteristics of the multi-transmitter system.

[0008] Preferably, step 2) includes: calculating the relationship between the output voltage and transmission efficiency and the radial displacement, axial displacement and deflection angle; adjusting the output voltage and transmission efficiency of the one-end multi-emitter system and the two-end multi-emitter system according to the radial displacement and axial displacement respectively to obtain the output voltage area; using the one-end multi-emitter magnetic resonance wireless power directional constant voltage output; and performing directional constant voltage output by dynamically adjusting the axial distance of the transceiver coil and the center distance of the transmitting coil.

[0009] Preferably, step 3) includes: controlling the output current signal of the secondary side with the compensation capacitor current of the primary side, controlling the output voltage signal of the secondary side with the current output by the inverter of the primary side, and verifying it through simulation; optimizing the information of load and coupling parameters by using a particle swarm algorithm; building a simulation model for simulation analysis to verify that the primary side information is estimated by the secondary side information; and charging the UAV with constant current and constant voltage through simulation.

[0010] As a preferred method, in step 4), the planning layer output is used As the expected input of the UAV trajectory tracking controller, the UAV thrust is completed in the tracking controller And the external torque of the three axes of the drone Calculate and use it as the underlying control input of the drone.

[0011] Preferably, in step 4), a UAV translational dynamics model is established by Newton's equations to establish the relationship between the linear acceleration of the center of mass of the UAV and the force acting on it; wherein The rotation matrix from the drone body coordinate system to the world coordinate system, are roll, pitch, and yaw angles, is the thrust generated by a single propeller; the rotational motion model of the UAV is established through the Euler equation, and the relationship between the resultant torque on the UAV and the UAV's own rotational angular velocity is established; is the UAV’s moment of inertia matrix, is the UAV’s angular velocity, is the distance from the motor to the center of mass, The torque generated by the four motors;

[0012] (1)

[0013] (2)

[0014] (3)

[0015] (4)

[0016] Combined with the drone balanced hovering state , by linearizing the UAV dynamics equation, we get the UAV x, y, z three-axis acceleration expression. In the UAV control process, we use the Euler angle in the world coordinate system. By linearizing the relationship between the UAV's own angular velocity and the Euler angle, we get the UAV angular velocity, which is the differential of the Euler angle (7);

[0017] (5)

[0018] (6)

[0019] (7)

[0020] in , , is a non-matching disturbance.

[0021] As a preferred method, in step 4), the quadcopter is used as the object and the inspection speed of the drone is defined. , blade power , inductive load power , rotor blade tip speed , rotor average induced speed , fuselage drag ratio ,The power consumption model of UAV inspection is as follows:

[0022] (8)

[0023] in , , , They represent blade angular velocity, air density, rotor radius, and drone mass, respectively. is the rotor disc area, is the rotor density, is the equivalent area of ​​the fuselage;

[0024] Define base station transmit power , base station transmitting antenna gain , UAV receiving antenna gain , the wireless charging power is

[0025] (9)

[0026] The calculation model of wireless energy transmission space loss ;

[0027] Define base station locations , when the drone moves to When the effective charging distance is reached, the maximum horizontal distance between the drone and the base station for charging is ;

[0028] When the charging phase starts at When , the distance between the drone and the base station is:

[0029] (10)

[0030] Charging power:

[0031] (11)

[0032] in ;

[0033] Drone from It takes time to reach the top of the base station satisfy ,At this stage, the UAV receives electrical energy as:

[0034] (12)

[0035] When the drone is hovering at height H, the charging power is constant:

[0036] (13)

[0037] During the hover time , the energy received by the drone is:

[0038] (14)

[0039] During the hovering phase, the drone consumes energy as follows:

[0040] (15)

[0041] When the drone reaches the top of the base station and still needs to be charged, the hovering time is:

[0042] (16)

[0043] in Arrival for drones The remaining energy.

[0044] Preferably, in step 5), the dynamic model of the inspection drone is equivalent to the following form:

[0045] (17)

[0046] in is the drone position state variable, is the equivalent input disturbance;

[0047] definition , given a power inspection drone system, the reduced-order extended state observer is constructed as follows:

[0048] (18)

[0049] in , , , , , is an adjustable parameter, and the system matrix is ​​as follows:

[0050] ;

[0051] Design the observer gain as follows:

[0052] (19)

[0053] in , ,when hour, ,when hour, .

[0054] As a preferred embodiment, in step 6), a virtual control function is introduced ,in ; Define tracking error ; Establish a virtual control function for the wireless charging process of the power inspection drone

[0055] (20)

[0056] in , Represents time-varying gain , is a parameter to be determined;

[0057] Inspection drones at preset times Enter the effective distance of the charging base station, and in the hover charging stage, the control error remains at 0, and the preset time Satisfy constraints ,in Effective charging distance for the base station.

[0058] Preferably, in step 6), the mixed obstacle Lyapunov function is defined as

[0059] (twenty one)

[0060] in ;

[0061] Will Taking the derivative with respect to time, we get

[0062] (twenty two)

[0063] for , , introduce the virtual control function, and get

[0064] (twenty three)

[0065] because , the above formula can be further expressed as:

[0066] (twenty four)

[0067] Therefore, the solution of the above formula is

[0068] (25)

[0069] Thus, we get ;for , ,get , ;when hour, ,thus, ; Get the inspection drone at the preset time Enter the effective distance of the charging base station.

[0070] The present invention provides a hovering wireless charging control method for a power inspection drone. The innovations of this technical solution are mainly reflected in the following aspects:

[0071] (1) Multi-transmitter MCR-WPT system

[0072] The present invention analyzes the coupled resonance theory of MCR-WPT technology, and then calculates the relationship between the various parameters of the single-emitter system and the transmission efficiency and output power based on circuit theory. In order to improve the transmission efficiency and optimize the output power, a multi-emitter method is proposed. A quantitative analysis is conducted on the multi-emitter magnetic resonance wireless power transmission system, and the parameters that affect the system transmission performance are comprehensively considered. On this basis, the single-emitter and multi-emitter systems are compared in three dimensions: output power, transmission efficiency, and effective transmission area. The results show the superiority of the multi-emitter system.

[0073] (2) Directional constant voltage output model

[0074] Aiming at the relative position offset problem of the transceiver coils when charging the drone, a directional constant voltage output strategy for multi-transmitter magnetic resonance wireless power transmission is proposed. The influence of axial displacement, radial displacement and angle deflection on the transmission performance of a single-transmitter system is studied, and then the method is extended to a multi-transmitter system to explore the change law of the axial displacement and radial displacement of the multi-transmitter system on the system performance. Determine the adjustment mechanism to maintain directional constant voltage output under the conditions of changing the center spacing of the transmitting coil, the axial distance of the transceiver coil and the load resistance.

[0075] (3) Optimize charging system control strategy

[0076] In view of the characteristics of the wireless charging load battery, the charging current and voltage are controlled by phase shifting and voltage regulation. Based on the reflection impedance theory, the output current signal of the secondary side is controlled by the compensation capacitor current of the primary side, and the output voltage signal of the secondary side is controlled by the current output by the inverter of the primary side. In view of the information of the load and coupling parameters, the optimization based on the particle swarm algorithm is adopted to transform the parameter identification problem into the algorithm optimization problem, which solves the problem that the traditional identification method needs to add additional equipment, increase the complexity of the system, and affect the transmission efficiency of the system.

[0077] (4) Design of wireless charging solution based on LCC-S compensation network

[0078] The various problems that multi-rotor drones will face in hovering wireless charging are analyzed, and the operating status of the compensation network under unexpected conditions is analyzed theoretically and verified experimentally. Since the LCC-S compensation network can solve the primary and secondary current surge problem during instantaneous overload through special design. Based on the wireless charging solution of the LCC-S compensation network, the engineering problems in the system MOSFET drive design are analyzed and the corresponding design solutions are given; a power control method based on a phase-shifted full-bridge is designed, the problems are analyzed, and solutions are proposed; and it is planned to verify the feasibility of the MOSFET design solution and the phase-shifted full-bridge power control solution through experiments.

[0079] The beneficial effects of the present invention include the following aspects:

[0080] (1) Improved the transmission efficiency and stability of the wireless charging system for power inspection drones

[0081] In order to solve the transmission efficiency problem of wireless charging system for electric drones, a multi-emitter magnetic resonance solution is proposed. Circuit modeling is performed on single-emitter and multi-emitter systems to analyze the relationship between transmission performance and system parameters. The maximum derivative method is used to obtain the optimal coupling coefficient and optimal load resistance, thereby improving transmission efficiency and output power and expanding the transmission area. In order to solve the problem of charging coil offset, a directional constant voltage output strategy is proposed to establish the relationship between transmission performance and axial displacement, radial displacement, and deflection angle, and to extend it to multi-emitter systems to establish a regulation mechanism. In view of the characteristics of lithium battery charging, the system adopts constant current and constant voltage control. Considering the impact of high-voltage transmission lines on communication equipment, a parameter identification and constant current and constant voltage control strategy based on bilateral LCC compensation is proposed. The secondary side parameters are identified through the reflection impedance theory, and phase shift voltage control is performed on the primary side.

[0082] (2) Improved the control accuracy of wireless charging for power inspection drones

[0083] Evaluate the impact of non-matching disturbances such as electromagnetic interference and wind speed changes on flight stability and energy efficiency, and provide a theoretical basis for the subsequent development of control strategies. A disturbance suppression strategy based on a full-time preset time-reduced extended state observer is proposed to effectively estimate and suppress multi-source disturbances such as electromagnetic interference and wind disturbances. A preset time control strategy based on obstacle Lyapunov technology is developed to manage the charging posture and trajectory of the drone during hovering wireless charging, ensuring that the drone has the optimal posture and position when it reaches the charging station, improving charging efficiency, and effectively suppressing electromagnetic interference and wind disturbances to ensure the stability and safety of the charging process.

[0084] (3) Optimized the hovering charging power setting and communication scheme of the power inspection drone

[0085] By optimizing the hovering wireless charging system of drones, the charging power and efficiency are improved, ensuring that drones can operate continuously for a long time during the inspection process. The communication technology between drones and charging base stations is optimized to ensure the timely transmission and feedback of charging control and monitoring data, and to ensure the smooth progress of drone inspection tasks. An intelligent control algorithm is developed to achieve effective power control and optimization of communication transmission when the drone is hovering, thereby ensuring the stability and safety of the drone charging process and improving operating efficiency. Improve the reliability and efficiency of the use of power inspection drones, provide more convenient and efficient solutions for power inspection work, and promote the application and promotion of drones in the power industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 It is the technical roadmap of the design and control strategy of the wireless charging system of the power inspection drone based on magnetic resonance in the present invention.

[0087] Figure 2 This is a circuit model diagram of a single emitter system in the present invention.

[0088] Figure 3 This is the system block diagram of the control strategy part in the present invention.

[0089] Figure 4 It is the technical roadmap of the full-time domain preset time control strategy for hovering wireless charging of power inspection drones in the present invention. DETAILED DESCRIPTION

[0090] The specific embodiments of the present invention will be described in detail below. In order to avoid too much unnecessary details, the well-known structures or functions will not be described in detail in the following examples. The approximate language used in the following examples can be used for quantitative expression, indicating that the quantity can be allowed to have a certain change without changing the basic function. Unless otherwise defined, the technical and scientific terms used in the following examples have the same meaning as those generally understood by those skilled in the art to which the present invention belongs.

[0091] 1. Design and control strategy of wireless charging system for power inspection drone based on magnetic resonance

[0092] Firstly, based on the magnetic coupling resonance theory, a multi-transmitter wireless power transmission system is constructed and optimized, and the optimal parameters are determined through quantitative analysis; secondly, a directional constant voltage output strategy is proposed under the coil offset state, and stable voltage output is achieved through dynamic adjustment; finally, a wireless charging system strategy based on phase-shift voltage regulation control is proposed for power inspection drones, and its effectiveness is verified through simulation to meet the constant current and constant voltage charging needs of drones. Figure 1 shown.

[0093] (1) Modeling and design of multi-transmitter magnetically coupled wireless power transfer system

[0094] First, based on the magnetic coupling resonance theory, the series resonance method is selected to build the MCR-WPT system. The single-transmitter MCR-WPT system is modeled to obtain the parameter characteristics that affect the output power and transmission efficiency, and finally the parameter characteristics of the single-transmitter optimal state are obtained.

[0095] Based on the single-emitter MCR-WPT system (the circuit model of the single-emitter system is as follows Figure 2As shown in the figure), a transmitter with the same parameters is added to generate a multi-transmitter MCR-WPT system. A quantitative analysis method is used to analyze the impact of input voltage amplitude and phase difference on output power and transmission efficiency. In the same way, the impact of operating frequency, coupling coefficient and load resistance on system transmission performance is quantitatively analyzed to determine the optimal state parameter characteristics of the multi-transmitter system. Finally, the transmission performance of the single-transmitter system and the multi-transmitter system is compared to prove the superiority of the multi-transmitter system.

[0096] (2) Directional constant voltage output strategy of wireless charging system under coil offset state

[0097] First, based on the coil mutual inductance calculation theory, the relationship between the output voltage and transmission efficiency and the radial displacement, axial displacement and deflection angle is calculated. Secondly, in order to solve the influence of the change of axial displacement and radial displacement on the output voltage and transmission efficiency of the system, the output voltage and transmission efficiency of the one-end multi-emitter system and the two-end multi-emitter system are analyzed from the radial displacement and axial displacement characteristics respectively, and the stable output voltage area is obtained. Considering the space condition limitations of general application occasions, a one-end multi-emitter magnetic resonance wireless power directional constant voltage output is generally adopted. The directional constant voltage output is achieved by dynamically adjusting the axial distance of the transceiver coil and the center distance of the transmitting coil.

[0098] (3) Control strategy of wireless power transmission system for power inspection drones

[0099] Based on the wireless charging system of magnetic resonance power inspection drone, the corresponding control strategy is proposed. The control scenario suitable for wireless power is selected by analyzing and comparing the applicable scenarios of DC-DC control, frequency modulation control, compensation network control and phase shift voltage regulation control.

[0100] A phase-shift voltage regulation control scheme is adopted. According to the principle of reflected impedance, the output current signal of the secondary side is controlled by the compensation capacitor current of the primary side. The output voltage signal of the secondary side is controlled by the current output by the inverter on the primary side, and verified by simulation. In view of the information of load and coupling parameters, an optimization based on a particle swarm algorithm is adopted to transform the parameter identification problem into an algorithm optimization problem, solving the problem that traditional identification methods require the addition of additional equipment, increase the complexity of the system, and affect the transmission efficiency of the system. Finally, a simulation model is built for simulation analysis to verify that the primary side information can be estimated by the secondary side information; according to the characteristics and requirements of the inspection drone, the drone is charged with constant current and constant voltage through simulation to verify the accuracy of the control strategy (the control strategy system block diagram is shown in the figure). Figure 3 as shown).

[0101] 2. Full-time preset time control strategy for hovering wireless charging of power inspection drones

[0102] Technical route such as Figure 4 shown.

[0103] (1) Power consumption and state of charge modeling for wireless charging drone power inspection

[0104] A cascade closed-loop control architecture is established to achieve flight trajectory tracking control of the inspection drone. The drone position control and attitude control are the core links of drone tracking control. The planning layer output As the expected input of the UAV trajectory tracking controller, the UAV thrust is completed in the tracking controller And the external torque of the three axes of the drone Calculate and use it as the underlying control input of the drone.

[0105] As a nonlinear system, the UAV translational dynamics model is first established through Newton's equations to establish the relationship between the linear acceleration of the center of mass of the UAV and the force acting on it; The rotation matrix from the drone body coordinate system to the world coordinate system, are roll, pitch, and yaw angles, is the thrust generated by a single propeller. Secondly, the rotational motion model of the UAV is established through the Euler equation, and the relationship between the resultant torque on the UAV and the UAV's own rotational angular velocity is established; is the UAV’s moment of inertia matrix, is the UAV’s angular velocity, The distance from the motor to the center of mass, Torque generated by four motors.

[0106] (A.1)

[0107] (A.2)

[0108] (A.3)

[0109] (A.4)

[0110] Considering that the slow-speed drone usually moves at a small angle and small posture, each state of the drone is a balanced state. Combined with the balanced hovering state of the drone By linearizing the UAV dynamics equation, we get the UAV x, y, z acceleration expressions. In the UAV control process, the Euler angle in the world coordinate system is used. By linearizing the relationship between the UAV's own angular velocity and the Euler angle, we get the UAV angular velocity, which is the differential of the Euler angle (A.7).

[0111] (A.5)

[0112] (A.6)

[0113] (A.7)

[0114] in , , is a non-matching disturbance.

[0115] Taking the quadcopter as the object, define the inspection speed of the drone , blade power , inductive load power , rotor blade tip speed , rotor average induced speed , fuselage drag ratio ,The UAV inspection power consumption model is as follows

[0116] (A.8)

[0117] in , , , They represent blade angular velocity, air density, rotor radius, and drone mass, respectively. is the rotor disc area, is the rotor density, is the equivalent area of ​​the fuselage.

[0118] Define base station transmit power , base station transmitting antenna gain , UAV receiving antenna gain , the wireless charging power is

[0119] (A.9)

[0120] The calculation model of wireless energy transmission space loss .

[0121] Define base station locations , when the drone moves to When the effective charging distance is reached, the maximum horizontal distance between the drone and the base station for charging is , otherwise the energy harvester cannot be triggered due to excessive energy space transmission loss.

[0122] Assume that the charging phase starts at , the distance change between the drone and the base station can be described as

[0123] (A.10)

[0124] The charging power is

[0125] (A.11)

[0126] in .

[0127] Drone from It takes time to reach the top of the base station satisfy ,At this stage, the UAV receives power

[0128] (A.12)

[0129] When the drone is hovering at height H, the charging power is constant:

[0130] (A.13)

[0131] Therefore, during the hover time , the drone receives energy

[0132] (A.14)

[0133] During the hovering phase, the drone consumes energy

[0134] (A.15)

[0135] When the drone reaches the top of the base station, it still needs to be charged, so the hovering time can be calculated as

[0136] (A.16)

[0137] in Arrival for drones The remaining energy.

[0138] Define base station locations , when the drone moves to When the effective charging distance is reached, the maximum horizontal distance between the drone and the base station for charging is , otherwise the energy harvester cannot be triggered due to excessive energy space transmission loss.

[0139] (2) UAV multi-source disturbance suppression strategy for large-scale power inspection

[0140] According to the equivalent input principle, the inspection UAV dynamics model (5) can be equivalent to the following form:

[0141] (A.17)

[0142] in is the drone position state variable, is the equivalent input disturbance.

[0143] definition , given a power inspection drone system, the reduced-order extended state observer is constructed as follows:

[0144] (A.18)

[0145] in , , , , , is an adjustable parameter, and the system matrix is ​​as follows:

[0146]

[0147] Design the observer gain as follows:

[0148] (A.19)

[0149] in , ,when hour, ,when hour, .

[0150] If the observer gain is properly configured so that is Hurwitz, then the observation error is within the preset time , converges to 0, and The error remains at 0.

[0151] when , defining the observation error , then the error system dynamics can be described as

[0152] (A.20)

[0153] in .

[0154] for , introducing the transformation , according to the observation error system, we have

[0155] (A.21)

[0156] in .

[0157] Select Parameters , then

[0158] (A.22)

[0159] The observation error equation is equivalent to

[0160] (A.23)

[0161] in .

[0162] Choose the right make and matrix For Hurwitz, therefore, Asymptotically stable, there is , ,for ,have

[0163] (A.24)

[0164] in addition, arrive The transformation expression is:

[0165] (A.25)

[0166] in , is a lower triangular matrix with .

[0167] Because when hour, is 1, when Approaching When , it decreases monotonically to 0, , therefore, the observation error is within the preset time Converges to 0.

[0168] when , , the error dynamic system is

[0169] (A.26)

[0170] definition , , establish the Lyapunov function

[0171] (A.27)

[0172] For a given Make For Hurwitz, , then the following inequality holds

[0173] (A.28)

[0174] therefore, , Established. Because, .when hour, Then, the observation error , the proof is complete.

[0175] (3) Full-time preset time control strategy for attitude trajectory of hovering wireless charging UAV

[0176] Introducing virtual control functions ,in . Define tracking error . Establish a virtual control function for the wireless charging process of the power inspection drone

[0177] (A.29)

[0178] in , Represents time-varying gain , It is a parameter to be determined.

[0179] Then, the inspection drone will Enter the effective distance of the charging base station, and in the hover charging stage, the control error remains at 0, and the preset time Satisfy constraints ,in Effective charging distance for the base station.

[0180] Define the mixed obstacle Lyapunov function as

[0181] (A.30)

[0182] in .

[0183] Will Taking the derivative with respect to time, we get

[0184] (A.31)

[0185] for , , introducing the virtual control function, we can get

[0186] (A.32)

[0187] because , the above formula can be further expressed as:

[0188] (A.33)

[0189] Therefore, the solution of the above equation can be defined as

[0190] (A.34)

[0191] So, we can get ,So Established. , , can be obtained , .when hour, ,therefore, The inspection drone can be obtained at the preset time Enter the effective distance of the charging base station, and during the hovering charging stage, the control error remains at 0.

[0192] 3. Hovering wireless charging power and communication optimization technology for power inspection drones

[0193] This project first analyzes the characteristics of the hovering wireless charging technology of power inspection drones, constructs the LCC-S compensation network used for wireless charging based on the LCC-S compensation network, and improves the stability and reliability of drone wireless charging; secondly, a primary and secondary side bidirectional communication solution based on frequency shift keying and shift keying is proposed, and a communication system model is constructed to avoid the interference that may be caused by traditional wireless communication modules at a very low cost. Finally, a charging system model is constructed based on the event trigger strategy, which is verified by experiments to achieve a stable effect.

[0194] (1) Analysis of technical characteristics of hovering wireless charging of power inspection drones

[0195] The LCC-S compensation network used in wireless charging based on the LCC-S compensation network includes a MOSFET drive design method and a power control method based on a phase-shifted full bridge. The MOSFET drive design scheme will introduce the bootstrap boost scheme and the independent isolation drive scheme, as well as the drive dead zone; the power control method based on the phase-shifted full bridge will introduce the implementation of the phase-shifted full bridge and the soft switching problem. Clarify the input and output parameters of the wireless charging system. The input of the system is 220V / 50Hz ordinary mains power; the charging object is the common 4S aviation lithium battery with a nominal voltage of 14.8V; the charging output should be greater than 16V and the power should be higher than 120W. The input end rectifies the mains power into DC power, and then converts the DC power into 215kHz AC power through the inverter and sends power through the primary coil; the receiving end rectifies the 215kHz AC power into DC power and outputs it directly to the battery.

[0196] (2) Wireless charging power setting scheme based on event triggering strategy

[0197] The primary actuator transmits electromagnetic power to the secondary side through the coupling of the primary and secondary coils. The power transmission process will be subject to various external interferences. The secondary observer judges the charging current state of the secondary side according to the event trigger conditions set in advance by the system. If the secondary charging current state meets the event trigger conditions, the secondary observer releases an event trigger signal to the primary actuator. After reading the event trigger signal released by the secondary observer, the primary controller adjusts the transmission power of the primary coil according to the control rules set in advance by the system, thereby achieving the purpose of stabilizing the secondary power output.

[0198] The external interference in power transmission is mainly caused by the attitude disturbance of the multi-rotor drone when it is hovering. The attitude disturbance will change the relative position of the primary and secondary coils, thereby causing the mutual inductance M of the primary and secondary coils to change. When the primary coil current does not change, if the primary and secondary coil mutual inductance M is disturbed, the secondary output voltage It will be disturbed accordingly. If the system load is a battery, the output power of the secondary side, that is, the charging power of the battery, will fluctuate accordingly. This fluctuation will affect the charging state of the battery, and the controller needs to adjust it in time. In addition to posture disturbances, the primary and secondary coils will also produce temperature rise during the power transmission process. This temperature rise will affect the impedance of each component in the primary and secondary coils. The change in impedance will also cause the coupling state of the primary and secondary coils to change, and then cause the secondary output power to change. In addition, if the system load is a battery, as the system charging process proceeds, the battery power will gradually increase. This change can be regarded as a change in the impedance of the system load. As a result, the output state of the system secondary will also change accordingly. Possible external interferences include power fluctuations of the AC input, which will cause the primary input voltage to The voltage is fluctuating, thus affecting the secondary output. If a PFC voltage regulator circuit is provided at the mains input of the circuit, the impact of the fluctuation on the overall power transmission of the system can be reduced.

[0199] (3) Bidirectional communication scheme between primary and secondary sides based on frequency shift keying and position shift keying

[0200] Based on the primary-to-secondary signal transmission scheme of frequency shift keying, the primary side modifies the operating frequency of the inverter circuit to carry the data signal in the conversion of the transmission frequency, and the secondary side decouples the data signal by reading the frequency conversion of the voltage in the receiving coil. Due to the change of the operating frequency, a basic parameter of wireless charging, all the working states of the system will change accordingly. Among them, whether the primary side phase-shifted full-bridge inverter circuit will enter a non-soft switching state, and whether the voltage of components such as the current in the primary and secondary coils and the resonant capacitor will increase sharply are all issues that need to be focused on in engineering applications. Therefore, in the process of signal transmission, the smaller the change in the operating frequency, the smaller the impact on the overall operation of the system, but the higher the accuracy requirement for the secondary side judgment, the designer needs to make a trade-off between the two to achieve a good balance.

[0201] The signal transmission scheme from the secondary side to the primary side based on shift keying can be simply described as follows: during the charging process, the secondary side actively modifies the load state to change the charging power, and the digital signal to be transmitted is carried by the change of the secondary side output power. The primary side detects the change of its own output power and decouples the change into the transmitted digital signal. The load change of the secondary side should not generate additional active power, resulting in a decrease in the overall transmission efficiency of the wireless charging system. In addition, the load change should not cause various adverse factors to the secondary side output object, such as overvoltage, overcurrent, surge, etc. At the same time, in actual engineering applications, designers can make full use of the voltage clamping characteristics when the load is a battery for circuit design.

[0202] 4. Development of autonomous charging system for power inspection drones based on wireless power transmission technology

[0203] First, the beacon is designed according to the image navigation method, the visual navigation coordinate system and camera imaging model are established, the beacon feature circle is detected by Hough transform, the corner points of the beacon rectangle are detected by Harris algorithm, and the method of solving the relative position between the UAV and the landing point is determined; in the case of night navigation, the guidance beacon light is designed by using light source auxiliary positioning. According to the requirements of the autonomous landing system, the hardware and software design is carried out. The hardware part mainly includes the design of the follower leg, the selection of the actuator, the installation of the quadcopter platform, and the circuit design of each module of the system. For the software part, the overall program design is carried out for the system integrated development environment.

[0204] Then, the landing coordinates of the drone are roughly located through the camera according to the principle of perspective transformation; the coordinates of the drone are accurately located through a scanning positioning method of laser distance sensing; finally, combined with the charging power self-test module, a single-coil charging method is adopted, and a two-axis sliding platform is used to adaptively align the transmitting coil of the charging module and the receiving coil of the drone to ensure the highest charging efficiency of the system.

[0205] In order to verify the reliability and charging efficiency of the autonomous charging system of the UAV, a test and verification environment for the autonomous charging system of the UAV was built. The functions of the system were realized through the joint debugging of software and hardware. Multiple flight tests were carried out. The program controlled the UAV to automatically land on the charging pile platform, and the charging module automatically aligned with the UAV. The flight was navigated according to the beacon image, and the landing stability was tested using the function of the follower leg; the error of the autonomous positioning of the UAV was measured according to the positioning method based on perspective transformation and the laser distance sensor scanning positioning method, and the alignment accuracy of the charging module was verified; finally, the system charging device was subjected to a wireless power transmission test according to the magnetic resonance coupling topology, and the charging efficiency of the system was tested and calculated.

[0206] The embodiments of the present invention are described in detail above, but the contents are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the scope of the present invention shall be included in the protection scope of the present invention.

Claims

1. A hovering wireless charging control method for a power inspection drone, characterized in that: The following steps are involved: 1) Modeling of multi-transmitter magnetically coupled wireless power transfer system; 2) Determine the directional constant voltage output parameters of the wireless charging system under the coil offset state; 3) Determine the control parameters of the wireless power transmission system of the power inspection drone; 4) Model the power consumption and charge state of the power inspection drone for wireless charging; 5) Determine the multi-source disturbance suppression parameters of the power inspection UAV; 6) Determine the full-time domain preset time control parameters of the attitude trajectory of the hovering wireless charging UAV.

2. The hovering wireless charging control method for a power inspection drone according to claim 1 is characterized in that: Step 1) includes: selecting a series resonance method to construct an MCR-WPT system; modeling a single-transmitter MCR-WPT system to obtain parameter characteristics that affect output power and transmission efficiency, and then obtaining parameter characteristics of the single-transmitter in the optimal state; based on the single-transmitter MCR-WPT system, adding a transmitter with the same parameters to generate a multi-transmitter MCR-WPT system; using a quantitative analysis method to determine the impact of input voltage amplitude and phase difference on output power and transmission efficiency, and using the same method to determine the impact of operating frequency, coupling coefficient and load resistance on system transmission performance, and thereby determine the optimal state parameter characteristics of the multi-transmitter system.

3. The hovering wireless charging control method for a power inspection drone according to claim 1 is characterized in that: Step 2) includes: calculating the relationship between the output voltage and transmission efficiency and the radial displacement, axial displacement and deflection angle; adjusting the output voltage and transmission efficiency of the one-end multi-emitter system and the two-end multi-emitter system from the radial displacement and axial displacement respectively to obtain the output voltage area; using the one-end multi-emitter magnetic resonance wireless power directional constant voltage output; and performing directional constant voltage output by dynamically adjusting the axial distance of the transceiver coil and the center distance of the transmitting coil.

4. The hovering wireless charging control method for a power inspection drone according to claim 1 is characterized in that: Step 3) includes: controlling the output current signal of the secondary side with the compensation capacitor current of the primary side, controlling the output voltage signal of the secondary side with the current output by the inverter of the primary side, and verifying it through simulation; optimizing the information of load and coupling parameters by using a particle swarm algorithm; building a simulation model for simulation analysis to verify that the primary side information is estimated by the secondary side information; and charging the drone with constant current and constant voltage through simulation.

5. The hovering wireless charging control method for a power inspection drone according to claim 1 is characterized in that: In step 4), the planning layer output p is used des , As the expected input of the UAV trajectory tracking controller, the UAV thrust u1 and the UAV three-axis combined external torque u2 are calculated in the tracking controller as the UAV bottom-level control input.

6. The hovering wireless charging control method for a power inspection drone according to claim 5 is characterized in that: In step 4), the UAV translational dynamics model is established by Newton's equations to establish the relationship between the linear acceleration of the center of mass of the UAV and the force acting on it; w R b is the rotation matrix from the drone body coordinate system to the world coordinate system, φ, θ, ψ are roll, pitch, and yaw angles respectively, F i is the thrust generated by a single propeller; the rotational motion model of the UAV is established through the Euler equation, and the relationship between the resultant torque on the UAV and the UAV's own rotational angular velocity is established; where I b is the UAV’s moment of inertia matrix, is the UAV’s angular velocity, l is the distance from the motor to the center of mass, M i The torque generated by the four motors; u1=F1+F2+F3+F4 (2) Combined with the drone's balanced hovering state (φ0~0,θ0~0,u 1,0 ~mg), by linearizing the UAV dynamics equation, the UAV x, y, z three-axis acceleration expression is obtained. In the process of UAV control, the Euler angle in the world coordinate system is used. By linearizing the relationship between the UAV's own angular velocity and the Euler angle, the UAV angular velocity is obtained, which is the differential of the Euler angle (7); Among them, d1, d2, and d3 are non-matching disturbances.

7. The hovering wireless charging control method for a power inspection drone according to claim 6 is characterized in that: In step 4), take the quadcopter as the object and define the inspection speed of the drone , blade power , inductive load power , rotor blade tip speed , rotor average induced speed , fuselage drag ratio ,The power consumption model of UAV inspection is as follows: (8) in , , , They represent blade angular velocity, air density, rotor radius, and drone mass, respectively. is the rotor disc area, is the rotor density, is the equivalent area of ​​the fuselage; Define base station transmit power , base station transmitting antenna gain , UAV receiving antenna gain , the wireless charging power is (9) The calculation model of wireless energy transmission space loss ; Define base station locations , when the drone moves to When the effective charging distance is reached, the maximum horizontal distance between the drone and the base station for charging is ; When the charging phase starts at When , the distance between the drone and the base station is: (10) Charging power: (11) in ; Drone from It takes time to reach the top of the base station satisfy ,At this stage, the UAV receives electrical energy as: (12) When the drone is hovering at height H, the charging power is constant: (13) During the hover time , the energy received by the drone is: (14) During the hovering phase, the energy consumed by the drone is: (15) When the drone reaches the top of the base station and still needs to be charged, the hovering time is: (16) in Arrival for drones The remaining energy.

8. The hovering wireless charging control method for a power inspection drone according to claim 1 is characterized in that: In step 5), the dynamic model of the inspection drone is equivalent to the following form: where x = [x i ] T is the drone position state variable, d e is the equivalent input disturbance; Define x n+1 =d e , given a power inspection drone system, the reduced-order extended state observer is constructed as follows: in α i =iα-(i-1) is an adjustable parameter, and the system matrix is ​​as follows: Design the observer gain as follows: in a i,i =1, when i<j, a i,j =0, when i>j, 9. The hovering wireless charging control method for a power inspection drone according to claim 1 is characterized in that: In step 6), a virtual control function α is introduced i , where α0 = y d , α n =u; define tracking error e i =x i -α i-1 ; Establish a virtual control function for the wireless charging process of the power inspection drone in Represents time-varying gain k i is a parameter to be determined; The inspection drone will be p Enter the effective distance of the charging base station, and in the hover charging stage, the control error remains at 0, and the preset time T p Satisfy constraints where x c Effective charging distance for the base station.

10. The hovering wireless charging control method for a power inspection drone according to claim 9 is characterized in that: In step 6), the mixed obstacle Lyapunov function is defined as in Taking the derivative of V with respect to time, we get For t∈[t0,T p ), θ=1, introduce the virtual control function, and get because The above formula can be further expressed as: Therefore, the solution of the above formula is Thus, we get for θ = 0, we get V(T p )=0; when When V(t)=0, Therefore, e i =0; get the inspection drone at the preset time T p Enter the effective distance of the charging base station.

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