A control method and system for wireless charging of inspection drones

Abstract

The embodiment of the present application discloses a control method and system for wireless charging of a patrol drone, which can ensure that the drone enters the effective charging distance of the charging base station within the preset time and has strong robustness. The method of the embodiment of the present application includes: querying the target base station closest to the target drone; obtaining the base station parameters of the target base station, the drone parameters of the target drone, and the target distance between the target drone and the target base station; calculating the patrol power and the charging power; calculating the preset time according to the current power, patrol power and charging power of the target drone; inputting the current first control information into the preset extended state observer, and using the extended state observer to calculate the state information and disturbance information of the target drone, the disturbance information including the non-matching disturbance; inputting the preset time, state information and disturbance information into the preset disturbance suppression controller, and obtaining the second control information output by the disturbance suppression controller.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of drone charging, and in particular to a control method and system for wireless charging of an inspection drone. Background Art

[0002] As we all know, as the main carrier of power transmission, the scale of the power transmission network has been expanding with the rapid growth of economic development and power demand, forming a national power transmission network with UHV and EHV as the main body. Power transmission line equipment has been exposed to the wild for a long time, and is affected by continuous mechanical tension, lightning flashover, material aging, icing and human factors, resulting in tower collapse, broken strands, wear, corrosion, dancing and other phenomena. Regular inspection of power lines has extremely important practical significance. With the rapid development and cross-penetration of communication technology, computer science, and unmanned system technology, drones have gained unprecedented development opportunities. By equipping infrared imagers, cameras and other detection devices, they have solved the problem of low efficiency of manual inspections, broken through the physiological limits of humans in extreme environments, and provided strong support and supplement for the intelligent and information development of new power systems. In practical applications, the flight time and range of drone inspections are limited by the battery life. If the battery life is increased by increasing the battery capacity, the power consumption will increase, and the problem of limited inspection power cannot be fundamentally solved. Through wireless charging technology, it is possible to flexibly, efficiently and timely replenish power, so that drones can better adapt to large-scale power inspections. In this context, wireless charging for power inspection, developing drone control technology, and improving work efficiency have become new research hotspots.

[0003] Facing the large-scale and complex power inspection environment, drones are faced with electromagnetic interference, environmental interference, wind interference, unmodeled dynamics and other factors, which seriously affect the normal operation of inspection equipment and data transmission accuracy in a non-matching form, and even cause system instability. In order to eliminate the disturbance effect, some control technologies use the permissibility method to simplify the disturbance into norm-bounded uncertainty, and use this as a constraint to design control methods so that the disturbance effect meets the performance index, making the system insensitive to it, or making its negative impact meet certain performance indicators, lacking the ability to actively compensate for disturbances. In addition, facing the wireless charging mechanism, during the inspection process, when the drone's state of charge (SoC) is less than the threshold, it needs to go from the current position to the charging base station. At this time, electric energy becomes a scarce resource. The existing control method only sets a higher threshold, but fails to consider the relationship between wireless charging and drone control performance during the inspection process. Under the constraint of limited power, it cannot guarantee that the drone reaches the effective charging distance within the charging base station within the set time, and the robustness is poor. Summary of the invention

[0004] The embodiments of the present application provide a control method and system for wireless charging of an inspection drone, which can ensure that the drone enters the effective charging distance of a charging base station within a preset time and has strong robustness.

[0005] A first aspect of an embodiment of the present application provides a control method for wireless charging of an inspection drone, comprising:

[0006] When the target UAV's battery level reaches the charging threshold, query the target base station closest to the target UAV;

[0007] Acquire base station parameters of the target base station, drone parameters of the target drone, and a target distance between the target drone and the target base station;

[0008] Calculating the inspection power according to the drone parameters and the preset inspection power consumption model, and calculating the charging power according to the base station parameters and the preset wireless charging model;

[0009] Calculate the preset time according to the current power of the target drone, the inspection power and the charging power;

[0010] Inputting the current first control information into a preset extended state observer, and using the extended state observer to calculate the state information and disturbance information of the target UAV, wherein the disturbance information includes a non-matching disturbance;

[0011] The preset time, the state information and the disturbance information are input into a preset disturbance suppression controller to obtain second control information output by the disturbance suppression controller.

[0012] Optionally, before querying the target base station closest to the target drone, the method further includes:

[0013] Establish a dynamic model for UAV power inspection;

[0014] An extended state observer is designed based on the UAV power inspection dynamics model.

[0015] Optionally, establishing a UAV power inspection dynamics model includes:

[0016] Establish the UAV translational dynamics model through Newton equations;

[0017] Establish the rotational motion model of the drone through the Euler equation;

[0018] Performing linearization processing on the translational dynamics model of the UAV to obtain a three-axis acceleration expression of the UAV, wherein the three-axis acceleration expression of the UAV includes information of non-matching disturbance;

[0019] In the process of UAV control, the Euler angle in the world coordinate system is used to generate the relationship between the angular velocity of the UAV and the Euler angle.

[0020] Optionally, the designing of an extended state observer according to the UAV power inspection dynamics model includes:

[0021] Generate an equivalent formula according to the UAV translational dynamics model, wherein the equivalent formula includes the UAV position state variable and the equivalent input disturbance;

[0022] An extended state observer is constructed according to the equivalent formula.

[0023] Optionally, before querying the target base station closest to the target drone, the method further includes:

[0024] A virtual control function is established for the wireless charging process of the power inspection drone, wherein the virtual control function includes information on the control error;

[0025] A hybrid barrier function is defined such that the control error remains within preset constraints.

[0026] Optionally, the calculating the inspection power according to the drone parameters and a preset inspection power consumption model includes:

[0027] extracting blade power, inductive load power, rotor tip speed, rotor average induced speed, fuselage drag ratio, blade angular velocity, air density, rotor radius, drone mass, rotor disc area, rotor density, and fuselage equivalent area from said drone parameters;

[0028] The inspection power is calculated by inputting the blade power, the inductive load power, the rotor tip speed, the rotor average induced speed, the fuselage drag ratio, the blade angular velocity, the air density, the rotor radius, the UAV mass, the rotor disc area, the rotor density, and the fuselage equivalent area into a preset inspection power consumption model.

[0029] Optionally, the calculating the charging power according to the base station parameters and a preset wireless charging model includes:

[0030] Obtaining base station transmit power and base station transmit antenna gain from the base station parameters;

[0031] Obtaining the drone receiving antenna gain from the drone parameters;

[0032] The charging power is calculated by inputting the base station transmission power, the base station transmission antenna gain and the drone receiving antenna gain into a preset wireless charging model.

[0033] A second aspect of an embodiment of the present application provides a control device for wireless charging of an inspection drone, comprising:

[0034] A query unit, configured to query a target base station closest to the target drone when the power of the target drone reaches a charging threshold;

[0035] An acquisition unit, configured to acquire base station parameters of the target base station, drone parameters of the target drone, and a target distance between the target drone and the target base station;

[0036] A first calculation unit, configured to calculate the inspection power according to the drone parameters and a preset inspection power consumption model, and to calculate the charging power according to the base station parameters and a preset wireless charging model;

[0037] A second calculation unit, configured to calculate a preset time according to the current power of the target UAV, the inspection power, and the charging power;

[0038] A first input unit, configured to input the current first control information into a preset extended state observer, and use the extended state observer to calculate the state information and disturbance information of the target UAV, wherein the disturbance information includes a non-matching disturbance;

[0039] The second input unit is used to input the preset time, the state information and the disturbance information into a preset disturbance suppression controller to obtain second control information output by the disturbance suppression controller.

[0040] Optionally, the system further includes:

[0041] The first establishment unit is used to establish a UAV power inspection dynamics model;

[0042] The design unit is used to design an extended state observer according to the UAV power inspection dynamics model.

[0043] Optionally, the first establishing unit includes:

[0044] The first building module is used to build the UAV translational dynamics model formula through Newton's equations;

[0045] The second building module is used to build the rotational motion model of the UAV through the Euler equation;

[0046] A linearization module is used to perform linearization processing on the translational dynamics model of the UAV to obtain a three-axis acceleration expression of the UAV, wherein the three-axis acceleration expression of the UAV includes information of non-matching disturbance;

[0047] The first generation module is used to use the Euler angle in the world coordinate system to generate a relationship between the angular velocity of the drone and the Euler angle during the drone control process.

[0048] Optionally, the design unit includes:

[0049] A second generating module is used to generate an equivalent formula according to the UAV translational dynamics model formula, wherein the equivalent formula includes the UAV position state variable and an equivalent input disturbance;

[0050] A construction module is used to construct an extended state observer according to the equivalent formula.

[0051] Optionally, the system further includes:

[0052] A second establishing unit is used to establish a virtual control function for the wireless charging process of the power inspection drone, wherein the virtual control function includes information of a control error;

[0053] A definition unit is used to define a mixed obstacle function so that the control error is kept within a preset constraint.

[0054] Optionally, the acquiring unit includes:

[0055] A first acquisition module is used to extract blade power, inductive load power, rotor tip speed, rotor average induced speed, fuselage drag ratio, blade angular velocity, air density, rotor radius, drone mass, rotor disc area, rotor density and fuselage equivalent area from the drone parameters;

[0056] The first calculation module is used to calculate the inspection power by inputting the blade power, the inductive load power, the rotor tip speed, the rotor average induced speed, the fuselage drag ratio, the blade angular velocity, the air density, the rotor radius, the UAV mass, the rotor disc area, the rotor density, and the fuselage equivalent area into a preset inspection power consumption model.

[0057] Optionally, the acquiring unit includes:

[0058] A second acquisition module is used to acquire the base station transmission power and the base station transmission antenna gain from the base station parameters;

[0059] A fourth acquisition module, used to acquire the drone receiving antenna gain from the drone parameters;

[0060] The second calculation module is used to calculate the charging power by inputting the base station transmission power, the base station transmission antenna gain and the drone receiving antenna gain into a preset wireless charging model.

[0061] A third aspect of the embodiment of the present application provides a control system for wireless charging of an inspection drone, including:

[0062] Processor, memory, input-output unit, and bus;

[0063] The processor is connected to the memory, the input and output unit, and the bus;

[0064] A program is stored in the memory, and the processor calls the program to execute the method in the first aspect and any possible implementation manner of the first aspect.

[0065] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, on which a program is stored. When the program is executed on a computer, the computer executes the method in the first aspect and any possible implementation of the first aspect.

[0066] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0067] The method provided in the embodiment of the present application uses an extended state observer to estimate the non-matching disturbances such as electromagnetic, wind interference, and unmodeled dynamics that are widely present in the power inspection process. In the case of real-time changes in the external environment and internal dynamics during large-scale power inspections, the designed disturbance suppression controller can still ensure the full-time domain preset time convergence and disturbance suppression performance of the UAV system, and can ensure that the UAV enters the effective charging distance of the charging base station within the preset time, with strong robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a flow chart of an embodiment of a control method for wireless charging of an inspection drone in an embodiment of the present application;

[0069] Figure 2 This is a structural schematic diagram of an embodiment of a control device for wireless charging of an inspection drone in an embodiment of the present application;

[0070] Figure 3 This is a structural schematic diagram of another embodiment of the control device for wireless charging of the inspection drone in the embodiment of the present application. DETAILED DESCRIPTION

[0071] The embodiment of the present application provides a control method for wireless charging of an inspection drone, which is used to ensure that the drone enters the effective charging distance of a charging base station within a preset time and has strong robustness.

[0072] The method of the present application can be applied to a server, a terminal or other devices with logic processing capabilities, and the present application does not limit this. For the convenience of description, the following description is made by taking the execution subject as a server as an example.

[0073] The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0074] See also Figure 1 In one embodiment of the present application, a control method for wireless charging of an inspection drone includes:

[0075] 101. When the power of the target UAV reaches the charging threshold, query the target base station closest to the target UAV;

[0076] In actual applications, a charging threshold is generally set for the target drone. When the target drone is performing a power inspection task, if the battery power drops to the charging threshold, the server will find the base station closest to the target drone. This base station is the target base station, and the target base station is the destination for the target drone to charge.

[0077] 102. Acquire base station parameters of the target base station, drone parameters of the target drone, and a target distance between the target drone and the target base station;

[0078] After the server obtains the target base station, it can obtain the base station parameters of the target base station, the drone parameters of the target drone, and the target distance between the target drone and the target base station. The base station parameters include the base station location, the base station wireless charging related information, etc. The drone parameters include a large amount of target drone information, as well as wireless charging related information and drone power related information.

[0079] 103. Calculate the inspection power according to the drone parameters and the preset inspection power consumption model, and calculate the charging power according to the base station parameters and the preset wireless charging model;

[0080] Since the base station parameters include relevant information about the base station's wireless charging, and the drone parameters include relevant information about wireless charging and information related to the drone's power, the server can apply the drone parameters and the base station parameters to the preset wireless charging model and inspection power consumption model, calculate the inspection power of the target drone using the inspection power consumption model, and calculate the charging power of the target drone using the wireless charging model.

[0081] 104. Calculate a preset time according to the current power of the target UAV, the inspection power, and the charging power;

[0082] After calculating the inspection power and charging power of the target UAV, the server can obtain the current power of the target UAV, and then use the current power, inspection power and charging power to calculate the preset time. The preset time is the time it takes for the target UAV to fly from the current position to the target base station. The server needs to ensure that the target UAV reaches the target base station within the preset time.

[0083] 105. Input the current first control information into a preset extended state observer, and use the extended state observer to calculate the state information and disturbance information of the target UAV, wherein the disturbance information includes a non-matching disturbance;

[0084] The server can input the current first control information of the target UAV into a preset extended state observer, and use the extended state observer to calculate the state information and disturbance information of the target UAV. The state information includes position, speed and other information, while the disturbance information includes internal disturbance and external disturbance. The external disturbance is a non-matching disturbance. The extended state observer can estimate the non-matching disturbances such as electromagnetic, wind interference, and unmodeled dynamics that are widely present in the power inspection process.

[0085] 106. Input the preset time, the state information, and the disturbance information into a preset disturbance suppression controller to obtain second control information output by the disturbance suppression controller.

[0086] After obtaining the preset time, state information and disturbance information, the server can input this information into the preset disturbance suppression controller, and the disturbance suppression controller can output the second control information, which can suppress the disturbance information and ensure that the control error and observation error of the target UAV converge within the preset time in the full time domain. It should be noted that in the process of the target UAV flying to the target base station, step 105 and step 106 form a closed loop, the control information output by the disturbance suppression controller is used as the input of the extended state observer, and the state information and disturbance information output by the extended state observer are used as the input of the disturbance suppression controller, and the two form a closed loop to constitute the cascade control of the target UAV.

[0087] In this embodiment, when the power of the target UAV reaches the charging threshold, the server queries the target base station that is closest to the target UAV at this time, and then obtains the base station parameters, the UAV parameters and the target distance, and uses the preset inspection power consumption model to calculate the inspection power, and the preset wireless charging model to calculate the charging power, and then calculates the preset time for the target UAV to fly to the target base station through the current power of the target UAV, the inspection power, and the charging power, and then inputs the current first control information into the preset extended state observer to obtain state information and disturbance information, and then inputs the state information and disturbance information into the disturbance suppression controller to obtain second control information, and the second information can act on the extended state observer, so that the extended state observer and the disturbance suppression controller form a closed-loop control of the target UAV, which can improve the stability of controlling the target UAV. At the same time, the extended state observer is used to estimate the non-matching disturbances such as electromagnetic, wind interference, and unmodeled dynamics that are widely present in the power inspection process. When the external environment and internal dynamics change in real time during large-scale power inspections, the designed disturbance suppression controller can still ensure the full-time domain preset time convergence and disturbance suppression performance of the UAV system, thereby ensuring that the UAV enters the effective charging distance of the charging base station within the preset time, and has strong robustness.

[0088] Specifically, in a possible embodiment, the design process is as follows:

[0089] Establish the power consumption and wireless charging model of UAV power inspection:

[0090] Taking the quad-rotor drone as the object, define the drone inspection speed V and blade power Inductive load power Rotor blade tip speed U p =RΩ, average induced rotor speed Fuselage drag ratio The power consumption model of drone inspection is as follows:

[0091]

[0092] Where Ω, ρ, R, and W represent the blade angular velocity, air density, rotor radius, and UAV mass, respectively. A is the rotor disk area, s is the rotor density, and S F is the equivalent area of ​​the fuselage.

[0093] Define the base station transmission power P t , base station transmitting antenna gain G t , UAV receiving antenna gain G u , the wireless charging power is:

[0094] P c (d) = P t +G t +Gu -C(d) (Formula 2)

[0095] The wireless energy transmission spatial loss calculation model C(d)=20lg{d}+20lg{f}-147.55dB.

[0096] Define the base station location x b , when the drone moves to x s When the effective charging distance is reached, the maximum horizontal distance between the drone and the base station for charging is x s +∫V t dt-x b , otherwise the energy harvester cannot be triggered due to excessive energy space transmission losses.

[0097] Assuming that the charging phase starts at t=0, the change in the distance between the drone and the base station can be described as:

[0098]

[0099] The charging power is as shown in Formula 4:

[0100]

[0101] Where, Ω=P t +G t +G u -20lg{f}-147.55.

[0102] Drone from x s It takes time to reach the top of the base station satisfy At this stage, the UAV receives electrical energy as shown in Formula 5

[0103]

[0104] When the drone hovers at a height of H, the charging power is constant as shown in Formula 6:

[0105] P c h =P t +G t +G u -PL(H) (Formula 6)

[0106] Therefore, during the hovering time T h , the drone receives energy as shown in Formula 7:

[0107]

[0108] During the hovering phase, the drone consumes energy as shown in Formula 8:

[0109] E uh =P(0)T h (Formula 8)

[0110] When the drone reaches the top of the base station, it still needs to be charged, so the hovering time can be calculated as shown in Formula 9:

[0111]

[0112] Among them, E s For drones to reach x s The remaining energy.

[0113] Define the base station location x b , when the drone moves to x s When the effective charging distance is reached, the maximum horizontal distance between the drone and the base station for charging is x s +∫V t dt-x b , otherwise the energy harvester cannot be triggered due to excessive energy space transmission losses.

[0114] Establishing the UAV power inspection dynamics model:

[0115] A cascade closed-loop control architecture is established to realize the flight trajectory tracking control of the inspection UAV. The UAV position control and attitude control are the core links of the UAV tracking control. The planning layer output p 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.

[0116] As a nonlinear system, the UAV translational dynamics model is first established through Newton's equations, as shown in Formula 10, 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. Secondly, the UAV rotational motion model is established through the Euler equation as shown in Formula 13, 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 Torque generated by four motors.

[0117]

[0118] u1=F1+F2+F3+F4 (Formula 11)

[0119]

[0120]

[0121] Considering that the slow-speed UAV motion state is usually a small angle and small posture motion, each state of the UAV is a balanced state. Combined with the balanced hovering state of the UAV (φ0~0,θ0~0,u 1,0 ~mg), and by linearizing the UAV dynamics equation, the UAV x, y, z three-axis acceleration expression is obtained as shown in Formula 14. In the process of UAV control, the Euler angle in the world coordinate system is used. The relationship between the UAV's own angular velocity and the Euler angle is shown in Formula 15. The UAV angular velocity is obtained by linearization, which is the differential of the Euler angle (such as Formula 16).

[0122]

[0123] Among them, d1, d2, and d3 are non-matching disturbances.

[0124] Design of a time-dilated state observer with preset full-time domain:

[0125] According to the equivalent input principle, the acceleration expression of the drone's x, y, and z axes can be equivalent to the following formula 17:

[0126]

[0127] where x = [x i ] T is the drone position state variable, d e is the equivalent input disturbance.

[0128] Define x n+1 =d e , given a power inspection drone system, the reduced-order extended state observer is constructed as follows:

[0129]

[0130] in ζ=[ζ1,…,ζ n ] T , α i =iα-(i-1) is an adjustable parameter, and the system matrix is ​​as follows:

[0131] G=I n×n

[0132] Design the observer gain as follows:

[0133]

[0134] in a i,i =1, when i<j, a i,j =0, when i>j,

[0135] If the observer gain is properly configured so that is Hurwitz, then the observation error is within the preset time T p , converges to 0, and at t≥T p The error remains at 0.

[0136] Proof: When t0≤t≤T p , defining the observation error Then the error system dynamics can be described as

[0137]

[0138] where g=[g1,…,g n ] T .

[0139] For 1≤j≤i≤n, we introduce the transformation According to the observation error system, we have

[0140]

[0141] in,

[0142] Select Parameters Then we have:

[0143]

[0144] The observation error equation is equivalent to:

[0145]

[0146] in,

[0147] Choose the appropriate i Order n +l1s n-1 +…+l n The matrix G1 is Hurwitz, so, Asymptotically stable, there exists C>0, δ>0, for t∈[t0,t0+T), we have:

[0148]

[0149] in addition, arrive The transformation expression is:

[0150]

[0151] Where μ = η -1 , Q is a lower triangular matrix with

[0152] Since when t = t0, is 1 when t approaches t0+T p When , it decreases monotonically to 0, Therefore, the observation error is within the preset time T p Converges to 0.

[0153] When t ≥ T p , θ=0, the error dynamic system is

[0154]

[0155] definition r i =(i-1)α-(i-2), establish the Lyapunov function:

[0156] V O (ξ)=ξPξ T (Formula 27)

[0157] For a given κ i Let G2 be Hurwitz, for c>0, then the following inequality holds

[0158]

[0159] therefore, Established. Because,

[0160] When t ≥ T p When ||ξ||=0. Then, the observation error The proof is complete.

[0161] Full time domain preset time controller design:

[0162] Introducing virtual control function α i , where α0 = y d , α n =u. Define tracking error e i =x i -α i-1 . For the wireless charging process of the power inspection drone, a virtual control function is established:

[0163]

[0164] in, Represents time-varying gain k i It is a parameter to be determined.

[0165] Then, 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.

[0166] Proof: Define the mixed obstacle Lyapunov function as:

[0167]

[0168] in,

[0169] Taking the derivative of V with respect to time, we get:

[0170]

[0171] For t∈[t0,T p ), θ=1, and introduce the virtual control function, we can get:

[0172]

[0173] because The above formula can be further expressed as:

[0174]

[0175] Therefore, the solution of the above equation can be defined as:

[0176]

[0177] So, we can get, then Established.

[0178] θ=0, we can get V(T p )=0.

[0179] when When V(t)=0, therefore, e i = 0. It can be obtained that the inspection drone is within the preset time T p Enter the effective distance of the charging base station, and during the hovering charging stage, the control error remains at 0, and the proof is complete.

[0180] In a possible embodiment, it is assumed that the power of the drone is 62.49 W when flying and 82.46 W when hovering, the battery capacity is 3830 mAh, the voltage is 12 V, Pt = 58 dBW, Gt = 58 dBi, f = 915 MHZ, η = 0.6, and the selected parameters are shown in Table 1.

[0181] Select the preset time dilation state observer parameter as l i =1, κ1=5, κ2=45, ensuring that G1 and G2 are Hurwitz. Parameters α=0.9, m=10, and the barrier Lyapunov function performance parameter is set to ρ=0.2cos(t)+0.5. The preset time controller parameters are k1=9.5,k2=5. According to the drone parameters and wireless charging behavior characteristics, the preset time is calculated as T p =8s. Consider different initial states X1(0) = [0 2 0] T and X2(0) = [0 5 0] T , X3(0) = [0 10 0] T The full-time domain preset time expansion state observation performance proposed by the present invention is as follows Figure 2 , 3 As shown, unlike the traditional algorithm, for different initial conditions, in the effective observation system state and the expansion state, the observation error converges to 0 at the preset time error and remains stable in the full time domain. The full time domain preset time controller proposed in the present invention controls the error, and through the barrier Lyapunov function, the control error is within the preset constraint.

[0182] It should be noted that the above examples are merely illustrative and do not limit specific numerical values.

[0183] See also Figure 2 , an embodiment of the control device for wireless charging of the inspection drone in the embodiment of the present application includes:

[0184] A query unit 201 is used to query the target base station closest to the target drone when the power of the target drone reaches a charging threshold;

[0185] An acquisition unit 202 is used to acquire base station parameters of the target base station, drone parameters of the target drone, and a target distance between the target drone and the target base station;

[0186] The first calculation unit 203 is used to calculate the inspection power according to the UAV parameters and the preset inspection power consumption model, and calculate the charging power according to the base station parameters and the preset wireless charging model;

[0187] The second calculation unit 204 calculates the preset time according to the current power of the target UAV, the inspection power and the charging power;

[0188] A first input unit 205 is used to input the current first control information into a preset extended state observer, and use the extended state observer to calculate the state information and disturbance information of the target UAV, wherein the disturbance information includes a non-matching disturbance;

[0189] The second input unit 206 is used to input the preset time, the state information and the disturbance information into a preset disturbance suppression controller to obtain second control information output by the disturbance suppression controller.

[0190] In this embodiment, when the power of the target UAV reaches the charging threshold, the query unit 201 queries the target base station that is closest to the target UAV at this time, and then the acquisition unit 202 acquires the base station parameters, the UAV parameters and the target distance, the first calculation unit 203 calculates the inspection power using the preset inspection power consumption model, and the preset wireless charging model calculates the charging power, and the second calculation unit 204 calculates the preset time for the target UAV to fly to the target base station through the current power of the target UAV, the inspection power, and the charging power, and then the first input unit 205 inputs the current first control information into the preset extended state observer to obtain state information and disturbance information, and then the second input unit 206 inputs the state information and disturbance information into the disturbance suppression controller to obtain second control information, and the second information can act on the extended state observer, so that the extended state observer and the disturbance suppression controller form a closed-loop control on the target UAV, which can improve the stability of controlling the target UAV. At the same time, the extended state observer is used to estimate the non-matching disturbances such as electromagnetic, wind interference, and unmodeled dynamics that are widely present in the power inspection process. When the external environment and internal dynamics change in real time during large-scale power inspections, the designed disturbance suppression controller can still ensure the full-time domain preset time convergence and disturbance suppression performance of the UAV system, thereby ensuring that the UAV enters the effective charging distance of the charging base station within the preset time, and has strong robustness.

[0191] See also Figure 3 Another embodiment of the control device for wireless charging of the inspection drone in the embodiment of the present application includes:

[0192] Processor 301, memory 302, input and output unit 303 and bus 304;

[0193] The processor 301 is connected to the memory 302, the input and output unit 303 and the bus 304;

[0194] The memory 302 stores a program, and the processor 301 calls the program to execute Figure 1 to Figure 2 Steps in the illustrated embodiment.

[0195] In this embodiment, the function of the processor 301 is the same as that of the aforementioned Figure 1 The steps in the illustrated embodiments correspond to each other and will not be described again here.

[0196] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0197] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0198] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0199] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0200] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, read-only memory), random access memory (RAM, random access memory), disk or optical disk and other media that can store program code.

Claims

1. A control method for wireless charging of an inspection drone, characterized in that: include: When the target UAV's battery level reaches the charging threshold, query the target base station closest to the target UAV; Acquire base station parameters of the target base station, drone parameters of the target drone, and a target distance between the target drone and the target base station; Calculate the inspection power according to the drone parameters and the preset inspection power consumption model, and calculate the charging power according to the base station parameters and the preset wireless charging model; Calculate the preset time according to the current power of the target drone, the inspection power and the charging power; Inputting the current first control information into a preset extended state observer, and using the extended state observer to calculate the state information and disturbance information of the target UAV, wherein the disturbance information includes a non-matching disturbance; The preset time, the state information and the disturbance information are input into a preset disturbance suppression controller to obtain second control information output by the disturbance suppression controller; wherein the disturbance suppression controller is established according to a virtual control function; the virtual control function is: Among them, e i is the tracking error; T p The preset time for the drone to enter the effective distance of the charging base station; μ = η -1 , and λ i represents the time-varying gain, θ,k i is a parameter to be determined; Before querying the target base station closest to the target drone, the method further includes: Establish a dynamic model for UAV power inspection; An extended state observer is designed based on the UAV power inspection dynamics model.

2. The method according to claim 1, characterized in that The establishment of the UAV power inspection dynamics model includes: Establish the UAV translational dynamics model through Newton equations; Establish the rotational motion model of the drone through the Euler equation; Performing linearization processing on the translational dynamics model of the UAV to obtain a three-axis acceleration expression of the UAV, wherein the three-axis acceleration expression of the UAV includes information of non-matching disturbance; In the process of UAV control, the Euler angle in the world coordinate system is used to generate the relationship between the angular velocity of the UAV and the Euler angle.

3. The method according to claim 2, characterized in that The design of an extended state observer based on the UAV power inspection dynamics model includes: Generate an equivalent formula according to the UAV translational dynamics model, wherein the equivalent formula includes the UAV position state variable and the equivalent input disturbance; An extended state observer is constructed according to the equivalent formula.

4. The method according to claim 1, characterized in that: Before querying the target base station closest to the target drone, the method further includes: A virtual control function is established for the wireless charging process of the power inspection drone, wherein the virtual control function includes information on the control error; A hybrid barrier function is defined such that the control error remains within preset constraints.

5. The method according to any one of claims 1 to 4, characterized in that The calculating the inspection power according to the UAV parameters and the preset inspection power consumption model includes: extracting blade power, inductive load power, rotor tip speed, rotor average induced speed, fuselage drag ratio, blade angular velocity, air density, rotor radius, drone mass, rotor disc area, rotor density, and fuselage equivalent area from said drone parameters; The inspection power is calculated by inputting the blade power, the inductive load power, the rotor tip speed, the rotor average induced speed, the fuselage drag ratio, the blade angular velocity, the air density, the rotor radius, the UAV mass, the rotor disc area, the rotor density, and the fuselage equivalent area into a preset inspection power consumption model.

6. The method according to any one of claims 1 to 4, characterized in that The calculating the charging power according to the base station parameters and the preset wireless charging model includes: Obtaining base station transmit power and base station transmit antenna gain from the base station parameters; Obtaining the drone receiving antenna gain from the drone parameters; The charging power is calculated by inputting the base station transmission power, the base station transmission antenna gain and the drone receiving antenna gain into a preset wireless charging model.

7. A control system for wireless charging of an inspection drone, characterized in that: include: A query unit, configured to query a target base station closest to the target drone when the power of the target drone reaches a charging threshold; An acquisition unit, configured to acquire base station parameters of the target base station, drone parameters of the target drone, and a target distance between the target drone and the target base station; A first calculation unit, configured to calculate the inspection power according to the drone parameters and a preset inspection power consumption model, and to calculate the charging power according to the base station parameters and a preset wireless charging model; A second calculation unit calculates a preset time according to the current power of the target UAV, the inspection power and the charging power; A first input unit, configured to input the current first control information into a preset extended state observer, and use the extended state observer to calculate the state information and disturbance information of the target UAV, wherein the disturbance information includes a non-matching disturbance; The second input unit is used to input the preset time, the state information and the disturbance information into a preset disturbance suppression controller to obtain second control information output by the disturbance suppression controller; wherein the disturbance suppression controller is established according to a virtual control function; the virtual control function is: Among them, e i is the tracking error; T p The preset time for the drone to enter the effective distance of the charging base station; μ = η -1 , and λ i represents the time-varying gain, θ,k i is a parameter to be determined; The control system further comprises: The first establishment unit is used to establish a UAV power inspection dynamics model; The design unit is used to design an extended state observer according to the UAV power inspection dynamics model.

8. A control system for wireless charging of an inspection drone, characterized in that: include: Processor, memory, input-output unit, and bus; The processor is connected to the memory, the input and output unit, and the bus; A program is stored in the memory, and the processor calls the program to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a program stored thereon, wherein when the program is executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 6.

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