Hydrogen-powered unmanned aerial vehicle power control method and related device

Abstract

This invention discloses a power control method and related apparatus for a hydrogen-powered unmanned aerial vehicle (UAV), relating to the field of data processing technology. The method includes: monitoring the operating status of various components of the hydrogen-powered UAV to obtain status monitoring data; determining operational energy consumption data based on the status monitoring data; performing load analysis based on the status monitoring data; determining power regulation data for the hydrogen-electric power system of the UAV based on the load data and operational energy consumption data; performing flight state transition analysis on the hydrogen-powered UAV based on rotor weights to obtain flight state transition data; determining optimal power allocation data based on the flight state transition data combined with power regulation data; constructing an adaptive controller; and controlling the power output of the hydrogen-electric power system of the UAV based on the adaptive controller and the optimal power allocation data. This invention effectively improves the endurance of hydrogen-powered UAVs and achieves a more ideal power control effect.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and device for controlling the electrical power of a hydrogen-powered drone. Background Technology

[0002] Traditional compound-wing inspection drones typically use lithium batteries for power. In actual inspection scenarios, due to takeoff and landing, target location travel, and maintaining safety redundancy, the effective working time of traditional drones is generally less than 60 minutes, indicating a significant need to improve their endurance. Hydrogen power is a novel drone energy system. Its core is the use of hydrogen as the drone's energy source. Hydrogen is converted from chemical energy to electrical energy through a fuel cell. This electrical energy, combined with a lithium battery and managed by an energy management system, powers the drone. Using hydrogen to power the drone significantly extends its endurance, making hydrogen-powered drones a key research focus for various companies. Furthermore, the electrical control of hydrogen-powered drones is paramount. Currently, hydrogen-powered drones often determine power allocation and regulation solely based on operational energy consumption, neglecting the impact of load and flight state transitions on electrical control. This results in the endurance of hydrogen-powered drones failing to achieve the expected improvement. Adaptive control of the power system's electrical output during operation is also a key research focus for companies. Incorporating adaptive control can further enhance the drone's endurance and flight efficiency. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a method and related device for the power control of hydrogen-powered drones, which effectively improves the endurance of hydrogen-powered drones and makes the power control of hydrogen-powered drones achieve a more ideal effect.

[0004] To address the aforementioned technical problems, this invention provides an electrical power control method for a hydrogen-powered unmanned aerial vehicle (UAV), the method comprising:

[0005] The operational status of each component of the hydrogen-powered drone is monitored to obtain status monitoring data, and operational energy consumption data is determined based on the status monitoring data.

[0006] Load analysis is performed based on the status monitoring data to obtain load data, and the power control data of the hydrogen-electric power system of the hydrogen-powered UAV is determined based on the load data and the operation energy consumption data.

[0007] Flight state transition analysis of hydrogen-powered UAVs is performed based on rotor weights to obtain flight state transition data, and power optimization allocation data is determined based on the flight state transition data and the power regulation data.

[0008] An adaptive controller is constructed, and the power output of the hydrogen-electric power system of the hydrogen-powered UAV is controlled based on the adaptive controller and the power optimization allocation data.

[0009] Optionally, the monitoring of the operational status of each component of the hydrogen-powered drone to obtain status monitoring data, and the determination of operational energy consumption data based on the status monitoring data, includes:

[0010] The sensor array based on the hydrogen-powered drone monitors the operating status of each component and obtains status monitoring data.

[0011] Divide the condition monitoring data into time periods to obtain condition monitoring data after time period division;

[0012] Based on the state monitoring data after time phase division, motor driving force analysis and power consumption analysis are performed to obtain motor driving force data and power consumption data.

[0013] The operational energy consumption data of hydrogen-powered drones was determined based on motor drive force data and power consumption data.

[0014] Optionally, the step of performing load analysis based on the status monitoring data to obtain load data, and determining the power control data of the hydrogen-electric power system of the hydrogen-powered UAV based on the load data and the operating energy consumption data, includes:

[0015] Data correlation is performed based on the status monitoring data of various components of the hydrogen-powered drone to obtain several corresponding correlation data.

[0016] Several related data points are fused to obtain fused data, and load analysis is performed on the fused data to obtain load correlation data for each component;

[0017] Correlation analysis is performed based on the load correlation data of each component to obtain load data;

[0018] Based on the load data and operational energy consumption data, combined with voltage control strategy, current control strategy and load characteristic control strategy, the power regulation data of the hydrogen-electric power system of the hydrogen-powered UAV is determined.

[0019] Optionally, the step of performing flight state transition analysis on the hydrogen-powered UAV based on rotor weights to obtain flight state transition data, and determining power optimization allocation data based on the flight state transition data and the power control data, includes:

[0020] Power demand analysis was performed on the flight state transition of the hydrogen-powered drone to obtain power demand data;

[0021] Rotor weight analysis is performed on the flight state transition of a hydrogen-powered drone to obtain rotor weight data, and flight state transition data is determined based on the power demand data and rotor weight data.

[0022] Based on the flight state transition data, combined with the hydrogen fuel cell model and the lithium battery model, the power allocation space is determined, and based on the power allocation space, combined with the power regulation data, the power optimization allocation data is determined.

[0023] Optionally, the rotor weight analysis for flight state transition of the hydrogen-powered UAV to obtain rotor weight data includes:

[0024] Acquire the conversion mode data of the hydrogen-powered drone and determine the upper and lower limits of the conversion speed of the hydrogen-powered drone;

[0025] Based on the conversion mode data, the upper limit of conversion speed, and the lower limit of conversion speed, the rotor weights of the hydrogen-powered UAV during flight state transition are analyzed to obtain rotor weight data.

[0026] Optionally, constructing the adaptive controller includes:

[0027] A fractional-order complex network system is constructed based on the object model of a hydrogen-powered drone, and a state error system is constructed based on the fractional-order complex network system.

[0028] A target proportional-integral-derivative PID controller is constructed based on the state error system and the internal model controller, and an adaptive controller is constructed based on the target PID controller and the Popov stability criterion.

[0029] Optionally, the proportional-integral-derivative PID controller constructed based on the state error system combined with the internal model controller includes:

[0030] Obtain the relative order of the object model, and construct the internal model controller based on the relative order using a low-pass filter function;

[0031] The transfer function is calculated based on the preset proportional coefficient, preset integral coefficient and preset derivative coefficient, and a conventional PID controller is generated based on the transfer function.

[0032] The parameters of a conventional PID controller are tuned to obtain a parameter-tuned PID controller. A target PID controller is then constructed based on the internal model controller, the state error system, and the parameter-tuned PID controller.

[0033] In addition, the present invention also provides an electrical power control device for a hydrogen-powered drone, the device comprising:

[0034] Operational energy consumption determination module: used to monitor the operating status of each component of the hydrogen-powered UAV, obtain status monitoring data, and determine operational energy consumption data based on the status monitoring data;

[0035] Power regulation determination module: used to perform load analysis based on the status monitoring data, obtain load data, and determine the power regulation data of the hydrogen-electric power system of the hydrogen-powered UAV based on the load data and the operation energy consumption data;

[0036] Power optimization allocation module: used to perform flight state transition analysis on hydrogen-powered UAV based on rotor weights, obtain flight state transition data, and determine power optimization allocation data based on the flight state transition data and the power regulation data;

[0037] Power output control module: used to construct an adaptive controller and control the power output of the hydrogen-electric power system of the hydrogen-powered UAV based on the adaptive controller and power optimization allocation data.

[0038] In addition, the present invention also provides an electronic device, which includes a processor and a memory. The memory is used to store instructions, and the processor is used to call the instructions in the memory to cause the electronic device to execute the above-described power control method for a hydrogen-powered drone.

[0039] In addition, the present invention also provides a computer-readable storage medium that stores computer instructions that, when executed on an electronic device, cause the electronic device to perform the above-described power control method for a hydrogen-powered drone.

[0040] In this embodiment of the invention, load analysis is performed based on the state monitoring data of the hydrogen-powered UAV to obtain load data. Based on the load data and operational energy consumption data, the power regulation data of the hydrogen-electric power system of the UAV is determined. This introduces load analysis of the hydrogen-powered UAV, and combining load data with operational energy consumption data makes the analyzed power regulation data more accurate. Flight state transition analysis is performed on the hydrogen-powered UAV based on rotor weights to obtain flight state transition data. Based on the flight state transition data and the power regulation data, optimal power allocation data is determined. Rotor weight analysis allows for a more comprehensive analysis of the flight state transition data of the hydrogen-powered UAV. The resulting optimal power allocation data, obtained by combining the flight state transition data and power regulation data, is more adapted to the actual operation process of the hydrogen-powered UAV. An adaptive controller is constructed, and based on the adaptive controller and the optimal power allocation data, the power output of the hydrogen-electric power system of the UAV is controlled, effectively improving the endurance of the hydrogen-powered UAV and achieving a more ideal power control effect. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating the power control method for a hydrogen-powered drone in an embodiment of the present invention.

[0043] Figure 2 This is a flowchart illustrating the power control method for a hydrogen-powered drone according to another embodiment of the present invention.

[0044] Figure 3 This is a schematic diagram of the structure of the power control device for a hydrogen-powered drone in an embodiment of the present invention.

[0045] Figure 4 This is a schematic diagram of the structural composition of the electronic device in an embodiment of the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Example 1

[0048] Please see Figure 1 , Figure 1 This is a flowchart illustrating the power control method for a hydrogen-powered drone according to an embodiment of the present invention. The method includes:

[0049] S11: Monitor the operating status of each component of the hydrogen-powered drone, obtain status monitoring data, and determine the operational energy consumption data based on the status monitoring data;

[0050] In the specific implementation of this invention, the monitoring of the operating status of each component of the hydrogen-powered drone to obtain status monitoring data, and the determination of operational energy consumption data based on the status monitoring data, includes: monitoring the operating status of each component based on the sensor combination of the hydrogen-powered drone to obtain status monitoring data; dividing the status monitoring data into time stages to obtain status monitoring data after time stage division; performing motor drive force analysis and power consumption analysis based on the status monitoring data after time stage division to obtain motor drive force data and power consumption data; and determining the operational energy consumption data of the hydrogen-powered drone based on the motor drive force data and power consumption data.

[0051] Specifically, the operational status of each component of the hydrogen-powered drone is monitored using a sensor suite. This suite includes a gyroscope, accelerometer, magnetometer, barometer, load cell, current sensor, and temperature sensor. This sensor suite monitors the operational status of the drone's components, such as monitoring the motor temperature using a temperature sensor. The monitoring data is then divided into time periods according to a preset time cycle. Based on this time-phased data, motor drive force and energy consumption are analyzed. The data is input into a neural network model to obtain motor drive force and energy consumption data, with the motor drive force representing the drone's motor drive force. Finally, the operational energy consumption data for the hydrogen-powered drone is determined from these two data points.

[0052] S12: Perform load analysis based on the status monitoring data to obtain load data, and determine the power control data of the hydrogen-electric power system of the hydrogen-powered UAV based on the load data and the operation energy consumption data.

[0053] In the specific implementation of this invention, the step of performing load analysis based on the status monitoring data to obtain load data, and determining the power regulation data of the hydrogen-electric power system of the hydrogen-powered UAV based on the load data and the operating energy consumption data, includes: performing data association based on the status monitoring data of each component of the hydrogen-powered UAV to obtain several corresponding association data; performing data fusion on the several association data to obtain fused data, and performing load analysis on the fused data to obtain load association data of each component; performing correlation analysis based on the load association data of each component to obtain load data; and determining the power regulation data of the hydrogen-electric power system of the hydrogen-powered UAV based on the load data and the operating energy consumption data combined with voltage control strategy, current control strategy and load characteristic control strategy.

[0054] Specifically, data association is performed based on the condition monitoring data of various components of the hydrogen-powered drone. Weighing data for each component is extracted from the condition monitoring data, and the weighing data of each component is synchronized and associated according to the data acquisition timestamp to obtain several corresponding associated data sets. These associated data sets are then fused using a Bayesian network to obtain fused data. Load analysis is performed on the fused data, which is then input into a machine learning model to associate the load conditions between different parts, obtaining load association data for each component. Finally, correlation analysis is performed based on the load association data of each component, using linear regression to perform correlation regression analysis on the load association data of each component, obtaining the load data. Based on the load data and operational energy consumption data, combined with voltage control strategy, current control strategy, and load characteristic control strategy, the power regulation data of the hydrogen-electric power system of the hydrogen-powered UAV is determined. The voltage control strategy includes controlling the output power and current to maintain voltage stability, the current control strategy includes controlling the output current to maintain current stability, and the load characteristic strategy includes adjusting reactive power to improve the power factor. The voltage control strategy, current control strategy, and load characteristic control strategy ensure the coordinated operation of the hydrogen-electric power system and its components. The voltage control strategy, current control strategy, and load characteristic control strategy are deployed in the power regulation model. Complex data and operational energy consumption data are input into the power regulation model for analysis to obtain the power regulation and distribution of the hydrogen-electric power system of the hydrogen-powered UAV to its components, such as the output power ratio of the hydrogen-electric power system to its components. This is the power regulation data. The hydrogen-electric power system includes a hydrogen storage system, a fuel cell system, a fuel cell controller, a lithium battery, and an energy management module.

[0055] S13: Perform flight state transition analysis on the hydrogen-powered UAV based on rotor weights to obtain flight state transition data, and determine power optimization allocation data based on the flight state transition data and the power control data.

[0056] In the specific implementation of this invention, the step of performing flight state transition analysis on the hydrogen-powered UAV based on rotor weights to obtain flight state transition data, and determining optimal power allocation data based on the flight state transition data and the power regulation data, includes: performing power demand analysis on the hydrogen-powered UAV during flight state transitions to obtain power demand data; performing rotor weight analysis on the hydrogen-powered UAV during flight state transitions to obtain rotor weight data, and determining flight state transition data based on the power demand data and rotor weight data; determining the power supply allocation space based on the flight state transition data and a hydrogen fuel cell model and a lithium battery model, and determining optimal power allocation data based on the power supply allocation space and the power regulation data.

[0057] Furthermore, the rotor weight analysis for flight state transition of the hydrogen-powered UAV to obtain rotor weight data includes: acquiring the transition mode data of the hydrogen-powered UAV and determining the upper limit and lower limit of the transition speed of the hydrogen-powered UAV; and performing rotor weight analysis for flight state transition of the hydrogen-powered UAV based on the transition mode data, the upper limit and lower limit of the transition speed to obtain rotor weight data.

[0058] Specifically, a power demand analysis is performed on the flight mode transitions of the hydrogen-powered drone. Flight mode transitions include switching from rotorcraft to fixed-wing flight and vice versa. The power demand for switching between rotorcraft and fixed-wing flight is analyzed to obtain power demand data. The transition mode data of the hydrogen-powered drone is acquired, including preset weights for fixed-wing and rotorcraft flight (e.g., a preset weight of 0 for fixed-wing flight and 1 for rotorcraft flight). Weight ranges are set based on these preset weights, and upper and lower limits for the transition speed of the hydrogen-powered drone are determined. These upper and lower limits are also determined based on the drone's parameter library for different flight mode transitions. Rotor weight analysis is performed on the flight state transition of a hydrogen-powered UAV based on the conversion mode data, the upper limit of the conversion speed, and the lower limit of the conversion speed. The difference between the current speed of the hydrogen-powered UAV and the lower limit of the conversion speed is divided by the difference between the upper limit of the conversion speed and the lower limit of the conversion speed to obtain the calculated value. The rotor weight data is obtained by subtracting the calculated value from the preset weight of the conversion mode data. This rotor weight data can be used to characterize the weight of the control variables of the rotor. The flight state transition data is determined based on the power demand data and the rotor weight data. That is, the flight state transition data is composed of the power demand data and the rotor weight data. Based on the flight state transition data, combined with the hydrogen fuel cell model and the lithium battery model, the power allocation space is determined. The hydrogen fuel cell consumption of the UAV per unit time is obtained. A hydrogen fuel cell model is established based on the hydrogen fuel cell consumption. The output power, output current, and state of charge of the lithium battery in the charge-discharge cycle are obtained. A lithium battery model is established based on the output power, output current, and state of charge of the lithium battery in the charge-discharge cycle. The battery output power is determined based on the flight state transition data, combined with the hydrogen fuel cell model and the lithium battery model. The battery output power is discretized in a preset continuous interval to obtain a power set. The preset continuous interval can be the interval between the minimum battery output power and the maximum battery output power. The distribution characteristics of hydrogen fuel are analyzed in the power set. Based on the distribution characteristics, a power allocation space is established. Based on the power allocation space and the power regulation data, the power optimization allocation data is determined. A neural network model of the hydrogen-electric power system of the hydrogen-powered UAV is constructed through a near-end strategy optimization algorithm. Based on the neural network model, the power allocation space and power regulation data are used to analyze the power optimization allocation of each component of the hydrogen-electric power system to obtain the corresponding power optimization allocation data.

[0059] S14: Construct an adaptive controller and control the power output of the hydrogen-electric power system of the hydrogen-powered UAV based on the adaptive controller and the power optimization allocation data.

[0060] In the specific implementation of this invention, the construction of the adaptive controller includes: constructing a fractional-order complex network system based on the object model of the hydrogen-powered UAV, and constructing a state error system based on the fractional-order complex network system; constructing a target proportional-integral-derivative PID controller based on the state error system combined with an internal model controller, and constructing an adaptive controller based on the target PID controller combined with the Popov stability criterion.

[0061] Furthermore, the construction of a proportional-integral-derivative PID controller based on the state error system combined with the internal model controller includes: obtaining the relative order of the object model and constructing an internal model controller based on the relative order using a low-pass filter function; calculating a transfer function based on preset proportional coefficients, preset integral coefficients, and preset derivative coefficients, and generating a conventional PID controller based on the transfer function; performing parameter tuning on the conventional PID controller to obtain a parameter-tuned PID controller, and constructing a target PID controller based on the internal model controller, the state error system, and the parameter-tuned PID controller.

[0062] Specifically, a fractional-order complex network system is constructed based on the object model of a hydrogen-powered drone. By abstracting the hydrogen-powered drone entity, the drone is treated as the controlled object. The controlled object and its corresponding function are abstracted at a predetermined level to obtain its corresponding object model. Differential equations corresponding to the object model are established, and the fractional order is determined by the fractional derivative, which is the fractional derivative in the Caputo sense. The fractional order is used to construct a fractional-order complex network system using the corresponding differential equations. The fractional-order complex network system adds infinite memory and hereditary properties through the fractional derivative, and also increases the degrees of freedom, making the obtained fractional-order complex network system more accurate. A state error system is constructed based on the fractional-order complex network system, and a state equation is constructed. This state equation is constructed to consider the equivalent perturbations corresponding to nonlinear states. The state error system between the object model and the fractional-order complex network system is constructed through the state equation. The relative order of the object model is obtained, as are the basis functions of the units into which the object model is divided. Solution vectors are determined based on the basis functions. The relative order of each unit is determined based on its basis functions and corresponding solution vectors. An internal model controller is constructed based on these relative orders using a low-pass filter function. Specifically, the final internal model controller is generated by combining the relative order, the low-pass filter function, and a preset template for the internal model controller. The low-pass filter function ensures the robustness of the internal model controller. A transfer function is calculated based on preset proportional, integral, and derivative coefficients. A conventional proportional-integral-differential (PID) controller is generated based on this transfer function. A conventional PID controller can be quickly generated using the transfer function via a PID interface function. For parameter tuning of a conventional PID controller, an empirical trial-and-error method can be used. Based on experience, the controller parameters are initially set to a single value. In the closed-loop control system, disturbances are applied by changing the setpoint, and the transient response curve is observed on a recorder. The proportional, integral, and derivative parameters are tuned sequentially until a satisfactory transient response is obtained, resulting in a parameter-tuned PID controller. A target PID controller is then constructed based on the internal model controller, the state error system, and the parameter-tuned PID controller. An equivalent disturbance is observed on the state error system using a state observer to obtain an extended state vector. The state observer can treat uncertainties or disturbances in the system as one of the states in the system. The parameters of the parameter-positive definite PID controller are optimized based on the extended state vector. The control parameters of the optimized PID controller and the internal model controller are then fused to obtain the target PID controller.An adaptive controller is constructed based on the target PID controller and the Popov stability criterion. The Popov stability criterion is a method for analyzing the stability of nonlinear control systems. It requires that the state vector of the control system remain bounded when the input of the control system is limited to a subset of all possible input sets. The conditions for ensuring controller stability can be derived based on the Popov stability criterion. A nonlinear characteristic function is set, and the equilibrium point of the controller is determined based on the Popov stability criterion and the nonlinear characteristic function. The stability condition is determined based on the equilibrium point of the controller. The Popov stability criterion is added to the target PID controller to form the final adaptive controller, which enhances the stability of the adaptive controller. Based on the adaptive controller and the optimized power distribution data, the power output of the hydrogen-electric power system of the hydrogen-powered UAV is controlled. The power output is distributed to various components of the hydrogen-powered UAV according to the optimized power distribution data. The power output deviation is detected, and the power adjustment parameters are determined based on the adaptive controller and the power output deviation. The power output is adjusted according to the adjustment parameters to achieve better power control.

[0063] In this embodiment of the invention, load analysis is performed based on the state monitoring data of the hydrogen-powered UAV to obtain load data. Based on the load data and operational energy consumption data, the power regulation data of the hydrogen-electric power system of the UAV is determined. This introduces load analysis of the hydrogen-powered UAV, and combining load data with operational energy consumption data makes the analyzed power regulation data more accurate. Flight state transition analysis is performed on the hydrogen-powered UAV based on rotor weights to obtain flight state transition data. Based on the flight state transition data and the power regulation data, optimal power allocation data is determined. Rotor weight analysis allows for a more comprehensive analysis of the flight state transition data of the hydrogen-powered UAV. The resulting optimal power allocation data, obtained by combining the flight state transition data and power regulation data, is more adapted to the actual operation process of the hydrogen-powered UAV. An adaptive controller is constructed, and based on the adaptive controller and the optimal power allocation data, the power output of the hydrogen-electric power system of the UAV is controlled, effectively improving the endurance of the hydrogen-powered UAV and achieving a more ideal power control effect.

[0064] Example 2

[0065] Please see Figure 2 , Figure 2 This is a flowchart illustrating the power control method for a hydrogen-powered drone according to another embodiment of the present invention, the method comprising:

[0066] S201: Monitor the operating status of each component of the hydrogen-powered drone, obtain status monitoring data, and determine the operational energy consumption data based on the status monitoring data;

[0067] S202: Perform load analysis based on the status monitoring data to obtain load data, and determine the power control data of the hydrogen-electric power system of the hydrogen-powered UAV based on the load data and the operation energy consumption data.

[0068] S203: Perform power demand analysis on the flight state transition of hydrogen-powered drones to obtain power demand data;

[0069] S204: Perform rotor weight analysis on the flight state transition of the hydrogen-powered drone, obtain rotor weight data, and determine flight state transition data based on the power demand data and rotor weight data;

[0070] S205: Based on the flight state transition data, combined with the hydrogen fuel cell model and the lithium battery model, determine the power supply allocation space, and based on the power supply allocation space, combined with the power regulation data, determine the power optimization allocation data;

[0071] S206: Construct an adaptive controller and control the power output of the hydrogen-electric power system of the hydrogen-powered UAV based on the adaptive controller and power optimization allocation data.

[0072] In this embodiment of the invention, load analysis is performed based on the state monitoring data of the hydrogen-powered UAV to obtain load data. Based on the load data and operational energy consumption data, the power regulation data of the hydrogen-electric power system of the UAV is determined. This introduces load analysis of the hydrogen-powered UAV, and combining load data with operational energy consumption data makes the analyzed power regulation data more accurate. Flight state transition analysis is performed on the hydrogen-powered UAV based on rotor weights to obtain flight state transition data. Based on the flight state transition data and the power regulation data, optimal power allocation data is determined. Rotor weight analysis allows for a more comprehensive analysis of the flight state transition data of the hydrogen-powered UAV. The resulting optimal power allocation data, obtained by combining the flight state transition data and power regulation data, is more adapted to the actual operation process of the hydrogen-powered UAV. An adaptive controller is constructed, and based on the adaptive controller and the optimal power allocation data, the power output of the hydrogen-electric power system of the UAV is controlled, effectively improving the endurance of the hydrogen-powered UAV and achieving a more ideal power control effect.

[0073] Example 3

[0074] Please see Figure 3 , Figure 3 This is a schematic diagram of the structural composition of the power control device for a hydrogen-powered drone according to an embodiment of the present invention. The device includes:

[0075] Operational energy consumption determination module 31: used to monitor the operating status of each component of the hydrogen-powered UAV, obtain status monitoring data, and determine operational energy consumption data based on the status monitoring data;

[0076] Power regulation determination module 32: used to perform load analysis based on the status monitoring data, obtain load data, and determine the power regulation data of the hydrogen-electric power system of the hydrogen-powered UAV based on the load data and the operation energy consumption data;

[0077] Power optimization allocation module 33: used to perform flight state transition analysis on hydrogen-powered UAV based on rotor weights, obtain flight state transition data, and determine power optimization allocation data based on the flight state transition data and the power regulation data;

[0078] Power output control module 34: used to construct an adaptive controller and control the power output of the hydrogen-electric power system of the hydrogen-powered UAV based on the adaptive controller and power optimization allocation data.

[0079] In the specific implementation of this invention, the specific implementation of the device item can be referred to the implementation of the method item above, and will not be repeated here.

[0080] In this embodiment of the invention, load analysis is performed based on the state monitoring data of the hydrogen-powered UAV to obtain load data. Based on the load data and operational energy consumption data, the power regulation data of the hydrogen-electric power system of the UAV is determined. This introduces load analysis of the hydrogen-powered UAV, and combining load data with operational energy consumption data makes the analyzed power regulation data more accurate. Flight state transition analysis is performed on the hydrogen-powered UAV based on rotor weights to obtain flight state transition data. Based on the flight state transition data and the power regulation data, optimal power allocation data is determined. Rotor weight analysis allows for a more comprehensive analysis of the flight state transition data of the hydrogen-powered UAV. The resulting optimal power allocation data, obtained by combining the flight state transition data and power regulation data, is more adapted to the actual operation process of the hydrogen-powered UAV. An adaptive controller is constructed, and based on the adaptive controller and the optimal power allocation data, the power output of the hydrogen-electric power system of the UAV is controlled, effectively improving the endurance of the hydrogen-powered UAV and achieving a more ideal power control effect.

[0081] This invention provides a computer-readable storage medium storing a computer program. When executed by a processor, this program implements the power control method for a hydrogen-powered drone according to any of the above embodiments. The computer-readable storage medium includes, but is not limited to, any type of disk (including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic cards, or optical cards. In other words, the storage device includes any medium that stores or transmits information in a readable form by a device (e.g., a computer, a mobile phone), and can be a read-only memory, a disk, or an optical disk, etc.

[0082] Example 4

[0083] Please see Figure 4 , Figure 4 This is a schematic diagram of the structural composition of the electronic device in an embodiment of the present invention.

[0084] This invention also provides an electronic device, such as... Figure 4 As shown, the electronic device includes a memory 41, a processor 43, and a computer program 42 stored in the memory 41 and executable on the processor 43. Those skilled in the art will understand that... Figure 3The illustrated electronic device does not constitute a limitation on all devices and may include more or fewer components than illustrated, or combine certain components. Memory 41 can be used to store computer program 42 and various functional modules. Processor 43 runs the computer program 42 stored in memory 41, thereby performing various functional applications and data processing of the device. Memory can be internal memory or external memory, or both. Internal memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or random access memory. External memory may include hard disks, floppy disks, ZIP disks, USB flash drives, magnetic tapes, etc. Processor 43 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, a single-chip microcomputer, or a processor 43, or any conventional processor, etc. The processors and memories disclosed in this invention include, but are not limited to, these types of processors and memories. The processors and memories disclosed in this invention are merely examples and not intended to be limiting.

[0085] As one embodiment, the electronic device includes: one or more processors 43, a memory 41, and one or more computer programs 42, wherein the one or more computer programs 42 are stored in the memory 41 and configured to be executed by the one or more processors 43, and the one or more computer programs 42 are configured to execute the power control method of the hydrogen-powered drone in any of the above embodiments. For specific implementation processes, please refer to the above embodiments, which will not be repeated here.

[0086] In this embodiment of the invention, load analysis is performed based on the state monitoring data of the hydrogen-powered UAV to obtain load data. Based on the load data and operational energy consumption data, the power regulation data of the hydrogen-electric power system of the UAV is determined. This introduces load analysis of the hydrogen-powered UAV, and combining load data with operational energy consumption data makes the analyzed power regulation data more accurate. Flight state transition analysis is performed on the hydrogen-powered UAV based on rotor weights to obtain flight state transition data. Based on the flight state transition data and the power regulation data, optimal power allocation data is determined. Rotor weight analysis allows for a more comprehensive analysis of the flight state transition data of the hydrogen-powered UAV. The resulting optimal power allocation data, obtained by combining the flight state transition data and power regulation data, is more adapted to the actual operation process of the hydrogen-powered UAV. An adaptive controller is constructed, and based on the adaptive controller and the optimal power allocation data, the power output of the hydrogen-electric power system of the UAV is controlled, effectively improving the endurance of the hydrogen-powered UAV and achieving a more ideal power control effect.

[0087] Furthermore, the above provides a detailed description of the power control method and related devices for a hydrogen-powered drone provided by the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for controlling the electrical power of a hydrogen-powered unmanned aerial vehicle, characterized in that, The method includes: The operational status of each component of the hydrogen-powered drone is monitored to obtain status monitoring data, and operational energy consumption data is determined based on the status monitoring data. Load analysis is performed based on the status monitoring data to obtain load data, and the power control data of the hydrogen-electric power system of the hydrogen-powered UAV is determined based on the load data and the operation energy consumption data. Flight state transition analysis of hydrogen-powered UAVs is performed based on rotor weights to obtain flight state transition data, and power optimization allocation data is determined based on the flight state transition data and the power regulation data. An adaptive controller is constructed, and the power output of the hydrogen-electric power system of the hydrogen-powered UAV is controlled based on the adaptive controller and the power optimization allocation data. The step of performing load analysis based on the status monitoring data to obtain load data, and determining the power regulation data of the hydrogen-electric power system of the hydrogen-powered UAV based on the load data and operational energy consumption data, includes: performing data association based on the status monitoring data of each component of the hydrogen-powered UAV to obtain several corresponding association data; performing data fusion on the several association data to obtain fused data, and performing load analysis on the fused data to obtain load association data for each component; performing correlation analysis based on the load association data of each component to obtain load data; and determining the power regulation data of the hydrogen-electric power system of the hydrogen-powered UAV based on the load data and operational energy consumption data combined with voltage control strategy, current control strategy, and load characteristic control strategy. The step of performing flight state transition analysis on a hydrogen-powered UAV based on rotor weights to obtain flight state transition data, and determining optimal power allocation data based on the flight state transition data and the power regulation data, includes: performing power demand analysis on the hydrogen-powered UAV during flight state transitions to obtain power demand data; performing rotor weight analysis on the hydrogen-powered UAV during flight state transitions to obtain rotor weight data, and determining flight state transition data based on the power demand data and rotor weight data; determining the power supply allocation space based on the flight state transition data combined with a hydrogen fuel cell model and a lithium battery model, and determining optimal power allocation data based on the power supply allocation space combined with the power regulation data. The rotor weight analysis for flight state transition of the hydrogen-powered UAV, to obtain rotor weight data, includes: acquiring the transition mode data of the hydrogen-powered UAV and determining the upper limit and lower limit of the transition speed of the hydrogen-powered UAV; and performing rotor weight analysis for flight state transition of the hydrogen-powered UAV based on the transition mode data, the upper limit and lower limit of the transition speed to obtain rotor weight data.

2. The power control method for a hydrogen-powered unmanned aerial vehicle according to claim 1, characterized in that, The monitoring of the operational status of each component of the hydrogen-powered drone, obtaining status monitoring data, and determining operational energy consumption data based on the status monitoring data includes: The sensor array based on the hydrogen-powered drone monitors the operating status of each component and obtains status monitoring data. Divide the condition monitoring data into time periods to obtain condition monitoring data after time period division; Based on the state monitoring data after time phase division, motor driving force analysis and power consumption analysis are performed to obtain motor driving force data and power consumption data. The operational energy consumption data of hydrogen-powered drones was determined based on motor drive force data and power consumption data.

3. The power control method for a hydrogen-powered unmanned aerial vehicle according to claim 1, characterized in that, The construction of the adaptive controller includes: A fractional-order complex network system is constructed based on the object model of a hydrogen-powered drone, and a state error system is constructed based on the fractional-order complex network system. A target proportional-integral-derivative PID controller is constructed based on the state error system and the internal model controller, and an adaptive controller is constructed based on the target PID controller and the Popov stability criterion.

4. The power control method for a hydrogen-powered unmanned aerial vehicle according to claim 3, characterized in that, The proportional-integral-derivative PID controller constructed by combining the state error system with the internal model controller includes: Obtain the relative order of the object model, and construct the internal model controller based on the relative order using a low-pass filter function; The transfer function is calculated based on the preset proportional coefficient, preset integral coefficient and preset derivative coefficient, and a conventional PID controller is generated based on the transfer function. The parameters of a conventional PID controller are tuned to obtain a parameter-tuned PID controller. A target PID controller is then constructed based on the internal model controller, the state error system, and the parameter-tuned PID controller.

5. An electrical power control device for a hydrogen-powered unmanned aerial vehicle, characterized in that, The device includes: Operational energy consumption determination module: used to monitor the operating status of each component of the hydrogen-powered UAV, obtain status monitoring data, and determine operational energy consumption data based on the status monitoring data; Power regulation determination module: used to perform load analysis based on the status monitoring data, obtain load data, and determine the power regulation data of the hydrogen-electric power system of the hydrogen-powered UAV based on the load data and the operation energy consumption data; Power optimization allocation module: used to perform flight state transition analysis on hydrogen-powered UAV based on rotor weights, obtain flight state transition data, and determine power optimization allocation data based on the flight state transition data and the power regulation data; Power output control module: used to construct an adaptive controller and control the power output of the hydrogen-electric power system of the hydrogen-powered UAV based on the adaptive controller and power optimization allocation data; The step of performing load analysis based on the status monitoring data to obtain load data, and determining the power regulation data of the hydrogen-electric power system of the hydrogen-powered UAV based on the load data and operational energy consumption data, includes: performing data association based on the status monitoring data of each component of the hydrogen-powered UAV to obtain several corresponding association data; performing data fusion on the several association data to obtain fused data, and performing load analysis on the fused data to obtain load association data for each component; performing correlation analysis based on the load association data of each component to obtain load data; and determining the power regulation data of the hydrogen-electric power system of the hydrogen-powered UAV based on the load data and operational energy consumption data combined with voltage control strategy, current control strategy, and load characteristic control strategy. The step of performing flight state transition analysis on a hydrogen-powered UAV based on rotor weights to obtain flight state transition data, and determining optimal power allocation data based on the flight state transition data and the power regulation data, includes: performing power demand analysis on the hydrogen-powered UAV during flight state transitions to obtain power demand data; performing rotor weight analysis on the hydrogen-powered UAV during flight state transitions to obtain rotor weight data, and determining flight state transition data based on the power demand data and rotor weight data; determining the power supply allocation space based on the flight state transition data combined with a hydrogen fuel cell model and a lithium battery model, and determining optimal power allocation data based on the power supply allocation space combined with the power regulation data. The rotor weight analysis for flight state transition of the hydrogen-powered UAV, to obtain rotor weight data, includes: acquiring the transition mode data of the hydrogen-powered UAV and determining the upper limit and lower limit of the transition speed of the hydrogen-powered UAV; and performing rotor weight analysis for flight state transition of the hydrogen-powered UAV based on the transition mode data, the upper limit and lower limit of the transition speed to obtain rotor weight data.

6. An electronic device, the electronic device comprising a processor and a memory, characterized in that, The memory is used to store instructions, and the processor is used to call the instructions in the memory to cause the electronic device to execute the power control method of the hydrogen-powered drone as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed on an electronic device, cause the electronic device to perform the power control method for a hydrogen-powered drone as described in any one of claims 1 to 4.

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