Hydrogen energy unmanned aerial vehicle health monitoring and management method based on hybrid excitation operator

Abstract

The application provides a hydrogen energy unmanned aerial vehicle health monitoring and management method based on a hybrid excitation operator, and belongs to the field of hydrogen energy unmanned aerial vehicles, and comprises the following steps: establishing a hydrogen fuel cell voltage estimator based on online parameter identification, estimating the voltage output in real time, generating a voltage residual with a voltage measurement value, and simultaneously acquiring the maximum output power of the fuel cell; designing a hybrid excitation operator, injecting high-frequency excitation into the fuel cell through a DC / DC converter, injecting low-frequency excitation that does not affect the flight stability of the active unmanned aerial vehicle, online calculating characteristic frequency impedance by using the injected excitation signal and the response voltage signal, and taking the characteristic frequency impedance as a fault diagnosis feature; designing a fault classifier, classifying faults into three categories of water flooding, membrane drying and oxygen deficiency based on the characteristic frequency impedance; and controlling components such as an air inlet fan and an exhaust valve based on the specific fault type diagnosed to recover from the fault. The application realizes online health management of the hydrogen energy unmanned aerial vehicle, and is suitable for hydrogen energy unmanned aerial vehicle systems that need online health management.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of hydrogen energy unmanned aerial vehicles, and particularly relates to a hydrogen energy unmanned aerial vehicle health monitoring and management method based on a hybrid excitation operator. BACKGROUND

[0002] Hydrogen energy unmanned aerial vehicles have the advantages of high energy density, long endurance time, and no pollution emissions, and play an important role in zero-carbon aviation, but their safety and reliability still need to be improved. Fuel cells, as the power core of hydrogen-powered unmanned aerial vehicles, their health management is crucial to improve the safety of the system and prolong its service life. The high dynamic and multi-scene characteristics of hydrogen energy unmanned aerial vehicles bring many challenges to the system health management: existing fuel cell fault diagnosis methods can be divided into two categories based on voltage and impedance according to the health indicators adopted. The high dynamic characteristics of unmanned aerial vehicles determine that the impedance-based method cannot be continuously online, because it relies on excitation injection, and continuous excitation injection will interfere with the flight of the unmanned aerial vehicle and even threaten the safety of the flight. The voltage-based method contains less health information, and it is difficult to distinguish different fault types, so it cannot take targeted recovery measures. Therefore, how to identify and classify fuel cell faults online is a major challenge. In addition, the change of environmental conditions brought by the multi-scene application of unmanned aerial vehicles will cause the characteristics of fuel cells to change, so that the residual index based on the static fuel cell model is no longer reliable, which will increase the false detection rate of faults, and the environmental adaptability of the management method is challenged.

[0003] Currently, there is no health management method specifically proposed for hydrogen energy unmanned aerial vehicles in the disclosed information, and there are studies on the fault diagnosis method of isolated fuel cells or fuel cell / lithium battery hybrid systems. Chinese patent application CN113447843A analyzes the electrochemical impedance spectrum of the fuel cell by establishing an equivalent circuit model, extracts the impedance spectrum characteristic parameters, and uses fuzzy rules to classify the fuel cell health loss degree into high, medium and low, but this method cannot be continuously operated online, and cannot take targeted recovery measures according to different health states. Chinese patent application CN112117475A trains a local reserved projection and learning vector quantization neural network based on voltage experimental data to diagnose the water flooding and membrane dry fault of the fuel cell, and Chinese patent application CN119165369A realizes fault diagnosis based on voltage by training a fuel cell fault diagnosis model, However, these methods rely on a large amount of experimental data and have high requirements for the deployment of hardware platforms, and at the same time, it is difficult to adapt to different fuel cells or cope with the fuel cell characteristic drift caused by changes in the unmanned aerial vehicle environment. In “C. Yan, J. Chen, H. Liu, L. Kumar, and H. Lu, “Health management for pem fuel cells based on an active fault tolerant control strategy,” IEEE Transactions on Sustainable Energy, vol. 12, no. 2, pp. 1311 1320, 2020”, an algorithm based on fuel cell voltage model for online fault detection and fault diagnosis through feature frequency impedance spectrum information is proposed, but the fuel cell model it adopts is a static model, without considering the inherent characteristics of fuel cell characteristic drift deviating from the preset model in the multi-scene application of unmanned aerial vehicles, lacking environmental adaptability. At the same time, the above methods do not consider the influence of fault monitoring or diagnosis algorithm on hydrogen energy unmanned aerial vehicle flight, nor do they consider the problem of excessive energy supply pressure in the excitation injection process, and cannot be directly applied to hydrogen energy unmanned aerial vehicles.

[0004] In summary, there is currently a lack of health monitoring and management methods designed for hydrogen energy unmanned aerial vehicles. In unmanned aerial vehicle applications, existing fuel cell fault diagnosis methods cannot achieve continuous monitoring and diagnosis, affect flight, have high energy supply pressure, and lack environmental adaptability, and cannot be directly applied to high-dynamic, multi-scenario hydrogen energy unmanned aerial vehicle applications. It is urgent to overcome the problem of closed-loop health management suitable for hydrogen energy unmanned aerial vehicles with online monitoring, diagnosis and recovery capabilities. SUMMARY

[0005] Aiming at the characteristics of multiple scenes and high dynamics of hydrogen energy unmanned aerial vehicle, there is no health management strategy specially designed for hydrogen energy unmanned aerial vehicle at present, and the existing fuel cell health management strategy cannot realize continuous monitoring and diagnosis, affects flight, has great energy supply pressure, and has insufficient environmental adaptability, and cannot be directly applied to hydrogen energy unmanned aerial vehicle, the present application provides a kind of hydrogen energy unmanned aerial vehicle health monitoring and management method based on hybrid excitation operator, first, a hydrogen fuel cell voltage estimator based on online parameter identification is established, the voltage output is estimated in real time, and the voltage residual is generated with the voltage measurement value, and the maximum output power of the fuel cell is obtained, so as to carry out health monitoring, and take emergency landing in the special case of insufficient maximum output power of fuel cell;Then, a hybrid excitation operator is designed, high-frequency excitation is injected into the fuel cell through the DC / DC converter, low-frequency excitation which does not affect the flight stability is injected into the unmanned aerial vehicle through the active and mobile unmanned aerial vehicle, the characteristic frequency impedance is calculated online by using the injected excitation signal and the response voltage signal, and it is used as the fault diagnosis feature;Secondly, a fault classifier is designed, and the fault is classified into three categories of water flooding, membrane dryness and oxygen deficiency based on the characteristic frequency impedance;Finally, based on the specific fault type diagnosed, the air fan, exhaust valve and other components are controlled to recover from the fault.The present application realizes the online health management of hydrogen energy unmanned aerial vehicle, realizes the functions of online fault monitoring and recovery, online fault diagnosis and other functions of fuel cell and unmanned aerial vehicle system, has the advantages of strong environmental adaptability, no damage to flight stability, reduction of energy subsystem pressure, etc., and is suitable for hydrogen energy unmanned aerial vehicle system which needs online health management.

[0006] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0007] A kind of hydrogen energy unmanned aerial vehicle health monitoring and management method based on hybrid excitation operator, comprising the following steps:

[0008] Firstly, a hydrogen fuel cell voltage estimator based on online parameter identification is established, the voltage output is estimated in real time, and the voltage residual is generated with the voltage measurement value, and the maximum output power of the fuel cell is obtained, so as to carry out health monitoring, and take emergency landing in the special case of insufficient maximum output power of fuel cell;

[0009] Secondly, a hybrid excitation operator is designed, high-frequency excitation is injected into the fuel cell through the DC / DC converter, low-frequency excitation which does not affect the flight stability is injected into the unmanned aerial vehicle through the active and mobile unmanned aerial vehicle, the characteristic frequency impedance is calculated online by using the injected excitation signal and the response voltage signal, and it is used as the fault diagnosis feature;

[0010] Thirdly, a fault classifier is designed, and the fault is classified into three categories of water flooding, membrane dryness and oxygen deficiency based on the characteristic frequency impedance;

[0011] Fourthly, based on the specific fault type diagnosed, the air fan, exhaust valve and other components are controlled to recover from the fault.

[0012] The beneficial effects of the present application compared with the prior art are:

[0013] The present application is aimed at the problem that the existing fuel cell health management strategy in hydrogen energy unmanned aerial vehicle design cannot realize continuous monitoring and diagnosis, affects flight, has great energy supply pressure, and has insufficient environmental adaptability, and cannot be directly applied to high dynamic and multi-scene hydrogen energy unmanned aerial vehicle applications, analyzes and designs a four-step system-level health monitoring and management framework including health monitoring, hybrid excitation operator injection, fault diagnosis, and fault recovery; The proposed systematic management framework of continuous monitoring-excitation injection-fault diagnosis-fault recovery realizes online continuous health monitoring and trigger-type fault diagnosis of hydrogen energy unmanned aerial vehicles through a fuel cell voltage estimator and a hybrid excitation operator, and classifies faults online through a feature frequency impedance as a diagnostic feature and takes recovery measures according to the fault category to realize closed-loop management; At the same time, the proposed hybrid excitation operator makes full use of the redundancy control channel of a quadrotor, changes the load power demand by designing a periodic yaw maneuver signal to inject a low-frequency excitation, and designs a sliding film flight controller to ensure the stability of the unmanned aerial vehicle trajectory tracking under the condition of low-frequency excitation injection, greatly compensates for the power fluctuation of the lithium battery during excitation injection, reduces the energy supply pressure of the energy system, and prolongs its service life. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 An algorithm framework diagram of a hydrogen energy unmanned aerial vehicle health monitoring and management method based on a hybrid excitation operator for embodiments of the present application;

[0015] Figure 2 A flowchart of the hybrid excitation operator in the present application;

[0016] Figure 3 A lithium battery output power graph when injecting low-frequency excitation using a DC / DC converter;

[0017] Figure 4 A lithium battery output power graph when injecting low-frequency excitation using the hybrid excitation operator of the present application;

[0018] Figure 5 A frequency graph of lithium battery output power fluctuations when injecting low-frequency excitation using a DC / DC converter and injecting low-frequency excitation using the hybrid excitation operator of the present application. DETAILED DESCRIPTION

[0019] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. In addition, the technical features involved in the various embodiments of the present application described below can be combined with each other as long as they do not conflict with each other. For example Figure 1 As shown in the drawings, the hydrogen energy unmanned aerial vehicle health monitoring and management method based on the hybrid excitation operator of the embodiment of the present application comprises the following steps:

[0020] The first step is to establish a hydrogen fuel cell voltage estimator based on online parameter identification, estimate the voltage output in real time, generate a voltage residual with the voltage measurement value, and obtain the maximum output power of the fuel cell, so as to perform health monitoring, and take emergency landing in the special case of insufficient maximum output power of the fuel cell;

[0021] The second step is to design a hybrid excitation operator, inject high-frequency excitation into the fuel cell through a DC / DC converter, actively inject low-frequency excitation which does not affect the flight stability through unmanned aerial vehicle maneuvering, calculate the characteristic frequency impedance online by using the injected excitation signal and the response voltage signal, and use it as a fault diagnosis feature;

[0022] The third step is to design a fault classifier, which classifies faults into three categories of water flooding, membrane drying and oxygen deficiency based on the characteristic frequency impedance;

[0023] The fourth step is to control the air inlet fan, exhaust valve and other exhaust components based on the specific fault type diagnosed to recover from the fault.

[0024] The present application realizes the functions of "control for sensing", online fault monitoring and recovery, online fault diagnosis, etc. of the hydrogen energy unmanned aerial vehicle, has the advantages of strong environmental adaptability, low energy subsystem pressure, etc., and is suitable for hydrogen energy unmanned aerial vehicle systems that need online health management.

[0025] Specifically, the first step comprises:

[0026] Step 1.1 establishes a fuel cell voltage model:

[0027] (1)

[0028] ;

[0029] wherein, is the voltage of a single fuel cell, is the Nernst potential determined by temperature and other factors, is the current density of the fuel cell, is the internal resistance of the fuel cell, is the limiting current density, , With unknown parameters, stack temperature, With anode hydrogen pressure and cathode oxygen pressure, respectively.

[0030] Define the unknown parameter vector , equation (1) can be expressed as where represents the measurement equation, and the superscript T represents the transpose of a matrix. It should be noted that the parameters in the fuel cell voltage model will change with the operating conditions and environmental factors. Therefore, an online parameter identification algorithm is designed to identify online to achieve accurate voltage estimation under dynamic environments.

[0031] Step 1.2 Design a voltage estimator based on an extended Kalman filter:

[0032] First, write the fuel cell voltage model as a discrete state-space equation:

[0033] ;

[0034] where subscript k represents the kth step, and are the process noise and measurement noise with known Gaussian distribution, respectively, and their covariance matrices are and , both with a mean of 0, is the Nernst potential energy calculated from the temperature, anode hydrogen pressure, and cathode oxygen pressure measured at the kth step, is the fuel cell current density at the kth step, and the algorithm design for online parameter identification is as follows:

[0035] ;

[0036] where the notation ^ represents the estimated value of the variable in question, the superscript - represents the prior estimate of the variable in question, is the state covariance matrix, is the output matrix.

[0037] Step 1.3 Update the model unknown parameter vector online through the algorithm for online parameter identification to obtain the real-time maximum power of the fuel cell :

[0038] ;

[0039] where is the number of single cells contained in the fuel cell, The effective reaction area of a single cell, The maximum power current point of the fuel cell, Satisfy:

[0040] ;

[0041] When , the emergency landing will be performed, the algorithm of online parameter identification ends, otherwise the subsequent steps are performed, wherein The power required for the UAV to hover.

[0042] At the same time, the voltage estimation residual error :

[0043] ;

[0044] Wherein, The measured value of the fuel cell voltage, The estimated value of the fuel cell voltage.

[0045] The voltage estimation residual error is calculated online, and when It is judged that a fault occurs when the set threshold is exceeded.

[0046] Specifically, as Figure 2 shown, the second step includes:

[0047] The mixed excitation operator is designed, the operator includes a high-frequency excitation signal for detecting the loss of proton transmission inside the fuel cell and a low-frequency excitation signal for detecting the mass transfer loss, which is injected into the system in the form of a current signal, and the signal generation process is as follows:

[0048] Step 2.1 The fuel cell current control signal is sent out by the DC / DC converter for injecting high-frequency excitation, wherein The fuel cell current, subscript des represents the desired signal, The high-frequency excitation amplitude, The high-frequency excitation signal frequency, The frequency corresponding to the intersection of the fuel cell impedance spectrum and the real axis is selected. The amplitude of the alternating component of each excitation current is selected to be 5-10% of the direct current component of the fuel cell operating current. The injection time of each excitation signal should be no less than 8 cycles.

[0049] Step 2.2 The periodic yaw command signal is sent to the UAV flight controller to generate periodic load demand power through the periodic yaw maneuver of the UAV, and then generate periodic fuel cell current for injecting low-frequency excitation, wherein The periodic yaw angle amplitude, The low-frequency excitation signal frequency, The frequency corresponding to the maximum value of the imaginary part of the impedance spectrum is selected.

[0050] The periodic yaw command brings strong nonlinear coupling between the pose state of UAV and the control input. To inject the excitation operator while ensuring the stability of the original flight path tracking, a flight controller based on sliding mode is designed, and its control law is:

[0051] ;

[0052] wherein, and are the tension and torque generated by the motor in the body coordinate system, i.e. the control input, and are the position vector and velocity vector of the UAV, is the first order time derivative of the desired trajectory, is the second order time derivative of the desired trajectory, is the gravity constant, is the angular velocity vector, is the transformation matrix from the body coordinate system to the inertial coordinate system, is the quaternion error determined by the current attitude angle, is the sliding mode vanishing term, , , and are adjustable parameters, is the integral sliding variable.

[0053] The motor speed can be determined by the control input:

[0054] ;

[0055] wherein, and are the tension and torque coefficients of the motor, is the distribution matrix determined by the quadrotor configuration and physical size, , , and are the speeds of each motor. After the motor speed is determined, the corresponding required power will be generated :

[0056] ;

[0057] wherein, , and are the voltage, current and speed of the i-th motor, , , , , are the internal resistance, KV value, no-load voltage, no-load current of the motor, respectively, is the auxiliary equipment power, including fuel cell controller, etc. The load power demand is injected into the fuel cell by the sinusoidal current through the fuel cell voltage model equation (1).

[0058] Step 2.3 Calculate the characteristic frequency by measuring the characteristic frequency and The sub-excitation current and the fuel cell voltage response online calculate the characteristic frequency impedance as the feature of fault diagnosis. The characteristic frequency impedance includes the high frequency impedance real part and the low frequency impedance imaginary part , which are calculated as follows:

[0059] ;

[0060] wherein, and are the real axis and imaginary axis components of the high frequency excitation current signal, respectively, and are the real axis and imaginary axis components of the high frequency response voltage signal, respectively, and are the real axis and imaginary axis components of the low frequency excitation current signal, respectively, and are the real axis and imaginary axis components of the low frequency response voltage signal, respectively, which are calculated as follows:

[0061] ;

[0062] ;

[0063] ;

[0064] ;

[0065] wherein, is the sampling start time, is the fuel cell voltage, is a positive integer. In this embodiment, and are selected as 1 kHz and 14 Hz, respectively, and the excitation current amplitude is selected as 4 A.

[0066] Specifically, the third step includes:

[0067] First, according to historical data and analysis, the is divided into high and medium categories, and the is divided into high, medium and low categories; then a fault classifier is designed according to mechanism analysis and expert knowledge, which includes the following fuzzy logic rules:

[0068] (1) If is medium and is low, the system state is normal;

[0069] (2) If is medium and is medium, the system state is water flooding;

[0070] (3) If is medium and is high, the system state is oxygen deficiency;

[0071] (4) If is high and is low, the system state is membrane dry;

[0072] (5) If is high and is medium, the system state is membrane dry;

[0073] (6) If is high and is high, the system state is oxygen deficiency.

[0074] The fault classifier is designed based on mechanism analysis and expert knowledge, and according to characteristic frequency impedance and the fuel cell fault is classified into three categories of water flooding, membrane dry and oxygen deficiency, which provides a basis for the fourth step of targeted recovery measures.

[0075] Specifically, the fourth step includes:

[0076] After detecting the fault, the actuators such as fans and valves will be controlled according to the diagnosis results to restore the fuel cell health as much as possible and ensure the safety of the unmanned aerial vehicle. When detecting the membrane dry fault, the speed of the cooling fan will be increased to reduce the fuel cell temperature to reduce the water evaporation rate; when detecting the water flooding fault, the purge time of the cathode drainage valve within one cycle will be increased to accelerate the discharge of excess water products in the fuel cell; when detecting the oxygen deficiency fault, the speed of the cathode air inlet fan will be increased to increase the oxygen supply amount per unit time. In the specific implementation case, when the membrane dry occurs, the speed of the cooling fan will be increased to 80%, when the water flooding is detected, the discharge cycle of the cathode drainage valve will be shortened from 4.5s to 2.5s to accelerate the discharge of product water, and when the oxygen deficiency is detected, the speed of the air inlet fan will be increased to 90%.

[0077] The actual flight power curve is used for comparison simulation, and the lithium battery power result in the injection excitation process is as shown in Figures 3-5 . The line in Figure 3 represents the lithium battery output power when using the DC / DC converter to inject low-frequency excitation. Figure 4The line in the figure represents the output power of the lithium battery when the low-frequency excitation is injected using the hybrid excitation operator of the application, and the power fluctuation of the lithium battery is significantly reduced, which is beneficial to prolong the service life of the lithium battery. Figure 5 The dashed line and the solid line in the figure respectively represent the frequency diagram of the output power fluctuation of the lithium battery when the low-frequency excitation is injected using the DC / DC converter and the hybrid excitation operator of the application, and the mean square error of the output power fluctuation of the lithium battery is reduced by 37% using the strategy of the application.

[0078] The contents not described in detail in the specification of the application belong to the prior art known to those skilled in the art.

Claims

1. A hydrogen energy unmanned aerial vehicle health monitoring and management method based on a hybrid excitation operator, characterized by, The method comprises the following steps: The first step is to establish a hydrogen fuel cell voltage estimator based on online parameter identification, estimate the voltage output in real time, generate a voltage residual with the measured voltage value, and obtain the maximum output power of the fuel cell to perform health monitoring and take emergency landing measures in the case of insufficient maximum output power of the fuel cell; The second step is to design a hybrid excitation operator, inject high-frequency excitation into the fuel cell through a DC / DC converter, inject low-frequency excitation that does not affect the flight stability of the UAV through active and maneuverable injection, calculate the characteristic frequency impedance online by using the injected high-frequency excitation and low-frequency excitation and the response voltage signal, and take the characteristic frequency impedance as the fault diagnosis feature; The third step is to design a fault classifier, which divides the fault types into three categories of water flooding, membrane drying and oxygen deficiency based on the characteristic frequency impedance; The fourth step is to control the exhaust components for fault recovery based on the specific fault type diagnosed.

2. The hydrogen energy unmanned aerial vehicle health monitoring and management method based on hybrid excitation operator according to claim 1, characterized in that, The first step comprises: The fuel cell voltage model is established as follows: (1) ； wherein, V is the single fuel cell voltage, T is the temperature dependent Nernst potential, J is the fuel cell current density, R is the fuel cell internal resistance, JLis the limiting current density, , V is the single fuel cell voltage, A is an unknown coefficient, T is the stack temperature, P is the pressure, P is the pressure, Define the model unknown parameter vector Then equation (1) is expressed as where denotes the measurement equation, and the superscript T denotes the transpose of a matrix.

3. The hydrogen energy unmanned aerial vehicle health monitoring and management method based on hybrid excitation operator according to claim 2, characterized in that, The first step further comprises: A fuel cell voltage estimator is designed based on an extended Kalman filter, the fuel cell voltage model is first written as a discrete state space equation form: ； where subscript k represents the kth step, is the model unknown parameter vector for the kth step, is the single cell fuel cell voltage for the kth step, and are the process noise and measurement noise with known Gaussian distribution, whose covariance matrices are and with mean 0, is the Nernst potential energy calculated from the measured temperature, anode hydrogen pressure and cathode oxygen pressure at the kth step, is the fuel cell current density for the kth step, and the algorithm design for online parameter identification is as follows: ； where the notation ^ represents the estimate of the variable in question, and the superscript - represents the prior estimate of the variable in question, is the state covariance matrix, is the output matrix.

4. The hydrogen energy unmanned aerial vehicle health monitoring and management method based on hybrid excitation operator according to claim 3, characterized in that, The first step further comprises: Updating a model unknown parameter vector on-line by an on-line parameter identification algorithm , obtaining real-time maximum power of the fuel cell : ； wherein is the number of monopolar cells contained in the fuel cell, is the effective reaction area of the monopolar cell, is the maximum power current point of the fuel cell, satisfies: ； When the emergency landing will be performed, the algorithm of online parameter identification ends, otherwise the subsequent steps are performed, in which the power required for the UAV to hover; At the same time, voltage estimation residuals are obtained by online parameter identification : ； wherein, is a fuel cell voltage measurement value, is a fuel cell voltage estimate; a voltage estimation residual is calculated online, and when the voltage estimation residual exceeds a set threshold then a fault is judged to have occurred.

5. The health monitoring and management method of hydrogen energy unmanned aerial vehicle based on hybrid excitation operator according to claim 2, characterized in that, In the second step, the hybrid excitation operator includes a high-frequency excitation signal for detecting the loss of proton transmission inside the fuel cell and a low-frequency excitation signal for detecting the mass transfer loss, which is injected in the form of a current signal, and the signal generation process includes: Fuel cell current control signal issued by DC / DC converter , for injecting high-frequency excitation, wherein is the fuel cell current, subscript des represents the desired signal, is the high-frequency excitation amplitude, is the high-frequency excitation signal frequency, is selected as the frequency corresponding to the intersection of the fuel cell impedance spectrum and the real axis, t represents time; the amplitude of the alternating component of each excitation current is selected as 5-10% of the direct current component of the fuel cell operating current, and the injection duration of the high-frequency excitation signal and the low-frequency excitation signal is not less than 8 cycles.

6. The hydrogen energy unmanned aerial vehicle health monitoring and management method based on hybrid excitation operator according to claim 5, characterized in that, The signal generation process further comprises issuing periodic yaw command signals to the drone flight controller generating periodic load demand power through periodic yaw maneuvers of the drone, and in turn periodic fuel cell current, for injection of the low frequency excitation, wherein is the periodic yaw angle amplitude, is the low frequency excitation signal frequency, is selected as the frequency corresponding to the maximum value of the imaginary part of the impedance spectrum; A UAV flight controller based on sliding mode is designed, and the control law is: ； wherein, and are the tension and torque generated by the motor in the body frame, i.e. the control input, is the z-axis component, are the x, y, z-axis components, and are the position and velocity vectors of the UAV, is the first time derivative of the desired trajectory, is the second time derivative of the desired trajectory, is the gravity constant, is the angular velocity vector, is the transformation matrix from the body frame to the inertial frame, is the quaternion error determined by the current attitude angle, is the sliding mode vanishing term, , , and are tunable parameters, is the integral sliding variable, is the desired angular velocity vector; The motor speed is determined by the control input: ； wherein, with T and M are the tension and moment coefficients of the motor respectively, is the distribution matrix determined by the quadrotor configuration and physical dimensions, , , and are the rotational speeds of the motors; the rotational speeds of the motors determine the corresponding required power : ； wherein, , with are the voltage, current and speed of the i-th motor, respectively, , , , , are the internal resistance, KV value, no-load voltage, no-load current of the motor, respectively, is the auxiliary power; the load power demand injects a sinusoidal current into the fuel cell through the fuel cell voltage model.

7. The hydrogen energy unmanned aerial vehicle health monitoring and management method based on hybrid excitation operator according to claim 6, characterized in that, The signal generation process further comprises calculating, as a feature for fault diagnosis, a characteristic frequency impedance by measuring the excitation current and fuel cell voltage response at high frequency excitation signal frequencies and low frequency excitation signal frequencies The characteristic frequency impedance comprises a high frequency impedance real part and a low frequency impedance imaginary part calculated as follows: ； wherein and are the real and imaginary components of the high frequency excitation current signal, respectively, and are the real and imaginary components of the high frequency response voltage signal, respectively, and are the real and imaginary components of the low frequency excitation current signal, respectively, and are the real and imaginary components of the low frequency response voltage signal, respectively, each component being calculated as follows: ； ； ； ； wherein is a sampling start time, is a fuel cell voltage, is a positive integer.

8. The health monitoring and management method of hydrogen energy unmanned aerial vehicle based on hybrid excitation operator according to claim 1, characterized in that, The third step comprises: Firstly, the high frequency impedance real part is divided into high and middle two classes according to historical data and analysis and the low frequency impedance imaginary part is divided into high, middle and low three classes. Then, the fault classifier is designed according to mechanism analysis and expert knowledge, which contains six fuzzy logic rules.

9. The hydrogen energy unmanned aerial vehicle health monitoring and management method based on hybrid excitation operator according to claim 8, characterized in that, The fuzzy logic rule is: (1) If is medium and is low, the system state is normal; (2) if is medium and is medium, the system state is waterflooded; (3) if is medium and is high, the system state is oxygen deficiency; (4) if is high and is low, then the system state is membrane dry; (5) if is high and is medium, then the system state is film dry; (6) If is high and is high, the system state is oxygen deficient.

10. The health monitoring and management method of hydrogen energy unmanned aerial vehicle based on hybrid excitation operator according to claim 1, characterized in that, The fourth step comprises: After detecting the fault, the actuator of the exhaust component is controlled according to the diagnosis result to restore the health of the fuel cell and ensure the safety of the UAV; when a membrane drying fault is detected, the speed of the cooling fan is increased to reduce the fuel cell temperature and reduce the water evaporation rate; when a water flooding fault is detected, the purge time of the cathode drain valve in one cycle is increased to accelerate the discharge of excess water products in the fuel cell; when an oxygen deficiency fault is detected, the speed of the cathode air inlet fan is increased to increase the oxygen supply per unit time.

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