A cooperative control method and system for a hydrogen fuel cell hybrid unmanned aerial vehicle

Abstract

This invention relates to the field of unmanned aerial vehicle (UAV) flight control technology, and more particularly to a cooperative control method and system for a hydrogen fuel cell hybrid UAV. The method establishes a cascaded state model of flight dynamics and energy dynamics, uses the actual total output power of the hybrid power system as an intermediate virtual control variable, introduces a disturbance observer to estimate lumped disturbances, and generates energy distribution control commands based on inversion sliding mode control to coordinate the distribution of output between the hydrogen fuel cell and the battery. This invention can achieve closed-loop coordination of flight control and energy management under conditions of slow fuel cell dynamic response and limited power ramp-up, improving the control accuracy, stability, and safety of UAVs in acceleration, climb, maneuvering, and complex disturbance environments.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) flight control technology, and more particularly to a cooperative control method and system for a hydrogen fuel cell hybrid UAV. Background Technology

[0002] With the increasing demand for long-endurance UAVs in scenarios such as power line inspection, pipeline inspection, geographic mapping, emergency communication, border patrol, marine monitoring, and disaster relief, traditional pure lithium battery UAVs, limited by battery energy density, discharge rate, and endurance, struggle to simultaneously meet the requirements of long-endurance flight and high-dynamic maneuverability. Hydrogen fuel cells, with their high energy density, short refueling time, and low operating emissions, are gradually becoming an important power source for long-endurance UAVs. Especially in fixed-wing, compound-wing, and vertical takeoff and landing (VTOL) UAVs, hydrogen fuel cells can provide a relatively stable and continuous power output during the cruise phase, thereby significantly extending the mission radius and endurance of the UAV.

[0003] However, hydrogen fuel cells are not ideal transient power sources. Affected by factors such as electrochemical reaction rates, hydrogen and oxygen supply systems, air compressors, humidity and temperature management, hydrogen fuel cells generally suffer from slow dynamic response, limited power ramp-up rates, and insufficient short-term overload capacity. When a drone is in situations such as takeoff, climb, acceleration, gust compensation, rapid attitude adjustment, or transitions between vertical / cruise modes, the propulsion system typically needs to obtain significant transient power within a short period. If the hydrogen fuel cell directly responds to flight control commands, the actual available power may lag behind the propulsion power demand, leading to increased speed or altitude tracking errors, slow attitude recovery, overcompensation by the lithium battery, bus voltage fluctuations, and even triggering the fuel cell power limiting protection. Therefore, in practical engineering, a hybrid power architecture combining a hydrogen fuel cell and a lithium battery is typically used, with the hydrogen fuel cell providing steady-state primary power and the lithium battery providing transient compensation power.

[0004] Regarding the aforementioned hybrid power architecture, various energy management methods have been proposed in existing technologies. For example, existing technical document CN113221258A discloses an energy management method for electric propulsion UAVs that combines propulsion power prediction (MPC). This method is used for electric propulsion UAVs powered by fuel cells and lithium batteries. It controls and schedules the hybrid power system using real-time flight data, optimizing energy management with the goal of minimizing hydrogen consumption during flight. It also incorporates propulsion power demand prediction based on deep neural networks to adapt to uncertainties arising from different flight conditions. Furthermore, it adjusts the output power of the fuel cell and lithium battery through optimization solutions, thereby improving the system's energy efficiency. This approach demonstrates that existing technologies have addressed the issues of uncertain propulsion power demand and complex energy scheduling in fuel cell-lithium battery hybrid UAVs, and have attempted to improve energy management effectiveness through power prediction and model predictive control.

[0005] However, the main technical focus of CN113221258A remains on optimizing the allocation of fuel cell and lithium battery output power based on the predicted propulsion power demand, which essentially falls under the category of energy management layer optimization scheduling. This type of method typically assumes that the flight control system has already provided the propulsion power demand, and the energy management system then performs subsequent responses and allocations based on this demand. In other words, there is still a degree of decoupling between the generation process of the flight control law and energy constraints such as the slow dynamic response and limited power ramp-up of the fuel cell. When the flight control system rapidly increases thrust or power demand under highly dynamic conditions, even if the energy management layer can identify the power shortfall, it often can only passively compensate for the power shortfall, making it difficult to actively constrain and coordinately correct the available power during the flight control command formation stage.

[0006] For example, existing technical document CN119088064A discloses a high-efficiency integrated planning and management method for hydrogen-powered UAVs. This method addresses the autonomous flight problem of hydrogen-powered UAVs in highly dynamic flight environments. First, it uses a trajectory planning method based on B-spline curves to plan a high-efficiency flight trajectory. Then, based on the planned trajectory information, it uses a global optimization method to provide a global SOC reference trajectory for subsequent energy management. Finally, in the real-time optimization and control of the hydrogen-lithium hybrid power system, it uses extended Kalman filtering to identify and update fuel cell model parameters, and provides a real-time optimized solution for online energy management through an adaptive equivalent hydrogen consumption minimization strategy. This scheme achieves high-efficiency integrated optimization of trajectory planning and energy management in hydrogen-powered UAVs, improving energy efficiency and making it suitable for long-endurance autonomous flight missions in complex environments.

[0007] However, CN119088064A focuses on integrated energy-saving optimization of trajectory planning, SOC reference, and online energy management. Specifically, it guides energy management strategies through high-efficiency trajectory planning and global SOC reference, improving overall energy utilization efficiency at both the mission planning and energy efficiency optimization levels. While this scheme incorporates trajectory planning information into the energy management process, its core remains the guidance of planning results for energy management. It does not further utilize the actual total output power of the hybrid power system as an intermediate virtual control variable for flight control, nor does it construct a unified control error surface for flight state tracking error and power tracking error, thus failing to synchronously generate flight control constraints and energy allocation commands using the same control law. Therefore, when the UAV experiences sudden wind disturbances, rapid climbs, or maneuvers, this type of method may still suffer from inconsistencies between the rate of change in flight control demands and the fuel cell's power supply response.

[0008] In summary, existing fuel cell hybrid unmanned aerial vehicle (UAV) technologies can improve energy management performance from the perspectives of propulsion power prediction, MPC optimization, trajectory planning, SOC reference trajectory, and adaptive equivalent hydrogen consumption optimization. They can also improve hydrogen economy and long-endurance flight capabilities to some extent. However, existing technologies generally rely on a series or weakly coupled architecture that allocates flight control output requirements to energy management, failing to fully resolve the contradiction between the slow dynamics of hydrogen fuel cells and the high dynamics of UAV flight control. Especially under power climb-limited conditions, if the speed, altitude, or attitude tracking commands generated by the flight control system are too aggressive, and the energy management system can only respond later, the actual total output power may fall short of the expected power demand, leading to problems such as increased tracking errors, energy system protection triggering, or reduced flight safety margins.

[0009] Therefore, there is an urgent need to propose a new collaborative control technology for hydrogen fuel cell energy management and flight control of unmanned aerial vehicles (UAVs). This technology should be able to introduce dynamic constraints of the energy system during the control law generation stage, taking into account flight state tracking requirements, actual available total power, fuel cell first-order hysteresis response, power ramp rate limit, and external disturbance compensation. This would form a closed-loop collaborative mechanism of flight performance requirements, available power, and control commands, thereby improving the stability, safety, and controllability of hydrogen fuel cell hybrid UAVs under acceleration, ramp, maneuver, and complex disturbance environments. Summary of the Invention

[0010] The technical objective of this invention is to address the problem that hydrogen fuel cell hybrid unmanned aerial vehicles (UAVs) are prone to mismatch between flight control commands and actual power supply capacity during acceleration, climb, maneuvering, and mode transitions. This is due to the slow dynamic response and limited power ramp of hydrogen fuel cells. Consequently, attitude or speed tracking performance deteriorates, lithium battery compensation pressure increases, and power limiting protection is even triggered. This invention proposes a cooperative control method for hydrogen fuel cell hybrid UAVs to achieve closed-loop coordination of flight control and power distribution under energy constraints, thereby improving the stability, safety, and controllability of the UAV under complex operating conditions. Firstly, to achieve the above objective, this invention adopts the following technical solution:

[0011] A cooperative control method for a hydrogen fuel cell hybrid unmanned aerial vehicle, the method comprising the following steps:

[0012] S1. Establish a cascaded state model between the flight state variables of the target flight control channel and the actual total output power of the hybrid power system, and introduce the first-order hysteresis response and power ramp-up constraint of the hydrogen fuel cell into the cascaded state model.

[0013] S2, construct a disturbance observer based on the flight state variables and the actual total output power to obtain the lumped disturbance estimate;

[0014] S3, using the actual total output power as an intermediate virtual control quantity, and generating the desired intermediate power command based on the target flight state, flight state tracking error and the lumped disturbance estimate;

[0015] S4. Construct a comprehensive sliding surface based on the power tracking error between the actual total output power and the expected intermediate power command, as well as the flight state tracking error, and generate an energy distribution control command based on the inversion sliding mode control.

[0016] S5, generate hydrogen fuel cell reference output power and battery reference output power according to the energy distribution control command, so that the actual total output power tracks the expected intermediate power command under the constraints of the first-order hysteresis response and power ramp rate, so as to perform closed-loop constraint on UAV flight control.

[0017] Preferably, the target flight control channel is any one of a speed channel, altitude channel, pitch angle channel, roll angle channel, yaw angle channel, or vertical speed channel; the flight state quantity is a real-time state quantity corresponding to the target flight control channel, and the target flight state is a desired state quantity output by the mission planning module or the flight control module.

[0018] And / or, the actual total output power of the hybrid power system is the sum of the actual output power of the hydrogen fuel cell and the actual output power of the battery referred to the power bus side; the actual output power of the hydrogen fuel cell is used to characterize the steady-state main power supply capability, and the actual output power of the battery is used to characterize the transient compensation power supply capability.

[0019] Preferably, in S1, the cascaded state model includes a flight dynamics part and an energy dynamics part; the flight dynamics part is used to characterize the relationship between the actual total output power, lumped disturbance and flight state change, and the energy dynamics part is used to characterize the relationship between energy distribution control command, hydrogen fuel cell slow dynamics, battery compensation and actual total output power change.

[0020] And / or, in S2, the lumped disturbance includes at least one of external wind field disturbance, flight dynamics modeling error, propulsion efficiency variation, motor output error, and energy system modeling error; the disturbance observer updates the lumped disturbance estimate in real time based on the flight state variables, the actual total output power, and known terms of the cascaded state model.

[0021] Preferably, in S3, the expected intermediate power command is determined jointly based on the target flight state change requirements, the flight state tracking error convergence requirements, and the lumped disturbance estimate compensation requirements, so that the expected intermediate power command simultaneously represents the power required for flight control and the power required for disturbance compensation.

[0022] Preferably, in S4, the integrated sliding surface is composed of a weighted average of flight state tracking error and power tracking error; when the flight state tracking error increases or the actual total output power lags behind the expected intermediate power command, the integrated sliding surface drives the energy distribution control command to synchronously correct the hydrogen fuel cell reference output power and the battery reference output power.

[0023] Preferably, in S5, the reference output power of the hydrogen fuel cell is constrained by an upper power limit, a lower power limit, and an upper power ramp-up limit; when the actual output power of the hydrogen fuel cell cannot meet the expected intermediate power command, the power gap is compensated by the battery reference output power; when the flight state tracking error and power tracking error decrease, the battery compensation power is reduced and the hydrogen fuel cell undertakes the steady-state main power supply.

[0024] Secondly, the present invention also provides a collaborative control system for hydrogen fuel cell energy management and flight control of unmanned aerial vehicles (UAVs). This system is used to implement the method described above, including: a flight status acquisition module, an energy status acquisition module, a cascaded state modeling module, a disturbance observation module, a virtual power command generation module, a comprehensive sliding surface construction module, a collaborative control law generation module, and a power allocation execution module.

[0025] The flight status acquisition module is used to acquire the flight status quantities and target flight status of the target flight control channel; the energy status acquisition module is used to acquire the actual total output power of the hybrid power system; the cascaded state modeling module is used to establish a cascaded state model between the flight status quantities and the actual total output power; the disturbance observation module is used to output the lumped disturbance estimate; the virtual power command generation module is used to use the actual total output power as an intermediate virtual control quantity and generate the expected intermediate power command; the integrated sliding surface construction module is used to construct an integrated sliding surface based on the flight status tracking error and the power tracking error; the cooperative control law generation module is used to generate energy allocation control commands; and the power allocation execution module is used to generate the hydrogen fuel cell reference output power and the battery reference output power based on the energy allocation control commands.

[0026] Preferably, the power distribution execution module includes a hydrogen fuel cell control unit, a battery management unit, and a motor drive unit; the hydrogen fuel cell control unit is used to adjust the reference output power of the hydrogen fuel cell according to the first-order hysteresis response and power ramp-up rate constraints, the battery management unit is used to compensate for the transient power gap of the hydrogen fuel cell, and the motor drive unit is used to perform propulsion control based on the actual total output power after combining the actual output power of the hydrogen fuel cell and the actual output power of the battery.

[0027] Thirdly, the present invention also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the method.

[0028] Fourthly, the present invention also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the method.

[0029] Compared to existing technologies that design flight control and energy management separately, this invention integrates flight state requirements, actual available total power, and energy distribution control into a single closed-loop collaborative control framework, under constraints such as slow dynamic response and limited power ramp-up of hydrogen fuel cells. By using the total output power of the hybrid power system as an intermediate virtual control variable and combining disturbance observation and inversion sliding mode control, it performs real-time power constraints and dynamic corrections on the speed, attitude, or altitude tracking process of the UAV, thereby effectively mitigating the energy mismatch problem caused by rapid changes in control commands and slow energy supply response. Under highly dynamic conditions such as UAV acceleration, climb, maneuvering flight, and mode transitions, it can avoid risks such as increased tracking errors, decreased flight quality, overcompensation of lithium batteries, triggering of fuel cell power limit protection, and even flight instability caused by insufficient transient power. At the same time, by using online estimation and compensation of lumped disturbances, it can also improve the disturbance rejection performance and state convergence speed under complex wind field or model uncertainty conditions, enabling the system to ensure control stability while taking into account the rationality of energy utilization, thereby improving the flight safety, control accuracy, power coordination capability, and overall controllability of hydrogen fuel cell hybrid UAVs in complex mission scenarios. Attached Figure Description

[0030] Figure 1 This is a flowchart illustrating the overall process of a cooperative control method for a hydrogen fuel cell hybrid unmanned aerial vehicle according to the present invention.

[0031] Figure 2 This is a structural block diagram of the collaborative control system of the present invention.

[0032] Figure 3 This is a diagram showing the relationship between the cascaded state-space model of flight dynamics and energy dynamics of this invention.

[0033] Figure 4 This is a diagram illustrating the disturbance observation and virtual power command generation of the present invention.

[0034] Figure 5 This is a diagram showing the relationship between closed-loop execution and feedback in this invention.

[0035] Figure 6 The speed tracking curves of the embodiments and comparative examples of the present invention under the condition of climbing and accelerating to a constant altitude of 1000m are shown.

[0036] Figure 7 The figures show the height variation curves of the embodiments and comparative examples of the present invention under the same working conditions.

[0037] Figure 8 The figures represent the time history curves of the expected intermediate power command, the actual total output power, the hydrogen fuel cell output power, and the battery output power in embodiments of the present invention.

[0038] Figure 9The velocity recovery curves of the embodiments and comparative examples of the present invention under gust wind disturbance conditions are shown. Detailed Implementation

[0039] The present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the following specific embodiments are only used to clearly and completely illustrate the present invention, and should not be construed as limiting the scope of protection of the present invention. Without departing from the concept of the present invention, those skilled in the art can make equivalent transformations or conventional substitutions to the controlled object, state selection method, module implementation method, and parameter tuning method, all of which should fall within the scope of protection of the present invention.

[0040] This embodiment uses the speed channel coordinated control of a hydrogen fuel cell hybrid UAV during the climb acceleration to a fixed altitude flight condition as an example for illustration. However, the present invention is not limited to the speed channel, and is also applicable to target flight control channels such as altitude, pitch angle, roll angle, yaw angle, or vertical speed.

[0041] I. Terminology Explanation

[0042] To facilitate understanding of this invention, the main terms in this specification are explained as follows.

[0043] 1. Target flight control channel: refers to the flight status channel that the UAV needs to control at present. It can be any one of the following: speed channel, altitude channel, pitch angle channel, roll angle channel, yaw angle channel or vertical speed channel.

[0044] 2. Flight state variables This refers to the real-time state quantity corresponding to the target flight control channel. For example, in the speed channel embodiment, This indicates the real-time flight speed of the drone; in the altitude channel embodiment, This indicates the drone's real-time altitude or vertical speed.

[0045] 3. Actual total output power : refers to the total power actually output by the hybrid power system to the power bus or propulsion side, preferably the sum of the actual output power of the hydrogen fuel cell and the actual output power of the battery.

[0046] 4. Target flight status : Refers to the desired flight state output by the mission planning module or flight control module, such as desired speed, desired altitude or desired attitude angle.

[0047] 5. Lumped disturbance : refers to the comprehensive disturbances that affect changes in flight status but are difficult to model completely and accurately, including external wind field disturbances, aerodynamic modeling errors, propulsion efficiency changes, motor output errors, and energy system modeling errors.

[0048] 6. Disturbance estimates : refers to the disturbance observer's observation of lumped disturbances The real-time estimation results.

[0049] 7. Expected intermediate power command : Refers to the total expected power value generated based on the target flight state, flight state tracking error and disturbance estimate, used to characterize the power support capability required for flight control.

[0050] 8. Power point tracking error : refers to the actual total output power With expected intermediate power command The deviation between them.

[0051] 9. Composite sliding surface : refers to the composite error surface composed of flight status tracking error and power tracking error, used to generate energy distribution control commands.

[0052] 10. Energy Distribution Control Command : Refers to the control quantity output by the co-controller, used to coordinate the reference output power of the hydrogen fuel cell and the reference output power of the battery.

[0053] II. System Structure

[0054] Combination Figure 2 and Figure 5 The collaborative control system of the hydrogen fuel cell hybrid unmanned aerial vehicle of the present invention includes a flight status acquisition module, an energy status acquisition module, a cascaded state modeling module, a disturbance observation module, a virtual power command generation module, a comprehensive sliding surface construction module, a collaborative control law generation module, and a power allocation execution module.

[0055] The flight status acquisition module is used to acquire the current flight status parameters of the UAV and the target flight status. Specifically, this module can be connected to the inertial measurement unit, airspeed indicator, altimeter, satellite positioning module, attitude calculation module, and mission planning module to obtain real-time speed, altitude, attitude angle, angular velocity, vertical velocity, and desired trajectory information. For speed channel control, the flight status acquisition module outputs the current flight speed and target flight speed; for altitude channel control, it outputs the current altitude and target altitude; and for attitude channel control, it outputs the current attitude angle and target attitude angle.

[0056] The energy status acquisition module is used to obtain the actual total output power of the hybrid power system. The hybrid power system includes a hydrogen fuel cell system, a battery system, a power bus, a motor driver, and a propulsion motor. The energy status acquisition module collects parameters such as the actual output power of the hydrogen fuel cell, the actual output power of the battery, the power bus voltage, the power bus current, the battery state of charge, the battery current, and the fuel cell operating status. It then converts the actual output power of the hydrogen fuel cell and the actual output power of the battery to the same power bus side to form the actual total output power.

[0057] Preferably, the actual total output power satisfies:

[0058] ;

[0059] in, This represents the actual total output power of the hybrid power system. This represents the actual output power of the hydrogen fuel cell. This represents the actual output power of the battery. All power figures above are converted to the power bus side.

[0060] The cascaded state modeling module is used to establish a cascaded state model of flight dynamics and energy dynamics. This module's role is not simply to establish a flight dynamics model, nor simply to establish an energy management model, but rather to place flight state variables and actual total output power into the same state chain, enabling the flight control target to directly perceive energy supply capabilities.

[0061] The disturbance observation module estimates the lumped disturbance in real time based on flight state variables, actual total output power, and the cascaded state model, and outputs the disturbance estimate. The virtual power command generation module uses the actual total output power as an intermediate virtual control variable and generates the desired intermediate power command based on the flight state tracking error and the disturbance estimate. The integrated sliding surface construction module constructs an integrated sliding surface based on the flight state tracking error and power tracking error. The cooperative control law generation module generates energy distribution control commands based on the integrated sliding surface. The power distribution execution module converts the energy distribution control commands into the hydrogen fuel cell reference output power and the battery reference output power, which are then executed by the motor drive unit for propulsion control.

[0062] Combination Figure 5 It can be seen that the hydrogen fuel cell branch is preferred to provide steady-state main power, while the battery branch is preferred to provide transient compensation power. When the expected intermediate power command increases rapidly but the hydrogen fuel cell cannot immediately follow due to response time and power ramp-up rate limitations, the battery branch compensates for the power gap; as the flight state stabilizes and the fuel cell output gradually builds up, the battery compensation power gradually decreases.

[0063] III. Specific Technical Route for Implementing the Method of the Invention

[0064] like Figure 1 As shown, the method of the present invention is executed according to S1 to S5. In each control cycle, the system first acquires the flight status and energy status, then establishes or updates the cascaded state model, then estimates the disturbance, generates the desired intermediate power command, constructs the integrated sliding surface, calculates the energy distribution control command, and distributes it to the hydrogen fuel cell system and the battery system for execution. Finally, it enters the next control cycle through flight status feedback and energy status feedback.

[0065] S1, Establish the cascaded state model

[0066] In S1, a cascaded state model is established between the flight state variables of the target flight control channel and the actual total output power of the hybrid power system, and the first-order hysteresis response and power ramp-up rate constraint of the hydrogen fuel cell are introduced into the cascaded state model.

[0067] Combination Figure 3 The preferred cascaded state model is as follows:

[0068] ;

[0069] in, For the target flight control channel, the flight state quantity. This represents the actual total output power of the hybrid power system. For energy distribution control commands, For aggregated disturbances, and For flight dynamics, a nonlinear function is known. and This is an energy system function that includes the response characteristics of a hydrogen fuel cell. for The first derivative with respect to time, for The first derivative with respect to time.

[0070] This invention departs from the traditional flight control logic of first outputting thrust or power demand and then passively allocating it through the energy management system. Instead, it uses the actual total output power of the hybrid power system. It is directly incorporated into the flight state equations, making it an intermediate state variable that affects changes in flight state. In other words, the flight controller considers how much power the current hybrid power system can actually provide when generating control commands, rather than having the energy management system determine whether it can supply power after the flight commands are generated.

[0071] In traditional decoupled architectures, flight controllers often focus only on speed, altitude, or attitude errors, directly increasing throttle, thrust, or motor power demands based on these errors. When the UAV is accelerating, climbing, or operating under wind disturbances, flight control commands may increase rapidly. However, hydrogen fuel cells, due to limitations in electrochemical reactions, gas supply systems, and thermal and humidity management systems, cannot quickly increase output power in a short period. Even with battery compensation, current spikes, bus voltage fluctuations, or fuel cell power limiting protection may occur. In contrast, this invention incorporates the actual total output power into the flight control closed loop through a cascaded state model, enabling the control system to recognize energy supply constraints at the state model level.

[0072] Preferably, the first-order hysteresis response of a hydrogen fuel cell can be described by the following relationship:

[0073] ;

[0074] in, The actual output power of the hydrogen fuel cell The first derivative with respect to time, This is the reference output power for the hydrogen fuel cell. is the response time constant of the hydrogen fuel cell.

[0075] Meanwhile, the reference output power of the hydrogen fuel cell meets the power ramp-up constraint:

[0076] ;

[0077] in, Reference output power for hydrogen fuel cells The first derivative with respect to time, This represents the upper limit of the permissible power ramp-up rate for hydrogen fuel cells.

[0078] The aforementioned first-order hysteresis response and power ramp-up rate constraints reflect the engineering fact that hydrogen fuel cells cannot respond as quickly as lithium batteries to transient high-power demands. In this invention, these constraints are not placed in isolation at the energy management layer, but are embedded in the cascaded state model and further participate in the generation of subsequent expected intermediate power commands and energy distribution control commands. Therefore, this step provides a model foundation for subsequent coordination between flight control and energy management.

[0079] In engineering implementation, those skilled in the art can obtain data from existing UAV flight mechanics models, power motor power models, and hydrogen fuel cell bench test data. , , , The specific values ​​or approximate mapping relationships. For example, in the speed control channel, This can represent the effects of aerodynamic drag, the component of gravity along the flight path, and basic propulsion efficiency on speed changes. This represents the driving effect of total output power on the change in velocity; in energy dynamics, It can represent the slow dynamics of fuel cells, cell voltage changes, and bus regulation dynamics. This indicates the control effect of energy distribution control commands on changes in total output power.

[0080] thus, Figure 3 The flight dynamics and energy dynamics components form a cascade relationship: energy distribution control commands First, it affects the actual total output power. Actual total output power Further affecting flight status quantities This technical approach is the basis for the difference between this invention and traditional independent energy management strategies.

[0081] S2, Constructing a disturbance observer

[0082] In S2, a disturbance observer is constructed based on the flight state variables and the actual total output power to obtain the lumped disturbance estimate.

[0083] Combination Figure 4 The disturbance observer preferably adopts the following form:

[0084] ;

[0085] in, This is the estimated value of the lumped disturbance. For the internal state variables of the perturbation observer, For the perturbation observer gain parameter, For flight state quantities, and For flight dynamics, a nonlinear function is known. This represents the actual total output power. for The first derivative with respect to time.

[0086] Preferably, the disturbance estimation error satisfies:

[0087] ;

[0088] in, For aggregated disturbances, This is the estimated value of the lumped disturbance. This is the upper bound for the preset disturbance estimation error.

[0089] In real-world flight environments, external gusts, aerodynamic parameter deviations, load variations, propulsion efficiency fluctuations, and energy system modeling errors all affect flight status changes. Without estimating these uncertainties, the controller might misinterpret external disturbances as insufficient energy supply, blindly increasing power demands and leading to excessive battery compensation; conversely, it might misinterpret energy system response lags as external disturbances, failing to compensate for power shortfalls in a timely manner. This invention uses a disturbance observer to estimate lumped disturbances in real time, enabling subsequent expected intermediate power commands to compensate for wind disturbances and model uncertainties.

[0090] like Figure 4 In the middle, flight state quantity and actual total output power Simultaneously, it enters the disturbance observer, making the disturbance estimate... It is correlated with current energy supply capacity, rather than being derived solely from flight state errors. Therefore, perturbation observations can more accurately serve the subsequent generation of virtual power commands.

[0091] S3, Generate the desired intermediate power command

[0092] In S3, the actual total output power is used as an intermediate virtual control quantity, and the desired intermediate power command is generated based on the target flight state, flight state tracking error and lumped disturbance estimate.

[0093] In traditional methods, flight controllers typically output thrust, motor speed, or throttle requirements directly, while the energy management system allocates power to the fuel cell and battery based on these requirements in a later stage. The drawback of this structure is that the flight controller is unaware of whether the fuel cell can respond quickly enough, nor does it consider whether the battery will experience current spikes due to excessive transient compensation. This invention, however, uses the actual total output power... As an intermediate virtual control variable, the flight control target is first converted into the desired intermediate power command. The power command is then tracked by the energy system.

[0094] Preferably, the flight status tracking error is defined as:

[0095] ;

[0096] in, For flight status tracking error, For real-time flight status quantities, The target is in flight status.

[0097] The desired intermediate power command can be expressed as:

[0098] ;

[0099] in, For the desired intermediate power command, for The inverse mapping, Target flight status The first derivative with respect to time, For flight dynamics, a nonlinear function is known. This is the estimated value of the lumped disturbance. Adjust the gain for flight status error. This refers to the flight status tracking error.

[0100] The engineering meaning of the above formula is: if you want the flight state According to the target flight status For convergence to occur, the system needs to provide total power matching this convergence requirement; simultaneously, this total power also needs to compensate for known flight dynamics terms and disturbance estimates. Therefore, the expected intermediate power command is... It is not a simple empirical power value, but a power command that simultaneously includes the requirements for changes in target flight status, flight error convergence, and disturbance compensation.

[0101] Combination Figure 4 In this step, the flight state quantity Target flight status and disturbance estimates These factors work together in the virtual power command generation module. If the UAV's speed is lower than the target speed during climb acceleration, the flight status tracking error will occur. prompt Increase; if a windward disturbance also exists, then the disturbance estimate is... It will also enter power command calculation, causing the system to increase power compensation in advance; if the drone approaches the target speed and target altitude, then Decrease The power output gradually decreases to avoid continuous high power output from the battery.

[0102] In this step, the actual total output power is no longer a passively measured value, but rather an intermediate virtual control object for flight control. Through this process, the flight controller no longer directly demands that the propulsion system immediately provide a large thrust, but instead first establishes a power target that the energy system can track. Subsequently, under the combined constraints of the fuel cell's slow dynamics and battery compensation capabilities, a cooperative control law drives the actual total output power to approach this target. In this way, a proactive coupling is formed between flight control and energy constraints, rather than a reactive remedy.

[0103] The fundamental problem with hydrogen fuel cell drones is not a lack of energy, but rather that while the fuel cell has sufficient steady-state energy, its transient power response is slow. If a traditional hierarchical flight control and energy management architecture is still used, the flight control system may continuously issue control commands exceeding the transient energy capacity. This invention addresses this issue by… Converting flight requirements into intermediate power requirements, and then constraining them through subsequent power tracking errors and integrated sliding surfaces, can significantly reduce the conflict between flight control commands and energy supply capabilities.

[0104] S4, Construct the integrated sliding surface and generate energy distribution control commands.

[0105] In S4, a comprehensive sliding surface is constructed based on the power tracking error between the actual total output power and the desired intermediate power command, as well as the flight state tracking error, and energy distribution control commands are generated based on the inversion sliding mode control.

[0106] First, define the power tracking error:

[0107] ;

[0108] in, For power tracking error, This represents the actual total output power. This is the desired intermediate power command.

[0109] The optimal structure for the integrated sliding surface is as follows:

[0110] ;

[0111] in, To integrate the sliding surface, For sliding surface parameters, For flight status tracking error, This represents the power tracking error.

[0112] Conventional sliding mode control typically constructs a sliding surface only around flight state errors, such as velocity or attitude errors, to form the control objective. However, the integrated sliding surface of this invention simultaneously incorporates flight state tracking errors and power tracking errors, thus including flight state deviations and energy supply deviations in the same convergence objective. In other words, the control system not only requires the UAV's speed, altitude, or attitude to be close to the desired values, but also requires the actual total output power to be close to the desired intermediate power command.

[0113] Based on the integrated sliding surface, the present invention preferably generates the following energy distribution control commands:

[0114] ;

[0115] in, For energy distribution control commands, for The inverse mapping, For the desired intermediate power command The first derivative with respect to time, For sliding surface parameters, and For flight dynamics, a nonlinear function is known. This represents the actual total output power. This is the estimated value of the lumped disturbance. Let the first derivative of the target's flight state with respect to time be denoted as . and For energy system functions, For sliding mode switching gain, For linear damping gain, It is a symbolic function.

[0116] The control law mentioned above Used to characterize the changing trend of the expected intermediate power command; Used to feed back flight status change requirements and disturbance compensation requirements to the energy control layer; Used to compensate for the known dynamics of energy systems; Used to enhance the system's robustness to disturbances and uncertainties; Used to improve the smoothness of sliding surface convergence.

[0117] Combination Figure 4 and Figure 5 It can be seen that the integrated sliding surface is not an isolated mathematical variable, but a core interface connecting flight control and energy management. If the flight state error is large but the actual total output power can track the expected intermediate power command in time, the power compensation requirement is relatively small; if both the flight state error and the power tracking error are large, the integrated sliding surface will drive the energy distribution control command to increase, causing the battery compensation branch to quickly participate and the fuel cell reference output power to gradually increase according to the allowable climb rate.

[0118] In traditional methods, flight error is regulated by flight control, and power error is regulated by energy management. These two are only connected in series via thrust or power demand, making it difficult to guarantee consistent convergence targets. This invention unifies the two types of errors through a combined sliding mode surface, enabling flight state convergence and power supply matching to be achieved simultaneously under the same control law. Therefore, this invention can better prevent the actual total output power from lagging behind flight demand under conditions of rapid climb, rapid acceleration, and gust disturbances, thereby reducing speed error, negative altitude deviation, and battery current surges.

[0119] Preferably, to prove the stability of the control closed loop, a Lyapunov function can be selected:

[0120] ;

[0121] in, It is a Lyapunov function. For the integrated sliding surface.

[0122] When the control parameters meet:

[0123] ;

[0124] in, For sliding mode switching gain, For sliding surface parameters, When the upper bound of the perturbation estimation error is given, we can obtain:

[0125] ;

[0126] in, Lyapunov function The first derivative with respect to time, For linear damping gain, For the integrated sliding surface.

[0127] Therefore, it can be seen that the integrated sliding surface has convergence, and the flight state tracking error and power tracking error can be compressed simultaneously.

[0128] S5 performs power allocation and forms closed-loop constraints.

[0129] In S5, the reference output power of the hydrogen fuel cell and the reference output power of the battery are generated according to the energy distribution control command, so that the actual total output power tracks the expected intermediate power command under the constraints of first-order hysteresis response and power ramp rate, so as to provide closed-loop constraints for the UAV flight control.

[0130] Combination Figure 5 The power distribution execution module first adjusts the reference output power of the hydrogen fuel cell according to the energy distribution control command. Since the hydrogen fuel cell is constrained by an upper power limit, a lower power limit, and an upper power ramp-up rate limit, its reference output power cannot change abruptly without restriction. Preferably, the reference output power of the hydrogen fuel cell is updated according to the limiting and ramp-up rate constraints in each control cycle.

[0131] When the actual output power of the hydrogen fuel cell fails to meet the expected intermediate power command, the battery reference output power is used to compensate for the power shortfall. Preferably, the battery reference output power is determined based on the difference between the expected intermediate power command and the actual output power of the hydrogen fuel cell, and is constrained by the battery state of charge, the upper limit of battery current, and the safe range of bus voltage.

[0132] In engineering implementation, if the UAV is in the climb and acceleration phase, the expected intermediate power command increases rapidly. The reference output power of the hydrogen fuel cell gradually increases according to the upper limit of the climb rate, and the battery branch provides transient compensation. When the UAV approaches the target speed and altitude, the expected intermediate power command decreases or tends to stabilize, and the battery compensation power decreases accordingly. The hydrogen fuel cell gradually takes over the steady-state main power supply. This avoids prolonged high-rate discharge of the battery and also avoids the fuel cell being forced to climb rapidly and triggering protection.

[0133] This step provides the necessary execution path for the engineering implementation of the control law of this invention. Figure 5 It can be seen that the power allocation execution module is not an independent energy optimizer, but rather the execution end of the cooperative control law. Its execution results are re-entered through flight status feedback and energy status feedback. Figure 1 The control flow shown forms a complete closed loop.

[0134] IV. Examples and Comparative Examples

[0135] (a) Test Platform

[0136] To verify the technical effects of this invention, a hydrogen fuel cell hybrid unmanned aerial vehicle (UAV) was used for semi-physical bench and closed-field flight verification. The UAV has a maximum takeoff mass of 26 kg, a main propulsion motor rated power of 4.2 kW, and a power bus rated voltage of 56 V. The hydrogen fuel cell has a rated continuous output power of 2.5 kW, a short-term allowable output power of 3.0 kW, a response time constant of 2.7 s, and an upper limit for the allowable power climb rate of 0.30 kW / s. The battery pack is 14 S / 20 Ah, with a continuous discharge capacity of 2.0 kW and a 10 s peak compensation capacity of 3.5 kW. The flight control calculation cycle is 0.02 s, and the state sampling frequency is 50 Hz.

[0137] The embodiment employs the cooperative control method of this invention. The comparative example uses a traditional open-loop decoupled control method, where the flight controller generates propulsion power demand based on speed or altitude errors, and the energy management layer allocates fuel cell and battery power using fixed rules, without constructing a virtual control quantity for the actual total output power or a comprehensive sliding surface.

[0138] (II) Test conditions

[0139] The test conditions involved climbing and accelerating to a fixed altitude of 1000m. The UAV's initial altitude was 820m, and the target altitude was 1000m; the initial speed was 16m / s, and the target speed was 24m / s. Each test was repeated three times, and the values ​​in the table are averaged. In the gust disturbance test, equivalent frontal and lateral disturbances were introduced during the climb phase to verify the disturbance recovery capability.

[0140] (III) Speed ​​Tracking Effect

[0141] Figure 6 The speed tracking curves of the embodiments and comparative examples of the present invention are shown under the condition of climbing and accelerating to a constant altitude of 1000m. Figure 6 It is evident that the comparative model exhibits significant lag and large overshoot during the acceleration phase, while the embodiment of the present invention can approach the target speed more quickly and with smaller overshoot.

[0142] The root mean square error of the velocity is calculated using the following formula:

[0143] ;

[0144] in, For the root mean square error of the velocity, The total number of sampling points. For the first The actual speed of each sampling point For the first The target speed at each sampling point.

[0145] Speed ​​overshoot is calculated using the following formula:

[0146] ;

[0147] in, For speed overshoot, To be the maximum actual speed during acceleration, The target speed.

[0148] Table 1 shows the velocity tracking results.

[0149] Table 1 Speed ​​Tracking Results

[0150]

[0151] As shown in Table 1, this invention achieves faster convergence of speed error through virtual control of actual total output power and integrated sliding surface control, proving that... Figure 6 The speed tracking curve shown is supported by data.

[0152] (iv) Height retention effect

[0153] Figure 7 The height variation curves of the embodiments of the present invention and the comparative examples are shown under the same operating conditions. Figure 7 As can be seen, the comparative model shows a significant negative altitude deviation during the climbing acceleration process, while the altitude trajectory of the embodiment of the present invention is closer to the target altitude trajectory.

[0154] The maximum negative height deviation is defined as:

[0155] ;

[0156] in, For the maximum negative height deviation, For the target altitude trajectory, This refers to the actual height.

[0157] Table 2 shows the height preservation results.

[0158] Table 2 Results of Height Maintenance

[0159]

[0160] From Table 2 and Figure 7 It can be seen that the present invention can reduce the drop in altitude caused by power response lag during climbing acceleration and improve altitude maintenance capability.

[0161] (v) Power synergy effect

[0162] Figure 8 The diagram illustrates the time history curves of the expected intermediate power command, actual total output power, hydrogen fuel cell output power, and battery output power in an embodiment of the present invention. Figure 8 It is evident that in the initial stage of accelerated ramp-up, the expected intermediate power command increases, and the output power of the hydrogen fuel cell increases slowly due to the ramp-up rate constraint, with the battery output power quickly compensating for the power gap; after entering the stable stage, the battery output power gradually decreases, and the hydrogen fuel cell takes over as the main power supply.

[0163] Table 3 provides power data at critical moments.

[0164] Table 3 Power Data at Critical Moments

[0165]

[0166] As shown in Table 3, the actual total output power is basically consistent with the sum of the output power of the hydrogen fuel cell and the battery. For example, at 30s, 2.41kW plus 0.76kW equals 3.17kW; at 45s, 2.56kW plus 0.28kW equals 2.84kW; and at 60s, 2.08kW plus 0.13kW equals 2.21kW. There are no power balance errors in the data.

[0167] The integral of power deficit is defined as:

[0168] ;

[0169] in, For the power deficit integral, For a moment The expected intermediate power command, For a moment The actual total output power and To examine the start and end times of the interval.

[0170] Table 4 presents the power coordination index.

[0171] Table 4 Power Coordination Index Table

[0172]

[0173] Depend on Figure 8As can be seen from Table 4, the present invention not only improves flight status tracking performance, but also reduces battery transient load and avoids protection intervention, proving that the power cooperative control of the present invention has practical technical effects.

[0174] (vi) Anti-disturbance effect of gusts

[0175] Figure 9 The velocity recovery curves of the embodiments of the present invention and the comparative examples are shown under gust wind disturbance conditions. Figure 9 As can be seen, after the gust disturbance occurs, the speed of the comparative model decreases significantly and has a long recovery tail; the speed decrease in the embodiment of the present invention is smaller and recovers to the target speed more quickly.

[0176] The absolute error of the velocity integral of the disturbance window is defined as:

[0177] ;

[0178] in, The absolute error is the integral of the perturbation window velocity. This refers to the actual flight speed. For the target flight speed, and The start and end times of the disturbance window.

[0179] Table 5 presents the results of the gust disturbance test.

[0180] Table 5 Results of Gust Disturbance Test

[0181]

[0182] Depend on Figure 9 As shown in Table 5, after the disturbance observer is combined with the integrated sliding surface, the system can identify the impact of gust disturbances on the flight state more quickly, and simultaneously correct the flight state and power state through the expected intermediate power command and energy distribution control command, thereby significantly improving the disturbance recovery capability.

[0183] (vii) Summary of Implementation Results

[0184] As can be seen from Tables 1 to 5, this invention, under conditions of slow dynamic response and limited power ramp-up in hydrogen fuel cells, can unify flight control and energy management into a single closed-loop control framework through a virtual control quantity of the actual total output power. Compared with the comparative example, this invention achieves significant improvements in speed tracking, altitude maintenance, power deficit, battery current surge, SOC consumption, and gust recovery. This is because this invention does not passively allocate energy after the flight control command is issued, but rather uses the actual total output power as an intermediate virtual control quantity during the control law generation stage. It unifies flight state tracking error and power tracking error into a comprehensive sliding surface and executes power allocation based on the first-order hysteresis response and power ramp-up rate constraints of the hydrogen fuel cell. Therefore, this invention can effectively alleviate the contradiction between rapid changes in flight control commands and slow energy supply response, improving the stability, safety, and controllability of hydrogen fuel cell hybrid UAVs under climbing, acceleration, maneuvering, and gust disturbance environments.

[0185] The foregoing description of embodiments of the present invention, through which those skilled in the art are able to implement or use the present invention, will be readily apparent to those skilled in the art. Various modifications to these embodiments will be readily apparent to those skilled in the art. The general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novelty disclosed herein.

[0186] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0187] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0188] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0189] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0190] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0191] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0192] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

Claims

1. A cooperative control method for a hydrogen fuel cell hybrid unmanned aerial vehicle, characterized in that, The method includes the following steps: S1. Establish a cascaded state model between the flight state variables of the target flight control channel and the actual total output power of the hybrid power system, and introduce the first-order hysteresis response and power ramp-up constraint of the hydrogen fuel cell into the cascaded state model. S2, construct a disturbance observer based on the flight state variables and the actual total output power to obtain the lumped disturbance estimate; S3, using the actual total output power as an intermediate virtual control quantity, and generating the desired intermediate power command based on the target flight state, flight state tracking error and the lumped disturbance estimate; S4. Construct a comprehensive sliding surface based on the power tracking error between the actual total output power and the expected intermediate power command, as well as the flight state tracking error, and generate an energy distribution control command based on the inversion sliding mode control. S5, generate hydrogen fuel cell reference output power and battery reference output power according to the energy distribution control command, so that the actual total output power tracks the expected intermediate power command under the constraints of the first-order hysteresis response and power ramp rate, so as to perform closed-loop constraint on UAV flight control.

2. The control method according to claim 1, characterized in that, The target flight control channel is any one of the following: speed channel, altitude channel, pitch angle channel, roll angle channel, yaw angle channel, or vertical speed channel; the flight state quantity is the real-time state quantity corresponding to the target flight control channel, and the target flight state is the expected state quantity output by the mission planning module or the flight control module. And / or, the actual total output power of the hybrid power system is the sum of the actual output power of the hydrogen fuel cell and the actual output power of the battery referred to the power bus side; the actual output power of the hydrogen fuel cell is used to characterize the steady-state main power supply capability, and the actual output power of the battery is used to characterize the transient compensation power supply capability.

3. The control method according to claim 1, characterized in that, In S1, the cascaded state model includes a flight dynamics part and an energy dynamics part; the flight dynamics part is used to characterize the relationship between the actual total output power, lumped disturbance and flight state changes, and the energy dynamics part is used to characterize the relationship between energy distribution control commands, hydrogen fuel cell slow dynamics, battery compensation and actual total output power changes. And / or, in S2, the lumped disturbance includes at least one of external wind field disturbance, flight dynamics modeling error, propulsion efficiency variation, motor output error, and energy system modeling error; the disturbance observer updates the lumped disturbance estimate in real time based on the flight state variables, the actual total output power, and known terms of the cascaded state model.

4. The control method according to claim 1, characterized in that, In S3, the expected intermediate power command is determined jointly based on the target flight state change requirements, the flight state tracking error convergence requirements, and the lumped disturbance estimate compensation requirements, so that the expected intermediate power command simultaneously represents the power required for flight control and the power required for disturbance compensation.

5. The control method according to claim 1, characterized in that, In S4, the integrated sliding surface is composed of a weighted average of flight state tracking error and power tracking error. When the flight state tracking error increases or the actual total output power lags behind the expected intermediate power command, the integrated sliding surface drives the energy distribution control command to synchronously correct the hydrogen fuel cell reference output power and the battery reference output power.

6. The control method according to claim 1, characterized in that, In S5, the reference output power of the hydrogen fuel cell is constrained by an upper power limit, a lower power limit, and an upper power ramp rate limit. When the actual output power of the hydrogen fuel cell cannot meet the expected intermediate power command, the power gap is compensated by the battery reference output power. When the flight state tracking error and power tracking error decrease, the battery compensation power is reduced and the hydrogen fuel cell takes over the steady-state main power supply.

7. A cooperative control system for a hydrogen fuel cell hybrid unmanned aerial vehicle, characterized in that, The system is used to implement the method described in any one of claims 1-6, comprising: a flight status acquisition module, an energy status acquisition module, a cascaded state modeling module, a disturbance observation module, a virtual power command generation module, a comprehensive sliding surface construction module, a cooperative control law generation module, and a power allocation execution module; The flight status acquisition module is used to acquire the flight status quantities and target flight status of the target flight control channel; the energy status acquisition module is used to acquire the actual total output power of the hybrid power system; the cascaded state modeling module is used to establish a cascaded state model between the flight status quantities and the actual total output power; the disturbance observation module is used to output the lumped disturbance estimate; the virtual power command generation module is used to use the actual total output power as an intermediate virtual control quantity and generate the expected intermediate power command; the integrated sliding surface construction module is used to construct an integrated sliding surface based on the flight status tracking error and the power tracking error; the cooperative control law generation module is used to generate energy allocation control commands; and the power allocation execution module is used to generate the hydrogen fuel cell reference output power and the battery reference output power based on the energy allocation control commands.

8. The system according to claim 7, characterized in that, The power distribution execution module includes a hydrogen fuel cell control unit, a battery management unit, and a motor drive unit. The hydrogen fuel cell control unit is used to adjust the reference output power of the hydrogen fuel cell according to the first-order hysteresis response and power ramp-up constraints. The battery management unit is used to compensate for the transient power deficit of the hydrogen fuel cell. The motor drive unit is used to perform propulsion control based on the actual total output power after combining the actual output power of the hydrogen fuel cell and the actual output power of the battery.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the method of any one of claims 1-6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the method of any one of claims 1-6.

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