Emergency return flight control method for unmanned aerial vehicle under power failure condition and medium

Abstract

The invention discloses an emergency homeward voyage control method for an unmanned aerial vehicle under a power failure condition and a medium. The method comprises the following steps: constructing a cascade PID path tracking controller; constructing a cascade model prediction path tracking controller; constructing a model prediction path tracking controller based on the extended state observer; a controller is determined according to the unmanned aerial vehicle power fault condition of emergency return, the unmanned aerial vehicle flight state, forced landing point information and external environment factors, and emergency return control is achieved. The emergency return path tracking controller is determined according to the return route provided by the current flight state, and the return route is tracked.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle path tracking control, and more particularly to an emergency return control method for an unmanned aerial vehicle in a power failure situation and a medium. BACKGROUND

[0002] In recent years, the explosive development of unmanned aerial vehicles has expanded their application to various industries. However, as unmanned aerial vehicles become more popular, power failure problems have become increasingly prominent. Light power failure may cause task failure or equipment damage, and heavy power failure may cause serious safety accidents and threaten personnel safety. In the case of known emergency return routes for unmanned aerial vehicles, existing control technologies (traditional PID control and LQR control) have significant limitations in observing external disturbances, including wind disturbance, and are difficult to ensure high-level flight stability and precise control, which may lead to flight trajectory deviation, attitude jitter, or even stall, which has become a major bottleneck for reliable operation in complex environments.

[0003] Therefore, it is necessary to develop an emergency return control method for an unmanned aerial vehicle in a power failure situation and a medium.

[0004] The information disclosed in the background section of this application is only intended to deepen the understanding of the general background of the present application and should not be considered as acknowledging or implying in any form that this information constitutes prior art known to those skilled in the art. SUMMARY

[0005] The present application provides an emergency return control method for an unmanned aerial vehicle in a power failure situation and a medium, which can determine which emergency return path tracking controller to use according to the return route provided by the current flight state.

[0006] In a first aspect, the present application provides an emergency return control method for an unmanned aerial vehicle in a power failure situation, comprising: constructing a cascade PID path tracking controller; constructing a cascade model predictive path tracking controller; constructing a model predictive path tracking controller based on an extended state observer; determining the controller according to the unmanned aerial vehicle power failure situation, the unmanned aerial vehicle flight state, the landing point information, and the external environmental factors to achieve emergency return control.

[0007] Preferably, the cascade PID path tracking controller is constructed by: the elevator channel is composed of a pitch stability control inner loop and a sink rate stability control outer loop; the aileron channel is composed of a roll stability control inner loop and a sideslip distance stability control outer loop; The rudder channel consists of a heading stability control loop; The throttle channel is set to the maximum throttle opening.

[0008] Preferably, the closed-loop control law for the sinking rate of the elevator channel is:

[0009] The closed-loop control law for the side offset of the aileron passage is:

[0010] The heading stability control law of the rudder channel:

[0011] in, , , These are pitch angle, roll angle, and sideslip angle, respectively. , These are the rate of change of height and the lateral offset, respectively. , , These are roll rate, pitch rate, and yaw rate, respectively. , The proportional and differential coefficients of the pitch angle. , , For the proportional, integral, and differential coefficients of the rate of change, , The proportional and differential coefficients of the roll angle. , , The proportional, integral, and differential coefficients of the lateral offset. This is the proportionality coefficient for the sideslip angle. is the differential coefficient of the heading angle.

[0012] Preferably, constructing a cascade model predictive path tracking controller includes: Reference path coordinates in three-dimensional space Based on the position and status of the unmanned aerial vehicle system model at any given time and speed state Calculate the acceleration of each axis; Based on the acceleration, the reference pitch angle, reference roll angle, and reference thrust of the UAV are calculated, and a state space model is constructed by combining the current attitude information of the UAV. Establish the model prediction equations and set the prediction time domain of the MPC controller as follows: l Control time domain is c The system is obtained in k Output model prediction equation at time t; Construct constraints and establish the cost function for path tracing; At each moment of the controller, the optimal control quantity of the system at that moment is solved by quadratic programming.

[0013] Preferably, the output model prediction equation is:

[0014] in, , , .

[0015] Preferably, the cost function for path tracing is:

[0016] in, , For the predicted time is i The weighting factor for the system output and control increment.

[0017] Preferably, constructing a model prediction path tracking controller based on an extended state observer includes: A third-order nonlinear system is constructed, and an extended state is introduced into the design of the ESO to determine the system's state equations, thereby determining the observation error. The tuning parameters are determined using the engineering parameter adjustment method. ESO uses the position observation error value as input to the PD controller to calculate the desired attitude angle, and then uses it as input to the MPC controller for path tracking.

[0018] Preferably, the controller, determined based on the drone's power failure during an emergency return, the drone's flight status and emergency landing point information, and external environmental factors, includes: If it is a single engine failure, determine whether the ratio of the horizontal distance to the relative altitude between the drone and the forced landing point is greater than the set value; If the ratio is greater than the set value, it is determined whether the current wind speed is less than 10m / s. If so, the controller is determined to be a cascaded PID path tracking controller; otherwise, the controller is determined to be a cascaded model predictive path tracking controller. If the ratio is not greater than the set value, then determine whether the current wind speed is less than 10 m / s. If yes, then determine that the controller is a cascaded model predictive path tracking controller. If no, then determine that the controller is a model predictive path tracking controller based on an extended state observer.

[0019] Preferably, the controller, determined based on the drone's power failure during an emergency return, the drone's flight status and emergency landing point information, and external environmental factors, includes: If the problem is a dual-engine failure, determine whether the ratio of the horizontal distance to the relative altitude between the drone and the forced landing point is greater than the set value. If the ratio is greater than the set value, it is determined whether the current wind speed is less than 10m / s. If so, the controller is determined to be a cascaded model predictive path tracking controller; otherwise, the controller is determined to be a model predictive path tracking controller based on an extended state observer. If the ratio is not greater than the set value, the controller is determined to be a model prediction path tracking controller based on an extended state observer.

[0020] Secondly, this disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned emergency return-to-home control method for unmanned aerial vehicles in the event of a power failure.

[0021] Its beneficial effects are as follows: When a drone experiences a power failure, the controller used during normal flight cannot accurately track the path. Existing technologies only design one type of controller, which cannot accurately track the path when facing changing environments and routes. When a drone makes an emergency return, factors such as the drone's flight status, destination distance, and wind speed will affect the drone's ability to effectively track the path. Designing different controllers according to different situations can effectively improve the accuracy and stability of the controller's path tracking.

[0022] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0023] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same parts.

[0024] Figure 1 A flowchart illustrating the steps of an emergency return-to-home control method for a UAV in the event of a power failure, according to an embodiment of the present invention, is shown.

[0025] Figure 2 A block diagram of a cascaded PID path tracking controller according to an embodiment of the present invention is shown.

[0026] Figure 3 A structural diagram of a cascaded model predictive path controller according to an embodiment of the present invention is shown.

[0027] Figure 4 A flowchart of the cascaded MPC path tracing controller according to an embodiment of the present invention is shown.

[0028] Figure 5 An ESO control block diagram according to an embodiment of the present invention is shown.

[0029] Figure 6 A structural diagram of an ESO-based MPC path tracing controller according to an embodiment of the present invention is shown.

[0030] Figure 7a and Figure 7b Schematic diagrams of path tracking simulation results for longitudinal height and lateral deviation according to an embodiment of the present invention are shown respectively.

[0031] Figure 8 A flowchart of the emergency return path tracking controller selection process according to an embodiment of the present invention is shown. Detailed Implementation

[0032] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0033] To facilitate understanding of the solutions and effects of the embodiments of the present invention, two specific application examples are given below. Those skilled in the art should understand that these examples are merely for the purpose of understanding the present invention, and any specific details therein are not intended to limit the present invention in any way.

[0034] Example 1

[0035] Figure 1 A flowchart illustrating the steps of an emergency return-to-home control method for a UAV in the event of a power failure, according to an embodiment of the present invention, is shown.

[0036] like Figure 1 As shown, the emergency return-to-home control method for this UAV in the event of a power failure includes: Step 101: Construct a cascaded PID path tracking controller; Step 102: Construct a cascade model predictive path tracking controller; Step 103: Construct a model-predictive path tracking controller based on an extended state observer; Step 104: Determine the controller based on the drone's power failure, flight status, emergency landing point information, and external environmental factors to achieve emergency return control.

[0037] In one example, building a cascaded PID path tracking controller includes: The elevator channel consists of an inner loop for pitch stabilization control and an outer loop for heave rate stabilization control. The aileron channel consists of an inner loop for roll stabilization control and an outer loop for side offset stabilization control; The rudder channel consists of a heading stability control loop; The throttle channel is set to the maximum throttle opening.

[0038] In one example, the closed-loop control law for the elevator channel's descent rate is:

[0039] The closed-loop control law for the side offset of the aileron passage is:

[0040] The heading stability control law of the rudder channel:

[0041] in, , , These are pitch angle, roll angle, and sideslip angle, respectively. , These are the rate of change of height and the lateral offset, respectively. , , These are roll rate, pitch rate, and yaw rate, respectively. , The proportional and differential coefficients of the pitch angle. , , For the proportional, integral, and differential coefficients of the rate of change, , The proportional and differential coefficients of the roll angle. , , The proportional, integral, and differential coefficients of the lateral offset. This is the proportionality coefficient for the sideslip angle. is the differential coefficient of the heading angle.

[0042] In one example, building a cascade model predicts a path tracing controller, which includes: Reference path coordinates in three-dimensional space Based on the position and status of the unmanned aerial vehicle system model at any given time and speed state Calculate the acceleration of each axis; Based on the acceleration, the reference pitch angle, reference roll angle, and reference thrust of the UAV are calculated, and a state space model is constructed by combining the current attitude information of the UAV. Establish the model prediction equations and set the prediction time domain of the MPC controller as follows: l Control time domain is c The system is obtained in k Output model prediction equation at time t; Construct constraints and establish the cost function for path tracing; At each moment of the controller, the optimal control quantity of the system at that moment is solved by quadratic programming.

[0043] In one example, the output model prediction equation is:

[0044] in, , , .

[0045] In one example, the cost function for path tracing is:

[0046] in, , For the predicted time is i The weighting factor for the system output and control increment.

[0047] In one example, building a model-predictive path tracking controller based on an extended state observer includes: A third-order nonlinear system is constructed, and an extended state is introduced into the design of the ESO to determine the system's state equations, thereby determining the observation error. The tuning parameters are determined using the engineering parameter adjustment method. ESO uses the position observation error value as input to the PD controller to calculate the desired attitude angle, and then uses it as input to the MPC controller for path tracking.

[0048] In one example, the controller is determined based on the following factors: drone power failure during emergency return, drone flight status and emergency landing point information, and external environmental factors: If it is a single engine failure, determine whether the ratio of the horizontal distance to the relative altitude between the drone and the forced landing point is greater than the set value; If the ratio is greater than the set value, it is determined whether the current wind speed is less than 10m / s. If so, the controller is determined to be a cascaded PID path tracking controller; otherwise, the controller is determined to be a cascaded model predictive path tracking controller. If the ratio is not greater than the set value, then determine whether the current wind speed is less than 10 m / s. If yes, then determine that the controller is a cascaded model predictive path tracking controller. If no, then determine that the controller is a model predictive path tracking controller based on an extended state observer.

[0049] In one example, the controller is determined based on the following factors: drone power failure during emergency return, drone flight status and emergency landing point information, and external environmental factors: If the problem is a dual-engine failure, determine whether the ratio of the horizontal distance to the relative altitude between the drone and the forced landing point is greater than the set value. If the ratio is greater than the set value, it is determined whether the current wind speed is less than 10m / s. If so, the controller is determined to be a cascaded model predictive path tracking controller; otherwise, the controller is determined to be a model predictive path tracking controller based on an extended state observer. If the ratio is not greater than the set value, the controller is determined to be a model prediction path tracking controller based on an extended state observer.

[0050] Specifically, the control objective of emergency return is to ensure the UAV flies along the flight path. This segment involves three-dimensional position control: longitudinally, pitch control is used to control the UAV's altitude, ensuring it tracks the flight path; laterally, roll control is used to ensure the lateral offset relative to the vertical plane containing the flight path is zero. For conventionally configured UAVs, the emergency return control performance requirements are a landing point position deviation of no more than 200m, a landing and ascent speed of no more than 3m / s, and a landing pitch angle of 3~9°.

[0051] Based on the influence of UAV flight status, engine status, emergency return location information, and external environmental factors, the emergency return control structure is selected according to certain rules, including cascade PID path tracking controller, cascade model predictive path tracking controller, and model predictive path tracking controller based on extended state observation.

[0052] (I) Constructing a Cascade PID Path Tracking Controller

[0053] Figure 2 A block diagram of a cascaded PID path tracking controller according to an embodiment of the present invention is shown.

[0054] like Figure 2 As shown, the elevator channel consists of an inner loop for pitch stabilization control and an outer loop for heave stabilization control; the aileron channel consists of an inner loop for roll stabilization control and an outer loop for side offset stabilization control; the rudder channel consists of a heading stabilization control loop; and the throttle channel is set to the maximum throttle opening.

[0055] Closed-loop control law for elevator channel descent rate:

[0056] Closed-loop control law for side offset of aileron passage:

[0057] The heading stability control law of the rudder channel:

[0058] in, , , These are pitch angle, roll angle, and sideslip angle, respectively. , These are the rate of change of height and the lateral offset, respectively. , , These are roll rate, pitch rate, and yaw rate, respectively. , The proportional and differential coefficients of the pitch angle. , , For the proportional, integral, and differential coefficients of the rate of change, , The proportional and differential coefficients of the roll angle. , , The proportional, integral, and differential coefficients of the lateral offset. This is the proportionality coefficient for the sideslip angle. The differential coefficients of the heading angle are used to set the proportional, integral, and derivative gain parameters of each channel PID controller using an adaptive optimization parameter tuning method.

[0059] (II) Constructing a cascade model for predicting path tracking controllers

[0060] Figure 3 A structural diagram of a cascaded model predictive path controller according to an embodiment of the present invention is shown.

[0061] Based on the Model Predictive Control (MPC) algorithm, a cascaded MPC path tracking controller is designed, such as... Figure 3 As shown, the outer loop is the position error control loop, and the inner loop is the MPC attitude control loop.

[0062] Reference path coordinates in three-dimensional space Based on the position and status of the unmanned aerial vehicle system model at any given time and speed state Calculate the acceleration of each axis. :

[0063] in , To control the system's stability coefficient, the UAV's reference pitch angle, reference roll angle, and reference thrust are calculated based on acceleration.

[0064] A state-space model is constructed based on the reference information provided by the outer-loop position controller and the current attitude information of the UAV, and a discretized model is obtained:

[0065] Establish the model prediction equations and set the prediction time domain of the MPC controller as follows: l Control time domain is c The system is obtained in k Output model prediction equation at time:

[0066] in , , The format is as follows:

[0067] Constructing constraints:

[0068] Establish the cost function for path tracing:

[0069] in, , For the predicted time is i The weighting factor for the system output and control increment.

[0070] Figure 4 A flowchart of the cascaded MPC path tracing controller according to an embodiment of the present invention is shown.

[0071] At each moment of the controller, the optimal control input for the current moment is solved using quadratic programming. The controller workflow is as follows: Figure 4 As shown.

[0072] (III) Constructing a model-predictive path tracking controller based on an extended state observer

[0073] Extended-State Observers (ESOs) expand the system's state space by adding virtual state variables, enabling effective observation and estimation of internal and external disturbances. ESOs require only the system's inputs and outputs, as well as its relative order, to expand the observation of system state variables, better capturing actual system behavior. They maintain good performance even in the face of unknown or uncertain system dynamics, improving control accuracy and stability. Compared to traditional cascade PID controllers and cascade MPC controllers, ESO-based MPC controllers can estimate and compensate for the total system disturbance in real time, thereby achieving active disturbance suppression and significantly improving system robustness.

[0074] Consider a third-order nonlinear system, its mathematical description is as follows:

[0075] in, Indicates external disturbance. Indicates control input, Indicates control output. b To represent system parameters, assuming the system state can be represented as... Then the above system can be represented as

[0076] In the design of ESO, an expansion state is introduced:

[0077] The state equation of the system can be described as follows:

[0078] The design of ESO is as follows:

[0079] in, Represented as state The estimated value, This is the state observation gain.

[0080] The observation error can then be expressed as:

[0081] Figure 5 An ESO control block diagram according to an embodiment of the present invention is shown.

[0082] when When the error is bounded, the above error equation can guarantee stability. An ESO control block diagram can be constructed using the above equation, as follows: Figure 5 As shown.

[0083] The only parameters that need to be tuned in the above structure are However, the position tracking system for UAVs is only a second-order system, and the parameters that need to be tuned are only... The parameters were set using the engineering parameter tuning method. Determine the tuning parameters:

[0084] Figure 6 A structural diagram of an ESO-based MPC path tracing controller according to an embodiment of the present invention is shown.

[0085] ESO uses the position observation error as input to the PD controller to calculate the desired attitude angle, and then uses it as input to the MPC controller for path tracking. Figure 6 This is a block diagram of the MPC path tracing controller based on ESO.

[0086] Figure 7a and Figure 7b Schematic diagrams of path tracking simulation results for longitudinal height and lateral deviation according to an embodiment of the present invention are shown respectively.

[0087] Based on the three control structures described above, a controller is designed, a path tracking controller and a UAV model are built, and path tracking simulation is performed. The simulation results are as follows: Figure 7a , Figure 7b As shown.

[0088] Figure 8 A flowchart of the emergency return path tracking controller selection process according to an embodiment of the present invention is shown.

[0089] Based on factors such as power failure in emergency return-to-home drones, drone flight status and emergency landing point information, and external environmental factors, a path tracking controller is selected according to certain rules. Figure 8 Flowchart for selecting an emergency return path tracking controller. First, determine if the UAV has dual engines. Then, determine if the ratio of the horizontal distance to the relative altitude between the UAV and the emergency landing point is greater than a set value (15). Finally, determine if the wind speed is less than 10 m / s. Based on these three determinations, select a suitable controller, specifically as follows: If it is a single engine failure, determine whether the ratio of the horizontal distance to the relative altitude between the drone and the forced landing point is greater than the set value; If the ratio is greater than the set value, it is determined whether the current wind speed is less than 10m / s. If yes, the controller is determined to be a cascaded PID path tracking controller; otherwise, the controller is determined to be a cascaded MPC path tracking controller. If the ratio is not greater than the set value, then determine whether the current wind speed is less than 10m / s. If yes, then determine that the controller is a cascaded MPC path tracking controller. If no, then determine that the controller is an ESO-based MPC path tracking controller.

[0090] If the problem is a dual-engine failure, determine whether the ratio of the horizontal distance to the relative altitude between the drone and the forced landing point is greater than the set value. If the ratio is greater than the set value, it is determined whether the current wind speed is less than 10m / s. If so, the controller is determined to be a cascaded MPC path tracking controller; otherwise, the controller is determined to be an ESO-based MPC path tracking controller. If the ratio is not greater than the set value, the controller is determined to be an ESO-based MPC path tracking controller.

[0091] Example 2

[0092] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned emergency return-to-home control method for a UAV in the event of a power failure.

[0093] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0094] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0095] Those skilled in the art should understand that the above description of the embodiments of the present invention is only intended to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.

[0096] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for emergency return control of a UAV in the event of a power failure, characterized by, The application relates to a controller for an unmanned aerial vehicle (UAV) in an emergency return mode. The controller comprises: a cascade PID path tracking controller; a cascade model prediction path tracking controller; a model prediction path tracking controller based on an extended state observer (ESO); 2.The emergency return control method of the UAV in the power failure situation according to claim 1, wherein, the controller is determined according to the power failure of the UAV in the emergency return mode, the flight state of the UAV, the information of a forced landing point and external environmental factors, and the emergency return control is realized. The cascade PID path tracking controller comprises: an elevator channel composed of an inner loop of pitch stability control and an outer loop of sink rate stability control; an aileron channel composed of an inner loop of roll stability control and an outer loop of sideslip distance stability control; a rudder channel composed of a heading stability control loop; 3.The emergency return control method of the UAV in the power failure situation according to claim 2, wherein, a throttle channel with a throttle opening degree being a maximum throttle opening degree. The sink rate closed-loop control law of the elevator channel is: The sideslip distance closed-loop control law of the aileron channel is: wherein, , , are a pitch angle, a roll angle, a side slip angle, respectively, , are a height rate of change, a side slip distance, respectively, , , are a roll angle rate, a pitch angle rate, a yaw angle rate, respectively, , is a proportional, a differential coefficient of the pitch angle, , , are a proportional, an integral, a differential coefficient of the height rate of change, , is a proportional, a differential coefficient of the roll angle, , , are a proportional, an integral, a differential coefficient of the side slip distance, is a proportional coefficient of the side slip angle, is a differential coefficient of the heading angle. 4.The emergency return control method of the UAV in the power failure situation according to claim 1, wherein, The heading stability control law of the rudder channel is: Reference path coordinates in three-dimensional space Based on the position and status of the unmanned aerial vehicle system model at any given time and speed state Calculate the acceleration of each axis; The cascade model prediction path tracking controller comprises: The model prediction equation is established, a prediction time domain of the MPC controller is set as l , a control time domain is set as c , and an output model prediction equation of the system at k is obtained. a state space model is constructed according to reference pitch angles, reference roll angles and reference thrusts calculated from accelerations and current attitude information of the UAV; a constraint is constructed, and a cost function of path tracking is established; 5.The emergency return control method of the UAV in the power failure case according to claim 4, wherein, at each moment of the controller, an optimal control amount of the current moment of the system is solved through quadratic programming. wherein , , . 6.The emergency return control method of the UAV in the power failure case according to claim 4, wherein, The output model prediction equation is: wherein, , is a weighting factor for the control increment to predict the system output at time i . 7.The emergency return control method of the UAV in the power failure situation according to claim 1, wherein, The cost function of path tracking is: The model prediction path tracking controller based on the ESO comprises: a third-order nonlinear system is constructed, an extended state is introduced in the design of the ESO, a state equation of the system is determined, and an observation error is determined; an engineering parameter tuning method is used to determine tuning parameters; 8.The emergency return control method of the UAV in the power failure situation according to claim 1, wherein, the ESO takes the error value of position observation as input to a PD controller to obtain an expected attitude angle, and takes the expected attitude angle as input to an MPC controller for path tracking. The controller determined according to the power failure of the UAV in the emergency return mode, the flight state of the UAV, the information of a forced landing point and external environmental factors comprises: if a single engine fails, whether the ratio of the horizontal distance between the UAV and the forced landing point to the relative height is greater than a set value is judged; if the ratio is greater than the set value, whether the current wind speed is less than 10 m / s is judged, if yes, the controller is determined to be the cascade PID path tracking controller, and if no, the controller is determined to be the cascade model prediction path tracking controller; 9.The emergency return control method of the UAV in the power failure situation according to claim 1, wherein, if the ratio is not greater than the set value, whether the current wind speed is less than 10 m / s is judged, if yes, the controller is determined to be the cascade model prediction path tracking controller, and if no, the controller is determined to be the model prediction path tracking controller based on the ESO. The controller determined according to the power failure of the UAV in the emergency return mode, the flight state of the UAV, the information of a forced landing point and external environmental factors comprises: if double engines fail, whether the ratio of the horizontal distance between the UAV and the forced landing point to the relative height is greater than a set value is judged; if the ratio is greater than the set value, whether the current wind speed is less than 10 m / s is judged, if yes, the controller is determined to be the cascade model prediction path tracking controller, and if no, the controller is determined to be the model prediction path tracking controller based on the ESO; if the ratio is not greater than the set value, the controller is determined to be the model prediction path tracking controller based on the ESO.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the emergency return control method of the unmanned aerial vehicle in the power failure case according to any one of claims 1-9.

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