Unmanned aerial vehicle track control method and system based on adaptive event triggering mechanism

Abstract

The invention relates to the technical field of unmanned aerial vehicle control, and provides an unmanned aerial vehicle trajectory control method and system based on an adaptive event trigger mechanism, and the method comprises the steps: combining an estimated value of external unit force interference and a first-order derivative of a position coordinate vector of an unmanned aerial vehicle based on a position signal expected value through a position controller; the control input of the position subsystem is obtained; after control input of a position subsystem is processed by an inverse solution device, attitude subsystem control input is obtained through an attitude controller in combination with a yaw angle reference value, an external torque interference estimated value and an attitude angle vector first-order derivative, and the attitude subsystem control input is updated by adopting a self-adaptive event triggering mechanism; the self-adaptive event triggering mechanism adjusts a triggering condition according to the variable quantity controlled and input by the attitude subsystem; and updating the position coordinate vector and the attitude angle vector of the unmanned aerial vehicle. And the flexibility and adaptability of unmanned aerial vehicle trajectory control are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle control, and in particular relates to a method and system for controlling the trajectory of an unmanned aerial vehicle based on an adaptive event triggering mechanism. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Quad-rotor drones are the most widely used, most versatile, and most deeply researched multi-rotor drones currently developed by various countries. For quad-rotor drones, achieving stable, efficient, and precise control is the key to improving their mission execution capabilities, which urgently requires the construction of a position and attitude control system that meets multiple needs to ensure this. First, considering that they are often affected by various external interferences including ground effects, gusts, and atmospheric turbulence during low-altitude flight, the designed control system needs to be sufficiently robust to cope with the adverse effects of external interference; second, in the actual flight control process, the calculation and measurement process of the sensor data related to it may have a certain degree of delay, so that the control signal cannot be transmitted to the actuator in time, which leads to a decrease in the controller efficiency; at the same time, as the actuator of the quad-rotor drone, the rotor speed is usually limited to a certain range, so the actuator saturation problem needs to be considered in the controller design process; finally, considering the limited computing and communication capabilities of the onboard flight control module of the quad-rotor drone, without reducing the performance of the controller, the controller design process should focus on the optimization of the algorithm structure and try to avoid the waste of computing and communication resources due to unnecessary sampling.

[0004] Existing research on flight control methods for quadrotor drones, such as publication number CN118034068A, patent "A fractional-order anti-saturation sliding mode control method and device for quadrotor drones"; publication number CN117311165A, patent "A specified fixed time control method for quadrotor drones based on disturbance observer"; publication number CN116795124A, patent "A quadrotor drone attitude control method based on dynamic event triggering"; publication number CN116225037A, patent "A quadrotor drone flight attitude adaptive event triggering control method" and other literatures mostly focus on the robust flight control problem under the influence of external interference and actuator saturation, or the flight control problem of quadrotor drones under the event trigger mechanism. However, there are few studies on the robust trajectory control problem of quadrotor drones under the event trigger mechanism that comprehensively considers the influence of external interference, input time lag and input saturation. Summary of the invention

[0005] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a UAV trajectory control method and system based on an adaptive event trigger mechanism. The designed adaptive event trigger mechanism automatically adjusts the trigger condition according to the change in the attitude subsystem control input, thereby improving the flexibility and adaptability of the UAV trajectory control.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A first aspect of the present invention provides a UAV trajectory control method based on an adaptive event triggering mechanism, which comprises:

[0008] Obtain the expected value of the position signal and the reference value of the yaw angle;

[0009] Based on the expected value of the position signal, combined with the estimated value of the external unit force disturbance and the first-order derivative of the position coordinate vector of the UAV, the control input of the position subsystem is obtained through the position controller;

[0010] The control input of the position subsystem is processed by the inverse solver, combined with the yaw angle reference value, the external torque disturbance estimation value and the first-order derivative of the attitude angle vector, and the attitude subsystem control input is obtained by the attitude controller, and the attitude subsystem control input is updated by an adaptive event trigger mechanism; the adaptive event trigger mechanism adjusts the trigger condition according to the change amount of the attitude subsystem control input;

[0011] Based on the control input of the position subsystem, the position coordinate vector of the UAV and the first-order derivative of the position coordinate vector of the UAV are updated through the position subsystem, and based on the position coordinate vector of the UAV, the external unit force interference estimate is updated through the position subsystem interference observer; based on the control input of the attitude subsystem, the attitude angle vector and the first-order derivative of the attitude angle vector are updated through the attitude subsystem, and based on the attitude angle vector, the external torque interference estimate is updated through the attitude subsystem interference observer.

[0012] Further, the adaptive event triggering mechanism is expressed as:

[0013]

[0014] in, t is the current time, σ and k are design parameters, is the adaptive update law of k, E(t)=U a (t i )-U a , t i is the sampling update time, U a is the control input of the attitude subsystem, e4 is the attitude tracking error, and Θ is the attitude angle vector.

[0015] Further, the position subsystem is expressed as:

[0016]

[0017] Among them, U p is the control input of the position subsystem, D p is the external unit force interference, m is the weight of the drone, g is the gravitational acceleration, ε=[0,0,1] T , X1 represents the position coordinate vector P of the drone, and X2 is the first-order derivative of the position coordinate vector of the drone

[0018] Further, the posture subsystem is expressed as:

[0019]

[0020] Among them, x n+1 is the intermediate variable, U Θ =g(Θ)U a , U a is the control input of the attitude subsystem, X3 is the attitude angle vector θ, and X4 is the first-order derivative of the attitude angle vector sat() is the saturation control input function, D Θ It is the external torque interference.

[0021] Further, the position controller is expressed as:

[0022]

[0023] Among them, U p is the control input of the position subsystem, is the estimated value of the external unit force interference, m is the weight of the drone, g is the gravitational acceleration, A1 and A2 are positive definite parameter matrices, P d is the expected value of the position signal, e1 is the position tracking error, and e2 is the first velocity tracking error.

[0024] Further, the posture controller is expressed as:

[0025]

[0026] Among them, U a is the control input of the attitude subsystem, Θ is the attitude angle vector, A4 is the positive definite parameter matrix, e3 is the attitude tracking error, e4 is the second velocity tracking error, is the estimated value of external torque disturbance, u c is the compensation controller, X 4d is the virtual control quantity.

[0027] Furthermore, the attitude angle vector includes a roll angle, a pitch angle and a yaw angle.

[0028] A second aspect of the present invention provides a UAV trajectory control system based on an adaptive event triggering mechanism, comprising:

[0029] A signal acquisition module, which is configured to: acquire an expected value of a position signal and a reference value of a yaw angle;

[0030] A position control module is configured to obtain a control input of a position subsystem through a position controller based on an expected value of the position signal, an estimated value of an external unit force disturbance and a first-order derivative of a position coordinate vector of the UAV;

[0031] The attitude control module is configured to: for the control input of the position subsystem, after being processed by the inverse solver, combine the yaw angle reference value, the external torque interference estimation value and the first-order derivative of the attitude angle vector, obtain the attitude subsystem control input through the attitude controller, and use the adaptive event trigger mechanism to update the attitude subsystem control input; the adaptive event trigger mechanism adjusts the trigger condition according to the change amount of the attitude subsystem control input;

[0032] The interference estimation module is configured as follows: based on the control input of the position subsystem, the position coordinate vector of the UAV and the first-order derivative of the position coordinate vector of the UAV are updated through the position subsystem, and based on the position coordinate vector of the UAV, the external unit force interference estimate is updated through the position subsystem interference observer; based on the control input of the attitude subsystem, the attitude angle vector and the first-order derivative of the attitude angle vector are updated through the attitude subsystem, and based on the attitude angle vector, the external torque interference estimate is updated through the attitude subsystem interference observer.

[0033] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the drone trajectory control method based on the adaptive event triggering mechanism as described above.

[0034] The fourth aspect of the present invention provides a computer device, including a computer-readable storage medium, a processor, and a computer program stored on the computer-readable storage medium and executable on the processor, wherein when the processor executes the program, the steps in the drone trajectory control method based on the adaptive event triggering mechanism as described above are implemented.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The adaptive event trigger mechanism designed in the present invention automatically adjusts the trigger condition according to the change in the attitude subsystem control input, thereby improving the flexibility and adaptability of the UAV trajectory control.

[0037] The present invention applies an interference observer to a quad-rotor UAV, effectively improving the anti-interference ability of the quad-rotor UAV control, enabling it to monitor external interference in real time and take timely measures, thereby improving the stability and flight safety of the quad-rotor UAV.

[0038] The present invention effectively solves the problems of attitude stability and trajectory precise tracking control of a quadrotor UAV when facing external interference, input delay and actuator saturation. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0040] Figure 1 is a flow chart of a UAV trajectory control method based on an adaptive event triggering mechanism according to Embodiment 1 of the present invention;

[0041] Figure 2 This is a model diagram of a 1 / 4 quad-rotor drone system according to the first embodiment of the present invention;

[0042] Figure 3 This is a tracking effect diagram of the trajectory control of the quad-rotor drone according to the first embodiment of the present invention;

[0043] Figure 4 This is a tracking effect diagram of the Euler angle of the quad-rotor drone according to the first embodiment of the present invention;

[0044] Figure 5 A scatter plot of the event triggering time intervals in the attitude subsystem of the quad-rotor drone according to the first embodiment of the present invention;

[0045] Figure 6 It is a structural diagram of a computer device according to a fourth embodiment of the present invention. DETAILED DESCRIPTION

[0046] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0047] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0048] Embodiment 1

[0049] This embodiment provides a UAV trajectory control method based on an adaptive event triggering mechanism.

[0050] The UAV trajectory control method based on the adaptive event triggering mechanism provided in this embodiment is suitable for Figure 2 The quadrotor drone shown.

[0051] In order to design a control system that better meets the actual flight requirements of a quad-rotor drone, the drone trajectory control method based on an adaptive event trigger mechanism provided in this embodiment comprehensively considers the influence of external interference, input time lag, and input saturation conditions, and proposes a robust trajectory tracking control strategy based on backstepping and adaptive event triggering mechanism. First, a nonlinear disturbance observer is used to estimate the unknown disturbances in the position and attitude loops; then, the Padé approximation and parameter adaptation methods are used to eliminate the adverse effects of input time lag and saturation; finally, in the framework of backstepping, combined with the adaptive event triggering mechanism, a robust tracking control strategy based on the adaptive event triggering mechanism is designed to achieve the trajectory tracking control goal of the quad-rotor drone.

[0052] The UAV trajectory control method based on the adaptive event trigger mechanism provided in this embodiment has the following specific implementation steps:

[0053] Step 1: Improve the existing quadrotor UAV model, fully consider the influence of external interference, input time lag and actuator saturation conditions, and build a quadrotor UAV dynamic model that meets actual control requirements.

[0054] In step 1, the dynamic characteristics of the quadrotor drone are analyzed, and the nonlinear dynamic model of the quadrotor drone with external interference, input delay and actuator saturation is constructed as follows:

[0055]

[0056] Where m is the weight of the quadcopter; g is the acceleration due to gravity; P = [x, y, z] T Represents the position coordinate vector of the quadrotor drone; Θ = [φ, θ, ψ] T are attitude angle vectors, representing roll angle, pitch angle and yaw angle respectively; U1 is the control input of the position subsystem; U Θ =g(Θ)U a , U a is the control input in the attitude subsystem, g() represents the control gain matrix function, which is a function of the attitude angle variable; ι is a small input time lag; ε=[0,0,1] T ;d p and D Θ Respectively represent external force and torque interference; R is the rotation matrix from the body coordinate system to the ground coordinate system; is the system state equation; Sat(U Θ) is the system saturation control input, and the specific form of the saturation control input function is as follows:

[0057]

[0058] in, Lower and upper bounds for each control input.

[0059] Step 2: According to the dynamic model of the position subsystem established in step 1, based on the given reference trajectory P d , the control input of the position subsystem is designed in the backstepping framework.

[0060] According to the dynamic model of the position subsystem established in step 1, based on the reference trajectory, the control input of the position subsystem is designed under the backstepping framework. The specific process is:

[0061] Replace P and X1 and X2 Then the dynamic model of the quadrotor drone position subsystem can be rewritten as follows:

[0062]

[0063] Among them, U p =U1Rε, external unit force disturbance

[0064] The position tracking error and first velocity tracking error of the quadrotor drone are defined as:

[0065] e1=X1-P d (4)

[0066] e2=X2-X 2d (5)

[0067] Among them, P d is the expected value of the position signal, X 2d is the virtual control quantity to be designed, which is designed as:

[0068]

[0069] Among them, A1 is the positive definite parameter matrix to be designed.

[0070] The control input of the position subsystem is designed as:

[0071]

[0072] Among them, A2 is the positive definite parameter matrix to be designed, is the estimated value of the external disturbance, which is obtained by the nonlinear disturbance observer of the following form:

[0073]

[0074] Among them, Z p is the system auxiliary variable, R p is the positive definite parameter matrix to be designed.

[0075] Control input U of the position subsystem p It can be written as U p =[U x ,U y ,U z ] T , Euler angle reference value ψ d The following relations are satisfied:

[0076]

[0077] Step 3: Based on the dynamic model of the attitude subsystem established in step 1 and the reference attitude information generated in step 2, a robust attitude tracking controller based on backstepping and adaptive event triggering mechanism is designed. At the same time, based on the Lyapunov stability method, the stability of the entire closed-loop system is analyzed.

[0078] The present invention uses the Padé approximation technique to process input delay, and the following can be obtained from Laplace transform and Taylor formula:

[0079]

[0080] Where h(·) is the Laplace transform and δ is the Laplace variable.

[0081] A new intermediate variable x n+1 Designed for Substituting it into the above formula and taking the inverse Laplace transform, we can get Then the attitude subsystem control input with delay can be rewritten as:

[0082] U Θ (t-ι)=x n+1 -U Θ (t) (12)

[0083] The Padé approximation technique is a rational function approximation method, which has advantages over the Taylor series in processing complex functions. However, when processing long delay systems, using rational functions to approximate a given function will increase the complexity of the system transfer function, resulting in a decrease in accuracy. Therefore, the input delay considered in the present invention is a smaller time-varying delay.

[0084] Combining the above analysis, replace Θ and X3 with X4. Then the dynamic model of the attitude subsystem of the quadrotor drone in (1) can be rewritten as follows:

[0085]

[0086] First, the attitude tracking error and the second velocity tracking error of the quadrotor drone are defined as:

[0087] e3=X3-θ d (14)

[0088]

[0089] Among them, Θ d is the expected value of the position signal (including φ d ,θ d and ψ d ), X 4d is the virtual control quantity to be designed, which is designed as:

[0090]

[0091] Among them, A3 is the positive definite parameter matrix to be designed.

[0092] Taking the first-order derivative of e4, we get:

[0093]

[0094] Wherein, f is the abbreviation of the system state equation f() in formula (1).

[0095] At this point, the robust controller of the attitude subsystem can be designed as:

[0096]

[0097] Among them, A4 is the positive definite parameter matrix to be designed, u c is a compensation controller, the specific form is as follows:

[0098]

[0099] Where W() is a known continuous function, ω is an unknown bounded constant, and it is assumed that the norm of the difference between the saturated input and the actual input is less than or equal to W()*ω, r1 is a design parameter, is the estimated value of ω, and its adaptive update rate is designed as follows:

[0100]

[0101] Among them, r2>0.

[0102] also, is the estimated value of the external torque disturbance, which is obtained by the nonlinear disturbance observer of the following form:

[0103]

[0104] Among them, Z Θ is the system auxiliary variable, R Θ is the positive definite parameter matrix to be designed.

[0105] The adaptive event triggering mechanism is designed as follows:

[0106]

[0107] in, t is the current time, σ and k are design parameters, E(t) = U a (t i )-U a ;t i is the sampling update time; when t∈[t i ,t i+1 ], the attitude subsystem control input U a Maintain the value of the previous moment unchanged. After the trigger, the attitude subsystem controls the input U a Change to U a (t i ) and time t i Update to t i+1 ; The adaptive update law of k is designed as:

[0108]

[0109] The adaptive event triggering mechanism designed in this embodiment can automatically adjust the triggering conditions according to the status and performance requirements of the system, thereby improving the flexibility and adaptability of the system.

[0110] The stability of the designed control system is verified by the Lyapunov method. Considering the position subsystem and attitude subsystem (3) and (13) of the quadrotor drone, the controllers are designed as (7) and (18) respectively. At the same time, in order to eliminate the adverse effects of external interference and input saturation, the nonlinear disturbance observer and adaptive rate are designed as shown in (8), (21) and (23); in addition, the adaptive event trigger mechanism is designed as shown in (22). By selecting appropriate parameters, the position and attitude tracking errors can be converged to ensure that all signals in the entire closed-loop system are consistently bounded. The specific analysis process is as follows:

[0111] First, choose the Lyapunov function:

[0112]

[0113] Then the first-order derivative of V is:

[0114]

[0115] At this time, choose appropriate parameters so that λ min (R p )-1>0, You can use Where Q = min{2A1, 2A2-1, 2A3, 2A4, 2R p -1,2R Θ -1, r2, l1}, Therefore, it can be concluded that V is convergent and all signals in the entire closed-loop system are uniformly bounded.

[0116] Step 4: Carry out numerical simulation verification based on the MATLAB simulation platform to verify that the designed control strategy can be effectively applied to the tracking control problem of the quadrotor drone and has good results. Figure 1 As shown, specifically including:

[0117] Step 401: Obtain the reference signal at the current time t, including the expected value P of the position signal d and the yaw angle reference value ψ d ;

[0118] Step 402: based on the expected value P of the position signal d , combined with the estimated value of the external unit force interference And the first derivative of the position coordinate vector of the quadrotor drone Through the position controller (i.e., formula (7)), the control input U of the position subsystem is obtained p ;

[0119] Step 403: Control input U for the position subsystem p , processed by the inverse solver (i.e., formulas (9) and (10)), we obtain φ d and θ d Then, combined with the yaw angle reference value ψ d , External torque disturbance estimate and the first derivative of the attitude angle vector The attitude subsystem control input U is obtained through the attitude controller (i.e., formula (18)): a , and then update the attitude subsystem control input U through the event-triggered strategy (i.e., formula (22)) a ;

[0120] Step 404: Through the speed distributor, the control input U of the position subsystem is p and attitude subsystem control input U a , respectively assigned to the position subsystem and attitude subsystem; based on the control input U of the position subsystem p, through the position subsystem (i.e., formula (3)), the position coordinate vector P (i.e., X1) of the quadrotor drone and the first-order derivative of the position coordinate vector of the quadrotor drone are obtained (i.e., X2); P passes through the position subsystem disturbance observer (i.e., formula (8)) to obtain the external unit force disturbance estimate Based on the attitude subsystem control input U a , after the attitude subsystem (i.e., formula (13)), the attitude angle vector θ and the first-order derivative of the attitude angle vector are obtained The attitude angle vector θ passes through the attitude subsystem disturbance observer (i.e., formula (21)) to obtain the external torque disturbance estimate The quadrotor drone trajectory is controlled based on the position coordinate vector P and the attitude angle vector θ of the quadrotor drone, and the process returns to step 401.

[0121] In step 4, a relevant simulation platform is built based on MATLAB / Simulink, and the designed control algorithm is substituted into the quadrotor UAV model for simulation testing to verify that the designed control strategy can be effectively applied to the trajectory tracking control problem of the quadrotor UAV under the influence of external interference, input delay and input saturation, and has good results.

[0122] The method of the present invention is simulated and analyzed below, and the designed control scheme is verified by Matlab / Simulink platform to be applicable to trajectory tracking control of quad-rotor UAV and has good control effect.

[0123] In combination with the requirements of the present invention for controller design and parameter adaptive update rate, the parameters are respectively taken as follows: Rp = diag{4; 4; 4}, R Θ =diag{60;60;54}, A1=diag{9;10;5}, A2=diag{4;4;2}, A3=diag{60;60;54}, A4=diag{70;70;63}; the input delay is selected as: ι=0.02+0.006sin(t); the parameters in the adaptive event trigger mechanism are k=4, σ=20, l1=5; the control input is limited to g(Θ)U am ≤g(Θ)U a ≤g(Θ)U aM , U am =[-5,-5,-5] T Nm,U aM =[5,5,5] T Nm. External interference options are:

[0124] d p =[0.15sin(0.6t),0.15sin(0.7t),0.15sin(t)]T N (26)

[0125] d Θ =[0.15sin(1.5t),0.15cos(t),0.15t] T Nm·m (27)

[0126] The desired trajectory signal and yaw angle signal are selected as:

[0127] P d =[sin(0.5t),sin(0.6t),0.2t] T m (28)

[0128] Ψ d =Π / 12deg (29)

[0129] Based on the above parameter settings, numerical simulation verification is carried out for the quadrotor drone. The simulation results are as follows Figure 3 , Figure 4 and Figure 5 shown. Figure 3 The spatial flight trajectory of the quadrotor drone under unknown external interference is Figure 3 It can be seen that even when the quadrotor drone is subject to unknown external interference, the designed controller can still enable the drone to maintain good flight performance, with fast response and good recovery effects. Figure 4 The curve of attitude angle change during the flight of the quadrotor drone under external interference, input saturation and input delay is shown in Figure 2. Figure 4 It can be clearly seen that the actual attitude angle can quickly track the reference signal, with a small steady-state error and high tracking accuracy. Figure 5 It is a scatter plot of the event trigger interval. It can be seen that the adaptive event trigger mechanism adopted by the present invention effectively reduces the number of triggers and saves communication resources. It is easy to find that the minimum value of the event trigger interval is 0.01, which means that the Zeno phenomenon is avoided. According to the above simulation results, it can be seen that for the trajectory tracking control problem of a quadcopter drone with input delay and input saturation, the finite time robust tracking control scheme proposed by the present invention is effective and feasible, and has the advantages of fast response and good control effect.

[0130] The UAV trajectory control method based on the adaptive event trigger mechanism provided in this embodiment fully utilizes the nonlinear disturbance observer, Padé approximation technology and parameter adaptation methods to design a robust trajectory tracking control scheme under the backstepping framework; at the same time, the adaptive event trigger mechanism is applied to the attitude subsystem of the quadrotor UAV to further reduce the complexity of the control algorithm and alleviate the calculation and communication pressure; it effectively solves the attitude stability and trajectory precision tracking control problems of the quadrotor UAV in the face of external interference, input delay and actuator saturation.

[0131] Embodiment 2

[0132] This embodiment provides a UAV trajectory control system based on an adaptive event triggering mechanism, which specifically includes:

[0133] A signal acquisition module is configured to: acquire an expected value of a position signal and a reference value of a yaw angle;

[0134] A position control module is configured to obtain a control input of a position subsystem through a position controller based on an expected value of the position signal, an estimated value of an external unit force disturbance and a first-order derivative of a position coordinate vector of the UAV;

[0135] The attitude control module is configured to: for the control input of the position subsystem, after being processed by the inverse solver, combine the yaw angle reference value, the external torque interference estimation value and the first-order derivative of the attitude angle vector, obtain the attitude subsystem control input through the attitude controller, and use the adaptive event trigger mechanism to update the attitude subsystem control input; the adaptive event trigger mechanism adjusts the trigger condition according to the change amount of the attitude subsystem control input;

[0136] The interference estimation module is configured as follows: based on the control input of the position subsystem, the position coordinate vector of the UAV and the first-order derivative of the position coordinate vector of the UAV are updated through the position subsystem, and based on the position coordinate vector of the UAV, the external unit force interference estimate is updated through the position subsystem interference observer; based on the control input of the attitude subsystem, the attitude angle vector and the first-order derivative of the attitude angle vector are updated through the attitude subsystem, and based on the attitude angle vector, the external torque interference estimate is updated through the attitude subsystem interference observer.

[0137] It should be noted here that each module in this embodiment corresponds to each step in Example 1 one by one, and the specific implementation process is the same, which will not be repeated here.

[0138] Embodiment 3

[0139] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the drone trajectory control method based on the adaptive event triggering mechanism as described in the first embodiment above are implemented.

[0140] Embodiment 4

[0141] This embodiment provides a computer device, such as Figure 6 As shown, it includes a display device, an input device, a computer-readable storage medium (volatile memory and non-volatile storage medium), a processor, a communication interface (i.e., a network interface), and a computer program stored on the computer-readable storage medium and executable on the processor, wherein the processor, the communication interface, and the computer-readable storage medium may be connected via a bus or other means. The communication interface is used to receive and send data, and when the processor executes the program, the steps in the method for controlling the trajectory of a drone based on an adaptive event triggering mechanism as described in the first embodiment above are implemented.

[0142] Among them, any reference to memory, storage, database or other media provided by the present application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0143] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0144] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0145] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0146] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. The UAV trajectory control method based on adaptive event triggering mechanism is characterized by: include: Obtain the expected value of the position signal and the reference value of the yaw angle; Based on the expected value of the position signal, combined with the estimated value of the external unit force disturbance and the first-order derivative of the position coordinate vector of the UAV, the control input of the position subsystem is obtained through the position controller; The control input of the position subsystem is processed by the inverse solver, combined with the yaw angle reference value, the external torque disturbance estimation value and the first-order derivative of the attitude angle vector, and the attitude subsystem control input is obtained by the attitude controller, and the attitude subsystem control input is updated by an adaptive event trigger mechanism; the adaptive event trigger mechanism adjusts the trigger condition according to the change amount of the attitude subsystem control input; Based on the control input of the position subsystem, the position coordinate vector of the UAV and the first-order derivative of the position coordinate vector of the UAV are updated through the position subsystem, and based on the position coordinate vector of the UAV, the external unit force interference estimate is updated through the position subsystem interference observer; based on the control input of the attitude subsystem, the attitude angle vector and the first-order derivative of the attitude angle vector are updated through the attitude subsystem, and based on the attitude angle vector, the external torque interference estimate is updated through the attitude subsystem interference observer.

2. The method for controlling the trajectory of a UAV based on an adaptive event triggering mechanism according to claim 1, characterized in that: The adaptive event triggering mechanism is expressed as: in, t is the current time, σ and k are design parameters, is the adaptive update law of k, E(t)=U a (t i )-U a , t i is the sampling update time, U a is the control input of the attitude subsystem, e4 is the attitude tracking error, and Θ is the attitude angle vector.

3. The method for controlling the trajectory of a UAV based on an adaptive event triggering mechanism according to claim 1, characterized in that: The position subsystem is represented as: Among them, U p is the control input of the position subsystem, D p is the external unit force interference, m is the weight of the drone, g is the gravitational acceleration, ε=[0,0,1] T , X1 represents the position coordinate vector P of the drone, and X2 is the first-order derivative of the position coordinate vector of the drone 4. The method for controlling the trajectory of a UAV based on an adaptive event triggering mechanism according to claim 1, characterized in that: The attitude subsystem is expressed as: Among them, x n+1 is the intermediate variable, U Θ =g(Θ)U a , U a is the control input of the attitude subsystem, X3 is the attitude angle vector θ, and X4 is the first-order derivative of the attitude angle vector sat() is the saturation control input function, D Θ It is the external torque interference.

5. The method for controlling the trajectory of a UAV based on an adaptive event triggering mechanism according to claim 1, characterized in that: The position controller is expressed as: Among them, U p is the control input of the position subsystem, is the estimated value of the external unit force interference, m is the weight of the drone, g is the gravitational acceleration, A1 and A2 are positive definite parameter matrices, P d is the expected value of the position signal, e1 is the position tracking error, and e2 is the first velocity tracking error.

6. The method for controlling the trajectory of a UAV based on an adaptive event triggering mechanism according to claim 1, characterized in that: The attitude controller is expressed as: Among them, U a is the control input of the attitude subsystem, Θ is the attitude angle vector, A4 is the positive definite parameter matrix, e3 is the attitude tracking error, e4 is the second velocity tracking error, is the estimated value of external torque disturbance, u c is the compensation controller, X 4d is the virtual control quantity.

7. The method for controlling the trajectory of a UAV based on an adaptive event triggering mechanism according to claim 1, characterized in that: The attitude angle vector includes a roll angle, a pitch angle, and a yaw angle.

8. The UAV trajectory control system based on the adaptive event trigger mechanism is characterized by: include: A signal acquisition module is configured to: acquire an expected value of a position signal and a reference value of a yaw angle; A position control module is configured to obtain a control input of a position subsystem through a position controller based on an expected value of the position signal, an estimated value of an external unit force disturbance and a first-order derivative of a position coordinate vector of the UAV; The attitude control module is configured to: for the control input of the position subsystem, after being processed by the inverse solver, combine the yaw angle reference value, the external torque interference estimation value and the first-order derivative of the attitude angle vector, obtain the attitude subsystem control input through the attitude controller, and use the adaptive event trigger mechanism to update the attitude subsystem control input; the adaptive event trigger mechanism adjusts the trigger condition according to the change amount of the attitude subsystem control input; The interference estimation module is configured as follows: based on the control input of the position subsystem, the position coordinate vector of the UAV and the first-order derivative of the position coordinate vector of the UAV are updated through the position subsystem, and based on the position coordinate vector of the UAV, the external unit force interference estimate is updated through the position subsystem interference observer; based on the control input of the attitude subsystem, the attitude angle vector and the first-order derivative of the attitude angle vector are updated through the attitude subsystem, and based on the attitude angle vector, the external torque interference estimate is updated through the attitude subsystem interference observer.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the UAV trajectory control method based on an adaptive event trigger mechanism as described in any one of claims 1 to 7 are implemented.

10. A computer device comprising a computer-readable storage medium, a processor, and a computer program stored in the computer-readable storage medium and executable on the processor, characterized in that: When the processor executes the program, the steps in the UAV trajectory control method based on the adaptive event trigger mechanism as described in any one of claims 1-7 are implemented.

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