A UAV trajectory control method and system based on adaptive event triggering mechanism

Abstract

This invention relates to the field of unmanned aerial vehicle (UAV) control technology, and provides a UAV trajectory control method and system based on an adaptive event-triggered mechanism. The method includes: obtaining the control input of the position subsystem through a position controller based on the expected value of the position signal, combined with the estimated value of external unit force disturbance and the first derivative of the UAV's position coordinate vector; processing the position subsystem control input through an inverse kinematics unit, and combining it with the yaw angle reference value, the estimated value of external torque disturbance, and the first derivative of the attitude angle vector, to obtain the attitude subsystem control input through an attitude controller, and updating the attitude subsystem control input using an adaptive event-triggered mechanism; the adaptive event-triggered mechanism adjusts the triggering conditions according to the changes in the attitude subsystem control input; and updating the UAV's position coordinate vector and attitude angle vector. This improves the flexibility and adaptability of UAV trajectory control.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) control technology, and particularly relates to a UAV trajectory control method and system based on an adaptive event triggering mechanism. Background Technology

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

[0003] Quadrotor UAVs are currently the most widely used, most functionally diverse, and most extensively researched type of multi-rotor UAV developed by various countries. For quadrotor UAVs, achieving stable, efficient, and precise control is crucial for enhancing their mission execution capabilities, necessitating the construction of a position and attitude control system that meets various requirements. Firstly, considering the various external disturbances they often experience during low-altitude flight, including ground effect, gusts, and atmospheric turbulence, the designed control system needs sufficient robustness to cope with the adverse effects of these disturbances. Secondly, during actual flight control, the calculation and measurement processes of related sensor data may experience a certain degree of delay, preventing control signals from being transmitted to the actuators in a timely manner, thus reducing controller efficiency. Simultaneously, as the actuators of quadrotor UAVs, the rotor speed is usually limited to a certain range, so actuator saturation needs to be considered during controller design. Finally, considering the limited computing and communication capabilities of the onboard flight control module of quadrotor UAVs, the controller design should focus on optimizing the algorithm structure without compromising controller performance, minimizing the waste of computing and communication resources due to unnecessary sampling.

[0004] Existing research on flight control methods for quadrotor UAVs, such as patents CN118034068A ("A Fractional-Order Anti-Saturation Sliding Mode Control Method and Device for Quadrotor UAVs"), CN117311165A ("A Fixed-Time Control Method for Quadrotor UAVs Based on Disturbance Observers"), CN116795124A ("A Quadrotor UAV Attitude Control Method Based on Dynamic Event Triggering"), and CN116225037A ("An Adaptive Event-Triggered Control Method for Quadrotor UAV Flight Attitude"), largely focuses on robust flight control problems under external disturbances and actuator saturation effects, or on quadrotor UAV flight control problems under event-triggered mechanisms. However, research on robust trajectory control of quadrotor UAVs that comprehensively considers external disturbances, input time delays, and input saturation effects under event-triggered mechanisms is still scarce. Summary of the Invention

[0005] To address the technical problems mentioned above, this invention provides a UAV trajectory control method and system based on an adaptive event triggering mechanism. The designed adaptive event triggering mechanism automatically adjusts the triggering conditions according to the changes in the attitude subsystem control input, thereby improving the flexibility and adaptability of UAV trajectory control.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The first aspect of the present invention provides a UAV trajectory control method based on an adaptive event-triggered mechanism, comprising:

[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 interference and the first derivative of the UAV's position coordinate vector, the control input of the position subsystem is obtained through the position controller;

[0010] For the control input of the position subsystem, after processing by the inverse solver, the attitude subsystem control input is obtained by combining the yaw angle reference value, the estimated value of the external torque disturbance, and the first derivative of the attitude angle vector through the attitude controller. The attitude subsystem control input is then updated using an adaptive event triggering mechanism. The adaptive event triggering mechanism adjusts the triggering conditions according to the changes in the attitude subsystem control input.

[0011] Based on the control input of the position subsystem, the position coordinate vector and the first derivative of the UAV's position coordinate vector are updated through the position subsystem. Based on the UAV's position coordinate vector, the estimated value of external unit force disturbance is updated through the position subsystem disturbance observer. Based on the control input of the attitude subsystem, the attitude angle vector and the first derivative of the attitude angle vector are updated through the attitude subsystem. Based on the attitude angle vector, the estimated value of external torque disturbance is updated through the attitude subsystem disturbance observer.

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

[0013]

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

[0015] Furthermore, the location subsystem is represented as:

[0016]

[0017] Among them, U p D is the control input for the position subsystem. p For external unit force interference, m is the weight of the UAV, g is the acceleration due to gravity, and ε = [0,0,1]. T X1 represents the position coordinate vector P of the UAV, and X2 is the first derivative of the UAV's position coordinate vector.

[0018] Furthermore, the attitude subsystem is represented as:

[0019]

[0020] Where, x n+1 U is an intermediate variable. Θ =g(Θ)U a U a X3 is the control input of the attitude subsystem, X4 is the attitude angle vector Θ, and X5 is the first derivative of the attitude angle vector. sat() is the saturation control input function, D Θ External torque interference.

[0021] Furthermore, the position controller is represented as:

[0022]

[0023] Among them, U p For the control input of the position subsystem, The external unit force disturbance is the estimated value, where m is the weight of the UAV, g is the gravitational acceleration, A1 and A2 are positive definite parameter matrices, and P is the external force disturbance. d Let e1 be the expected value of the position signal, e2 be the position tracking error, and e3 be the first velocity tracking error.

[0024] Furthermore, the attitude controller is represented as:

[0025]

[0026] Among them, U a Here, Θ is the control input to the attitude subsystem, A4 is the attitude angle vector, e3 is the attitude tracking error, and e4 is the second velocity tracking error. u is the estimated value of the external torque disturbance. c To compensate the controller, X 4d This is a virtual control variable.

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

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

[0029] The signal acquisition module is configured to acquire the expected value of the position signal and the reference value of the yaw angle.

[0030] The position control module is configured to obtain the control input of the position subsystem through the position controller based on the expected value of the position signal, the estimated value of the external unit force interference, and the first derivative of the UAV's position coordinate vector.

[0031] The attitude control module is configured to: process the control input of the position subsystem through an inverse solver, combine the yaw angle reference value, the estimated value of the external torque disturbance, and the first derivative of the attitude angle vector, obtain the attitude subsystem control input through the attitude controller, and update the attitude subsystem control input using an adaptive event triggering mechanism; the adaptive event triggering mechanism adjusts the triggering conditions according to the change in the attitude subsystem control input.

[0032] The disturbance estimation module is configured to: based on the control input of the position subsystem, update the UAV's position coordinate vector and the first derivative of the UAV's position coordinate vector through the position subsystem, and update the estimated value of the external unit force disturbance through the disturbance observer of the position subsystem based on the UAV's position coordinate vector; based on the control input of the attitude subsystem, update the attitude angle vector and the first derivative of the attitude angle vector through the attitude subsystem, and update the estimated value of the external torque disturbance through the disturbance observer of the attitude subsystem based on the attitude angle vector.

[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 above-described method for unmanned aerial vehicle trajectory control based on an adaptive event triggering mechanism.

[0034] A 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 the processor executes the program to implement the steps in the UAV trajectory control method based on the adaptive event triggering mechanism described above.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] The adaptive event triggering mechanism designed in this invention automatically adjusts the triggering conditions based on changes in the attitude subsystem control input, thereby improving the flexibility and adaptability of UAV trajectory control.

[0037] This invention applies an interference observer to a quadcopter drone, effectively improving the anti-interference capability of the quadcopter drone control, enabling it to monitor external interference in real time and take timely measures, thereby improving the stability and flight safety of the quadcopter drone.

[0038] This invention effectively solves the problem of attitude stability and precise trajectory tracking control of quadcopter drones under conditions of external interference, input delay, and actuator saturation. Attached Figure Description

[0039] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0040] Figure 1 This is a flowchart of the 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-inch quadcopter unmanned aerial vehicle system according to Embodiment 1 of the present invention;

[0042] Figure 3 This is a tracking effect diagram of the trajectory control of a quadcopter UAV according to Embodiment 1 of the present invention;

[0043] Figure 4 This is a tracking effect diagram of the Euler angles of the quadcopter UAV according to Embodiment 1 of the present invention;

[0044] Figure 5 This is a scatter plot of the event triggering time intervals in the attitude subsystem of the quadcopter UAV in Embodiment 1 of the present invention.

[0045] Figure 6 This is a schematic diagram of the structure of a computer device according to Embodiment 4 of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0047] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0048] Example 1

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

[0050] The UAV trajectory control method based on an adaptive event-triggered mechanism provided in this embodiment is applicable to, for example, Figure 2 The quadcopter drone shown.

[0051] To design a control system that better meets the actual flight requirements of quadcopter UAVs, this embodiment presents a UAV trajectory control method based on an adaptive event-triggered mechanism. Taking into account the effects of external disturbances, input time delay, and input saturation conditions, a robust trajectory tracking control strategy based on backstepping and an adaptive event-triggered mechanism is proposed. First, a nonlinear disturbance observer is used to estimate unknown disturbances in the position and attitude loops. Then, Padé approximation and parameter adaptation methods are used to eliminate the adverse effects of input time delay and saturation. Finally, within the backstepping framework, combined with the adaptive event-triggered mechanism, a robust tracking control strategy based on the adaptive event-triggered mechanism is designed to achieve the trajectory tracking control objective of the quadcopter UAV.

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

[0053] Step 1: Improve the existing quadcopter UAV model, fully consider the effects of external interference, input time delay and actuator saturation conditions, and construct a quadcopter UAV dynamic model that meets the actual control requirements.

[0054] In step 1, the dynamic characteristics of the quadcopter UAV are analyzed, and the nonlinear dynamic model of the quadcopter UAV under external disturbances, input delays, and actuator saturation is constructed as follows:

[0055]

[0056] Where m is the weight of the quadcopter drone; g is the acceleration due to gravity; P = [x, y, z] T The vector representing the position coordinates of the quadcopter drone; Θ = [φ, θ, ψ] T U1 is the attitude angle vector, representing the roll, pitch, and yaw angles, respectively; U2 is the control input of the position subsystem; U3 is the attitude angle vector, representing the roll, pitch, and yaw angles, respectively; U4 is the attitude angle vector, representing the roll, pitch, and yaw angles, respectively; U5 is the attitude angle vector, representing the roll, pitch, and yaw angles, respectively; U6 is the attitude angle vector, representing the roll, pitch, and yaw angles, respectively; Θ =g(Θ)U a U a Here, g() represents the control input to the attitude subsystem, which is a function of the control gain matrix function, a function of the attitude angle variable; ι represents a small input time delay; ε = [0,0,1] T ;d p and D Θ These represent external force and torque disturbances, respectively; R is the rotation matrix from the body coordinate system to the ground coordinate system. The system state equations are: Sat(U ΘThe saturation control input is the system saturation control input, and the specific form of the saturation control input function is as follows:

[0057]

[0058] in, Define the lower and upper bounds for each control input.

[0059] Step 2: Based on the dynamic model of the position subsystem established in Step 1, and based on the given reference trajectory P d The control input of the position subsystem is designed within the framework of the backstepping method.

[0060] Based on the dynamic model of the position subsystem established in step 1, and using the reference trajectory, the control input of the position subsystem is designed within the backstepping framework. The specific process is as follows:

[0061] Replace P and X1 with X2. The dynamic model of the position subsystem of the quadcopter UAV can then be rewritten as follows:

[0062]

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

[0064] Define the position tracking error and the first velocity tracking error of a quadcopter UAV as follows:

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

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

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

[0068]

[0069] Where A1 is the positive definite parameter matrix to be designed.

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

[0071]

[0072] Where A2 is the positive definite parameter matrix to be designed. The estimated value of the external disturbance is obtained from a nonlinear disturbance observer of the following form:

[0073]

[0074] Among them, Z p As a system auxiliary variable, R p This is the positive definite parameter matrix that needs 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 relationship must be 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, design a robust attitude tracking controller based on backstepping and an adaptive event triggering mechanism. Simultaneously, analyze the stability of the entire closed-loop system based on the Lyapunov stability method.

[0078] In this invention, the Padé approximation technique is used to handle the input delay, which can be obtained from the Laplace transform and Taylor formula:

[0079]

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

[0081] A new intermediate variable x n+1 Designed for Substituting it into the above equation and taking the inverse Laplace transform, we can obtain... The attitude subsystem control input with delay can then 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 that has advantages over Taylor series in handling complex functions. However, when dealing with long-delay systems, approximating a given function with a rational function increases the complexity of the system's transfer function, leading to a decrease in accuracy. Therefore, this invention considers a relatively small time-varying input delay.

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

[0085]

[0086] First, define the attitude tracking error and the second velocity tracking error of the quadcopter UAV as follows:

[0087] e3=X3-Θ d (14)

[0088]

[0089] Where, Θ d The expected value of the position signal (including φ) d θ d and ψ d ), X 4d The virtual control variable to be designed is designed as follows:

[0090]

[0091] Where A3 is the positive definite parameter matrix to be designed.

[0092] Taking the first derivative with respect to e⁴, we get:

[0093]

[0094] Where f is a simplified version of the system state equation f() in formula (1).

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

[0096]

[0097] Where A4 is the positive definite parameter matrix to be designed, u c The compensation controller takes the following form:

[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, and r1 is a design parameter. Given an estimated value for ω, its adaptive update rate is designed as follows:

[0100]

[0101] Where r2 > 0.

[0102] also, The estimated value of the external torque disturbance is obtained from the following nonlinear disturbance observer:

[0103]

[0104] Among them, Z Θ As a system auxiliary variable, R Θ This is the positive definite parameter matrix that needs 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, and E(t) = U a (t i )-U a ;t i The update time for sampling; when t∈[t i ,t i+1 At this time, the attitude subsystem control input U a Maintaining the value from the previous moment, after triggering, the attitude subsystem control input U... a Change to U a (t i And time t i Updated to t i+1 The adaptive update law for k is designed as follows:

[0108]

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

[0110] The stability of the designed control system was verified using the Lyapunov method. Considering the position and attitude subsystems of the quadcopter UAV (3) and (13), the controllers were designed as (7) and (18), respectively. In order to eliminate the adverse effects of external interference and input saturation, the nonlinear interference observer and adaptive rate were designed as shown in (8), (21) and (23); in addition, the adaptive event triggering mechanism was designed as shown in (22). By selecting appropriate parameters, the position and attitude tracking errors can be converged, ensuring that all signals in the entire closed-loop system are consistent and bounded. The specific analysis process is as follows:

[0111] First, choose the Lyapunov function:

[0112]

[0113] Then the first derivative of V is:

[0114]

[0115] At this point, choosing appropriate parameters will make λ min (R p )-1>0, That can make 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: Conduct numerical simulation verification using the MATLAB simulation platform to verify that the designed control strategy is effectively applicable to the tracking control problem of a quadcopter UAV and has good performance. For example... Figure 1 As shown, it specifically includes:

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

[0118] Step 402: Based on the expected value P of the location signal d Combined with the estimated value of external unit force interference The first derivative of the position coordinate vector of the quadcopter drone The control input U of the position subsystem is obtained through the position controller (i.e., formula (7)). p ;

[0119] Step 403: For the control input U of the position subsystem p After processing by the inverse solver (i.e., equations (9) and (10)), φ is obtained. 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., Equation (18)). a Then, the attitude subsystem control input U is updated through an event-triggered strategy (i.e., Equation (22)). a ;

[0120] Step 404: The control input U of the position subsystem is transmitted through the speed distributor. p and attitude subsystem control input U a The control input U is allocated to the position subsystem and attitude subsystem respectively; based on the position subsystem... pThrough the position subsystem (i.e., Equation (3)), the position coordinate vector P (i.e., X1) of the quadcopter UAV and the first derivative of the position coordinate vector of the quadcopter UAV are obtained. (i.e., X2); P passes through the position subsystem disturbance observer (i.e., Equation (8)) to obtain the estimated value of the external unit force disturbance. Based on attitude subsystem control input U a After passing through the attitude subsystem (i.e., formula (13)), the attitude angle vector Θ and the first derivative of the attitude angle vector are obtained. The attitude angle vector Θ is passed through the attitude subsystem disturbance observer (i.e., Equation (21)) to obtain the estimated value of the external torque disturbance. The quadrotor drone's trajectory is controlled based on its position coordinate vector P and attitude angle vector Θ, and the process returns to step 401.

[0121] In step 4, a simulation platform was built based on MATLAB / Simulink. The designed control algorithm was 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 following simulation analysis demonstrates that the designed control scheme can be used for trajectory tracking control of a quadcopter UAV and has good control performance, as verified by the Matlab / Simulink platform.

[0123] In accordance with the requirements of this invention for controller design and adaptive parameter update rate, the parameters are 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}; Input delay is selected as: ι=0.02+0.006sin(t); Parameters in the adaptive event triggering mechanism are k=4, σ=20, l1=5; 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 is selected as:

[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 follows:

[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 was carried out on a quadcopter UAV, and the simulation results are as follows: Figure 3 , Figure 4 and Figure 5 As shown. Figure 3 To determine the space flight trajectory of a quadcopter drone under unknown external interference, from Figure 3 As can be seen, even when there are unknown external interferences in the quadcopter drone, the designed controller can still enable the drone to maintain good flight performance, with fast response and good recovery. Figure 4 The curves showing the attitude angle changes during the flight of a quadcopter UAV under external interference, input saturation, and input delay are presented from... Figure 4 It can be clearly seen that the actual attitude angle can quickly track the reference signal, and the steady-state error is small, indicating high tracking accuracy. Figure 5 The scatter plot of the event trigger interval shows that the adaptive event triggering mechanism adopted in this invention effectively reduces the number of triggers and saves communication resources. It is easy to see that the minimum event trigger interval is 0.01, which means that Zeno's phenomenon is avoided. Based on the simulation results above, it can be seen that the finite-time robust tracking control scheme proposed in this invention is effective and feasible for the trajectory tracking control problem of quadcopter UAVs with input delay and input saturation, and has the advantages of fast response and good control effect.

[0130] The UAV trajectory control method based on an adaptive event triggering mechanism provided in this embodiment fully utilizes nonlinear disturbance observers, Padé approximation techniques, and parameter adaptation methods within the backstepping framework to design a robust trajectory tracking control scheme. Simultaneously, the adaptive event triggering mechanism is applied to the quadrotor UAV attitude subsystem, further reducing the complexity of the control algorithm and alleviating computational and communication pressures. This effectively solves the problems of attitude stability and accurate trajectory tracking control for quadrotor UAVs facing external disturbances, input delays, and actuator saturation.

[0131] Example 2

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

[0133] The signal acquisition module is configured to acquire the expected value of the position signal and the reference value of the yaw angle.

[0134] The position control module is configured to obtain the control input of the position subsystem through the position controller based on the expected value of the position signal, the estimated value of the external unit force interference, and the first derivative of the UAV's position coordinate vector.

[0135] The attitude control module is configured to: process the control input of the position subsystem through an inverse solver, combine the yaw angle reference value, the estimated value of the external torque disturbance, and the first derivative of the attitude angle vector, obtain the attitude subsystem control input through the attitude controller, and update the attitude subsystem control input using an adaptive event triggering mechanism; the adaptive event triggering mechanism adjusts the triggering conditions according to the change in the attitude subsystem control input.

[0136] The disturbance estimation module is configured to: based on the control input of the position subsystem, update the UAV's position coordinate vector and the first derivative of the UAV's position coordinate vector through the position subsystem, and update the estimated value of the external unit force disturbance through the disturbance observer of the position subsystem based on the UAV's position coordinate vector; based on the control input of the attitude subsystem, update the attitude angle vector and the first derivative of the attitude angle vector through the attitude subsystem, and update the estimated value of the external torque disturbance through the disturbance observer of the attitude subsystem based on the attitude angle vector.

[0137] It should be noted that each module in this embodiment corresponds one-to-one with each step in Embodiment 1, and their specific implementation processes are the same, so they will not be repeated here.

[0138] Example 3

[0139] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the UAV trajectory control method based on an adaptive event triggering mechanism as described in Embodiment 1 above.

[0140] Example 4

[0141] This embodiment provides a computer device, such as... Figure 6 As shown, the device 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. The processor, communication interface, and computer-readable storage medium can 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, it implements the steps in the UAV trajectory control method based on an adaptive event triggering mechanism as described in Embodiment 1 above.

[0142] Any references to memory, storage, database, or other media used in this application and 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. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0143] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0144] 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.

[0145] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment 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.

[0146] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A UAV trajectory control method based on an adaptive event-triggered mechanism, characterized in that, 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 interference and the first derivative of the UAV's position coordinate vector, the control input of the position subsystem is obtained through the position controller; For the control input of the position subsystem, after processing by the inverse solver, the attitude subsystem control input is obtained by combining the yaw angle reference value, the estimated value of the external torque disturbance, and the first derivative of the attitude angle vector through the attitude controller. The attitude subsystem control input is then updated using an adaptive event triggering mechanism. The adaptive event triggering mechanism adjusts the triggering conditions according to the changes in the attitude subsystem control input. Based on the control input of the position subsystem, the position coordinate vector and the first derivative of the UAV's position coordinate vector are updated through the position subsystem. Based on the UAV's position coordinate vector, the estimated value of external unit force disturbance is updated through the position subsystem disturbance observer. Based on the control input of the attitude subsystem, the attitude angle vector and the first derivative of the attitude angle vector are updated through the attitude subsystem. Based on the attitude angle vector, the estimated value of external torque disturbance is updated through the attitude subsystem disturbance observer. The adaptive event triggering mechanism is represented as follows: ;in, , t For the current time, and For design parameters, , The update time for sampling, For the control input of the attitude subsystem, e 4 represents the attitude tracking error. The attitude angle vector, g ( ) represents the control gain matrix function. f The system state equations; for Adaptive update law: ; The position controller is represented as: ;in, For the control input of the position subsystem, It's the weight of the drone. It is the acceleration due to gravity. and P is a positive definite parameter matrix. d The expected value of the position signal. For position tracking error, This represents the first speed tracking error; The estimated value of the external unit force disturbance is obtained from the nonlinear disturbance observer: ; As a system auxiliary variable, The positive definite parameter matrix to be designed is... The first derivative of the position coordinate vector of the UAV , This represents the position coordinate vector of a quadcopter drone. ; The attitude controller is represented as: ;in, For the control input of the attitude subsystem, It is a positive definite parameter matrix. For attitude tracking error, For the second speed tracking error, This is a virtual control variable; The compensation controller takes the following form: W() is a known continuous function, and ω is an unknown bounded constant. For design parameters, for The estimated value, with its adaptive update rate designed as follows: , ; The estimated value of the external torque disturbance is obtained from the following nonlinear disturbance observer: , As a system auxiliary variable, The positive definite parameter matrix to be designed is... The first derivative of the attitude angle vector , .

2. The UAV trajectory control method based on an adaptive event triggering mechanism as described in claim 1, characterized in that, The location subsystem is represented as follows: ； in, For the control input of the position subsystem, For external unit force interference, It's the weight of the drone. It is the acceleration due to gravity. , Represents the position coordinate vector of the UAV P , The first derivative of the position coordinate vector of the UAV .

3. The UAV trajectory control method based on an adaptive event triggering mechanism as described in claim 1, characterized in that, The attitude subsystem is represented as follows: ； in, As an intermediate variable, , For the control input of the attitude subsystem, Attitude angle vector , The first derivative of the attitude angle vector , sat ( ) represents the saturation control input function. External torque interference.

4. The UAV trajectory control method based on an adaptive event triggering mechanism as described in claim 1, characterized in that, The attitude angle vector includes roll angle, pitch angle, and yaw angle.

5. A UAV trajectory control system based on an adaptive event triggering mechanism, characterized in that, include: The signal acquisition module is configured to acquire the expected value of the position signal and the reference value of the yaw angle. The position control module is configured to obtain the control input of the position subsystem through the position controller based on the expected value of the position signal, the estimated value of the external unit force interference, and the first derivative of the UAV's position coordinate vector. The attitude control module is configured to: process the control input of the position subsystem through an inverse solver, combine the yaw angle reference value, the estimated value of the external torque disturbance, and the first derivative of the attitude angle vector, obtain the attitude subsystem control input through the attitude controller, and update the attitude subsystem control input using an adaptive event triggering mechanism; the adaptive event triggering mechanism adjusts the triggering conditions according to the change in the attitude subsystem control input. The disturbance estimation module is configured to: based on the control input of the position subsystem, update the UAV's position coordinate vector and the first derivative of the UAV's position coordinate vector through the position subsystem, and update the estimated value of the external unit force disturbance through the disturbance observer of the position subsystem based on the UAV's position coordinate vector; based on the control input of the attitude subsystem, update the attitude angle vector and the first derivative of the attitude angle vector through the attitude subsystem, and update the estimated value of the external torque disturbance through the disturbance observer of the attitude subsystem based on the attitude angle vector. The adaptive event triggering mechanism is represented as follows: ;in, , t For the current time, and For design parameters, , The update time for sampling, For the control input of the attitude subsystem, e 4 represents the attitude tracking error. The attitude angle vector, g ( ) represents the control gain matrix function. f The system state equations; for Adaptive update law: ; The position controller is represented as: ;in, For the control input of the position subsystem, It's the weight of the drone. It is the acceleration due to gravity. and P is a positive definite parameter matrix. d The expected value of the position signal. For position tracking error, This represents the first speed tracking error; The estimated value of the external unit force disturbance is obtained from the nonlinear disturbance observer: ; As a system auxiliary variable, The positive definite parameter matrix to be designed is... The first derivative of the position coordinate vector of the UAV , This represents the position coordinate vector of a quadcopter drone. ; The attitude controller is represented as: ;in, For the control input of the attitude subsystem, It is a positive definite parameter matrix. For attitude tracking error, For the second speed tracking error, This is a virtual control variable; The compensation controller takes the following form: W() is a known continuous function, and ω is an unknown bounded constant. For design parameters, for The estimated value, with its adaptive update rate designed as follows: , ; The estimated value of the external torque disturbance is obtained from the following nonlinear disturbance observer: , As a system auxiliary variable, The positive definite parameter matrix to be designed is... The first derivative of the attitude angle vector , .

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the UAV trajectory control method based on the adaptive event triggering mechanism as described in any one of claims 1-4.

7. A computer device comprising a computer-readable storage medium, a processor, and a computer program stored on the computer-readable storage medium and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the UAV trajectory control method based on an adaptive event triggering mechanism as described in any one of claims 1-4.

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