Control Method and System for Quadrotor UAVs under Actuator Partial Failure

Abstract

This invention relates to the field of fault-tolerant flight control technology for unmanned aerial vehicles (UAVs), and provides a control method and system for quadrotor UAVs under actuator partial failure. The method includes: obtaining the position subsystem control input based on the expected value of the position signal and combined with the linear velocity through a position subsystem controller; updating the position coordinate vector and linear velocity of the quadrotor UAV by incorporating a position dynamic model with a position loop auxiliary system; obtaining the attitude subsystem control input through an attitude subsystem controller after passing through an inverse kinematics unit, combined with the expected value of the yaw angle signal, the fault estimate, and the rate of change; obtaining the actual attitude system control input after an actuator failure; updating the attitude angle vector and rate of change of the quadrotor UAV by incorporating an attitude dynamic model with an attitude loop auxiliary system; and updating the fault estimate through a fault observer. This method is well-suited for fault-tolerant control problems in nonlinear UAV systems.

Description

Technical Field

[0001] This invention belongs to the field of fault-tolerant flight control technology for unmanned aerial vehicles (UAVs), and particularly relates to a control method and system for quadcopter UAVs under actuator failure. 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 drones, a type of rotary-wing drone, possess advantages such as simple structure, small size, vertical takeoff and landing (VTOL), and low cost. Maintaining a stable flight trajectory is crucial for quadrotor drones performing low-altitude missions, necessitating the development of a trajectory control system capable of meeting diverse mission requirements. First, the low-altitude flight environment of quadrotor drones is complex, with numerous unknown disturbances, including wind interference, requiring the designed flight control system to possess a certain level of anti-interference capability. Second, the individual rotors, acting as actuators, typically operate at high speeds, and prolonged operation inevitably leads to malfunctions such as failure, saturation, and jamming, posing flight safety issues for quadrotor drones, which are underactuated systems. Finally, to address the rapid and agile flight characteristics of quadrotor drones, the designed flight control system must possess not only high control precision but also a fast response speed.

[0004] Existing research on flight control methods for quadrotor UAVs, such as patents CN113359472B ("An Adaptive Robust Trajectory Tracking Control Method for Quadrotor UAVs"), CN115857356A ("An Adaptive Fault-Tolerant Control Method for Actuator Failures in Quadrotors"), CN117908576B ("An Anti-Saturation Finite-Time Fault-Tolerant Control Method for Quadrotor UAVs"), and CN116627156B ("An Attitude Disturbance Rejection Control Method for Quadrotor UAVs"), largely focuses on robust flight control under the influence of external disturbances and actuator saturation, or on fault-tolerant flight control under various fault conditions. However, research on finite-time fault-tolerant control of quadrotor UAVs that comprehensively considers external disturbances, actuator saturation, and partial failure scenarios is lacking. Summary of the Invention

[0005] To address the technical problems mentioned above, this invention provides a control method and system for quadrotor unmanned aerial vehicles (UAVs) under actuator partial failure. It considers the actual control input of the attitude system after actuator failure and designs an adaptive fault observer to estimate fault information in real time. It does not require prior knowledge of fault information and can achieve real-time fault estimation, making it well applicable to fault-tolerant control problems of nonlinear UAV systems.

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

[0007] The first aspect of the present invention provides a control method for a quadcopter unmanned aerial vehicle under actuator partial failure, comprising: acquiring expected values ​​of position signals and expected values ​​of yaw angle signals;

[0008] Based on the expected value of the position signal and combined with the linear velocity, the position subsystem controller obtains the control input of the position subsystem. Then, by adding the position dynamic model of the position loop auxiliary system, the position coordinate vector and linear velocity of the quadcopter UAV are updated.

[0009] For the position subsystem control input, after passing through the inverse solver, the attitude subsystem control input is obtained by combining the expected value of the yaw angle signal, the fault estimate, and the rate of change, and then by the attitude subsystem controller. After an actuator failure occurs, the actual attitude system control input is obtained. Based on the actual attitude subsystem control input, the attitude dynamic model with the addition of the attitude loop auxiliary is used to update the attitude angle vector and rate of change of the quadcopter UAV. Based on the actual attitude subsystem control input, the fault estimate is updated by the fault observer.

[0010] Furthermore, the position dynamic model of the added position loop auxiliary system is as follows:

[0011]

[0012] Where X1 represents the position coordinate vector of the quadcopter UAV, and X2 is the derivative of X1, representing the linear velocity. This is an estimate of X2. To estimate the error, sgn(·) is the sign function. and Let K1 and K2 be the estimation errors of the linear velocities in the x, y, and z directions, respectively, and K2 be positive definite parameter matrices, where 0 < σ < 1, and Ω is the linear velocity. p For robust compensation, m is the weight of the quadcopter UAV, g is the acceleration due to gravity, and ε = [0, 0, 1]. T U p This is the control input for the position subsystem.

[0013] Furthermore, the attitude dynamic model with the added attitude loop assistant is as follows:

[0014]

[0015] Where, Θ=[φ,θ,ψ] T The attitude angle vector, θ and ψ represent the roll angle, pitch angle, and yaw angle, respectively. They are respectively The validity index corresponding to the faults appearing in θ and ψ, X3 = Θ, This is an estimate of X4. To estimate the error,

[0016] and These represent the estimation errors of the rates of change in the x, y, and z directions, respectively. K3 and K4 are positive definite parameter matrices, and Ω... Θ For robust compensation, U Θ This is the control input for the attitude subsystem.

[0017] Furthermore, the fault observer is:

[0018]

[0019] in, Here, ξ and β are the fault estimates, and they are normal values. proj{-ξΠ} is the projection operator.

[0020] A second aspect of the present invention provides a quadcopter unmanned aerial vehicle (UAV) control system under actuator partial failure, comprising:

[0021] The signal acquisition module is configured to acquire the expected values ​​of the position signal and the yaw angle signal.

[0022] The position loop module is configured to: based on the expected value of the position signal and combined with the linear velocity, obtain the position subsystem control input through the position subsystem controller, and update the position coordinate vector and linear velocity of the quadcopter UAV by adding the position dynamic model of the position loop auxiliary system;

[0023] The attitude loop module is configured as follows: for the position subsystem control input, after passing through the inverse solver, it combines the expected value of the yaw angle signal, the fault estimate, and the rate of change, and obtains the attitude subsystem control input through the attitude subsystem controller; and after an actuator fault occurs, it obtains the actual attitude system control input; based on the actual attitude subsystem control input, it updates the attitude angle vector and rate of change of the quadcopter UAV by adding the attitude dynamic model of the attitude loop auxiliary device; and based on the actual attitude subsystem control input, it updates the fault estimate through the fault observer.

[0024] Furthermore, the position dynamic model of the added position loop auxiliary system is as follows:

[0025]

[0026] Where X1 represents the position coordinate vector of the quadcopter UAV, and X2 is the derivative of X1, representing the linear velocity. This is an estimate of X2. To estimate the error, sgn(·) is the sign function. and Let K1 and K2 be the estimation errors of the linear velocities in the x, y, and z directions, respectively, and K2 be positive definite parameter matrices, where 0 < σ < 1, and Ω is the linear velocity. p For robust compensation, m is the weight of the quadcopter UAV, g is the acceleration due to gravity, and ε = [0, 0, 1]. T U p This is the control input for the position subsystem.

[0027] Furthermore, the attitude dynamic model with the added attitude loop assistant is as follows:

[0028]

[0029] Where, Θ=[φ,θ,ψ] T The attitude angle vector, θ and ψ represent the roll angle, pitch angle, and yaw angle, respectively. They are respectively The validity index corresponding to the faults appearing in θ and ψ, X3 = Θ, This is an estimate of X4. To estimate the error,

[0030] and These represent the estimation errors of the rates of change in the x, y, and z directions, respectively. K3 and K4 are positive definite parameter matrices, and Ω... Θ For robust compensation, U Θ This is the control input for the attitude subsystem.

[0031] Furthermore, the fault observer is:

[0032]

[0033] in, Here, ξ and β are the fault estimates, and they are normal values. proj{-ξΠ} is the projection operator.

[0034] 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 quadcopter unmanned aerial vehicle control method under actuator partial failure as described above.

[0035] 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 quadcopter unmanned aerial vehicle control method under actuator partial failure as described above.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] This invention considers the actual control input of the attitude system after an actuator failure and designs an adaptive fault observer to estimate the fault information in real time. It does not require prior knowledge of the fault information and can realize real-time fault estimation, making it well applicable to the fault-tolerant control problem of nonlinear unmanned aerial vehicle systems.

[0038] This invention introduces a robust compensation term and a parameter adaptive method to eliminate the adverse effects of external interference and actuator saturation.

[0039] This invention comprehensively considers the effects of external interference, actuator saturation, and partial actuator failure, and proposes a finite-time robust trajectory fault-tolerant control strategy based on the backstepping method.

[0040] The finite-time fault-tolerant control method proposed in this invention can achieve semi-global practical finite-time stability and has the advantages of good robustness, fast convergence speed and strong anti-interference ability. While realizing fast and effective tracking of the position and attitude of the quadcopter UAV, it can also ensure the semi-global practical finite-time stability of the entire closed-loop system. Attached Figure Description

[0041] 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.

[0042] Figure 1 This is a flowchart of a quadcopter UAV control method under actuator failure according to Embodiment 1 of the present invention;

[0043] Figure 2 This is a model diagram of the quadcopter unmanned aerial vehicle system according to Embodiment 1 of the present invention;

[0044] Figure 3 This is a tracking effect diagram of the trajectory control of a quadcopter UAV according to Embodiment 1 of the present invention;

[0045] Figure 4 This is a tracking effect diagram of the Euler angles of the quadcopter UAV according to Embodiment 1 of the present invention;

[0046] Figure 5 This is an estimated effect diagram of partial failure of the quadcopter unmanned aerial vehicle system according to Embodiment 1 of the present invention;

[0047] Figure 6 This is a schematic diagram of the structure of a computer device according to Embodiment 4 of the present invention. Detailed Implementation

[0048] 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.

[0049] 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.

[0050] Example 1

[0051] This embodiment provides a control method for a quadcopter drone under actuator failure.

[0052] To design a control method that better meets the actual flight requirements of quadrotor UAVs, this invention comprehensively considers the effects of external disturbances, actuator saturation, and partial actuator failures, and proposes a finite-time robust trajectory fault-tolerant control strategy based on the backstepping method. First, a robust compensation term and parameter adaptive method are introduced to eliminate the adverse effects of external disturbances and actuator saturation. Then, an adaptive fault observer is designed to estimate fault information in real time. Finally, a finite-time robust fault-tolerant flight control strategy is designed within the backstepping framework, which achieves rapid and effective tracking of the quadrotor UAV's position and attitude while ensuring the semi-global practical finite-time stability of the entire closed-loop system.

[0053] The control method for a quadcopter drone under actuator failure provided in this embodiment is implemented in the following steps:

[0054] Step 1: Improve the existing quadcopter UAV model, fully considering the effects of external interference, actuator saturation, and partial actuator failure, and construct a quadcopter UAV dynamic model that meets actual control requirements, such as... Figure 2 As shown.

[0055] In step 1, the dynamic characteristics of the quadcopter UAV are analyzed, and the nonlinear dynamic model of the quadcopter UAV under external disturbances, actuator saturation, and partial actuator failure is constructed as follows:

[0056]

[0057] Where m is the weight of the quadcopter UAV, g is the gravitational acceleration, R is the transformation matrix from the body coordinate system to the ground coordinate system, ε is the unit vector in the vertical direction, and d p For disturbances occurring in the position subsystem, P = [x, y, z] T The vector representing the position coordinates of the quadcopter drone; Θ = [φ, θ, ψ] T These are attitude angle vectors, representing the roll angle, pitch angle, and yaw angle, respectively. For control input in the position subsystem,

[0058] U Θd =ρ i U Θ =[ρ1U φ ,ρ2U θ ,ρ3U ψ ] T This is the actual control input when the attitude subsystem experiences a partial actuator failure. For the desired control input of the attitude subsystem, 0 < ρ i <1 is an effectiveness index for actuator partial failure faults, when ρ i =1 indicates that the actuator is in normal working condition; ε = [0,0,1] T , and These represent external force and torque disturbance, respectively; Let be the rotation matrix from the body coordinate system to the ground coordinate system. The system state equations are as follows: Sat(U) is the system's control gain matrix. Θd The input for system saturation control is as follows:

[0059]

[0060] in, Define the lower and upper bounds for each control input.

[0061] 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 inverse step method. The specific process is as follows:

[0062] First, replace P and X1 with X2. Then the dynamic model of the quadcopter UAV position subsystem in (1) can be rewritten as follows:

[0063]

[0064] Among them, U p =U1Rε,D p =d p / m.

[0065] Then, the position loop auxiliary system is defined as follows:

[0066]

[0067] in, This is an estimate of X2. and Here is the positive definite parameter matrix to be designed; To estimate the error, X² is the derivative of P, representing the linear velocity. and σ represents the estimation error of the linear velocity corresponding to coordinates x, y, and z, respectively; sgn(·) is the sign function; 0 < σ < 1; The robust compensation term is introduced to compensate for external disturbances to the position subsystem; θ1>0 and κ1>0 are constants to be designed.

[0068] At this point, equation (3) can be rewritten as:

[0069]

[0070] The position tracking error and velocity tracking error of a quadcopter UAV are defined as follows:

[0071] e1 = X1 - P d (6)

[0072]

[0073] Among them, P d X is the expected value of the position signal. 2d The virtual control law to be designed is designed as follows:

[0074]

[0075] in, Let be the positive definite parameter matrix to be designed. e1 represents the tracking error of the position coordinates, e 11 e 12 e 13 These represent the tracking errors corresponding to coordinates x, y, and z, respectively.

[0076] The control input of the position subsystem can then be designed as follows:

[0077]

[0078] in, Let be the positive definite parameter matrix to be designed.

[0079] Choose the Lyapunov function as Its first derivative can be obtained as:

[0080]

[0081] Control input U of the position subsystem p It can be written as U p =[U x U y U z [ T The reference values ​​for Euler angles satisfy the following relationship:

[0082]

[0083] 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 an attitude tracking controller based on the backstepping method. Simultaneously, verify the stability of the design scheme using the Lyapunov stability method. The specific process is as follows:

[0084] Replace Θ and X3 with X4. Then the dynamic model of the attitude subsystem of the quadcopter UAV in (1) can be rewritten as:

[0085]

[0086] Where f() is the system state equation; G() is the control gain matrix; X4 is the first derivative of Θ, representing the rate of change; F Θ =diag{Sat(U φ ),Sat(U θ ),Sat(U ψ )};ρ Θ =[ρ1,ρ2,ρ2] T This is an effectiveness indicator for actuator failures.

[0087] Define the attitude loop auxiliary system as:

[0088]

[0089] in, This is an estimate of X4. The first derivative of the rate of change estimate, and Here is the positive definite parameter matrix to be designed; To estimate the error, and These represent the estimation errors of the rates of change corresponding to coordinates x, y, and z, respectively. To compensate for external disturbances to the attitude subsystem, robust compensation terms are introduced, where θ2>0 and κ2>0 are constants to be designed.

[0090] To accurately estimate fault information in real time, an adaptive fault observer of the following form is designed based on this auxiliary system:

[0091]

[0092] Where ξ and β are the positive constants to be designed. proj [ζ,1] {-ξΠ} is the projection operator, i.e., proj{-ξΠ}, which can be expressed as:

[0093]

[0094] Where J is the identity matrix, and Π has the following specific form:

[0095]

[0096] Furthermore, it can be proven that this projection function can project the estimated value onto the interval [ζ, 1]. The Lyapunov function is designed as follows: Its first derivative can be obtained as:

[0097]

[0098] At this point, equation (13) can be rewritten as:

[0099]

[0100] in, U Θ Sat() represents the saturation control input for the system.

[0101] Define the attitude tracking error and angular velocity tracking error of a quadcopter UAV:

[0102] e3=X3-Θ d (20)

[0103]

[0104] Where, Θ d ={Φ d ,θ d ,ψ d Let X be the expected value of the angle signal. 4d The virtual control law to be designed is designed as follows:

[0105]

[0106] in, This is the positive definite parameter matrix that needs to be designed.

[0107] The control input for the attitude subsystem is:

[0108]

[0109] in, Attitude angles The validity indicators corresponding to the faults appearing in θ and ψ The positive definite parameter matrix to be designed is... u c The compensation controller is designed in the following specific form:

[0110]

[0111] Where r1 > 0 is a design parameter. Given an estimated value for ω, its adaptive update rate is designed as follows:

[0112]

[0113] Where r2 > 0 are the parameters that need to be designed.

[0114] Design the Lyapunov function as follows: Substituting equations (20), (21), and (22) into the equations, we can obtain its first derivative as:

[0115]

[0116] 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 control inputs were designed as (9) and (23). In order to eliminate the adverse effects of actuator failure, the adaptive fault observer was designed as shown in (15) and (16). By selecting appropriate parameters, the position and attitude tracking errors can be converged, ensuring that all signals in the entire closed-loop system are semi-global practical finite-time stable. The specific analysis process is as follows:

[0117] Choose the Lyapunov function V = V p +V1+V2, then the first derivative of V is:

[0118]

[0119] At this point, choosing appropriate parameters will make That can make in 0 < μ1 < 1, 0 < μ2 < 1 This shows that all signals in the entire closed-loop system are semi-global, practically finite-time stable.

[0120] Step 4: Conduct 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 quadcopter UAV system and has good control effect.

[0121] In step 4, a simulation platform was built based on MATLAB / Simulink. The designed control algorithm was then substituted into the quadrotor UAV model for simulation testing. This verified 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, actuator saturation, and partial actuator failure, and that it has good performance.

[0122] like Figure 1 As shown, the specific execution steps are as follows:

[0123] Step (1): Obtain the expected value P of the location signal d and the expected value of the yaw angle signal ψ d .

[0124] Step (2): Based on the expected value P of the location signal d X2, through the position subsystem controller (formula (9)), the position subsystem control input U is obtained. p Based on the position subsystem control input U p By incorporating the position dynamic model of the position loop auxiliary system (Equation (5)), the position coordinate vector P (i.e., X1) and its first derivative of the quadcopter UAV are obtained. (i.e., X2).

[0125] Step (3): Based on the position subsystem control input U p and the expected value of the yaw angle signal ψ d By using the inverse solver (formulas (11) and (12)), φ is obtained. d and θ d .

[0126] Step (4), based on yaw angle signal expected value ψ d φ d θ d X4 and X5, through the attitude subsystem controller (formula (23)), obtain the control input U of the attitude subsystem. Θ Control input U based on attitude subsystem Θ After an actuator failure occurs, via actuator failure (U) Θd=ρ i U Θ =[ρ1U φ ,ρ2U θ ,ρ3U ψ ] T The subscripts 1, 2, and 3 correspond to... θ and ψ are used to obtain the actual control input when the attitude system has an actuator fault; based on the actual control input ρU of the attitude subsystem Θ By incorporating the attitude dynamic model with an attitude loop helper (Equation (19)), the attitude angle vector Θ (i.e., X3) and its first derivative are updated. (i.e., X4); based on the actual control input ρU of the attitude subsystem Θ The fault estimate is updated using the fault observer (Formulas (15) and (16)).

[0127] Step (5), return to step (2).

[0128] The following uses the Matlab / Simulink platform to verify that the control algorithm can be used for trajectory tracking control of a quadcopter UAV and has good results.

[0129] Consider a quadcopter drone system with the following parameters: mass m = 1.2 kg, gravitational acceleration g = 10 m / s². 2 Moment of inertia I x =6.22×10 -3 kg·m 2 I y =6.22×10 -3 kg·m 2 I z =1.3×10 -2 kg·m 2 The unknown external interference is selected as:

[0130]

[0131] The expected values ​​of the position signal and yaw angle signal are selected as follows:

[0132]

[0133] ψ d =π / 12deg (27)

[0134] The actuator failure is as follows:

[0135]

[0136] The controller parameters are selected as follows: K1 = diag{4; 4; 2}, K2 = diag{4; 4; 2}, A1 = diag{4; 4; 2}, A2 = diag{4; 4; 2}, A3 = diag{4; 4; 2}, A4 = diag{4; 4; 2}, T1 = diag{4; 4; 2}, υ1 = 20, ω1 = 20, σ = 0.9, K3 = diag{4; 4; 2}, K4 = diag{4; 4; 2}, A5 = diag{4; 4; 2}, A6 = diag{4; 4; 2}, A7 = diag{4; 4; 2}, A8 = diag{4; 4; 2}, T2 = diag{4; 4; 2}, υ2 = 20, ω2 = 20, ξ = 5, β = 0.0001. 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.

[0137] Numerical simulation verification was conducted on a hypersonic vehicle based on the above parameter settings. The simulation results are as follows: Figure 3 , Figure 4 , Figure 5 As shown. Figure 3 , Figure 4 The images show the tracking results for trajectory tracking and attitude tracking of a quadcopter drone, respectively. Figure 3 As can be seen, even in the presence of unknown external interference and actuator failures, the designed controller can still enable the quadcopter drone to maintain good flight performance, demonstrating fast response and good recovery. 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 simulation results show the actuator fault estimation curve. It can be seen that when the quadrotor UAV experiences partial failure at 10 seconds, the proposed adaptive fault observer can accurately estimate the fault within 5 seconds, and the specific fault information can be observed in real time throughout the simulation period. Based on the simulation results, it is clear that the finite-time robust tracking control scheme proposed in this invention is effective and feasible for the trajectory tracking control problem of quadrotor UAVs with actuator saturation and partial actuator failure, and has the advantages of fast response and good control effect.

[0138] The quadrotor UAV control method provided in this embodiment under actuator partial failure faults, within the backstepping framework, designs a finite-time fault-tolerant tracking control scheme by utilizing robust compensation terms, parameter adaptation, and fault observers. This effectively solves the attitude stability and trajectory accurate tracking control problems of quadrotor UAVs when facing external interference and actuator saturation / partial actuator failure faults.

[0139] Example 2

[0140] This embodiment provides a quadcopter unmanned aerial vehicle (UAV) control system under actuator partial failure, which specifically includes:

[0141] The signal acquisition module is configured to acquire the expected values ​​of the position signal and the yaw angle signal.

[0142] The position loop module is configured to: based on the expected value of the position signal and combined with the linear velocity, obtain the position subsystem control input through the position subsystem controller, and update the position coordinate vector and linear velocity of the quadcopter UAV by adding the position dynamic model of the position loop auxiliary system;

[0143] The attitude loop module is configured as follows: for the position subsystem control input, after passing through the inverse solver, it combines the expected value of the yaw angle signal, the fault estimate, and the rate of change, and obtains the attitude subsystem control input through the attitude subsystem controller; and after an actuator fault occurs, it obtains the actual attitude system control input; based on the actual attitude subsystem control input, it updates the attitude angle vector and rate of change of the quadcopter UAV by adding the attitude dynamic model of the attitude loop auxiliary device; and based on the actual attitude subsystem control input, it updates the fault estimate through the fault observer.

[0144] Furthermore, the position dynamic model of the added position loop auxiliary system is as follows:

[0145]

[0146] Where X1 represents the position coordinate vector of the quadcopter UAV, and X2 is the derivative of X1, representing the linear velocity. ~

[0147] Let X2 be the estimated value of X2, and X2 be the estimation error. sgn(·) is the sign function. and Let K1 and K2 be the estimation errors of the linear velocities in the x, y, and z directions, respectively, and K2 be positive definite parameter matrices, where 0 < σ < 1, and Ω is the linear velocity. p For robust compensation, m is the weight of the quadcopter UAV, g is the acceleration due to gravity, and ε = [0, 0, 1]. T U p This is the control input for the position subsystem.

[0148] Furthermore, the attitude dynamic model with the added attitude loop assistant is as follows:

[0149]

[0150] Where, Θ=[φ,θ,ψ] T The attitude angle vector, θ and ψ represent the roll angle, pitch angle, and yaw angle, respectively. They are respectively The validity index corresponding to the faults appearing in θ and ψ, X3 = Θ, This is an estimate of X4. To estimate the error,

[0151] and These represent the estimation errors of the rates of change in the x, y, and z directions, respectively. K3 and K4 are positive definite parameter matrices, and Ω... Θ For robust compensation, U Θ This is the control input for the attitude subsystem.

[0152] Furthermore, the fault observer is:

[0153]

[0154] in, Here, ξ and β are the fault estimates, and they are normal values. proj{-ξΠ} is the projection operator.

[0155] 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.

[0156] Example 3

[0157] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the quadcopter UAV control method under actuator partial failure as described in Embodiment 1 above.

[0158] Example 4

[0159] This embodiment provides a computer device, such as... Figure 6As shown, the system 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 transmit data, and when the processor executes the program, it implements the steps in the quadcopter UAV control method under actuator partial failure as described in Embodiment 1 above.

[0160] 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.

[0161] 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 1 A device that provides the functions specified in one or more boxes.

[0162] 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.

[0163] 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.

[0164] 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 control method for a quadcopter unmanned aerial vehicle (UAV) under actuator partial failure, characterized in that, include: Obtain the expected values ​​of the position signal and the yaw angle signal; Based on the expected value of the position signal and combined with the linear velocity, the position subsystem controller obtains the control input of the position subsystem. Then, by adding the position dynamic model of the position loop auxiliary system, the position coordinate vector and linear velocity of the quadcopter UAV are updated. For the position subsystem control input, after passing through the inverse kinematics, the expected value of the yaw angle signal, the fault estimate, and the rate of change are combined with the yaw angle signal, and the attitude subsystem controller is used to obtain the attitude subsystem control input. After an actuator failure occurs, the actual attitude system control input is obtained. Based on the actual attitude subsystem control input, the attitude dynamic model with the addition of an attitude loop auxiliary device is used to update the attitude angle vector and rate of change of the quadcopter UAV. Based on the actual attitude subsystem control input, the fault estimate is updated through the fault observer. The position dynamic model of the added position loop auxiliary system is as follows: Where X1 represents the position coordinate vector of the quadcopter UAV, and X2 is the derivative of X1, representing the linear velocity. This is an estimate of X2. To estimate the error, , For symbolic functions, , and These represent the estimation errors of the linear velocities in the x, y, and z directions, respectively. and It is a positive definite parameter matrix. , For robust compensation, That's the weight of the quadcopter drone. It is the acceleration due to gravity. , This is the control input for the position subsystem.

2. The quadcopter UAV control method under actuator partial failure as described in claim 1, characterized in that, The attitude dynamic model with the added attitude loop assistant is: in, The attitude angle vector, φ , θ and ψ These represent the roll angle, pitch angle, and yaw angle, respectively. , , , They are respectively φ , θ and ψ The validity indicators corresponding to the faults that occurred in the process = , = , for The estimated value, To estimate the error, , , and They are respectively x , y , z The estimation error of the rate of change in direction, and It is a positive definite parameter matrix. For robust compensation, This is the control input for the attitude subsystem.

3. The quadcopter UAV control method under actuator partial failure as described in claim 1, characterized in that, The fault observer is: in, This is a fault estimate. and For positive integers, , It is a projection operator.

4. A quadcopter unmanned aerial vehicle (UAV) control system under actuator failure, characterized in that, include: The signal acquisition module is configured to acquire the expected values ​​of the position signal and the yaw angle signal. The position loop module is configured to: based on the expected value of the position signal and combined with the linear velocity, obtain the position subsystem control input through the position subsystem controller, and update the position coordinate vector and linear velocity of the quadcopter UAV by adding the position dynamic model of the position loop auxiliary system; The attitude loop module is configured to: for the position subsystem control input, after passing through the inverse solver, combine the expected value of the yaw angle signal, the fault estimate, and the rate of change, and obtain the attitude subsystem control input through the attitude subsystem controller; and obtain the actual attitude system control input after an actuator failure occurs; based on the actual attitude subsystem control input, update the attitude angle vector and rate of change of the quadcopter UAV by adding the attitude dynamic model of the attitude loop auxiliary device; Based on the actual control input of the attitude subsystem, the fault estimate is updated through the fault observer.

5. The quadcopter UAV control system under actuator partial failure as described in claim 4, characterized in that, The position dynamic model of the added position loop auxiliary system is as follows: in, X 1 represents the position coordinate vector of the quadcopter drone. X 2 is X The derivative of 1 represents the linear velocity. for X The estimated value of 2, To estimate the error, , For symbolic functions, , and They are respectively x , y and z The estimation error of linear velocity in the direction, and It is a positive definite parameter matrix. , For robust compensation, That's the weight of the quadcopter drone. It is the acceleration due to gravity. , This is the control input for the position subsystem.

6. The quadcopter UAV control system under actuator partial failure as described in claim 4, characterized in that, The attitude dynamic model with the added attitude loop assistant is: in, The attitude angle vector, φ , θ and ψ These represent the roll angle, pitch angle, and yaw angle, respectively. , , , They are respectively φ , θ and ψ The validity indicators corresponding to the faults that occurred in the process = , = , for The estimated value, To estimate the error, , , and They are respectively x , y , z The estimation error of the rate of change in direction, and It is a positive definite parameter matrix. For robust compensation, This is the control input for the attitude subsystem.

7. The quadcopter UAV control system under actuator partial failure as described in claim 4, characterized in that, The fault observer is: in, This is a fault estimate. and For positive integers, , It is a projection operator.

8. 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 quadcopter UAV control method under actuator partial failure as described in any one of claims 1-3.

9. 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 quadcopter unmanned aerial vehicle control method under actuator partial failure as described in any one of claims 1-3.

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