Quad-rotor unmanned aerial vehicle control method and system under actuator partial failure fault

Abstract

The invention relates to the technical field of unmanned aerial vehicle fault-tolerant flight control, and provides a quad-rotor unmanned aerial vehicle control method and system under an actuator partial failure fault, and the method comprises the steps: obtaining a position subsystem control input through a position subsystem controller based on a position signal expected value in combination with a linear speed, and carrying out the control of a quad-rotor unmanned aerial vehicle; the position coordinate vector and the linear speed of the four-rotor unmanned aerial vehicle are updated by adding a position dynamic model of a position loop auxiliary system; after position subsystem control input passes through an inverse solution device, attitude subsystem control input is obtained through an attitude subsystem controller in combination with a yaw angle signal expected value, a fault estimation value and a change rate, attitude system actual control input is obtained after an actuator fault occurs, and attitude loop assist control is achieved by adding an attitude dynamic model of an attitude loop assist device. Updating the attitude angle vector and the change rate of the quadrotor unmanned aerial vehicle; and updating a fault estimation value through a fault observer. The method can be well suitable for the fault-tolerant control problem of a nonlinear unmanned aerial vehicle system.

Description

Technical Field

[0001] The present invention belongs to the technical field of fault-tolerant flight control of unmanned aerial vehicles, and in particular to a control method and system for a quad-rotor unmanned aerial vehicle under partial actuator failure. 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 a type of rotary-wing drones with the advantages of simple structure, small size, vertical take-off and landing, and low cost. For quad-rotor drones, it is crucial to maintain a good flight trajectory when performing low-altitude flight missions, which requires the construction of a trajectory control system that can meet the needs of multiple missions to ensure it. First of all, the low-altitude flight environment of quad-rotor drones is complex, and there are many unknown disturbances represented by wind interference, which requires the designed flight control system to have a certain anti-interference ability. Secondly, as the actuators, each rotor usually operates at a high speed, and failure, saturation, and jamming are inevitable during its long-term operation, which makes the quad-rotor drone, which is an under-actuated system, have flight safety problems. Finally, in order to cope with the fast and flexible flight characteristics of quad-rotor drones, the designed flight control system should have a fast response speed in addition to high control accuracy.

[0004] Existing research on flight control methods for quadrotor drones, such as publication number CN113359472B, patent "A quadrotor drone adaptive robust trajectory tracking control method"; publication number CN115857356A, patent "A quadrotor aircraft actuator fault adaptive fault-tolerant control method"; publication number CN117908576B, patent "Anti-saturation quadrotor drone finite-time fault-tolerant control method"; publication number CN116627156B, patent "A quadrotor drone attitude anti-disturbance control method" and other literatures mostly focus on the robust flight control problem under the influence of external interference and actuator saturation, or the fault-tolerant flight control problem of quadrotor drones under various fault conditions. However, there is no research on the finite-time fault-tolerant control problem of quadrotor drones under the comprehensive consideration of external interference, actuator saturation and partial failure. Summary of the invention

[0005] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a control method and system for a quadrotor UAV under a partial actuator failure, takes into account the actual control input of the attitude system after the actuator failure occurs, and designs an adaptive fault observer to perform real-time estimation of the fault information. It does not require prior knowledge of the fault information, can achieve real-time fault estimation, and can be well applied to the fault-tolerant control problem of nonlinear UAV systems.

[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 control method for a quadrotor drone under a partial actuator failure, which comprises: obtaining an expected value of a position signal and an expected value of a yaw angle signal;

[0008] Based on the expected value of the position signal and the linear velocity, after obtaining the control input of the position subsystem through the position subsystem controller, the position coordinate vector and linear velocity of the quadrotor drone are updated by adding the position dynamic model of the position loop auxiliary system;

[0009] For the position subsystem control input, after passing through the inverse solver, combined with the expected value of the yaw angle signal, the fault estimation value and the change rate, the attitude subsystem control input is obtained through the attitude subsystem controller, and after an actuator failure occurs, the actual control input of the attitude system is obtained; based on the actual control input of the attitude subsystem, the attitude angle vector and the change rate of the quadrotor drone are updated by adding the attitude dynamic model of the attitude loop assistant; based on the actual control input of the attitude subsystem, the fault estimation value is updated through the fault observer.

[0010] Furthermore, the position dynamic model adding the position loop auxiliary system is:

[0011]

[0012] Among them, X1 represents the position coordinate vector of the quadrotor drone, X2 is the derivative of X1, representing the linear velocity, is the estimated value of X2, is the estimation error, sgn(·) is the sign function, and are the estimated errors of the linear velocity in the x, y and z directions respectively, K1 and K2 are positive definite parameter matrices, 0<σ<1, Ω p is the robust compensation term, m is the weight of the quadrotor drone, g is the gravitational acceleration, ε=[0,0,1] T , U p Control input for the position subsystem.

[0013] Furthermore, the posture dynamic model with the posture ring assistant added is:

[0014]

[0015] where Θ = [φ,θ,ψ] T is the attitude angle vector, θ and ψ represent the roll angle, pitch angle and yaw angle respectively, They are The effectiveness index corresponding to the faults in θ and ψ, X3 = θ, is the estimated value of X4, is the estimation error,

[0016] and are the estimated errors of the rate of change in the x, y, and z directions respectively, K3 and K4 are positive definite parameter matrices, Ω Θ is the robust compensation term, U Θ It is the control input of the attitude subsystem.

[0017] Furthermore, the fault observer is:

[0018]

[0019] in, is the fault estimate, ξ and β are positive constants, proj{-ξΠ} is the projection operator.

[0020] A second aspect of the present invention provides a quadrotor drone control system under a partial actuator failure, comprising:

[0021] A signal acquisition module, configured to: acquire an expected value of a position signal and an expected value of a yaw angle signal;

[0022] The position loop module is configured to: based on the expected value of the position signal and the linear velocity, after obtaining the control input of the position subsystem through the position subsystem controller, update the position coordinate vector and linear velocity of the quadrotor drone 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, combined with the expected value of the yaw angle signal, the fault estimation value and the change rate, the attitude subsystem control input is obtained through the attitude subsystem controller, and after an actuator failure occurs, the actual control input of the attitude system is obtained; based on the actual control input of the attitude subsystem, the attitude angle vector and the change rate of the quadrotor drone are updated by adding the attitude dynamic model of the attitude loop assistant; based on the actual control input of the attitude subsystem, the fault estimation value is updated through the fault observer.

[0024] Furthermore, the position dynamic model adding the position loop auxiliary system is:

[0025]

[0026] Among them, X1 represents the position coordinate vector of the quadrotor drone, X2 is the derivative of X1, representing the linear velocity, is the estimated value of X2, is the estimation error, sgn(·) is the sign function, and are the estimated errors of the linear velocity in the x, y and z directions respectively, K1 and K2 are positive definite parameter matrices, 0<σ<1, Ω p is the robust compensation term, m is the weight of the quadrotor drone, g is the gravitational acceleration, ε=[0,0,1] T , U p Control input for the position subsystem.

[0027] Furthermore, the posture dynamic model with the posture ring assistant added is:

[0028]

[0029] where Θ = [φ,θ,ψ] T is the attitude angle vector, θ and ψ represent the roll angle, pitch angle and yaw angle respectively, They are The effectiveness index corresponding to the faults in θ and ψ, X3 = θ, is the estimated value of X4, is the estimation error,

[0030] and are the estimated errors of the rate of change in the x, y, and z directions respectively, K3 and K4 are positive definite parameter matrices, Ω Θ is the robust compensation term, U Θ It is the control input of the attitude subsystem.

[0031] Furthermore, the fault observer is:

[0032]

[0033] in, is the fault estimate, ξ and β are positive constants, 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 method for controlling a quadrotor drone under a partial actuator failure as described above.

[0035] The fourth aspect of the present invention provides 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, wherein when the processor executes the program, the steps in the method for controlling a quadrotor drone under a partial actuator failure as described above are implemented.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The present invention takes into account the actual control input of the attitude system after an actuator failure occurs, and designs an adaptive fault observer to perform real-time estimation of fault information. It does not require prior knowledge of the fault information and can achieve real-time fault estimation. It can be well applied to the fault-tolerant control problem of nonlinear UAV systems.

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

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

[0040] The finite-time fault-tolerant control method proposed in the present invention can achieve semi-global practical finite-time stability, and has the advantages of good robustness, fast convergence speed, strong anti-interference ability, etc. While realizing fast and effective tracking of the position and attitude of the quadrotor drone, it can also ensure the semi-global practical finite-time stability of the entire closed-loop system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] 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.

[0042] Figure 1 is a flow chart of a method for controlling a quad-rotor drone under a partial actuator failure according to Embodiment 1 of the present invention;

[0043] Figure 2 This is a model diagram of a four-rotor drone system according to Embodiment 1 of the present invention;

[0044] 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;

[0045] 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;

[0046] Figure 5 This is an estimated effect diagram of a partial failure of a quad-rotor drone system according to Embodiment 1 of the present invention;

[0047] Figure 6 It is a structural diagram of a computer device according to a fourth embodiment of the present invention. DETAILED DESCRIPTION

[0048] 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.

[0049] 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.

[0050] Embodiment 1

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

[0052] In order to design a control method that is more in line with the actual flight requirements of quadcopters, this paper comprehensively considers the influence of external interference, actuator saturation and partial actuator failure, and proposes a finite-time robust trajectory fault-tolerant control strategy based on backstepping. First, the robust compensation term and parameter adaptive method are introduced to eliminate the adverse effects of external interference 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 under the framework of backstepping, which can achieve fast and effective tracking of the position and attitude of the quadcopters while ensuring the semi-global practical finite-time stability of the entire closed-loop system.

[0053] The control method of the quadrotor drone under the condition of partial actuator failure provided in this embodiment has the following specific implementation steps:

[0054] Step 1: Improve the existing quadrotor UAV model, fully consider the influence of external interference, actuator saturation and actuator partial failure, and build a quadrotor UAV dynamic model that meets the actual control requirements, such as Figure 2 shown.

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

[0056]

[0057] Among them, m is the weight of the quadrotor drone, 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 is the interference in the position subsystem, 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; is the control input in the position subsystem,

[0058] U Θd =ρ i U Θ =[ρ1U φ ,ρ2U θ ,ρ3U ψ ] T is the actual control input when the attitude subsystem has a partial actuator failure. is the desired control input of the attitude subsystem, 0<ρ i <1 is the effectiveness index of the actuator partial failure fault, when ρ i =1 means the actuator is in normal working state; ε = [0,0,1] T , and Represent the external force and torque disturbance respectively; is the rotation matrix from the body coordinate system to the ground coordinate system, is the system state equation, is the control gain matrix of the system; Sat(U Θd ) is the system saturation control input, and its specific form is as follows:

[0059]

[0060] in, Lower and upper bounds for each control input.

[0061] Step 2: According to the dynamic model of the position subsystem established in step 1, based on the given reference trajectory P d , design the control input of the position subsystem under the backstepping framework. The specific process is:

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

[0063]

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

[0065] Then, define the position loop auxiliary system as:

[0066]

[0067] in, is the estimated value of X2, and is the positive definite parameter matrix to be designed; is the estimation error, X2 is the derivative of P, representing the linear velocity, and are the estimated errors of the linear velocities corresponding to the coordinates x, y, and z, respectively; sgn(·) is the sign function; 0<σ<1; Robust compensation term introduced to compensate for external disturbance of position subsystem; θ1>0 and κ1>0 are constants to be designed.

[0068] At this time, formula (3) can be rewritten as:

[0069]

[0070] The position tracking error and velocity tracking error of the quadrotor drone are defined as:

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

[0072]

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

[0074]

[0075] in, is the positive definite parameter matrix to be designed, e1 is the tracking error of the position coordinate, e 11 、e 12 、e 13 are the tracking errors corresponding to coordinates x, y, and z respectively.

[0076] Then the control input of the position subsystem can be designed as:

[0077]

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

[0079] Choose the Lyapunov function as Its first-order 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 value of the Euler angle satisfies the following relationship:

[0082]

[0083] Step 3: According to 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. At the same time, the stability of the design scheme is verified by 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 quadrotor drone 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-order derivative of Θ, representing the rate of change, F Θ =diag{Sat(U φ ),Sat(U θ ),Sat(U ψ )}; Θ =[ρ1,ρ2,ρ2] T It is an effectiveness indicator for partial failure of the actuator.

[0087] Define the attitude loop auxiliary system as:

[0088]

[0089] in, is the estimated value of X4, is the first derivative of the estimated rate of change, and is the positive definite parameter matrix to be designed; is the estimation error, and are the estimated errors of the rates of change corresponding to the coordinates x, y, and z, respectively. The robust compensation term introduced to compensate for the external disturbance of the attitude subsystem, θ2>0 and κ2>0 are constants to be designed.

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

[0091]

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

[0093]

[0094] Among them, J is the unit matrix, and the specific form of Π is:

[0095]

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

[0097]

[0098] At this time, formula (13) can be rewritten as:

[0099]

[0100] in, U Θ is the control input of the system, and Sat() represents the saturated control input.

[0101] Define the attitude tracking error and angular velocity tracking error of the quadrotor drone:

[0102] e3=X3-θ d (20)

[0103]

[0104] Among them, Θ d ={Φ d ,θ d ,ψ d} is the expected value of the angle signal, X 4d is the virtual control rate to be designed, which is designed as:

[0105]

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

[0107] Then the control input of the attitude subsystem is:

[0108]

[0109] in, The attitude angle The effectiveness index corresponding to the faults in θ and ψ, is the positive definite parameter matrix to be designed, u c For the compensation controller, the specific form is designed as follows:

[0110]

[0111] Among them, r1>0 is the design parameter, is the estimated value of ω, and its adaptive update rate is designed as follows:

[0112]

[0113] Among them, r2>0 is the parameter to be designed.

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

[0115]

[0116] 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 quadcopter, the control input is designed as (9) and (23). At the same time, in order to eliminate the adverse effects of actuator failure, the adaptive fault observer is designed as shown in (15) and (16). 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 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-order derivative of V is:

[0118]

[0119] At this time, choose appropriate parameters so that You can use in 0<μ1<1,0<μ2<1 It can be seen that all signals in the entire closed-loop system are semi-globally practical finite-time stable.

[0120] 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 UAV system and has good control effect.

[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, actuator saturation and partial actuator failure, and has good results.

[0122] like Figure 1 The specific execution steps are as follows:

[0123] Step (1), obtain the expected value P of the position signal d and the expected value of the yaw angle signal ψ d .

[0124] Step (2), based on the expected value P of the position 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 adding the position dynamic model of the position loop auxiliary system (Formula (5)), the position coordinate vector P (i.e., X1) of the quadrotor drone and its first-order derivative 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 , through the inverse solver (Formula (11) and (12)), we get φ d and θ d .

[0126] Step (4), based on Expected value of yaw angle signal ψ d ,φ d ,θ d , and X4, through the attitude subsystem controller (Formula (23)), the control input U of the attitude subsystem is obtained Θ ; Based on the control input U of the attitude subsystem Θ , after an actuator failure occurs, the actuator failure (U Θd=ρ i U Θ =[ρ1U φ ,ρ2U θ ,ρ3U ψ ] T ), the subscripts 1, 2, and 3 correspond to θ, ψ, to obtain the actual control input of the attitude system when there is an actuator failure; based on the actual control input ρU of the attitude subsystem Θ By adding the attitude dynamic model of the attitude loop assistant (Formula (19)), the attitude angle vector Θ (i.e., X3) and its first-order derivative are updated. (ie, X4); based on the actual control input ρU of the attitude subsystem Θ , through the fault observer (Formula (15) and (16)), update the estimated value of the fault

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

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

[0129] Consider a quadrotor drone system with the following parameters: quadrotor drone mass m = 1.2 kg, gravity acceleration g = 10 m / s 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 ; External unknown interference options are:

[0130]

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

[0132]

[0133] ψ d =π / 12deg (27)

[0134] The actuator partial failure fault is:

[0135]

[0136] The controller parameters are chosen as 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] Based on the above parameter settings, numerical simulation verification is carried out for hypersonic aircraft. The simulation results are as follows Figure 3 , Figure 4 , Figure 5 shown. Figure 3 , Figure 4 The following are the tracking effects of trajectory tracking and attitude tracking of a quad-rotor drone. Figure 3 It can be seen that even when there are unknown external interferences and actuator failures, the designed controller can still enable the quadcopter to maintain good flight performance, with fast response and good recovery effects. 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 is the actuator fault estimation curve. It can be seen that when a partial failure occurs in the quadrotor UAV at the 10th second, the proposed adaptive fault observer can accurately estimate the fault within 5 seconds, and the specific fault information can be observed in real time during the entire simulation period. According to the above simulation results, it can be seen that for the trajectory tracking control problem of the quadrotor UAV with actuator saturation and partial actuator failure, the finite-time robust tracking control scheme proposed in the present invention is effective and feasible, and has the advantages of fast response and good control effect.

[0138] The quadrotor UAV control method under the actuator partial failure fault provided in this embodiment designs a finite-time fault-tolerant tracking control scheme by utilizing robust compensation terms, parameter adaptation, and fault observer methods under the backstepping framework, thereby effectively solving the attitude stability and trajectory precise tracking control problems of the quadrotor UAV when facing external interference, actuator saturation, and actuator partial failure.

[0139] Embodiment 2

[0140] This embodiment provides a quadrotor drone control system under a partial actuator failure, which specifically includes:

[0141] A signal acquisition module, configured to: acquire an expected value of a position signal and an expected value of a yaw angle signal;

[0142] The position loop module is configured to: based on the expected value of the position signal and the linear velocity, after obtaining the control input of the position subsystem through the position subsystem controller, update the position coordinate vector and linear velocity of the quadrotor drone 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, combined with the expected value of the yaw angle signal, the fault estimation value and the change rate, the attitude subsystem control input is obtained through the attitude subsystem controller, and after an actuator failure occurs, the actual control input of the attitude system is obtained; based on the actual control input of the attitude subsystem, the attitude angle vector and the change rate of the quadrotor drone are updated by adding the attitude dynamic model of the attitude loop assistant; based on the actual control input of the attitude subsystem, the fault estimation value is updated through the fault observer.

[0144] Furthermore, the position dynamic model adding the position loop auxiliary system is:

[0145]

[0146] Among them, X1 represents the position coordinate vector of the quadrotor drone, X2 is the derivative of X1, representing the linear velocity, ~

[0147] is the estimated value of X2, X2 is the estimation error, sgn(·) is the sign function, and are the estimated errors of the linear velocity in the x, y and z directions respectively, K1 and K2 are positive definite parameter matrices, 0<σ<1, Ω p is the robust compensation term, m is the weight of the quadrotor drone, g is the gravitational acceleration, ε=[0,0,1] T , U p Control input for the position subsystem.

[0148] Furthermore, the posture dynamic model with the posture ring assistant added is:

[0149]

[0150] where Θ = [φ,θ,ψ] T is the attitude angle vector, θ and ψ represent the roll angle, pitch angle and yaw angle respectively, They are The effectiveness index corresponding to the faults in θ and ψ, X3 = θ, is the estimated value of X4, is the estimation error,

[0151] and are the estimated errors of the rate of change in the x, y, and z directions respectively, K3 and K4 are positive definite parameter matrices, Ω Θ is the robust compensation term, U Θ It is the control input of the attitude subsystem.

[0152] Furthermore, the fault observer is:

[0153]

[0154] in, is the fault estimate, ξ and β are positive constants, proj{-ξΠ} is the projection operator.

[0155] 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.

[0156] Embodiment 3

[0157] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps in the method for controlling a quad-rotor drone under a partial actuator failure as described in the first embodiment above are implemented.

[0158] Embodiment 4

[0159] This embodiment provides a computer device, such as Figure 6As 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 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, the steps in the method for controlling a quad-rotor drone under a partial actuator failure as described in the first embodiment above are implemented.

[0160] 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).

[0161] 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 1 A device that provides the functions specified in a block or multiple blocks.

[0162] 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 comprising 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.

[0163] 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.

[0164] 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. A quadrotor drone control method under partial actuator failure, characterized in that: include: Obtaining expected values ​​of position signals and yaw angle signals; Based on the expected value of the position signal and the linear velocity, after obtaining the control input of the position subsystem through the position subsystem controller, the position coordinate vector and linear velocity of the quadrotor drone are updated by adding the position dynamic model of the position loop auxiliary system; For the position subsystem control input, after passing through the inverse solver, combined with the expected value of the yaw angle signal, the fault estimation value and the change rate, the attitude subsystem control input is obtained through the attitude subsystem controller, and after an actuator failure occurs, the actual control input of the attitude system is obtained; based on the actual control input of the attitude subsystem, the attitude angle vector and change rate of the quadrotor drone are updated by adding the attitude dynamic model of the attitude loop assistant; Based on the actual control input of the attitude subsystem, the fault estimation value is updated through the fault observer.

2. The method for controlling a quadrotor drone under a partial actuator failure as claimed in claim 1, characterized in that: The position dynamic model of the position loop auxiliary system is: Among them, X1 represents the position coordinate vector of the quadrotor drone, X2 is the derivative of X1, representing the linear velocity, is the estimated value of X2, is the estimation error, sgn(·) is the sign function, and are the estimated errors of the linear velocity in the x, y and z directions respectively, K1 and K2 are positive definite parameter matrices, 0<σ<1, Ω p is the robust compensation term, m is the weight of the quadrotor drone, g is the gravitational acceleration, ε=[0,0,1] T , U p Control input for the position subsystem.

3. The method for controlling a quadrotor drone under a partial actuator failure as claimed in claim 1, characterized in that: The posture dynamic model with the posture ring assistant is: where Θ = [φ,θ,ψ] T is the attitude angle vector, θ and ψ represent the roll angle, pitch angle and yaw angle respectively, They are The effectiveness index corresponding to the faults in θ and ψ, X3 = θ, is the estimated value of X4, is the estimation error, and are the estimated errors of the rate of change in the x, y, and z directions respectively, K3 and K4 are positive definite parameter matrices, Ω Θ is the robust compensation term, U Θ It is the control input of the attitude subsystem.

4. The method for controlling a quadrotor drone under a partial actuator failure as claimed in claim 1, characterized in that: The fault observer is: in, is the fault estimate, ξ and β are positive constants, proj{-ξΠ} is the projection operator.

5. A quadrotor drone control system with a partial actuator failure, characterized in that: include: A signal acquisition module, configured to: acquire an expected value of a position signal and an expected value of a yaw angle signal; The position loop module is configured to: based on the expected value of the position signal and the linear velocity, after obtaining the control input of the position subsystem through the position subsystem controller, update the position coordinate vector and linear velocity of the quadrotor drone by adding the position dynamic model of the position loop auxiliary system; The attitude loop module is configured as follows: for the position subsystem control input, after passing through the reverse decoder, combined with the expected value of the yaw angle signal, the fault estimation value and the change rate, the attitude subsystem control input is obtained through the attitude subsystem controller, and after the actuator failure occurs, the actual control input of the attitude system is obtained; based on the actual control input of the attitude subsystem, the attitude angle vector and the change rate of the quadrotor drone are updated by adding the attitude dynamic model of the attitude loop assistant; Based on the actual control input of the attitude subsystem, the fault estimation value is updated through the fault observer.

6. The quadrotor drone control system under partial actuator failure as claimed in claim 6, characterized in that: The position dynamic model of the position loop auxiliary system is: Among them, X1 represents the position coordinate vector of the quadrotor drone, X2 is the derivative of X1, representing the linear velocity, is the estimated value of X2, is the estimation error, sgn(·) is the sign function, and are the estimated errors of the linear velocity in the x, y and z directions respectively, K1 and K2 are positive definite parameter matrices, 0<σ<1, Ω p is the robust compensation term, m is the weight of the quadrotor drone, g is the gravitational acceleration, ε=[0,0,1] T , U p Control input for the position subsystem.

7. The quadrotor drone control system under partial actuator failure as claimed in claim 6, characterized in that: The posture dynamic model with the posture ring assistant is: where Θ = [φ,θ,ψ] T is the attitude angle vector, θ and ψ represent the roll angle, pitch angle and yaw angle respectively, They are The effectiveness index corresponding to the faults in θ and ψ, X3 = θ, is the estimated value of X4, is the estimation error, and are the estimated errors of the rate of change in the x, y, and z directions respectively, K3 and K4 are positive definite parameter matrices, Ω Θ is the robust compensation term, U Θ It is the control input of the attitude subsystem.

8. The quadrotor drone control system under partial actuator failure as claimed in claim 6, characterized in that: The fault observer is: in, is the fault estimate, ξ and β are positive constants, proj{-ξΠ} is the projection operator.

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 of the quad-rotor drone control method under a partial actuator failure as described in any one of claims 1 to 4 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 of the quadrotor drone control method under a partial actuator failure as described in any one of claims 1-4 are implemented.

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