A Fixed-Time Control Method for Rotary-Wing Unmanned Aerial Vehicles in Unknown Environments

Abstract

This invention discloses a fixed-time control method for a rotary-wing unmanned aerial vehicle (UAV) in unknown environments. The steps are as follows: 1. Constructing a nonlinear system model of the UAV's position loop and attitude loop, simultaneously considering system uncertainty, output constraints, and unknown external disturbances; 2. Designing a safe desired tracking signal, obtaining the remaining desired signals through inverse kinematics, calculating the errors of the position loop and attitude loop, and designing a barrier Lyapunov function based on the characteristics of the nonlinear system model of the UAV's position and attitude loops, and selecting appropriate upper and lower bounds; 3. Designing a tracking controller based on the barrier Lyapunov function, combined with a fuzzy logic system, a fixed-time disturbance observer, a filter, and an adaptive backstepping method. This invention has the advantage of enabling a rotary-wing UAV to track a preset desired tracking signal within a fixed time while ensuring system safety and stability, under the influence of output constraints, system uncertainty, and unknown external disturbances.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and more specifically to a fixed-time control method for rotary-wing UAVs designed for unknown environments. Background Technology

[0002] In recent years, rotary-wing unmanned aerial vehicles (UAVs) have achieved remarkable performance in high-altitude photography, airway inspection, earthquake relief, epidemic prevention and disinfection, and targeted material delivery, thanks to their advantages such as hovering, vertical takeoff and landing, wide applicability, and ability to fly in any direction. Rotary-wing UAV systems are characterized by high nonlinearity, strong coupling, time-varying behavior, and open-loop instability. Achieving flight objectives such as hovering, forward and backward flight, and autonomous takeoff and landing is extremely challenging. However, given the high application value of rotary-wing UAVs in various real-world fields, developing autonomous flight control technology for them is of profound significance. Undoubtedly, advanced autonomous flight control technology is the core to solving the aforementioned problems.

[0003] During flight, rotorcraft drones are susceptible to stability issues due to factors such as engine vibration and changes in the external environment (e.g., gusts of wind). Particularly during flight control system operation, neglecting the impact of system uncertainties and external disturbances can lead to performance degradation. Furthermore, rotorcraft drones inherently employ various safety protection mechanisms during flight, such as position constraints. Therefore, the designed controller must ensure that system state variables remain within safe boundaries to safely and effectively complete the preset tasks. Failure to address the constraints encountered during drone flight in a timely manner could result in irreversible losses such as system loss of control or crashes.

[0004] Furthermore, because drones need to respond rapidly to complex working environments, the controller's response speed is particularly important. Traditional control systems typically do not consider this issue, ultimately resulting in the drone system reaching asymptotic stability, but the settling time cannot be guaranteed. Therefore, there is an urgent need to design a control strategy that can control the system's settling time. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a fixed-time control method for rotary-wing unmanned aerial vehicles (UAVs) in unknown environments. This method enables the UAV to track a preset desired tracking signal within a fixed time while ensuring system safety and stability, taking into account output constraints, system uncertainties, and the effects of unknown external interference. Furthermore, it solves the problem of output limitations during flight.

[0006] This invention is achieved through the following technical solution:

[0007] A fixed-time control method for rotary-wing unmanned aerial vehicles (UAVs) in unknown environments includes the following steps:

[0008] Step 1: Simultaneously consider system uncertainties, output constraints, and unknown external disturbances to construct a nonlinear system model of the rotorcraft UAV's position loop and attitude loop;

[0009] Step 2: Design the desired safety tracking signal X d ,Y d Z d ,ψ d The remaining desired signal φ is obtained through inverse kinematics. d ,θ d The errors z1 and z2 of the position loop and z3 and z4 of the attitude loop are calculated. Based on the nonlinear system model characteristics of the position loop and attitude loop of the rotary-wing UAV, the corresponding obstacle Lyapunov function is designed, and the appropriate upper and lower bounds are selected according to the output requirements.

[0010] Step 3: Based on the obstacle Lyapunov function from Step 2, and combining the fuzzy logic system with a fixed-time perturbation observer, filter, and adaptive backstepping method, design a tracking controller.

[0011] Furthermore, in step one, the nonlinear system model of the rotorcraft UAV's position loop and attitude loop is constructed as follows:

[0012]

[0013] Where: P = [X, Y, Z] T X, Y, and Z represent the positions in the three directions of the geodetic coordinate system, and ε = [u, v, w]. T Let u, v, and w be the velocities along the X, Y, and Z axes in the geodetic coordinate system, respectively, and Ξ = [φ, θ, ψ]. T Let φ, θ, and ψ represent the roll, pitch, and yaw angles along the X, Y, and Z axes, respectively, in the body coordinate system, and ν = [p, q, r]. T p, q, r are the roll angular velocity, pitch angular velocity, and yaw angular velocity along the X, Y, and Z axes in the body coordinate system. It is a rotation matrix, F m This is the main rotor thrust, G = [0, 0, mg] T This represents the weight of the rotary-wing drone, and J is the moment of inertia matrix. It is system uncertainty. This is the unknown disturbance vector under attitude angle and attitude angular velocity.

[0014] Furthermore, the barrier Lyapunov function in step two is designed as follows:

[0015]

[0016] Where: b i >0, (i=1,2,3) are pre-designed error constraints, z 1i Let b represent the i-th column of z1. i , (i = 1, 2, 3) are pre-designed error safety boundaries.

[0017] Furthermore, the controller is selected as follows:

[0018]

[0019] Where: u1 is the position loop controller, u2 is the attitude transition controller, 0 < ι < 1, ρ > 1, ζ1 > 0, ζ2 > 0 are the parameters to be designed. It is the matrix to be designed. It is the output of the dynamic surface. It is an estimate under system uncertainty. It is an estimation of the position loop and attitude loop perturbations. It is the parameter of the barrier Lyapunov function.

[0020] Furthermore, the fixed-time perturbation observer is:

[0021]

[0022] in It is an estimation of position loop and attitude loop perturbations. As an auxiliary variable, These are the parameters to be designed.

[0023] The present invention also provides a device including a memory and a processor, wherein the memory is used to store a computer program; and the processor is used to execute the above-described fixed-time control method for rotary-wing unmanned aerial vehicles in unknown environments.

[0024] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, enables fixed-time control of a rotary-wing unmanned aerial vehicle.

[0025] The beneficial effects of this invention are as follows:

[0026] 1. This invention addresses the output constraints of unmanned aerial vehicles (UAVs). By selecting reasonable upper and lower bounds for error and utilizing a barrier Lyapunov function to constrain the output, it ensures the safe flight of the rotary-wing UAV. To address system uncertainties and unknown external disturbances, a fuzzy logic system and a fixed-time disturbance observer are employed for estimation. Simultaneously, fixed-time stability theory is used to guarantee the UAV's response time. Verification has shown that the fixed-time disturbance-resistant tracking controller designed in this invention can successfully track a preset desired tracking signal while ensuring system safety and stability.

[0027] 2. This invention simultaneously considers output constraints, system uncertainties, and unknown external disturbances, constructing a nonlinear system model of the position and attitude loops of a rotorcraft UAV that requires rapid response. This invention utilizes the real-time output and output constraints of the rotorcraft UAV system to design a class of adaptive fixed-time algorithms. Addressing issues such as system uncertainty, unknown external disturbances, and response time, this invention employs fuzzy logic systems and fixed-time disturbance observer methods to approximate these issues, and uses fixed-time stability theory to design an adaptive fixed-time tracking controller to achieve the safe flight intentions of the rotorcraft UAV system. Attached Figure Description

[0028] Figure 1 This is a system control flowchart of the present invention. Detailed Implementation

[0029] A fixed-time control method for rotary-wing unmanned aerial vehicles (UAVs) in unknown environments includes the following steps:

[0030] Step 1: Simultaneously consider system uncertainties, output constraints, and unknown external disturbances to construct a nonlinear system model of the rotorcraft UAV's position loop and attitude loop;

[0031]

[0032] Where: P = [X, Y, Z] T X, Y, and Z represent the positions in the three directions of the geodetic coordinate system, and ε = [u, v, w]. T Let u, v, and w be the velocities along the X, Y, and Z axes in the geodetic coordinate system, respectively, and Ξ = [φ, θ, ψ]. T Let φ, θ, and ψ represent the roll, pitch, and yaw angles along the X, Y, and Z axes, respectively, in the body coordinate system, and ν = [p, q, r]. T p, q, r are the roll angular velocity, pitch angular velocity, and yaw angular velocity along the X, Y, and Z axes in the body coordinate system. It is a rotation matrix, F m This is the main rotor thrust, G = [0, 0, mg] T This represents the weight of the rotary-wing drone, and J is the moment of inertia matrix. It is system uncertainty. The unknown disturbance vector under attitude angle and attitude angular velocity;

[0033] Step 2: Ensure the desired tracking signal X corresponding to the positions X, Y, Z and yaw angle ψ. d ,Y d Z d ,ψ d Its first derivative is bounded, and the remaining desired signal φ can be obtained by inverse solution. d ,θd Calculate the position loop errors z1, z2 (specifically P and P). d The errors z1, z2 (the error between ε and the virtual control law α1) and z3, z4 (specifically Ξ and Ξ) of the attitude loop. d The error z3 and the error z4 between ν and the virtual control law α2 are combined with the nonlinear system model characteristics of the rotor UAV position loop and attitude loop. The corresponding obstacle Lyapunov function is designed to constrain the output error z1, and the appropriate upper and lower bounds are selected according to the output requirements.

[0034] The barrier Lyapunov function is:

[0035]

[0036] b i >0, (i=1,2,3) are pre-designed error constraints, z 1i Let the i-th column of z1 be...

[0037] in

[0038] b i , (i = 1, 2, 3) are pre-designed error safety boundaries.

[0039] Step 3: Based on the obstacle Lyapunov function from Step 2, and combining the fuzzy logic system with a fixed-time perturbation observer, filter, and adaptive backstepping method, design a tracking controller.

[0040] The controller selection is as follows:

[0041]

[0042] Where u1 is the position loop controller, u2 is the attitude transition controller, and 0 < ι < 1, ρ > 1, ζ1 > 0, ζ2 > 0 are the parameters to be designed. It is the matrix to be designed. It is the output of the dynamic surface. It is an estimate under system uncertainty. It is an estimation of the position loop and attitude loop perturbations. It is the parameter of the barrier Lyapunov function.

[0043] The fixed-time perturbation observer is:

[0044]

[0045] in As an auxiliary variable, These are the parameters to be designed.

[0046] The controller design process is as follows:

[0047] (1) For the location subsystem of unmanned aerial vehicles;

[0048] a. Design the virtual control law α1:

[0049]

[0050] Where 0 < ι < 1, ρ > 1, a 1i1 >0,a 1i2 >0 represents the parameter to be designed; α 1i ,P di Representing α1,P d The first column;

[0051] b. Design of a fixed-time disturbance observer:

[0052] c. Filter design:

[0053]

[0054] Where λ1 is the filter output signal. λ1 is the time derivative, and τ1 is a positive definite diagonal matrix;

[0055] d. Designing fuzzy adaptive laws

[0056]

[0057] Where γ1>0, ξ1>0 are the parameters to be designed;

[0058] e. Design a fixed-time controller;

[0059] (2) For the attitude subsystem, the controller design process is as follows:

[0060] a. Design the virtual control law α2 as follows:

[0061]

[0062] in, The matrix to be designed;

[0063] b. Design a fixed-time disturbance observer;

[0064] C. Filter Design:

[0065]

[0066] Where λ2 is the filter output signal. λ is the time derivative of λ², and τ² is a positive definite diagonal matrix;

[0067] d. Design a fuzzy adaptive law by combining the attitude subsystem, the interference observer, and the filter.

[0068]

[0069] Where γ2>0, ξ2>0 are the parameters to be designed.

[0070] e. Design a fixed-time controller.

[0071] 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.

[0072] Example 1

[0073] Please see Figure 1 A fixed-time control method for rotary-wing unmanned aerial vehicles (UAVs) in unknown environments includes the following steps:

[0074] Step 1: Simultaneously consider output constraints, system uncertainties, and unknown external disturbances to construct a nonlinear system model of the rotorcraft UAV's position loop and attitude loop.

[0075] The process of establishing the nonlinear system model of the rotorcraft UAV's position loop and attitude loop in step one is as follows:

[0076] a. Nonlinear model of position and attitude loops of rotary-wing UAVs

[0077] Considering output constraints, system uncertainties, and unknown external disturbances, combined with the aerodynamics and kinematics of the rotary-wing UAV, the nonlinear models of the position loop and attitude loop are described as follows:

[0078]

[0079] Where: P = [X, Y, Z] T ε=[u,v,w] T The position and velocity vectors of the rotary-wing UAV, Ξ=[φ,θ,ψ] T ν=[p,q,r] T Let be the attitude angle and attitude angular velocity vector of the rotary-wing UAV. It is a rotation matrix, F m This is the main rotor thrust, G = [0, 0, mg] T This represents the weight of the rotary-wing drone, and J is the moment of inertia matrix. It is system uncertainty. Here is the unknown disturbance vector under attitude angle and attitude angular velocity; the attitude rotation matrix is...

[0080]

[0081] To facilitate controller design, system (1) can be rewritten as a position subsystem and an attitude subsystem:

[0082] The location subsystem is expressed as follows:

[0083]

[0084] The attitude subsystem is expressed as follows:

[0085]

[0086] Where u1 = RF m , F ν =J -1 ν×Jν,

[0087] Before designing the controller, the following assumptions and lemmas are required:

[0088] Assumption 1: The derivatives of the external disturbance vectors d1 and d2 are bounded, that is, there exists a positive constant β such that the following applies to the disturbance vector d1. i Time derivative of (i = 1, 2) The inequality holds:

[0089] Assumption 2: Preset desired tracking signal Θ d =[X d ,Y d Z d ,ψ d ] T Both its r-th derivative and its r-th derivative are bounded (r = 1, 2), that is, they have a positive constant δ. j (j = 1, ..., 4) such that the following inequality always holds:

[0090]

[0091] Assumption 3: The roll and pitch angles of the rotary-wing UAV are always kept within a reasonable range (-π / 2, π / 2).

[0092] Lemma 1: For any variable If there exist a3 > 0, a4 > 0, a5 > 0, then the following inequality holds.

[0093]

[0094] Lemma 2: For ρ > 1, 0 < ι < 1, a i If > 0, then we have

[0095]

[0096] Lemma 3: For b i >0, The following inequalities always hold true

[0097]

[0098] Lemma 4: For o1≥o2>0, ρ>1, the following inequality always exists.

[0099]

[0100] Considering that rotary-wing UAVs are often subject to output constraints, the upper and lower bounds of the system output error are defined as z. 1,up =[b1,b2,b3] T With z 1,low =[-b1,-b2,-b3] T .

[0101] Furthermore, during flight, rotorcraft UAVs are susceptible to factors such as engine vibration and changes in the external environment (e.g., gusts of wind), which can affect system stability. The unknown external interference signal d(t) satisfies the norm boundedness condition, meaning there exists a positive constant β such that the following inequality holds.

[0102] To handle system uncertainty, a fuzzy logic system will be used to approximate it, and the design is as follows.

[0103]

[0104] in For the matrix to be designed, To approximate the error, σ i It is a positive real number.

[0105] Substituting these values ​​into the position subsystem and attitude subsystem respectively yields the following results:

[0106]

[0107] and

[0108]

[0109] in It is a complex disturbance.

[0110] The tracking error design of the position subsystem is as follows:

[0111] z1 = PP d (14)

[0112] z2 = ε - α1 (15)

[0113] Where α1 is the virtual control law of the position subsystem.

[0114] To achieve the output constraint, the barrier Lyapunov function is designed as follows:

[0115]

[0116] Where z 1i Let b represent the i-th column of z1. i , (i = 1, 2, 3) are pre-designed error safety boundaries.

[0117] Step 3: Combining the obstacle Lyapunov function, fuzzy logic system, fixed-time perturbation observer, and fixed-time stability theory, design a tracking controller based on the adaptive backstepping method; the controller design process in Step 3 is as follows:

[0118] a. For the UAV's position subsystem, the virtual control law α1 is designed as follows:

[0119]

[0120] Where 0 < ι < 1, ρ > 1, a 1i1 >0,a 1i2 >0 represents the parameter to be designed; α 1i ,P di Representing α1,P d The first column.

[0121] To compensate for unknown disturbances, a fixed-time disturbance observer is designed as follows:

[0122]

[0123] in, As an auxiliary variable, Let β1 > 0 be the matrix to be designed, and let β1 > 0 be the parameter to be designed. Define the error variable. Then there is

[0124]

[0125] b. To avoid directly differentiating the virtual control law α1, let it pass through a filter of the following form:

[0126]

[0127] Where λ1 is the filter output signal. Let λ1 be the time derivative, τ1 be a positive definite diagonal matrix, and define the error variable. Then there is

[0128]

[0129] Where B1 is a continuous function vector, and under the given initial value, it satisfies ||B1(·)||≤B1.

[0130] By combining the position subsystem (12), the obstacle Lyapunov function (16), the interference observer (18), and the filter (20), a fuzzy adaptive law is designed. The fixed-time controller u1 is as follows:

[0131]

[0132] Where γ1>0, ξ1>0 are the parameters to be designed. For the matrix to be designed,

[0133] For the attitude subsystem, a tracking controller based on the adaptive backstepping method is designed by combining fuzzy logic systems, fixed-time perturbation observers, and fixed-time stability theory. The controller design process is as follows:

[0134] a. For the position subsystem of a rotary-wing UAV, the virtual control law α2 is designed as follows:

[0135]

[0136] in, The matrix to be designed.

[0137] To compensate for unknown disturbances, a fixed-time disturbance observer is designed as follows:

[0138]

[0139] in, As an auxiliary variable, Let β2 be the matrix to be designed, β2 > 0 be the parameters to be designed, and define the error variable. Then there is

[0140]

[0141] b. To avoid directly differentiating the virtual control law α2, let it pass through a filter of the following form:

[0142]

[0143] Where λ2 is the filter output signal. Let λ² be the time derivative, τ² be a positive definite diagonal matrix, and define the error variable. Then there is

[0144]

[0145] Where B2 is a continuous function vector, and under the given initial value, it satisfies ||B2(·)||≤B2.

[0146] By combining the attitude subsystem (21), the interference observer (25), and the filter (27), a fuzzy adaptive law is designed. The fixed-time controller u2 is as follows:

[0147]

[0148] Where γ2>0, ξ2>0 are the parameters to be designed. The matrix to be designed.

[0149] Based on the above analysis and discussion, the following conclusions are reached:

[0150] Conclusion 1: For a rotary-wing UAV system with output constraints, system uncertainties, and external disturbances, considering the obstacle Lyapunov function as (16), the fixed-time disturbance observer is designed as Equations (18) and (25), the virtual control input is designed as Equations (17) and (24), the control input is designed as Equations (23) and (30), the fuzzy adaptive law is designed as Equations (22) and (29), and the filter is designed as Equations (20) and (27). The system is actually fixed-time bounded. The tracking error of the system and the observer error of the observer can converge within a fixed time. The convergence time does not depend on the initial conditions of the system, and the convergence speed of the observer can be made faster than the convergence speed of the state by adjusting the parameters.

[0151] Proof: Let the Lyapunov function be...

[0152]

[0153] Differentiating V with respect to time t yields

[0154]

[0155] in in It is D i The upper bound of >0.

[0156] By Lemma 2, we can obtain

[0157]

[0158] Substituting (33) into (32) and rearranging, we get

[0159]

[0160] in

[0161] From Lemma 3 we can obtain

[0162]

[0163] Substituting (35)-(38) into (34) gives

[0164]

[0165] Where c2 = min{c1, 2c1, 3} 1-ρ c1}.

[0166] After further transformation, we can obtain

[0167]

[0168] From Lemma 2, we can obtain the following inequality.

[0169]

[0170] Substituting (41)-(43) into (40) gives

[0171]

[0172] in

[0173] Assumption Then you can get

[0174]

[0175] Then (44) can be rewritten as

[0176]

[0177] Where B = c², S = 5 1-ρ c2,W = W3.

[0178] To ensure the stability of the closed-loop system, relevant parameters ι,ρ,Λ1,Λ2,a need to be designed. 111 ,a 121 ,a 131 ,ζ1,ζ2,a 112 ,a 122 ,

[0179] a 132 ,a 21 ,a 22 ,a 31 ,a 32 ,a 41 ,a 42,β1,β2,Γ1,Γ2,ξ1,ξ2,γ1,γ2,b1,b2,b3 make (47) true, and the conclusion is proved.

[0180] The barrier Lyapunov function designed in this invention ensures that the output error is always kept within a pre-defined safe range. A fuzzy logic system and a fixed-time perturbation observer are used to approximate the system's uncertainties and external disturbances. The observer error can be made to reach an ideal state before the system stabilizes by design parameters, further improving system stability. All system states are bounded and can track the desired signal. Furthermore, the settling time is determined solely by the design parameters.

[0181] Based on the above steps, a rotary-wing UAV can track a preset desired tracking signal while ensuring system safety and stability, taking into account constant attitude angle and altitude constraints as well as the influence of unknown external interference.

[0182] The present invention also provides an apparatus comprising: a memory and a processor, wherein the memory is used to store a computer program; and the processor is used to execute the computer program to implement a fixed-time control method for a rotary-wing unmanned aerial vehicle (UAV) oriented to an unknown environment.

[0183] A computer-readable storage medium storing a computer program that, when executed by a processor, enables fixed-time control of a rotary-wing unmanned aerial vehicle.

[0184] It should be noted that although the present invention has been described through the above embodiments, the present invention may have many other embodiments. Without departing from the spirit and scope of the present invention, those skilled in the art can obviously make various corresponding changes and modifications to the present invention, but all such changes and modifications should fall within the scope of protection of the appended claims and their equivalents.

Claims

1. A fixed-time control method for a rotary-wing unmanned aerial vehicle (UAV) in an unknown environment, characterized in that: Includes the following steps: Step 1: Simultaneously consider system uncertainties, output constraints, and unknown external disturbances to construct a nonlinear system model of the rotorcraft UAV's position loop and attitude loop; Step 2: Design the desired safety tracking signal X d ,Y d Z d ,ψ d The remaining desired signal φ is obtained through inverse kinematics. d ,θ d The errors z1 and z2 of the position loop and z3 and z4 of the attitude loop are calculated. Based on the nonlinear system model characteristics of the position loop and attitude loop of the rotary-wing UAV, the corresponding obstacle Lyapunov function is designed, and the appropriate upper and lower bounds are selected according to the output requirements. Step 3: Based on the obstacle Lyapunov function from Step 2, and combining the fuzzy logic system with a fixed-time perturbation observer, filter, and adaptive backstepping method, design a tracking controller. By combining the position subsystem, the obstacle Lyapunov function, the interference observer, and the filter, a fuzzy adaptive law is designed. The fixed-time controller u1 is as follows: By combining the attitude subsystem, interference observer, and filter, a fuzzy adaptive law is designed. The fixed-time controller u2 is as follows: Where u1 is the controller for the position loop and u2 is the controller for the attitude loop. 0 < ι < 1, ρ > 1, γ1 > 0, ξ1 > 0, γ2 > 0, ξ2 > 0 are the parameters to be designed. It is the matrix to be designed. It is the output of the dynamic surface. It is an estimate of system uncertainty. This is an estimate of the position loop and attitude loop perturbations, where H is the attitude rotation matrix used in the coordinate transformation of the UAV system, and J is the moment of inertia matrix. It is the parameter of the barrier Lyapunov function.

2. The fixed-time control method for a rotary-wing unmanned aerial vehicle (UAV) in an unknown environment according to claim 1, characterized in that: In step one, the nonlinear system model of the rotorcraft UAV's position loop and attitude loop is constructed as follows: Where: P = [X, Y, Z] T X, Y, and Z represent the positions in the three directions of the geodetic coordinate system, and ε = [u, v, w]. T Let u, v, and w be the velocities along the X, Y, and Z axes in the geodetic coordinate system, respectively, and Ξ = [φ, θ, ψ]. T Let φ, θ, and ψ represent the roll, pitch, and yaw angles along the X, Y, and Z axes, respectively, in the body coordinate system, and ν = [p, q, r]. T p, q, r are the roll angular velocity, pitch angular velocity, and yaw angular velocity along the X, Y, and Z axes in the body coordinate system. It is a rotation matrix, F m This is the main rotor thrust, G = [0, 0, mg] T It is the weight of the rotary-wing drone. It is system uncertainty. The unknown disturbance vector is defined by the attitude angle and attitude angular velocity.

3. The fixed-time control method for a rotary-wing unmanned aerial vehicle (UAV) in an unknown environment according to claim 1, characterized in that: The obstacle Lyapunov function in step two is: Where: b i >0, (i=1,2,3) are pre-designed error constraints, z 1i Let b represent the i-th column of z1. i , (i = 1, 2, 3) are pre-designed error safety boundaries.

4. The fixed-time control method for a rotary-wing unmanned aerial vehicle (UAV) in an unknown environment according to claim 1, characterized in that: The fixed-time perturbation observer is: in It is an estimation of the position loop and attitude loop perturbations. As an auxiliary variable, These are the parameters to be designed.

5. A device, characterized in that: It includes a memory and a processor, wherein the memory is used to store computer programs; the processor is used to execute any one of the above claims 1 to 4 for fixed-time control of a rotary-wing unmanned aerial vehicle in an unknown environment.

6. A computer-readable storage medium, characterized in that: It stores a computer program that, when executed by a processor, can control the rotary-wing drone at fixed intervals.

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