Non-planar six-rotor unmanned aerial vehicle logistics fault-tolerant control method based on high-order all-drive system

Abstract

The invention discloses a non-planar six-rotor unmanned aerial vehicle logistics fault-tolerant control method based on a high-order all-drive system, and relates to the field of unmanned aerial vehicle control, in particular to the non-planar six-rotor unmanned aerial vehicle logistics fault-tolerant control method based on the high-order all-drive system. The objective of the invention is to solve the problem that in the express delivery process of a non-planar six-rotor unmanned aerial vehicle, an actuator fails, so that the express cannot be delivered. The method comprises the following steps: step 1, establishing an actuator fault model considering incomplete failure; obtaining a fault model of the non-planar six-rotor unmanned aerial vehicle carrying the asymmetric load; step 2, establishing a second-order all-drive system; introducing a nonlinear integral term and a variable parameter to form a new sliding mode surface; obtaining the input u of the unmanned aerial vehicle when the unmanned aerial vehicle does not fail; inverse solution is carried out to obtain ideal control input U of the actuator; the deviation between u and U is calculated to serve as compensation after the unmanned aerial vehicle breaks down, a compensated control law is obtained based on the compensation after the unmanned aerial vehicle breaks down, and the compensated control law is a controller.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle control, and in particular to a non-planar six-rotor unmanned aerial vehicle logistics fault-tolerant control method based on a high-order all-wheel drive system. Background Art

[0002] With the rapid development of e-commerce, the express delivery industry is facing growing transportation demand and fierce market competition. Traditional delivery methods can no longer meet the challenges of modern logistics. Traffic congestion, insufficient delivery in remote areas, and long package processing time have made the express delivery industry urgently need to seek innovative solutions. As an emerging means of transportation, drones are gradually showing great potential in express delivery with their high efficiency and unique advantages. First, drones can avoid ground traffic congestion and quickly deliver packages to the destination, meeting the e-commerce industry's demand for fast delivery and improving customer satisfaction. Secondly, the flexibility of drones enables them to cover remote areas or areas with inconvenient transportation, solving blind spots that traditional delivery methods cannot cover, thereby optimizing the balance of logistics services. In addition, drones can also significantly reduce the cost of manual delivery and improve delivery efficiency, especially during peak hours, alleviating the work pressure of couriers. With the continuous advancement of technology, the application prospects of drones in the express delivery industry are becoming broader.

[0003] Hexacopter drones have many advantages in express delivery. First, they are stable. The hexacopter design provides better stability and control, allowing them to fly safely in strong winds and complex environments. Secondly, they have a strong load-bearing capacity. Compared with quadrotors, the drone's extra rotors enable it to carry heavier cargo, making it suitable for transporting a variety of packages. In addition, the six rotors provide a certain degree of redundancy, and if one or two rotors fail, the drone can still fly, thereby improving safety. In terms of maneuverability, hexacopter drones have more flexible control capabilities, making it easier to hover, turn, and avoid obstacles, and adapt to complex flight requirements in urban environments. At the same time, when moving horizontally, hexacopter drones can reduce the need for tilting, thereby improving transportation efficiency and shortening delivery time.

[0004] Compared with the non-planar six-rotor all-wheel drive design, traditional six-rotor drones face many challenges in complex environments. Traditional six-rotors usually adopt a planar layout and rely on six independent rotors to provide lift and thrust, but they have limitations in complex maneuvers, energy efficiency and adaptability. For example, traditional six-rotors consume more power, limiting endurance; and their larger structure and rotor layout affect maneuverability, portability and storage convenience. In extreme weather and complex terrain, adaptability is poor, and the additional rotors increase power consumption, while also making fault detection and maintenance more complicated. The non-planar six-rotor all-wheel drive design successfully overcomes these limitations through its unique construction concept, showing better flight performance and adaptability. The biggest feature of this design is that the rotors are no longer limited to a planar layout, but are distributed at multiple angles in three-dimensional space, allowing the drone to maneuver flexibly in a wider flight space. The non-planar six-rotor drone uses the reasonable configuration of the rotors at different spatial angles to enable each rotor to provide more balanced lift and thrust in different flight attitudes. This layout enables the drone to achieve more precise control and faster response, especially in complex environments. For example, in narrow urban streets or mountainous environments, the non-planar design allows the drone to effectively avoid obstacles and achieve a more flexible flight path. This feature allows the non-planar six-rotor drone to easily bypass obstacles such as tall buildings and trees when performing express delivery tasks, ensuring that the delivery tasks are completed efficiently and safely. Another significant advantage of the non-planar six-rotor design is that it can achieve static hovering in any posture. Traditional six-rotor drones may have difficulty maintaining stability in certain flight postures, especially when encountering wind speed changes or complex airflows. The non-planar six-rotor can maintain static hovering even at high altitudes or in complex terrain through the optimization of its rotor layout and control system, thus providing great guarantee for accurate delivery. In terms of energy efficiency, the non-planar six-rotor is more efficient than the traditional six-rotor. Because its rotor layout can more accurately control the power output of each rotor, the thrust of each rotor can be more reasonably distributed during flight, which effectively reduces unnecessary energy waste. This feature is particularly important when flying for a long time or needing to cover a long distance, especially when performing express delivery tasks. Energy efficiency optimization can effectively improve the work efficiency of the drone and reduce the risk of insufficient power. Compared with traditional six-rotors, the rapid response capability of non-planar six-rotors enables the drone to take off quickly and reach the target location quickly. After receiving the mission instructions, the flight control system can quickly adjust the flight attitude and path to achieve efficient and accurate express delivery. The non-planar six-rotor drone not only improves the stability of the drone, but also optimizes the design of the power distribution and control system, allowing it to show better adaptability and maneuverability in complex environments. Compared with traditional six-rotor drones, its unique advantages greatly improve the convenience of drone application in express delivery.

[0005] This non-planar design with a reasonable configuration of rotors at different spatial angles does show significant advantages in overcoming the under-actuated characteristics of common multi-rotor configurations. By ensuring that the control allocation matrix is ​​full rank, six independent control variables can be realized, so that the drone does not need additional tilt when moving horizontally, and the drone can achieve stable and continuous flight within a small angle range. This feature reduces the need for additional rotation and stabilization devices in express delivery, thereby increasing the possibility of modular design. The simplified design not only reduces production costs, but also significantly reduces the failure rate and enhances the reliability of drones in express delivery. The advantages of this design are particularly prominent in the face of complex environmental factors such as strong winds or sudden weather. When responding to emergency delivery needs, rapid response capabilities are the key to ensuring timely delivery. When performing express delivery tasks, non-planar six-rotor drones will inevitably encounter body failures such as sensor failures, battery failures, axis failures, and actuator failures. These failures will affect the completion of the mission and may also cause the drone to crash and be damaged. Summary of the invention

[0006] The purpose of the present invention is to solve the problem that the express delivery cannot be delivered due to actuator failure of the non-planar six-rotor UAV during the delivery of express delivery, and to propose a non-planar six-rotor UAV logistics fault-tolerant control method based on the high-order all-wheel drive system theory.

[0007] A non-planar six-rotor UAV logistics fault-tolerant control method based on high-order full-drive system theory. The specific process is as follows:

[0008] Step 1: Establish an actuator fault model considering non-complete failure; obtain a fault model of a non-planar six-rotor drone carrying an asymmetric load considering failure;

[0009] Step 2: Based on the fault model of the non-planar six-rotor drone carrying asymmetric load established in step 1, design a controller; the specific process is as follows:

[0010] Step 21: Establish a second-order full-drive system;

[0011] Step 22: Introduce a nonlinear integral term H(e) and a variable parameter k D , forming a new sliding surface s;

[0012] Step 23: Based on the second-order full-drive system and the new sliding surface s, the control law of the integral sliding mode is obtained;

[0013] Step 24: Proof of Lyapunov stability;

[0014] Step 25: Based on the fault model considering incomplete failure and the integral sliding mode control law, the ideal control input U of the actuator is obtained; based on the ideal control input U of the actuator, the input u of the UAV when the UAV does not fail is obtained;

[0015] Step 26: Based on the second-order full drive system and actuator fault model, inversely solve the control input U after the actuator efficiency loss r , according to the actuator fault model, the ideal control input U of the actuator is obtained;

[0016] Step 27: Calculate the deviation κ=uU between the input u of the UAV obtained in step 25 when the UAV does not fail and the ideal control input U of the actuator obtained in step 26. κ is used as compensation after the UAV fails. Based on the compensation κ after the UAV fails, the compensated control law is obtained, and the compensated control law is the controller.

[0017] The beneficial effects of the present invention are:

[0018] In order to solve the problem that the UAV cannot deliver the express due to actuator failure during the delivery of express, this paper proposes a controller design method based on a high-order all-wheel drive system. The controller designed by the high-order all-wheel drive system can effectively improve the robustness of the UAV system in the face of failures or abnormal situations, and ensure its adaptability to external disturbances and system uncertainties during execution. At the same time, this method can also enhance the rapid response capability of the system, and ensure that the goods can be delivered quickly in emergency delivery. In addition, the designed controller has strong fault tolerance. When some actuators fail, the system can compensate through the control algorithm to ensure the stable flight of the UAV and continue to complete the express delivery task. This control method of the high-order all-wheel drive system not only improves the reliability of the UAV, but also provides an effective solution for the application of UAVs in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the present invention;

[0020] Figure 2 The following are the position tracking curves and error curves of the drone. a is the position tracking curve in the x-axis direction, b is the position tracking curve in the y-axis direction, c is the position tracking curve in the z-axis direction, and d is the position tracking error.

[0021] Figure 3 The following are the attitude tracking curves and error curves of the UAV, where a is the roll angle tracking curve, b is the yaw angle tracking curve, c is the pitch angle tracking curve, and d is the attitude tracking error;

[0022] Figure 4The following are the position tracking curve and error curve of the UAV after the actuator failure. a is the position tracking curve after the actuator failure, and b is the position tracking error after the actuator failure.

[0023] Figure 5 Figure 1 is the attitude tracking curve and error curve of the UAV after the actuator failure. a is the attitude tracking curve after the actuator failure, and b is the attitude tracking error after the actuator failure.

[0024] Figure 6 Schematic diagram of the payload carried by the all-wheel drive six-rotor drone. DETAILED DESCRIPTION

[0025] Specific implementation method 1: This implementation method is a non-planar six-rotor UAV logistics fault-tolerant control method based on a high-order full-drive system. The specific process is as follows:

[0026] Step 1: Establish an actuator fault model considering non-complete failure; obtain a fault model of a non-planar six-rotor drone carrying an asymmetric load considering failure;

[0027] This paper mainly studies the design of fault-tolerant control strategies when actuators have multiplicative and additive faults at the same time;

[0028] Step 2: Based on the fault model of the non-planar six-rotor drone carrying asymmetric load established in step 1, design a controller; the specific process is as follows:

[0029] Step 21: Establish a second-order full-drive system;

[0030] Step 22: Introduce a nonlinear integral term H(e) and a variable parameter k D , forming a new sliding surface s;

[0031] Step 23: Based on the second-order full-drive system and the new sliding surface s, the control law of the integral sliding mode is obtained;

[0032] Step 24: Proof of Lyapunov stability;

[0033] Step 25: Based on the fault model formula (3) considering incomplete failure and the integral sliding mode control law formula (29), the ideal control input U of the actuator (actual control law of the control system) is obtained; based on the ideal control input U of the actuator, the input u of the UAV when the UAV does not fail is obtained;

[0034] Step 26: Based on the second-order full drive system (12) and the actuator fault model (3), the control input U after the actuator efficiency loss is obtained by inverse solution. r , according to the actuator fault model, the ideal control input U of the actuator (actual control law of the control system) is obtained;

[0035] Step 27. Calculate the deviation κ=uU between the input u of the UAV obtained in step 25 when the UAV does not fail and the ideal control input U of the actuator obtained in step 26. κ is used as compensation after the UAV fails. Based on the compensation κ after the UAV fails, the compensated control law is obtained. The compensated control law is the controller (Formula 37).

[0036] The high-end all-wheel drive system is a second-order all-wheel drive system.

[0037] Specific implementation method 2: This implementation method is different from specific implementation method 1 in that in step 1, an actuator fault model considering non-complete failure is established; a fault model of a non-planar six-rotor drone carrying an asymmetric load considering a fault is obtained; the specific process is:

[0038] Establish a mathematical model for actuator multiplicative faults with effective factors; Establish a mathematical model for actuator additive faults;

[0039] Based on the mathematical model of actuator multiplicative fault with effective factors and the mathematical model of actuator additive fault, a fault model considering incomplete failure is established;

[0040] The dynamic model of the fully-driven six-rotor aircraft is derived according to the Newton-Euler method; the control input vector u of the UAV is obtained based on the dynamic model of the fully-driven six-rotor aircraft:

[0041] Obtain the inertial matrix of a multirotor drone carrying an asymmetric payload;

[0042] Based on the dynamic model of the fully-driven six-rotor aircraft and the inertia matrix of the multi-rotor UAV carrying an asymmetric load, the dynamic equation of the aircraft rotating around the center of mass when carrying a load is obtained;

[0043] Based on the fault model considering incomplete failure, the dynamic model of the fully driven six-rotor aircraft, and the dynamic equation of the aircraft rotating around the center of mass when carrying a load, the fault model of the non-planar six-rotor UAV carrying an asymmetric load is obtained;

[0044] In the control system of a six-rotor drone, the actuator is a very important actuator in the control system of a six-rotor drone. As the actuator of the drone, the healthy operation of the six motors is the basic guarantee for the normal flight of the drone. Common actuator failures include: multiplicative failure of the actuator and additive failure of the actuator.

[0045] This paper mainly studies the design of fault-tolerant control strategies when actuators have multiplicative and additive faults at the same time;

[0046] The other steps and parameters are the same as those in the first embodiment.

[0047] Specific implementation method three: This implementation method is different from specific implementation methods one or two in that the mathematical model of actuator multiplicative fault with effective factors is established; the mathematical model of actuator additive fault is established;

[0048] Based on the mathematical model of actuator multiplicative fault with effective factors and the mathematical model of actuator additive fault, a fault model considering incomplete failure is established;

[0049] The dynamic model of the fully-driven six-rotor aircraft is derived according to the Newton-Euler method; the control input vector u of the UAV is obtained based on the dynamic model of the fully-driven six-rotor aircraft:

[0050] Obtain the inertial matrix of a multirotor drone carrying an asymmetric payload;

[0051] Based on the dynamic model of the fully-driven six-rotor aircraft and the inertia matrix of the multi-rotor UAV carrying an asymmetric load, the dynamic equation of the aircraft rotating around the center of mass when carrying a load is obtained;

[0052] Based on the fault model considering incomplete failure, the dynamic model of the fully driven six-rotor aircraft, and the dynamic equation of the aircraft rotating around the center of mass when carrying a load, the fault model of the non-planar six-rotor UAV carrying an asymmetric load is obtained;

[0053] In the control system of the six-rotor drone, the actuator is a very important actuator in the control system of the six-rotor drone. As the actuator of the drone, the healthy operation of the six motors is the basic guarantee for the normal flight of the drone. Common actuator failures include: multiplicative failure of the actuator and additive failure of the actuator;

[0054] The specific process is:

[0055] Step 1: Establish a mathematical model of actuator multiplicative fault with effective factors; expressed as:

[0056] U r =τU (1)

[0057] in,

[0058] U r represents the control input after the efficiency loss of the actuator (six actuators of the six-rotor), U r =[U r1 U r2 U r3 U r4 U r5 U r6 ] T , U r1 represents the control input after the efficiency loss of actuator 1, U r2represents the control input after the efficiency loss of actuator 2, U r3 represents the control input after the efficiency loss of actuator 3, U r4 represents the control input after the efficiency loss of actuator 4, U r5 represents the control input after the efficiency loss of actuator 5, U r6 represents the control input after the efficiency loss of actuator 6, and the upper corner table T represents the transposition;

[0059] U represents the ideal control input of the actuator, U=[U1 U2 U3 U4 U5 U6] T , U1 represents the ideal control input of actuator 1, U2 represents the ideal control input of actuator 2, U3 represents the ideal control input of actuator 3, U4 represents the ideal control input of actuator 4, U5 represents the ideal control input of actuator 5, and U6 represents the ideal control input of actuator 6;

[0060] τ represents the effective factor of the actuator failure, τ∈[0,1], when τ=1, it means that the actuator has no failure; when τ=0, it means that the actuator has complete efficiency loss; when 0<τ<1, it means that the actuator has partial efficiency loss;

[0061] Step 1 and 2: Establish a mathematical model of actuator additive fault; expressed as:

[0062] U r =U+σc (2)

[0063] Where c represents the actuator additive fault;

[0064] σ takes the value of 0 or 1. When σ takes the value of 0, it indicates that the actuator has no additive fault; when σ takes the value of 1, it indicates that the actuator has an additive fault.

[0065] Step 1: Based on the mathematical model of actuator multiplicative fault with effective factors and the mathematical model of actuator additive fault, a fault model considering incomplete failure is established; it is expressed as:

[0066] U r =τU+σc(0<τ<1) (3)

[0067] Step 14: Derived the dynamic model of the fully driven six-rotor aircraft according to the Newton-Euler method; expressed as:

[0068]

[0069] Where, F represents the total force on the fully-driven six-rotor aircraft, and M represents the total torque on the fully-driven six-rotor aircraft;

[0070] P = [x, y, z] Trepresents the position vector, x represents the position along the x-axis of the earth coordinate system, y represents the position along the y-axis of the earth coordinate system, and z represents the position along the z-axis of the earth coordinate system;

[0071] represents the velocity vector, represents the acceleration vector;

[0072] Ω=[p,q,r] T represents the angular velocity, p represents the angular velocity of the roll angle φ, q represents the angular velocity of the pitch angle θ, and r represents the angular velocity of the yaw angle ψ;

[0073] represents the angular acceleration, represents the second-order derivative of Ω;

[0074] m represents the total mass of the fully-driven hexacopter;

[0075] J represents the body inertia matrix of the fully-driven six-rotor aircraft;

[0076] g represents the acceleration due to gravity, which is 9.81ms -2 , e3 means [0 0 1] T ;

[0077] n=[n1 n2 n3 n4 n5 n6] T Indicates the control input, that is, the magnitude of the thrust generated by the propeller, n1 indicates the magnitude of the thrust generated by propeller 1, n2 indicates the magnitude of the thrust generated by propeller 2, n3 indicates the magnitude of the thrust generated by propeller 3, n4 indicates the magnitude of the thrust generated by propeller 4, n5 indicates the magnitude of the thrust generated by propeller 5, and n6 indicates the magnitude of the thrust generated by propeller 6;

[0078] is the air resistance, k x , k y , k z is the air resistance coefficient, represents the velocity along the x-axis of the Earth coordinate system, represents the velocity along the y-axis of the Earth coordinate system, Represents the velocity along the z-axis of the Earth coordinate system, and diag() represents a diagonal matrix;

[0079] F(α) represents the control efficiency matrix, α is the angle of rotation of the rotor around the direction of the arm, sα and cα represent sinα and cosα respectively;

[0080] The control efficiency matrix F(α) is expressed as follows:

[0081]

[0082] In the formula,

[0083] T′ represents the intermediate variable,

[0084] W represents the intermediate variable,

[0085] L is the rotor radius of the UAV, is the constant parameter of the UAV;

[0086] Step 15: Based on the dynamic model of the fully-driven six-rotor aircraft, the control input vector u of the drone is obtained:

[0087] Based on formulas (4) and (5), the control input vector u of the drone is derived:

[0088] It is expressed as:

[0089]

[0090] In the formula, u represents the control input vector of the UAV, u1 represents the input value of u one, u2 represents the input value of u two, u3 represents the input value of u three, u4 represents the input value of u four, u5 represents the input value of u five, and u6 represents the input value of u six;

[0091] Step 16: Obtain the inertial matrix of the multi-rotor drone carrying an asymmetric payload;

[0092] The specific process is:

[0093] When the UAV carries an asymmetric load, it is assumed that in the aircraft body coordinate system O b X b Y b Z b The aircraft carries a rigid body load under it, and the load is located on the plane O of the aircraft body coordinate system. b X b Y b Below, the load is regarded as a point mass, and the load mass is m load , the load is from the aircraft body coordinate plane O b X b Z b The distance is d y , the load is from the aircraft body coordinate plane O b Y b Z b The distance is d x , the load is from the aircraft body coordinate plane O b X b Y b The distance is d z ;

[0094] The opposite direction of load gravity is Zb Axis, X b Axis and Z b Axis vertical, Y b Axis and X b Z b The plane is vertical;

[0095] The aircraft with asymmetric load is divided into a bare part and a loaded part, and the moment of inertia of the bare part and the loaded part about each axis of the aircraft body coordinate system is calculated respectively; the specific process is:

[0096] X b Axis: Bare metal part to X b The moment of inertia of the shaft is I x , the load part is X b The shaft moment of inertia is

[0097] Y b Axis: Bare metal part to Y b The moment of inertia of the shaft is I y , the load part is Y b The shaft moment of inertia is

[0098] Z b Axis: Bare metal part to Z b The moment of inertia of the shaft is I z , the load part is Z b The shaft moment of inertia is

[0099] When calculating the inertia product, it is also divided into the bare metal part and the load part. The aircraft has a centrally symmetrical shape. In the aircraft body coordinate system, all the inertia products of the bare metal part are zero, so the inertia product of the load part can be obtained:

[0100]

[0101] In the formula, I xy The mutual inertia product of the load rotating around the x-axis and the y-axis, I yx The mutual inertia product of the load rotating around the y-axis and the x-axis, I xz The mutual inertia product of the load rotating around the x-axis and the z-axis, I zx The mutual inertia product of the load rotating around the z-axis and the x-axis, I yz The mutual inertia product of the load rotating around the y-axis and the z-axis, I zy represents the mutual related inertia product of the load rotating around the z-axis and the y-axis;

[0102] In summary, the inertia matrix of the multi-rotor drone carrying an asymmetric load is as follows:

[0103]

[0104]

[0105] In the formula, J1 represents the inertial matrix of the multi-rotor drone;

[0106] Step 17: Based on the dynamic model of the fully-driven six-rotor aircraft and the inertia matrix of the multi-rotor UAV carrying an asymmetric load, obtain the dynamic equation of the aircraft rotating around the center of mass when carrying the load;

[0107] According to formulas (4) and (8), the dynamic equation of the aircraft rotating around the center of mass when carrying a load is obtained;

[0108] It is expressed as:

[0109]

[0110] In the formula, represents the angular acceleration of the roll angle φ, represents the angular acceleration of the pitch angle θ, represents the angular acceleration of the yaw angle ψ;

[0111] make

[0112]

[0113] In the formula, D1 represents the intermediate variable, D2 represents the intermediate variable, and D3 represents the intermediate variable;

[0114] Step 18: Based on the fault model considering incomplete failure, the dynamic model of the fully-driven six-rotor aircraft, and the dynamic equation of the aircraft rotating around the center of mass when carrying a load, a fault model of the non-planar six-rotor UAV carrying an asymmetric load is obtained;

[0115] Based on (3), (4), (9), and (10), the fault model of the non-planar six-rotor UAV carrying asymmetric loads considering faults is obtained;

[0116] It is expressed as:

[0117]

[0118] In the formula, represents the acceleration along the x-axis of the Earth coordinate system, represents the acceleration along the y-axis of the Earth coordinate system, Represents the acceleration along the z-axis of the Earth's coordinate system.

[0119] The other steps and parameters are the same as those in the first or second embodiment.

[0120] Specific implementation method 4: This implementation method is different from the specific implementation methods 1 to 3 in that a second-order full-drive system is established in step 21; the expression is:

[0121]

[0122] In the formula, E represents the constant matrix, represents a continuous vector function, t represents time; represents n′×n′ dimensional real matrix space, u represents input vector, v represents external signal; n′ represents a positive integer;

[0123] represents the state variable, express The first derivative of express The second derivative of

[0124] in Meet the following full drive conditions:

[0125]

[0126] In the formula, det represents the determinant of the matrix, R n′ represents n′-dimensional real vector space;

[0127] When the second-order full-drive system (12) satisfies the full-drive condition (13), for any given matrix A1, A0∈R n′×n′ The linear steady-state closed-loop system is obtained through the control law (14);

[0128]

[0129] Where u=[u1 u2 u3 u4 u5 u6] T , the control input U after the actuator efficiency loss r Expressed as

[0130] U r =[U r1 U r2 U r3 U r4 U r5 U r6 ] T (15)

[0131] In the formula, A1 represents n′×n′ dimensional real matrix space, A0 represents n′×n′ dimensional real matrix space, R n′×n′ represents the n′×n′ dimensional real matrix space; u represents the control input vector of the UAV, represents n′×n′ dimensional real matrix space, v represents the external signal;

[0132]

[0133] v=[0 0 g 0 0 0] T (18)

[0134] Wherein, diag(B1,B2,B3,B4,B5,B6) represents a diagonal matrix, B1, B2, B3, B4, B5, B6 represent intermediate variables; d1, d2, d3 represent intermediate variables;

[0135] in

[0136]

[0137] The tracking error e1 and the first-order derivative e2 of the tracking error e1 are defined as:

[0138]

[0139] in, represents the state variable, is the expected input of the state variable, e2 represents the first-order derivative of e1, express The first derivative of express The first derivative of .

[0140] The other steps and parameters are the same as those in Specific Embodiments 1 to 3.

[0141] Specific implementation method 5: This implementation method is different from the specific implementation methods 1 to 4 in that a nonlinear integral term H(e) and a variable parameter k are introduced in step 22. D , forming a new sliding surface s, as shown below:

[0142]

[0143] Among them, s represents the sliding surface, k D represents a constant, k P represents a constant, k I represents a constant, H(e) represents the nonlinear integral term H(e), e2(0) represents the initial condition of e2, e1(0) represents the initial condition of e1, represents the first-order derivative of H(e), h(e) represents the first-order derivative of H(e); e represents the error;

[0144] in

[0145]

[0146] β eis a positive constant;

[0147] β represents a constant greater than 0, β=diag(β x ,β y ,β z ,β φ ,β θ ,β ψ ), β x represents a constant, β y represents a constant, β z represents a constant, β φ represents a constant, β θ represents a constant, β ψ represents a constant; β x , β y , β z , β φ , β θ , β ψ For setting;

[0148] Taking the derivative of H(e), we get a nonlinear function of the following form:

[0149]

[0150] The other steps and parameters are the same as those in Specific Embodiments 1 to 4.

[0151] Specific implementation method 6: This implementation method is different from the specific implementation methods 1 to 5 in that the control law of the integral sliding mode is obtained based on the second-order full drive system and the new sliding mode surface s in steps 2 and 3; the specific process is:

[0152] By taking the derivative of formula (22), we get:

[0153]

[0154] in, represents the rate of change of state over time, represents the first-order derivative of e2, represents the first-order derivative of H(e);

[0155] in, Combining (12) and (25), we obtain:

[0156]

[0157] in, express The second-order derivative of express The second derivative of

[0158] k D represents a constant, kP represents a constant, k I represents a constant;

[0159] k D =diag(k Dx ,k Dy ,k Dz ,k Dφ ,k Dθ ,k Dψ )

[0160] k I =diag(k Ix ,k Iy ,k Iz ,k Iφ ,k Iθ ,k Iψ )

[0161] k P =diag(k Px ,k Py ,k Pz ,k Pφ ,k Pθ ,k Pψ )

[0162] Among them, diag() represents a diagonal matrix;

[0163] k Dx represents a constant, k Dy represents a constant, k Dz represents a constant, k Dφ represents a constant, k Dθ represents a constant, k Dψ represents a constant; k Dx , k Dy , k Dz , k Dφ , k Dθ , k Dψ For setting;

[0164] k Ix represents a constant, k Iy represents a constant, k Iz represents a constant, k Iφ represents a constant, k Iθ represents a constant, k Iψ represents a constant; k Ix , k Iy , k Iz , k Iφ , k Iθ , k Iψ For setting;

[0165] k Px represents a constant, kPy represents a constant, k Pz represents a constant, k Pφ represents a constant, k Pθ represents a constant, k Pψ represents a constant; k Px , k Py , k Pz , k Pφ , k Pθ , k Pψ For setting;

[0166] In order to make the system state approach the sliding surface at a faster speed and thus achieve a fast and effective system response, the exponential convergence law is used here:

[0167]

[0168] In the exponential reaching law, εsigm(s) is the constant speed reaching term;

[0169] ε represents a constant, ε=diag(ε x ,ε y ,ε z ,ε φ ,ε θ ,ε ψ ), ε x represents a constant, ε y represents a constant, ε z represents a constant, ε φ represents a constant, ε θ represents a constant, ε ψ represents a constant; ε, ε x , ε y , ε z , ε φ , ε θ , ε ψ For setting;

[0170] in b represents a constant (set);

[0171] ks is the exponential approach term, k represents a constant, k = diag(k x ,k y ,k z ,k φ ,k θ ,k ψ ), k x represents a constant, k y represents a constant, k z represents a constant, k φ represents a constant, k θ represents a constant, k ψ represents a constant; k, kx , k y , k z , k φ , k θ , k ψ For setting;

[0172] Combining (26) and (27), we get:

[0173]

[0174] in, represents a continuous vector function, represents the n′×n′ dimensional real matrix space;

[0175] Based on (28), the control input U after the efficiency loss of the actuator (six actuators of the six-rotor) is obtained r , U r As the control law of the integral sliding mode; it is expressed as:

[0176]

[0177] U r Represents the control input after the efficiency loss of the actuator (six actuators for a hexacopter).

[0178] The other steps and parameters are the same as those in Specific Implementation Methods 1 to 5.

[0179] Specific implementation method 7: This implementation method is different from any one of the specific implementation methods 1 to 6 in that the Lyapunov stability proof in step 24 is:

[0180]

[0181] Where V represents the Lyapunov function, represents the derivative of the Lyapunov function;

[0182] Substituting expression (29) into expression (31), we obtain:

[0183]

[0184] is negatively determined, thereby ensuring the stability of the control system (ensuring the stability of the present invention as a whole).

[0185] The other steps and parameters are the same as those in Specific Embodiments 1 to 6.

[0186] Specific implementation eight: This implementation differs from any one of specific implementations one to seven in that, in step 25, based on the fault model formula (3) considering non-complete failure and the control law formula (29) of the integral sliding mode, the ideal control input U (actual control law of the control system) of the actuator is obtained; based on the ideal control input U of the actuator, the input u of the drone when the drone does not fail is obtained;

[0187] It is expressed as:

[0188]

[0189] where τ = diag(τ x ,τ y ,τ z ,τ φ ,τ θ ,τ ψ ),c=diag(c x ,c y ,c z ,c φ ,c θ ,c ψ );

[0190] τ represents a constant, τ x represents a constant, τ y represents a constant, τ z represents a constant, τ φ represents a constant, τ θ represents a constant, τ ψ represents a constant; τ, τ x , τ y , τ z , τ φ , τ θ , τ ψ For setting;

[0191] c represents a constant, c x represents a constant, c y represents a constant, c z represents a constant, c φ represents a constant, c θ represents a constant, c ψ represents a constant; c, c x 、c y 、c z 、c φ 、c θ 、c ψ For setting;

[0192] When τ=I 6×6 , when σ is a zero matrix, when the UAV does not fail, the input u of the UAV is:

[0193]

[0194] The other steps and parameters are the same as those in Specific Embodiments 1 to 7.

[0195] Specific embodiment 9: This embodiment is different from any one of specific embodiments 1 to 8 in that, in step 26, based on the second-order full drive system (12) and the actuator fault model (3), the control input U after the actuator efficiency loss is obtained by inverse solution r , according to the actuator fault model, the ideal control input U of the actuator (actual control law of the control system) is obtained;

[0196] It is expressed as:

[0197]

[0198] Substituting the fault model (3) into formula (35), the ideal control input U of the actuator (actual control law of the control system) is obtained as:

[0199]

[0200] The other steps and parameters are the same as those in Specific Embodiments 1 to 8.

[0201] Specific implementation ten: This implementation is different from any one of specific implementations one to nine in that, in step twenty-seven, the deviation κ=uU between the input u of the drone obtained in step twenty-five when the drone does not fail and the ideal control input U of the actuator obtained in step twenty-six is ​​calculated, κ is used as compensation after the drone fails, and a compensated control law is obtained based on the compensation κ after the drone fails, and the compensated control law is the controller;

[0202] The specific process is:

[0203] The deviation κ=uU between the input u of the UAV obtained in step 25 when the UAV does not fail and the ideal control input U of the actuator obtained in step 26 is calculated. κ is used as compensation after the UAV fails. Based on the compensation κ after the UAV fails, the compensated control law is obtained. The compensated control law is:

[0204]

[0205] Among them, U κ represents the control law after compensation.

[0206] The other steps and parameters are the same as those in Specific Embodiments 1 to 9.

[0207] Example:

[0208] In order to verify and demonstrate the effectiveness of the controller designed in the present invention for the tracking control method of a non-planar six-rotor UAV based on high-order all-wheel drive system theory, the following numerical simulation experiments were carried out.

[0209] The parameters of the drone are: mass m = 12kg, m load =8kg, t≥1s, m load =1kg, gravitational acceleration g = 9.81N / kg, I x =0.2kg·m 2 ,I y =0.2kg·m 2 ,I z =0.37kg·m 2 ,k x =0.001N / (s·m 2 ),k y =0.001N / (s·m 2 ),k z =0.001N / (s·m 2 ), Rotor radius L = 0.5m

[0210] , Table 1 and Table 2 are controller parameters and fault effectiveness factor parameters respectively.

[0211] Table 1 Controller parameters

[0212]

[0213] Table 2 Fault effectiveness factor parameters

[0214]

[0215] Select position reference trajectory x d =sin(πt)m,y d =sin(0.5πt)m,z d =0.25tm; attitude reference trajectory θ d =0.4cos(πt)rad,ψ d =0.2sin(0.5πt)rad. The numerical experimental results are as follows Figure 2 , 3 As shown;

[0216] Figure 2 and Figure 3The position and attitude tracking curves and errors of the six-rotor drone are shown respectively. The system can achieve good tracking within 0.5s, and the tracking error is less than 0.005m, which shows that the controller has high tracking accuracy and good tracking performance, and can accurately and quickly reach the designated location to perform tasks. The load change of the drone at 1s has little effect on the error of the drone's tracking position and attitude, indicating that the system has good robustness and can perform express delivery tasks well.

[0217] Figure 4 and Figure 5 The position and attitude tracking curves and errors of the six-rotor UAV are shown respectively when the actuator fails.

[0218] As can be seen from the figure, the drone had a fault at 2 seconds, the maximum position tracking error was less than 0.006m, the attitude tracking error was less than 0.01rad, and it successfully recovered to normal after 0.5 seconds. After the fault occurred, the tracking errors of position and attitude were less affected, indicating that the system has excellent fault tolerance. This performance enables the drone to effectively avoid delays caused by faults when performing express delivery in complex environments, ensuring timely delivery of mission targets, reflecting its high reliability and mission completion capabilities in practical applications.

[0219] The present invention may also have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A non-planar six-rotor UAV logistics fault-tolerant control method based on a high-order full-drive system, characterized by: The specific process of the method is: Step 1: Establish an actuator fault model considering non-complete failure; obtain a fault model of a non-planar six-rotor drone carrying an asymmetric load considering failure; Step 2: Based on the fault model of the non-planar six-rotor drone carrying asymmetric load established in step 1, design a controller; the specific process is as follows: Step 21: Establish a second-order full-drive system; Step 22: Introduce a nonlinear integral term H(e) and a variable parameter k D , forming a new sliding surface s; Step 23: Based on the second-order full-drive system and the new sliding surface s, the control law of the integral sliding mode is obtained; Step 24: Lyapunov stability proof; Step 25: Based on the fault model considering incomplete failure and the integral sliding mode control law, the ideal control input U of the actuator is obtained; based on the ideal control input U of the actuator, the input u of the UAV when the UAV does not fail is obtained; Step 26: Based on the second-order full drive system and actuator fault model, inversely solve the control input U after the actuator efficiency loss r ,According to the actuator fault model, the ideal control input U of the actuator is obtained; Step 27: Calculate the deviation κ=uU between the input u of the UAV obtained in step 25 when the UAV does not fail and the ideal control input U of the actuator obtained in step 26. κ is used as compensation after the UAV fails. Based on the compensation κ after the UAV fails, the compensated control law is obtained, and the compensated control law is the controller.

2. According to claim 1, a non-planar six-rotor UAV logistics fault-tolerant control method based on a high-order full-drive system is characterized by: In the step 1, an actuator fault model considering non-complete failure is established; a fault model of a non-planar six-rotor drone carrying an asymmetric load considering a fault is obtained; the specific process is: Establish a mathematical model for actuator multiplicative faults with effective factors; Establish a mathematical model for actuator additive faults; Based on the mathematical model of actuator multiplicative fault with effective factors and the mathematical model of actuator additive fault, a fault model considering incomplete failure is established; The dynamic model of the fully-driven six-rotor aircraft is derived according to the Newton-Euler method; the control input vector u of the UAV is obtained based on the dynamic model of the fully-driven six-rotor aircraft: Obtain the inertial matrix of a multirotor drone carrying an asymmetric payload; Based on the dynamic model of the fully-driven six-rotor aircraft and the inertia matrix of the multi-rotor UAV carrying an asymmetric load, the dynamic equation of the aircraft rotating around the center of mass when carrying a load is obtained; Based on the fault model considering incomplete failure, the dynamic model of the full-drive six-rotor aircraft, and the dynamic equation of the aircraft rotating around the center of mass when carrying a load, the fault model of the non-planar six-rotor UAV carrying an asymmetric load considering faults is obtained.

3. The method for controlling the logistics of a non-planar six-rotor UAV based on a high-order all-wheel drive system according to claim 2 is characterized by: The mathematical model of multiplicative fault of the actuator with effective factors is established; the mathematical model of additive fault of the actuator is established; Based on the mathematical model of actuator multiplicative fault with effective factors and the mathematical model of actuator additive fault, a fault model considering incomplete failure is established; The dynamic model of the fully-driven six-rotor aircraft is derived according to the Newton-Euler method; the control input vector u of the UAV is obtained based on the dynamic model of the fully-driven six-rotor aircraft: Obtain the inertial matrix of a multirotor drone carrying an asymmetric payload; Based on the dynamic model of the fully-driven six-rotor aircraft and the inertia matrix of the multi-rotor UAV carrying an asymmetric load, the dynamic equation of the aircraft rotating around the center of mass when carrying a load is obtained; Based on the fault model considering incomplete failure, the dynamic model of the fully driven six-rotor aircraft, and the dynamic equation of the aircraft rotating around the center of mass when carrying a load, the fault model of the non-planar six-rotor UAV carrying an asymmetric load is obtained; The specific process is: Step 1: Establish a mathematical model of actuator multiplicative fault with effective factors; expressed as: U r =τU(1) in, U r represents the control input after the actuator efficiency loss, U r =[U r1 U r2 U r3 U r4 U r5 U r6 ] Τ , U r1 represents the control input after the efficiency loss of actuator 1, U r2 represents the control input after the efficiency loss of actuator 2, U r3 represents the control input after the efficiency loss of actuator 3, U r4 represents the control input after the efficiency loss of actuator 4, U r5 represents the control input after the efficiency loss of actuator 5, U r6 represents the control input after the efficiency loss of actuator 6, and the upper corner table T represents the transposition; U represents the ideal control input of the actuator, U=[U1 U2 U3 U4 U5 U6] Τ , U1 represents the ideal control input of actuator 1, U2 represents the ideal control input of actuator 2, U3 represents the ideal control input of actuator 3, U4 represents the ideal control input of actuator 4, U5 represents the ideal control input of actuator 5, and U6 represents the ideal control input of actuator 6; τ represents the effective factor of the actuator failure, τ∈[0,1], when τ=1, it means that the actuator has no failure; when τ=0, it means that the actuator has complete efficiency loss; when 0<τ<1, it means that the actuator has partial efficiency loss; Step 1 and 2: Establish a mathematical model of actuator additive fault; expressed as: And r =U+σc (2) Where c represents the actuator additive fault; σ takes the value of 0 or 1. When σ takes the value of 0, it indicates that the actuator has no additive fault; when σ takes the value of 1, it indicates that the actuator has an additive fault. Step 1: Based on the mathematical model of actuator multiplicative fault with effective factors and the mathematical model of actuator additive fault, a fault model considering incomplete failure is established; it is expressed as: U r =τU+σc (0<τ<1) (3) Step 14: Derived the dynamic model of the fully driven six-rotor aircraft according to the Newton-Euler method; expressed as: Where, F represents the total force on the fully-driven six-rotor aircraft, and M represents the total torque on the fully-driven six-rotor aircraft; P = [x, y, z] T represents the position vector, x represents the position along the x-axis of the earth coordinate system, y represents the position along the y-axis of the earth coordinate system, and z represents the position along the z-axis of the earth coordinate system; represents the velocity vector, represents the acceleration vector; Ω=[p,q,r] T represents the angular velocity, p represents the angular velocity of the roll angle φ, q represents the angular velocity of the pitch angle θ, and r represents the angular velocity of the yaw angle ψ; represents the angular acceleration, represents the second-order derivative of Ω; m represents the total mass of the fully-driven hexacopter; J represents the body inertia matrix of the fully-driven six-rotor aircraft; g represents the acceleration due to gravity, which is 9.81ms -2 , e3 represents [0 0 1] T ; n=[n1 n2 n3 n4 n5 n6] T Indicates the control input, that is, the magnitude of the thrust generated by the propeller, n1 indicates the magnitude of the thrust generated by propeller 1, n2 indicates the magnitude of the thrust generated by propeller 2, n3 indicates the magnitude of the thrust generated by propeller 3, n4 indicates the magnitude of the thrust generated by propeller 4, n5 indicates the magnitude of the thrust generated by propeller 5, and n6 indicates the magnitude of the thrust generated by propeller 6; is the air resistance, k x , k y , k z is the air resistance coefficient, represents the velocity along the x-axis of the Earth coordinate system, represents the velocity along the y-axis of the Earth coordinate system, Represents the velocity along the z-axis of the Earth coordinate system, and diag() represents a diagonal matrix; F(α) represents the control efficiency matrix, α is the angle of rotation of the rotor around the direction of the arm, sα and cα represent sinα and cosα respectively; The control efficiency matrix F(α) is expressed as follows: In the formula, T′ represents the intermediate variable, W represents the intermediate variable, L is the rotor radius of the UAV, is the constant parameter of the UAV; Step 15: Based on the dynamic model of the fully-driven six-rotor aircraft, the control input vector u of the drone is obtained: It is expressed as: In the formula, u represents the control input vector of the UAV, u1 represents the input value of u one, u2 represents the input value of u two, u3 represents the input value of u three, u4 represents the input value of u four, u5 represents the input value of u five, and u6 represents the input value of u six; Step 16: Obtain the inertial matrix of a multi-rotor drone carrying an asymmetric payload; the specific process is as follows: When the UAV carries an asymmetric load, it is assumed that in the aircraft body coordinate system O b X b Y b Z b The aircraft carries a rigid body load under it, and the load is located on the plane O of the aircraft body coordinate system. b X b Y b Below, the load is regarded as a point mass, and the load mass is m load , the load is from the aircraft body coordinate plane O b X b Z b The distance is d y , the load is from the aircraft body coordinate plane O b Y b Z b The distance is d x , the load is from the aircraft body coordinate plane O b X b Y b The distance is d z ; The aircraft with asymmetric load is divided into a bare part and a loaded part, and the moment of inertia of the bare part and the loaded part about each axis of the aircraft body coordinate system is calculated respectively; the specific process is: X b Axis: Bare metal part to X b The moment of inertia of the shaft is I x , the load part is X b The shaft moment of inertia is Y b Axis: Bare metal part to Y b The moment of inertia of the shaft is I y , the load part is Y b The shaft moment of inertia is Z b Axis: Bare metal part to Z b The moment of inertia of the shaft is I z , the load part is Z b The shaft moment of inertia is When calculating the inertia product, it is also divided into the bare metal part and the load part. The aircraft has a centrally symmetrical shape. In the aircraft body coordinate system, all the inertia products of the bare metal part are zero, so the inertia product of the load part can be obtained: In the formula, I xy The mutual inertia product of the load rotating around the x-axis and the y-axis, I yx The mutual inertia product of the load rotating around the y-axis and the x-axis, I xz The mutual inertia product of the load rotating around the x-axis and the z-axis, I zx The mutual inertia product of the load rotating around the z-axis and the x-axis, I yz The mutual inertia product of the load rotating around the y-axis and the z-axis, I zy represents the mutual related inertia product of the load rotating around the z-axis and the y-axis; In summary, the inertia matrix of the multi-rotor drone carrying an asymmetric load is as follows: In the formula, J1 represents the inertial matrix of the multi-rotor drone; Step 17: Based on the dynamic model of the fully-driven six-rotor aircraft and the inertia matrix of the multi-rotor UAV carrying an asymmetric load, the dynamic equation of the aircraft rotating around the center of mass when carrying a load is obtained; it is expressed as: In the formula, represents the angular acceleration of the roll angle φ, represents the angular acceleration of the pitch angle θ, represents the angular acceleration of the yaw angle ψ; make In the formula, D1 represents the intermediate variable, D2 represents the intermediate variable, and D3 represents the intermediate variable; Step 18: Based on the fault model considering non-complete failure, the dynamic model of the fully driven six-rotor aircraft, and the dynamic equation of the aircraft rotating around the center of mass when carrying a load, the fault model of the non-planar six-rotor UAV carrying an asymmetric load is obtained; it is expressed as: In the formula, represents the acceleration along the x-axis of the Earth coordinate system, represents the acceleration along the y-axis of the Earth coordinate system, Represents the acceleration along the z-axis of the Earth's coordinate system.

4. The method for controlling the logistics of a non-planar six-rotor UAV based on a high-order all-wheel drive system according to claim 3 is characterized by: In step 21, a second-order full-drive system is established; the expression is: In the formula, E represents the constant matrix, represents a continuous vector function, t represents time; represents n′×n′ dimensional real matrix space, u represents input vector, v represents external signal; n′ represents a positive integer; represents the state variable, express The first derivative of express The second derivative of in Meet the following full drive conditions: In the formula, det represents the determinant of the matrix, R n′ represents n′-dimensional real vector space; When the second-order full-drive system (12) satisfies the full-drive condition (13), for any given matrix A1, A0∈R n′×n′ The linear steady-state closed-loop system is obtained through the control law (14); Where u=[u1 u2 u3 u4 u5 u6] Τ , the control input U after the actuator efficiency loss r Expressed as IN r =[U r1 IN r2 IN r3 IN r4 IN r5 IN r6 ] Τ (15) In the formula, A1 represents n′×n′ dimensional real matrix space, A0 represents n′×n′ dimensional real matrix space, R n′×n′ represents the n′×n′ dimensional real matrix space; u represents the control input vector of the UAV, represents n′×n′ dimensional real matrix space, v represents the external signal; v=[0 0 g 0 0 0] Τ (18) Wherein, diag(B1,B2,B3,B4,B5,B6) represents a diagonal matrix, B1, B2, B3, B4, B5, B6 represent intermediate variables; d1, d2, d3 represent intermediate variables; in The tracking error e1 and the first-order derivative e2 of the tracking error e1 are defined as: in, represents the state variable, is the expected input of the state variable, e2 represents the first-order derivative of e1, express The first derivative of express The first derivative of .

5. The method for controlling the logistics of a non-planar six-rotor UAV based on a high-order all-wheel drive system according to claim 4 is characterized by: In step 22, a nonlinear integral term H(e) and a variable parameter k are introduced. D , forming a new sliding surface s, as shown below: Among them, s represents the sliding surface, k D represents a constant, k P represents a constant, k I represents a constant, H(e) represents a nonlinear integral term, e2(0) represents the initial condition of e2, e1(0) represents the initial condition of e1, represents the first-order derivative of H(e), h(e) represents the first-order derivative of H(e); e represents the error; in β e is a positive constant; β represents a constant greater than 0, β=diag(β x ,β y ,β z ,β φ ,β θ ,β ψ ), β x represents a constant, β y represents a constant, β z represents a constant, β φ represents a constant, β θ represents a constant, β ψ represents a constant; Taking the derivative of H(e), we get a nonlinear function of the following form:

6. The method for controlling the logistics of a non-planar six-rotor UAV based on a high-order all-wheel drive system according to claim 5 is characterized by: In the steps 2 and 3, the control law of the integral sliding mode is obtained based on the second-order full drive system and the new sliding mode surface s; the specific process is: By taking the derivative of formula (22), we get: in, represents the rate of change of state over time, represents the first-order derivative of e2, represents the first derivative of H(e); in, Combining (12) and (25), we obtain: in, express The second-order derivative of express The second derivative of k D represents a constant, k P represents a constant, k I represents a constant; k D =diag(k Dx ,k Dy ,k Dz ,k Dφ ,k Dθ ,k Dψ ) k I =diag(k Ix ,k Iy ,k Iz ,k Iφ ,k Iθ ,k Iψ ) k P =diag(k Px ,k Py ,k Pz ,k Pφ ,k Pθ ,k Pψ ) Among them, diag() represents a diagonal matrix; k Dx represents a constant, k Dy represents a constant, k Dz represents a constant, k Dφ represents a constant, k Dθ represents a constant, k Dψ represents a constant; k Ix represents a constant, k Iy represents a constant, k Iz represents a constant, k Iφ represents a constant, k Iθ represents a constant, k Iψ represents a constant; k Px represents a constant, k Py represents a constant, k Pz represents a constant, k Pφ represents a constant, k Pθ represents a constant, k Pψ represents a constant; Using exponential convergence law: εsigm(s) is the isokinetic approach term; ε represents a constant, ε=diag(ε x ,ε y ,ε z ,ε φ ,ε θ ,ε ψ ), ε x represents a constant, ε y represents a constant, ε z represents a constant, ε φ represents a constant, ε θ represents a constant, ε ψ represents a constant; in b represents a constant; ks is the exponential approach term, k represents a constant, k = diag(k x ,k y ,k z ,k φ ,k θ ,k ψ ), k x represents a constant, k y represents a constant, k z represents a constant, k φ represents a constant, k θ represents a constant, k ψ represents a constant; Combining (26) and (27), we get: in, represents a continuous vector function, represents the n′×n′ dimensional real matrix space; Based on (28), the control input U after the actuator efficiency loss is obtained r , U r As the control law of the integral sliding mode; it is expressed as: U r Represents the control input after actuator efficiency losses.

7. The method for controlling the logistics of a non-planar six-rotor UAV based on a high-order all-wheel drive system according to claim 6 is characterized by: The Lyapunov stability proof in step 24 is: Where V represents the Lyapunov function, represents the derivative of the Lyapunov function; Substituting expression (29) into expression (31), we obtain: is negatively determined, thus ensuring the stability of the control system.

8. The method for controlling the logistics of a non-planar six-rotor UAV based on a high-order all-wheel drive system according to claim 7 is characterized by: In the step 25, based on the fault model considering non-complete failure and the control law of integral sliding mode, the ideal control input U of the actuator is obtained; based on the ideal control input U of the actuator, the input u of the UAV when the UAV does not fail is obtained; It is expressed as: Where τ=diag(τ x ,t y ,t z ,t φ ,t θ ,t ψ ),c=diag(c x ,c y ,c z ,c φ ,c θ ,c ψ ); τ represents a constant, τ x represents a constant, τ y represents a constant, τ z represents a constant, τ φ represents a constant, τ θ represents a constant, τ ψ represents a constant; c represents a constant, c x represents a constant, c y represents a constant, c z represents a constant, c φ represents a constant, c θ represents a constant, c ψ represents a constant; When τ=I 6×6 , when σ is a zero matrix, when the UAV does not fail, the input u of the UAV is:

9. The method for controlling the logistics of a non-planar six-rotor UAV based on a high-order all-wheel drive system according to claim 8, characterized in that: In step 26, based on the second-order full drive system and the actuator fault model, the control input U after the actuator efficiency loss is obtained by reverse analysis. r ,According to the actuator fault model, the ideal control input U of the actuator is obtained; It is expressed as: Substituting the fault model (3) into formula (35), the ideal control input U of the actuator is obtained as:

10. The method for controlling the logistics of a non-planar six-rotor UAV based on a high-order all-wheel drive system according to claim 9, characterized in that: In the step 27, the deviation κ=uU between the input u of the drone obtained in step 25 when the drone does not fail and the ideal control input U of the actuator obtained in step 26 is calculated, and κ is used as compensation after the drone fails. Based on the compensation κ after the drone fails, the compensated control law is obtained, and the compensated control law is the controller; the specific process is: The deviation κ=uU between the input u of the UAV obtained in step 25 when the UAV does not fail and the ideal control input U of the actuator obtained in step 26 is calculated. κ is used as compensation after the UAV fails. Based on the compensation κ after the UAV fails, the compensated control law is obtained. The compensated control law is: Among them, U κ represents the control law after compensation.

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