A finite-time anti-disturbance control method for a multi-UAV hanging transportation system

Abstract

The present invention discloses a finite-time self-disturbance rejection control method for a multi-UAV suspension transport system. The method comprises the following steps: a finite-time super-helical sliding mode observer is used to observe and compensate for load errors and lumped disturbances thereof, a finite-time sliding mode controller is constructed, the total pulling force for controlling load motion is determined, and an optimal pulling force distribution scheme is solved. An external disturbance is introduced, and a leader-follower cluster model is constructed with the load as a virtual leader. A finite-time reduced-order proportional differential observer and a finite-time controller are designed to perform formation finite-time control and determine the expected trajectory of each UAV. An actuator fault model and a virtual control quantity are introduced to construct a UAV dynamics model and a lumped disturbance is introduced to reconstruct a state error system. A finite-time reduced-order generalized parameter estimation observer and a finite-time non-singular terminal integral sliding mode fault-tolerant controller are designed to perform UAV position loop control and attitude loop control, and finite-time self-disturbance rejection control for a multi-UAV suspension transport system is realized.

Description

Technical Field

[0001] The present invention relates to the field of control of a quad-rotor UAV cluster suspension transportation system, and in particular to a finite-time self-disturbance rejection control method for a multi-UAV suspension transportation system. Background Art

[0002] At present, quadcopter drone technology has been applied to the field of object hanging transportation, especially in scenarios such as emergency rescue, construction sites and small cargo delivery, where drones can independently complete the transportation of lightweight objects. This method has the characteristics of flexible operation and rapid deployment. However, as the weight and volume of objects increase, a single drone encounters bottlenecks in carrying capacity, stability maintenance and flight endurance. These limitations make it difficult for a single drone to meet the transportation needs of heavier and larger objects.

[0003] The limitation of a single drone in the transportation of suspended objects has prompted the development of technology in the direction of integration, that is, to achieve the suspension and transportation of objects through the collaborative operation of multiple drones. The joint work of multiple drones can disperse the load and improve the overall transportation capacity. This clustering method can not only transport heavier objects, but also enhance the stability and reliability of the transportation process through the mutual cooperation between drones. The cluster system can also optimize energy consumption, extend the operation time and improve transportation efficiency through reasonable task allocation.

[0004] Although multi-UAV cluster hanging object transportation brings a series of advantages, the implementation process of existing technologies still faces several challenges, which are directly related to the safety, stability and efficiency of the transportation process. The first is the unstable motion of the load object under wind disturbance, which makes accurate trajectory tracking difficult, especially in emergency missions or time-sensitive situations, where the cluster needs to respond quickly and operate stably. Secondly, it is difficult to accurately establish the dynamic model of the load object and estimate its air friction. These problems not only affect the safe transportation of the load, but also have a negative impact on the energy consumption and flight performance of the drone. In addition, the overall modeling accuracy of the drone cluster is insufficient, and both the cluster and the individual drone are susceptible to wind disturbance, which requires the control strategy to be highly robust to cope with environmental uncertainty and the nonlinear characteristics of the system. Ensuring system stability is crucial, because any instability in the cluster may lead to the failure of the entire system. Therefore, the solution of these key issues is crucial to the development of multi-UAV cluster hanging object transportation technology. Summary of the invention

[0005] Purpose of the invention: The present invention aims to provide a finite-time self-disturbance rejection fault-tolerant control method for a quadrotor UAV cluster suspension transport system based on multiple reduced-order observers.

[0006] Technical solution: The finite-time anti-disturbance control method for a multi-UAV hanging transportation system of the present invention comprises the following steps:

[0007] (1) Construct a load dynamics model with multi-source disturbances; design a finite-time super-helical sliding mode observer to observe and compensate for the error of the load dynamics model with multi-source disturbances and the lumped disturbance of the load; construct a finite-time sliding mode controller based on the improved power reaching law according to the observed values ​​of the error of the load dynamics model and the observed values ​​of the lumped disturbance of the load to determine the total pulling force to control the load motion;

[0008] (2) Taking the minimization of total tension as the objective function, setting constraints based on tension balance and the upper limit of rope tension, solving the optimal tension distribution scheme, and determining the tension of each UAV;

[0009] (3) Introduce external disturbances, take the load as a virtual leader, and construct Leader-Follower A leader-follower cluster model is proposed. For this model, a finite-time reduced-order proportional differential observer is designed. According to the estimated value of the finite-time reduced-order proportional differential observer, a finite-time controller is designed to perform formation finite-time control and determine the expected trajectory of each UAV.

[0010] (4) Considering the reaction tension of the rope on the UAV, air friction, external wind disturbance and multi-source disturbance factors, the actuator fault model and virtual control quantity are introduced to construct the UAV dynamics model. According to the UAV position and attitude information and its tracking signal information, the UAV state error system is determined, and the lumped disturbance is introduced to reconstruct the state error system;

[0011] (5) In order to reconstruct the state error system by introducing lumped disturbances, a finite-time reduced-order generalized parameter estimation observer and a speed sensorless finite-time non-singular terminal integral sliding mode fault-tolerant controller are designed by combining linear regression and dynamic regression extension techniques to perform UAV position loop control and attitude loop control, thereby realizing finite-time self-disturbance rejection control of a multi-UAV suspension transport system.

[0012] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: 1. The present invention, in view of the multi-source influences such as inaccurate modeling, external environmental disturbances, and air resistance during the movement of the load, uses a multi-factorial dynamic model and a compensation control algorithm to identify and adjust the motion deviation in real time within a limited time, thereby realizing an accurate compensation mechanism, significantly improving the tracking accuracy of the load on the desired trajectory in a complex environment, and enhancing the robustness of the control; 2. The present invention adopts a series of finite-time reduced-order observers to accurately identify and compensate for the disturbances encountered by the load, the cluster, and a single UAV, significantly enhancing the performance and robustness of the entire UAV cluster suspension transportation system in an uncertain environment, and ensuring that the observer still maintains excellent stability and reliability in the face of model deviations and external interference; 3. The present invention incorporates the high-order derivative information of the desired signal, which is usually difficult to express with a closed-form solution, into the category of lumped disturbances and estimates it using the observer, effectively bypassing the problem of the missing closed-form solution of the desired signal, and is easier to apply in practice; 4. The present invention adopts an optimal allocation strategy to optimize the tension of the rope on the load, ensuring that the sum of the total tension is minimized, while The tension provided by each drone is balanced at the same time, ensuring that the tension of each rope does not exceed its upper limit, significantly extending the endurance of the drone cluster hanging transportation system and improving the transportation efficiency; 5. In the present invention, each component of the entire cluster hanging transportation system adopts a finite time control strategy, which greatly improves the response speed and operational flexibility of the system; 6. The present invention aims at the single drone actuator failure that may occur in the cluster hanging transportation system, such as partial motor failure and actuator bias failure. The present invention ensures that the system can still operate stably when these problems occur, greatly enhancing the reliability of the system; 7. The present invention accurately estimates the state change rate of the system through an observer, significantly reducing the dependence on the speed sensor, thereby simplifying the system structure and reducing costs, improving the system integration, and enhancing its economy; 8. The present invention constructs an innovative non-singular terminal integral sliding mode controller, which effectively avoids the singular value problem, improves the convergence speed and approach speed of the system, reduces system jitter, and further optimizes the overall performance. Through these technical innovations, the control quantity output is smoother, and the control accuracy and robustness of the drone in a complex environment are significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a flow chart of the present invention;

[0014] Figure 2 A schematic diagram of the structure of load finite time control in an embodiment of the present invention;

[0015] Figure 3 Schematic diagram of a leader-follower cluster model in an embodiment of the present invention;

[0016] Figure 4 Schematic diagram of the structure of the limited time control of the formation in an embodiment of the present invention;

[0017] Figure 5 Schematic diagram of the structure of the finite-time self-disturbance rejection fault-tolerant control of a single UAV in an embodiment of the present invention;

[0018] Figure 6 It is a schematic diagram of the structure of the finite-time active disturbance rejection control of the multi-UAV hanging transportation system in an embodiment of the present invention;

[0019] Figure 7 This is a simulation effect diagram of the finite-time anti-disturbance control method for a multi-UAV hanging transportation system in an embodiment of the present invention;

[0020] Figure 8 It is a simulation diagram of the motion error of the hanging load in the embodiment of the present invention;

[0021] Fig. 9 This is a simulation diagram of the horizontal position of a multi-UAV cluster in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be further described below in conjunction with the accompanying drawings.

[0023] The finite-time anti-disturbance control method for a multi-UAV suspension transportation system of the present invention comprises the following steps:

[0024] (1) Construct a load dynamics model with multi-source disturbances; design a finite-time superhelical sliding mode observer to observe and compensate for the errors in the load dynamics model with multi-source disturbances and the lumped disturbances of the load; construct a finite-time sliding mode controller based on the improved power reaching law according to the observed values ​​of the errors in the load dynamics model and the observed values ​​of the lumped disturbances of the load to determine the total pulling force to control the load motion.

[0025] The load is connected to n quadcopters by n ropes, and the ropes transmit the tension provided by the drones to control the load to move along a predetermined trajectory. Assuming that the load is an object with a regular shape, the rope connection point is the center of gravity of the load, and the load does not rotate. Considering the influence of multiple sources of disturbances such as air friction, external wind disturbance, and modeling uncertainty, the dynamic model of the load is established as follows:

[0026] ;

[0027] in, Indicates the weight of the load; , , Indicates the position coordinates of the load; , , The corresponding speed represents the load; , , Then the corresponding represents the acceleration of the load; represents the acceleration due to gravity; , , Respectively The components of force in the x, y, and z directions, represents the total force provided to the load by all drones through the rope; ; is the vector representation of the total pulling force, , , are the unit vectors along the x-axis, y-axis, and z-axis respectively; , represents the tension applied by the i-th UAV to the load through the i-th rope; represents the air resistance coefficient; , , They represent the projected area of ​​the load in the xyz axis direction respectively; , , Represents external disturbances, such as wind disturbances.

[0028] Define the error of the load:

[0029] ;

[0030] And considering the load dynamics model, the error system is obtained:

[0031] ;

[0032] in, , , represents the desired position trajectory of the load, , , Indicates the error between the expected position and the actual position of the load. , , represents the second-order derivative of the error, , , Represents the second derivative of the desired position trajectory of the load.

[0033] The multi-source disturbance effects and the second-order derivatives of the load's expected position trajectory are uniformly regarded as lumped disturbances, and the lumped disturbance of the load is defined as:

[0034] ;

[0035] Then the error system of the load can be written as

[0036] ;

[0037] in, , , is the lumped disturbance of the load. Assuming that the external disturbance is differentiable and their derivatives are bounded, we can get a positive constant , , The derivative of the lumped disturbance satisfies: , , .

[0038] Design a finite-time superhelical sliding mode observer:

[0039] ;

[0040] in, , , is the error of the finite-time super-helical sliding mode observer , , Observed value of , , for , , The first derivative of ; , , is the error derivative of the finite-time super-helical sliding mode observer , , Observed value of , , for , , The first derivative of ; , , is the lumped perturbation of the load by the finite-time super-helical sliding mode observer , , Observed value of , , for , , The first derivative of ; represents the symbolic function, , indicating the directions of the x, y, and z axes.

[0041] Design the sliding surface function of finite-time sliding mode control:

[0042] ;

[0043] The power reaching law of the selected variant:

[0044] ;

[0045] The finite-time sliding mode controller is obtained:

[0046] ;

[0047] in, , , is the sliding surface function , , The derivative and reaching law of ; , , Indicates the error between the expected position and the actual position of the load , , The first derivative of ; , , , , , , , , is the sliding mode parameter, and , , , , , , , is the reaching law parameter, and , ; Indicates absolute value.

[0048] Using the observer to calculate the error derivative , , Observed value , , and the observer for the lumped disturbance , , Observed value , , Replace the controller design , , and , , , then the finite-time sliding surface function and the finite-time sliding mode controller become

[0049] ;

[0050] .

[0051] (2) Taking the minimization of total tension as the objective function, constraints are set according to the tension balance and the upper limit of the rope tension, and the optimal tension distribution scheme is solved to determine the tension of each UAV.

[0052] Load Control Input Total Force It is obtained by transmitting the tension provided by n drones through n ropes. The tension provided by different ropes is recorded as , the direction of the tension provided by different ropes is recorded as ,in , represents the position coordinates of the i-th drone, represents the position coordinates of the load, represents the Euclidean norm. Therefore, the total force It can be expressed as .

[0053] When allocating the pulling force of the drones, the power consumption problem must be taken into consideration. The pulling force on each rope should be as balanced as possible, while the sum of the pulling force of each rope should be minimized to ensure that the drones have a longer endurance when transporting loads. The objective function is defined as

[0054] ;

[0055] The constraint is that the tension on each rope cannot exceed its maximum bearing capacity: , ;

[0056] The tension provided by the rope must be balanced with the control force of the load: ; The tension provided by the rope cannot be negative: , .

[0057] Therefore, we can get the following optimization problem. The optimization goal is to find a set of , so that the objective function Minimum:

[0058] ;

[0059] ;

[0060] ;

[0061] ;

[0062] in, and To optimize the weight coefficient, The magnitude of the pulling force provided by the i-th UAV through the i-th rope, Representing a collection All elements in The maximum value of Representing a collection All elements in The minimum value of represents the maximum load that the i-th rope can bear. By finding a set of constraints that meet the objective function Smallest , which is the expected traction of each drone ,and It represents the component of the expected traction force of the i-th UAV in the xyz axis direction. represents the direction of the tension provided by the i-th rope.

[0063] (3) Introduce external disturbances, take the load as a virtual leader, and construct Leader-Follower A leader-follower cluster model is proposed. For this model, a finite-time reduced-order proportional differential observer is designed. A finite-time controller is designed based on the estimated value of the finite-time reduced-order proportional differential observer to perform finite-time control of the formation and determine the expected trajectory of each UAV.

[0064] Since the movement of the payload is completely dependent on the control of the drone, the payload does not need to receive the drone's status information. On the contrary, in order to ensure that the drone can move around the payload accurately, they need to obtain relevant status information of the payload in real time. Based on this requirement, the leader-follower formation model is selected to build the control system.

[0065] like Figure 3 As shown, the load is taken as the virtual leader, and the straight-line distance between the i-th UAV and the load is defined as , Horizontal distance , longitudinal distance and height error .

[0066] ;

[0067] in, , , , They represent the position and yaw angle of the load respectively. Since the rotation of the load is not considered, is a fixed value, , , They represent the position of the i-th UAV respectively.

[0068] Taking the derivative and introducing external disturbances, we get the dynamic equation:

[0069] ;

[0070] ;

[0071] ;

[0072] in, , , are the lateral distances between the i-th UAV and the payload , longitudinal distance and height error The derivative of , , is the general term for internal coupling terms and external disturbances, , , is the formation desired control input, They represent the yaw angular velocity of the load respectively. Since the rotational motion of the load is not considered, is a fixed value, is zero, , , For external disturbance.

[0073] by As an example, design a finite-time reduced-order proportional-differential observer

[0074] ;

[0075] Among them, the intermediate variable , intermediate variable , and It is a disturbance and the perturbation derivative The observed value of , are observer parameters, is the lateral distance between the i-th UAV and the payload. Similarly, the disturbances can be obtained respectively and the perturbation derivative Observed value and , disturbance and the perturbation derivative Observed value and .

[0076] Design a finite-time controller:

[0077] ;

[0078] in, , , is the expected lateral distance, expected longitudinal distance and expected height error between the i-th UAV and the payload, is the symbolic function, , , is a design parameter with a value range of Therefore, the expected trajectory of the i-th UAV can be inferred based on the expected control input of the formation:

[0079] ;

[0080] The control structure diagram of this part is as follows Figure 4 shown.

[0081] (4) Considering the reaction tension of the rope on the UAV, air friction, external wind disturbance and multi-source disturbance factors, the actuator fault model and virtual control quantity are introduced to construct the UAV dynamics model. According to the UAV position and attitude information and its tracking signal information, the UAV state error system is determined, and the lumped disturbance is introduced to reconstruct the state error system.

[0082] Since the movement of the quadcopter is completely dependent on the lift generated by the rotation of the motor-driven blades, once the blades are damaged or the motor fails, the normal operation of the actuator will be difficult to guarantee. In this case, the drone is likely to fail to fly according to the planned trajectory. Such problems can be classified as actuator failures. Assuming that the quadcopter is affected by the partial failure of the motor and the bias failure, the fault model can be expressed as

[0083] ;

[0084] in, represents the desired control input of the controller, , represents the actual control input considering the fault, represents the failure factor of the actuator, , ; Indicates the actuator fault bias value, represents the paranoia factor, ; When the actuator operates normally, , ; When the actuator has only a partial failure, , ; When the actuator only has a paranoid fault, , ; When there are two kinds of faults in the actuator, , ;

[0085] Based on comprehensive consideration of multiple disturbance factors such as the reaction force of the rope on the drone, air friction, external wind disturbance, and modeling uncertainty, the actuator failure condition was introduced and the drone dynamics model was constructed:

[0086] ;

[0087] in, , , represents the roll angle, pitch angle and yaw angle of the i-th UAV; , , They represent the moment of inertia around the xyz axis of the body coordinate system; m represents the mass of the drone, represents the acceleration due to gravity, represents the moment of inertia of a single propeller, , , is the translational air resistance coefficient, , , represents the damping torque coefficient, , , , , , Indicates external interference. represents the sum of the rotational speeds of the four propellers, where , , , Indicates the speed of each propeller.

[0088] The relationship between the rotation speed of each propeller and the force it produces is

[0089] ;

[0090] in, represents the propeller lift coefficient, represents the propeller's anti-torque coefficient, Indicates the radius of the fuselage.

[0091] Introduction of virtual control , , Decouple the position control channel and attitude control channel

[0092] ;

[0093] in, , , is the virtual control quantity of the position loop, Represents the controller's expected control input. By performing attitude inverse analysis on the virtual control quantity, the controller's expected control input and attitude's expected input are obtained:

[0094] ;

[0095] By introducing virtual control variables and actuator failures, the UAV dynamics model can be simplified as

[0096] ;

[0097] in, , , It represents the component of the expected traction force of the i-th UAV in the xyz-axis direction.

[0098] Define the tracking error of the quadrotor position and attitude angle:

[0099] ;

[0100] ;

[0101] ;

[0102] ;

[0103] ;

[0104] ;

[0105] in, , , , , , represents the state tracking error of each control channel of the i-th UAV, , , , , , is the expected position and attitude of the i-th UAV, then the error system of the UAV is

[0106] .

[0107] The coupling relationship in each control channel of the UAV system, the influence of multi-source disturbances and other difficult-to-handle parts are all regarded as lumped disturbances:

[0108] ;

[0109] Then the state error system can be simplified as

[0110] ;

[0111] ;

[0112] in, , , , , , represents the aggregate disturbance of each control channel, , , , , , represents the state tracking error of each control channel of the i-th UAV, , , , , , is the rate of change of state tracking error, , , , , , is the second-order derivative of the state tracking error.

[0113] (5) In order to reconstruct the state error system by introducing lumped disturbances, a finite-time reduced-order generalized parameter estimation observer and a speed sensorless finite-time non-singular terminal integral sliding mode fault-tolerant controller are designed by combining linear regression and dynamic regression extension techniques to perform UAV position loop control and attitude loop control, thereby realizing finite-time self-disturbance rejection control of a multi-UAV suspension transport system.

[0114] Aiming at the state error system of the UAV, a finite-time reduced-order generalized parameter estimation observer is designed by combining the linear regression principle and dynamic regression extension technology. Taking the control channel as an example, define the measurable variables , , unmeasured variables ; Then rewrite the dynamic equation of the channel into the following state space form:

[0115] ;

[0116] in, Representation and The first constant of interest, , represents the translational air resistance coefficient in the x-axis direction, Representation and Unrelated variables, , is the virtual control amount of the x control channel, is the second parameter to be estimated, , is the expected x position of the i-th UAV The second-order derivative of It represents the reaction force of the component force of the expected traction force of the i-th UAV in the x-axis direction, represents the external perturbation on the x control channel.

[0117] Construct parameter estimates:

[0118] ;

[0119] in, for The reconstruction state, is a virtual state parameter, represents the state transition matrix.

[0120] Next, the rewritten state space is dynamically expanded:

[0121] ;

[0122] ;

[0123] ;

[0124] ;

[0125] ;

[0126] ;

[0127] ;

[0128] in, and is the intermediate variable, is the observer gain, , , , for , , , The first derivative of , , , for , , The initial value of , and t is the current time.

[0129] Satisfies the following linear regression equation:

[0130] ;

[0131] in, is the measurable signal defined, , is an intermediate variable.

[0132] Design an observer for this linear regression equation:

[0133] ;

[0134] in, , , , is the dynamic expansion amount, , , , are the filter parameters, is the measurable signal defined, , is the drone state variable, , , is the observer gain, , is the intermediate variable, , is a virtual state parameter, is the second parameter to be estimated, s is the differential operator, , For virtual state parameters The estimated value of is the second parameter to be estimated The estimated value of is the determinant function, is the adjoint matrix function, is the acceleration due to gravity, , , , for , , , The first-order derivative of . Then the lumped disturbance estimated by the observer is , the estimated state change rate is

[0135] Since the six control channels of the position loop and attitude loop have similar structures, The control channels are the same.

[0136] Design of finite-time fast non-singular terminal integral sliding surface function based on simplified state error system:

[0137] ;

[0138] in, , , , , , , are the sliding surface functions of the six control channels respectively, , , , , is the sliding mode control parameter, , and are all positive odd numbers and . Combining the adaptive theory, an improved adaptive sliding mode reaching law is designed, and the hyperbolic tangent function is used instead of the sign function:

[0139] ;

[0140] ;

[0141] in, is the sliding mode reaching law, It is an adaptive reaching function. In the traditional reaching law, the sign function is discontinuous near the zero point, which can easily cause high-frequency oscillation of the output, which will have a great adverse effect on the operation of the UAV system. Therefore, the hyperbolic tangent function is selected. In comparison, the hyperbolic tangent function has continuity, and the sliding surface switching process is smoother, which can effectively reduce the jitter and instability of the UAV system.

[0142] ;

[0143] ;

[0144] in, is the adaptive parameter, is the adaptive gain coefficient, is the hyperbolic tangent parameter, is the power exponent of the adaptive parameter. Combining the rate of change information and disturbance information estimated by the finite-time reduced-order generalized parameter estimation observer, a speed sensorless finite-time non-singular terminal integral sliding mode fault-tolerant controller can be formed:

[0145] ;

[0146] ;

[0147] ;

[0148] ;

[0149] ;

[0150] ;

[0151] ;

[0152] in, , , , , , , is the desired control quantity of the position loop and attitude loop, is the adaptive approach function, is the adaptive parameter, , , , , , , , , , , is the non-singular terminal integral sliding mode adjustable parameter, ,s j is the sliding surface function, , , , is the actuator failure factor, , , is the translational air resistance coefficient, , , Represents the damping torque coefficient.

[0153] At this point, the construction of the finite-time fast non-singular terminal integral sliding mode fault-tolerant controller has been completed. The control structure diagram of this component is as follows: Figure 4 shown.

[0154] The overall flow chart of the finite-time self-disturbance rejection and fault-tolerant control of the multi-UAV cluster hanging transportation system is as follows: Figure 1 As shown in the structure diagram Figure 6 shown.

[0155] In the MATLAB2021 environment, a simulation verification test is carried out on a finite-time self-disturbance rejection fault-tolerant control scheme of a drone cluster hanging transportation system designed in the present invention:

[0156] A simulation module was built in MATLAB / SIMULINK to conduct the whole system simulation experiment. The model parameters used for the quadrotor are shown in Table 1, and the load model parameters are shown in Table 2.

[0157] Table 1 Quadrotor UAV model parameters

[0158] ;

[0159] Table 2 Load model parameters

[0160] ;

[0161] The expected motion trajectory of the load is set as

[0162] ;

[0163] ;

[0164] ;

[0165] ;

[0166] The initial position and yaw angle are set to

[0167] ;

[0168] The number of drones is selected as 4, and the expected relative distance between the drone and the payload is

[0169] ;

[0170] ;

[0171] ;

[0172] ;

[0173] Set the initial state of the 4 drones to

[0174] ;

[0175] ;

[0176] The external disturbance is set to

[0177] ;

[0178] ;

[0179] ;

[0180] ;

[0181] ;

[0182] The simulation effect is as follows Figure 7 As shown in the figure, the motion error of the suspended load is as follows: Figure 8 As shown in the figure, the actual motion trajectory of the visible load is basically consistent with the expected motion trajectory; the position simulation of the drone cluster is shown in Fig. 9 As shown, the drone cluster includes drone No. 1, drone No. 2, drone No. 3 and drone No. 4, which can still track the target trajectory well in the presence of disturbance. The superiority of the present invention is verified through simulation experiments.

Claims

1. A finite-time anti-disturbance control method for a multi-UAV suspension transportation system, characterized in that: The following steps are involved: (1) Construct a load dynamics model with multi-source disturbances; design a finite-time super-helical sliding mode observer to observe and compensate for the error of the load dynamics model with multi-source disturbances and the lumped disturbance of the load; construct a finite-time sliding mode controller based on the improved power reaching law according to the observed values ​​of the error of the load dynamics model and the observed values ​​of the lumped disturbance of the load to determine the total pulling force to control the load motion; (2) Taking the minimization of total tension as the objective function, setting constraints based on tension balance and the upper limit of rope tension, solving the optimal tension distribution scheme, and determining the tension of each UAV; (3) Introduce external disturbances, take the load as a virtual leader, and construct Leader-Follower A leader-follower cluster model is proposed. For this model, a finite-time reduced-order proportional differential observer is designed. According to the estimated value of the finite-time reduced-order proportional differential observer, a finite-time controller is designed to perform formation finite-time control and determine the expected trajectory of each UAV. (4) Considering the reaction tension of the rope on the UAV, air friction, external wind disturbance and multi-source disturbance factors, the actuator fault model and virtual control quantity are introduced to construct the UAV dynamics model. According to the UAV position and attitude information and its tracking signal information, the UAV state error system is determined, and the lumped disturbance is introduced to reconstruct the state error system; (5) For the state error system reconstructed by introducing lumped disturbances, a finite-time reduced-order generalized parameter estimation observer and a finite-time non-singular terminal integral sliding mode fault-tolerant controller without velocity sensor are designed to perform UAV position loop control and attitude loop control, thus realizing finite-time self-disturbance rejection control of a multi-UAV hanging transport system; In step (1), the finite-time super-helical sliding mode observer is ； in, Indicates the error between the expected position and the actual position of the load. is the error of the finite-time super-helical sliding mode observer Observed value of for The first derivative of ; is the error derivative of the finite-time super-helical sliding mode observer Observed value of for The first derivative of ; is the lumped perturbation of the load by the finite-time super-helical sliding mode observer Observed value of for The first derivative of ; represents a symbolic function; , , is a normal number, satisfying , , , is the derivative of the lumped disturbance of the load; Indicates the weight of the load; , , Respectively The components of force in the x, y, and z directions, represents the total force provided to the load by all drones through the rope; , indicating the directions of the x, y, and z axes; The finite-time sliding mode controller based on the improved power reaching law is: ； ； ； ； in, is the sliding surface function; , , is the sliding mode parameter, and , ; , is the reaching law parameter, and , ; Indicates absolute value; is the vector representation of the total pulling force, , , are unit vectors along the x-axis, y-axis, and z-axis respectively; they represent the tension applied by the ith drone to the load through the ith rope.

2. According to claim 1, the finite-time auto-disturbance rejection control method for a multi-UAV suspension transportation system is characterized in that: In step (2), the objective function for ； The constraints are ； ； ； in, and To optimize the weight coefficient, i=1,2,…n, Representing a collection All elements in The maximum value of Representing a collection All elements in The minimum value of represents the maximum bearing force that the i-th rope can withstand, represents the direction of the tension provided by the i-th rope.

3. According to claim 2, the finite-time auto-disturbance rejection control method for a multi-UAV suspension transportation system is characterized in that: In step (3), the finite-time reduced-order proportional derivative observer is ； ； ； ； in, , is the intermediate variable, , are the parameters of the finite-time reduced-order proportional derivative observer, is the formation desired control input, and is an external disturbance and the external perturbation derivative The observed value of , , are the lateral distance, longitudinal distance and height error between the i-th UAV and the payload respectively; , , express , , The first derivative of .

4. According to claim 3, the finite-time auto-disturbance rejection control method for a multi-UAV suspension transportation system is characterized in that: In step (3), the finite time controller is ； in, , , is the expected lateral distance, expected longitudinal distance and expected height error between the i-th UAV and the payload, , , is a design parameter with a value range of ; The first-order derivative of the desired trajectory of the i-th UAV is ； in, Represents the yaw angle of the load, and the desired trajectory can be obtained by integrating , , .

5. According to claim 4, the finite-time auto-disturbance rejection control method for a multi-UAV suspension transportation system is characterized in that: In step (4), the actuator fault model is ； in, represents the desired control input of the controller, , represents the actual control input considering the fault, represents the failure factor of the actuator, , ; Indicates the actuator fault bias value, represents the paranoia factor, ; When the actuator operates normally, , ; When the actuator has only a partial failure, , ; When the actuator only has a paranoid fault, , ; When there are two kinds of faults in the actuator, , ; The virtual control quantity is ； in, , , is the virtual control quantity of the position loop, , , represents the roll angle, pitch angle and yaw angle of the i-th UAV; Introducing the actuator fault model and virtual control quantity, the UAV dynamics model is constructed as follows: ； in, , , represents the position of the i-th drone, , , They represent the moment of inertia around the xyz axis of the body coordinate system, m represents the mass of the drone, represents the acceleration due to gravity, represents the moment of inertia of a single propeller, , , is the translational air resistance coefficient, , , represents the damping torque coefficient, , , , , , Indicates external interference, , represents the sum of the rotational speeds of the four propellers, where , , , Indicates the speed of each propeller; , , It represents the component of the expected traction force of the i-th UAV in the xyz-axis direction.

6. According to the finite time auto-disturbance rejection control method for a multi-UAV suspension transportation system of claim 5, it is characterized in that: In step (4), the lumped disturbance is introduced to reconstruct the state error system as ； ； in, , , , , , represents the aggregate disturbance of each control channel, , , , , , represents the state tracking error of each control channel of the i-th UAV, , , , , , is the rate of change of state tracking error, , , , , , is the second-order derivative of the state tracking error.

7. The finite-time auto-disturbance rejection control method for a multi-UAV suspension transportation system according to claim 6 is characterized in that: In step (5), the x-channel of the finite-time reduced-order generalized parameter estimation observer is ； in, , , , is the dynamic expansion amount, , , , are the filter parameters, is the measurable signal defined, , is the drone state variable, , , is the observer gain, , is the intermediate variable, , is a virtual state parameter, is the second parameter to be estimated, s is the differential operator, , For virtual state parameters The estimated value of is the second parameter to be estimated The estimated value of is the determinant function, is the adjoint matrix function, is the acceleration due to gravity, , , , for , , , The first derivative of .

8. The finite-time auto-disturbance rejection control method for a multi-UAV suspension transportation system according to claim 7 is characterized in that: In step (5), the speed sensorless finite-time non-singular terminal integral sliding mode fault-tolerant controller is: ； ； ； ； ； ； ； in, , , , , , is the desired control quantity of the position loop and attitude loop, is the adaptive approach function, is the adaptive parameter, , , , , , , , , , , is the non-singular terminal integral sliding mode adjustable parameter, ,s j is the sliding surface function, , , , is the actuator failure factor, , , is the translational air resistance coefficient, , , represents the damping torque coefficient, .

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