Time-varying delay output feedback control method and system based on barrier function

Abstract

The invention provides a time-varying delay output feedback control method and system based on a barrier function, and solves the problems of communication delay and field of view (FOV) constraint when an unmanned vehicle formation communicates with an ultra-wide-angle camera. Firstly, a second-order oscillator and an output feedback controller are used for feeding back the position and the direction of an adjacent vehicle through exchange control quantity, and the problem of time-varying delay in ultra-wide-angle camera position calculation is solved. And secondly, aiming at the problem that the radiation range of a target sensed by a camera is limited, an FOV constraint potential function is adopted to carry out optimization design on a sliding mode curved surface, and the stability of a closed-loop control system is analyzed by applying a Lyapunov method. Finally, the effectiveness of the consensus scheme is verified through a simulation experiment by processing communication delay and FOV constraint problems under two different initial conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned vehicle formation control, and relates to a time-varying delayed output feedback control method and system of an unmanned vehicle formation with error communication constraints based on a potential barrier function. Background Art

[0002] Many scholars have conducted extensive research on the rendezvous problem of unmanned vehicle formations. In the paper Optimal rendezvous trajectory for unmanned aerial-ground vehicles, a control formula is proposed to ensure the rendezvous of the controlled objects by tracking the optimal trajectory. In the paper Autonomous trajectory planning for rendezvous and proximity operations by conic optimization, the solution to the rendezvous control is obtained by converting the rendezvous control problem into a loose formula. In the paper Antenna array synthesis with clusters of unmanned aerial vehicles, an effective method for solving the collision-free trajectory and rendezvous formation of multiple UGVs (Unmanned Ground Vehicles) with motion constraints is given.

[0003] In addition, since the external environment is constantly changing, it will be difficult for UGV clusters to perform tasks. In order to deploy UGVs to form an ideal formation and thus achieve better performance, scholars have conducted extensive research on the consensus problem. Yang Liu introduced a distributed dynamic output feedback protocol using consensus control in the literature Dynamic output feedback control for consensus of multi-agent systems: an H∞ approach. In order to avoid the singularity problem, an adaptive singularity avoidance fixed-time consensus control scheme was designed in the literature Singularity avoidance adaptive output-feedback fixed-time consensus control for multiple autonomous underwater vehicles subject to nonlinearities. In the literature Distributed semiglobal consensus with relative output feedback and input saturation under directed switching networks, an observer-based distributed MAS control scheme was proposed to solve the consensus problem of linear multi-agent systems with input saturation and directed switching method topology. Importantly, in practical applications, the communication acquisition of unmanned vehicles mainly depends on cameras, as can be seen in the literature Task-Driven Formation of Nonholonomic Vehicles With Communication Constraints. However, the field of view of the camera is limited. Without considering FOV constraints, other autonomous vehicles or external conditions may exceed the field of view, resulting in collision risks.

[0004] In order to solve the problem of limited radiation range of the camera sensing target, researchers use potential barrier functions based on FOV constraints to optimize the sliding surface. In the literature Constrained image-based visual servoing using barrier functions, potential functions are applied to transform characteristic dynamics into equivalent dynamics with total state constraints. In the literature Integral barrier Lyapunov function-based three-dimensional low-order integrated guidance and control design with seeker's field-of-view constraint, a three-dimensional low-order IGC scheme based on FOV constraints is proposed to solve the problem of the target being outside the FOV when the control target adjusts its attitude. Due to the very complex actual application conditions, the above methods must use velocity sensors to estimate the control value of the unmanned vehicle. The unmanned underwater vehicle system is very sensitive to sensor failures and attitude and velocity sensors, so additional filters are required, which can be referred to the literature Consensus of Euler-Lagrange systems using only position measurements. In addition, there are time-varying delays in the detection and calculation of ultra-wide-angle cameras, which may cause the unmanned vehicle to miss the best time to perform the task. However, existing methods cannot pass their coordinates to achieve consistency, which is crucial for the stability of the UGV cluster under external interference.

[0005] In general, the time-varying delay in the calculation of the ultra-wide-angle camera position of the unmanned vehicle formation and the limited radiation range of the perceived target are difficult to solve effectively by traditional methods, especially when the speed of the unmanned vehicle is unpredictable, which may lead to a decrease in the stability of the formation under external interference. Therefore, it is necessary to study a time-varying delay output feedback control scheme based on the potential barrier function. Summary of the invention

[0006] In order to overcome the deficiencies of the above-mentioned prior art, the present invention proposes a time-varying delay output feedback control method based on communication constraints for the intersection control problem of unmanned vehicle formations, so as to solve the communication delay and field of view constraint problems faced when using ultra-wide-angle cameras. First, the time-varying delay problem in position calculation is solved; at the same time, an output feedback controller is designed to feedback the position and direction of adjacent vehicles by exchanging controller variables. Since the target radiation range perceived by the camera is limited, the FOV constraint barrier function is used to optimize the design of the sliding surface, and the Lyapunov method is used to analyze the stability of the closed-loop control system.

[0007] The technical solution adopted by the present invention is: a time-varying delay output feedback control method based on a potential barrier function, comprising the following steps: Step 1, establish a dynamic model of a group of N unmanned vehicles to describe the motion state of the unmanned vehicles; Step 2: Describe the parameters in the dynamic model through consensus control, define the control objectives of the unmanned vehicle formation, and make assumptions about the environmental constraints of the unmanned vehicles under the control objectives, where the environmental constraints include field of view constraints and time delay constraints; Step 3, combining the field of view constraint and the time delay constraint to design the sliding membrane surface, so as to facilitate the controller design in a unified system, includes the following sub-steps; Step 3.1, introduce position error and attitude error, and set the effective range for the relative angle between any two vehicles in the formation to ensure that the unmanned vehicle formation can communicate normally; Step 3.2, using the communication constraint function and the potential barrier function to perform design analysis on the angle error expectation, so that the communication constraints of all unmanned vehicles can be processed in a unified architecture; Step 3.3, analyze the position error in combination with the potential barrier function, and design the sliding surface according to the deterministic equivalent principle of linear motion dynamics; Step 4, design an output feedback controller based on a second-order oscillator so that the system state quickly reaches the preset sliding membrane surface and maintains stable operation on this sliding membrane surface to achieve the control target.

[0008] Furthermore, the kinetic model established in step 1 is expressed as follows: (1) (2) Where (1) and (2) represent angular motion and linear motion respectively, Indicates The translational speed of the unmanned vehicle, Indicates The angular velocity of the unmanned vehicle, vector It is expressed as: (3) Among them, := indicates assignment; and Indicates The Cartesian coordinates of the unmanned vehicle, Indicates the direction of the unmanned vehicle. Respectively express Find the first-order derivative, and is the control input, expressed as: (4) In the formula, and is the torque at the wheel, denote the inertia matrix of the vehicle and the mass of the machine respectively, is the radius of the wheel, is the axle length of the wheel.

[0009] Furthermore, in step 2, it is assumed that a positioning sensor is installed on each unmanned vehicle to provide Reliable measurement value requires the unmanned vehicle to be at the meeting point Converge to form a formation and obtain a common direction ,for and the unmanned vehicle vector relative to the unknown formation center , They represent the expected horizontal and vertical distances of the final position of the unmanned vehicle from the center of the formation, respectively, which ultimately makes the Cartesian position Converge to ; definition , then the control goal can be divided into two sub-goals: 1) The unmanned vehicle is required to join the formation near a non-predetermined assembly point, which is described as: (5) 2) Each unmanned vehicle has the same angular velocity and the same heading, which can be described as: (6) In the formula, is the communication delay time, Public direction for unmanned vehicle platooning, It is The Cartesian coordinates of the unmanned vehicle, Indicates the direction of the unmanned vehicle. Indicates The translational speed of the unmanned vehicle, Indicates The angular velocity of the unmanned vehicle.

[0010] Furthermore, the specific implementation of step 3.1 is as follows; For position error: (7) For attitude error: (8) In the formula, is a parameter and , It is The neighboring unmanned vehicles of the unmanned vehicles are gathered together. They are The steering parameters of the translational velocity and angular velocity of the unmanned vehicle, They are The steering parameters of the translational velocity and angular velocity of the unmanned vehicle, For the The driverless car arrived The time delay of the driverless car, is the communication delay time.

[0011] Furthermore, the communication constraints are defined in step 3.2 as follows: (9) in, It is The Cartesian coordinates of the unmanned vehicle, It is The Cartesian coordinates of the unmanned vehicle, Indicates the direction of the unmanned vehicle. Indicates The unmanned vehicle and The relative angle of the unmanned vehicle, Indicates approximation to; The angle-based barrier function updates the output according to the communication constraint and is expressed as: (10) In the formula is the maximum angle between the principal axis and the incident ray, is a tunable parameter and ,set up For the The number of unmanned vehicles is relative to The angle of an unmanned vehicle; The distance-based barrier function is expressed as: (11) In the formula, z i and z j Respectively The unmanned vehicle and The Cartesian coordinates of the unmanned vehicle, is an adjustable parameter, is the communication radius, satisfying the condition , and due to the existence of time delay, the parameter Cannot be too small to ensure that the unmanned vehicle stays within the upper bound of the time delay within the field of view Inside; So the potential barrier function can be expressed as : (12).

[0012] Furthermore, in step 3.3, the synovial surface is designed as: (13) In the formula, is the state variable, is a constant, Respectively express Find the first and second order derivatives, is an external input, mapping It is a passive mapping.

[0013] Furthermore, the specific implementation of step 4 is as follows: First, design the constraint function : (15) in, , , is the independent variable, and is the desired angle and desired speed, Used to simplify expressions; The overall control scheme is as follows: For linear motion dynamics: (16) (17) For angular motion dynamics: (18) (19) In the formula, and is the control input, is a constant, is a constant, Respectively express Find the first-order derivative; In addition, the function It is redefined to enhance its compatibility with output feedback controllers, thus making the expression more convenient; (20) In the formula is a positive number, is a vector that satisfies , is a function of the communication delay time. , Vector for driverless car.

[0014] Furthermore, the method also includes step 5, in which the expected accuracy of the controller is effectively proved using the Lyapunov function.

[0015] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention proposes a novel time-varying delayed output feedback control method based on potential barrier function, which has high control accuracy and stability.

[0016] (2) Considering the communication constraints, an output feedback FOV constraint consensus control is proposed to ensure fast convergence and high control accuracy.

[0017] (3) The FOV constraint barrier function is used to solve the unmanned vehicle communication problem with limited viewing angle.

[0018] (4) Design an output feedback controller based on a second-order oscillator to solve the time-varying delay problem and to feedback the position and direction of adjacent vehicles by exchanging controller variables and. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The figure is a flow chart of an embodiment of the present invention.

[0020] Figure 2 This is a UGV intersection trajectory diagram when the initial position is given in an embodiment of the present invention.

[0021] Figure 3 The speed and angle errors of the unmanned vehicle provided in the embodiment of the present invention.

[0022] Figure 4 This is the control torque of the UGV provided in the embodiment of the present invention. DETAILED DESCRIPTION

[0023] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0024] like Figure 1 As shown, the time-varying delay output feedback control method based on the potential barrier function provided by the embodiment of the present invention includes the following steps: Step 1: Build a model of a group of N unmanned vehicles and mathematically model the unmanned vehicle formation; The model with N unmanned vehicles is expressed as: (1) (2) Where (1) and (2) represent angular motion and linear motion respectively, Indicates The translational speed of the unmanned vehicle, Indicates The angular velocity of the unmanned vehicle, mapping is a passive mapping. The vector It is expressed as: (3) Among them, := indicates assignment; and Indicates The Cartesian coordinates of the unmanned vehicle, Indicates the direction of the unmanned vehicle. Respectively express Find the first-order derivative, and is the control input, which can be expressed as: (4) In the formula, and is the torque at the wheel, denote the inertia matrix of the vehicle and the mass of the machine respectively, is the radius of the wheel, is the axle length of the wheel.

[0025] Step 2: Set control objectives and consensus expectations, assuming the field of view constraints and time delays that the autonomous vehicle must deal with.

[0026] Assume that a positioning sensor is installed on each unmanned vehicle to provide Reliable measurement value. Converge to form a formation and obtain a common direction .for and the unmanned vehicle vector relative to the unknown formation center , They represent the expected horizontal and vertical distances of the final position of the unmanned vehicle from the center of the formation, respectively, which ultimately makes the Cartesian position Converge to .

[0027] definition , then the control objective can be divided into two sub-objectives.

[0028] 1) The unmanned vehicle is required to join the formation near a non-predetermined assembly point, which can be described as: (5) 2) Each unmanned vehicle has the same angular velocity and the same heading, which can be described as: (6) Where t is the communication delay time, Public direction for unmanned vehicle platooning, is the position parameter of the unmanned vehicle.

[0029] Under the above control objectives, the following assumptions are made about the environmental constraints on the unmanned vehicle: Assumption 1: Only consider measuring coordinates and angles , assuming that The unmanned vehicle can communicate with the Here, it is generally believed that if the communication flow is bidirectional, the two unmanned vehicles and If there is a communication connection between them, the communication flow will not be lost. Therefore, the following assumptions 2 and 3 are valid.

[0030] Assumption 2: There exists Car to The time delay of a vehicle is , let the function is bounded, and its second-order derivative is also bounded. In addition, The upper bound of is known.

[0031] Assumption 3: The communication of unmanned vehicle platoons can be modeled using undirected graphs, static graphs, and connected graphs.

[0032] Step 3: Design the sliding surface considering the field of view constraint and time delay to facilitate controller design in a unified system, including the following sub-steps; Step 3.1, introduce communication constraints on errors, design a barrier function, and set a valid range for the relative angle between any two vehicles in the formation to ensure that the formation can communicate normally; The consensus error of each pair of unmanned vehicles is defined as follows: For position error: (7) For attitude error: (8) In the formula, is a parameter and , They are The steering parameters of the translational velocity and angular velocity of the unmanned vehicle and , For the Car to The time delay of a vehicle, is the communication delay time.

[0033] Step 3.2, analyze the angle error in combination with the potential barrier function so that the communication constraints of all unmanned vehicles can be processed in a unified architecture; Since speed measurement is often affected by noise and sensor failure, the present invention introduces a barrier function, which is a continuous function that can keep the control target within the field of view through two components (angle and distance), which is more conducive to communication constraints; First, define the communication constraints as follows; (9) in, Indicates Car and The relative angle of the car, Indicates approximation to; The angle-based barrier function can update the output according to the communication constraint, which is expressed as: (10) In the formula is the maximum angle between the principal axis and the incident ray, is a tunable parameter and ,set up For driverless cars Relative to Angle The distance-based barrier function is expressed as: (11) In the formula, is an adjustable parameter, is the communication radius, satisfying the condition , and due to the existence of time delay, the parameter Cannot be too small to ensure that the unmanned vehicle stays within the upper bound of the time delay within the field of view Inside.

[0034] So the potential barrier function can be expressed as ; (12) Step 3.3, analyze the position error in combination with the potential barrier function, and design the sliding surface according to the deterministic equivalent principle of linear motion dynamics; In this step, the deterministic equivalence principle of the linear motion dynamics and the angular motion dynamics of the unmanned vehicle are considered, and the sliding membrane surface is designed as: (13) In the formula, is the state variable, is a constant, Respectively express Find the first and second order derivatives, is an external input, mapping It is a passive mapping.

[0035] In order to achieve consistency in consensus error, if and , then there exists a real value , so that for all , .in, , is the force of the torsion spring. The unknown quantities are integrated using the following relationship: (14) Theorem 1: According to the model (1) and the dynamic controller (14), Assumption 2 holds, and the constant Exists in all , , , , and .

[0036] Step 4: Design an output feedback controller based on a second-order oscillator so that the system state quickly reaches the preset sliding membrane surface and maintains stable operation on this surface to achieve the consensus goal.

[0037] First, design the constraint function : (15) in, , , is the independent variable, and is the desired angle and desired speed, where Used to simplify expressions.

[0038] In this step, the overall controller will be designed. The overall control scheme is as follows: For linear motion dynamics: (16) (17) For angular motion dynamics: (18) (19) In the formula, and is the control input, is a constant, is a constant, Respectively express Find the first-order derivative.

[0039] In addition, the function has been redefined to enhance its compatibility with output feedback controllers, making the expression more convenient.

[0040] (20) In the formula is a positive number, ,function is bounded and has the characteristics of continuous motivation. Therefore, the following can be summarized: Proposition 1: The system described by Assumptions 1 to 3 operates in a closed loop with the controller given by (7), (8), and (16). In this configuration, the desired goal of leaderless consensus control defined by (5) and (6) can be achieved as long as the following conditions hold: (twenty one) (twenty two) Step 5: The expected accuracy of the time-varying delayed output feedback control method based on the barrier function is effectively proved using the Lyapunov function.

[0041] Theorem 2: For the system , are all bounded, all belong to . Further established , indicating the consensus error is essentially bounded. Therefore, it can be concluded that , which shows that The second-order derivative with respect to time is also inherently bounded. Finally, we can conclude that as time goes by, Approaching zero or .

[0042] For Theorem 2, the energy-based Lyapunov-Krasovskii functional is adopted, which is defined as follows: (twenty three) (twenty four) (25) (26) According to Theorem 2, we introduce ,function Similar to the energy function, and can be assimilated into the kinetic energy term, represents the potential energy term associated with the spring stiffness . In Equivalent to .

[0043] Will Substitution The derivative of , we get: (27) because , (28) and , we can get: (28) The final result is , known ,and ,

[0044] Then the following equation can be derived:

[0045] Finally, based on the closed-loop system, the Lyapunov-Krasovskii function is obtained.

[0046] The derivative of is: (29) For any , Is positive.

[0047] Further analysis The trajectory behavior of ≡0. Then we have the following theorem: Theorem 3: If ≡0, then , In the trajectory of are all bounded, all belong to Finally, it can be concluded that as time goes by → 0.

[0048] The proof of Theorem 3 is the same as Theorem 2, and we have: (30) (31) (32) In this case, as shown in (25), >0, and The definition is similar, Replace with ,Will Replace with . Function along The derivative of the trajectory, ≡ 0, the following conditions are met: (33) in, The positive factors of are obtained according to condition (22).

[0049] It's obvious and are bounded and belong to Therefore, according to Theorem 2, we can deduce The limit when it approaches infinity is equal to zero, that is, .

[0050] Theorem 4: In ≡0, Converging to 0 is correct. (34) (35) item Asymptotically converges to zero. Consider the first limit in (11) and make the following argument. Assume is uniformly bounded, and belong and Also belongs to In addition, considering Assumption 3, we can infer that: (37) (38) This means that, according to Barbalat's lemma, In addition, it is worth noting that

[0051] (39) To understand this, consider .because , then according to the need, So, is uniformly continuous and tends to zero. In addition, from (38), we can see that approaches zero. Therefore, it can be concluded that The conclusion is that The second limit in (11) is given by , ,and Converges to zero.

[0052] To gain a clearer understanding, according to the following equation: (40) By definition Reason , , ..., The vector and As by , , ..., The vector composed of can be expressed as: (41) like = 0 and = 0, it means , and there exists a vector , so that , or equivalently, for all , .

[0053] Therefore, it can be obtained that if , and Approaching zero, (42) (43) In order to determine the effectiveness and stability of UGV consistency control based on an ultra-wide-angle camera, the present invention uses a MATLAB simulator to perform numerical simulation experiments.

[0054] Based on the references, a UGV system suitable for the controller designed by the present invention is selected, assuming that the four UGVs (unmanned vehicles) have the same inertia and geometric parameters, which are defined as follows:

[0055] The information from the ultra-wide-angle camera will generate communication delays due to detection and calculation. In order to simulate the time-varying delay , using random signals following normal distribution , , the sampling time is 10ms. Due to the time lag and time variation, when the controller gain is large, the second-order system oscillates violently or is completely unstable, so the control gain is set to .

[0056] For all , in the Eq. (40) will Persistent Activation Function . In addition, the periodic function can be described as: The sampling time and other parameters have the same values.

[0057] The ability of the UGV cluster to reach a consensus state under the method designed by the present invention is verified through simulation experiments. The control parameters and expected values ​​are shown in Table 1.

[0058] Table 1 Control parameters and expected values

[0059] In the table, and Respectively The initial position and initial angle of each UGV, and is the vector of distance required to reach the center point.

[0060] Figure 2 This is the UGV intersection trajectory diagram when the initial position is given in the embodiment of the present invention, where the horizontal axis is The position of , the ordinate is the position of y; Figure 3 The speed and angle error of the unmanned vehicle provided in the embodiment of the present invention, the horizontal axis is time, the vertical axis of the upper part is posture, and the vertical axis of the lower part is position error; Figure 4 This is the control torque of the UGV provided in the embodiment of the present invention. The horizontal axis is time, the vertical axis of the upper part is the steering torque, and the vertical axis of the lower part is the moving torque.

[0061] from Figure 2 It can be seen that the UGV cluster can form the required formation, which means that the UGV cluster reaches the consensus state in a shorter time, proving that the scheme can successfully achieve the consensus goal.

[0062] from Figure 3 As shown, the speed and angle errors converge to about 0, indicating that the control method proposed in the present invention is effective and convergent.

[0063] from Figure 4 It can be seen that in order to keep other UGVs and the external environment within the field of view of the camera's limited observation angle, the value of the control torque will be adjusted with the viewing angle of other UGVs, which proves that it is feasible to optimize the sliding surface through the field of view constraint function.

[0064] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A time-varying delayed output feedback control method based on a potential barrier function, characterized in that: The steps include: Step 1: Create a group The dynamic model of the unmanned vehicles is used to describe the motion state of the unmanned vehicle formation; Step 2: Describe the parameters in the dynamic model through consensus control, define the control objectives of the unmanned vehicle formation, and make assumptions about the environmental constraints of the unmanned vehicles under the control objectives, where the environmental constraints include field of view constraints and time delay constraints; Step 3, combining the field of view constraint and the time delay constraint to design the sliding membrane surface, so as to facilitate the controller design in a unified system, includes the following sub-steps; Step 3.1, introduce position error and attitude error, and set the effective range for the relative angle between any two unmanned vehicles in the unmanned vehicle formation to ensure that the unmanned vehicle formation can communicate normally; Step 3.2, using the communication constraint function and the potential barrier function to perform design analysis on the angle error expectation, so that the communication constraints of all unmanned vehicles can be processed in a unified architecture; Step 3.3, analyze the position error in combination with the potential barrier function, and design the sliding surface according to the deterministic equivalent principle of linear motion dynamics; Step 4, design an output feedback controller based on a second-order oscillator so that the system state quickly reaches the preset sliding membrane surface and maintains stable operation on this sliding membrane surface to achieve the control target.

2. The time-varying delay output feedback control method based on potential barrier function according to claim 1, characterized in that: The kinetic model established in step 1 is expressed as follows: (1) (2) Where (1) and (2) represent angular motion and linear motion respectively, Indicates The translational speed of the unmanned vehicle, Indicates The angular velocity of the unmanned vehicle, vector It is expressed as: (3) Among them, := indicates assignment; and Indicates The Cartesian coordinates of the unmanned vehicle, Indicates the direction of the unmanned vehicle. Respectively express Find the first-order derivative, and is the control input, expressed as: (4) In the formula, and is the torque at the wheel, denote the inertia matrix of the vehicle and the mass of the machine respectively, is the radius of the wheel, is the axle length of the wheel.

3. The time-varying delayed output feedback control method based on potential barrier function according to claim 1, characterized in that: In step 2, it is assumed that a positioning sensor is installed on each unmanned vehicle to provide Reliable measurement value requires the unmanned vehicle to be at the meeting point Converge to form a formation and obtain a common direction ,for and the unmanned vehicle vector relative to the unknown formation center , They represent the expected horizontal and vertical distances of the final position of the unmanned vehicle from the center of the formation, respectively, which ultimately makes the Cartesian position Converge to ; definition , then the control goal is divided into two sub-goals: 1) The unmanned vehicle is required to join the formation near a non-predetermined assembly point, which is described as: (5) 2) Each unmanned vehicle has the same angular velocity and the same heading, which can be described as: (6) In the formula, is the communication delay time, Public direction for unmanned vehicle platooning, It is The Cartesian coordinates of the unmanned vehicle, Indicates the direction of the unmanned vehicle. Indicates The translational speed of the unmanned vehicle, Indicates The angular velocity of the unmanned vehicle.

4. The time-varying delayed output feedback control method based on potential barrier function according to claim 1, characterized in that: The specific implementation of step 3.1 is as follows; For position error: (7) For attitude error: (8) In the formula, is a parameter and , It is The neighboring unmanned vehicles of the unmanned vehicles are gathered together. They are The steering parameters of the translational velocity and angular velocity of the unmanned vehicle, They are The steering parameters of the translational velocity and angular velocity of the unmanned vehicle, For the The driverless car arrived The time delay of the driverless car, is the communication delay time.

5. The time-varying delay output feedback control method based on potential barrier function as claimed in claim 4, characterized in that: The communication constraints defined in step 3.2 are as follows: (9) in, It is The Cartesian coordinates of the unmanned vehicle, It is The Cartesian coordinates of the unmanned vehicle, Indicates the direction of the unmanned vehicle. Indicates The unmanned vehicle and The relative angle of the unmanned vehicle, Indicates approximation to; Angle-based barrier function Update the output according to the communication constraints, expressed as: (10) In the formula is the maximum angle between the principal axis and the incident ray, is a tunable parameter and ,set up For the The number of unmanned vehicles is The angle of an unmanned vehicle; Distance-based barrier function It is expressed as: (11) In the formula, and Respectively The unmanned vehicle and The Cartesian coordinates of the unmanned vehicle, is an adjustable parameter, R is the communication radius, satisfying the condition ; So the potential barrier function is expressed as : (12)。 6. The time-varying delay output feedback control method based on potential barrier function according to claim 5, characterized in that: In step 3.3, the synovial surface is designed as: (13) In the formula, is the state variable, is a constant, Respectively express Find the first and second order derivatives, is an external input.

7. The time-varying delay output feedback control method based on potential barrier function according to claim 6, characterized in that: The specific implementation of step 4 is as follows: First, design the constraint function : (15) in, , , is the independent variable, and is the desired angle and desired speed, Used to simplify expressions; The overall control scheme is as follows: For linear motion dynamics: (16) (17) For angular motion dynamics: (18) (19) In the formula, and is the control input, is a constant, is a constant, Respectively express Find the first-order derivative; In addition, the function It is redefined to enhance its compatibility with output feedback controllers, thus making the expression more convenient; (20) In the formula is a positive number, is a vector that satisfies , is a function of the communication delay time. , Vector for driverless car.

8. The time-varying delay output feedback control method based on potential barrier function as claimed in claim 1, characterized in that: The method also includes step 5, which uses the Lyapunov function to effectively prove the expected accuracy of the controller.

9. A time-varying delayed output feedback control system based on a potential barrier function, characterized in that: It comprises a processor and a memory, the memory is used to store program instructions, and the processor is used to call the stored instructions in the memory to execute the time-varying delay output feedback control method based on the potential barrier function as claimed in any one of claims 1 to 8.

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