UWB-based finite time unmanned aerial vehicle cooperative control method

Abstract

The invention relates to the technical field of unmanned aerial vehicles, and discloses a UWB-based finite time unmanned aerial vehicle cooperative control method, which comprises the following steps: acquiring relative position data of an unmanned aerial vehicle; designing a virtual piloting target, and calculating the relative position expectation of each unmanned aerial vehicle at each moment; designing a finite time controller model; designing a trajectory tracking controller model; an unmanned aerial vehicle cooperative control system is divided into an outer ring and an inner ring for respective control. According to a task flight planning application data structure, it is guaranteed that a certain distance is kept during cooperation of multiple unmanned aerial vehicle formations, nonlinear control convergence of relative trajectory errors is achieved by designing a finite time controller, the unmanned aerial vehicles are controlled to be converged to be kept at the relative distance within set time, and the unmanned aerial vehicle formations are controlled to be coordinated. Therefore, formation operation in a complex scene is realized. According to the invention, the unmanned aerial vehicle can quickly reach a task target area and realize formation keeping or changing in finite time, and task development is supported.

Description

Technical Field

[0001] The present invention relates to the technical field of high - end equipment manufacturing, especially the technical field of unmanned aerial vehicles, and specifically discloses a finite - time cooperative control method for unmanned aerial vehicles based on UWB. Background Technique

[0002] Quadrotor Unmanned Aerial Vehicles (QUAVs) have the characteristics of vertical take - off and landing, simple structure, strong maneuverability, etc. Therefore, they have broad application prospects in the fields of rescue, surveillance, inspection, surveying and mapping, etc., attracting extensive attention from the industrial and academic circles. However, a quadrotor is a typical under - actuated, strongly - coupled non - linear system. When multiple unmanned aerial vehicles perform tasks and conduct formation cooperative control, it is often necessary to complete it within a finite time.

[0003] Ultra Wide Band (UWB) technology is a wireless carrier communication technology. It does not use sinusoidal carriers, but uses nanosecond - level non - sinusoidal narrow pulses to transmit data. Therefore, the spectrum range it occupies is very wide. UWB technology has the advantages of low system complexity, low transmit signal power spectral density, being insensitive to channel fading, low interception ability, high positioning accuracy, etc. It is especially suitable for high - speed wireless access in indoor and other dense multipath places, and can provide relative positioning and mutual transmission of position information for unmanned aerial vehicles. Summary of the Invention

[0004] The present invention provides a finite - time cooperative control method for unmanned aerial vehicles based on UWB, including the following steps:

[0005] S1. Obtain the relative position data of the unmanned aerial vehicles;

[0006] S2. Design a virtual leader target, use the relative position data of the unmanned aerial vehicles obtained in S1, set the relative position targets of each unmanned aerial vehicle relative to the virtual leader target, take the relative position of the initial formation as the expectation, and calculate the relative position expectation of each unmanned aerial vehicle at each moment;

[0007] S3. Design a finite - time controller model according to the relative position expectation;

[0008] S4. Design a trajectory - tracking controller model according to the finite - time controller model designed in S3;

[0009] S5. According to the trajectory - tracking controller model designed in S4, divide the unmanned aerial vehicle cooperative control system into an outer loop and an inner loop, and control them separately.

[0010] Further, S3 includes:

[0011] S3.1. Establish a finite - time controller model and abstract it into a double - integral system:

[0012]

[0013] where x = (x1, x2) T is the state, x1 is the relative position error, x2 is the change rate of the relative position error, u is the desired thrust of the UAV motor, and d(t) is a bounded disturbance;

[0014] S3.2. Calibrate the finite-time controller model using the data to determine whether the internal relevant parameters and the characterization relationship meet the data.

[0015] Furthermore, S4 includes:

[0016] S4.1. Define the trajectory tracking controller model according to the finite-time controller model in S3:

[0017]

[0018] where k1, k2, α, α1, and α2 are all control debugging parameters, k1 > 0, k2 > 0, 0 < α < 1, and α2 = 2α1 / (1 + α2);

[0019] S4.2. Perform simulation optimization according to the calibrated finite-time controller model in S3.2.

[0020] Furthermore, S5 includes:

[0021] The outer loop is controlled using the finite-time controller model in S3;

[0022] The inner loop is controlled using PID, and the speed attitude and speed control laws are:

[0023]

[0024] where U(t) is the thrust of the corresponding four motors, err(t) is the error between the inner loop state quantity changing with time and the desired value, kp is the proportional control parameter, T i is the integral time, and T d is the differential time.

[0025] The present invention uses a UAV equipped with a UWB module to obtain relative positioning position information. At the same time, a finite-time UAV cooperative control method is designed to achieve the formation mission flight operation of the UAV. The UAV can quickly reach the mission target area within a finite time and maintain or change the formation shape, supporting the mission implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is the control system structure diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments. Unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the ordinary meanings understood by those of ordinary skill in the field to which the present invention belongs.

[0028] Install a UWB positioning module on the drone, and install a UWB receiving base station on the ground. The relative distance can be measured and each drone can be notified to plan the relative position relationship of each drone. The three-dimensional relative distance between each drone can be located through the UWB positioning module, and can be broadcast to each drone and transmitted to the drone flight controller through the relevant interface. The drone is equipped with a UWB module, and information broadcasting can be set. When the drones approach each other, the positions can be transmitted to each other to obtain the positioning information such as the position and speed of the surrounding cooperative drones.

[0029] In a specific embodiment of the present invention, the following steps are included:

[0030] S1. Use UWB (Ultra-Wideband) technology to obtain the relative position data of the drones.

[0031] The UWB positioning system performs two-way time-of-flight (ToF) ranging between the base stations deployed in the environment and the tags on the drones to achieve high-precision relative position measurement. Combining the multi-base station layout, the TDOA (Time Difference of Arrival) or AOA (Angle of Arrival) algorithm can be used to calculate the position of the drone relative to the virtual leader target. At the same time, the Kalman Filter or Unscented Kalman Filter (UKF) is used to optimize the measurement data to reduce noise interference and improve the positioning accuracy and stability. The obtained UWB relative position data will be used as the basic input for the drone formation control and provide support for setting the relative position reference for the virtual leader target in the subsequent steps.

[0032] S2. Design a virtual leader target that moves according to the designed mission track. Use the relative position information obtained by UWB in step S1 to set the relative position target of each drone relative to the virtual leader target and convert it into the relative position quantity relative to other drones.

[0033] In a specific embodiment of the present invention, a virtual leader target is designed to achieve mission track planning, with the relative position of the initial formation as the expectation, and the relative position expectation of each drone is calculated at each moment.

[0034] S3. Design a finite-time controller model according to the relative position expectation to achieve relative position control within a finite time, which mainly includes the following steps:

[0035] S3.1. Establish a finite-time controller model and abstract it into a double-integral system:

[0036]

[0037] where \(x=(x_1,x_2)\) T is the state, \(x_1\) is the relative position error, \(x_2\) is the relative position error change rate, \(u\) is the expected thrust of the UAV motor, which is the control quantity, and \(d(t)\) is the bounded disturbance;

[0038] S3.2. Calibrate the finite-time controller model using data to determine whether the internal relevant parameters and the characterization relationship meet the data.

[0039] S4. Design a trajectory tracking controller, including:

[0040] S4.1. Define a trajectory tracking controller model according to the finite-time controller model in step S3:

[0041]

[0042] where \(k_1>0\), \(k_2>0\), \(0 < \alpha < 1\), \(\alpha_2 = 2\alpha_1 / (1 + \alpha_2)\), and \(k_1\), \(k_2\), \(\alpha\), \(\alpha_1\), and \(\alpha_2\) are all control debugging parameters. The trajectory tracking controller can ensure that the Lyapunov stability hypothesis within a finite time is satisfied, enabling the UAV to converge to the relative position.

[0043] S4.2. According to the calibrated finite-time controller model in S3.2, perform simulation optimization to confirm each control parameter and ensure that the simulation results meet the task requirements.

[0044] In a specific embodiment of the present invention, actual flight verification is carried out in step 4.

[0045] S5. According to the trajectory tracking controller model in S4, divide the UAV cooperative control system into an outer loop and an inner loop, and control them separately.

[0046] According to the trajectory tracking controller model in S4, divide the UAV cooperative control system into two parts: an outer loop and an inner loop. The control system structure diagram is as Figure 1 shown.

[0047] The outer loop is controlled using the finite-time controller model in S3, and the relative position error and the relative position error change rate are defined as \(x_1\) and \(x_2\) respectively.

[0048] The inner loop is controlled using traditional PID, and the speed attitude and speed control laws are as follows:

[0049]

[0050] Among them, U(t) is the thrust of the corresponding four motors, err(t) is the error between the inner-loop state quantity changing with time and the expectation, kp is the proportional control parameter, T i is the integral time, T d is the differential time.

[0051] The expectation of the inner loop is the desired speed and angle of the UAV input by the outer loop, and the control quantity is the pulling force of the UAV. Appropriate control parameters are set for the above formula to achieve the control of the inner loop of the UAV.

[0052] In a specific embodiment of the present invention, through simulation optimization, the tuning of each inner-loop PID parameter is realized to meet the task execution requirements.

[0053] In a specific embodiment of the present invention, a coordinate system is established, and the UAV model is defined as:

[0054]

[0055] Among them, x, y, and z represent the position coordinates of the UAV in the inertial coordinate system, represents the speed of the UAV in the inertial coordinate system (i.e., the derivative of the position with respect to time), is the acceleration of the UAV, u1 is the total thrust, m is the mass of the UAV, φ, θ, ψ represent the Euler angles of the UAV, represents the angular velocity of the UAV, represents the angular acceleration of the UAV, k x , k y , k z represent the air resistance coefficients, f x , f y , f z represent the external forces, d x (t), d y (t), d z (t) represents additional random disturbances or modeling errors.

[0056] The trajectory tracking controller model is:

[0057]

[0058] Among them, x1 is the relative position error, x2 is the relative position error change rate, both are uniformly represented in vector form, and the desired thrust u of the UAV motor can be solved. The desired thrust u of the UAV motor is input into the inner loop, and the speed attitude and speed control law are:

[0059]

[0060] According to the above speed attitude and speed control laws, the control quantities u1 to u4 are solved, that is, the expected thrusts of the four motors of the UAV, so as to realize the control of the UAV.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A finite-time UAV cooperative control method based on UWB, characterized in that An ultra-wideband (UWB) positioning module is installed on the unmanned aerial vehicle (UAV), which includes the following steps: S1. Obtain the relative position data of the UAVs; S2. Design a virtual leading target. Using the relative position data of the UAVs obtained in S1, set the relative position targets of each UAV relative to the virtual leading target, and calculate the relative position expectations of each UAV at each moment with the relative positions of the initial formation as the expectations; S3. Design a finite-time controller model based on the relative position expectations; S4. Design a trajectory tracking controller model based on the finite-time controller model designed in S3; S5. According to the trajectory tracking controller model designed in S4, divide the UAV cooperative control system into an outer loop and an inner loop, and control them separately.

2. The finite-time UAV cooperative control method based on UWB according to claim 1, characterized in that S3 It includes: S3.

1. Establish a finite-time controller model and abstract it as a double-integral system: where \(x=(x_1,x_2)\) T is the state, \(x_1\) is the relative position error, \(x_2\) is the change rate of the relative position error, \(u\) is the desired thrust of the UAV motor, and \(d(t)\) is a bounded disturbance; S3.

2. Calibrate the finite-time controller model using data to determine whether the internal relevant parameters and the characterization relationship meet the data.

3. A finite-time UAV cooperative control method based on UWB according to claim 2, characterized in that S4 It includes: S4.

1. Define a trajectory tracking controller model according to the finite-time controller model in S3: where k1, k2, α, α1, and α2 are all control debugging parameters, k1>0, k2>0, 0<α<1, and α2 = 2α1 / (1 + α2); S4.

2. Perform simulation optimization according to the calibrated finite-time controller model in S3.

2.

4. A finite-time UAV cooperative control method based on UWB according to claim 3, wherein S5 It includes: The outer loop is controlled using the finite-time controller model in S3; The inner loop is controlled using PID, and the speed attitude and speed control laws are: Among them, U(t) is the thrust of the corresponding four motors, err(t) is the error between the inner-loop state quantity changing with time and the expectation, kp is the proportional control parameter, T i is the integral time, and T d is the differential time.

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