A communication and control integrated design method for UAV platforms

Abstract

The present invention discloses a communication and control integrated design method for an unmanned aerial vehicle (UAV) platform, comprising the following steps: constructing a UAV dynamics model; determining a line-of-sight link based on a path loss index and a random phase, and imposing an average transmission power constraint on each node to determine the UAV's signal-to-noise ratio at time τ; maximizing the node's average transmission data rate based on a preset UAV trajectory and node transmission power; optimizing and solving the result of maximizing the node's average transmission data rate based on an accurate penalty function; and solving the problem that the existing work of the UAV only obtains segmented trajectories, cannot be tracked due to ignoring dynamic constraints, and has a more obvious degradation in communication performance, which is insufficient for the UAV-assisted data acquisition system to plan trajectories.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) data acquisition technology, and in particular to a communication and control integrated design method for an UAV platform. Background Art

[0002] In recent years, drones (UAVs) have been widely studied as aerial platforms in next-generation wireless networks. Typical examples include mobile aerial base stations, mobile data acquisition devices, aerial monitors, and aerial communication relays. Due to their high mobility and on-demand deployment, UAVs are expected to play a vital role in wireless sensor networks. Compared to ground-based base stations, using UAVs to collect data from distributed sensors offers significant flexibility and efficiency. Furthermore, UAV-assisted data collection can be applied in complex terrain, and the strong line-of-sight link established between ground terminals and UAVs can effectively improve communication performance.

[0003] At present, trajectory optimization for UAV-assisted data collection has been widely studied. Reference [1] studied the throughput maximization problem by optimizing the flight trajectory, the transmission power of the UAV and the node, and proposed a solution method based on sequential convex optimization, which is implemented in an alternating optimization manner. Reference [2] studied the UAV data collection problem by designing trajectories to maximize energy efficiency, and proposed a solution method based on state space model and sequential convex approximation technology. Reference [3] considered the scenario of a UAV collecting data from multiple sensor nodes, minimized the task execution time by optimizing the design of the UAV flight trajectory points and node allocation, and solved it using convex approximation technology. Reference [4] studied the problem of UAV collecting data from IoT devices with time constraints, maximized the number of IoT devices that can be served by designing radio resource allocation and flight trajectory, and proposed a low-complexity suboptimal solution method based on sequential convex approximation technology. In reference [5], in order to improve the average throughput and reduce the interruption probability, an offline joint design scheme of flight trajectory and transmission power was proposed. Reference [6] studied the UAV data collection problem of multiple nodes, and optimized the age of collected data by jointly designing discrete flight trajectories and node associations. Reference [7] developed a reinforcement learning-based method to optimize flight trajectory and throughput in a drone-assisted IoT system.

[0004] The authors and titles of the papers are as follows:

[0005] [1]Zeng Y, Zhang R, and Lim T, Throughput maximization for UAV-enabled mobile relaying systems.

[0006] [2]Zeng Y,and Zhang R,Energy-Efficient UAV communication withtrajectory optimization。

[0007] [3]Yuan X,Hu Y,Zhang J,and Schmeink A,Joint user scheduling and UAVtrajectory design on completion time minimization for UAV-aided datacollection。

[0008] [4]Samir M,Sharafeddine S,Assi C,Nguyen T,and Ghrayeb A,UAVtrajectory planning for data collection from time-constrained IoT devices。

[0009] [5]Feng T,Xie L,Yao J,and Xu J,UAV-enabled data collection forwireless sensor networks with distributed beamforming。

[0010] [6]Liu J,Tong P,Wang X,Bai B,and Dai H,UAV-aided data collection forinformation freshness in wireless sensor networks。

[0011] [7]Nguyen K,Duong T,Do-Duy T,Claussen H,and Hanzo L,3D UAV trajectoryand data collection optimisation via deep reinforcement learning。

[0012] Most existing research efforts have only considered the UAV's flight speed or acceleration constraints, without analyzing its motion capabilities based on the forces acting on it. This ignores the UAV's dynamic constraints. Furthermore, existing work assumes that the UAV's speed and acceleration remain constant within each time interval, resulting in an optimized trajectory consisting of a series of piecewise line segments. However, a UAV is a complex dynamic system governed by a set of differential equations, including both kinematic and dynamic equations. If the UAV's dynamic constraints are ignored in trajectory design, the planned trajectory may not be tracked well by the actual controller, resulting in severe degradation of communication performance. Summary of the Invention

[0013] In order to overcome the shortcomings of the existing technology, the present invention provides an integrated communication and control design method for UAV platforms, which solves the problems that the existing work of UAVs only obtains segmented trajectories, cannot be tracked due to ignoring dynamic constraints, and the communication performance deteriorates more significantly, and the UAV-assisted data acquisition system is insufficient in planning trajectories.

[0014] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions:

[0015] A communication and control integrated design method for an unmanned aerial vehicle platform includes the following steps:

[0016] S101, build a UAV dynamics model;

[0017] S102: Determine the line-of-sight link based on the path loss index and the random phase, and impose an average transmit power constraint on each node to determine the signal-to-noise ratio of the UAV at time τ;

[0018] S103, based on the preset UAV trajectory and node transmission power, maximize the average node transmission data rate;

[0019] S104: Optimize and solve the processing result of maximizing the average node transmission data rate based on the exact penalty function.

[0020] Furthermore, building a UAV dynamics model includes the following steps:

[0021] Set up a system with M ground nodes and multiple IoT devices. The IoT devices send the collected data to the nodes near them.

[0022] All nodes upload data to the UAV by adopting orthogonal transmission. Let M∈D{1,2,…,M} represent the set of all nodes, and let Represents the coordinates of the mth node;

[0023] The drone flies from a given starting point to a given end point at a fixed height H and a given time interval [0, T]. represents the coordinates of the UAV at time τ, where Represents a transpose operation;

[0024] At time τ, the positions of the drone on the x-axis and y-axis are set to x(τ) and y(τ), and the velocities are set to v x (τ) and v y (τ), so the expression of the UAV's dynamic model is as follows:

[0025]

[0026] Where g represents the acceleration due to gravity, α(τ) and ψ(τ) represent the angle of attack and heading, and C d Indicates the fuselage drag coefficient, m u Indicates the quality of the drone.

[0027] Furthermore, based on the path loss index and random phase, the line-of-sight link is determined, and the average transmit power constraint is imposed on each node. The signal-to-noise ratio of the UAV at time τ is determined by the following steps:

[0028] Determine line-of-sight links based on path loss exponent and random phase, and fully compensate for the Doppler effect caused by drone motion;

[0029] Assume that the data ν uploaded by the node is a circularly symmetric complex Gaussian random variable The signal transmitted by the mth node is Among them, P m (τ)≥0 is the transmission power of the mth node, and the average transmission power constraint is imposed on each node. Its expression is as follows:

[0030]

[0031] Among them, P m (τ) is the transmission power of the node, T is the task execution time, is the average transmission power of the node, τ is the time;

[0032] Based on the line-of-sight link, the node transmission signal and additive white Gaussian noise, the signal received by the drone is determined, and its expression is as follows:

[0033]

[0034] Among them, h m is the line-of-sight link, q(τ) is the signal received by the UAV, is the signal transmitted by the mth node, z is additive Gaussian white noise, φ m (τ) is the channel phase shift of the mth ground node, is the signal phase of the mth ground node;

[0035] Assume that the channel phase shift is estimated online by the node, set Constructive signal superposition is achieved at the UAV receiver, and the signal-to-noise ratio expression of the UAV at time τ is as follows:

[0036]

[0037] Among them, P m (τ) is the transmission power of the node, ν is the data uploaded by the node, a circularly symmetric complex Gaussian random variable, z is additive Gaussian white noise, To find the expected sign, β0 represents the channel power per unit length (1m), σ 2 is the noise power, represents the Euclidean distance between the UAV and the mth node, and ι is the path loss index.

[0038] Furthermore, based on the preset UAV trajectory and node transmission power, maximizing the average node transmission data rate includes the following steps:

[0039] The node adaptively changes the transmit power based on the channel bandwidth and signal-to-noise ratio;

[0040] By adaptively changing the transmission power of the node, the average transmission data rate is determined, and its expression is as follows:

[0041]

[0042] Among them, R ave (T) is the average transmission data rate, R(τ) is the node adaptive change of transmission power, and T is the task execution time;

[0043] Based on the preset UAV trajectory and node transmission power, the average node transmission data rate is maximized, and its expression is as follows;

[0044]

[0045] st(1),(2)

[0046] P m (τ)≥0,

[0047] |α(τ)|≤α max ,|ψ(τ)|≤ψ max ,

[0048]

[0049] x(0)=x0,y(0)=y0,

[0050] x(T)=x_F,y(T)=y_F.

[0051] in, is the trajectory of the UAV, P m (τ) is the transmission power of the node, α(τ) and ψ(τ) represent the angle of attack and heading angle respectively, and the trajectory of the UAV Depend on Determine that the position of the drone on the x-axis and y-axis are x(τ) and y(τ), and the speed is v x (τ) and v y (τ), α max is the maximum value of the attack angle, ψ max is the maximum value of the angle of attack, x0 and y0 are the horizontal and vertical coordinates of the initial point on the x-axis and y-axis respectively, and are the initial velocities in the x-axis and y-axis directions, respectively. F and y F Expressed as the horizontal and vertical coordinates of the end point on the x-axis and y-axis respectively.

[0052] Furthermore, optimizing and solving the result of maximizing the average node transmission data rate based on the exact penalty function includes the following steps:

[0053] Optimize the UAV dynamics model based on the state vector and the control vector to obtain the optimized UAV dynamics model;

[0054] The optimized UAV dynamics model is abbreviated as in, Indicates the differentiation of the state vector, g() is the function, τ is the time, Indicates that the control parameterization The state vector under is the control parameter;

[0055] The new objective function constructed based on the exact penalty function handles continuous inequality state constraints, and its expression is as follows:

[0056]

[0057] Where ∈ represents the introduced decision variable, R ave (T) represents the average transmission rate calculated using time discretization, represents the velocity continuity constraint violation, Denotes the terminal constraint violation, which are expressed as:

[0058]

[0059] Among them, E i ∈(0,1),i=1,2,...,M+1 are all given constants, is the penalty parameter, δ, γ, β are constants satisfying δ>0, γ>0, β>2;

[0060] Therefore, problem (P1) is transformed into the following problem form:

[0061]

[0062] st(13),0≤∈≤∈ max ,

[0063]

[0064] x(0)=x0,y(0)=y0,

[0065] Among them, ∈ max is the maximum value of the optimization variable, For the i-th control variable in [τ n-1 ,τ n ], α max is the maximum value of the attack angle, ψ max is the maximum value of the angle of attack, x0 and y0 are the coordinates of the initial point on the x-axis and y-axis respectively, and are the magnitudes of the initial velocity in the x-axis and y-axis directions respectively;

[0066] Calculate Relative to and the gradient of ∈, the problem can be obtained by standard optimization methods such as sequential quadratic programming In addition, the local optimal solution of problem (P1) can be obtained by adjusting ∈ and To solve a series of problems get.

[0067] Furthermore, optimizing the UAV dynamics model based on the state vector and the control vector includes the following steps:

[0068] The state vector s(τ) is defined as follows:

[0069]

[0070] Among them, x(τ) and y(τ) are the coordinates of the drone on the x-axis and y-axis respectively, and v x (τ) and v y (τ) are the components of the UAV velocity in the x-axis and y-axis respectively;

[0071] The control vector c(τ) is defined as follows:

[0072]

[0073] Among them, α(τ) is the angle of attack of the UAV, ψ(τ) is the angle of attack of the UAV, and P(τ) represents the UAV transmission power vector. P i (τ),i=1,...,M is the transmission power of the UAV to the i-th ground node;

[0074] The time interval [0, T] is discretized into N subintervals [τ n-1 ,τ n ), The length of each subinterval is ξ = T / N;

[0075]

[0076] Shows the control variable c i (τ) in the subinterval [τ n-1 ,τ n ) on the approximation value;

[0077] In the subinterval [τ n-1 ,τ n ) is converted into the following expression:

[0078]

[0079] Among them, at time τ, the position and velocity of the drone on the x-axis and y-axis are x(τ), y(τ), v x (τ) and v y (τ), To find the derivative with respect to x(τ), is the derivative with respect to y(τ), g is the acceleration due to gravity, C t is the fuselage drag coefficient, m u is the weight of the drone, The attack angle of the UAV in the subinterval [τ n-1 ,τ n ) on the value, The angle of attack of the UAV in the subinterval [τ n-1 ,τ n ) on the value.

[0080] The beneficial effects of this application are: setting the transmission power and flight trajectory of each node based on dynamic constraints to improve the average data transmission rate;.

[0081] By jointly designing the transmit power and flight trajectory of each node while taking into account dynamic constraints, the average transmission data rate is maximized. A state-space model is used to address the challenges posed by dynamic constraints, transforming the initial optimization problem into a state-constrained dynamic programming problem. Control parameterization techniques are then combined with an exact penalty function approach. Furthermore, a high-quality solution can be obtained using existing numerical algorithms, such as sequential quadratic programming. Compared to existing drone-assisted data collection efforts, the drone trajectories obtained by the proposed method are easier to follow and exhibit minimal degradation in communication performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0083] Figure 1 This is a schematic diagram of the steps of a communication and control integrated design method for UAV platforms of the present invention;

[0084] Figure 2 This is a schematic diagram of the planned trajectory and actual trajectory in the literature [5];

[0085] Figure 3 are the planned trajectory and actual trajectory of the method proposed in this invention;

[0086] Figure 4 It is a comparison curve of average data throughput under different task times. DETAILED DESCRIPTION

[0087] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0088] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0089] Example 1:

[0090] A communication and control integrated design method for an unmanned aerial vehicle platform includes the following steps:

[0091] S101, build a UAV dynamics model;

[0092] Building a UAV dynamics model involves the following steps:

[0093] Set up a system with M ground nodes and multiple IoT devices. The IoT devices send the collected data to the nodes near them.

[0094] All nodes upload data to the UAV by adopting orthogonal transmission. Let M∈D{1,2,…,M} represent the set of all nodes, and let Represents the coordinates of the mth node;

[0095] The drone flies from a given starting point to a given end point at a fixed height H and a given time interval [0, T]. represents the coordinates of the UAV at time τ, where Represents a transpose operation;

[0096] At time τ, the positions of the drone on the x-axis and y-axis are set to x(τ) and y(τ), and the velocities are set to v x (τ) and v y (τ), so the expression of the UAV's dynamic model is as follows:

[0097]

[0098] Where g represents the acceleration due to gravity, α(τ) and ψ(τ) represent the angle of attack and heading, and C d Indicates the fuselage drag coefficient, m u Indicates the quality of the drone.

[0099] S102: Determine the line-of-sight link based on the path loss index and the random phase, and impose an average transmit power constraint on each node to determine the signal-to-noise ratio of the UAV at time τ;

[0100] Determining the line-of-sight link based on the path loss index and random phase, and imposing an average transmit power constraint on each node, determining the signal-to-noise ratio of the drone at time τ includes the following steps:

[0101] The line-of-sight link is determined based on the path loss exponent and random phase, and the Doppler effect caused by the UAV motion is fully compensated. The expression for determining the line-of-sight link based on the path loss exponent and random phase is as follows:

[0102]

[0103] Among them, h m For line-of-sight links, represents the Euclidean distance between the UAV and the mth node, φ m (τ) is the random phase, ι is the path loss exponent, h m For line-of-sight links, β0 represents the channel power per unit length (1 m);

[0104] Assume that the data ν uploaded by the node is a circularly symmetric complex Gaussian random variable The signal transmitted by the mth node is Among them, P m (τ)≥0 is the transmission power of the mth node, and the average transmission power constraint is imposed on each node. Its expression is as follows:

[0105]

[0106] Among them, P m (τ) is the transmission power of the node, T is the task execution time, is the average transmission power of the node, τ is the time;

[0107] Based on the line-of-sight link, the node transmission signal and additive white Gaussian noise, the signal received by the drone is determined, and its expression is as follows:

[0108]

[0109] Among them, h m is the line-of-sight link, q(τ) is the signal received by the UAV, is the signal transmitted by the mth node, z is additive Gaussian white noise, φ m (τ) is the channel phase shift of the mth ground node, is the signal phase of the mth ground node;

[0110] Assume that the channel phase shift is estimated online by the node, set Constructive signal superposition is achieved at the UAV receiver, and the signal-to-noise ratio expression of the UAV at time τ is as follows:

[0111]

[0112] Among them, P m (τ) is the transmission power of the node, ν is the data uploaded by the node, a circularly symmetric complex Gaussian random variable, z is additive Gaussian white noise, To find the expected sign, β0 represents the channel power per unit length (1m), σ 2 is the noise power, represents the Euclidean distance between the UAV and the mth node, and ι is the path loss index.

[0113] S103, based on the preset UAV trajectory and node transmission power, maximize the average node transmission data rate;

[0114] Based on the preset UAV trajectory and node transmit power, maximizing the average node transmission data rate includes the following steps:

[0115] The node adaptively changes the transmit power based on the channel bandwidth and signal-to-noise ratio, and its expression is as follows:

[0116] R(τ)=Blog2(1+SNR(τ))

[0117] Where B represents the channel bandwidth, SNR(τ) is the signal-to-noise ratio of the UAV at time τ, and R(τ) is the node’s adaptively changed transmit power.

[0118] By adaptively changing the transmission power of the node, the average transmission data rate is determined, and its expression is as follows:

[0119]

[0120] Among them, R ave (T) is the average transmission data rate, R(τ) is the node adaptive change of transmission power, T is;

[0121] Based on the preset UAV trajectory and node transmission power, the average node transmission data rate is maximized; for example, through the UAV trajectory and the node's transmit power To maximize the average transmission data rate R ave (T), whose expression is as follows:

[0122]

[0123] st(1),(2)

[0124] P m (τ)≥0,

[0125] |α(τ)|≤α max ,|ψ(τ)|≤ψ max ,

[0126]

[0127] x(0)=x0,y(0)=y0,

[0128] x(T)=x_F,y(T)=y_F.

[0129] in, is the trajectory of the UAV, P m (τ) is the transmission power of the node, α(τ) and ψ(τ) represent the angle of attack and heading angle respectively, and the trajectory of the UAV Depend on Determine that the position of the drone on the x-axis and y-axis are x(τ) and y(τ), and the speed is v x (τ) and v y (τ), α max is the maximum value of the attack angle, ψ max is the maximum value of the angle of attack, x0 and y0 are the horizontal and vertical coordinates of the initial point on the x-axis and y-axis respectively, and are the initial velocities in the x-axis and y-axis directions, respectively. F and y F Expressed as the horizontal and vertical coordinates of the end point on the x-axis and y-axis respectively.

[0130] S104, optimizing and solving the processing result of maximizing the average transmission data rate of the nodes based on the precise penalty function;

[0131] Based on the exact penalty function, the average node transmission data rate maximization processing result is optimized and solved.

[0132] The following steps are included:

[0133] Optimize the UAV dynamics model based on the state vector and the control vector to obtain the optimized UAV dynamics model;

[0134] The optimized UAV dynamics model is abbreviated as Indicates the differentiation of the state vector, g(·) is the function, τ is the time, Indicates that the control parameterization The state vector under is the control parameter;

[0135] The new objective function constructed based on the exact penalty function handles continuous inequality state constraints, and its expression is as follows:

[0136]

[0137] Where ∈ represents the introduced decision variable, R ave (T) represents the average transmission rate calculated using time discretization, represents the velocity continuity constraint violation, Denotes the terminal constraint violation, which are expressed as:

[0138]

[0139] Among them, E i ∈(0,1),i=1,2,...,M+1 are all given constants, is the penalty parameter, δ, γ, β are constants satisfying δ>0, γ>0, β>2;

[0140] Therefore, problem (P1) is transformed into the following problem form:

[0141]

[0142] st(13),0≤∈≤∈ max ,

[0143]

[0144] x(0)=x0,y(0)=y0,

[0145] Among them, ∈ max is the maximum value of the optimization variable, For the i-th control variable in [τ n-1 ,τ n ], α max is the maximum value of the attack angle, ψ max is the maximum value of the angle of attack, x0 and y0 are the coordinates of the initial point on the x-axis and y-axis respectively, and are the magnitudes of the initial velocity in the x-axis and y-axis directions respectively;

[0146] Calculate Relative to and the gradient of ∈, the problem can be obtained by standard optimization methods such as sequential quadratic programming In addition, the local optimal solution of problem (P1) can be obtained by adjusting ∈ and To solve a series of problems get.

[0147] Optimizing the UAV dynamics model based on the state vector and control vector includes the following steps:

[0148] The state vector s(τ) is defined as follows:

[0149]

[0150] Among them, x(τ) and y(τ) are the coordinates of the drone on the x-axis and y-axis respectively, and v x (τ) and v y (τ) are the components of the UAV velocity in the x-axis and y-axis respectively;

[0151] The control vector c(τ) is defined as follows:

[0152]

[0153] Among them, α(τ) is the angle of attack of the UAV, ψ(τ) is the angle of attack of the UAV, and P(τ) represents the UAV transmission power vector. P i (τ),i=1,...,M is the transmission power of the UAV to the i-th ground node;

[0154] The time interval [0, T] is discretized into N subintervals [τ n-1 ,τ n ), The length of each subinterval is ξ = T / N;

[0155]

[0156] Shows the control variable c i (τ) in the subinterval [τ n-1 ,τ n ) on the approximation value;

[0157] In the subinterval [τ n-1 ,τ n ) is converted into the following expression:

[0158]

[0159] Among them, at time τ, the position and velocity of the drone on the x-axis and y-axis are x(τ), y(τ), v x (τ) and v y (τ), To find the derivative with respect to x(τ), is the derivative with respect to y(τ), g is the acceleration due to gravity, C t is the fuselage drag coefficient, m u is the weight of the drone, The attack angle of the UAV in the subinterval [τ n-1 ,τ n ) on the value, The angle of attack of the UAV in the subinterval [τ n-1 ,τ n ) on the value.

[0160] For example, a multi-agent system consists of 1 UAV and 10 ground nodes in a two-dimensional space. The positions of the ground nodes are o1 = [20m, 10m], o2 = [30m, 28m], o3 = [46m, 0], o4 = [56m, 24m], o5 = [94m, 168m], o6 = [100m, 200m], o7 = [112m, 176m], o8 = [162m, 0], o9 = [178m, 40m] and o 10 =[200m,6m]. To demonstrate the superiority of the proposed scheme, the planning method in [5] that does not consider the dynamic model (1) is used as a benchmark for comparison. Unless otherwise specified, the parameters of the problem are shown in Table 1.

[0161] Table 1 Simulation parameter settings

[0162]

[0163] When the task time is set to 40 seconds, Figure 1 and Figure 2 The planned trajectory and actual trajectory obtained by the proposed method and reference [5] are plotted respectively. The actual trajectory is obtained by tracking the planned trajectory using a proportional-integral-derivative controller, which is constructed using a six-degree-of-freedom model. The actual trajectory is obtained as shown in Figure 3 As shown. Figure 1 and Figure 2 As shown in Figure 2, the planned trajectories of both schemes attempt to approach the node during the flight from the starting point to the end point in order to improve the system throughput. However, compared with the scheme proposed in this paper, the actual trajectory of the reference [5] fails to reach the end point. This is because the UAV dynamics are ignored, so the UAV controller cannot fully track the planned trajectory to complete the main task.

[0164] The average throughput of different trajectories varies with time. Figure 4 As shown. Figure 4 As shown in Figure 5, the average throughput increases gradually with time. In addition, the average throughput of the planned trajectory under both schemes is higher than the average throughput of the actual trajectory. This further confirms that if the dynamic constraints are ignored or simplified, performance may degrade, but the performance loss of the proposed method is much smaller than that of the literature [5], showing the superiority of the proposed method.

[0165] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0166] The terms "first", "second" and "third" etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can, for example, be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0167] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0168] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0169] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0170] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A communication and control integrated design method for UAV platforms, characterized in that: The following steps are involved: S101. Constructing a UAV dynamics model includes the following steps: set up M A system with a ground node and multiple IoT devices, where the IoT devices send the collected data to the nodes nearby; All nodes upload data to the UAV by adopting orthogonal transmission. Let M∈D{1,2,…,M} represent the set of all nodes, and let Indicates the m The coordinates of the nodes; Drone at a fixed altitude H and a given time interval [0, T ]Fly from the given starting point to the given end point, let T ∈[0, T ],make q ( τ )=( x ( τ ), y ( τ )) indicates that the drone is at time τ The coordinates of , where (·) represents the transpose operation; exist τ At this moment, set the drone to x axis, y The positions of the axes are x ( τ )and y ( τ ), the speeds are v x ( τ )and v y ( τ ), so the expression of the UAV's dynamic model is as follows: in, g represents the acceleration due to gravity, α ( τ )and ψ ( τ ) represents the angle of attack and heading angle, C d represents the fuselage drag coefficient, m u Indicates the quality of the drone; S102, determine the line-of-sight link based on the path loss index and random phase, and impose an average transmission power constraint on each node to determine the UAV's τ The signal-to-noise ratio at each moment; S103, based on the preset UAV trajectory and node transmission power, maximize the average node transmission data rate; S104, optimizing and solving the processing result of maximizing the average node transmission data rate based on the exact penalty function, including the following steps: Optimize the UAV dynamics model based on the state vector and the control vector to obtain the optimized UAV dynamics model; The optimized UAV dynamics model is abbreviated as ;in, represents the differentiation of the state vector, g (·) is a function, τ For time, Indicates that the control parameterization The state vector under is the control parameter; The new objective function constructed based on the exact penalty function handles continuous inequality state constraints, and its expression is as follows: in represents the introduced decision variables, R ave ( T ) represents the average transmission rate calculated using time discretization, represents the velocity continuity constraint violation, Denotes the terminal constraint violation, which are expressed as: in, E i ∈(0,1), i =1,2,…, M +1 are all given constants, is the penalty parameter, δ 、 γ 、 β is satisfied δ >0, γ >0, β A constant > 2; Therefore, problem (P1) is transformed into the following problem form: in, is the maximum value of the optimization variable, For the i The control variables are [ τ n-1 ,τ n ], α max is the maximum value of the attack angle, ψ max is the maximum value of the angle of attack, x 0 and y 0 are the initial points x Axis and y The horizontal and vertical coordinates of the axis, v x 0 and v y 0 The initial speed is x Axis and y Size in the axial direction; Calculate Relative to and The gradient of the problem can be obtained by standard optimization methods such as sequential quadratic programming In addition, the local optimal solution of problem (P1) can be obtained by adjusting and To solve a series of problems get; Optimizing the UAV dynamics model based on the state vector and control vector includes the following steps: Define the state vector s ( τ ) is as follows: in, x ( τ )and y ( τ ) are the coordinates of the drone on axis and axis respectively, v x ( τ )and v y ( τ ) are the speeds of the drone at x Axis and y The weight of the axis; Define the control vector c ( τ ) is as follows: in, α ( τ ) is the angle of attack of the UAV, ψ ( τ ) is the angle of attack of the UAV, represents the UAV transmission power vector, , P i ( τ )=1, …, M For drones i The transmit power of each ground node; The time interval [0, T ] is discretized into N Subintervals [ τ n-1 ,τ n ), , the length of each subinterval is ξ = T / N ; The control variable c i ( τ ), Approximately , in, represents the characteristic function, which is expressed as , Indicates the i The control variables are in the sub-interval [ τ n-1 ,τ n ) on the approximation value; In the subinterval [ τ n-1 ,τ n ) is converted into the following expression: Among them, τ At this moment, the drone is x axis, y The position and speed of the axis are x ( τ ), y ( τ ), v x ( τ )and v y ( τ ), For about x ( τ ) to find the derivative, For about y ( τ ) to find the derivative, g is the acceleration due to gravity, C t is the fuselage drag coefficient, m u is the weight of the drone, The angle of attack of the UAV in the subinterval [ τ n-1 ,τ n ) on the value, The angle of attack of the UAV is in the subinterval [ τ n-1 ,τ n ) on the value.

2. The communication and control integrated design method for UAV platforms according to claim 1 is characterized in that: The line-of-sight link is determined based on the path loss index and random phase, and the average transmission power constraint is imposed on each node to determine the UAV τ The signal-to-noise ratio at a given moment includes the following steps: Determine line-of-sight links based on path loss exponent and random phase, and fully compensate for the Doppler effect caused by drone motion; Set the data uploaded by the node v is a circularly symmetric complex Gaussian random variable , No. m The signal transmitted by the node is ,in, P m ( τ )≥0 is the m The transmission power of each node is calculated and the average transmission power constraint is imposed on each node. The expression is as follows: in, P m ( τ ) is the node’s transmit power, T is the task execution time, is the average transmit power of the node, τ For time; Determine the signal received by the drone based on the line-of-sight link, the node transmission signal and additive white Gaussian noise; Assume that the channel phase shift is estimated online by the node, set φ m ( τ )=- ϕ m ( τ ) constructive signal superposition is achieved at the UAV receiver, and the UAV τ The expression of the signal-to-noise ratio at the moment is as follows: in, P m ( τ ) is the node’s transmit power, v The data uploaded by the node is a circularly symmetric complex Gaussian random variable, z is additive white Gaussian noise, To find the expected sign, β 0 represents the channel power per unit length (1m), σ 2 is the noise power, Indicates drone and m The Euclidean distance between nodes, l is the path loss index.

3. The communication and control integrated design method for UAV platforms according to claim 2 is characterized in that: The expression for determining the line-of-sight link based on the path loss exponent and the random phase is as follows: in, h m ( τ ) is the signal link, ϕ m ( τ ) is a random phase, h m It is a line-of-sight link.

4. The communication and control integrated design method for UAV platforms according to claim 2 is characterized in that: The signal received by the drone is determined based on the line-of-sight link, the node transmission signal and the additive white Gaussian noise. The expression is as follows: in, h m For line-of-sight links, q ( τ ) is the signal received by the drone, z is additive white Gaussian noise, , ϕ m ( τ ) is the m The ground node channel phase shift, φ m ( τ ) is the m The signal phase of each ground node.

5. The communication and control integrated design method for UAV platforms according to claim 1 is characterized in that: The method of maximizing the average node transmission data rate based on the preset UAV trajectory and node transmission power includes the following steps: The node adaptively changes the transmit power based on the channel bandwidth and signal-to-noise ratio; By adaptively changing the transmit power of the nodes, the average transmission data rate is determined; Based on the preset UAV trajectory and node transmission power, the average node transmission data rate is maximized.

6. The communication and control integrated design method for UAV platforms according to claim 5 is characterized in that: The average transmission data rate is determined by adaptively changing the transmission power of the node, and its expression is as follows: in, R ( τ ) is the node adaptively changing the transmission power, T The task execution time.

7. The communication and control integrated design method for UAV platforms according to claim 5, characterized in that: Based on the preset UAV trajectory and node transmission power, the average node transmission data rate is maximized, and its expression is as follows: in, is the trajectory of the drone, P m ( τ ) is the transmission power of the node, the trajectory of the UAV Depend on Decision, drone in x axis, y The positions of the axes are x ( τ )and y ( τ ), the speeds are v x ( τ )and v y ( τ ), α max is the maximum value of the attack angle, ψ max is the maximum value of the angle of attack, x 0 and y 0 are the initial points x Axis and y The horizontal and vertical coordinates of the axis, v x 0 and v y 0 The initial speed is x Axis and y The size of the axis, x F and y F Respectively, the end points are x Axis and y The horizontal and vertical coordinates of the axis.

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