CN117240342B - General sensing and control integrated method in industrial Internet of things - Google Patents
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Abstract
The invention provides a communication and control integrated method in an industrial Internet of things, which belongs to the technical field of wireless communication and comprises the following steps: introducing the unmanned aerial vehicle into an industrial Internet of things system, and providing a communication model of a communication, perception and control integrated method; the sensing system and the control system are introduced to improve the communication performance of the industrial Internet of things, and the data transmission rate of the industrial Internet of things system is improved by adopting a communication, sensing and control integrated method and a control activation method; the method comprises the steps of providing a non-convex problem of combining and optimizing automatic guided trolley communication power and wireless access point perceived wave beams to maximize the ratio of communication rate to total power consumption, introducing a punishment term and a Taylor expansion method to convert the non-convex problem into a convex problem, and designing a double-layer continuous convex approximation method based on the punishment term to improve the communication rate of an industrial Internet of things system and reduce the total power consumption of the system. According to the invention, the unmanned aerial vehicle can be perceived under the condition that the communication rate is reduced due to the change of the channel condition, so that the motion control or the antenna angle control can be realized.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a communication and control integrated method in the industrial Internet of things.
Background
In industrial internet of things systems, sensors are widely deployed to collect sensory data, which is then passed back over a wireless channel for processing by a controller. Generally, the wider the coverage, the more the number of sensors, the greater the difficulty of data feedback, and with the development of unmanned systems, unmanned aerial vehicles and automated guided vehicles are widely used in the industrial field to assist in data transmission. However, timely acquisition of accurate channel conditions is challenging due to the mobility of the drones and the automated guided vehicles. Furthermore, in harsh industrial environments, limited transmit power of the sensor may lead to increased transmission errors and even transmission failures. Therefore, when transferring data, good channel conditions need to be guaranteed to ensure data transmission.
Therefore, the industrial Internet of things system not only needs high-quality transmission capability, but also needs high-precision perception capability. The concept of communication awareness integration is proposed, which aims to share the same spectrum and infrastructure to obtain gains, such as awareness-assisted communication and communication-assisted awareness. However, how to improve the compactness of sensing and communication and reduce mutual interference is a highly desirable problem. And, in order to support high-speed data and large-scale connection over limited spectrum resources, it is important to study multiple access technology of next-generation wireless systems. While the non-orthogonal multiple access technique provides an additional field to separate users on the same resource block to detect superimposed user information using the serial interference cancellation technique. The prior researches show that the sensing and communication are periodically carried out in the time domain under the same transmission condition, and the improvement of the system performance is limited. Meanwhile, in the unmanned aerial vehicle auxiliary system, performing only sensing without performing a control operation may further cause a deterioration in channel conditions.
In summary, the problems of the prior art are: due to the complexity of the industrial wireless transmission environment, the accuracy and stability of data transmission cannot be guaranteed only by the sensor; because of the mobility of unmanned aerial vehicles and automatic guided vehicles, timely obtaining accurate channel conditions is challenging; how to balance communication and perceived performance in the communication perception integrated technology, how to couple the communication and perceived performance and how to operate after perceiving the state information of the unmanned aerial vehicle are still needed to be discussed for an industrial internet of things system, and how to guarantee the channel condition is still needed to be discussed.
Disclosure of Invention
According to the technical problems set forth above: due to the complexity of the industrial wireless transmission environment, the accuracy and stability of data transmission cannot be guaranteed only by the sensor; because of the mobility of unmanned aerial vehicles and automatic guided vehicles, timely obtaining accurate channel conditions is challenging; how to balance communication and perceived performance in the communication perception integration technology, how to couple the communication and perceived performance to the industrial Internet of things system still needs to be discussed, and how to operate after the state information of the unmanned aerial vehicle is perceived to ensure the channel condition, so as to provide a communication perception control integration method in the industrial Internet of things, which comprises the following steps:
s1, introducing an unmanned aerial vehicle into an industrial Internet of things system, and providing a communication model of a communication, sensing and control integrated method;
S2, introducing a sensing system and a control system to improve the communication performance of the industrial Internet of things, and adopting a communication, sensing and control integrated method and a control activation method to improve the data transmission rate of the industrial Internet of things system;
S3, providing a non-convex problem of combining and optimizing automatic guiding of the communication power of the trolley and the sensing beam of the wireless access point to maximize the ratio of the communication rate to the total power consumption, introducing a punishment item and a Taylor expansion method to convert the non-convex problem into a convex problem, and designing a double-layer continuous convex approximation method based on the punishment item to improve the communication rate of the industrial Internet of things system and reduce the total power consumption of the system.
Further, the method for introducing the unmanned aerial vehicle into the industrial internet of things system provides a communication model of a communication, perception and control integrated method, which comprises the following steps:
s11, in the industrial Internet of things system which is considered to introduce the unmanned aerial vehicle, the wireless access point is provided with a linear antenna array with the number of antennas of N=4, the unmanned aerial vehicle and the automatic guiding trolley are both provided with horn antennas, and signals received by the unmanned aerial vehicle are expressed as follows:
yu＝hsa+hsx+ω,
Wherein, Representing a communication signal sent by an automatic guided vehicle,/>Representing perceived signals transmitted by a wireless access point,/>Representing a flat fading wireless channel between an automated guided vehicle and a drone,/>Representing a wireless channel between a wireless access point and the unmanned aerial vehicle, wherein ω represents additive noise with a mean value of zero and a variance of N 0;
In wireless transmission based on a non-orthogonal multiple access technology, a serial interference cancellation technology is generally used for canceling a signal of a weak user from a received signal of a strong user, so as to improve a signal-to-interference-and-noise ratio, and according to different received powers, a data transmission rate R of an unmanned aerial vehicle realized by using the serial interference cancellation technology is expressed as:
Where Δ denotes a decoding interval, P c denotes a transmission power of a communication signal, P s denotes a power of a received sensing signal of the unmanned aerial vehicle, and when a serial interference cancellation condition is satisfied, the sensing signal or the communication signal is cancelled, but if the serial interference cancellation condition is not satisfied, the sensing signal still affects a data transmission rate as interference.
Further, the expression of the flat fading wireless channel h between the automatic guided vehicle and the unmanned aerial vehicle is as follows:
h＝hphfhm
1) Path loss h p: the path loss is described by a free space propagation model, expressed as:
Wherein G t and G r are transmit and receive signal gains, respectively, λ=f/c is the signal wavelength, where c is the speed of light, f is the occupied frequency band, and d is the distance between the transceivers;
2) Multipath fading h f: multipath fading is typically described by a rayleigh distribution whose probability distribution function is:
Wherein, Is a fading channel with a root mean square value of α=2;
3) Misalignment fading h m: assuming that the emission beam of the unmanned aerial vehicle covers an area S with a radius of R, and simultaneously, the radius of the beam coverage area of the automatic guiding trolley is R, the distance is d, wherein R is more than or equal to 0 and less than or equal to R m,Rm, and the maximum radius of the beam when the distance is d, and in addition, l is the misalignment error between the beam center O AGV of the automatic guiding trolley and the beam center O UAV of the unmanned aerial vehicle, and the misalignment is according to the relation between l and R
The cases are divided into three types, with the misalignment fading expressed as:
Wherein the equivalent beam width is represented by R e, e θ is the angle error of the horn antenna, the misalignment error only considers the position errors in the x-axis and y-axis directions, and is represented by x 1 and x 2, respectively, namely In addition, when l=0, the received power of the unmanned aerial vehicle may be represented as P 0, which is obtained by the following formula:
Wherein, Erf (·) is a gaussian error function, then the equivalent beamwidth R e is expressed as:
further, the sensing system and the control system are introduced to improve the communication performance of the industrial Internet of things, and the communication, sensing and control integrated method and the control activation method are adopted to improve the data transmission rate of the industrial Internet of things system, which is specifically as follows:
S21, key indexes of the sensing system comprise covariance matrixes of sensing beams determined by the transmitting signals, wherein the covariance matrixes are as follows:
Where w i is the transmit beam vector of the sensing signal, and M is the number of sensing targets, then the power of the sensing signal transmitted by the wireless access point is:
Pt＝Tr(Rw)
Then the first time period of the first time period, The perceived beam pattern in the direction is:
Wherein, The steering vector for the wireless access point, d 0, the antenna spacing, and the perceived channel h s are expressed as:
Wherein β is a channel gain value 1m away from the wireless access point, H represents the height of the automatic guided vehicle or the unmanned aerial vehicle, Z m and Z a represent the x-axis and y-axis coordinates of the automatic guided vehicle or the unmanned aerial vehicle and the wireless access point, respectively, and therefore, the power of the wireless access point perceived unmanned aerial vehicle is represented as:
Only consider the wave beam of horizontal direction, assume that wireless access point is located the origin of coordinate axis, do not consider the vertical direction, according to automatic guiding dolly and unmanned aerial vehicle's positional information, horizontal angle is:
Wherein q xm and q ym are the positions of the perception target in the x-axis and y-axis directions, respectively;
S22, a model of the unmanned aerial vehicle control system is as follows:
1) Unmanned aerial vehicle motion control: the discrete time control model is:
xk+1＝Adxk+Bduk+wk,
Wherein x k is the state of the unmanned aerial vehicle at time k, a d and B d are system parameter matrices in a discrete time model, u k is a motion control input, W k is disturbance caused by additive white gaussian noise with mean value of zero and variance of W, and assuming that the sampling state of the wireless access point is perfect at each time slot k, namely y k＝xk, the controller calculates the control input of the unmanned aerial vehicle as:
uk＝Kyk,
K is control gain, and for a rotor unmanned aerial vehicle with the speed of V k, propulsion power consumption is modeled as follows:
Wherein U tip is rotor tip speed, V o is average rotor induced speed, d o is fuselage resistance ratio, P b and P i are blade profile power and induced power, ρ, s and w o are air density, rotor solidity and blade angular velocity, respectively, and unmanned aerial vehicle speed in discrete domain V k is calculated by the following formula:
Wherein, Q 1,k(qx1,qy1,qz1) and Q 2,k(qx2,qy2,qz2) respectively represent the position information of the automatic guided vehicle and the unmanned aerial vehicle at the time k, Q x,qy,qz respectively represents the x, y and z axis position information, and delta 0 represents the time interval;
2) Horn antenna angle control: the distance and the relative angle between the unmanned aerial vehicle and the automatic guiding trolley can be obtained through sensing measurement, the direction of the horn antenna on the unmanned aerial vehicle can be represented by a pitch angle theta and an azimuth angle phi, and the unmanned aerial vehicle and the automatic guiding trolley keep relative motion, so that an antenna control model is simplified, the azimuth angle between the unmanned aerial vehicle and the automatic guiding trolley is considered to be kept optimally constant, and the angle of the horn antenna pointing to the direction of the equipment is calculated to be theta d according to the knowledge of a trigonometric function, wherein:
then, the error of the horn antenna pointing direction at the kth time is e θ,k＝θk-θd,k,θk, which is the antenna pointing angle of the unmanned aerial vehicle at the kth time, obtained through the built-in sensor of the unmanned aerial vehicle, and the direction of the horn antenna can be controlled by using the controller, and the control output is expressed as:
zk＝Kaθk,
Wherein, K a is control gain, and unmanned aerial vehicle horn antenna angle control's discrete time control model is:
θk+1＝Aaθk+Bazk+wa,k,
Wherein A a and B a are respectively a system parameter matrix, w a,k is disturbance at k moment,
S23, because the unmanned aerial vehicle motion control power consumption is large, unmanned aerial vehicle antenna angle control is introduced, and then a communication, sensing and control integrated method is provided, and an appropriate control mode of the unmanned aerial vehicle is judged and selected according to error conditions to obtain a higher data transmission rate of the industrial Internet of things system.
Further, the communication, sensing and control integrated method and the process of judging and selecting a proper control mode of the unmanned aerial vehicle according to error conditions to obtain a higher data transmission rate of the industrial Internet of things system are as follows:
The automatic guiding trolley sends the acquired sensor information to the unmanned aerial vehicle through the horn antenna, meanwhile, the wireless access point sends a sensing signal to acquire the position information of the automatic guiding trolley and the unmanned aerial vehicle, and then a controller in the wireless access point calculates a misalignment error, which is a key index for activating a control system of the unmanned aerial vehicle. The controller in the wireless access point makes a judgment according to the misalignment error l related to the beam radius R of the unmanned aerial vehicle, when l > R, the wireless access point sends out unmanned aerial vehicle motion control and antenna angle control instructions, otherwise, only sends out antenna angle control instructions, wherein the control activation coefficient epsilon k is.
Further, the expression of the non-convex problem that maximizes the ratio of the communication rate to the total power consumption is as follows:
s31, jointly optimizing communication power and a perceived beam vector to obtain the ratio of the maximized communication rate to the total power consumption:
s.t. C1:ε∈{0,1},
C4:Pt≤Pl,
C6:0＜|h|2Pc＜Ps.
Wherein: c1 represents the 0-1 constraint controlling the activation coefficients, C2 and C3 are constraints ensuring similar induced power levels in different target directions, where P d is the set minimum power difference, ε [ -90, 90 ] is the perceived angular range, C4 is the maximum transmit power constraint, where P l represents the perceived upper power limit, C6 is the serial interference cancellation constraint, and it is challenging to obtain a globally optimal solution for the proposed problem P 0 because the quadratic form of the covariance matrix makes the constraints C2-C6 non-convex.
Further, the process of introducing the penalty term and the taylor expansion method to convert the non-convex problem into the convex problem is as follows:
s32, firstly defining auxiliary variables Wherein/>Rank (W k) =1, then P 0 is rewritten as:
s.t. C1-C6,
C8:rank(Wk)＝1.
The semi-definite relaxation method is considered as an effective method for solving the rank-one constraint C8, the general solution obtained by the semi-definite relaxation method is reconstructed into a rank-one solution by using a eigenvalue decomposition or gaussian randomization method, the rank-one constraint is converted into a penalty term in an objective function, and then the problem is solved by using a continuous convex approximation algorithm, then the rank-one constraint is equivalent to:
Where i·i * is the kernel norm, i.e. the sum of the singular values of the matrix, |·i 2 is the spectral norm, i.e. the largest singular value of the matrix, so if the matrix W k is a rank one matrix, the above equation holds, otherwise since W k is a semi-positive definite matrix, the sum of the singular values is always larger than the largest singular value, i.e. |w k||*-||Wk||2 > 0, a penalty term is introduced in the objective function in order to get a rank one matrix, with the result that:
s.t. C1-C6,
where η is a penalty factor only if it tends to 0, i.e When the rank of W k tends to be +.;
S33, in this case, the major non-convexity of P 1.1 comes from the second term of the penalty term, with it at the point First-order taylor expansion at place of:
Wherein: Is with/> The P 1.1 problem is approximated as the following problem for the feature vector corresponding to the maximum feature value:
s.t. C1-C6,
s34, only if η is small enough, the penalty term is closer to the rank one constraint, but this creates a new problem: under the condition that eta is small enough, the penalty term approaches infinity, at the moment, the penalty term becomes the dominant term of the objective function, and the real objective function value cannot be obtained, so that the attenuation coefficient epsilon is introduced, a large value eta is initialized, then gradually reduced to a small enough value through eta= epsilon eta, and an integral suboptimal solution is obtained, wherein 0 < epsilon < 1, when the penalty term is small enough, namely I W k||*-||Wk||2＜ε2, the iteration is jumped out,
Further, the solving process of the double-layer continuous convex approximation method based on the penalty term is as follows:
1) Initializing: feasible taylor expansion point The reduction coefficients e, iteration thresholds e 1 and e 2, n=1;
2) When W k||*-||Wk||2≥ε2, performing inner iteration;
3) When (when) When according to/>Solve for P 1.2 and based on/>Updating taylor expansion points/>n＝n+1；
4) When (when)When we jump out of the inner iteration,/>Values of/>η＝∈η；
5) When ||w k||*-||Wk||2＜ε2, the operation is ended.
Compared with the prior art, the invention has the following advantages:
According to the method for integrating communication and control in the industrial Internet of things, provided by the invention, the unmanned aerial vehicle can be perceived to realize motion control or antenna angle control under the condition that the communication rate is reduced due to channel condition change, so that the overall performance of the unmanned aerial vehicle auxiliary industrial Internet of things system is effectively improved.
For the reasons, the invention can be widely popularized in the fields of wireless communication and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a scene graph used in an embodiment of the invention;
fig. 3 (a) is a diagram of the best situation of the alignment of the automatic guided vehicle and the unmanned aerial vehicle antenna beam, (b) is a situation of small deviation of the alignment of the automatic guided vehicle and the unmanned aerial vehicle antenna beam, and (c) is a situation of large deviation of the alignment of the automatic guided vehicle and the unmanned aerial vehicle antenna beam;
FIG. 4 is a flowchart of a control activation method according to an embodiment of the present invention;
FIG. 5 is a graph showing the comparison of the change of objective function values and penalty term values with iteration times for different numbers of sensing antennas according to the embodiment of the present invention;
FIG. 6 is a graph showing the data rate versus misalignment error under different conditions for eliminating serial interference according to an embodiment of the present invention;
FIG. 7 is a graph of data rate versus upper power limit for various misalignment conditions provided by embodiments of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As shown in fig. 1, the embodiment of the invention provides a method for integrating communication and control in an industrial internet of things, which comprises the following steps:
s1, introducing an unmanned aerial vehicle into an industrial Internet of things system, and providing a communication model of a communication, sensing and control integrated method;
S2, introducing a sensing system and a control system to improve the communication performance of the industrial Internet of things, and adopting a communication, sensing and control integrated method and a control activation method to improve the data transmission rate of the industrial Internet of things system;
S3, providing a non-convex problem of combining and optimizing automatic guiding of the communication power of the trolley and the sensing beam of the wireless access point to maximize the ratio of the communication rate to the total power consumption, introducing a punishment item and a Taylor expansion method to convert the non-convex problem into a convex problem, and designing a double-layer continuous convex approximation method based on the punishment item to improve the communication rate of the industrial Internet of things system and reduce the total power consumption of the system.
The steps S1/S2/S3 are sequentially executed;
FIG. 2 is a scene graph used in an embodiment of the invention;
further, the method for introducing the unmanned aerial vehicle into the industrial internet of things system provides a communication model of a communication, perception and control integrated method, which comprises the following steps:
S11, in a system of introducing the industrial Internet of things of the unmanned aerial vehicle, the wireless access point is provided with a linear antenna array with the number of N=4. Both the drone and the automated guided vehicle are equipped with horn antennas. The signal received by the drone may be expressed as:
yu＝hsa+hsx+ω,
Wherein, Representing a communication signal sent by an automatic guided vehicle,/>Representing perceived signals transmitted by a wireless access point,/>Representing a flat fading wireless channel between an automated guided vehicle and a drone,/>Representing the wireless channel between the wireless access point and the drone, ω represents additive noise with zero mean and N 0 variance.
In wireless transmission based on non-orthogonal multiple access techniques, a serial interference cancellation technique is generally used to cancel the signal of a weak user from the received signal of a strong user to improve the signal-to-interference-and-noise ratio. Depending on the received power, the data transmission rate achieved using the serial interference cancellation technique can be expressed as:
Wherein Δ represents a decoding interval, P c represents a transmission power of a communication signal, and P s represents a power of a received perception signal of the unmanned aerial vehicle. When the serial interference cancellation condition is satisfied, the perceived signal or the communication signal may be cancelled. But if the serial interference cancellation condition is not met, the perceived signal still affects the data transmission rate as interference.
Fig. 3 (a) is a diagram of the best situation of the alignment of the automatic guided vehicle and the unmanned aerial vehicle antenna beam, (b) is a situation of small deviation of the alignment of the automatic guided vehicle and the unmanned aerial vehicle antenna beam, and (c) is a situation of large deviation of the alignment of the automatic guided vehicle and the unmanned aerial vehicle antenna beam;
s12, flat fading wireless channels include path loss, multipath fading and misalignment fading. The expression of the flat fading wireless channel h between the automatic guiding trolley and the unmanned aerial vehicle is as follows:
h＝hphfhm.
1) Path loss h p: the path loss can be described by a free space propagation model expressed as:
Where G t and G r are transmit and receive signal gains, respectively, λ=f/c is the signal wavelength, where c is the speed of light, f is the occupied frequency band, and d is the distance between the transceivers.
2) Multipath fading h f: multipath fading is typically described by a rayleigh distribution whose probability distribution function is:
Wherein, Is a fading channel with a root mean square value of α=2.
3) Misalignment fading h m: let us assume that the transmit beam of the drone covers an area S of radius R. Meanwhile, the radius of the beam coverage area of the automatic guided trolley is R, the distance is d, and R is more than or equal to 0 and less than or equal to R m,Rm, and the maximum radius of the beam is the distance d. Furthermore, l is the misalignment error between the beam center O AGV of the automated guided vehicle and the beam center O UAV of the drone. The misalignment can be classified into three types according to the relationship between l and R. Misalignment fading can be expressed as:
Where the beam width is denoted by R e and e θ is the angle error of the horn antenna. In this context, misalignment errors take into account only positional errors in the x-axis and y-axis directions, denoted by x 1 and x 2, respectively, i.e In addition, when l=0, the received power may be expressed as P 0, which may be obtained by the following equation:
Wherein, Erf (·) is a gaussian error function. Then, R e can be expressed as:
further, the sensing system and the control system are introduced to improve the communication performance of the industrial Internet of things, and the communication, sensing and control integrated method and the control activation method are adopted to improve the data transmission rate of the industrial Internet of things system, which is specifically as follows:
The key index of the sensing function is a covariance matrix of the sensing beam determined by the transmitting signal. The covariance matrix is as follows:
Where w i is the transmit beam vector of the perceived signal and M is the number of perceived objects. Then the power of the wireless access point transmit the perceived signal is:
Pt＝Tr(Rw).
Then the first time period of the first time period, The perceived beam pattern in the direction is:
Wherein, Represents the steering vector of the wireless access point, and d 0 represents the antenna spacing. Then the perceived channel h s is denoted as:
Where β is the channel gain value at 1m from the wireless access point. H denotes the height of the automated guided vehicle or drone, and Z m and Z a denote the x, y coordinates of the automated guided vehicle or drone and the wireless access point, respectively. Thus, the wireless access point aware drone's power is expressed as:
The present application only considers beams in the horizontal direction. The wireless access point is assumed to be located at the origin of the coordinate axes, irrespective of the vertical direction. According to the position information of automatic guiding trolley and unmanned aerial vehicle, horizontal angle is:
Where q xm and q ym are the positions of the perception target in the x-axis and y-axis directions, respectively.
S22, because the unmanned aerial vehicle motion control power consumption is larger, unmanned aerial vehicle antenna angle control is further introduced, and a proper control mode is judged and selected according to conditions to obtain higher data transmission rate.
1) Unmanned aerial vehicle motion control: the discrete time control model is:
xk+1＝Adxk+Bduk+wk,
Wherein x k is the state of the unmanned aerial vehicle at time k, and A d and B d are the system parameter matrices in the discrete time model. u k is the motion control input and W k is the disturbance caused by additive white gaussian noise with zero mean and W variance. It is assumed that the sampling state of the wireless access point is perfect, i.e., y k＝xk, at each time slot k. The controller may calculate the control input of the unmanned aerial vehicle as:
uk＝Kyk,
Where K is the control gain. For a rotary-wing drone with a speed V k, the propulsion power consumption can be modeled as:
Wherein U tip is rotor tip speed, v o is average rotor induced speed, d o is airframe resistance ratio, P b and P i are blade profile power and induced power respectively, and ρ, s and w o are air density, rotor solidity and blade angular speed respectively. The speed of the unmanned aerial vehicle in the discrete domain may then be calculated by the following equation:
Wherein, Q 1,k(qx1,qy1,qz1) and Q 2,k(qx2,qy2,qz2) respectively represent position information of the automatic guided vehicle and the unmanned aerial vehicle at the time k, respectively represent x, y, z axis position information, and Δ 0 represents a time interval.
2) Horn antenna angle control: the distance and relative angle between the drone and the automated guided vehicle may be obtained through perceptual measurement. The direction of the horn antenna on the unmanned aerial vehicle can be represented by a pitch angle theta and an azimuth angle phi. Since the drone and the automated guided vehicle remain in relative motion, the antenna control model is simplified herein, with the azimuth angle between them being considered to remain optimally constant. From knowledge of the trigonometric function, the angle of the horn antenna pointing device direction may be calculated as θ d, where:
Then, the error of the horn antenna pointing direction at the kth time is e θ,k＝θk-θd,k,θk, which is the antenna pointing angle of the unmanned aerial vehicle at the kth time, and the angle can be obtained by the built-in sensor of the unmanned aerial vehicle. We can use the controller to control the direction of the feedhorns, the control output can be expressed as:
zk＝Kaθk,
Where K a is the control gain. The discrete time control model for controlling the horn antenna angle of the unmanned aerial vehicle is as follows:
θk+1＝Aaθk+Bazk+wa,k,
Wherein, a a and B a are respectively a system parameter matrix, and w a,k is a disturbance at k time.
S23, the position of the unmanned aerial vehicle can seriously influence the transmission rate, so that the position of the unmanned aerial vehicle is required to be adjusted according to the channel condition and the transmission requirement. However, the change in position may result in energy consumption. Therefore, there is a need to study control activation schemes to meet the transmission rate requirements between the automated guided vehicles and the drones and to reduce overall energy consumption. Because the unmanned aerial vehicle motion control power consumption is large, unmanned aerial vehicle antenna angle control is introduced, and then a communication, sensing and control integrated method is provided, and an appropriate control mode of the unmanned aerial vehicle is judged and selected according to error conditions to obtain a higher data transmission rate of the industrial Internet of things system;
FIG. 4 is a flowchart of a control activation method according to an embodiment of the present invention;
Further, the communication, sensing and control integrated method and the process of judging and selecting a proper control mode of the unmanned aerial vehicle according to error conditions to obtain a higher data transmission rate of the industrial Internet of things system are as follows:
the automatic guiding trolley sends the acquired sensor information to the unmanned aerial vehicle through the horn antenna, meanwhile, the wireless access point sends a sensing signal to acquire the position information of the automatic guiding trolley and the unmanned aerial vehicle, and then a controller in the wireless access point calculates a misalignment error, which is a key index for activating a control system of the unmanned aerial vehicle. The control activation coefficients are:
The wireless access point makes a determination based on the misalignment error/associated with the drone beam radius R. When l > R, the wireless access point will send out the unmanned plane motion control and antenna angle control instruction, otherwise only send out the antenna angle control instruction.
Further, the expression of the non-convex problem that maximizes the ratio of the communication rate to the total power consumption is as follows:
S31, jointly optimizing communication power and a sensing beam vector, and maximizing the ratio of communication rate to total power consumption:
s.t. C1:ε∈{0,1},
C4:Pt≤Pl,
C6:0＜|h|2Pc＜Ps.
Wherein C1 represents a 0-1 constraint that controls the activation coefficient. C2 and C3 are constraints that ensure similar induced power levels in different target directions, where P d is the set minimum power difference, ψ e [ -90 °,90 ° ] is the perceived angle range. C4 is the maximum transmit power constraint, where P l represents the perceived power upper limit. C6 is a serial interference cancellation condition constraint. Since the quadratic form of the covariance matrix makes the constraints C2-C6 non-convex, it is challenging to obtain a globally optimal solution for the proposed problem P 0.
S32, firstly defining auxiliary variablesWherein/>Rank (W k) =1 then P 0 can be rewritten as:
s.t. C1-C6,
C8:rank(Wk)＝1.
The semi-definite relaxation method is considered as an effective method to solve rank one constraint C8. The general solution obtained by the semi-definite relaxation method can be reconstructed into a rank-one solution using eigenvalue decomposition or gaussian randomization methods. However, this may lead to a large performance penalty and the feasibility of reconstructing the matrix cannot be guaranteed. To avoid these drawbacks, we translate the rank-one constraint into a penalty term in the objective function and then solve the problem with a continuous convex approximation algorithm. Then, rank one constraint is equivalent to:
where I * is the kernel norm, i.e., the sum of the singular values of the matrix, || 2 is the norm of the spectrum, i.e. the largest singular value of the matrix. Thus, if matrix W k is a rank-one matrix, then the above equation holds. Otherwise, since W k is a semi-positive definite matrix, the sum of the singular values is always greater than the largest singular value, i.e., ||W k||*-||Wk‖2 >0. To get a rank-one matrix we introduce a penalty term in the objective function, with the result that:
s.t. C1-C6,
where η is a penalty factor only if it tends to 0, i.e Toward +..
S33, in this case, the major non-convexity of P 1.1 comes from the second term of the penalty term, which we use at the pointFirst-order taylor expansion at place of:
Wherein the method comprises the steps of Is with/>And the feature vector corresponding to the maximum feature value. Then the P 1.1 problem can be approximated as the following:
s.t. C1-C6,
S34, only if eta is small enough, the penalty term is closer to the rank-one constraint. But this creates a new problem: under the condition that eta is small enough, the penalty term approaches infinity, and at the moment, the penalty term becomes the dominant term of the objective function, and the real objective function value cannot be obtained. So introducing the decay factor e, by initializing a large value η and then gradually reducing it to a sufficiently small value by η= e, where 0 < 1, an overall sub-optimal solution can be obtained. When the penalty term is sufficiently small, i.e., ||W k||*-‖Wk‖2＜ε2, the iteration is skipped.
Further, the solving process of the double-layer continuous convex approximation method based on the penalty term is as follows:
1) Initializing: feasible taylor expansion point The reduction coefficients e, iteration thresholds e 1 and e 2, n=1;
2) When II W k‖*-‖Wk‖2≥ε2, performing inner iteration;
3) When (when) When according to/>Solve for P 1.2 and based on/>Updating taylor expansion points/>n＝n+1；
4) When (when)When we jump out of the inner iteration,/>Values of/>η＝∈η；
5) When ||w k||*-||Wk||2＜ε2, the operation is ended.
In order to verify the effectiveness of the method of the present invention, the effect of the application of the present invention will be described in detail with reference to simulation.
Simulation conditions
In the simulation scenario, the initial setup wireless access point is equipped with n=4 antennas. The upper limit of communication and sensing power is P l =20dBm, and the power of noiseThe perceived power interval P d = 5. The beam radii of the unmanned aerial vehicle and the automatic guided vehicle at the distance d are r=1 and r=0.5, respectively. Unmanned aerial vehicle consumption calculates initial value: tip speed U tip = 120 for rotor blades; average rotor induction speed v o = 4.03; fuselage resistance ratio d o = 0.6; blade profile power and induction power were P b =79.9 and P i =88.6, respectively; air density, rotor solidity, and blade angular velocity are ρ=1.225, s=0.05, and w o =0.503, respectively. The initial value of the iterative simulation is epsilon=0.4 and epsilon 1＝0.1,ε2＝10-6. Initial positions Q 1 (-20,34.6,0) and Q 2 (20,34.6,10).
Simulation content and result analysis
Simulation 1: and comparing and analyzing the change of the objective function value and the punishment item value along with the iteration times under different sensing antenna numbers.
As can be seen from fig. 5, for a given penalty factor, the target value converges to a stable value after a number of iterations, the penalty term converges to almost zero, although the number of antennas of the wireless access point varies. But an increase in the number of antennas will cause the target value and penalty value to fluctuate with an increase in the number of iterations. Further, when n=8, the perceived power increases rapidly, which further causes the denominator of the objective function to increase, making the target value smaller. The figure shows that the proposed convergence based on the two-layer penalty algorithm is better.
Simulation 2: the data rate is changed along with the misalignment error under different serial interference elimination conditions by comparison analysis.
As can be seen from fig. 6, for the case where the serial interference cancellation condition is satisfied or not, the communication rate is larger regardless of the misalignment error value when the serial interference cancellation condition is satisfied. Thus, the unmanned aerial vehicle motion control and antenna angle control involved in the proposed communication, perception and control schemes can achieve higher data transmission rates at lower energy costs. Adjusting the misalignment error can increase the communication rate, which further verifies the necessity of communication, sensing, and control integration.
Simulation 3: the data rate varies with the upper power limit for different misalignments.
As can be seen in fig. 7, the transmission rate increases with increasing power budget in all misalignment situations. And, the rate in the optimal case is approximately twice that in the case of large deviation, with the advantage of 0.2bit/Hz/s in the case of small deviation. Therefore, the proposed method can properly perform control operation under a limited power budget, improving the transmission rate.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (1)
1. The integrated method for communication and control in the industrial Internet of things is characterized by comprising the following steps of:
s1, introducing an unmanned aerial vehicle into an industrial Internet of things system, and providing a communication model of a communication, sensing and control integrated method;
S2, introducing a sensing system and a control system to improve the communication performance of the industrial Internet of things, and adopting a communication, sensing and control integrated method and a control activation method to improve the data transmission rate of the industrial Internet of things system;
S3, providing a non-convex problem of combining and optimizing automatic guiding of the communication power of the trolley and the sensing beam of the wireless access point to maximize the ratio of the communication rate to the total power consumption, introducing a punishment item and a Taylor expansion method to convert the non-convex problem into a convex problem, and designing a double-layer continuous convex approximation method based on the punishment item to improve the communication rate of the industrial Internet of things system and reduce the total power consumption of the system;
The unmanned aerial vehicle is introduced into an industrial Internet of things system, and a communication model for providing a communication, perception and control integrated method is as follows:
s11, in the industrial Internet of things system which is considered to introduce the unmanned aerial vehicle, the wireless access point is provided with a linear antenna array with the number of antennas of N=4, the unmanned aerial vehicle and the automatic guiding trolley are both provided with horn antennas, and signals received by the unmanned aerial vehicle are expressed as follows:
yu＝hsa+hsx+ω,
Wherein, Representing a communication signal sent by an automatic guided vehicle,/>Representing perceived signals transmitted by a wireless access point,/>Representing a flat fading wireless channel between an automated guided vehicle and a drone,/>Representing a wireless channel between a wireless access point and the unmanned aerial vehicle, wherein ω represents additive noise with a mean value of zero and a variance of N 0;
according to the difference of the received power, the data transmission rate R of the unmanned aerial vehicle realized by using the serial interference elimination technology is expressed as follows:
Wherein Δ represents a decoding interval, P c represents a transmission power of a communication signal, P s represents a power of a received sensing signal of the unmanned aerial vehicle, when a serial interference cancellation condition is satisfied, the sensing signal or the communication signal is cancelled, but if the serial interference cancellation condition is not satisfied, the sensing signal still affects a data transmission rate as interference;
the expression of the flat fading wireless channel h between the automatic guiding trolley and the unmanned aerial vehicle is as follows:
h＝hphfhm
1) Path loss h p: the path loss is described by a free space propagation model, expressed as:
Wherein G t and G r are transmit and receive signal gains, respectively, λ=f/c is the signal wavelength, where c is the speed of light, f is the occupied frequency band, and d is the distance between the transceivers;
2) Multipath fading h f: multipath fading is typically described by a rayleigh distribution whose probability distribution function is:
Wherein, Is a fading channel with a root mean square value of α=2;
3) Misalignment fading h m: assuming that the emission beam of the unmanned aerial vehicle covers an area S with a radius R, and meanwhile, the radius R and the distance d of the beam coverage area of the automatic guiding trolley are equal to or less than 0 and equal to or less than R m,Rm, and the maximum radius of the beam when the distance d is equal to or less than 0 and less than R m,Rm, in addition, l is a misalignment error between a beam center O AGV of the automatic guiding trolley and a beam center O UAV of the unmanned aerial vehicle, the misalignment conditions are divided into three types according to the relation between l and R, and the misalignment fading is expressed as:
Wherein the equivalent beam width is represented by R e, e θ is the angle error of the horn antenna, the misalignment error only considers the position errors in the x-axis and y-axis directions, and is represented by x 1 and x 2, respectively, namely In addition, when l=0, the received power of the unmanned aerial vehicle may be represented as P 0, which is obtained by the following formula:
Wherein, Erf (·) is a gaussian error function, then the equivalent beamwidth R e is expressed as:
The sensing system and the control system are introduced to improve the communication performance of the industrial Internet of things, and the communication, sensing and control integrated method and the control activation method are adopted to improve the data transmission rate of the industrial Internet of things system, which is specifically as follows:
S21, key indexes of the sensing system comprise covariance matrixes of sensing beams determined by the transmitting signals, wherein the covariance matrixes are as follows:
Where w i is the transmit beam vector of the sensing signal, and M is the number of sensing targets, then the power of the sensing signal transmitted by the wireless access point is:
Pt＝Tr(Rw)
Then the first time period of the first time period, The perceived beam pattern in the direction is:
Wherein, The steering vector for the wireless access point, d 0, the antenna spacing, and the perceived channel h s are expressed as:
Wherein β is a channel gain value 1m away from the wireless access point, H represents the height of the automatic guided vehicle or the unmanned aerial vehicle, Z m and Z a represent the x-axis and y-axis coordinates of the automatic guided vehicle or the unmanned aerial vehicle and the wireless access point, respectively, and therefore, the power of the wireless access point perceived unmanned aerial vehicle is represented as:
Only consider the wave beam of horizontal direction, assume that wireless access point is located the origin of coordinate axis, do not consider the vertical direction, according to automatic guiding dolly and unmanned aerial vehicle's positional information, horizontal angle is:
Wherein q xm and q ym are the positions of the perception target in the x-axis and y-axis directions, respectively;
S22, a model of the unmanned aerial vehicle control system is as follows:
1) Unmanned aerial vehicle motion control: the discrete time control model is:
xk+1＝Adxk+Bduk+wk,
Wherein x k is the state of the unmanned aerial vehicle at time k, a d and B d are system parameter matrices in a discrete time model, u k is a motion control input, W k is disturbance caused by additive white gaussian noise with mean value of zero and variance of W, and assuming that the sampling state of the wireless access point is perfect at each time slot k, namely y k＝xk, the controller calculates the control input of the unmanned aerial vehicle as:
uk＝Kyk,
K is control gain, and for a rotor unmanned aerial vehicle with the speed of V k, propulsion power consumption is modeled as follows:
Wherein U tip is rotor tip speed, V o is average rotor induced speed, d o is fuselage resistance ratio, P b and P i are blade profile power and induced power, ρ, s and w o are air density, rotor solidity and blade angular velocity, respectively, and unmanned aerial vehicle speed in discrete domain V k is calculated by the following formula:
Wherein, Q 1,k(qx1,qy1,qz1) and Q 2,k(qx2,qy2,qz2) respectively represent the position information of the automatic guided vehicle and the unmanned aerial vehicle at the time k, Q x,qy,qz respectively represents the x, y and z axis position information, and delta 0 represents the time interval;
2) Horn antenna angle control: the distance and the relative angle between the unmanned aerial vehicle and the automatic guiding trolley can be obtained through sensing measurement, the direction of the horn antenna on the unmanned aerial vehicle can be represented by a pitch angle theta and an azimuth angle phi, and the unmanned aerial vehicle and the automatic guiding trolley keep relative motion, so that an antenna control model is simplified, the azimuth angle between the unmanned aerial vehicle and the automatic guiding trolley is considered to be kept optimally constant, and the angle of the horn antenna pointing to the direction of the equipment is calculated to be theta d according to the knowledge of a trigonometric function, wherein:
then, the error of the horn antenna pointing direction at the kth time is e θ,k＝θk-θd,k,θk, which is the antenna pointing angle of the unmanned aerial vehicle at the kth time, obtained through the built-in sensor of the unmanned aerial vehicle, and the direction of the horn antenna can be controlled by using the controller, and the control output is expressed as:
zk＝Kaθk,
Wherein, K a is control gain, and unmanned aerial vehicle horn antenna angle control's discrete time control model is:
θk+1＝Aaθk+Bazk+wa,k,
Wherein A a and B a are respectively a system parameter matrix, w a,k is disturbance at k moment,
S23, because the unmanned aerial vehicle motion control power consumption is large, unmanned aerial vehicle antenna angle control is introduced, and then a communication, sensing and control integrated method is provided, and an appropriate control mode of the unmanned aerial vehicle is judged and selected according to error conditions to obtain a higher data transmission rate of the industrial Internet of things system;
the communication, perception and control integrated method and the process for judging and selecting the proper control mode of the unmanned aerial vehicle according to the error condition to obtain the higher data transmission rate of the industrial Internet of things system are as follows:
The automatic guiding trolley sends the acquired sensor information to the unmanned aerial vehicle through the horn antenna, meanwhile, the wireless access point sends a sensing signal to acquire the position information of the automatic guiding trolley and the unmanned aerial vehicle, and then a controller in the wireless access point calculates a misalignment error, which is a key index for activating a control system of the unmanned aerial vehicle; the controller in the wireless access point makes a judgment according to a misalignment error l related to the beam radius R of the unmanned aerial vehicle, when l > R, the wireless access point sends out unmanned aerial vehicle motion control and antenna angle control instructions, otherwise, only sends out antenna angle control instructions, wherein the control activation coefficient epsilon k is as follows:
The expression of the non-convex problem that maximizes the ratio of communication rate to total power consumption is as follows:
s31, jointly optimizing communication power and a perceived beam vector to obtain the ratio of the maximized communication rate to the total power consumption:
Wherein: c1 represents a 0-1 constraint controlling the activation coefficients, C2 and C3 are constraints ensuring similar induced power levels in different target directions, where P d is a set minimum power difference, ε [ -90, 90 ] is the perceived angular range, C4 is the maximum transmit power constraint, where P l represents the perceived upper power limit, C6 is the serial interference cancellation constraint, and it is challenging to obtain a globally optimal solution for the proposed problem P 0 because the quadratic form of the covariance matrix makes the constraints C2-C6 non-convex;
The process of introducing penalty term and Taylor expansion method to convert non-convex problem into convex problem is as follows:
s32, firstly defining auxiliary variables Where W k≥0,rank(Wk) =1, then P 0 is rewritten as:
The semi-definite relaxation method is considered as an effective method for solving the rank-one constraint C8, a solution obtained by the semi-definite relaxation method is reconstructed into a rank-one solution by using a eigenvalue decomposition or gaussian randomization method, the rank-one constraint is converted into a penalty term in an objective function, and then a continuous convex approximation algorithm is used for solving the problem, then the rank-one constraint is equivalent to:
Where i·i * is the kernel norm, i.e. the sum of the singular values of the matrix, |·i 2 is the spectral norm, i.e. the largest singular value of the matrix, so if the matrix W k is a rank one matrix, the above equation holds, otherwise since W k is a semi-positive definite matrix, the sum of the singular values is always larger than the largest singular value, i.e. |w k||*-||Wk||2 > 0, a penalty term is introduced in the objective function in order to get a rank one matrix, with the result that:
where η is a penalty factor only if it tends to 0, i.e When the rank of W k tends to be +.;
S33, in this case, the non-convexity of P 1.1 comes from the second term of the penalty term, with it at the point First-order taylor expansion at place of:
Wherein: Is with/> The P 1.1 problem is approximated as the following problem for the feature vector corresponding to the maximum feature value:
s34, only if η is small enough, the penalty term is closer to the rank one constraint, but this creates a new problem: under the condition that eta is small enough, the penalty term approaches infinity, at the moment, the penalty term becomes the dominant term of the objective function, and the real objective function value cannot be obtained, so that an attenuation coefficient epsilon is introduced, a large value eta is initialized, then the eta is gradually reduced to a small enough value through eta= epsilon eta, and an integral suboptimal solution is obtained, wherein 0 < epsilon < 1, and when the penalty term is small enough, namely I W k||*-||Wk||2＜ε2, iteration is jumped out;
the solving process of the double-layer continuous convex approximation method based on the penalty term is as follows:
1) Initializing: feasible taylor expansion point The reduction coefficients e, iteration thresholds e 1 and e 2, n=1;
2) When W k||*-||Wk||2≥ε2, performing inner iteration;
3) When (when) When according to/>Solve for P 1.2 and based on/>Updating taylor expansion points/>n＝n+1；
4) When (when)When we jump out of the inner iteration,/>Values of/>η＝∈η；
5) When ||w k||*-||Wk||2＜ε2, the operation is ended.
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