Communication-perception-control joint design method for unmanned aerial vehicle

Abstract

The invention discloses a communication-perception-control combined design method for an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: based on a state-space equation of an unmanned aerial vehicle system, unmanned aerial vehicle motor control quantity and transmitting power constraints, system initial state constraints, unmanned aerial vehicle safe flight constraints, and communication service quality constraints; an unmanned aerial vehicle communication-perception-control joint optimization problem is constructed according to the residual energy constraint when the unmanned aerial vehicle completes the task and the unmanned aerial vehicle flight terminal position constraint; based on time scale transformation, control variable parameterization and precise penalty function constraint processing, an unmanned aerial vehicle communication-perception-control joint optimization problem is converted into a nonlinear programming problem; and solving the nonlinear programming problem based on a gradient method to obtain a control strategy of the unmanned aerial vehicle. The method is more efficient and practical when a complex unmanned aerial vehicle control system is processed, and can effectively guide the unmanned aerial vehicle to complete a sensing task for a non-cooperative target especially in a dynamically changing environment.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle control, and in particular to a communication-perception-control joint design method for unmanned aerial vehicles. Background Art

[0002] Drones are considered to be an efficient mobile aerial platform that can improve the quality of communication and perception. In addition, by controlling the flight altitude of the drone, a strong line-of-sight air-to-ground link can be easily established, providing a larger communication and sensing coverage. Equipped with advanced sensors, drones can perform a wide range of critical tasks such as traffic monitoring, industrial inspections, and disaster relief operations.

[0003] In order to fully exploit the potential of drone and synaesthesia integration, there are many studies on the optimization design of drone networks, including drone trajectory planning and synaesthesia resource allocation. However, most studies mainly optimize communication, perception, and control separately, which leads to poor matching between them and affects the overall performance. In many research works, only the kinematic characteristics of the drone are considered to simplify the complexity of the problem. In the drone kinematic trajectory planning scheme, the drone is simplified as a mass point, ignoring the impact of the drone body rotation, and only considering its center of mass movement in three-dimensional space, resulting in the actual communication and perception performance of the drone being lower than expected. Summary of the invention

[0004] The present invention provides a communication-perception-control joint design method for unmanned aerial vehicles. The method is a comprehensive method that closely combines communication, perception and control. By optimizing the motion trajectory and transmission power of the unmanned aerial vehicle, the method can sense non-cooperative moving targets and transmit monitoring data back to a base station, thereby optimizing system performance.

[0005] In order to solve the above problems, the technical solution adopted by the present invention is as follows:

[0006] The present invention provides a communication-perception-control joint design method for an unmanned aerial vehicle, comprising:

[0007] Based on the state space equation of the UAV system, the UAV motor control quantity and transmission power constraints, the system initial state constraints, the UAV safe flight constraints, the communication service quality constraints, the remaining energy constraints when the UAV completes the mission, and the UAV flight terminal position constraints, the UAV communication-perception-control joint optimization problem (P1) is constructed;

[0008] Based on time scale transformation, control variable parameterization and exact penalty function constraint processing, the UAV communication-perception-control joint optimization problem (P1) is transformed into a nonlinear programming problem (P σ, ∈);

[0009] Gradient-based methods for nonlinear programming problems (P σ, ∈) is solved to obtain the control strategy of the UAV.

[0010] As a further description of the above technical solution, the state space equation of the UAV system takes into account the position of the UAV, the position of the target, the position of the base station, the velocity vector of the UAV, the angular velocity of rotation around the three coordinate axes in the UAV body coordinate system, the value of the UAV's quaternion, the remaining energy of the UAV, the communication throughput, the amount of perception information, the amount of UAV motor control, the communication power, the perception power, the UAV body mass, the body air resistance coefficient, the moment of inertia of the body coordinate axis, the moment of inertia of the motor rotor around the center axis of the body, the sum of the speeds of all UAV motors, the body damping coefficient, the power of each UAV motor, and the communication and perception gain when the distance between the UAV and the target is 1 meter.

[0011] As a further description of the above technical solution, the UAV motor control quantity and transmission power constraints are:

[0012] U 1,min ≤u 1 (t)≤U 1,max ;|u j (t)|≤U j,max ; j = 2, 3, 4;

[0013] 0≤P c (t)≤P max ; 0≤P r (t)≤P max ; t∈[0,T]

[0014] Among them, U 1,min , U 1,max and U j,max is the upper and lower limits of the capacity calculated according to the motor performance, which is a constant parameter, P max is the maximum transmission power; u 1 (t) is the control quantity of the first motor of the drone, u j (t) is the control quantity of the jth motor of the UAV; P c (t) is the communication power of the UAV at time t; P r (t) is the perceived power of the UAV at time t; T is the task completion time.

[0015] As a further description of the above technical solution, the communication service quality constraint is:

[0016]

[0017] Among them, Q c (t) is the communication throughput of the UAV at time t; Q c (t) is the derivative with respect to time, i.e., the communication rate of the UAV at time t; Q r (t) is the amount of information sensed by the UAV at time t; Q r (t) is the derivative with respect to time, i.e., the sensing rate of the UAV at time t; R v is the data rate of the monitoring video; T is the task completion time.

[0018] As a further description of the above technical solution, the time scale transformation is used to transform the state space equation of the UAV system The variable time interval t∈[0,T] is converted into a fixed integration interval s∈[0,1], and the s-domain space is defined. Then the linear mapping of the time t domain to the s domain has the following relationship:

[0019]

[0020] Among them, T is the task completion time, x(t) is the state quantity of the UAV system, and u(t) is the control quantity of the UAV system.

[0021] As a further description of the above technical solution, the control variable parameterization includes:

[0022] The continuous control quantity in the s domain is discretized into K equally spaced intervals;

[0023] With parameter variable σ i,k , k∈{0,1,…,K-1,K} represents the interval [s k-1 ,s k ) equivalent control quantity,

[0024] where s 0 =0,s K =1, K is the number of equally spaced piecewise parameterized variables;

[0025] The control variables are expressed as

[0026]

[0027] in,

[0028]

[0029] The amount of control Recorded as

[0030]

[0031] i∈{1,2,…,6}.

[0032] As a further description of the above technical solution, the precise penalty function constraint processing includes:

[0033] The flight terminal position constraint of the UAV communication-perception-control joint optimization problem (P1) is transformed into:

[0034] Δ e =∈ -α [(x(1)-x F ) 2 +(y(1)-y F ) 2 +(z(1)-z F ) 2 ]

[0035] Among them, α>0, α is the preset penalty weight coefficient, ∈ -α is the penalty weight, (x F ,y F ,z F ) is the coordinate of the UAV’s flight endpoint, x(1), y(1), z(1) are the coordinates of the UAV at time 1;

[0036] The remaining energy constraint of the UAV when completing the task in the UAV communication-perception-control joint optimization problem (P1) is transformed into:

[0037] Δ ine =∈ -α min{0,E r (1)+∈ β W 0} 2

[0038] Where β>0, β is the preset scaling penalty weight coefficient; E r (1) is the remaining energy of the UAV at time 1; W 0 For E r (1) The scaling cardinality of β is the penalty weight; ∈ β W 0 is the relaxation factor of the inequality constraint; min{} is the minimum function;

[0039] The UAV safe flight constraints and communication service quality constraints of the UAV communication-perception-control joint optimization problem (P1) are transformed into:

[0040]

[0041] Among them, W 1 h is the scaling base of the drone height z(s), min is the minimum safe flight altitude, W 2 The communication channel capacity of the UAV and drone perception rate The scaling factor, h max is the maximum safe flight altitude, R v The data rate for monitoring video.

[0042] As a further description of the above technical solution, the nonlinear programming problem (P σ, ∈) is:

[0043] (P σ,∈ ):

[0044] sC 0 :

[0045] C 1 :U 1,min ≤σ 1,k ≤U 1,max ,|σ j,k |≤U j,max ,j=2,3,4,

[0046] 0≤σ m,k ≤P max ,m=5,6,k∈{0,1,…,K-1,K}

[0047] C 2 :T>0

[0048] C 3 :x(0)=x 0

[0049] C 8 :∈>0

[0050] Among them, Q r (1) is the amount of perceptual information accumulated from time 0 to 1; δ>0, δ is the degree cardinality of the penalty variable factor ∈, γ≥2 is the penalty coefficient of ∈, δ∈ γ is the penalty term for ∈; U 1,min , U 1,max and U i,max is the upper and lower limits of the capacity calculated according to the motor performance, which is a constant parameter, P max is the maximum transmit power; x 0 This is the initial state of the system.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] Can optimize the trajectory planning and transmission power configuration of drones;

[0053] Compared with the traditional method, the UAV adopts state space modeling, which jointly considers the control performance, communication and perception performance of the UAV, and provides conditions for the joint design of communication-perception-control. Then, the present invention adopts the control parameterization method to convert the infinite-dimensional control variables into finite-dimensional control parameters, reducing the number of variables to be optimized. In addition, the method avoids the increase in the computational complexity caused by the increase in the state of the UAV after the introduction of UAV dynamics by optimizing the control of the UAV rather than the state of the UAV. After the present invention adopts the precise penalty function to transform the state constraints, it has a lower computational complexity, which makes it more efficient and practical when dealing with complex UAV control systems; especially in a dynamically changing environment, the present invention can effectively guide the UAV to complete the perception task of non-cooperative targets and ensure the accurate return of data.

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the embodiments of the present invention are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0056] Figure 1 is a flow chart of the communication-perception-control joint design method of the UAV described in the embodiment;

[0057] Figure 2 Schematic diagram of parameterization of control variables in the embodiment.

[0058] Figure 3 is a trajectory diagram of a UAV in a simulation of an embodiment;

[0059] Figure 4 is a histogram comparing the perceptual mutual information of targets with different speeds in the simulation of the embodiment. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0061] Please refer to Figure 1 The present invention provides a communication-perception-control joint design method for unmanned aerial vehicles, which is as follows:

[0062] 1. Establish the state space equation of the UAV system. The specific process is as follows:

[0063] Define the state quantity of the system as

[0064]

[0065] The control amount is

[0066] Then the state space equation of the system is:

[0067]

[0068] Where: is the coordinate of the UAV at time t;

[0069] is the position of the ground target at time t;

[0070] p B is the location coordinates of the base station; is the velocity vector of the UAV at time t;

[0071] is the angular velocity of the drone body around the three coordinate axes at time t;

[0072] is the value of the quaternion at time t; E(t) is the residual energy at time t; Q c (t) is the communication throughput at time t; Q r (t) is the amount of perceived information at time t;

[0073] is the motor control quantity of the UAV at time t; P c (t) is the communication power at time t; P r (t) is the perceived power at time t; m is the mass of the drone; g is the gravitational acceleration;

[0074] D x (t) = C dx |v x (t)|v x (t), D y (t) = C dy |v y (t)|v y (t), D z (t) = C dz |v z (t)|v z (t),

[0075] Cdx , C dy and C dz is the air resistance coefficient of the fuselage;

[0076] I x ,I y ,I z are the moment of inertia matrices of the body coordinate axes,

[0077] I m is the moment of inertia of the motor rotor around the central axis of the machine body;

[0078] Ω(t) is the sum of the speeds of all motors of the drone at time t;

[0079] D mx (t) = C dmx |ω x (t)|ω x (t), D my (t) = C dmy |ω y (t)|ω y (t), D mz (t) = C dmz |ω z (t)|ω z (t),

[0080] C dmx , C dmy and C dmz is the damping coefficient of the fuselage;

[0081] P n (t) is the power of the nth motor of the drone at time t, n = {1, 2, 3, 4}; λ c and λ r They are the communication and perception gains at a distance of 1 meter, respectively;

[0082] Represents the matrix transpose in mathematical algebra; is the differential of x(t) with respect to time t, and other expressions are similar; ||·|| represents the binary norm.

[0083] In order to simplify the expression, the state space equation of the above UAV system is recorded as

[0084]

[0085] 2. UAV communication-perception-control joint optimization problem (P1), specifically:

[0086] (P1):

[0087] sC 0: t∈[0,T]

[0088] C 1 :U 1,min ≤u 1 (t)≤U 1,max ,|u j (t)|≤U j,max ,j=2,3,4,

[0089] 0≤P c (t)≤P max ,0≤P r (t)≤P max ,t∈[0,T]

[0090] C 2 :T>0

[0091] C 3 :x(0)=x 0

[0092] C 4 :h min ≤z(t)≤h max ,t∈[0,T]

[0093] C 5 : t∈[0,T]

[0094] C 6 :E r (T)≥0

[0095] C 7 :x(T)=x F ,y(T)=y F ,z(T)=z F

[0096] In problem (P1), constraint C 0 is the state space equation of the UAV system;

[0097] C 1 is the UAV motor control quantity and transmission power constraint, U 1,min , U 1,max and U i,max is the upper and lower limits of the capacity calculated according to the motor performance, which is a constant parameter, P max is the maximum transmit power;

[0098] C 2 Ensure that the task completion time T conforms to the actual physical meaning;

[0099] C 3 is the system initial state constraint, x 0is the initial state of the system;

[0100] C 4 To ensure safe flight of drones, h min is the minimum safe flight altitude, h max The highest safe flight altitude;

[0101] C 5 is the communication service quality constraint, the communication channel capacity is greater than the amount of sensor data required to be transmitted, where R v To monitor the data rate of the video;

[0102] C 6 The remaining energy of the drone is required to be greater than or equal to 0 when completing the mission;

[0103] C 7 is the flight terminal position constraint of the UAV, (x F ,y F ,z F ) are the end point position coordinates.

[0104] 3. Perform time scale transformation on the state space equation of the UAV system, as follows:

[0105] The variable time interval t∈[0,T] is converted into a fixed integration interval s∈[0,1] through time scale transformation.

[0106] Define the s-domain space, and the linear mapping of the time t-domain to the s-domain has the following relationship:

[0107]

[0108] 4. Control variable parameterization, as follows:

[0109] like Figure 2 As shown in the figure, the continuous control quantity in the s domain is discretized into K equally spaced intervals, and the parameter variable σ i,k , k∈{0,1,…,K-1,K} represents the interval [s k-1 ,s k ) equivalent control quantity, where s 0 =0,s K = 1, K is the number of equally spaced segmented parameterized variables. The control variable is expressed as

[0110]

[0111] in

[0112]

[0113] The control quantity u(t) is expressed as

[0114] i∈{1,2,…,6}.

[0115] 5. Precise penalty function constraint processing, as follows:

[0116] The terminal position constraint C in problem (P1) 7 , which is converted into

[0117] Δ e =∈ -α [(x(1)-x F ) 2 +(y(1)-y F ) 2 +(z(1)-z F ) 2 ]

[0118] Among them, α>0, α is the preset penalty weight coefficient, ∈ -α is the penalty weight.

[0119] For the terminal energy inequality constraint C in problem (P1) 6 , which is converted into

[0120] Δ ine =∈ -α min{0,E r (1)+∈ β W 0} 2

[0121] Where β>0, β is the preset scaling penalty weight coefficient; E r (1) is the remaining energy of the UAV at time 1; W 0 For E r (1) The scaling cardinality of β is the penalty weight; ∈ β W 0 is the relaxation factor of the inequality constraint; min{} is the minimum value function, and its operation example is as follows:

[0122]

[0123] For the process state inequality constraint C in problem (P1) 4 ,C 5 , is converted into

[0124]

[0125] Among them, W 1 is the scaling base of the drone height z(s), W 2 The communication channel capacity of the UAV and drone perception rate The zoom level base.

[0126] 6. Based on time scale transformation, control variable parameterization and precise penalty function constraint processing, the UAV communication-perception-control joint optimization problem (P1) is transformed into a nonlinear programming problem (P σ,∈ ):

[0127] (P σ,∈ ):

[0128] sC 0 :

[0129] C 1 :U 1,min ≤σ 1,k ≤U 1,max ,|σ j,k |≤U j,max ,j=2,3,4,

[0130] 0≤σ m,k ≤P max ,m=5,6,k∈{0,1,…,K-1,K}

[0131] C 2 :T>0

[0132] C 3 :x(0)=x 0

[0133] C 8 :∈>0

[0134] In the formula, Q r (1) is the amount of perceptual information accumulated from 0 to 1; δ>0 is the degree cardinality of the penalty variable factor ∈, γ≥2 is the penalty coefficient of ∈, δ∈ γ is the penalty term for ∈.

[0135] 7. For nonlinear programming problems (P σ,∈ ), and use gradient-based methods to solve it, such as sequential quadratic programming (SQP), and finally obtain the control strategy of the UAV, that is, the control quantity u(t).

[0136] The communication-perception-control joint design method for unmanned aerial vehicles described in the present invention is simulated below.

[0137] Consider a drone communication and perception integrated system flying from the starting point to the end point, sensing a single target during the flight and transmitting the sensing data back to the ground base station in real time. The entire flight mission is limited by the airborne energy. Set the starting coordinates of the drone to The end point coordinates are The base station coordinates are Given the airborne energy E total =30kJ. Other parameters related to the UAV communication and perception integration system are shown in Table 1.

[0138] In problems (P1) and (P2), the initial states of the drone are set to

[0139]

[0140] and

[0141]

[0142] The maximum acceleration of the drone is set to 26m / s 2 .

[0143] Table 1 Parameters of UAV communication and perception integrated system

[0144]

[0145] Consider two situations of the target to be measured, one is a static target (the target is stationary at a fixed position) and the other is a target in uniform linear motion.

[0146] Set the position coordinates of the stationary target to

[0147] The initial position of the moving target is Move uniformly along the negative direction of the X-axis, considering three movement speeds of 5m / s, 10m / s and 15m / s respectively.

[0148] Set the constant parameter reference values ​​in the exact penalty function to α=2,β=2,γ=3,W 0 =W 1 =W 2 =10.

[0149] Figure 3 The trajectory diagram of the UAV and the target is shown when the target moves at 5m / s. Figure 3 As shown in the figure, in order to obtain better perception performance, the UAV first flies in the direction close to the target. However, due to the limitation of communication capability, the UAV must maintain a stable perception distance with the target to ensure that the communication capacity is greater than the perception rate. When the energy consumption reaches a certain level, the UAV starts to fly towards the flight destination and away from the target, causing the perception speed to gradually decrease, and finally reaches the destination before the energy is exhausted. The simulation results show that the UAV needs to strike a balance between the flight destination and the flight target according to the onboard energy consumption to ensure that the UAV can successfully return to the destination and achieve maximum perception mutual information without violating the communication constraints.

[0150] Figure 4 The comparison scheme does not take into account the dynamic equation of the UAV, resulting in the actual perceived mutual information being lower than that of the method of the present invention.

[0151] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A communication-perception-control joint design method for unmanned aerial vehicles, characterized in that: include: Based on the state space equation of the UAV system, the UAV motor control quantity and transmission power constraints, the system initial state constraints, the UAV safe flight constraints, the communication service quality constraints, the remaining energy constraints when the UAV completes the mission, and the UAV flight terminal position constraints, the UAV communication-perception-control joint optimization problem (P1) is constructed; Based on time scale transformation, control variable parameterization and exact penalty function constraint processing, the UAV communication-perception-control joint optimization problem (P1) is transformed into a nonlinear programming problem (P σ,∈ ); Gradient-based methods for nonlinear programming problems (P σ,∈ ) is solved to obtain the control strategy of the UAV.

2. The design method according to claim 1, characterized in that: The state space equation of the UAV system takes into account the position of the UAV, the position of the target, the position of the base station, the velocity vector of the UAV, the angular velocity of the rotation around the three coordinate axes in the UAV body coordinate system, the value of the UAV's quaternion, the remaining energy of the UAV, the communication throughput, the amount of perception information, the UAV motor control amount, the communication power, the perception power, the UAV body mass, the body air resistance coefficient, the moment of inertia of the body coordinate axis, the moment of inertia of the motor rotor around the center axis of the body, the sum of the speeds of all UAV motors, the body damping coefficient, the power of each UAV motor, and the communication and perception gain when the distance between the UAV and the target is 1 meter.

3. The design method according to claim 1, characterized in that: The UAV motor control quantity and transmission power constraints are: U 1,min ≤u1(t)≤U 1,max ;|u j (t)|≤U j,max ;j=2,3,4; 0≤P c (t)≤P max ;0≤P r (t)≤P max ;t∈[0,T] Among them, U 1,min , U 1,max and U j,max is the upper and lower limits of the capacity calculated according to the motor performance, which is a constant parameter, P max is the maximum transmission power; u1(t) is the control quantity of the first motor of the drone, u j (t) is the control quantity of the jth motor of the UAV; P c (t) is the communication power of the UAV at time t; P r (t) is the perceived power of the UAV at time t; T is the task completion time.

4. The design method according to claim 1, characterized in that: The communication service quality constraints are: Among them, Q c (t) is the communication throughput of the UAV at time t; Q c (t) is the derivative with respect to time, i.e., the communication rate of the UAV at time t; Q r (t) is the amount of information sensed by the UAV at time t; Q r (t) is the derivative with respect to time, i.e., the sensing rate of the UAV at time t; R v is the data rate of the monitoring video; T is the task completion time.

5. The design method according to claim 1, characterized in that: The time scale transformation is used to transform the state space equations of the UAV system The variable time interval t∈[0,T] is converted into a fixed integration interval s∈[0,1], and the s-domain space is defined. Then the linear mapping of the time t domain to the s domain has the following relationship: Among them, T is the task completion time, x(t) is the state quantity of the UAV system, and u(t) is the control quantity of the UAV system.

6. The design method according to claim 5, characterized in that: Control variable parameterization includes: The continuous control quantity in the s domain is discretized into K equally spaced intervals; With parameter variable σ i,k , k∈{0,1,…,K-1,K} represents the interval [s k-1 ,s k ) equivalent control quantity, Where s0=0, s K =1, K is the number of equally spaced piecewise parameterized variables; The control variables are expressed as in, The amount of control Recorded as 7. The design method according to claim 6, characterized in that: Exact penalty function constraint processing includes: The flight terminal position constraint of the UAV communication-perception-control joint optimization problem (P1) is transformed into: Δ e (∈ -α [(x(1)-x F ) 2 +(y(1)-y F ) 2 +(z(1)-z F ) 2 ] Among them, α>0, α is the preset penalty weight coefficient, ∈ -α is the penalty weight, (x F ,y F ,z F ) is the coordinate of the UAV’s flight endpoint, x(1), y(1), z(1) are the coordinates of the UAV at time 1; The remaining energy constraint of the UAV when completing the task in the UAV communication-perception-control joint optimization problem (P1) is transformed into: Δ ine =∈ -α min{0,E r (1)+∈ β W0} 2 Where β>0, β is the preset scaling penalty weight coefficient; E r (1) is the remaining energy of the drone at time 1; W0 is E r (1) The scaling cardinality of β is the penalty weight; ∈ β W0 is the relaxation factor of the inequality constraint; min{} is the minimum function; The UAV safe flight constraints and communication service quality constraints of the UAV communication-perception-control joint optimization problem (P1) are transformed into: Where W1 is the scaling base of the drone height z(s), h min is the minimum safe flight altitude, W2 is the communication channel capacity of the UAV and drone perception rate The scaling factor, h max is the maximum safe flight altitude, R v The data rate for monitoring video.

8. The design method according to claim 7, characterized in that: Nonlinear programming problem (P σ, ∈) is: (P σ,∈ ): C1:U 1,min ≤σ 1,k ≤U 1,max ,|σ j,k |≤U j,max ,j=2,3,4,0≤σ m,k ≤P max ,m=5,6,k∈{0,1,…,K-1,K} C2:T>0 C3:x(0)=x0 C8:∈＞0 Among them, Q r (1) is the amount of perceptual information accumulated from time 0 to 1; δ>0, δ is the degree cardinality of the penalty variable factor ∈, γ≥2 is the penalty coefficient of ∈, δ∈ γ is the penalty term for ∈; U 1,min , U 1,max and U i,max is the upper and lower limits of the capacity calculated according to the motor performance, which is a constant parameter, P max is the maximum transmission power; x0 is the initial state of the system.

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