A communication-sensing-control joint design method for unmanned aerial vehicles

Abstract

The application discloses a communication-sensing-control joint design method for a UAV, and relates to the technical field of UAV control, and comprises the following steps: constructing a UAV communication-sensing-control joint optimization problem based on a state space equation of a UAV system, motor control quantity and transmission power constraints, system initial state constraints, UAV safe flight constraints, communication service quality constraints, residual energy constraints when the UAV completes a task, and UAV flight terminal position constraints; converting the UAV communication-sensing-control joint optimization problem into a nonlinear programming problem based on time scale transformation, control variable parameterization and accurate penalty function constraint processing; and solving the nonlinear programming problem based on a gradient method to obtain a control strategy of the UAV. The application is more efficient and practical in processing a complex UAV control system, and can effectively guide the UAV to complete a sensing task on a non-cooperative target in a dynamically changing environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle control, in particular to a communication-sensing-control joint design method for unmanned aerial vehicles. BACKGROUND

[0002] Unmanned aerial vehicles (UAVs) are considered as an efficient mobile aerial platform that can improve the quality of communication and sensing. Moreover, by controlling the flight altitude of UAVs, strong line-of-sight air-to-ground links can be easily established, thus providing greater communication and sensing coverage. Equipped with advanced sensors, UAVs can perform a wide range of critical tasks such as traffic monitoring, industrial inspection, and disaster rescue operations.

[0003] In order to fully exploit the potential of UAVs and the integration of communication and sensing, there are currently many researches on the optimal design of UAV networks, including UAV trajectory planning and communication-sensing resource allocation. However, most of the researches mainly optimize communication, sensing and control separately, which leads to poor matching between each other and affects the overall performance. In many research works, only the kinematic characteristics of UAVs are considered to simplify the problem complexity. In the kinematic trajectory planning scheme of UAVs, the UAV is simplified as a point mass, ignoring the influence of the rotation of the UAV body, and only considering the center of mass motion in three-dimensional space, which leads to the actual communication and sensing performance of the UAV being less than expected. SUMMARY

[0004] The present application provides a communication-sensing-control joint design method for unmanned aerial vehicles, which is a comprehensive method that closely integrates communication, sensing and control, and optimizes the motion trajectory and transmission power of the unmanned aerial vehicle to achieve the functions of sensing and monitoring non-cooperative moving targets and data backhaul to the base station, so as to optimize the system performance.

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

[0006] The present application provides a communication-sensing-control joint design method for unmanned aerial vehicles, comprising:

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

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

[0009] The gradient-based method is used for solving a nonlinear programming problem (P σ, The control strategy of the UAV is obtained.

[0010] As a further description of the above technical solution, the state space equation of the UAV system considers the position of the UAV, the position of the target, the position of the base station, the speed vector of the UAV, the rotation angular velocity of the UAV around three coordinate axes in the body coordinate system, the value of the quaternion of the UAV, the remaining energy of the UAV, the communication throughput, the perception information amount, the motor control amount of the UAV, the communication power, the perception power, the mass of the UAV body, the air resistance coefficient of the body, the moment of inertia of the body coordinate axis, the moment of inertia of the motor rotor around the body center axis, the rotation speed of all motors of the UAV, the body damping coefficient, the power of each motor of the UAV, 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 motor control amount and the transmission power constraint are:

[0012] U 1,min ≤u1(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] Wherein, U 1,min , U 1,max and U j,max are upper and lower limits of the ability calculated according to the motor performance, and are constant parameters, P max is the maximum transmission power; u1(t) is the control amount of the first motor of the UAV, u j (t) is the control amount 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 perception power of the UAV at time t; and T is the task completion time.

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

[0016]

[0017] Wherein, Q c (t) is the communication throughput of the UAV at time t; is Q c(t) the derivative with respect to time, i.e., the communication rate of the UAV at time t; Q r (t) the amount of perception information of the UAV at time t; Q is the data rate of the monitoring video; T is the task completion time. r (t) the derivative with respect to time, i.e., the perception rate of the UAV at time t; R v Q 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 variable time interval t∈[0, T] of the state space equation of the UAV system into a fixed integral interval s∈[0, 1], define the s domain space, then the time t domain is linearly mapped to the s domain, and the relationship is as follows:

[0019]

[0020] Wherein, 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 segmented and discretized into K equal interval segments;

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

[0024] Wherein, s0=0, s K =1, and K is the number of variable parameterization of equal interval segmentation;

[0025] The control variable is represented as

[0026]

[0027] Wherein,

[0028]

[0029] The control quantity is denoted 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 unmanned aerial vehicle communication-sensing-control joint optimization problem (P1) is converted into:

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

[0035] Wherein, α > 0, α is a pre-set penalty weight coefficient, ∈ -α is a penalty weight, (x F , y F , z F ) is the coordinate of the unmanned aerial vehicle flight end position, x(1), y(1), z(1) is the coordinate of the unmanned aerial vehicle at time 1;

[0036] The remaining energy constraint of the unmanned aerial vehicle communication-sensing-control joint optimization problem (P1) when the unmanned aerial vehicle completes the task is converted into:

[0037] Δ ine =∈ -α min{0,E r (1)+∈ β W0} 2

[0038] Wherein, β > 0, β is a pre-set scaling penalty weight coefficient; E r (1) is the remaining energy of the unmanned aerial vehicle at time 1; W0 is the scaling degree base of E r (1); ∈ β is a penalty weight; ∈ β W0 is the relaxation factor of the inequality constraint; min{} is the minimum value function;

[0039] The unmanned aerial vehicle safety flight constraint and communication service quality constraint of the unmanned aerial vehicle communication-sensing-control joint optimization problem (P1) are converted into:

[0040]

[0041] Wherein, W1 is the scaling degree base of the unmanned aerial vehicle height z(s), h min is the minimum safe flight height, W2 is the scaling degree base of the unmanned aerial vehicle communication channel capacity and the unmanned aerial vehicle sensing rate , h max is the maximum safe flight height, R v is the data rate of the monitoring video.

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

[0043] (P σ,∈ ):

[0044] s.t.C0:

[0045] C1: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] C2:T>0

[0048] C3:x(0)=x0

[0049] C8:∈>0

[0050] Wherein, Q r (1) is the cumulative amount of perception information from 0 to 1 moment;δ>0, δ is the degree base of penalty variable factor ∈, γ≥2 is the penalty coefficient of ∈, δ∈ γ It is the penalty term for ∈;U 1,min , U 1,max and U i,max are the upper and lower limits of the ability calculated according to the motor performance, are constant parameters, P max is the maximum transmitting power;x0 is the initial state of the system.

[0051] Compared with the prior art, the beneficial effects of the present application are:

[0052] Optimize the trajectory planning and transmitting power configuration problem of unmanned aerial vehicle;

[0053] Compared with the traditional method, the unmanned aerial vehicle adopts state space modeling, and the control performance, communication and perception performance of the unmanned aerial vehicle are considered jointly, so that the communication-perception-control joint design is provided. Then, the control parameterization method is adopted to convert the infinite-dimensional control variable into the finite-dimensional control parameter, so that the number of to-be-optimized variables is reduced. In addition, the method optimizes the control of the unmanned aerial vehicle instead of the state of the unmanned aerial vehicle, so that the increase of the state of the unmanned aerial vehicle caused by the introduction of the dynamics of the unmanned aerial vehicle is avoided, and the increase of the calculation complexity is avoided. The application adopts the accurate penalty function to convert the state constraint, so that the calculation complexity is low, which makes it more efficient and practical in processing the complex unmanned aerial vehicle control system; especially in the dynamically changing environment, the application can effectively guide the unmanned aerial vehicle to complete the perception task of the non-cooperative target, and ensure the accurate return of data.

[0054] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the following will specifically describe the embodiments of the present application, and the accompanying drawings will be described in detail as follows. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments, and it should be understood that the following drawings only show some embodiments of the present application, and should not be regarded as a limitation to the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

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

[0057] Figure 2 It is a schematic diagram of the parameterization of the control variable in the embodiment.

[0058] Figure 3 It is a trajectory diagram of the unmanned aerial vehicle in the simulation of the embodiment;

[0059] Figure 4 It is a perception mutual information comparison histogram of different speed targets in the simulation of the embodiment. DETAILED DESCRIPTION

[0060] In order to make the purposes, technical solutions and advantages of the embodiments of the present application more clear, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are a part of the embodiments of the present application, rather than all the embodiments.

[0061] Please refer to Figure 1 The present application provides a communication-perception-control joint design method for unmanned aerial vehicle, specifically 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 quantity is

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

[0067]

[0068] In the formula: 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 position coordinate of the base station; is the velocity vector of the UAV at time t;

[0071] is the rotation angular velocity around the three coordinate axes in the UAV body coordinate system 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 perception information amount 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 perception power at time t; m is the mass of the UAV body; 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 body;

[0076] I x , I y , and I z are the moment of inertia matrix of the body coordinate axis, respectively,

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

[0078] Ω(t) is the speed of all motors of the UAV 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 are the damping coefficients of the body;

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

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

[0083] For simplicity of expression, the state space equation of the above UAV system is denoted as

[0084]

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

[0086] (P1):

[0087] s.t. C0: t∈[0,T]

[0088] C1:U 1,min ≤u1(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] C2:T>0

[0091] C3:x(0)=x0

[0092] C4:h min ≤z(t)≤h max ,t∈[0,T]

[0093] C5: t∈[0,T]

[0094] C6:E r (T)≥0

[0095] C7:x(T)=x F ,y(T)=y F ,z(T)=z F

[0096] Constraint C0 in problem (P1) is the state space equation of the UAV system;

[0097] C1 is the motor control quantity and transmission power constraint of the UAV, U 1,min , U 1,max and U i,max are the upper and lower limits of the ability calculated according to the motor performance, which are constant parameters, and P max is the maximum transmission power;

[0098] C2 ensures that the task completion time T meets the actual physical meaning;

[0099] C3 is the initial state constraint of the system, and x0 is the initial state of the system;

[0100] C4 is the safe flight constraint of the UAV, h min is the minimum safe flight altitude, and h max is the maximum safe flight altitude;

[0101] C5 is the communication quality of service constraint, and the communication channel capacity is greater than the required transmission of perception data, wherein R vTo monitor the data rate of the video;

[0102] C6 requires the remaining energy of the UAV to complete the task to be greater than or equal to 0;

[0103] C7 is the flight terminal position constraint of the UAV, (x F ,y F ,z F ) is the terminal position coordinates.

[0104] 3. Time scale transformation is performed on the state space equation of the UAV system, as follows:

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

[0106] The s-domain space is defined, and the time t-domain is linearly mapped to the s-domain as follows:

[0107]

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

[0109] As shown in Figure 2 , the continuous control variable in the s-domain is segmented and discretized into K equidistant interval segments, and the parameter variable σ i,k , k∈{0,1,…,K-1,K} represents the interval [s k-1 ,s k ) equivalent control variable, where s0=0, s K =1, and K is the number of variable parameterization of equidistant segmentation. The control variable is represented as

[0110]

[0111] where

[0112]

[0113] The control variable u(t) is denoted as

[0114] i∈{1,2,…,6}.

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

[0116] The terminal position constraint C7 in the problem (P1) is converted into

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

[0118] where, α > 0, α is a pre-set penalty weight coefficient, ∈ -α is a penalty weight.

[0119] For the terminal energy inequality constraint C6 in problem (P1), it is transformed into

[0120] Δ ine = ∈ -α min{0, E r (1) + ∈ β W0} 2

[0121] where, β > 0, β is a pre-set scaling penalty weight coefficient; E r (1) is the residual energy of the UAV at time 1; W0 is the scaling degree base of E r (1); ∈ β is a penalty weight; ∈ β W0 is a relaxation factor of the inequality constraint; min{} is a minimum function, and its operation example is as follows:

[0122]

[0123] For the process state inequality constraints C4, C5 in problem (P1), they are transformed into

[0124]

[0125] where, W1 is the scaling degree base of the UAV height z(s), and W2 is the scaling degree base of the UAV communication channel capacity and the UAV perception rate .

[0126] 6. Based on time scale transformation, control variable parameterization and accurate 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] s.t.C0:

[0129] C1:U 1,min ≤ σ 1,k ≤ U 1,max , |σ j,k | ≤ Uj,max j = 2, 3, 4,

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

[0131] C2: T > 0

[0132] C3: x(0) = x0

[0133] C8: ∈ > 0

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

[0135] 7. For the nonlinear programming problem (P σ,∈ ), a gradient-based method is used to solve it, such as sequential quadratic programming (SQP), and finally the control strategy of the unmanned aerial vehicle, i.e. the control amount u(t) is obtained.

[0136] The communication-perception-control joint design method for unmanned aerial vehicles described in the application is simulated as follows.

[0137] Consider that the unmanned aerial vehicle communication and perception integrated system flies from the starting point to the ending point, perceives a single target during flight, and transmits the perception data back to the ground base station in real time, and the entire flight task is limited by the on-board energy. The starting point coordinates of the unmanned aerial vehicle are set as The ending point coordinates are set as The base station coordinates are set as The given on-board energy E total = 30 kJ, and other unmanned aerial vehicle communication and perception integrated system related parameters are shown in Table 1.

[0138] In problems (P1) and (P2), the initial state of the unmanned aerial vehicle is set as

[0139]

[0140] and

[0141]

[0142] The maximum acceleration of the unmanned aerial vehicle is set as 26 m / s 2 .

[0143] Table 1 Unmanned aerial vehicle communication and perception integrated system parameters

[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 The motion is uniform along the negative direction of the X-axis, and three motion speeds of 5m / s, 10m / s and 15m / s are considered respectively.

[0148] The reference values ​​of the constant parameters in the exact penalty function are set to α=2, β=2, γ=3, W0=W1=W2=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, to achieve better perception performance, the drone first flies toward the target. However, due to communication capacity limitations, the drone must maintain a stable perception distance with the target to ensure that the communication capacity exceeds the perception rate. When energy consumption reaches a certain level, the drone begins to fly toward the destination, away from the target, causing the perception rate to gradually decrease, ultimately reaching the destination before energy is exhausted. Simulation results show that the drone needs to strike a balance between the flight destination and the target based on onboard energy consumption to ensure that the drone can successfully return to the destination and achieve maximum perception mutual information without violating communication constraints.

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

[0151] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A communication-perception-control joint design method for unmanned aerial vehicles, characterized by: 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,∈ Solve and obtain the control strategy of the UAV; 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 pre-set penalty weight coefficient, ∈ -α is the penalty weight, (x F ,y F ,z F ) is the coordinate of the drone’s flight endpoint, x(1), y(1), z(1) are the coordinates of the drone 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 UAV at time 1; W0 is E r (1) the scaling cardinality; ∈ 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: Among them, W1 is the scaling base of the drone height z(s), h min is the minimum safe flight altitude, W2 is the UAV communication channel capacity and drone perception rate The scaling factor, h max is the highest safe flight altitude, R v The data rate for monitoring video.

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 quantity, 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 central 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,max and U j,max is the upper limit of capacity calculated according to motor performance, U 1,min is the lower limit of capacity calculated according to motor performance, U 1,min 、U 1,max and U j,max are all constant parameters, 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 sensing power of the UAV at time t; T is the mission 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) The derivative with respect to time, i.e., the communication rate of the UAV at time t; Q r (t) is the amount of perception information of the UAV at time t; Q r (t) The derivative with respect to time, i.e., the perception 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 3, characterized in that: Time scale transformation is used to transform the state space equations of the UAV system into The variable time interval t∈[0,T] is converted into a fixed integration interval s∈[0,1], and the s-domain space is defined. 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 control amount Recorded as 7. The design method according to claim 6, characterized in that: Nonlinear programming problem P σ,∈ for: 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 ∈; x0 is the initial state of the system.

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