Time-varying full-state constraint fixed-point time cooperative control method

Abstract

The invention relates to a time-varying full-state constraint fixed-point time cooperative control method, and belongs to the field of intelligent cooperative control of a fixed wing formation system. Comprising the following steps: 1, establishing a six-degree-of-freedom dynamic model of a fixed wing formation system; 2, aiming at the six-degree-of-freedom dynamical model established in the step 1, establishing a thrust and control surface actuator fault model included in the six-degree-of-freedom dynamical model; 3, establishing a formation system topological relation comprising a plurality of six-degree-of-freedom dynamical models in the step 1; 4, defining and establishing a fixed time performance function for realizing fixed-point time state constraint; 5, solving a speed layer adjacency error and an attitude layer adjacency error; 6, constructing a barrier Lyapunov function of a speed layer and an attitude layer; 7, designing a speed layer thrust control law and an adaptive updating law; and 8, designing an attitude layer virtual control law, a control surface control law and a self-adaptive updating law. The problem of formation fixed point time fault-tolerant control of the fixed wing formation system under full-state constraint is solved.

Description

Technical Field

[0001] The invention relates to a time-varying full-state constrained fixed-point time cooperative control method, belonging to the field of intelligent cooperative control of fixed-wing formation systems. Background Art

[0002] Since formation collaboration technology can effectively improve the effectiveness of cluster tasks and reduce costs, it has achieved rapid development in formation control, speed and attitude synchronization control. The existing formation system model usually adopts a three-degree-of-freedom model, and only considers output constraints when performing state constraints. This is obviously different from the six-degree-of-freedom model that actually requires full-state constraints. At present, in the field of strict feedback systems, the use of obstacle Lyapunov functions for full-state constraints has become relatively mature, but research on six-degree-of-freedom fixed-wing formation systems is very scarce.

[0003] Existing research results show that emergencies often occur in complex environments, and the formation system needs to achieve rapid convergence of attitude and speed to avoid risks. Although the formation system uses a finite time control method to achieve convergence of attitude and speed within a finite time, the convergence time cannot be set in advance. At present, some progress has been made in the field of multi-agent and formation systems in fixed-point time control methods, but there is a lack of in-depth research on six-degree-of-freedom fixed-wing formation systems. In addition, component aging and external interference can cause actuator failures, and fault-tolerant control methods need to be applied to enhance system robustness and reduce accident rates. Summary of the invention

[0004] The purpose of the present invention is to solve the deficiencies of the above-mentioned prior art and to provide a time-varying full-state constrained fixed-point time collaborative control method, which solves the problem of fixed-point time fault-tolerant control of fixed-wing formation systems under full-state constraints.

[0005] The time-varying full-state constrained fixed-point time coordinated control method of the present invention is special in that it comprises the following steps:

[0006] Step 1: Establish a six-degree-of-freedom dynamic model of the fixed-wing formation system;

[0007] Step 2: establishing thrust and rudder actuator fault models included in the six-degree-of-freedom dynamics model established in step 1;

[0008] Step 3: Establish the topological relationship of the formation system including multiple six-degree-of-freedom dynamic models in step 1;

[0009] Step 4: define and establish a fixed-time performance function for implementing the fixed-point time state constraint;

[0010] Step 5: Based on the topological relationship of the formation system in step 3, the velocity layer adjacency error and the attitude layer adjacency error are solved respectively;

[0011] Step 6: Based on the fixed time performance function in step 4, construct the obstacle Lyapunov function of the speed and attitude layers respectively;

[0012] Step 7: Based on the velocity layer adjacency error in step 5, a velocity layer thrust control law and an adaptive update law are designed to realize the state constraint of the velocity layer;

[0013] Step 8. Based on the attitude layer adjacency error in step 5, in order to realize the state constraint of the attitude layer, the attitude layer virtual control law, the rudder control law and the adaptive update law are designed.

[0014] Preferably, in step 1, the six-degree-of-freedom dynamic model of the fixed-wing formation system is established as follows:

[0015]

[0016] Among them, I i =[x i ,y i ,z i ] T is the inertial position, v i =[u i ,v i ,w i ] T is the linear speed, represents the attitude angle, ω i =[p i ,q i ,r i ] T is the angular velocity, and the thrust vector along the body axis is δ i =[δ ai ,δ ei ,δ ri ] T is the control input, the gravity acceleration is g = [0,0,g z ] T , the external disturbances of velocity and attitude layers are and δ ei ,δ ai ,δ ri Represents the elevator, aileron and rudder respectively, the inertia tensor matrix The expression is:

[0017] is the transformation matrix from the body coordinate system to the inertial coordinate system, is the conversion matrix of angular velocity and attitude angle time derivative, and the expressions are:

[0018]

[0019] Among them, S a and C a Represent sin(a) and cos(a) respectively. For pneumatic power, is the torque, and the expressions are:

[0020]

[0021] in, is the dynamic pressure, α i is the angle of attack, β i is the sideslip angle, ρ is the air density, B i is the wingspan, S i is the wing surface area, is the mean chord length, K Di ,K Yi ,K Li ,K l ' i ,K′ Mi ,K n ' i are dimensionless aerodynamic coefficients, and the conversion matrix The expression is:

[0022]

[0023] The control effectiveness matrix is:

[0024]

[0025] Preferably, in step 2, the thrust and rudder actuator actuator fault models are respectively:

[0026]

[0027] Among them, P xi0 represents the thrust control input, is the thrust drive efficiency, P xif It is a thrust bounded fault.

[0028]

[0029] Among them, δ i0 =[δ ai0 ,δ ei0 ,δ ri0 ] T , δ if =[δ aif ,δ eif ,δ rif ]T The actuator is stuck or has an offset fault. is the rudder actuator efficiency, δ ji0 represents the rudder actuator control input, δ jif The rudder actuator is stuck or has an offset fault.

[0030] Preferably, in step 3, the topological relationship of the formation system including the six-degree-of-freedom dynamic model in step 1 is established as follows: The meaning is as follows:

[0031] is an undirected graph representing the interaction topology between fixed-wing systems. represents the set of fixed-wing systems, For the edge set, define the adjacency matrix When n i and n j When connected, a ij =a ji =1, otherwise a ij =a ji = 0, define the Laplace matrix as: Λ = Δ-A, where Δ is the in-degree matrix. i The connection matrix is ​​denoted as: It includes all the Node n j .

[0032] Preferably, in step 4, the fixed time performance function is defined as:

[0033] Definition 1: A smooth function ρ(t) is called a fixed-time performance function if the following conditions are satisfied:

[0034] 1) ρ(t) is positive and decreases with time within t∈[0,T);

[0035] 2) For all t ≥ T, ρ(t) ≡ τ ∞ >0, where τ ∞ is the positive parameter to be designed;

[0036] According to Definition 1, the fixed time performance function in step 4 is established as:

[0037]

[0038] in, and r is a positive constant.

[0039] Preferably, in step 5, the derivation method for solving the velocity and attitude layer adjacency error is:

[0040]

[0041] in, is the tracking error, λ i1 ,λ i2 ,λ i3 and λ i4 It is a positive constant used to adjust the convergence speed of the combined velocity. Based on the velocity and attitude layer adjacency errors, the velocity layer adjacency error e vi and posture adjacency error Derivative:

[0042]

[0043] in,

[0044] Approximating Uncertain Term Using RBF Neural Network

[0045]

[0046] Among them, v i =[v i1 ,v i2 ,v i3 ] T , Preferably, in order to realize the state constraint of the velocity layer, based on the fixed time performance function, the following velocity layer state constraint Γ is constructed: 1 ={e vi |-l vi (t) <e vi <h vi (t),l vi (t)>0,h vi (t)>0}, the velocity layer barrier Lyapunov function described in step 6 is established as:

[0047]

[0048] in, Ξ vi , is the adaptive parameter, γ vi1 ,γ vi2 ,γ vi3 is a normal number, is the fixed-point time performance function constructed in step 4 and satisfies:

[0049]

[0050] Preferably, in order to realize the state constraint of the attitude layer, based on the fixed time performance function, the following attitude layer state constraint Γ is constructed: 2={e mi ∣-l mi (t) <e mi <h mi (t),l mi (t)>0andh mi (t)>0, m=φ,θ,ψ}, the attitude layer obstacle Lyapunov function described in step 6 is established as:

[0051]

[0052] in, m={φ,θ,ψ}, is the adaptive parameter, γ ωi1 ,γ ωi2 is a positive constant, e ωi =ω i -ω di ,ω di is the virtual control law to be designed, is the fixed-point time performance function constructed in step 4 and satisfies:

[0053]

[0054] The term in the attitude layer obstacle Lyapunov function described in step 6 of the differentiation have to:

[0055] Approximating the Uncertain Term ζ′ Using RBF Neural Network ωi +ξ ωi +d ωi :

[0056]

[0057] in, Preferably, in step 7, the velocity layer thrust control law is:

[0058]

[0059] in, Preferably, in step seven, the speed layer adaptation law is as follows:

[0060]

[0061] Among them, γ vi1 ,γ vi2 ,γ vi3 ,σ vi1 ,σ vi2 and σ vi3 is the positive parameter to be designed, and the adaptive parameter satisfies θvi =β vi sup t≥0 |T xif |,β vi =1 / ρ Ti , the estimated error is Preferably, in step eight, the attitude layer virtual control law is:

[0062]

[0063] in,

[0064] is a real diagonal matrix.

[0065] Preferably, in step eight, the attitude layer control surface control law is as follows:

[0066]

[0067] in, I = diag{1,1,1}.

[0068] Preferably, in step eight, the attitude layer adaptive update law is as follows:

[0069]

[0070] Among them, γ ωi1 ,γ ωi2 ,σ ωi1 ,σ ωi2 is a positive number, and the adaptive parameters satisfy

[0071] β ωi =1 / g ωi The estimated error is

[0072] The beneficial effects of the present invention are:

[0073] 1. For the speed and attitude layers of the fixed-wing system, the fixed-point time fault-tolerant control strategy of the formation under the full-state constraints of the speed and attitude layers is designed respectively. By combining the fixed-point time performance function and the obstacle Lyapunov function, the fixed-point time control under the full-state constraints of the multi-fixed-wing formation system is realized;

[0074] 2. Considering the thrust and rudder actuator failures in the system, an adaptive update law for fault parameters is designed to solve the actuator failure problem.

[0075] The present invention designs a time-varying full-state constrained fixed-point time cooperative control method to solve the cooperative control problem of a six-degree-of-freedom fixed-wing formation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a flow chart of a time-varying full-state constrained fixed-point time cooperative control method of the present invention;

[0077] Figure 2 is the roll-on angle tracking error diagram;

[0078] Figure 3 is the pitch angle tracking error diagram;

[0079] Figure 4 is the heading angle tracking error diagram;

[0080] Figure 5 It is the velocity tracking error diagram. DETAILED DESCRIPTION

[0081] For better understanding and implementation, a specific implementation detailed description of a time-varying full-state constrained fixed-point time cooperative control method of the present invention is given below in conjunction with the accompanying drawings.

[0082] Example 1

[0083] A time-varying full-state constrained fixed-point time coordinated control method of this embodiment, Figure 1 The flowchart of the method of the present invention is shown, and the method of the present invention specifically comprises the following steps:

[0084] Step 1: Establish a six-degree-of-freedom dynamic model of the fixed-wing system;

[0085] In step 1, the six-degree-of-freedom dynamics model of the fixed-wing system is as follows:

[0086]

[0087] Among them, I i =[x i ,y i ,z i ] T is the inertial position, v i =[u i ,v i ,w i ] T is the linear speed, represents the attitude angle, ω i =[p i ,q i ,r i ] T is the angular velocity, and the thrust vector along the body axis is δi =[δ ai ,δ ei ,δ ri ] T is the control input. The acceleration due to gravity is g = [0, 0, g z ] T , the external disturbances of velocity and attitude layers are and δ ei ,δ ai ,δ ri Represent the elevator, aileron and rudder respectively. The inertia tensor matrix The expression is:

[0088]

[0089] is the transformation matrix from the body coordinate system to the inertial coordinate system, is the conversion matrix of angular velocity and attitude angle time derivative, and the expressions are:

[0090]

[0091] Among them, S a and C a Represent sin(a) and cos(a) respectively. For pneumatic power,

[0092] is the torque, and the expressions are:

[0093]

[0094] in, is the dynamic pressure, α i is the angle of attack, β i is the sideslip angle, ρ is the air density, B i is the wingspan, S i is the wing surface area, is the mean chord length, K Di ,K Yi ,K Li ,K l ' i ,K′ Mi ,K n ' i are dimensionless aerodynamic coefficients, and the conversion matrix The expression is:

[0095]

[0096] The control effectiveness matrix is:

[0097]

[0098] Step 2: Design thrust and rudder actuator fault models for the six-degree-of-freedom dynamics model in step 1;

[0099] In step 2, the thrust and rudder actuator fault models are:

[0100]

[0101] Among them, δ i0 =[δ ai0 ,δ ei0 ,δ ri0 ] T , δ if =[δ aif ,δ eif ,δ rif ] T ,

[0102] (j = a, e, r) is the rudder actuator efficiency, δ ji0 represents the rudder actuator control input, δ jif The rudder actuator is stuck or has an offset fault.

[0103]

[0104] Among them, P xi0 represents the thrust control input, is the thrust drive efficiency, P xif It is a thrust bounded fault.

[0105] Step 3: Establish a formation system topology relationship including multiple six-degree-of-freedom fixed-wing models in step 1;

[0106] The topological relationship of step three The meaning is as follows:

[0107] is an undirected graph representing the interaction topology between fixed-wing systems. represents the set of fixed-wing systems, is the edge set. Define the adjacency matrix When n i and n j When connected, a ij =a ji =1, otherwise a ij =a ji = 0. Define the Laplacian matrix as: Λ = Δ-A, where Δ is the in-degree matrix. Node n i The connection matrix is ​​denoted as: It includes all the Node n j .

[0108] Step 4: define and establish a fixed-time performance function for implementing the fixed-point time state constraint;

[0109] The definition and expression of the fixed-point time performance function in step 4 are as follows:

[0110] Definition 1: A smooth function ρ(t) is called a fixed-time performance function if the following conditions are satisfied:

[0111] 1) ρ(t) is positive and decreases with time within t∈[0,T);

[0112] 2) For all t ≥ T, ρ(t) ≡ τ ∞ >0, where τ ∞ is the positive parameter to be designed.

[0113] The fixed time performance function is:

[0114]

[0115] in, and r is a normal number.

[0116] Step 5: Based on the topological model in step 3, solve the adjacency errors of the velocity and attitude layers respectively;

[0117] In the step 5, V r and is the speed and attitude to be tracked, and the adjacency error solution of the speed and attitude layer is:

[0118]

[0119] in, is the tracking error, λ i1 ,λ i2 ,λ i3 and λ i4 It is a positive constant used to adjust the convergence speed of the combined velocity.

[0120] The velocity layer adjacency error e vi and posture adjacency error Derivative:

[0121]

[0122] in,

[0123] Approximating the Uncertain Term ζ in Claim 5 Using RBF Neural Network vi +d vi :

[0124]

[0125] Among them, v i =[v i1 ,v i2 ,v i3 ] T , Meets the boundedness condition.

[0126] Step 6: Based on the fixed time performance function in step 4, construct the obstacle Lyapunov function of the speed and attitude layers respectively;

[0127] In step 6, in order to realize the speed layer state constraint, a speed layer state constraint Γ is established based on a fixed time performance function. 1 ={e vi |-l vi (t) <e vi <h vi (t),l vi (t)>0,h vi (t)>0}, the designed velocity layer Lyapunov function is:

[0128]

[0129] in, Ξ vi , is the adaptive parameter, γ vi1 ,γ vi2 ,γ vi3 is a normal number, is the fixed-point time performance function constructed in step 4 and satisfies:

[0130]

[0131] In step 6, in order to realize the state constraint of the attitude layer, the attitude layer state constraint Γ is constructed based on the fixed time performance function. 2 ={e mi ∣-l mi (t) <e mi <h mi (t),l mi (t)>0andh mi (t)>0,m=φ,θ,ψ}, the designed attitude layer Lyapunov function is:

[0132]

[0133] in, m={φ,θ,ψ}, is the adaptive parameter, γ ωi1 ,γ ωi2 is a positive constant, e ωi =ω i -ω di ,ω di is the virtual control law to be designed, is the fixed-point time performance function constructed in step 4 and satisfies:

[0134]

[0135] Step 7: Based on the velocity layer adjacency error in step 5, a velocity layer thrust control law and an adaptive update law are designed;

[0136] In step 7, the velocity layer thrust control law is:

[0137]

[0138] in,

[0139] In step 7, the velocity layer adaptive law is:

[0140]

[0141] Among them, γ vi1 ,γ vi2 ,γ vi3 ,σ vi1 ,σ vi2 and σ vi3 is the positive parameter to be designed, and the adaptive parameter satisfies θ vi =β vi sup t≥0 |T xif |,β vi =1 / ρ Ti , the estimated error is Differentiation step six have to:

[0142]

[0143] Approximating the Uncertain Term ζ′ Using RBF Neural Network ωi +ξ ωi +d ωi :

[0144]

[0145] in, Meets the boundedness condition.

[0146] Step 8. Based on the attitude adjacency error in step 5, design the attitude layer virtual control law, rudder surface control law and adaptive update law.

[0147] The virtual control law of the attitude layer in step eight is:

[0148]

[0149] in,

[0150] is a real diagonal matrix.

[0151] The attitude layer control surface control law in step eight is:

[0152]

[0153] in, I = diag{1,1,1}.

[0154] The attitude layer adaptation law described in step eight is:

[0155]

[0156] Among them, γ ωi1 ,γ ωi2 ,σ ωi1 ,σ ωi2 is a positive number, and the adaptive parameters satisfy β ωi =1 / g ωi The estimated error is

[0157] Example 2

[0158] Considering that the six-degree-of-freedom fixed-wing system is a more common system than the fixed-wing system, a six-degree-of-freedom fixed-wing formation composed of three fixed-wing systems is taken as an example to further prove the effectiveness of the proposed time-varying full-state constrained fixed-point time cooperative control method. The adjacency matrix of the six-degree-of-freedom fixed-wing system is:

[0159]

[0160] The initial states of the three fixed-wing systems are:

[0161] V 1 =45m / s,V 2 =V 3 =40m / s,ω 1 =ω2 =ω 3 =[0,0,0] T deg / s,

[0162]

[0163] The external disturbance suffered by the attitude layer is:

[0164]

[0165] The actuator failures suffered by the attitude layer are:

[0166] In t∈[3,10], the actuator efficiency matrix is The bias matrix is ​​δ if =diag{0,0,0}.

[0167] The execution task background is set as follows:

[0168] Three six-degree-of-freedom fixed-wing systems perform the formation speed and attitude fixed-point time tracking task. It is required to achieve the convergence of the combined speed to 50m / s within 7s and the attitude angle to [φ] within 5s under the consideration of external disturbances and actuator failures and asymmetric constraints on speed and state. r ,θ r ,ψ r ] T =[5,5,5] T deg,.

[0169] The fixed-point time performance function parameters and asymmetric constraint parameters are: τ ∞ =0.5,ε=1, r=0.1,θ=0.5In(11 / 9).

[0170] The attitude tracking error of the fixed-wing system is shown in Figure 2-4 , speed tracking error see Figure 5 The attitude angle tracking error and velocity tracking error are obviously always within the constraint range during the entire flight process, and the state constraint can be indirectly realized through the error constraint. Within the controllable range, the error range is determined by the asymmetric constraint coefficient The smaller the coefficient, the faster the performance function converges and the smaller the constraint range. The time is determined by the time parameter T of the performance function between fixed points. if Decision, T if The smaller the value, the faster the convergence. The convergence accuracy is determined by the parameter τ ∞ Determine, τ ∞The smaller the value, the higher the accuracy. The simulation shows that this method not only effectively implements the asymmetric full-state constraint of the fixed-wing system and solves the actuator failure problem, but also can preset the convergence time and accuracy of the fixed-wing system formation.

[0171] The above calculation examples of the present invention are only used to explain the calculation model and calculation process of the present invention in detail, and are not intended to limit the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. A time-varying full-state constrained fixed-point time cooperative control method, characterized in that The following steps are involved: Step 1: Establish a six-degree-of-freedom dynamic model of the fixed-wing formation system; Step 2: establishing thrust and rudder actuator fault models included in the six-degree-of-freedom dynamics model established in step 1; Step 3: Establish the topological relationship of the formation system including multiple six-degree-of-freedom dynamic models in step 1; Step 4: define and establish a fixed-time performance function for implementing the fixed-point time state constraint; Step 5: Based on the topological relationship of the formation system in step 3, the velocity layer adjacency error and the attitude layer adjacency error are solved respectively; Step 6: Based on the fixed time performance function in step 4, construct the obstacle Lyapunov function of the speed and attitude layers respectively; Step 7: Based on the velocity layer adjacency error in step 5, a velocity layer thrust control law and an adaptive update law are designed to realize the state constraint of the velocity layer; Step 8. Based on the attitude layer adjacency error in step 5, in order to realize the state constraint of the attitude layer, the attitude layer virtual control law, the rudder control law and the adaptive update law are designed.

2. A time-varying full-state constrained fixed-point time cooperative control method according to claim 1, characterized in that: In step 1, the six-degree-of-freedom dynamic model of the fixed-wing formation system is established as follows: Among them, I i =[x i ,y i ,z i ] T is the inertial position, v i =[u i ,v i ,w i ] T is the linear speed, represents the attitude angle, ω i =[p i ,q i ,r i ] T is the angular velocity, and the thrust vector along the body axis is δ i =[δ ai ,δ ei ,δ ri ] T is the control input, the gravity acceleration is g = [0,0,g z ] T , the external disturbances of velocity and attitude layers are and δ ei ,δ ai ,δ ri Represents the elevator, aileron and rudder respectively, the inertia tensor matrix The expression is: is the transformation matrix from the body coordinate system to the inertial coordinate system, is the conversion matrix of angular velocity and attitude angle time derivative, and the expressions are: Among them, S a and C a Represent sin(a) and cos(a) respectively. For pneumatic power, is the torque, and the expressions are: in, is the dynamic pressure, α i is the angle of attack, β i is the sideslip angle, ρ is the air density, B i is the wingspan, S i is the wing surface area, is the mean chord length, K Di ,K Yi ,K Li ,K′ li ,K′ Mi ,K′ ni are dimensionless aerodynamic coefficients, and the conversion matrix The expression is: The control effectiveness matrix is:

3. A time-varying full-state constrained fixed-point time cooperative control method according to claim 1, characterized in that: In step 2, the thrust and rudder actuator fault models are: Among them, P xi0 represents the thrust control input, is the thrust drive efficiency, P xif For thrust bounded fault: Among them, δ i0 =[δ ai0 ,δ ei0 ,δ ri0 ] T , δ if =[δ aif ,δ eif ,δ rif ] T The actuator is stuck or has an offset fault. is the rudder actuator efficiency, δ ji0 represents the rudder actuator control input, δ jif The rudder actuator is stuck or has an offset fault.

4. A time-varying full-state constrained fixed-point time cooperative control method according to claim 1, characterized in that: In step 3, the topological relationship of the formation system including the six-degree-of-freedom dynamic model in step 1 is established as follows: The meaning is as follows: is an undirected graph representing the interaction topology between fixed-wing systems. represents the set of fixed-wing systems, For the edge set, define the adjacency matrix When n i and n j When connected, a ij =a ji =1, otherwise a ij =a ji = 0, define the Laplace matrix as: Λ = Δ-A, where Δ is the in-degree matrix. i The connection matrix is ​​denoted as: It includes all the Node n j .

5. A time-varying full-state constrained fixed-point time cooperative control method according to claim 1, characterized in that: In step 4, the fixed time performance function is defined as: Definition 1: A smooth function ρ(t) is called a fixed-time performance function if the following conditions are satisfied: 1) ρ(t) is positive and decreases with time within t∈[0,T); 2) For all t ≥ T, ρ(t) ≡ τ ∞ >0, where τ ∞ is the positive parameter to be designed; According to Definition 1, the fixed time performance function in step 4 is established as: in, and r is a positive constant.

6. A time-varying full-state constrained fixed-point time cooperative control method according to claim 1, characterized in that: In step 5, the derivative method for solving the velocity and attitude layer adjacency error is: in, is the tracking error, λ i1 ,λ i2 ,λ i3 and λ i4 It is a positive constant used to adjust the convergence speed of the combined velocity. Based on the velocity and attitude layer adjacency errors, the velocity layer adjacency error e vi and posture adjacency error Derivative: in, Approximating Uncertain Term Using RBF Neural Network Among them, v i =[v i1 ,v i2 ,v i3 ] T , Meets the boundedness condition.

7. A time-varying full-state constrained fixed-point time cooperative control method according to claim 1 or 6, characterized in that: In order to realize the state constraint of the velocity layer, based on the fixed time performance function, the following velocity layer state constraint Γ1 = {e vi |-l vi (t) <e vi <h vi (t),l vi (t)>0,h vi (t)>0}, the velocity layer barrier Lyapunov function described in step 6 is established as: in, Ξ vi , is the adaptive parameter, γ vi1 ,γ vi2 ,γ vi3 is a positive constant, is the fixed-point time performance function constructed in step 4 and satisfies: In order to realize the state constraint of the attitude layer, based on the fixed time performance function, the following attitude layer state constraint Γ2 = {e mi ∣-l mi (t) <e mi <h mi (t),l mi (t)>0andh mi (t)>0, m=φ,θ,ψ}, the attitude layer obstacle Lyapunov function described in step 6 is established as: in, m={φ,θ,ψ}, is the adaptive parameter, γ ωi1 ,γ ωi2 is a positive constant, e ωi =ω i -ω di ,ω di is the virtual control law to be designed, is the fixed-point time performance function constructed in step 4 and satisfies: The term in the attitude layer obstacle Lyapunov function described in step 6 of the differentiation have to: Approximating the Uncertain Term ζ′ Using RBF Neural Network ωi +ξ ωi +d ωi : in, Meets the boundedness condition.

8. A time-varying full-state constrained fixed-point time cooperative control method according to claim 1, characterized in that: In step 7, the velocity layer thrust control law is: in, In step 7, the speed layer adaptation law is as follows: Among them, γ vi1 ,γ vi2 ,γ vi3 ,σ vi1 ,σ vi2 and σ vi3 is the positive parameter to be designed, and the adaptive parameter satisfies θ vi =β vi sup t≥0 |T xif |,β vi =1 / ρ Ti , the estimated error is 9. A time-varying full-state constrained fixed-point time cooperative control method according to claim 1, characterized in that: In step eight, the attitude layer virtual control law is: in, is a real diagonal matrix; In step eight, the attitude layer control surface control law is as follows: in, 10. A time-varying full-state constrained fixed-point time cooperative control method according to claim 9, characterized in that: In step eight, the attitude layer adaptive update law is as follows: Among them, γ ωi1 ,γ ωi2 ,σ ωi1 ,σ ωi2 is a positive number, and the adaptive parameters satisfy β ωi =1 / g ωi The estimated error is

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