Full-cushion hovercraft multi-control surface vector coordination control method

Abstract

A kind of full-cushion hovercraft multi-control surface vector coordination control method, it is related to hovercraft motion control technical field, in view of the problem that the control effect is poor by using single control surface from low speed to high speed in the process of hovercraft navigation, the control surface in the existing hovercraft model is air duct propeller, air rudder and vector nozzle.In the process of hovercraft operation, the controller for processing the force and torque output by the control surface selects the controller of the application, which will produce better control benefit, and has better robustness, high convergence accuracy, good control effect and other characteristics.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hovercraft motion control, in particular to a full-pad hovercraft multi-control surface vector coordination control method. BACKGROUND

[0002] In the aspect of safe navigation control of the hovercraft, a multi-operation surface strategy is proposed, a force (moment) coordination distribution module is used, a bow nozzle is used to provide lateral force to offset the sideslip angle, and an air rudder is used to provide a turning moment to make the heading track the desired trajectory, but the control effect of using a single control surface is poor during the hovercraft navigation from low speed to high speed. SUMMARY

[0003] The purpose of the present application is to solve the problem of poor control effect of using a single control surface during the hovercraft navigation from low speed to high speed, and to propose a full-pad hovercraft multi-control surface vector coordination control method.

[0004] The technical scheme adopted by the present application to solve the above technical problem is:

[0005] A full-pad hovercraft multi-control surface vector coordination control method, the method comprising the following steps:

[0006] Step one: establishing a four-degree-of-freedom kinematics model and a dynamics model of the full-pad hovercraft, and designing a fixed-time non-singular fast terminal sliding mode controller according to the four-degree-of-freedom kinematics model and the dynamics model of the full-pad hovercraft;

[0007] Step two: obtaining the total force output by the fixed-time non-singular fast terminal sliding mode controller, and combining the force and turning moment of the air duct propeller, the air rudder and the vector nozzle to establish a multi-control surface control distribution equation;

[0008] Step three: constructing an objective function according to the total force and the turning moment of the air duct propeller, the air rudder and the vector nozzle;

[0009] Step four: solving according to the objective function and the multi-control surface control distribution equation to obtain the turning moment of the air duct propeller, the air rudder and the vector nozzle.

[0010] Further, the kinematics model is represented as:

[0011]

[0012] Wherein, u, v, p, r respectively represent the longitudinal velocity, lateral velocity, roll angle velocity and turning angle velocity of the hovercraft in the ship body coordinate system, x, y, ψ respectively represent the position of the ship body centroid in the north-east coordinate system, the roll angle and the heading angle, Representing x, y, respectively The first derivative of ψ.

[0013] Furthermore, the dynamic model is expressed as:

[0014]

[0015] Where m represents the state vector, J x J z F represents the moments of inertia about the x-axis and z-axis, respectively. x ,F y M represents the net external force on the hull in the x-axis and y-axis directions, respectively. x M z These represent the resultant moments of the ship's hull about the x-axis and z-axis, respectively. This represents the first derivative of u. This represents the first derivative of v. This represents the first derivative of p. Let r be the first derivative.

[0016] Furthermore, the sliding surface of the fixed-time non-singular fast terminal sliding mode controller is represented as follows:

[0017]

[0018] Where a2 > 1, i=u,ψ, k1>0 and k2>0, sig a (·)=|x a sign(·),

[0019] Where sign(·) is the sign function, S i The sliding surface is represented by k1, k2, a1, and a2, which are adjustment coefficients. k1, k2, a1, and a2 are used to ensure the upper bound of the convergence time.

[0020] Furthermore, the control law of the fixed-time non-singular fast terminal sliding mode controller is expressed as follows:

[0021]

[0022] Where, τ r e represents the resultant force. u Indicates speed error, F represents the derivative of the desired velocity. xD J represents the longitudinal resultant force. Z E represents the turning torque. ψ Indicates heading error, diag represents the mathematical symbol, and Λ represents the symbolic formula. Let α and β represent the expected forward derivative, and let α and β represent positive constants.r1 and r2 denotes a constant, denotes the second derivative of the desired heading.

[0023] Further, the multi-operating surface coordinated control equation is expressed as

[0024] τ = BU + s

[0025] τ = [X Y N p N r N n ] T

[0026]

[0027]

[0028] wherein τ denotes the total resultant force output by the upper controller, B denotes the control distribution matrix, U denotes a function about the pitch angle, the rudder angle, the vector nozzle rotation angle, s denotes the error value, X denotes the longitudinal control force, Y denotes the lateral control force, N p denotes the turning moment of the air duct propeller, N r denotes the turning moment of the air rudder, N n denotes the turning moment of the vector nozzle, (x n1 ,y n1 ) and (x n2 ,y n2 ) denote the vector nozzle coordinates located at the bow of the ship, (x p1 ,y p1 ) and (x p2 ,y p2 ) denote the vector nozzle coordinates located at the stern of the ship, (x r1 ,y r1 ) and (x r2 ,y r2 ) denote the air rudder coordinates, respectively denote the left and right pitch angles, δ1, δ2 respectively denote the left and right rudder angles, θ1, θ2 respectively denote the left and right vector nozzle rotation angles, f T , f xr , f yr respectively denote the force of the air duct propeller, the longitudinal force of the air rudder, the lateral force of the air rudder, F xn1 , F xn2 respectively denote the longitudinal force of the left and right bow nozzles, F yn1 , F yn2 respectively denote the lateral force of the left and right bow nozzles.

[0029] Further, the target function is expressed as:

[0030]

[0031] wherein WP represents the consumed energy of the air duct propeller when the air cushion vehicle is running, W>0 represents a weight matrix, P represents the power when the air duct propeller is running, (u-u0) T R(u-u0) represents the rate of change of the pitch angle of the air duct propeller, the rudder angle of the air rudder, and the rotation angle of the vector jet pipe, R>0 represents a weight matrix, s T Qs represents a penalty term of error, s>0 represents an error value, Q>0 represents a weight matrix, δ represents the rudder angle of the air rudder, and θ represents the angle of the vector jet pipe rotation.

[0032] Further, the solving in the fourth step is performed by a particle swarm algorithm.

[0033] Further, the particle swarm algorithm is a multi-strategy improved particle swarm algorithm, and the multi-strategy improved particle swarm algorithm specifically performs the following steps:

[0034] Step 1: initialize the position, speed, particle quantity and iteration number of the algorithm in the particle;

[0035] Step 2: calculate the fitness value and optimal position of each particle, and select the global optimal fitness value and global optimal position;

[0036] Step 3: according to the particle speed and position updating formula in the particle swarm algorithm, update the speed and position of the particle under the condition of meeting the constraint condition;

[0037] Step 4: repeat step 2, compare each particle with the previous optimal fitness value, and select the self-optimal fitness value and optimal position;

[0038] Step 5: update the global optimal fitness value and global optimal position;

[0039] Step 6: repeat steps 2 to 5 until the minimum error is met or the maximum iteration number is reached;

[0040] Step 7: output the finally obtained global optimal fitness value and global optimal position.

[0041] Further, the speed formula and position formula in the multi-strategy improved particle swarm algorithm are represented as:

[0042]

[0043] wherein w represents an inertia weight, w max represents the upper limit of the inertia weight of the algorithm, w min represents the lower limit of the inertia weight of the algorithm iteration, k represents the current iteration number of the particle swarm algorithm, k maxdenotes the maximum number of iterations of the particle swarm algorithm, c1 and c2 are learning factors, c 1_max , c 1_min , c 2_max , c 2_min are upper and lower limits of learning factors c1 and c2, r1 and r2 are random numbers in the range of [0, 1], and υ is a constraint factor, denotes the i-th particle in space, denotes the current position of the i-th particle, denotes the current speed of the i-th particle, the superscript d denotes the d-th dimension of the solution space, w k denotes the inertia weight of the k-th iteration, denotes the particle motion speed of the global optimal position, and Sine() denotes the sign of the chaotic mapping.

[0044] The beneficial effects of the present application are:

[0045] The present application manipulates the total of air duct propeller, air rudder and vector nozzle in the existing air cushion ship model. In the process of air cushion ship operation, the controller for processing the force and torque output by the control surface selects the controller of the present application to produce better control benefits, and has better robustness, high convergence accuracy, good control effect and the like. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 is a coordinate system rotation schematic diagram;

[0047] Figure 2 is an air cushion ship vector propulsion flow chart;

[0048] Figure 3 is an air cushion ship propulsion and control system installation drawing;

[0049] Figure 4 is a vector nozzle model diagram;

[0050] Figure 5 is a PSO algorithm flow chart. DETAILED DESCRIPTION

[0051] It should be particularly noted that the various embodiments disclosed in the present application can be combined with each other without conflict.

[0052] Embodiment one: a full-cushion air cushion ship multi-control surface vector coordination control method according to the present embodiment, the method comprising the following steps:

[0053] Step one: establish a four-degree-of-freedom kinematics model and a dynamics model of a full-cushion air cushion ship, and design a fixed-time non-singular fast terminal sliding mode controller according to the four-degree-of-freedom kinematics model and the dynamics model of the full-cushion air cushion ship.

[0054] Step two: obtain the total force of the fixed time non-singular fast terminal sliding mode controller output, and combine the force and turning moment of the air duct propeller, air rudder and vector nozzle to establish a multi-control surface control distribution equation;

[0055] Step three: according to the total force and the turning moment of the air duct propeller, air rudder and vector nozzle, a target function is constructed;

[0056] Step four: according to the target function and the multi-control surface control distribution equation, the turning moment of the air duct propeller, air rudder and vector nozzle distribution is obtained.

[0057] The air cushion vehicle multi-control surface coordinated control distribution model comprises:

[0058] The four-degree-of-freedom kinematic model of the air cushion vehicle is as follows:

[0059]

[0060] In the formula, u, v, p and r respectively represent the longitudinal velocity, lateral velocity, roll angle velocity and turning angle velocity of the air cushion vehicle in the ship coordinate system; x, y, And ψ respectively represent the position, roll angle and heading angle of the ship center of mass in the north-east coordinate system.

[0061] When the air cushion vehicle sails on the water surface, the pitch and heave can be ignored, so that the six-degree-of-freedom model is simplified into a four-degree-of-freedom model. Therefore, the simplified air cushion vehicle dynamics model is as follows:

[0062]

[0063] In the formula, m is a system state vector; J x ,J z are the moments of inertia around the x-axis and the z-axis respectively; F x ,F y are the resultant external forces of the ship in the x-axis and y-axis directions respectively; M x ,M z are the resultant moments of the ship around the x-axis and z-axis respectively.

[0064] F x ,F y ,M x ,M z The specific expressions of F

[0065]

[0066] In the formula: subscript a is air power, h is water power, m is air momentum force, p is propeller force, r is air rudder force, and n is vector nozzle force.

[0067] The sliding surface of the fixed-time non-singular fast terminal sliding mode controller is represented as:

[0068]

[0069] where a2>1, i=u,ψ,k1>0 and k2>0,sig a (·)=|x| a sign(·),

[0070] where sign(·) is the sign function,S i represents the sliding surface,k1,k2,a1,a2 represent the tuning coefficients,k1,k2,a1,a2 are used to ensure the upper bound of the convergence time.When S i =0,e i and converges to zero in a fixed time T,and the upper bound of the convergence time is:

[0071]

[0072] The force and torque output by the fixed-time fast terminal sliding mode controller are:

[0073]

[0074] The model of the vectoring nozzle is:

[0075] The longitudinal force of the air cushion vehicle received by the left bow nozzle and the right bow nozzle are respectively:

[0076] F xn1 =T n1 gcosα n1

[0077] F xn2 =T n2 gcosα n2

[0078] The lateral force of the left and right bow nozzles are respectively:

[0079] F yn1 =T n1 gsinα n1

[0080] F yn2 =T n2 gsinα n2

[0081] In the formula,T n1 ,T n2 are the output thrust of the left and right bow nozzles respectively;α n1 ,αn2 These represent the rotation angles of the left and right bow nozzles, respectively.

[0082] The turning torque is:

[0083] N n =F xn1 y n1 +F yn1 x n1 +F xn2 y n2 +F yn2 x n2

[0084] Vector nozzle installation locations: The vector nozzles are located at two points on the bow of the hull, with coordinates (x, y) on the hull. n1 ,y n1 ), (x n2 ,y n2 The two air-ducted propellers located at the stern of the hull are installed at positions (x...). p1 ,y p1 ), (x p2 ,y p2 The two air rudders located behind the air duct propeller are installed at positions (x...). r1 ,y r1 ), (x r2 ,y r2 These six devices together constitute the multi-handling surfaces and control system of the fully-cushioned hovercraft.

[0085] propeller model

[0086] The output thrust of a propeller is related to the propeller pitch angle, rotational speed, and the relative wind speed during the ship's navigation. The specific calculation formula is shown below:

[0087]

[0088] In the formula, T pl ,T pr The thrust of the left and right propellers respectively; These are the pitch angles of the left and right propellers, respectively; n l ,n r These represent the rotational speeds of the left and right propellers, respectively; u a This represents the longitudinal component of the wind speed along the propeller shaft.

[0089] The thrust generated by the propeller along the longitudinal axis is:

[0090] T xp =T pl +T pr

[0091] The thrust generated by the propeller in the lateral axis is:

[0092] T yp = 0

[0093] When the thrust generated by the left and right propellers is different, a yawing moment can be generated. The yawing moment received by the hovercraft is:

[0094] N p = -T pl y p1 -T pr y p2

[0095] Airfoil model

[0096] The longitudinal force generated by a single airfoil is:

[0097] F xri = 2p ri S d c xi

[0098] The lateral force generated by a single airfoil is:

[0099] F yri = 2p ri S d c yi

[0100] In the formula, p ri is the aerodynamic pressure; S d is the area of a single airfoil blade; and c i is the air dynamic coefficient.

[0101] The yawing moment is:

[0102] N r = -y r1 F xr1 +x r1 F yr1 -y r2 F xr2 +x r2 F yr2

[0103] The multi-control surface coordination control distribution equation is established according to the model, as follows:

[0104]

[0105] However, how to switch the actuators to control the movement of the hovercraft at different speeds becomes a problem, that is, there is a problem of multi-control surface coordination control switching. The present application solves this problem by introducing a switching function.

[0106] The switching function ξ(u) is expressed as:

[0107]

[0108] In the formula, u1 and u2 are the optimal speed thresholds for turning using different control surfaces, and the specific values are determined by the actual operating characteristics of the air cushion vehicle. When the air cushion vehicle is sailing at low speed, the propeller and the bow nozzle are used to provide longitudinal force and turning moment. When the air cushion vehicle is sailing at high speed, the air vane, the propeller and the bow nozzle are used together to provide longitudinal force and turning moment.

[0109] Rewritten in vector form, it is as follows:

[0110]

[0111] According to the expression, the relationship between the longitudinal control force, the lateral control force, the turning control moment output by the upper controller and the force and moment generated by each control surface can be known.

[0112] The application mainly calculates the longitudinal control force and the turning control moment required during sailing according to the expected state and the real-time state of the air cushion vehicle. However, the air duct propeller, the air vane and the vector nozzle in the lower control surface receive input signals about the pitch angle, the rudder angle and the rotation angle of the vector nozzle. The force and moment output by the upper controller of the air cushion vehicle are reasonably distributed to the lower control surface and converted into the input angle of each control surface. A set of multi-control surface control distribution equations need to be established.

[0113] τ = BU + s

[0114] τ = [X Y N p N r N n ] T

[0115]

[0116] U = [F p1 F p2 F xr1 F yr1 F xr2 F yr2 F xn1 F yn1 F xn2 F yn2 ] T

[0117] In the formula, τ is the total force (moment) output by the upper control system, B is the control distribution matrix, U is the function of the pitch angle, the rudder angle and the rotation angle of the vector nozzle, and s is the error value.

[0118] Let U be rewritten as:

[0119]

[0120] wherein, are respectively left and right pitch angle, δ1, δ2 are respectively left and right rudder angle, θ1, θ2 are respectively left and right vector nozzle rotation angle

[0121] The left and right air rudder of the air cushion vehicle model used in this application is a set of linkage system with δ1=δ2=δ, and the left and right vector nozzles are a set of linkage system, so θ1=θ2=θ.

[0122] Rudder-prop-bow nozzle thrust coordinated distribution optimization objective

[0123] Under the premise of considering the loss of each propulsion device and the safe navigation condition of the air cushion vehicle, and trying to minimize the error between the control command and the distribution command, the objective function is represented as:

[0124] Objective function:

[0125]

[0126] wherein, WP represents the energy consumed by the air duct propeller when the air cushion vehicle is running, W>0 is a weight matrix, and P is the power of the air duct propeller when it is running; (u-u0) T R(u-u0) represents the change rate of the pitch angle of the air duct propeller, the air rudder angle, and the vector nozzle rotation angle, wherein R>0 W>0 is a weight matrix; s T Qs represents the penalty term of the error, s>0 is the error value, Q>0 is a weight matrix, and by adjusting the value of the third term (error value), the error between the output force and moment of the upper controller and the total force and moment generated by the control surface is adjusted, so as to accelerate the solution of the problem.

[0127] The first term in the objective function represents the energy consumption of the left and right air propellers; the second term in the objective function represents the change rate of the pitch angle, the rudder angle, and the bow nozzle; and the third term in the objective function represents the penalty term of the error between the control command and the distribution command, wherein s is a slack variable, and the purpose is to ensure that a feasible solution can be obtained for the optimization problem under any condition.

[0128] The final rudder-prop-bow nozzle thrust coordinated distribution objective function and constraint condition are:

[0129]

[0130] Constraint condition:

[0131] τ=B·U+s

[0132] s min ≤s i ≤s max

[0133]

[0134] δ min ≤δ i ≤δ max

[0135] θ min ≤θ i ≤θ max

[0136] Genetic algorithm and particle swarm algorithm are used to solve the allocation optimization problem. In view of the problem that the accuracy of ordinary particle swarm algorithm is not high, a multi-strategy improved particle swarm algorithm is proposed. Firstly, the inertia weight w is improved. In order to avoid the algorithm falling into local optimum, the inertia weight w is linearly reduced with the increase of iteration number, and the chaotic Sine mapping is introduced. The mapping has good ergodicity, which ensures the good global search ability in the later period while meeting the fast convergence, reduces the possibility of falling into local optimal solution in the search, and the improved inertia weight w expression is:

[0137]

[0138] where w max , w min are the upper and lower bounds of inertia weight respectively; k and k max are the current iteration number and the maximum iteration number respectively; r1, r2 are random numbers in [0,1].

[0139] The learning factors c1 and c2 in the traditional particle swarm algorithm do not change with iteration. The fixed learning factor shows a decline in convergence in iteration, and the global search ability of the algorithm decreases, which is easy to fall into local optimal solution. Although the introduction of nonlinear learning factor can increase the global search ability of the algorithm to a certain extent, it cannot adapt to the change of the environment to determine the change of the learning factor with the iteration process. Therefore, the sin function is used to construct the nonlinear learning factor, which focuses on the learning speed of individuals and groups at different iteration periods, as follows:

[0140]

[0141] In this algorithm, the spatial position of the particle is set as the pitch angle of the left and right air duct propellers of the air cushion vehicle, the left and right air rudder angle, and the rotation angle of the left and right vector jet. The definition domain of the particle space in motion in each dimension space is set as the physical angle constraint of the four actuators. The fitness function in the algorithm The coordination control distribution optimization objective function set in the multi-surface coordination control equation is set.

[0142] On the basis of the above, the improved algorithm can be applied to the multi-surface coordination distribution optimization of the full-cushion air cushion vehicle, so as to realize the optimal control of the air cushion vehicle under the multi-surface vector control.

[0143] The speed formula and the position formula of the improved particle swarm algorithm are as follows:

[0144]

[0145] The specific steps of the multi-strategy improved particle swarm algorithm are as follows:

[0146] Step 1: initialize the position, speed, particle number, algorithm iteration number and other parameters of the particle in the algorithm;

[0147] Step 2: calculate the fitness value and the optimal position of each particle, and select the global optimal fitness value and the global optimal position;

[0148] Step 3: according to the particle speed and position update formula in the particle swarm algorithm, update the speed and position of the particle under the condition of meeting the constraint condition;

[0149] Step 4: repeat Step 2, compare each particle with the previous optimal fitness value, and select the self-optimal fitness value and the optimal position;

[0150] Step 5: update the global optimal fitness value and the global optimal position;

[0151] Step 6: repeat Step 2-Step 5 until the minimum error set by the program is met or the maximum iteration number of the program is reached.

[0152] Step 7: output the global optimal fitness value and the global optimal position calculated by the program.

[0153] It should be noted that the specific embodiments are only an explanation and description of the technical solutions of the present application, and cannot limit the protection scope. Any partial change made according to the claims and the specification of the present application shall still fall within the protection scope of the present application.

Claims

1. A method for vector coordination control of multiple control surfaces of a full-cushion hovercraft, characterized in that The method comprises the following steps: Step one: establishing a four-degree-of-freedom kinematic model and a dynamic model of the full-pad hovercraft, and designing a fixed-time non-singular fast terminal sliding mode controller according to the four-degree-of-freedom kinematic model and the dynamic model of the full-pad hovercraft; Step two: obtaining total force output by the fixed-time non-singular fast terminal sliding mode controller, and combining the force and turning moment of the air duct propeller, the air rudder and the vector nozzle to establish a multi-control surface control distribution equation; Step three: constructing an objective function according to the total force and the turning moment of the air duct propeller, the air rudder and the vector nozzle; Step four: solving the objective function and the multi-control surface control distribution equation to obtain the turning moment of the air duct propeller, the air rudder and the vector nozzle; The control law of the fixed-time non-singular fast terminal sliding mode controller is represented as: wherein represents the resultant force, represents the velocity error, represents the derivative of the desired velocity, represents the longitudinal resultant force, represents the turning moment, represents the heading error, represents the mathematical symbol, represents the symbolic formula, represents the derivative of the desired heading, and represents the normal number, and represents the constant, represents the second derivative of the desired heading.

2. The method of claim 1, wherein the method is characterized by The kinematic model is represented as: wherein Vx, Vy, Vp, Vr respectively denote the longitudinal velocity, the lateral velocity, the roll angular velocity and the yaw angular velocity of the air cushion vehicle in the body coordinate system, X, Y, P respectively denote the position, the roll angle and the heading angle of the body center of mass in the North-East coordinate system, , , , respectively denote the first derivative of 3. The method of claim 2, wherein The dynamic model is represented as: wherein denotes the state vector, denote the moments of inertia about the axis and the axis, denote the resultant external forces of the ship in the axis and the axis direction, denote the resultant moments of the ship about the axis and the axis, denotes the first derivative of , denotes the first derivative of , denotes the first derivative of , denotes the first derivative of .

4. The method of claim 3, wherein The sliding surface of the fixed-time non-singular fast terminal sliding mode controller is represented as: wherein , , and , , wherein is a sign function, denotes a sliding surface, , , , denotes a tuning coefficient, , , , for ensuring an upper bound of the convergence time.

5. A method of vector control of multiple control surfaces of a hovercraft according to claim 4, characterized in that The multi-operation surface coordination control equation is represented as wherein, represents the total resultant force output by the upper controller, represents the control distribution matrix, represents a function of the pitch angle, rudder angle, and vector nozzle rotation angle, represents an error value, represents a longitudinal control force, represents a lateral control force, represents a yawing moment of the air duct propeller, represents a yawing moment of the air rudder, represents a yawing moment of the vector nozzle, and represents a vector nozzle coordinate located at the bow of the hull, and represents a vector nozzle coordinate located at the stern of the hull, and represents an air rudder coordinate, respectively represent left and right pitch angles, respectively represent left and right rudder angles, respectively represent left and right vector nozzle rotation angles, , , respectively represent a force of the air duct propeller, a longitudinal force of the air rudder, and a lateral force of the air rudder, , respectively represent longitudinal forces of left and right bow nozzles, , respectively represent lateral forces of left and right bow nozzles.

6. A method of vector coordinated control of multiple control surfaces of a hovercraft according to claim 5, characterized in that The objective function is represented as: wherein, represents the energy consumed by the air-cushion boat air-duct propeller when running, represents a weight matrix, represents the power when the air-duct propeller is running, represents the rate of change of the pitch angle of the air-duct propeller, the rudder angle of the air-rudder, the rotation angle of the vector nozzle, represents a weight matrix, represents a penalty term for error, represents an error value, represents a weight matrix, represents the rudder angle of the air-rudder, represents the angle of rotation of the vector nozzle.

7. The method of claim 1, wherein The solving in the step four is performed by a particle swarm algorithm.

8. The method of claim 7, wherein the control method is characterized by The particle swarm algorithm is a multi-strategy improved particle swarm algorithm, and the multi-strategy improved particle swarm algorithm specifically performs the following steps: Step 1: initializing the position, speed, particle quantity and algorithm iteration times of particles in the algorithm; Step 2: calculating the fitness value and optimal position of each particle, and selecting the global optimal fitness value and global optimal position; Step 3: updating the speed and position of the particles according to the particle speed and position updating formula in the particle swarm algorithm under the condition of meeting the constraint condition; Step 4: repeating step 2, comparing each particle with the previous optimal fitness value, and selecting the own optimal fitness value and optimal position; Step 5: updating the global optimal fitness value and global optimal position; Step 6: repeating steps 2 to 5 until the set minimum error is met or the maximum iteration times are reached; Step 7: outputting the finally obtained global optimal fitness value and global optimal position.

9. A method of vector control of multiple control surfaces of a hovercraft according to claim 8, characterized in that The speed formula and position formula in the multi-strategy improved particle swarm algorithm are represented as: in, Indicates inertia weight, This represents the upper bound of the algorithm's inertia weights. This represents the lower bound of the algorithm's iterative inertia weights. This indicates the current iteration number of the particle swarm optimization algorithm. This represents the maximum number of iterations in the particle swarm optimization algorithm. , As a learning factor, , , , Learning factor , The upper and lower limits, This represents two random numbers whose values ​​are in the range [0,1]. As a constraint factor, The first in the space One particle, Indicates the first The current position of each particle. Indicates the first The current velocity of the particle, superscript Indicates the first 3D solution space This represents the inertia weight in the k-th iteration. The velocity of the particle at the globally optimal position. Symbols representing chaotic mappings.

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