Multi-control-surface vector coordination control method for full-hovering hovercraft

Abstract

The invention discloses a multi-control-surface vector coordination control method for a full-hovering hovercraft, relates to the technical field of hovercraft motion control, and aims to solve the problem that the control effect is poor when a single control surface is used from a low speed to a high speed in the sailing process of the hovercraft. In an existing hovercraft model, an air duct propeller, an air rudder and a thrust vectoring nozzle are arranged on control surfaces. In the operation process of the hovercraft, the controller for processing the force and torque output by the control surface is selected to generate better control benefits, and the method has the advantages of being better in robustness, high in convergence precision, good in control effect and the like.

Description

Technical Field

[0001] The invention relates to the technical field of hovercraft motion control, and in particular to a multi-control surface vector coordinated control method for a full-lift hovercraft. Background Art

[0002] In terms of safe navigation control of hovercraft, a multi-control surface strategy is proposed, which adopts a force (torque) coordinated distribution module, uses the bow nozzle to provide lateral force to offset the sideslip angle, and uses the air rudder to provide a turning torque to make the heading track the desired trajectory. However, when the hovercraft navigates from low speed to high speed, the control effect of using a single control surface is poor. Summary of the invention

[0003] The purpose of the present invention is to propose a multi-control surface vector coordinated control method for a full-lift hovercraft in order to solve the problem of poor control effect of a single control surface when the hovercraft is sailing from low speed to high speed.

[0004] The technical solution adopted by the present invention to solve the above technical problems is:

[0005] A method for vector coordinated control of multiple control surfaces of a full-lift air cushion vehicle, the method comprising the following steps:

[0006] Step 1: Establish a four-degree-of-freedom kinematic model and a dynamic model of the full-lift hovercraft, and design a fixed-time non-singular fast terminal sliding mode controller based on the four-degree-of-freedom kinematic model and the dynamic model of the full-lift hovercraft;

[0007] Step 2: Obtain the total force output by the fixed-time non-singular fast terminal sliding mode controller, and establish the multi-control surface control allocation equation by combining the forces of the air duct propeller, air rudder and vector nozzle with the bow torque;

[0008] Step 3: Construct the objective function according to the resultant force of the bow torque of the air duct propeller, air rudder and vector nozzle, as well as the total resultant force;

[0009] Step 4: Solve the objective function and the multi-control surface control allocation equation to obtain the bow torque distributed by the air duct propeller, air rudder and vector nozzle.

[0010] Furthermore, the kinematic model is expressed as:

[0011]

[0012] Among them, u, v, p, r represent the longitudinal velocity, transverse velocity, heel angular velocity and bow angular velocity of the hovercraft in the hull coordinate system respectively, and x, y, ψ represents the position of the ship's center of mass in the northeast coordinate system, the heel angle and the heading angle, Represents x, y, The first derivative of ψ.

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

[0014]

[0015] Where m represents the state vector, J x ,J z Denotes the moment of inertia around the x-axis and z-axis respectively, F x ,F y They represent the combined external forces on the hull in the x-axis and y-axis directions, M x ,M z They represent the resultant moments of the hull around the x-axis and the z-axis respectively, represents the first-order derivative of u, represents the first-order derivative of v, represents the first-order derivative of p, represents the first derivative of r.

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

[0017]

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

[0019] where sign(·) is the sign function, S i represents the sliding surface, k1, k2, a1, a2 represent the debugging coefficients, and k1, k2, a1, a2 are used to ensure the upper limit of the convergence time.

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

[0021]

[0022] Among them, τ r represents the combined force, e u represents the speed error, represents the derivative of the desired velocity, F xD is the longitudinal force, J Z is the bow moment, e ψ represents heading error, diag represents mathematical symbol, Λ represents symbolic formula, represents the derivative of the desired heading, α and β represent positive constants,r1 and r2 represents a constant, Represents the second derivative of the desired heading.

[0023] Furthermore, the multi-operation surface coordination control equation is expressed as

[0024] τ=BU+s

[0025] τ=[XYN p N r N n ] T

[0026]

[0027]

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

[0029] Furthermore, the objective function is expressed as:

[0030]

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

[0032] Furthermore, the solution in step 4 is performed by a particle swarm algorithm.

[0033] Furthermore, 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, velocity, number of particles and number of algorithm iterations in the algorithm;

[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 update formula in the particle swarm algorithm, update the particle speed and position under the constraints;

[0037] Step 4: Repeat step 2, compare each particle with the previous optimal fitness value, and select its own optimal fitness value and optimal position;

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

[0039] Step 6: Repeat steps 2 to 5 until the set minimum error is met or the maximum number of iterations is reached;

[0040] Step 7: Output the final global optimal fitness value and global optimal position.

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

[0042]

[0043] Among them, w represents the inertia weight, w max represents the upper bound of the algorithm inertia weight, w min represents the lower bound of the algorithm iteration inertia weight, k represents the current number of iterations of the particle swarm algorithm, and k maxrepresents the maximum number of iterations of the particle swarm algorithm, c1 and c2 are learning factors, and c 1_max 、c 1_min 、c 2_max 、c 2_min are the upper and lower limits of the learning factors c1 and c2, r1 and r2 represent two random numbers in the range [0,1], and υ is the constraint factor. represents the i-th particle in space, represents the current position of the i-th particle, represents the current speed of the ith particle, the superscript d represents the d-dimensional solution space, and w k represents the inertia weight of the kth iteration, It represents the particle movement speed at the global optimal position, and Sine() represents the symbol of the chaotic map.

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

[0045] In the existing hovercraft model, the control surfaces of the present application are air duct propellers, air rudders and vector nozzles. During the operation of the hovercraft, the controller for processing the force and torque output by the control surface uses the controller of the present application, which will produce better control benefits and has the characteristics of better robustness, high convergence accuracy, good control effect, etc. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0047] Figure 2 Flowchart for vector propulsion of hovercraft;

[0048] Figure 3 Installation diagram for the propulsion and control system of the hovercraft;

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

[0050] Figure 5 This is the flow chart of the PSO algorithm. DETAILED DESCRIPTION

[0051] It should be particularly noted that, in the absence of conflict, the various embodiments disclosed in this application can be combined with each other.

[0052] Specific implementation method 1: A method for coordinated control of multiple control surfaces of a full-lift hovercraft vector according to this implementation method comprises the following steps:

[0053] Step 1: Establish a four-degree-of-freedom kinematic model and a dynamic model of the full-lift hovercraft, and design a fixed-time non-singular fast terminal sliding mode controller based on the four-degree-of-freedom kinematic model and the dynamic model of the full-lift hovercraft;

[0054] Step 2: Obtain the total force output by the fixed-time non-singular fast terminal sliding mode controller, and establish the multi-control surface control allocation equation by combining the forces of the air duct propeller, air rudder and vector nozzle with the bow torque;

[0055] Step 3: Construct the objective function according to the resultant force of the bow torque of the air duct propeller, air rudder and vector nozzle, as well as the total resultant force;

[0056] Step 4: Solve the objective function and the multi-control surface control allocation equation to obtain the bow torque distributed by the air duct propeller, air rudder and vector nozzle.

[0057] The establishment of the hovercraft multi-control surface coordinated control allocation model includes:

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

[0059]

[0060] Where, u, v, p, r represent the longitudinal velocity, transverse velocity, heel angular velocity and bow angular velocity of the hovercraft in the hull coordinate system respectively; x, y, ψ represents the position of the center of mass of the ship in the northeast coordinate system, the heel angle and the heading angle respectively.

[0061] When the hovercraft is sailing on the water, the pitch and heave can be ignored, thus simplifying the six-degree-of-freedom model to a four-degree-of-freedom model. Therefore, the simplified dynamic model of the hovercraft is as follows:

[0062]

[0063] Where m is the system state vector; J x ,J z The moment of inertia around the x-axis and z-axis respectively; F x ,F y are the combined external forces on the hull in the x-axis and y-axis directions respectively; M x ,M z are the resultant moments of the hull around the x-axis and the z-axis respectively.

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

[0065]

[0066] Where: subscript a is aerodynamic force, h is hydrodynamic force, 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 expressed as:

[0068]

[0069] Among them, 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 debugging coefficients, and k1, k2, a1, a2 are used to ensure the upper bound of the convergence time. i = 0, e i and It converges to zero within a fixed time T, and the upper bound of the convergence time is:

[0071]

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

[0073]

[0074] Vector nozzle model:

[0075] The longitudinal forces of the left bow nozzle and the right bow nozzle on the hovercraft are:

[0076] F xn1 =T n1 gcosα n1

[0077] F xn2 =T n2 gcosα n2

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

[0079] F yn1 =T n1 gsinα n1

[0080] F yn2 =T n2 gsinα n2

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

[0082] The bow turning moment is:

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

[0084] Installation position of vector nozzle: The two vector nozzles located at the bow of the hull have coordinates on the hull (x n1 ,y n1 )、(x n2 ,y n2 ); The two air duct propellers located at the stern of the hull are installed at (x p1 ,y p1 )、(x p2 ,y p2 ); The two air rudders located behind the propeller in the air duct are installed at (x r1 ,y r1 )、(x r2 ,y r2 ). These six devices together constitute the multi-control surface and control system of the full-lift hovercraft.

[0085] Propeller Model

[0086] The output thrust of the propeller is related to the pitch angle of the propeller, the rotation speed and the relative wind speed of the ship during navigation. The specific calculation formula is shown as follows:

[0087]

[0088] Where, T pl ,T pr The thrust of the left and right propellers respectively; are the pitch angles of the left and right propellers respectively; n l ,n r are the rotation speeds of the left and right propellers respectively; u a is the longitudinal component of wind speed on the propeller shaft.

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

[0090] T xp =T pl +T pr

[0091] The thrust generated by the propeller on the horizontal axis is:

[0092] T yp =0

[0093] When the thrusts generated by the left and right propellers are different, a bow moment can be generated. The bow moment on the hovercraft is:

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

[0095] Air rudder model

[0096] The longitudinal force generated by the unilateral air rudder is:

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

[0098] The lateral force generated by the unilateral air rudder is:

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

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

[0101] The bow turning 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 coordinated control distribution equation is established according to the model as follows:

[0104]

[0105] However, how to switch the actuators at different speeds to control the movement of the hovercraft becomes a problem, that is, there is a problem of coordinated control switching of multiple control surfaces. This application solves this problem by introducing a switching function.

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

[0107]

[0108] Wherein, u1, u2 are the optimal speed thresholds for turning using different control surfaces alone. The specific values ​​are determined by the actual operating characteristics of the hovercraft. When the hovercraft is sailing at a low speed, the propeller and bow nozzle are used to provide longitudinal force and turning moment. When the hovercraft is sailing at a high speed, the air rudder, propeller and bow nozzle are used together to provide longitudinal force and turning moment.

[0109] Rewritten into vector form, it looks like this:

[0110]

[0111] According to the expression, we can know the relationship between the longitudinal control force, lateral control force, and rotational control torque output by the upper-level controller and the force and torque generated by each control surface.

[0112] This application mainly calculates the longitudinal control force and rotation control torque required during navigation based on the expected state and real-time state of the hovercraft. However, the air duct propeller, air rudder and vector nozzle in the lower control surface receive input signals about the pitch angle, rudder angle and rotation angle of the vector nozzle. In order to reasonably distribute the force and torque output by the upper controller of the hovercraft to the lower control surface and convert them into the input angles of each control surface, a set of multi-control surface control distribution equations needs to be established.

[0113] τ=BU+s

[0114] τ=[XYN 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] Where τ is the total resultant force (torque) output by the upper control system, B is the control allocation matrix, U is the function of pitch angle, rudder angle, and vector nozzle rotation angle, and s is the error value.

[0118] Rewrite U as:

[0119]

[0120] In the formula, are the left and right pitch angles, δ1 and δ2 are the left and right rudder angles, θ1 and θ2 are the left and right vector nozzle rotation angles

[0121] The left and right air rudders of the hovercraft model used in this application are a linkage system with δ1=δ2=δ, and the left and right vector nozzles are a linkage system with θ1=θ2=θ.

[0122] Optimization target of rudder-propeller-bow nozzle thrust coordination distribution

[0123] Taking into account the loss of each propulsion device and the safe navigation conditions of the hovercraft, and minimizing the error between the control command and the distribution command, the objective function is expressed as:

[0124] Objective function:

[0125]

[0126] Where WP represents the energy consumed by the air duct propeller when the hovercraft is running, W>0 is the weight matrix, and P is the power of the air duct propeller when it is running; (u-u0) T R(u-u0) represents the rate of change of the pitch angle of the air duct propeller, the air rudder angle, and the rotation angle of the vector nozzle, where R>0W>0 is the weight matrix; s T Qs represents the penalty term of the error, s>0 is the error value, Q>0 is the weight matrix, and by adjusting the value of the third term (error value), the size of the error between the output force and torque of the upper-level controller and the total force and torque generated by the control surface can be adjusted 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, rudder angle, and bow nozzle; the third term in the objective function represents the penalty term for the error between the control instruction and the assigned instruction, where s is a slack variable, the purpose of which is to ensure that a feasible solution can be obtained for the optimization problem under any circumstances.

[0128] The final rudder-propeller-bow nozzle thrust coordination distribution objective function and constraints are:

[0129]

[0130] Constraints:

[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. Aiming at the problem of low accuracy of ordinary particle swarm algorithm, a multi-strategy improved particle swarm algorithm is proposed. The algorithm first improves the inertia weight w. In order to avoid the algorithm falling into the 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. While satisfying the fast convergence, it also ensures good global search ability in the later stage, reducing the possibility of falling into the local optimal solution in the search. The improved inertia weight w expression is:

[0137]

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

[0139] Learning factors c1 and c2 will not change with iterations in traditional particle swarms. Fixed learning factors show decreased convergence during iterations, decreased global search capabilities of the algorithm, and are prone to falling into the problem of local optimal solutions. Although the introduction of nonlinear learning factors can increase the global search capabilities of the algorithm to a certain extent, it will not determine the changes in the learning factors in accordance with the adaptive environmental changes during the iteration process. Therefore, the sin function is used to construct nonlinear learning factors, focusing on the learning speed of individuals and groups in different iteration periods, as follows:

[0140]

[0141] The spatial position of the particle in this algorithm Set the pitch angle of the left and right air duct propellers of the hovercraft, the left and right air rudder angles, and the rotation angles of the left and right vector nozzles. Set the domain of the particle in each dimension space when it moves in the space to the physical angle constraints of the four actuators. Set the fitness function in the algorithm It is set as the coordinated control allocation optimization objective function set in the multi-control surface coordinated control equation.

[0142] Based on the above, the improved algorithm can be applied to the coordinated allocation optimization of multiple control surfaces of the full-lift hovercraft to achieve the optimal control of the hovercraft under vector control of multiple control surfaces.

[0143] The speed formula and 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:

[0146] Step 1: Initialize the parameters of the algorithm, such as the position, speed, number of particles, and number of algorithm iterations;

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

[0148] Step 3: According to the particle speed and position update formula in the particle swarm algorithm, update the particle speed and position under the constraints;

[0149] Step 4: Repeat Step 2, compare each particle with the previous optimal fitness value, and select its own optimal fitness value and 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 number of program iterations is reached.

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

[0153] It should be noted that the specific implementation is only an explanation and description of the technical solution of the present invention, and cannot be used to limit the scope of protection of the rights. Any partial changes made according to the claims and description of the present invention should still fall within the scope of protection of the present invention.

Claims

1. A method for vector coordinated control of multiple control surfaces of a full-lift hovercraft, characterized in that The method comprises the following steps: Step 1: Establish a four-degree-of-freedom kinematic model and a dynamic model of the full-lift hovercraft, and design a fixed-time non-singular fast terminal sliding mode controller based on the four-degree-of-freedom kinematic model and the dynamic model of the full-lift hovercraft; Step 2: Obtain the total force output by the fixed-time non-singular fast terminal sliding mode controller, and establish the multi-control surface control allocation equation by combining the forces of the air duct propeller, air rudder and vector nozzle with the bow torque; Step 3: Construct the objective function according to the resultant force of the bow torque of the air duct propeller, air rudder and vector nozzle, as well as the total resultant force; Step 4: Solve the objective function and the multi-control surface control allocation equation to obtain the bow torque distributed by the air duct propeller, air rudder and vector nozzle.

2. The method for vector coordinated control of multiple control surfaces of a full-lift hovercraft according to claim 1 is characterized in that The kinematic model is expressed as: Among them, u, v, p, r represent the longitudinal velocity, transverse velocity, heel angular velocity and bow angular velocity of the hovercraft in the hull coordinate system respectively, and x, y, ψ represents the position of the ship's center of mass in the northeast coordinate system, the heel angle and the heading angle, Represents x, y, The first derivative of ψ.

3. The method for vector coordinated control of multiple control surfaces of a full-lift hovercraft according to claim 2 is characterized in that The kinetic model is expressed as: Where m represents the state vector, J x ,J z Denotes the moment of inertia around the x-axis and z-axis respectively, F x ,F y They represent the combined external forces on the hull in the x-axis and y-axis directions, M x ,M z They represent the resultant moments of the hull around the x-axis and the z-axis respectively, represents the first-order derivative of u, represents the first-order derivative of v, represents the first-order derivative of p, represents the first derivative of r.

4. The method for vector coordinated control of multiple control surfaces of a full-lift hovercraft according to claim 3 is characterized in that The sliding surface of the fixed-time non-singular fast terminal sliding mode controller is expressed as: Among them, a2>1, k1>0 and k2>0, sig a (·)=|x a sign(·), where sign(·) is the sign function, S i represents the sliding surface, k1, k2, a1, a2 represent the debugging coefficients, and k1, k2, a1, a2 are used to ensure the upper limit of the convergence time.

5. The method for vector coordinated control of multiple control surfaces of a full-lift hovercraft according to claim 4 is characterized in that The control law of the fixed-time non-singular fast terminal sliding mode controller is expressed as: Among them, τ r represents the combined force, e u represents the speed error, represents the derivative of the desired velocity, F xD is the longitudinal force, J Z is the bow moment, e ψ represents heading error, diag represents mathematical symbol, Λ represents symbolic formula, represents the derivative of the desired heading, α and β represent positive constants, r1 and r2 represents a constant, Represents the second derivative of the desired heading.

6. The method for vector coordinated control of multiple control surfaces of a full-lift hovercraft according to claim 5 is characterized in that The multi-operation surface coordinated control equation is expressed as τ=BU+s τ=[XYN p N r N n ] T Where τ represents the total resultant force output by the upper controller, B represents the control allocation matrix, U represents the function of pitch angle, rudder angle, and vector nozzle rotation angle, s represents the error value, X represents the longitudinal control force, Y represents the lateral control force, and N p Indicates the bow moment of the air duct propeller, N r Indicates the turning moment of the air rudder, N n represents the bow moment of the vector nozzle, (x n1 ,y n1 ) and (x n2 ,y n2 ) represents the coordinates of the vector nozzle located at the bow of the ship, (x p1 ,y p1 ) and (x p2 ,y p2 ) represents the coordinates of the vector nozzle located at the stern of the hull, (x r1 ,y r1 ) and (x r2 ,y r2 ) represents the air rudder coordinates, represents the left and right pitch angles, δ1 and δ2 represent the left and right rudder angles, θ1 and θ2 represent the left and right vector nozzle rotation angles, respectively. T 、f xr 、f yr They represent the force of the air duct propeller, the longitudinal force of the air rudder, and the lateral force of the air rudder, respectively. xn1 、F xn2 Respectively represent the longitudinal force of the left and right bow nozzles, F yn1 、F yn2 Represent the lateral forces of the left and right bow nozzles respectively.

7. The method for vector coordinated control of multiple control surfaces of a full-lift hovercraft according to claim 6 is characterized in that The objective function is expressed as: Where WP represents the energy consumed by the air duct propeller when the hovercraft is running, W>0 represents the weight matrix, P represents the power of the air duct propeller when it is running, (u-u0) T R(u-u0) represents the rate of change of the pitch angle of the air duct propeller, the air rudder angle, and the rotation angle of the vector nozzle. R>0 represents the weight matrix, s T Qs represents the penalty term of the error, s>0 represents the error value, Q>0 represents the weight matrix, δ represents the rudder angle of the air rudder, and θ represents the rotation angle of the vector nozzle.

8. The method for vector coordinated control of multiple control surfaces of a full-lift hovercraft according to claim 1 is characterized in that The solution in step 4 is performed by a particle swarm algorithm.

9. The method for vector coordinated control of multiple control surfaces of a full-lift hovercraft according to claim 8 is characterized in that The particle swarm algorithm is a multi-strategy improved particle swarm algorithm, which specifically performs the following steps: Step 1: Initialize the position, velocity, number of particles and number of algorithm iterations in the algorithm; Step 2: Calculate the fitness value and optimal position of each particle, and select the global optimal fitness value and global optimal position; Step 3: According to the particle speed and position update formula in the particle swarm algorithm, update the particle speed and position under the constraints; Step 4: Repeat step 2, compare each particle with the previous optimal fitness value, and select its own optimal fitness value and optimal position; Step 5: Update the global optimal fitness value and the global optimal position; Step 6: Repeat steps 2 to 5 until the set minimum error is met or the maximum number of iterations is reached; Step 7: Output the final global optimal fitness value and global optimal position.

10. The method for vector coordinated control of multiple control surfaces of a full-lift hovercraft according to claim 8, characterized in that The speed formula and position formula in the multi-strategy improved particle swarm algorithm are expressed as: Among them, w represents the inertia weight, w max represents the upper bound of the algorithm inertia weight, w min represents the lower bound of the algorithm iteration inertia weight, k represents the current number of iterations of the particle swarm algorithm, and k max represents the maximum number of iterations of the particle swarm algorithm, c1 and c2 are learning factors, and c 1_max 、c 1_min 、c 2_max 、c 2_min are the upper and lower limits of the learning factors c1 and c2, r1 and r2 represent two random numbers in the range [0,1], and υ is the constraint factor. represents the i-th particle in space, represents the current position of the i-th particle, represents the current speed of the ith particle, the superscript d represents the d-dimensional solution space, and w k represents the inertia weight of the kth iteration, It represents the particle movement speed at the global optimal position, and Sine() represents the symbol of the chaotic map.

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