A method of distributing thrust for vector propulsion of a full-pad hovercraft

Abstract

A kind of thrust distribution method of full-cushion hovercraft vector propulsion, it is related to hovercraft motion control technical field, in view of the problem that the auxiliary effect of the existing side wind door, a control surface, for the rotation of hovercraft is poor, the application adds a vector control surface, vector nozzle, in the existing hovercraft model, vector nozzle can rotate 360 °, produce vector force facing any one direction.After adding vector nozzle, the control surface of hovercraft changes into air duct propeller, air rudder and vector nozzle.In the process of hovercraft operation, the vector control force produced by the real-time change of rotation angle of vector nozzle to hovercraft can assist the rotation of hovercraft.Moreover, the technical scheme of the application makes the rotation control benefit of hovercraft higher.

Description

Technical Field

[0001] This invention relates to the field of hovercraft motion control technology, specifically a thrust distribution method for vector propulsion of a fully-cushioned hovercraft. Background Technology

[0002] Previously built hovercraft in China did not have vectoring nozzles as a control surface; instead, they were equipped with side doors. The purpose of the side doors is to provide additional turning torque at the bow and stern of the hovercraft when it is turning at low speeds, thus assisting in its rotation. However, the side doors are not very effective at assisting the hovercraft in turning. Summary of the Invention

[0003] The purpose of this invention is to address the problem that the side vents, as a control surface, have a poor effect on the turning assistance of hovercraft in the prior art, and to propose a thrust distribution method for vector propulsion of a fully-cushioned hovercraft.

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

[0005] A thrust distribution method for vector propulsion of a fully cushioned air-cushioned vehicle includes the following steps:

[0006] Step 1: Establish a four-degree-of-freedom kinematic and dynamic model of the fully-cushioned hovercraft, and design an upper-level controller based on the four-degree-of-freedom kinematic and dynamic model of the fully-cushioned hovercraft;

[0007] Step 2: Obtain the total resultant force τ output by the upper controller, and establish the multi-control surface control allocation equation by combining the turning torque of the air duct propeller, air rudder and vector nozzle;

[0008] Step 3: Construct the objective function based on the resultant force of the turning torque of the air duct propeller, air rudder, and vector nozzle, and the total resultant force τ;

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

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

[0011]

[0012] Where u, v, p, r represent the longitudinal velocity, lateral velocity, heel angular velocity, and turning 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, respectively. 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 multi-operation surface control allocation equation is expressed as:

[0017] τ=BU+s

[0018] τ=[XYN p N r N n ] T

[0019]

[0020]

[0021] Where τ represents the total resultant force output by the upper-level 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 represents the longitudinal control force. p The thrust torque (N) of an air-ducted propeller is expressed as... r The rudder's turning torque, N n The thrust torque of the vectoring nozzle, (x) n1 ,y n1 ) and (x n2 ,y n2 (x) represents the coordinates of the vector nozzle located at the bow of the ship. p1 ,y p1 ) and (x p2 ,y p2 (x) represents the coordinates of the vector nozzle located at the stern of the ship. r1 ,y r1 ) and (x r2,y r2 () indicates the coordinates of the air rudder. δ1 and δ2 represent the left and right pitch angles, respectively; θ1 and θ2 represent the left and right rudder angles, respectively; and f represents the left and right vector nozzle rotation angles, respectively. T f xr f yr F represents the forces of the air duct propeller, the longitudinal force of the air rudder, and the lateral force of the air rudder, respectively. xn1 F xn2 F represents the longitudinal force of the left and right bow nozzles, respectively. yn1 F yn2 These represent the lateral forces of the left and right bow nozzles, respectively.

[0022] Furthermore, the turning torque of the air duct propeller is expressed as:

[0023] N p =-T pl y p1 -T pr y p2

[0024] Among them, T pl ,T pr These represent the thrust of the left and right propellers, respectively.

[0025] Furthermore, the steering torque of the air rudder is expressed as:

[0026] N r =-y r1 F xr1 +x r1 F yr1 -y r2 F xr2 +x r2 F yr2

[0027] Among them, F xr1 F yr1 F xr2 F yr2 These represent the longitudinal and lateral forces provided by the left and right aerodynamic rudders, respectively.

[0028] Furthermore, the turning torque of the vector nozzle is expressed as:

[0029] N n =F xn1 y n1 +F yn1 x n1 +F xn2 y n2 +F yn2 x n2 .

[0030] Furthermore, the objective function is expressed as:

[0031]

[0032] Where WP represents the energy consumed by the air duct propeller during the operation of the hovercraft, W > 0 represents the weight matrix, P represents the power of the air duct propeller during operation, and (u - u0) T R(u-u0) represents the rate of change of the pitch angle, air rudder angle, and vector nozzle rotation angle of the air-ducted propeller; R > 0 represents the weight matrix. T Qs represents the penalty term for the error, s > 0 represents the error value, Q > 0 represents the weight matrix, δ represents the rudder angle of the aerodynamic rudder, and θ represents the rotation angle of the vector nozzle.

[0033] Furthermore, the solution is obtained using the particle swarm optimization algorithm.

[0034] Furthermore, the particle swarm optimization algorithm is an improved adaptive compression factor particle swarm optimization algorithm, in which the velocity formula and position formula are expressed as follows:

[0035]

[0036] Where w represents the inertia weight, w start w represents the initial iteration inertia weight of the algorithm. end The inertia weight represents the end of the algorithm's iteration, and t represents the current iteration number of the particle swarm optimization algorithm. max Let represent the maximum number of iterations in the particle swarm optimization algorithm, ζ represent the compression factor, λ be a positive number, c1 and c2 be learning factors, r1 and r2 be two random numbers in the range [0,1], and υ be the constraint factor. Represents the i-th particle in space. This represents the current position of the i-th particle. Let represent the current velocity of the i-th particle, and the superscript d denotes the d-th dimension of the solution space.

[0037] The beneficial effects of this invention are:

[0038] This application adds a vectoring nozzle as a new control surface to the existing hovercraft model. The vectoring nozzle can rotate 360°, generating a vector force in any direction. With the addition of the vectoring nozzle, the hovercraft's control surfaces become an air-ducted propeller, air rudders, and the vectoring nozzle. During hovercraft operation, the vectoring nozzle's real-time changes in rotation angle generate a vector control force that assists the hovercraft's rotational motion. Furthermore, the technical solution of this application results in higher efficiency in hovercraft rotational control. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of coordinate system rotation;

[0040] Figure 2 A flowchart of the vector propulsion process for hovercraft;

[0041] Figure 3 Installation diagram of the hovercraft's propulsion and control system;

[0042] Figure 4 This is a model diagram of a vector nozzle;

[0043] Figure 5 This is a flowchart of the PSO algorithm. Detailed Implementation

[0044] It should be noted that, where there is no conflict, the various embodiments disclosed in this application can be combined with each other.

[0045] Specific implementation method one: Refer to Figure 1 This embodiment describes a thrust distribution method for vector propulsion of a fully cushioned hovercraft, comprising the following steps:

[0046] Step 1: Establish a four-degree-of-freedom kinematic and dynamic model of the fully-cushioned hovercraft, and design an upper-level controller based on the four-degree-of-freedom kinematic and dynamic model of the fully-cushioned hovercraft;

[0047] Step 2: Obtain the total resultant force τ output by the upper controller, and establish the multi-control surface control allocation equation by combining the turning torque of the air duct propeller, air rudder and vector nozzle;

[0048] Step 3: Construct the objective function based on the resultant force of the turning torque of the air duct propeller, air rudder, and vector nozzle, and the total resultant force τ;

[0049] Step 4: Solve the objective function and the multi-control surface control allocation equation to obtain the yaw torque allocated to the air duct propeller, air rudder, and vector nozzle.

[0050] The establishment of the multi-control surface coordinated control allocation model for hovercraft includes:

[0051] The four-degree-of-freedom kinematic model of the hovercraft is shown below:

[0052]

[0053] In the formula, u, v, p, r represent the longitudinal velocity, lateral velocity, heel angular velocity, and turning angular velocity of the hovercraft in the hull coordinate system, respectively; 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, respectively.

[0054] When a hovercraft navigates on the water surface, 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:

[0055]

[0056] In the formula, m is the system state vector; J x J z Moments of inertia about the x-axis and z-axis, respectively; F x ,F y These are the net external forces acting on the hull along the x-axis and y-axis, respectively; M x M z These are the resultant moments of the ship's hull about the x-axis and z-axis, respectively.

[0057] F x ,F y M x M z The specific expressions are as follows:

[0058]

[0059] In the formula: subscript a represents aerodynamic force, h represents hydrodynamic force, m represents aerodynamic momentum force, p represents propeller force, r represents aerodynamic rudder force, and n represents vector nozzle force.

[0060] Vector nozzle model:

[0061] The longitudinal forces exerted on the hovercraft by the port bow nozzle and the starboard bow nozzle are as follows:

[0062] F xn1 =T n1 gcosα n1

[0063] F xn2 =T n2 gcosα n2

[0064] The lateral forces of the left and right bow nozzles are as follows:

[0065] F yn1 =T n1 gsinα n1

[0066] F yn2 =T n2 gsinα n2

[0067] In the formula, T n1 ,T n2 These represent the output thrust of the left and right bow nozzles, respectively; α n1 ,α n2These represent the rotation angles of the left and right bow nozzles, respectively.

[0068] The turning torque is:

[0069] N n =F xn1 y n1 +F yn1 x n1 +F xn2 y n2 +F yn2 x n2

[0070] 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 vector propulsion and control system of the fully-cushioned hovercraft.

[0071] propeller model

[0072] 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:

[0073]

[0074] 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.

[0075] The thrust generated by the propeller along the longitudinal axis is:

[0076] T xp =T pl +T pr

[0077] The thrust generated by the propeller on the horizontal axis is:

[0078] T yp =0

[0079] When the thrust generated by the left and right propellers is different, a turning torque can be generated. The turning torque experienced by the hovercraft is:

[0080] N p =-T pl y p1 -T pr y p2

[0081] Air rudder model

[0082] The longitudinal force generated by a single-sided aerodynamic rudder is:

[0083] F xri =2p ri S d c xi

[0084] The lateral force generated by a single-sided aerodynamic rudder is:

[0085] F yri =2p ri S d c yi

[0086] In the formula, p ri For pneumatic pressure; S d c is the area of ​​a single air rudder blade. i It represents the aerodynamic coefficient.

[0087] The turning torque is:

[0088] N r =-y r1 F xr1 +x r1 F yr1 -y r2 F xr2 +x r2 F yr2

[0089] Based on the model, the multi-operation-face coordinated control allocation equations are established as follows:

[0090]

[0091] However, how to switch actuators to control the motion of the hovercraft at different speeds becomes a problem, namely, the problem of multi-control surface coordinated control switching. This study solves this problem by introducing a switching function.

[0092] The expression for the switching function ξ(u) is:

[0093]

[0094] In the formula, u1 and u2 are the optimal speed thresholds for turning using different control surfaces individually, and the specific values ​​are determined by the actual operating characteristics of the hovercraft.

[0095] Rewritten in vector form, as shown below:

[0096]

[0097] The expression shows the relationship between the longitudinal control force, lateral control force, and slewing control torque output by the upper controller and the forces and torques generated by each control surface.

[0098] This invention primarily relies on the desired and real-time states of the hovercraft. The upper-level controller calculates the longitudinal control force and turning control torque required during navigation. However, the lower-level control surfaces—the air duct propeller, air rudder, and vector nozzle—receive input signals regarding pitch angle, rudder angle, and vector nozzle rotation angle. To rationally distribute the forces and torques output by the upper-level controller to the lower-level control surfaces and convert them into input angles for each surface, a set of multi-control surface control allocation equations needs to be established.

[0099] τ=BU+s

[0100] τ=[XYN p N r N n ] T

[0101]

[0102] U = [F p1 F p2 F xr1 F yr1 F xr2 F yr2 F xn1 F yn1 F xn2 F yn2 ] T

[0103] In the formula, 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.

[0104] Rewrite U as:

[0105]

[0106] In the formula, Let δ1 and δ2 be the left and right pitch angles, respectively, and θ1 and θ2 be the left and right rudder angles, respectively.

[0107] The hovercraft model used in this invention has a linkage system with left and right air rudders having δ1=δ2=δ, and a linkage system with left and right vector nozzles having θ1=θ2=θ.

[0108] Optimization objective for coordinated thrust distribution between rudder, propeller, and bow nozzle

[0109] Considering the losses of each propulsion device and the safe navigation conditions of the hovercraft, and under the premise of minimizing the error between control commands and allocation commands, the objective function is expressed as:

[0110] Objective function:

[0111]

[0112] In the formula, WP represents the energy consumed by the air duct propeller during the operation of the hovercraft, W > 0 is the weight matrix, and P is the power of the air duct propeller during operation; (u - u0) T R(u-u0) represents the rate of change of the pitch angle, air rudder angle, and vector nozzle rotation angle of the air-ducted propeller, where R > 0 W > 0 is the weight matrix; s T Qs represents the error penalty term, s>0 is the error value, Q>0 is the weight matrix. By adjusting the value of the third term (error value), the magnitude of the error between the output force and torque of the upper controller and the total force and torque generated by the control surface is adjusted, thus accelerating the solution of the problem.

[0113] The first term in the objective function represents the energy consumption of the left and right air propellers; the second term represents the pitch angle, rudder angle, and rate of change of the bow nozzle; the third term represents the penalty term for the error between the control command and the allocation command, where s is a slack variable, the purpose of which is to ensure that the optimization problem can be solved feasiblely under any circumstances.

[0114] The final objective function and constraints for the coordinated thrust distribution between the rudder, propeller, and bow nozzle are as follows:

[0115]

[0116] Constraints:

[0117] τ=B·U+s

[0118] s min ≤s i ≤s max

[0119]

[0120] δ min ≤δ i ≤δ max

[0121] θ min ≤θ i ≤θ max

[0122] This paper proposes an improved adaptive compression factor PSO algorithm to solve the allocation optimization problem using genetic algorithms and particle swarm optimization (PSO). Addressing the low accuracy of the standard PSO algorithm, the algorithm first introduces a variable inertia weight to avoid getting trapped in local optima. The algorithm then considers the spatial positions of the particles. The pitch angles of the left and right air duct propellers, the left and right air rudder angles, and the rotation angles of the left and right vector nozzles are set for the hovercraft. The domain of the particle's motion in each dimension is set as the physical angular constraints of the four actuators. The fitness function in the algorithm is... Set as the objective function for coordinated control allocation optimization in the multi-operation-face coordinated control equations.

[0123] We design an adaptive compression factor to improve the convergence speed of the algorithm; at the same time, we modify the learning factor to enhance the algorithm's global search capability in the early stage and its local search capability in the later stage.

[0124] Based on the above, the improved algorithm can be applied to the multi-control surface coordination and allocation optimization of the fully cushioned hovercraft, so as to achieve optimal control of the hovercraft under the vector control of multiple control surfaces.

[0125] An improved adaptive compressibility factor particle swarm optimization algorithm is used to solve the coordinated control allocation problem of multiple control surfaces for hovercraft. The following improvements are made to the ordinary particle swarm optimization algorithm:

[0126] Set inertia weight

[0127]

[0128] In the formula, w s t art w represents the initial iterative inertia weights of the algorithm. end The inertia weight at the end of the algorithm iteration, t is the current iteration number of the particle swarm optimization algorithm, T max This represents the maximum number of iterations for the particle swarm optimization algorithm.

[0129] Set adaptive compression factor

[0130]

[0131] In the formula, ζ is the compression factor, λ is a positive number, t is the current iteration number of the particle swarm optimization algorithm, and Tmax This represents the maximum number of iterations for the particle swarm optimization algorithm.

[0132] Set adaptive learning factor

[0133]

[0134]

[0135] The improved velocity and position formulas for the particle swarm optimization algorithm are shown below:

[0136]

[0137] The specific steps of the improved adaptive compression factor particle swarm algorithm are as follows:

[0138] Step 1: Initialize parameters such as particle position, velocity, number of particles, and number of algorithm iterations in the algorithm;

[0139] Step 2: Calculate the fitness value and optimal position of each particle, and select the globally optimal fitness value and globally optimal position;

[0140] Step 3: Update the particle velocity and position according to the particle velocity and position update formula in the particle swarm algorithm, while satisfying the constraints.

[0141] Step 4: Repeat step 2, compare each particle with its previous best fitness value, and select its own best fitness value and best position;

[0142] Step 5: Update the global optimal fitness value and the global optimal position;

[0143] Step 6: Repeat steps 2-5 until the minimum error set by the program is met or the maximum number of iterations of the program is reached.

[0144] Step 7: Output the global optimal fitness value and global optimal position calculated by the program.

[0145] It should be noted that the specific embodiments are merely explanations and illustrations of the technical solution of the present invention and should not be used to limit the scope of protection. Any modifications made in accordance with the claims and specification of the present invention that are only partial should still fall within the protection scope of the present invention.

Claims

1. A method of distributing thrust for vector propulsion of a full-cushion hovercraft, characterized by The method comprises the following steps: Step 1: establishing a four-degree-of-freedom kinematics model and a four-degree-of-freedom dynamics model of the full-pad air cushion vehicle, and designing an upper controller according to the four-degree-of-freedom kinematics model and the four-degree-of-freedom dynamics model of the full-pad air cushion vehicle; Step 2: obtaining a total force τ output by the upper controller, and establishing a multi-control surface control distribution equation in combination with turning moment of the air duct propeller, the air rudder and the vector jet pipe; Step 3: constructing an objective function according to the total force τ and the turning moment of the air duct propeller, the air rudder and the vector jet pipe; Step 4: 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 jet pipe; The multi-control surface control distribution equation is expressed as: τ = BU + s τ = [X Y N p N r N n ] T where τ denotes the total force outputted from the upper controller, B denotes a control allocation matrix, U denotes a function with respect to the pitch angle, the rudder angle, and the vector nozzle rotation angle, s denotes an error value, X denotes a longitudinal control force, Y denotes a lateral control force, N p denotes a yawing moment of the air duct propeller, N r denotes a yawing moment of the air rudder, N n denotes a yawing moment of the vector nozzle, (x n1 ,y n1 ) and (x n2 ,y n2 ) denote vector nozzle coordinates at the bow of the ship, (x p1 ,y p1 ) and (x p2 ,y p2 ) denote vector nozzle coordinates at the stern of the ship, (x r1 ,y r1 ) and (x r2 ,y r2 ) denote air rudder coordinates, denote left and right pitch angles, δ1 and δ2 denote left and right rudder angles, θ1 and θ2 denote left and right vector nozzle rotation angles, f T , f xr , and f yr denote force of the air duct propeller, longitudinal force of the air rudder, and lateral force of the air rudder, respectively, F xn1 and F xn2 denote longitudinal forces of left and right bow nozzles, respectively, and F yn1 and F yn2 denote lateral forces of left and right bow nozzles, respectively.

2. A method of distributing thrust for vector propulsion of a hovercraft as claimed in claim 1, characterised in that The kinematics model is expressed as: where u, v, p, r are the longitudinal, lateral, roll angular and yaw angular velocities of the air cushion vehicle in the body coordinate system, x, y, ψ are the position, roll angle and heading angle of the body center of mass in the North-East coordinate system, are the first derivatives of x, y, ψ.

3. A method of distributing thrust for vector propulsion of a hovercraft as claimed in claim 2, characterised in that The dynamics model is expressed as: where m represents the state vector, J x z Ixx and Izz represent the moments of inertia about the x and z axes, respectively, F x y Fx and Fy represent the resultant external forces on the hull in the x and y directions, respectively, M x z Mx and Mz represent the resultant moments about the x and z axes, respectively, u' represents the first derivative of u, v' represents the first derivative of v, p' represents the first derivative of p, r' represents the first derivative of r.​​​ 4. A method of distributing thrust for vector propulsion of a hovercraft as claimed in claim 3, characterised in that The turning moment of the air duct propeller is expressed as: N p = -T pl y p1 -T pr y p2 where T pl ,T pr represent the thrust of the left and right propellers, respectively.

5. A method of distributing thrust for vector propulsion of a hovercraft as claimed in claim 4, characterised in that The turning moment of the air rudder is expressed as: N r = -y r1 F xr1 +x r1 F yr1 -y r2 F xr2 +x r2 F yr2 where F xr1 , F yr1 , F xr2 , F yr2 denote the longitudinal and lateral forces provided by the left and right airfoils, respectively.

6. A method of distributing thrust for vector propulsion of a hovercraft as claimed in claim 5, characterised in that The turning moment of the vector jet pipe is expressed as: N n = F xn1 y n1 + F yn1 x n1 + F xn2 y n2 + F yn2 x n2 .

7. A method of distributing thrust for vector propulsion of a hovercraft as claimed in claim 6, characterised in that The objective function is expressed as: 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, 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 rotation.

8. A method of distributing thrust for vector propulsion of a hovercraft as claimed in claim 1, characterised in that The solving is performed by a particle swarm algorithm.

9. A method of distributing thrust for vector propulsion of a hovercraft as claimed in claim 8, characterised in that The particle swarm algorithm is an improved adaptive compression factor particle swarm algorithm, and the velocity formula and the position formula in the improved adaptive compression factor particle swarm algorithm are expressed as: where w represents the inertia weight, w start represents the initial iteration inertia weight of the algorithm, w en d represents the final iteration inertia weight of the algorithm, t represents the current iteration number of the particle swarm algorithm, T max represents the maximum iteration number of the particle swarm algorithm, ζ represents the compression factor, λ is a positive number, c1 and c2 are learning factors, r1 and r2 represent two random numbers with a value range of [0, 1], and υ is a constraint factor, represents the i-th particle in the space, represents the current position of the i-th particle, represents the current motion speed of the i-th particle, and the superscript d represents the d-dimensional solution space.

Images