A Multi-Constraint Control Method for Air Cushion Vehicles Based on a Preset Performance Function with Composite Error Transformation

Abstract

The present application provides an air-cushion vehicle multi-constraint control method based on a preset performance function with composite error transformation, which relates to the technical field of air-cushion vehicle motion control. The method establishes a motion mathematical model of a fully-lifted air-cushion vehicle, designs an observer to estimate and compensate for system uncertainties in real time, and introduces a non-linear mapping function and a biological excitation model to handle asymmetric time-varying constraints; based on the preset performance function with composite error transformation, a position control law and a speed control law are designed, effectively avoiding the problems of side-slip and tail-swing during high-speed navigation, and improving the trajectory tracking accuracy and system stability; in addition, through the disturbance observer and the composite error transformation technology, the dependence on initial conditions is eliminated, and the robustness of the system is enhanced. The present invention can significantly improve the trajectory tracking accuracy, stability and safety of the air-cushion vehicle in complex environments, is applicable to various complex environments such as water surfaces, land, swamps, etc., and has broad application prospects.

Description

Technical Field

[0001] The present invention relates to the technical field of hovercraft motion control, and specifically to a multi-constraint control method based on a preset performance function with composite error transformation. Background Art

[0002] A fully air-cushioned hovercraft is a cross-media special ship that hovers above water surfaces, land, swamps and other complex environments through a cushioning system, and is widely used in civilian fields and naval equipment, etc. However, since a hovercraft is easily affected by external environmental factors (such as wind, waves, load changes, etc.) during operation, there are large errors in its control system, resulting in poor navigation accuracy and stability. To solve the above problems, current research focuses on multi-model fusion control and non-linear solution optimization: Existing research has proposed a multi-condition weight coefficient iteration method based on reinforcement learning to improve the track stability by dynamically adjusting the actuator strategy; Another research uses the Newton iteration method combined with model linearization to improve the cushioning pressure solution accuracy, but the real-time performance and anti-interference ability are still limited; Active disturbance rejection control (ADRC) compensates for disturbances through an extended state observer, but there are still problems with fuzzy preset performance boundaries under multi-constraint conditions. Therefore, most of the existing hovercraft control methods lack effective consideration of multi-constraint conditions and cannot achieve precise motion control. Summary of the Invention

[0003] To solve the above problems, the present invention designs a trajectory tracking method based on a preset performance function with composite error transformation, and under the premise of considering the transient and steady-state performance, constrains the control accuracy, directly constrains the state variables of the hovercraft system, avoids the problem of sideslip and tail swing during high-speed navigation, and improves the safety performance.

[0004] The present invention specifically provides the following technical solutions:

[0005] A multi-constraint control method for a hovercraft based on a preset performance function with composite error transformation, the control method comprising the following steps:

[0006] S1. Obtain the hull data and experimental data of the fully air-cushioned hovercraft, convert the non-affine mathematical model into an angular velocity control model in vector form, define the system state variables and introduce the uncertainty term, and construct a fully air-cushioned hovercraft vector dynamics model including dynamic parameters and external disturbances;

[0007] S2. Based on the fully air-cushioned hovercraft vector dynamics model, design a non-linear disturbance observer, and the observer estimates and compensates for the system uncertainty problem in hovercraft trajectory tracking;

[0008] S3. Design a non-linear mapping function and construct a bio-inspired model;

[0009] S4. Design the position control law and speed control law based on the preset performance function of the composite error transformation;

[0010] S5. Based on the disturbance observer, combine the composite error transformation technology of continuously differentiable time-varying functions to eliminate the dependence on initial conditions; and couple the sliding mode control with the improved mapping function to synchronously constrain the speed, turning rate, and sideslip angle within the asymmetric safety interval;

[0011] S6. Dynamically adjust the parameters according to the actual operating state and environmental changes of the hovercraft, and verify the effectiveness of the control method.

[0012] Optionally, in the S1, the angular velocity control model is specifically:

[0013] ;

[0014] Where, is the pose vector of the hovercraft in the global coordinate system; is the rotation matrix, composed of the roll angle and the heading angle ; is the velocity vector in the body coordinate system; is the inertia matrix; is the instantaneous acceleration of, is the Coriolis-centripetal force matrix; is the control input vector; is the external disturbing force; is the total uncertainty, that is, the damping matrix;

[0015] Expand the angular velocity control model into a vector form, define , the vector dynamic model of the fully air-cushioned hovercraft is specifically:

[0016] ;

[0017] Where, is the non-linear coupling term, is the position and attitude vector of the hovercraft in the global coordinate system; is the residual disturbance.

[0018] Optionally, in the S2, the process of the non-linear disturbance observer for estimation and compensation is specifically:

[0019] ;

[0020] Where, is the dynamic mixing coefficient; is a preset performance function, i.e., the gain matrix, corresponding to the inertia matrix in the observer , the Coriolis matrix , and the damping matrix compensation weights; is a non - linear compensation function for dealing with the non - linear coupling terms in the hovercraft dynamics model ; is the observation error.

[0021] Optionally, in the S3, the non - linear mapping function directly processes the asymmetric time - varying constraints, and the is specifically expressed as:

[0022] ;

[0023] where and are the upper and lower dynamic boundaries of the state quantity under time constraints, and and are the static reference values under the upper and lower limits constraints.

[0024] Optionally, in the S3, the function of the bio - inspired model is:

[0025] ;

[0026] where is the upper bound, is the passive attenuation rate; is the action potential of the neuron, and are the excitatory and inhibitory inputs respectively. [[ID=5%]]

[0027] Optionally, in the S4, the position control law is, after executing the S3, to perform envelope constraints on the longitudinal position deviation and the lateral position deviation specifically as:

[0028] ;

[0029] where is a preset performance function to be defined.

[0030] Optionally, the speed control law includes the longitudinal speed and lateral speed desired control laws, specifically as:

[0031] ;

[0032] where and are the longitudinal and lateral speed desired control rates respectively; , , are the heading angle, pitch angle, and roll angle respectively; and are the target longitudinal coordinate and lateral coordinate of the hovercraft in the global coordinate system respectively; and are the preset performance functions of the longitudinal and lateral position deviations respectively; and are the longitudinal and lateral normalized tracking errors; is the weight coefficient.

[0033] Optionally, in the S5, design a speed control law based on the improved consistent non - linear mapping function combined with the bio - inspired model, specifically:

[0034] ;

[0035] where is the control torque for driving the hovercraft to rotate around the vertical axis Z; , , are the moments of inertia of the hovercraft around the X - axis, Y - axis, and Z - axis respectively; is the damping torque around the Z - axis; and are the rotation rates of the hovercraft around the longitudinal axis and the transverse axis respectively; is the hover damping coefficient; is a constant; is the turning resistance component of the hovercraft; and are the linear and non - linear components of the turning resistance of the hovercraft respectively; is the adjustment resistance term in the weight of the control law; is the equivalent action radius of the contact area between the hover and the water surface; is the longitudinal velocity component; is the lateral error velocity.

[0036] Optionally, in the S6, build a motion control simulation platform for the fully - lifted hovercraft based on MATLAB to conduct a simulation experiment on the control method; the simulation parameters include the mass, moment of inertia, thrust coefficient, and external disturbance of the hovercraft.

[0037] Optionally, the simulation experiment includes designing multiple motion scenarios to verify the performance of the control method under different working conditions; the multiple motion scenarios include straight - line navigation, curve navigation, and sharp turns.

[0038] The present invention has the following beneficial technical effects: The present invention provides an air-cushion vehicle multi-constraint control method based on a composite error transformation preset performance function. 1) In the present invention, in view of the traditional air-cushion vehicle control method, during the trajectory tracking process, due to the lack of effective handling of system uncertainties and external disturbances, the tracking error is often large, especially in complex environments (such as wind and waves, load changes, etc.). By introducing composite error transformation and preset performance function, the tracking error can be strictly constrained within a preset range, significantly improving the trajectory tracking accuracy; 2) By designing a disturbance observer to estimate and compensate the system uncertainties in real time and combining with the preset performance function, the robustness of the system is significantly enhanced. Even in the presence of strong disturbances, the system can still operate stably. By introducing an improved consistent non-linear mapping function, the speed, turning rate, and sideslip angle of the air-cushion vehicle can be directly constrained, effectively avoiding the sideslip and tail-swing problems during high-speed navigation and improving the navigation safety; 3) By designing a non-linear mapping function, it can directly handle asymmetric time-varying constraints and uniformly handle the constrained and unconstrained cases, significantly improving the applicability and flexibility of the control method. By introducing the composite error transformation technology, the initial deviation of the air-cushion vehicle position can be compressed into a definable range, eliminating the dependence on initial conditions and improving the stability and reliability of the control method. The control method proposed by the present invention has wide applicability and can be applied to the motion control of fully air-cushioned air-cushion vehicles in different environments (such as water surface, land, swamp, etc.), with high generality and popularization value. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0040] Figure 1 FIG. is a control flow chart of a fully air-cushioned air-cushion vehicle provided for an embodiment of the present invention.

[0041] Figure 2 FIG. is a schematic diagram of a multi-constraint control method for an air-cushioned vehicle based on a composite error transformation preset performance function provided for an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The following will describe the embodiments of the present application in detail with reference to the drawings.

[0043] The following describes the implementation modes of the present application through specific examples with labels. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all embodiments. The present application can also be implemented or applied through other different specific implementation modes. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope protected by the present application.

[0044] It should be noted that the following describes various aspects of embodiments within the scope of the appended claims. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and the structure and / or function of any of the labels described herein are illustrative only. Based on the present application, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number and aspects set forth herein can be used to implement an apparatus and / or practice a method.

[0045] It should also be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner.

[0046] In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the practice can be carried out without these details of the labels.

[0047] The object of the present invention is to provide an air-cushion vehicle multi-constraint control method based on a preset performance function of composite error transformation.

[0048] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation modes.

[0049] Refer to Figure 1 , which shows a control flow chart of a fully air-cushioned air-cushion vehicle according to an embodiment of the present application.

[0050] Figure 1The control logic is a multi-actuator collaborative control architecture based on closed-loop feedback. Its core process takes the desired navigation trajectory as the input target, generates multi-degree-of-freedom tracking error signals by collecting the position, heading, and attitude data of the hovercraft in real time, and then the motion controller uses a non-linear decoupling algorithm to convert the error into independent control components (such as longitudinal thrust and lateral torque), and introduces a preset performance function to dynamically constrain the error amplitude and convergence rate to avoid overshoot or actuator saturation. The decoupled commands are optimally distributed to actuators such as propellers, air rudders, and bow nozzles through a control allocator, and finally the disturbance deviation is corrected in real time through sensor closed-loop feedback to form a complete control loop of "trajectory input - error calculation - command distribution - dynamic compensation".

[0051] Figure 2 The control logic is then Figure 1 a technical refinement and implementation expansion of the architecture, with its core focusing on the precise regulation of lift pressure and multi-degree-of-freedom motion.

[0052] Specifically, Figure 2 A hovercraft multi-constraint control method based on a composite error transformation preset performance function according to an embodiment of the present application is shown, and the method includes the following steps:

[0053] S1. Obtain the hull data and experimental data of the fully-lifted hovercraft, convert the non-affine mathematical model into an angular velocity control model in vector form, define system state variables and introduce uncertainty terms, and construct a fully-lifted hovercraft vector dynamics model including dynamic parameters and external disturbances.

[0054] In the S1, the angular velocity control model is specifically:

[0055] ;

[0056] Wherein, is the pose vector of the hovercraft in the global coordinate system; is the rotation matrix, composed of the roll angle and the heading angle ; is the velocity vector in the hull coordinate system; is the inertia matrix; is the instantaneous acceleration of, is the Coriolis-centripetal force matrix; is the control input vector; is the external disturbing force; is the total uncertainty, that is, the damping matrix, including linear / nonlinear damping such as fluid resistance and air resistance.

[0057] Specifically, , representing the position and attitude of the hovercraft in the global coordinate system, corresponding to the three-dimensional position coordinates, is the roll angle, is the pitch angle, is the heading angle; represents the linear velocity and angular velocity of the hovercraft in the hull coordinate system, is the longitudinal linear velocity, is the lateral linear velocity, is the vertical linear velocity, is the roll angular velocity, is the pitch angular velocity, is the heading angular velocity;

[0058] is the rotation matrix, representing the pose η mapped to the global coordinate system. R1 is the linear velocity rotation matrix composed of , θ, and R2 is the angular velocity rotation matrix composed of , θ, ; Among them, the specific expressions of R1 and R2 are as follows: ;

[0059] The inertia matrix M contains a symmetric positive definite matrix of the mass and moment of inertia of the hovercraft, reflecting the inertial characteristics of the hull's translational and rotational motions. The specific expression is as follows:

[0060] ;

[0061] Coriolis-centripetal force matrix is the virtual force / moment generated by the coupling of the hull's motion, which is related to the velocity vector ν and reflects the nonlinear dynamic characteristics. The specific expression is as follows:

[0062] ;

[0063] Among them, m is the total mass of the hull, with the unit of kg, is the moment of inertia about the x-axis of the hull coordinate system, with the unit of kg·m², is the moment of inertia about the y-axis of the hull coordinate system, with the unit of kg·m², is the moment of inertia about the z-axis of the hull coordinate system, with the unit of kg·m².

[0064] characterizes the external disturbing force, including the forces / moments exerted on the hull by external environments such as wind waves and ocean currents. The specific expression is as follows:

[0065] ;

[0066] ;

[0067] Among them, is the fluid density, is the pressure, the internal pressure of the air cushion or the hydrostatic pressure, is the moving speed of the hovercraft relative to the air; g is the acceleration due to gravity, implicit in the hydrostatic term; are the total resistance components of the hull in the x, y, and z directions, respectively, which are composed of the superposition of aerodynamic resistance, fluid resistance, shear force, etc.; are the moment resistance components about the x, y, and z axes, respectively; , are the components of the aerodynamic force in the x and y directions, respectively; is the air cushion momentum force, related to the air cushion flow rate. Specifically , where Q is the volume flow rate, such as the flow rate of the air cushion fan; is the buoyancy or vertical supporting force generated by the hydrostatic pressure; is the shear force, related to the fluid viscosity effect: is the force generated by the air cushion pressure; is the tilt angle of the hovercraft or the fluid action direction angle; is the characteristic length, such as the air cushion length or the hull contact surface length; is the fluid layer height or the air cushion thickness; is the air cushion width; is the hull roll angle or the geometric shape correction coefficient; is the reference height, such as the initial fluid layer thickness; is the shear action characteristic length; is the reference area, such as the air cushion projection area or the fluid action surface area; is the air cushion characteristic length for pressure distribution calculation; is the general force / moment coefficient, is the aerodynamic angle of attack coefficient acting in the x direction; , are the aerodynamic moment coefficients about the x-axis and z-axis, respectively; Shear force coefficient; is the water density; is the air density. The core parameters in the above formulas have been given, and the definitions of the remaining relevant parameters can be directly obtained or inferred according to the task scenario, and no repeated explanation is given here.

[0068] The units and values of the relevant parameters in the above hovercraft model are specifically shown in Table 1.

[0069] Table 1. Relevant parameters in the hovercraft model

[0070] .

[0071] Expand the formula into vector form, consider the hovercraft model with system uncertainties, and define , and rewrite it as:

[0072] ;

[0073] where is the non - linear coupling term, is the position and attitude vector of the hovercraft in the global coordinate system; is the residual disturbance.

[0074] Step S2. Use the full - lift hovercraft vector mathematical model established in Step S1. For the system uncertainty problem in hovercraft trajectory tracking, design the following observer for estimation and compensation:

[0075] ;

[0076] where is the dynamic mixing coefficient; is the preset performance function, i.e., the gain matrix, corresponding to the compensation weights of the inertia matrix , the Coriolis matrix , and the damping matrix in the observer respectively; is the non - linear compensation function used to handle the non - linear coupling term in the hovercraft dynamics model; is the observation error. Specifically, , and . Where specifically represents the dynamic coupling error between the system pose η and the observed state ξ, including the influence of unmodeled dynamics or external disturbances. is the observed value vector, representing the estimated values of the linear velocity and angular velocity in the body coordinate system; is the observation error vector, representing the estimated deviations of the three - axis position and three - axis torque. The estimation error . are respectively expressed as correction values in different intervals and can ensure that the observation error converges to the origin. The correction function is as follows:

[0077] ;

[0078] where H1(e) is the hybrid correction term, including the time function h(t), the gain matrix Θ, and the error scaling factor ; H2(e) is the second - order correction term, strengthening the time dependence; H3(e) is the third - order correction term for high - order error suppression; is the convergence parameter, determined by solving the linear matrix inequality LMI, used to ensure the exponential convergence of the observation error; It can be written as follows:

[0079] ;

[0080] Among them, N1(e) is a nonlinear sliding mode correction term, which contains the gain l1 and the sign function sign(e); N2(e) is a mid-order sliding mode correction term, which reduces chattering; N3(e) is a first-order sliding mode correction term, which acts directly on the error; is the Lipschitz constant, are the parameters to be designed and satisfy: , and .

[0081] Step S3 specifically includes three sub-steps, namely:

[0082] Step 3-1: Design a nonlinear mapping function that can directly handle asymmetric time-varying constraints without changing its own structure, and can uniformly handle constrained and unconstrained situations. The designed function :

[0083] ;

[0084] in, and Define the state quantity under time constraint separately The upper and lower dynamic boundaries of and are the static benchmark values under upper and lower limit constraints respectively.

[0085] Step 3-2: Design a bioinspiration model. The bioinspiration model represents the rate of change of the action potential ζi of neuron i over time. Therefore, the bioinspiration model function is defined as:

[0086] ;

[0087] ;

[0088] in, is the upper bound, representing the maximum saturation value of the neuronal action potential, limiting the positive excitation amplitude of the excitatory input; is the passive decay rate, which describes the natural decay rate of neurons when there is no external input; is the action potential of the neuron, which represents the membrane potential state of neuron i and is the state variable of the model; and They are excitatory and inhibitory inputs, respectively, triggered according to the polarity of the input signal a.

[0089] Step 3-3: Design the position control law based on the preset performance function of the composite error transformation. On the premise of performing Step 3-2, for the longitudinal position deviation and the lateral position deviation make the envelope constraints as:

[0090] ;

[0091] where is the preset performance function to be defined. Preferably, select the exponential preset performance function.

[0092] Redefine the transformation error as follows:

[0093] ;

[0094] where and .

[0095] where , are the normalized errors, which scale the original error into the preset performance boundary; is the time-varying mixing coefficient, 0 ≤ ≤ 1, which is used to dynamically adjust the ratio of the nonlinear tanh to the linear error transformation. is the hyperbolic tangent function, which is used to limit the amplitude of the transformed error and enhance the robustness. is to transform the constrained original error into the unconstrained new error through nonlinear mapping, which is convenient for controller design. The transformed error satisfies the inequality , then the tracking error will be within the preset performance range.

[0096] Therefore, design the following desired control laws for the longitudinal and lateral velocities:

[0097] ;

[0098] where and are the desired control laws for the longitudinal and lateral velocities respectively; , , are the heading angle, pitch angle and roll angle respectively; and are the target longitudinal coordinate and lateral coordinate of the hovercraft in the global coordinate system respectively; and are the preset performance functions of the longitudinal and lateral position deviations respectively; and are the longitudinal and lateral normalized tracking errors; is the weight coefficient.

[0099] Step S5: Design a velocity control law based on the improved uniform nonlinear mapping function. On the premise of performing Step 3-3, introduce the improved uniform nonlinear mapping function for the designed in Step 3-1. To avoid the differential explosion problem and simplify the control design, introducing the bio-inspired model designed in Step 3-2, we can obtain: ;

[0100] where is the control torque for driving the hovercraft to rotate around the vertical axis Z; , , are the moments of inertia of the hovercraft around the X-axis, Y-axis, and Z-axis respectively; is the damping torque around the Z-axis; and are the rotational rates of the hovercraft around the longitudinal axis and the lateral axis respectively; is the hover damping coefficient; is a constant; is the turning resistance component of the hovercraft; and are the linear and nonlinear components of the turning resistance of the hovercraft respectively; is the adjustment resistance term is the weight of the control law; is the equivalent action radius of the contact area between the hover and the water surface; is the longitudinal velocity component; is the lateral error velocity.

[0101] Step S6. Dynamically adjust the parameters according to the actual operating state and environmental changes of the hovercraft, and verify the effectiveness of the control method.

[0102] To verify the effectiveness of the present invention, system simulation and experimental verification were carried out. The specific implementation steps are as follows:

[0103] 1. Simulation environment construction: Use MATLAB / Simulink to build a motion control simulation platform for the surface-effect ship. Set the simulation parameters, including the mass, moment of inertia, thrust coefficient, external disturbances (such as wind and waves) of the hovercraft, etc. Design a variety of typical motion scenarios, such as straight navigation, curve navigation, sharp turn, etc., to verify the performance of the control method under different working conditions.

[0104] 2. Simulation result analysis: In the comparative experiment, the trajectory tracking accuracy and stability of the control method based on the preset performance function with composite error transformation proposed in the present invention are compared with those of traditional control methods (such as PID control and sliding mode control), the robustness of the system in the presence of external disturbances and uncertainties is analyzed, and the estimation and compensation effects of the observer on the system uncertainties are verified. The simulation results show that the sideslip and tail-swing problems of the hovercraft during high-speed navigation are significantly improved, verifying the effective constraint of the preset performance function on the state variables.

[0105] The actuator constraints of the fully air-cushioned hovercraft are the rudder angle constraint, the thrust speed constraint, and the pitch angle constraint. Considering the influence of the mechanical performance on the propeller drive mechanism and the rudder of the hovercraft, their motion ranges and rates are limited. Therefore, bounded constraints on the control quantity and control increment and bounded constraints on the yaw rate of the hull are introduced, which are respectively ;

[0106] where 、 are the minimum and maximum values of the propeller thrust and torque respectively; and are the minimum and maximum allowable change amounts of the control increment respectively; 、 are the minimum and maximum values of the yaw rate of the hull respectively; is the current iteration number, is the control time domain length, is the total number of steps.

[0107] Experimental verification: Experiments are carried out on the actual fully air-cushioned hovercraft platform, and actual operation data are collected. By comparing the simulation results with the experimental data, the actual application effect of the control method is verified. The experimental results show that the control method proposed in the present invention can significantly improve the trajectory tracking accuracy and stability of the hovercraft, and effectively avoid the sideslip and tail-swing problems at the same time.

[0108] Parameter adaptive adjustment: According to the actual operation state and environmental changes of the hovercraft, the parameters in the preset performance function and the control law are dynamically adjusted to further improve the control accuracy and robustness.

[0109] Multi-objective optimization: Combining with the multi-objective optimization algorithm, the parameters in the control law are optimized to achieve the best balance among trajectory tracking accuracy, energy consumption, and safety.

[0110] Based on the same inventive concept, according to another aspect of the present invention, an embodiment of the present invention also provides a computer device, including:

[0111] At least one processor; and

[0112] The memory stores a computer program that can be run on the processor, and the processor executes the steps of any of the above control methods when executing the program.

[0113] Based on the same inventive concept, according to another aspect of the present invention, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer program instructions. When the computer program instructions are executed by a processor, the steps of any of the above control methods are performed.

[0114] Finally, it should be noted that those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM). The above-mentioned computer program embodiments can achieve the same or similar effects as the corresponding embodiments of any of the above-mentioned methods.

[0115] Furthermore, the apparatuses and devices disclosed in the embodiments of the present invention may typically be various electronic terminal devices, such as mobile phones, personal digital assistants (PDAs), tablet computers (PADs), smart televisions, etc., or large terminal devices, such as servers. Therefore, the scope of protection disclosed in the embodiments of the present invention should not be limited to a specific type of apparatus or device. The client disclosed in the embodiments of the present invention may be implemented in any of the above-mentioned electronic terminal devices in the form of electronic hardware, computer software, or a combination of both.

[0116] In addition, the method disclosed in the embodiment of the present invention can also be implemented as a computer program executed by a CPU, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the CPU, the above functions defined in the method disclosed in the embodiment of the present invention are performed.

[0117] In addition, the above method steps and system units can also be implemented using a controller and a computer-readable storage medium for storing a computer program that enables the controller to implement the above steps or unit functions.

[0118] The above are exemplary embodiments disclosed in the present invention, but it should be noted that various changes and modifications may be made without departing from the scope of the embodiments disclosed in the claims. The functions, steps and / or actions of the method claims according to the disclosed embodiments described herein do not need to be performed in any marked order. In addition, although the elements disclosed in the embodiments of the present invention may be described or required in individual form, they may also be understood as multiple unless expressly limited to the singular.

[0119] In this specification, for the same or similar parts among the various embodiments, reference can be made to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the embodiments described later, the description is relatively simple, and for the relevant parts, reference can be made to the partial description of the foregoing embodiments.

[0120] As described above, the foregoing are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A multi-constraint control method for hovercraft based on a preset performance function with composite error transformation, characterized in that The control method includes the following steps: S1. Obtain the hull data and experimental data of the surface effect ship, transform the non - affine mathematical model into an angular velocity control model in vector form, define the system state variables and introduce the uncertainty terms, and construct a surface effect ship vector dynamics model including dynamic parameters and external disturbances; S2. Based on the surface effect ship vector dynamics model, design a non - linear disturbance observer, and the observer estimates and compensates for the system uncertainty problem in the trajectory tracking of the hovercraft; In the S2, the process of the non - linear disturbance observer for estimation and compensation is specifically: Among them, is the pose vector observation value of the hovercraft under nonlinear disturbances in the global coordinate system; is the rotation matrix, which consists of the roll angle and the heading angle ψ; τ is the control input vector; F is the external disturbance force; is the observation value of the residual disturbance; μ is the dynamic mixing coefficient; H1(e), H2(e), H3(e) are preset performance functions, i.e., gain matrices, corresponding to the compensation weights of the inertia matrix M, the Coriolis-centripetal force matrix C(v), and the damping matrix D in the observer respectively; N1(e), N2(e), N3(e) are nonlinear compensation functions used to handle the nonlinear coupling terms in the hovercraft dynamics model is the observation error; S3. Design a non-linear mapping function and construct a bio-inspired model; the non-linear mapping function F c directly processes asymmetric time-varying constraints, and the F c is specifically expressed as: where ξ cr (t) and ξ cl (t) are the upper and lower dynamic boundaries of the state variable x under time constraints, and are the static reference values under the upper and lower constraints respectively; S4. Design the position control law and speed control law based on the preset performance function of the composite error transformation; in the S4, the position control law includes, after performing the S3, respectively performing envelope constraints on the longitudinal position deviation x e and the lateral position deviation y e Specifically, it is as follows: where ρ x , ρ y is a preset performance function to be defined; S5. Based on the disturbance observer, combined with the composite error transformation technique of continuously differentiable time-varying functions, eliminate the initial condition dependence; and couple with the sliding mode control and the non-linear mapping function to synchronously constrain the speed, yaw rate and sideslip angle within the asymmetric safety interval; in the S5, design a consistent non-linear mapping function F c Combine the speed control law of the bio-inspired model, specifically: where τ r is the control moment for driving the hovercraft to rotate about the vertical axis Z; J x , J y , J z are the moments of inertia of the hovercraft about the X-axis, Y-axis, and Z-axis respectively; M zD is the damping moment about the Z-axis; p and q are the rotational rates of the hovercraft about the longitudinal axis and the transverse axis respectively; is the hover damping coefficient; k r1 is a constant; F r is the turning resistance component of the hovercraft; F r1 and F r2 are the linear and non-linear components of the turning resistance of the hovercraft respectively; α r is the weight of the adjustment resistance term F r1 in the control law; r e is the equivalent action radius of the contact area between the hover and the water surface; u is the longitudinal velocity component; v e is the lateral error velocity; S6. Dynamically adjust the parameters according to the actual operating state and environmental changes of the hovercraft, and verify the effectiveness of the control method.

2. The multi-constraint control method for an air-cushion vehicle based on a preset performance function with composite error transformation according to claim 1, wherein In the S1, the angular velocity control model is specifically: wherein, is the pose vector of the hovercraft in the global coordinate system; is the rotation matrix, which consists of the roll angle and the heading angle ψ; is the velocity vector in the body coordinate system; M is the inertia matrix; is 's instantaneous acceleration, is the Coriolis-centripetal force matrix; τ is the control input vector; F is the external disturbance force; D is the total uncertainty, i.e., the damping matrix; Expand the angular velocity control model into a vector form and define The vector dynamics model of the fully air-cushioned hovercraft is specifically as follows: where f(η,ξ) is the non - linear coupling term and Q is the residual disturbance.

3. A hovercraft multi-constraint control method based on a preset performance function with composite error transformation according to claim 1, characterized in that, In the S3, the function of the bio - inspired model is: where G is the upper bound and P is the passive decay rate; is the action potential of the neuron, and γ(a) and are the excitatory and inhibitory inputs, respectively.

4. A hovercraft multi-constraint control method based on a preset performance function of composite error transformation according to claim 3, characterized in that The velocity control law includes the longitudinal velocity and lateral velocity desired control laws, specifically: Among them, α u and α v are the longitudinal and lateral velocity desired control rates respectively; ψ, θ, are the heading angle, pitch angle and roll angle respectively; and are the target longitudinal coordinate and lateral coordinate of the hovercraft in the global coordinate system respectively; and are the preset performance functions of the longitudinal and lateral position deviations respectively; δ x and δ y are the longitudinal and lateral normalized tracking errors; w is the weight coefficient.

5. A hovercraft multi-constraint control method based on a preset performance function with composite error transformation according to claim 1, characterized in that In the S6, build a motion control simulation platform for the surface effect ship based on MATLAB to conduct a simulation experiment on the control method; The simulation parameters include the mass, moment of inertia, thrust coefficient, and external disturbance of the hovercraft.

6. A hovercraft multi-constraint control method based on a preset performance function with composite error transformation according to claim 5, characterized in that The simulation experiment includes designing multiple motion scenarios to verify the performance of the control method under different working conditions; The multiple motion scenarios include straight - line navigation, curve navigation, and sharp turns.

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