Fault-tolerant obstacle avoidance control method for safe berthing of unmanned surface ship

Abstract

The invention relates to a fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface ship, and the method comprises the steps: constructing a control density function containing obstacle information, constructing a fault-tolerant model prediction control frame based on the control density function and a kinetic equation of a reference system model, and controlling the berthing of the unmanned surface ship through the output of the fault-tolerant model prediction control frame. Wherein the auxiliary control law, the adaptive law and the Lyapunov stability criterion are constructed based on a kinematics equation of a reference system model, and the Lyapunov stability criterion, the auxiliary control law and the adaptive law are applied to a fault-tolerant model prediction control framework. Based on a control density function containing obstacle information, a fault-tolerant model prediction control framework is constructed, and effective obstacle avoidance of complex ocean obstacles can be realized. The Lyapunov stability criterion, the auxiliary control law and the adaptive law are applied to a fault-tolerant model prediction control framework, and the safety berthing stability of the unmanned surface ship under the multi-obstacle condition can be guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of unmanned boat berthing control, and in particular to a fault-tolerant obstacle avoidance control method for safe berthing of unmanned surface ships. Background Art

[0002] Autonomous berthing is a typical task for unmanned surface vessels (USVs) in various maritime operations, such as maritime rescue, geological surveys, and autonomous refueling. However, the complex ocean environment and the physical constraints of USVs present two major challenges to their safe berthing. First, unpredictable internal propeller failures and external ocean disturbances—including wind, waves, and currents—can affect the maneuverability of USVs. These multimodal disturbances pose a challenge to the safe berthing of USVs. Second, in addition to multimodal disturbances, the maneuverability of USVs is also limited by their physical constraints, such as underactuated nonlinear dynamics and propeller saturation. Furthermore, USVs must avoid obstacles in their operating environment, such as reefs and other vessels. These diverse constraints also pose challenges to the safe berthing of USVs.

[0003] Fuzzy backstepping control methods based on fuzzy logic systems can effectively approximate multimodal disturbances without requiring extensive data training. They combine feedback controller design with the selection of Lyapunov functions to ensure system stability. This method has been widely used for the safe berthing of unmanned surface vessels. Furthermore, model predictive control (MPC), an optimization-based time-domain control method, has also been widely used for the safe berthing of unmanned surface vessels to effectively address various constraints on unmanned surface vessels. Fuzzy backstepping control methods have a simple structure and can effectively ensure system stability, but they are deficient in handling system constraints. Although model predictive control can explicitly handle system constraints, effective obstacle avoidance typically requires a long time domain, which increases the system's computational burden and makes stability verification extremely complex.

[0004] However, the above technologies have not fully considered the diversity of marine obstacles. Marine obstacles are of various types, and whether it is possible to naturally construct obstacle avoidance constraints suitable for marine obstacles is crucial for the safe berthing of unmanned surface vessels. Summary of the Invention

[0005] The present invention provides a fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel, aiming to solve at least one of the technical problems existing in the prior art.

[0006] The technical solution of the present invention is a fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel, comprising:

[0007] S100. Construct the kinematic and dynamic equations of unmanned surface vessels;

[0008] S200, generating kinematic and dynamic equations of a reference system model according to the set trajectory information of the reference system;

[0009] S300, constructing a control density function containing obstacle information;

[0010] S400, constructing a fault-tolerant model predictive control framework based on the control density function and the dynamic equation of the reference system model;

[0011] S500, controlling the berthing of the unmanned surface vessel through the output of the fault-tolerant model predictive control framework;

[0012] wherein, based on the kinematic equations of the reference system model, auxiliary control laws and adaptive laws are constructed;

[0013] Construct Lyapunov stability criterion;

[0014] The Lyapunov stability criterion, the auxiliary control law and the adaptive law are applied to the fault-tolerant model predictive control framework.

[0015] According to some embodiments of the present invention, in step S100, the kinematic and dynamic equations of the unmanned surface vessel are expressed as follows:

[0016]

[0017] In the formula, η(t)=[x(t),y(t),φ(t)] T represents the position information of the unmanned surface vessel, x(t) and y(t) represent the x-axis and y-axis positions of the unmanned surface vessel, φ(t) represents the heading of the unmanned surface vessel, [x(t), y(t), φ(t)] T is the transpose of [x(t), y(t), φ(t)], V(t) = [u(t), v(t), r(t)] T represents the speed information of the unmanned surface vessel, u(t) represents the surge of the unmanned surface vessel, v(t) represents the roll of the unmanned surface vessel, and r(t) represents the yaw of the unmanned surface vessel. [u(t), v(t), r(t)] T It is the transpose of [u(t), v(t), r(t)], G represents the coordinate transformation matrix, represents the derivative of η(t), is the mass matrix, represents the derivative of V(t), represents the Coriolis centripetal force matrix, D represents the damping matrix, τ′(t) is the force applied after the tugboat propulsion system fails, τ l (t) is external ocean disturbance including wind, waves and currents.

[0018] According to some embodiments of the present invention, in step S200, the set reference system trajectory information includes position information of the reference system and speed information of the reference system, wherein:

[0019] The position information of the reference system is expressed as follows:

[0020] η d =[x d ,y d ,φ d ] T ,

[0021] in,

[0022] Where η d is the position information of the reference system, x d and y d represents the position of the reference system, i.e., the position of the x-axis and y-axis of the reference system, φ d represents the heading of the reference system, [x d ,y d ,φ d ] T is [x d ,y d ,φ d ] is the transpose of Represents x d The derivation of represents y d The derivative of

[0023] The speed information of the reference system is expressed as follows:

[0024] V d =[u d ,v d ,r d ] T ,in,

[0025]

[0026] v d =0,

[0027]

[0028] Where V d is the velocity information of the reference system, u d 、v d and r d is the velocity variable of the reference system, u d represents the oscillation of the reference system, v d represents the roll of the reference system, rd represents the yaw of the reference system, [u d ,v d ,r d ] T is[u d ,v d ,r d ] is the transpose of Represents x d The derivation of represents y d The derivation of Represents x d The second-order derivative of represents y d The second-order derivative of .

[0029] According to some embodiments of the present invention, in step S200, the kinematic and dynamic equations of the reference system model are expressed as follows:

[0030]

[0031] Where, represents the kinematic equation of the reference system model, η d is the position information of the reference system, V d is the velocity information of the reference system, G d represents the coordinate transformation matrix, τ d is the dynamic equation of the reference system model, is the mass matrix, Indicates V d The derivation of represents the Coriolis centripetal force matrix, and D represents the damping matrix.

[0032] According to some embodiments of the present invention, in step S300, constructing a control density function including obstacle information includes the following steps:

[0033] An unsafe set is constructed to determine whether the current reference system is in an unsafe region. The unsafe set is expressed as:

[0034] X uk :={x∈X:h k (x)≤0},

[0035] Where, X uk is the unsafe set, X represents the feasible space set of all states, x represents the state of the current reference system, h k is a function that describes the characteristics of the obstacle;

[0036] Construct a sensing set to determine whether the current reference system is within the sensing area. The sensing set is expressed as:

[0037] X ck :={x∈X:c k (x)≤0}\X uk ,

[0038] Where, X ck is the sensing set, c k is a function that describes the characteristics of the sensing area;

[0039] Constructing a first auxiliary function, constructing a second auxiliary function based on the first auxiliary function, and constructing a smooth inverse convex function based on the unsafe set, the sensing set, and the second auxiliary function to determine whether the current state is in the unsafe area; wherein,

[0040]

[0041] Where p k (x) is the first auxiliary function, ψ k (x) is the second auxiliary function, exp(·) is an exponential function with the mathematical constant e as the base, Ψ k (x) is the smooth inverse convex function;

[0042] Based on the smooth inverse convex function, the control density function containing obstacle information is constructed, which is expressed as follows:

[0043]

[0044] Wherein, α>0 is a constant, ρ(x) is the control density function, represents the information used to encode the unsafe set, k represents the kth obstacle, Indicates the distance from the unmanned surface vessel to the target point.

[0045] According to some embodiments of the present invention, in step S400, a fault-tolerant model predictive control framework is constructed based on the control density function and the dynamic equation of the reference system model, which is expressed as follows:

[0046]

[0047] Where, represents the objective function of optimization, K(δ) is the sampling period function, T represents the total control time, is the state error, and Represents the predicted state and predicted input, x d(k) is the reference system state, u is the optimal output, Q, R and P are the weights, namely the first weight, the second weight and the third weight, is the control error, u d (k) is the reference system input, F(·) represents the matrix form of the dynamic equation of the reference system model, dk represents the integral variable, x e (T) is the terminal state error, represents the initial value of the predicted state, x(t0) represents the initial value of the state, u max represents the thruster saturation value, Δ represents the change in density function, ρ(x) is the control density function, represents the gradient, To predict the next state, is the next prediction input, V(·) is the Lyapunov function, represents the partial derivative, represents the initial value of the prediction input, represents the auxiliary control law under the initial value of the predicted state, represents a density function constructed based on the information of the prediction state and the prediction input, express The derivative of .

[0048] According to some embodiments of the present invention, constructing an auxiliary control law and an adaptive law based on the kinematic equations of the reference system model includes:

[0049] Based on the kinematic equation and position error of the reference system model, a first conversion variable is constructed; a second conversion variable is obtained through the first conversion variable and the coordinate transformation matrix; and a third conversion variable is obtained based on the first conversion variable, which are respectively expressed as follows:

[0050]

[0051]

[0052] Where η e =η(t)-η d represents the position error, η(t) represents the position information of the unmanned surface vessel, φ(t) represents the heading of the unmanned surface vessel, represents the kinematic equation of the reference system model, η d is the position information of the reference system, is the first conversion variable, v b is the second conversion variable, G T represents the transpose of G, G represents the coordinate transformation matrix, e is the third conversion variable, represents the derivative of η(t), represents η e The derivative of

[0053] Based on the second conversion variable and the position error, the auxiliary control law and the adaptive law are constructed, which are respectively expressed as follows:

[0054]

[0055] Where θ(x) is the auxiliary control law, and represents the adaptive law, namely the first adaptive law and the second adaptive law, represents the approximate value of the control matrix L, represents the estimated value of the fuzzy logic system weight ω, s represents the activation function of the fuzzy logic system, is the mass matrix, Indicates v b The derivation of represents the Coriolis centripetal force matrix, Represents the approximation error of the fuzzy logic system The estimated value of , D represents the damping matrix, i=1,2,3 represents the first control gain constant, the second control gain constant and the third control gain constant, represents the total control gain constant, e T represents the transpose of e.

[0056] According to some embodiments of the present invention, constructing a Lyapunov stability criterion includes:

[0057] Based on the position error, a first Lyapunov function is constructed, and a second Lyapunov function is obtained according to the first Lyapunov function and the third conversion variable, which are respectively expressed as follows:

[0058]

[0059] Where M * (φ(t))=G T (φ(t))MG(φ(t)), is the first Lyapunov function, is the second Lyapunov function, represents η e The transpose of , tr represents the trace of the matrix, represents the difference between the control matrix L and the approximation of the control matrix L, express The transpose of represents the difference between the fuzzy logic system weight ω and the estimated value of the fuzzy logic system weight ω, express The transpose of

[0060] By taking the derivative of the second Lyapunov function, we obtain the Lyapunov stability criterion, which is expressed as:

[0061]

[0062] Where, for The derivative of is the Lyapunov stability criterion, D * (v,φ(t))=G(φ(t))MG T (φ(t)), Represents the approximation error of the fuzzy logic system The upper bound of .

[0063] The technical solution of the present invention also relates to an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the fault-tolerant obstacle avoidance control method for the safe berthing of the unmanned surface vessel as described above.

[0064] The technical solution of the present invention also relates to a storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the fault-tolerant obstacle avoidance control method for the safe berthing of the unmanned surface vessel as described above.

[0065] The beneficial effects of the present invention include: constructing the kinematic and dynamic equations of the unmanned surface vessel, generating the kinematic and dynamic equations of the reference system model based on the trajectory information of the set reference system, constructing a control density function containing obstacle information, constructing a fault-tolerant model predictive control framework based on the control density function and the dynamic equations of the reference system model, and controlling the berthing of the unmanned surface vessel through the output of the fault-tolerant model predictive control framework, wherein, based on the kinematic equations of the reference system model, auxiliary control laws and adaptive laws are constructed, and the Lyapunov stability criterion is constructed, and the Lyapunov stability criterion, auxiliary control laws, and adaptive laws are applied to the fault-tolerant model predictive control framework. Constructing a fault-tolerant model predictive control framework based on the control density function containing obstacle information is conducive to achieving effective obstacle avoidance of complex marine obstacles. Applying the Lyapunov stability criterion, auxiliary control laws, and adaptive laws to the fault-tolerant model predictive control framework is conducive to ensuring the stability of the safe berthing of the unmanned surface vessel in multi-obstacle situations.

[0066] In addition, additional aspects and advantages of the present invention will be set forth in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is an optional flow chart of the fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel in an embodiment of the present invention.

[0068] Figure 2 This is a schematic diagram of an optional application scenario of the fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel in an embodiment of the present invention.

[0069] Figure 3 This is an optional design flow chart of the fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other unless there is a conflict.

[0071] It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature or indirectly fixed or connected to the other feature. Furthermore, terms such as "upper," "lower," "left," "right," "top," and "bottom" used in this disclosure are intended solely to describe the relative positions of the components of the disclosure as shown in the accompanying drawings.

[0072] In addition, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art. The terms used in this specification are only for describing specific embodiments and are not intended to limit the present invention. The term "and / or" as used herein includes any combination of one or more of the related listed items.

[0073] It should be understood that although the terms first, second, third, etc. may be used to describe various elements in the present invention, these elements should not be limited to these terms. These terms are only used to distinguish elements of the same type from each other. For example, a first element may also be referred to as a second element, and similarly, a second element may also be referred to as a first element without departing from the scope of the present invention.

[0074] Reference Figures 1 to 3 In some embodiments, the technical solution of the present invention is a fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel, including but not limited to steps S100 to S500. Each step is introduced in turn below.

[0075] Step S100: Construct the kinematic and dynamic equations of the unmanned surface vessel.

[0076] Specifically, an unmanned surface vessel is an unmanned surface vessel that is mainly used to perform dangerous tasks that are not suitable for manned vessels.

[0077] In a specific embodiment, the kinematic and dynamic equations of a 3-DOF unmanned surface vessel are constructed.

[0078] In some embodiments, in step S100, the kinematic and dynamic equations of the unmanned surface vessel are expressed as follows:

[0079]

[0080] In the formula, η(t)=[x(t),y(t),φ(t)] T represents the position information of the unmanned surface vessel, x(t) and y(t) represent the x-axis and y-axis positions of the unmanned surface vessel, φ(t) represents the heading of the unmanned surface vessel, [x(t), y(t), φ(t)] T is the transpose of [x(t), y(t), φ(t)], V(t) = [u(t), v(t), r(t)] T represents the speed information of the unmanned surface vessel, u(t) represents the surge of the unmanned surface vessel, v(t) represents the roll of the unmanned surface vessel, and r(t) represents the yaw of the unmanned surface vessel. [u(t), v(t), r(t)] T It is the transpose of [u(t), v(t), r(t)], G represents the coordinate transformation matrix, represents the derivative of η(t), is the mass matrix, represents the derivative of V(t), represents the Coriolis centripetal force matrix, D represents the damping matrix, τ′(t) is the force applied after the tugboat propulsion system fails, τ l (t) is external ocean disturbance including wind, waves and currents.

[0081] It should be noted that τ′(t)=τ-τ f , where τ=[τ u ,τ v ,τ r ] T represents the force applied by the thruster, τ u is the force in the longitudinal direction of the propeller, τ v is the force in the roll direction of the thruster, τ r is the force in the yaw direction exerted by the thruster, τ f is the loss force due to thruster failure, [τ u ,τ v ,τ r ] T represents [τu ,τ v ,τ r ] is the transpose of ].

[0082] It can be understood that the kinematic and dynamic equations of the 3-DOF unmanned surface vessel are constructed based on the world Cartesian coordinate system and the coordinate system of the unmanned surface vessel itself.

[0083] Step S200: Generate kinematic and dynamic equations of the reference system model according to the set trajectory information of the reference system.

[0084] The reference system is a reference system model. Specifically, the kinematic and dynamic equations of an unmanned surface vessel are mathematical models that describe its motion on the water surface. These equations consist of both kinematic and dynamic equations. A reference system model is a commonly used concept in control system design, used to describe the desired system behavior or performance. The reference system model is used to design controllers so that the controlled system can mimic or follow the behavior of the reference system model.

[0085] In some embodiments, in step S200, the trajectory information of the reference system is set to include the position information of the reference system and the speed information of the reference system, wherein:

[0086] The position information of the reference system is expressed as follows:

[0087] η d =[x d ,y d ,φ d ] T ,

[0088] in,

[0089] Where η d is the position information of the reference system, x d and y d represents the position of the reference system, i.e., the position of the x-axis and y-axis of the reference system, φ d represents the heading of the reference system, [x d ,y d ,φ d ] T is [x d ,y d ,φ d ] is the transpose of Represents x d The derivation of represents y d The derivative of

[0090] The speed information of the reference system is expressed as follows:

[0091] V d =[u d ,v d ,r d ] T ,in,

[0092]

[0093] v d =0,

[0094]

[0095] Where V d is the speed information of the reference system, u d 、v d and r d is the velocity variable of the reference system, u d represents the oscillation of the reference system, v d represents the roll of the reference system, r d represents the yaw of the reference system, [u d ,v d ,r d ] T is[u d ,v d ,r d ] is the transpose of Represents x d The derivation of represents y d The derivation of Represents x d The second-order derivative of represents y d The second-order derivative of .

[0096] Specifically, X d =[x d ,y d ,φ d ,u d ,v d ,r d ] T , X d is the trajectory information of the set reference system, [x d ,y d ,φ d ,u d ,v d ,r d ] T means [x d ,y d ,φ d ,u d ,vd ,r d ] is the transpose of ].

[0097] In some embodiments, in step S200, the kinematic and dynamic equations of the reference system model are expressed as follows:

[0098]

[0099] Where, represents the kinematic equation of the reference system model, η d is the position information of the reference system, V d is the speed information of the reference system, G d represents the coordinate transformation matrix, τ d is the dynamic equation of the reference system model, is the mass matrix, Indicates V d The derivation of represents the Coriolis centripetal force matrix, and D represents the damping matrix.

[0100] Specifically, is the mass (inertia) matrix. A transformation matrix is ​​a matrix used to transform the coordinates or vectors of a point between different coordinate systems. The Coriolis / Centrifugal matrix and the damping matrix are important mathematical tools used to describe the dynamic characteristics of a system. The Coriolis / Centrifugal matrix describes the effects of the Coriolis force and centripetal force on the system dynamics caused by the rotating reference frame. The damping matrix describes the forces or torques in the system caused by the damping effect. The mass matrix is ​​a matrix used to describe the mass distribution of an object in mechanical and dynamic systems.

[0101] Step S300: Constructing a control density function containing obstacle information.

[0102] Specifically, the control density function is a function used in control theory to describe the distribution of control inputs or states of a system.

[0103] In some embodiments, in step S300, constructing a control density function containing obstacle information includes the following steps:

[0104] Construct an unsafe set to determine whether the current reference system is in an unsafe area. The unsafe set is expressed as:

[0105] X uk :={x∈X:h k (x)≤0},

[0106] Where, X uk is an unsafe set, X represents the feasible space set of all states, x represents the state of the current reference system, h k is a function that describes the characteristics of the obstacle;

[0107] Construct a sensing set to determine whether the current reference system is in the sensing area. The sensing set is expressed as:

[0108] X ck :={x∈X:c k (x)≤0}\X uk ,

[0109] Where, X ck is the induction set, c k is a function that describes the characteristics of the sensing area;

[0110] Construct a first auxiliary function, construct a second auxiliary function based on the first auxiliary function, and construct a smooth inverse convex function based on the unsafe set, the sensing set, and the second auxiliary function to determine whether the current state is in an unsafe area; wherein,

[0111]

[0112] Where p k (x) is the first auxiliary function, ψ k (x) is the second auxiliary function, exp(·) is the exponential function with the mathematical constant e as the base, Ψ k (x) is a smooth inverse convex function;

[0113] Based on the smooth inverse convex function, a control density function containing obstacle information is constructed, which is expressed as follows:

[0114]

[0115] Where α>0 is a constant, ρ(x) is the control density function, represents the information used to encode the unsafe set, k represents the kth obstacle, Indicates the distance from the unmanned surface vessel to the target point.

[0116] Specifically, h k is the function describing the obstacle characteristics (distance function), c k is a function that describes the characteristics of the sensing area (distance function).

[0117] Specifically, the unsafe set is a set defined in system analysis that contains points that lead to unstable or unacceptable state of the system. When designing a control system, it is usually desirable to avoid the state of the system entering the unsafe set.

[0118] A sensing set is a set of states associated with a system's initial state and control inputs that describes all possible states the system can reach within a finite timeframe. This set is particularly useful in model predictive control (MPC) because it helps predict the system's future behavior and design control strategies to avoid unsafe states. The sensing set is often used to analyze the reachability of a system, ensuring that the system will not reach an unsafe state within a given timeframe.

[0119] Step S400: constructing a fault-tolerant model predictive control framework based on the control density function and the dynamic equations of the reference system model.

[0120] Specifically, the fault-tolerant model predictive control framework is a control strategy that combines the concepts of model predictive control (MPC) and fault-tolerant control to improve the reliability and robustness of the system.

[0121] In some embodiments, in step S400, a fault-tolerant model predictive control framework is constructed based on the control density function and the dynamic equation of the reference system model, which is expressed as follows:

[0122]

[0123] Where, represents the objective function of optimization, K(δ) is the sampling period function, T represents the total control time, is the state error, and Represents the predicted state and predicted input, x d (k) is the reference system state, u is the optimal output, Q, R and P are the weights, namely the first weight, the second weight and the third weight, is the control error, u d (k) is the reference system input, F(·) represents the matrix form of the dynamic equation of the reference system model, dk represents the integral variable, x e (T) is the terminal state error, represents the initial value of the predicted state, x(t0) represents the initial value of the state, u max represents the thruster saturation value, Δ represents the change in density function, ρ(x) is the control density function, represents the gradient, To predict the next state, is the next prediction input, V(·) is the Lyapunov function, represents the partial derivative, represents the initial value of the prediction input, represents the auxiliary control law under the initial value of the predicted state, represents the density function constructed based on the information of the predicted state and the predicted input, express The derivative of .

[0124] It should be noted that The matrix form of the dynamic equations representing the reference system model, represents the previous step prediction state, Represents the previous step prediction input. It represents the density function constructed by controlling the density function based on the information of the predicted state and the predicted input. The Lyapunov function includes the first Lyapunov function and the second Lyapunov function.

[0125] In some embodiments, auxiliary control laws and adaptive laws are constructed based on the kinematic equations of a reference system model.

[0126] Specifically, auxiliary control law and adaptive law are constructed to ensure the stability of the entire control system.

[0127] In some embodiments, the auxiliary control law and the adaptive law are constructed based on the kinematic equations of the reference system model, including:

[0128] Based on the kinematic equations and position errors of the reference system model, the first conversion variable is constructed; the second conversion variable is obtained through the first conversion variable and the coordinate transformation matrix; the third conversion variable is obtained according to the first conversion variable, which are respectively expressed as follows:

[0129]

[0130] Where η e =η(t)-η d represents the position error, η(t) represents the position information of the unmanned surface ship, φ(t) represents the heading of the unmanned surface ship, represents the kinematic equation of the reference system model, η d is the position information of the reference system, is the first conversion variable, v b is the second conversion variable, G T represents the transpose of G, G represents the coordinate transformation matrix, e is the third conversion variable, represents the derivative of η(t), represents η e The derivative of

[0131] Based on the second conversion variable and the position error, the auxiliary control law and the adaptive law are constructed, which are expressed as follows:

[0132]

[0133] Where θ(x) is the auxiliary control law, and represents the adaptive law, namely the first adaptive law and the second adaptive law, represents the approximate value of the control matrix L, represents the estimated value of the fuzzy logic system weight ω, s represents the activation function of the fuzzy logic system, is the mass matrix, Indicates v b The derivation of represents the Coriolis centripetal force matrix, Represents the approximation error of the fuzzy logic system The estimated value of , D represents the damping matrix, i=1,2,3 represents the first control gain constant, the second control gain constant and the third control gain constant, represents the total control gain constant, e T represents the transpose of e.

[0134] Specifically, a fuzzy logic system is a computing system based on fuzzy logic principles. It processes information that is fuzzy or uncertain, rather than traditional binary logic (i.e., true or false, 0 or 1). Fuzzy logic systems can simulate the human decision-making process when dealing with complex problems, especially in the presence of incomplete, imprecise, or ambiguous information.

[0135] In some embodiments, a Lyapunov stability criterion is constructed.

[0136] In some embodiments, constructing a Lyapunov stability criterion includes:

[0137] Based on the position error, the first Lyapunov function is constructed, and the second Lyapunov function is obtained according to the first Lyapunov function and the third conversion variable, which are expressed as follows:

[0138]

[0139] Where M * (φ(t))=G T (φ(t))MG(φ(t)), is the first Lyapunov function, is the second Lyapunov function, represents η e The transpose of , tr represents the trace of the matrix, represents the difference between the control matrix L and the approximation of the control matrix L, express The transpose of represents the difference between the fuzzy logic system weight ω and the estimated value of the fuzzy logic system weight ω, express The transpose of

[0140] Taking the derivative of the second Lyapunov function, we get the Lyapunov stability criterion, which is expressed as:

[0141]

[0142] Where, for The derivative of is the Lyapunov stability criterion, D * (v,φ(t))=G(φ(t))MG T (φ(t)), Represents the approximation error of the fuzzy logic system The upper bound of .

[0143] Specifically, the Lyapunov function and the Lyapunov stability criterion are important concepts in control theory and system stability analysis. Auxiliary control laws are additional control strategies used in control system design to assist the primary control law in improving system performance. They are typically used to address problems that are difficult to solve with the primary control law, such as disturbance suppression, nonlinear compensation, or tracking high-precision trajectories. Adaptive laws are a special type of control law that automatically adjust control parameters based on real-time feedback from the system to adapt to changes in system parameters or the external environment. The main purpose of adaptive control is to improve the robustness and adaptability of the system.

[0144] In some embodiments, the Lyapunov stability criterion, auxiliary control law, and adaptive law are applied to a fault-tolerant model predictive control framework.

[0145] It can be understood that the Lyapunov stability criterion is conducive to ensuring the stability of the fault-tolerant model predictive control framework.

[0146] Step S500: controlling the berthing of the unmanned surface vessel through the output of the fault-tolerant model prediction control framework.

[0147] Reference Figure 3 In one possible implementation, a fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel includes:

[0148] Establish the kinematic and dynamic equations of a 3-DOF unmanned surface vessel;

[0149] Based on the position information of the reference berth, a reference system model is constructed to match the dimensions of the kinematic and dynamic equations of the unmanned surface vessel;

[0150] Construct a control density function containing obstacle information to provide preconditions for the construction of obstacle avoidance constraints;

[0151] Design a fault-tolerant model predictive control framework;

[0152] The stability of the fault-tolerant model predictive control framework is proved using the Lyapunov stability criterion.

[0153] It should be understood that the fault-tolerant obstacle avoidance control method for safe berthing of unmanned surface vessels proposes a new fault-tolerant model predictive control framework that uniformly considers multimodal disturbances and various constraints to achieve fault tolerance and safety for unmanned surface vessel berthing in multi-obstacle environments. Multimodal disturbances are used as a constraint in the fault-tolerant model predictive control framework. The framework is modeled using a fuzzy backstepping scheme, and the closed-loop stability of the fault-tolerant model predictive control framework is demonstrated by combining Lyapunov functions.

[0154] As can be seen, the fault-tolerant obstacle avoidance control method for safe berthing of unmanned surface vessels (USVs) achieves safe berthing under multimodal disturbances, multiple constraints, and multiple obstacles, contributing to system stability. Based on the control density function, obstacle avoidance constraints are constructed to effectively avoid complex marine obstacles. Furthermore, using backstepping control techniques, auxiliary control laws and Lyapunov functions are designed and incorporated into the fault-tolerant model predictive control framework as constraints to ensure stability.

[0155] In a specific embodiment, a first conversion variable is constructed based on the kinematic equation of the reference system model and the position error; a second conversion variable is obtained through the first conversion variable and the coordinate transformation matrix; and a third conversion variable is obtained based on the first conversion variable, which are respectively expressed as follows:

[0156]

[0157] Where η e =η(t)-η d represents the position error, η(t) represents the position information of the unmanned surface ship, φ(t) represents the heading of the unmanned surface ship, represents the kinematic equation of the reference system model, η d is the position information of the reference system, is the first conversion variable, v b is the second conversion variable, G T represents the transpose of G, G represents the coordinate transformation matrix, e is the third conversion variable, represents the derivative of η(t), represents η e The derivative of

[0158] Based on the position error, the first Lyapunov function is constructed, and the second Lyapunov function is obtained according to the first Lyapunov function and the third conversion variable, which are expressed as follows:

[0159]

[0160] Where M * (φ(t))=G T (φ(t))MG(φ(t)), is the first Lyapunov function, is the second Lyapunov function, represents η e The transpose of , tr represents the trace of the matrix, represents the difference between the control matrix L and the approximation of the control matrix L, express The transpose of represents the difference between the fuzzy logic system weight ω and the estimated value of the fuzzy logic system weight ω, express The transpose of

[0161] Based on the second conversion variable and the position error, the auxiliary control law and the adaptive law are constructed, which are expressed as follows:

[0162]

[0163] Where θ(x) is the auxiliary control law, and represents the adaptive law, namely the first adaptive law and the second adaptive law, represents the approximate value of the control matrix L, represents the estimated value of the fuzzy logic system weight ω, s represents the activation function of the fuzzy logic system, is the mass matrix, Indicates v b The derivation of represents the Coriolis centripetal force matrix, Represents the approximation error of the fuzzy logic system The estimated value of , D represents the damping matrix, i=1,2,3 represents the first control gain constant, the second control gain constant and the third control gain constant, represents the total control gain constant, e T represents the transpose of e;

[0164] Taking the derivative of the second Lyapunov function, we get the Lyapunov stability criterion, which is expressed as:

[0165]

[0166] Where, for The derivative of is the Lyapunov stability criterion, D * (v,φ(t))=G(φ(t))MG T (φ(t)), Represents the approximation error of the fuzzy logic system The upper bound of .

[0167] As can be seen, the kinematic and dynamic equations of the unmanned surface vessel are constructed. Based on the trajectory information of the reference system, the kinematic and dynamic equations of the reference system model are generated. A control density function that includes obstacle information is constructed. Based on the control density function and the dynamic equations of the reference system model, a fault-tolerant model predictive control framework is constructed. The output of the fault-tolerant model predictive control framework is used to control the berthing of the unmanned surface vessel. Based on the kinematic equations of the reference system model, auxiliary control laws and adaptive laws are constructed. The Lyapunov stability criterion is constructed and applied to the fault-tolerant model predictive control framework. The fault-tolerant model predictive control framework constructed based on the control density function that includes obstacle information facilitates effective obstacle avoidance for complex marine obstacles. Applying the Lyapunov stability criterion, auxiliary control laws, and adaptive laws to the fault-tolerant model predictive control framework helps ensure the stability of the unmanned surface vessel during safe berthing in multi-obstacle situations.

[0168] An embodiment of the present invention further provides an electronic device comprising a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program, implements the aforementioned fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel. The electronic device can be any intelligent terminal, including a computer.

[0169] An embodiment of the present invention further provides a storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel.

[0170] It should be appreciated that the method steps in the embodiments of the present invention can be implemented or executed by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory. The method can use standard programming techniques. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. In addition, for this purpose, the program can be run on a programmed application-specific integrated circuit.

[0171] Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by the context. The processes described herein (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed collectively on one or more processors, by hardware, or a combination thereof. The computer program includes a plurality of instructions that can be executed by one or more processors.

[0172] Further, the methods can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, etc. Various aspects of the present invention can be implemented as machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, RAM, ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the processes described herein. In addition, the machine-readable code, or portions thereof, can be transmitted over a wired or wireless network. When such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor, the invention described herein includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques of the present invention, the present invention can also include the computer itself.

[0173] The computer program can be applied to input data to perform the functions described herein, thereby converting the input data to generate output data that is stored in a non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the converted data represents a physical and tangible object, including a specific visual depiction of the physical and tangible object produced on the display.

[0174] The above description is merely a preferred embodiment of the present invention. The present invention is not limited to the aforementioned embodiments. As long as the technical effects of the present invention are achieved by the same means, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention. Within the scope of protection of the present invention, various modifications and variations of the technical solutions and / or implementation methods are possible.

Claims

1. A fault-tolerant obstacle avoidance control method for safe berthing of unmanned surface vessels, characterized in that: include: S100. Construct the kinematic and dynamic equations of unmanned surface vessels; S200, generating kinematic and dynamic equations of a reference system model according to the set trajectory information of the reference system; S300, constructing a control density function containing obstacle information; S400, constructing a fault-tolerant model predictive control framework based on the control density function and the dynamic equation of the reference system model; S500, controlling the berthing of the unmanned surface vessel through the output of the fault-tolerant model prediction control framework; wherein, based on the kinematic equations of the reference system model, auxiliary control laws and adaptive laws are constructed; Construct Lyapunov stability criterion; The Lyapunov stability criterion, the auxiliary control law and the adaptive law are applied to the fault-tolerant model predictive control framework.

2. The fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel according to claim 1, characterized in that: In step S100, the kinematic and dynamic equations of the unmanned surface vessel are expressed as follows: In the formula, η(t)=[x(t),y(t),φ(t)] T represents the position information of the unmanned surface vessel, x(t) and y(t) represent the x-axis and y-axis positions of the unmanned surface vessel, φ(t) represents the heading of the unmanned surface vessel, [x(t), y(t), φ(t)] T is the transpose of [x(t), y(t), φ(t)], V(t) = [u(t), v(t), r(t)] T represents the speed information of the unmanned surface vessel, u(t) represents the surge of the unmanned surface vessel, v(t) represents the roll of the unmanned surface vessel, and r(t) represents the yaw of the unmanned surface vessel. [u(t), v(t), r(t)] T It is the transpose of [u(t), v(t), r(t)], G represents the coordinate transformation matrix, represents the derivative of η(t), is the mass matrix, represents the derivative of V(t), represents the Coriolis centripetal force matrix, D represents the damping matrix, τ′(t) is the force applied after the tugboat propulsion system fails, τ l (t) is external ocean disturbance including wind, waves and currents.

3. The fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel according to claim 2, characterized in that: In step S200, the trajectory information of the reference system includes the position information and the speed information of the reference system, wherein: The position information of the reference system is expressed as follows: or d =[x d ,y d ,f d ] T , in, Where η d is the position information of the reference system, x d and y d represents the position of the reference system, i.e., the position of the x-axis and y-axis of the reference system, φ d represents the heading of the reference system, [x d ,y d ,φ d ] T is [x d ,y d ,φ d ], Represents x d The derivation of represents y d The derivative of The speed information of the reference system is expressed as follows: V d =[u d ,v d ,r d ] T ,in, v d ＝0， Where V d is the velocity information of the reference system, u d 、v d and r d is the velocity variable of the reference system, u d represents the oscillation of the reference system, v d represents the roll of the reference system, r d represents the yaw of the reference system, [u d ,v d ,r d ] T is[u d ,v d ,r d ], Represents x d The derivation of represents y d The derivation of Represents x d The second-order derivative of represents y d The second-order derivative of .

4. The fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel according to claim 3, characterized in that: In step S200, the kinematic and dynamic equations of the reference system model are expressed as follows: Where, represents the kinematic equation of the reference system model, η d is the position information of the reference system, V d is the velocity information of the reference system, G d represents the coordinate transformation matrix, τ d is the dynamic equation of the reference system model, is the mass matrix, Indicates V d The derivation of represents the Coriolis centripetal force matrix, and D represents the damping matrix.

5. The fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel according to claim 1, characterized in that: In step S300, constructing a control density function containing obstacle information includes the following steps: An unsafe set is constructed to determine whether the current reference system is in an unsafe region. The unsafe set is expressed as: X uk :={x∈X:h k (x)≤0}, Where, X uk is the unsafe set, X represents the feasible space set of all states, x represents the state of the current reference system, h k is a function that describes the characteristics of the obstacle; Construct a sensing set to determine whether the current reference system is within the sensing area. The sensing set is expressed as: X ck :={x∈X:c k (x)≤0}\X uk , Where, X ck is the sensing set, c k is a function that describes the characteristics of the sensing area; Constructing a first auxiliary function, constructing a second auxiliary function based on the first auxiliary function, and constructing a smooth inverse convex function based on the unsafe set, the sensing set, and the second auxiliary function to determine whether the current state is in the unsafe area; wherein, Where p k (x) is the first auxiliary function, ψ k (x) is the second auxiliary function, exp(·) is an exponential function with the mathematical constant e as the base, Ψ k (x) is the smooth inverse convex function; Based on the smooth inverse convex function, the control density function containing obstacle information is constructed, which is expressed as follows: Wherein, α>0 is a constant, ρ(x) is the control density function, represents the information used to encode the unsafe set, k represents the kth obstacle, Indicates the distance from the unmanned surface vessel to the target point.

6. The fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel according to claim 1, characterized in that: In step S400, a fault-tolerant model predictive control framework is constructed based on the control density function and the dynamic equation of the reference system model, which is expressed as follows: Where, represents the objective function of optimization, K(δ) is the sampling period function, T represents the total control time, is the state error, and Represents the predicted state and predicted input, x d (k) is the reference system state, u is the optimal output, Q, R and P are the weights, namely the first weight, the second weight and the third weight, is the control error, u d (k) is the reference system input, F(·) represents the matrix form of the dynamic equation of the reference system model, dk represents the integral variable, x e (T) is the terminal state error, represents the initial value of the predicted state, x(t0) represents the initial value of the state, u max represents the thruster saturation value, Δ represents the change in density function, ρ(x) is the control density function, represents the gradient, To predict the next state, is the next prediction input, V(·) is the Lyapunov function, represents the partial derivative, represents the initial value of the prediction input, represents the auxiliary control law under the initial value of the predicted state, represents a density function constructed based on the information of the prediction state and the prediction input, express The derivative of .

7. The fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel according to claim 1, characterized in that: Based on the kinematic equations of the reference system model, auxiliary control laws and adaptive laws are constructed, including: Based on the kinematic equation and position error of the reference system model, a first conversion variable is constructed; a second conversion variable is obtained through the first conversion variable and the coordinate transformation matrix; and a third conversion variable is obtained based on the first conversion variable, which are respectively expressed as follows: Where η e =η(t)-η d represents the position error, η(t) represents the position information of the unmanned surface vessel, φ(t) represents the heading of the unmanned surface vessel, represents the kinematic equation of the reference system model, η d is the position information of the reference system, is the first conversion variable, v b is the second conversion variable, G T represents the transpose of G, G represents the coordinate transformation matrix, e is the third conversion variable, represents the derivative of η(t), represents η e The derivative of Based on the second conversion variable and the position error, the auxiliary control law and the adaptive law are constructed, which are respectively expressed as follows: Where θ(x) is the auxiliary control law, and represents the adaptive law, namely the first adaptive law and the second adaptive law, represents the approximate value of the control matrix L, represents the estimated value of the fuzzy logic system weight ω, s represents the activation function of the fuzzy logic system, is the mass matrix, Indicates v b The derivation of represents the Coriolis centripetal force matrix, Represents the approximation error of the fuzzy logic system The estimated value of , D represents the damping matrix, i=1,2,3 represents the first control gain constant, the second control gain constant and the third control gain constant, represents the total control gain constant, e T represents the transpose of e.

8. The fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel according to claim 7, characterized in that: Construct the Lyapunov stability criterion, including: Based on the position error, a first Lyapunov function is constructed, and a second Lyapunov function is obtained according to the first Lyapunov function and the third conversion variable, which are respectively expressed as follows: Where M * (φ(t))=G T (φ(t))MG(φ(t)), is the first Lyapunov function, is the second Lyapunov function, represents η e The transpose of , tr represents the trace of the matrix, represents the difference between the control matrix L and the approximation of the control matrix L, express The transpose of represents the difference between the fuzzy logic system weight ω and the estimated value of the fuzzy logic system weight ω, express The transpose of By taking the derivative of the second Lyapunov function, we obtain the Lyapunov stability criterion, which is expressed as: Where, for The derivative of is the Lyapunov stability criterion, D * (v,φ(t))=G(φ(t))MG T (φ(t)), Represents the approximation error of the fuzzy logic system The upper bound of .

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it implements the fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel as described in any one of claims 1 to 8.

10. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the fault-tolerant obstacle avoidance control method for safe berthing of an unmanned surface vessel according to any one of claims 1 to 8 is implemented.

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