AUV (Autonomous Underwater Vehicle) energy optimization method and system, electronic equipment and storage medium

Abstract

The invention provides an AUV energy optimization method and system, electronic equipment and a storage medium, and the method comprises the steps: carrying out the discrete processing of a preset AUV kinematic model and an AUV kinetic model, and obtaining an AUV error kinetic model; obtaining an expected path and a state variable according to zero path error popularity in a preset state space and the AUV error dynamical model; acquiring corresponding energy consumption according to the state variable of each unit step length in the expected path; acquiring real-time ocean current interference data of the to-be-optimized AUV, and fitting the real-time navigation speed of the to-be-optimized AUV according to the ocean current interference data; determining the priority of a plurality of optimization targets in the optimization process of the to-be-optimized AUV according to a preset LO-MOMPC; and obtaining convergence conditions of the plurality of optimization targets according to the priorities, and performing energy optimization on the to-be-optimized AUV according to the convergence conditions.

Description

Technical Field

[0001] The present application belongs to the field of artificial intelligence, and in particular to methods, systems, electronic devices and storage media for AUV energy optimization. Background Art

[0002] In today's era, autonomous underwater vehicles (AUVs) play an increasingly important role in marine scientific research, marine resource exploration, environmental monitoring, and military applications. However, the complexity, time-varying nature, and uncertainty of the underwater environment, as well as the limited energy supply of underwater vehicles, severely restrict their operating time and scope. Therefore, how to efficiently utilize limited energy resources and extend the operating time of underwater vehicles has become a key issue in current research and application.

[0003] Traditional AUVs usually adopt a single-objective optimization control strategy. Although this method can meet a certain specific performance requirement, its performance in multi-task or complex environments is often unsatisfactory. Especially in multi-task scenarios, it is difficult for a single-objective optimization strategy to take into account multiple aspects such as the vehicle's energy consumption, path planning, and task execution efficiency. In recent years, multi-objective optimization technology has gradually been introduced into the energy management of underwater vehicles to improve the overall performance of the vehicle by optimizing multiple objectives at the same time. However, existing multi-objective optimization methods are usually based on static models and predefined control strategies, which are difficult to cope with complex and changeable underwater environments. In addition, these methods also have certain limitations in terms of computational complexity and real-time performance, which limits their promotion and popularization in practical applications.

[0004] Model Predictive Control (MPC), as an advanced control method, has received extensive attention in the energy optimization of AUVs in recent years. MPC can achieve more efficient energy management and control accuracy in complex environments by performing forward predictions in each control cycle and dynamically adjusting the control strategy based on the prediction results. However, there is no optimized energy consumption item in the cost function of MPC, and traditional MPC methods usually only optimize a single performance indicator, which is difficult to meet the needs of multi-objective optimization. Summary of the invention

[0005] The main purpose of the embodiments of the present invention is to provide a method, system, electronic device and storage medium for AUV energy optimization, so that an AUV operating in an underwater environment with multiple interference targets can fully consider multiple interference factors and quickly and accurately obtain the optimal energy consumption.

[0006] In a first aspect, a method for AUV energy optimization is provided, the method comprising:

[0007] Discrete the preset autonomous underwater vehicle AUV kinematic model and AUV dynamic model to obtain the AUV error dynamic model;

[0008] According to the zero path error manifold in the preset state space and the AUV error dynamics model, control signal constraints are performed to obtain the expected path and state variables, wherein the expected path is the expected path when the AUV to be optimized is on a horizontal plane, and the state variables are the state variables when the AUV to be optimized is on a horizontal plane;

[0009] Obtaining corresponding energy consumption according to the state variable of each unit step in the expected path;

[0010] Acquire real-time ocean current disturbance data of the AUV to be optimized, and fit the real-time navigation speed of the AUV to be optimized according to the ocean current disturbance data;

[0011] Determining the priorities of multiple optimization targets in the optimization process of the AUV to be optimized according to the preset LO-MOMPC;

[0012] The convergence conditions of the multiple optimization objectives are acquired according to the priorities, and energy optimization is performed on the AUV to be optimized according to the convergence conditions.

[0013] In a possible implementation, the AUV dynamics model of the AUV in an ocean current environment is: where η = [x, y, ψ] T is the position and direction vector expressed in the inertial coordinate system, including position and yaw angle, v b Represents the velocity vector of the ocean current or wave in the inertial coordinate system.

[0014] v=[u,v,r] T is the relative velocity vector expressed in the hull coordinate system, and the control vector u=[F u ,F v ,F r ] T is the generalized thrust and torque, b represents the model uncertainty, and the state vector x = [x, y, ψ, u, v, r] T It consists of the position and velocity of the AUV, and J(ψ) is the rotation matrix from the vehicle coordinate system to the inertial coordinate system.

[0015] M=M RB +M A , C(v)=C RB (v)+C A (v), D(v), g(η) represent the mass matrix, M RB Rigid body mass matrix, C RB(v) Coriolis centripetal matrix, M A The hydrodynamic added mass matrix, C A (v)Added mass Coriolis centripetal matrix, Coriolis force matrix and damping matrix, restoring torque.

[0016] In another possible implementation, the formula of the expected path is:

[0017]

[0018] The mapping p is a mathematical expression of the path, which is smooth and bounded; the path parameter s is closed but may be unbounded, that is, Definition: For a function p(s), its derivative with respect to s is denoted by p′(s), and its time derivative is denoted by The meaning of the subscript under the mapping p: state vector x = [x, y, ψ, u, v, r] T , which consists of the position and velocity of the AUV.

[0019] In another possible implementation, after the step of acquiring corresponding energy consumption according to the state variable of each unit step in the expected path, the method further includes:

[0020] Obtain secondary energy consumption, the formula of secondary energy consumption is: The percentage formula of the secondary energy consumption is: Among them, T sim is the navigation time, T is the control time, v s is the control input of the LO-MOMPC controller at time i, v s ′ is the control input of the conventional MPC in the prior art at time i.

[0021] In another possible implementation, fitting the real-time navigation speed of the AUV to be optimized according to the ocean current disturbance data includes:

[0022] According to the formula Get the real-time navigation speed, where F u is the thrust in the AUV forward direction, ρ is the fluid density, D is the propeller diameter, C p is the power coefficient, W is the ocean current speed, P h is the energy consumption used to support non-propulsion systems or auxiliary systems, E is the energy of the AUV to be optimized, P is the instantaneous power of the AUV to be optimized, R is the travel distance of the AUV to be optimized, is the instantaneous energy consumption of the AUV to be optimized, V is the navigation speed of the AUV to be optimized, and the formula of the navigation speed is: Among them, J is the speed coefficient.

[0023] In another possible implementation manner, the LO-MOMPC determines the priorities of the multiple targets according to a lexicographic sorting method.

[0024] In a second aspect, a system for AUV energy optimization is provided, the system comprising:

[0025] The AUV error dynamics model acquisition module is used to discretize the preset autonomous underwater vehicle AUV kinematic model and AUV dynamics model to obtain the AUV error dynamics model;

[0026] An expected path and state variable acquisition module is used to perform control signal constraints according to the zero path error flow in the preset state space and the AUV error dynamics model, and acquire an expected path and state variables, wherein the expected path is the expected path when the AUV to be optimized is on a horizontal plane, and the state variables are the state variables when the AUV to be optimized is on a horizontal plane;

[0027] An energy consumption acquisition module, used to acquire corresponding energy consumption according to the state variable of each unit step in the expected path;

[0028] A navigation speed acquisition module, used to acquire real-time ocean current interference data of the AUV to be optimized, and fit the real-time navigation speed of the AUV to be optimized according to the ocean current interference data;

[0029] A priority determination module, used to determine the priorities of multiple optimization targets in the optimization process of the AUV to be optimized according to a preset LO-MOMPC;

[0030] An energy optimization module is used to obtain the convergence conditions of the multiple optimization objectives according to the priorities, and perform energy optimization on the AUV to be optimized according to the convergence conditions.

[0031] In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for AUV energy optimization provided in the first aspect is implemented.

[0032] In a fourth aspect, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the method for AUV energy optimization provided in the first aspect is implemented.

[0033] The beneficial effect of the present application is that an AUV operating in an underwater environment with multiple interference targets can fully consider multiple interference factors and quickly and accurately obtain optimal energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in describing the embodiments of the present application.

[0035] Figure 1 A flowchart of a method for AUV energy optimization provided by one embodiment of the present invention;

[0036] Figure 2 A structural diagram of an AUV energy optimization system provided by one embodiment of the present invention;

[0037] Figure 3 A schematic diagram of the physical structure of an electronic device provided by the present invention.

[0038] Specific implementation method

[0039] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar modules or modules with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present invention.

[0040] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, modules and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, modules, components and / or groups thereof. It should be understood that when we refer to a module as being "connected" or "coupled" to another module, it may be directly connected or coupled to the other modules, or there may be intermediate modules. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any modules and all combinations of one or more associated listed items.

[0041] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation method of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0042] The technical solution of the present application and how to solve the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0043] like Figure 1FIG. 1 is a flow chart of a method for AUV energy optimization provided by an embodiment of the present invention, wherein the method comprises:

[0044] Step S101, discretizing a preset autonomous underwater vehicle AUV kinematic model and an AUV dynamic model to obtain an AUV error dynamic model;

[0045] Step S102, according to the zero path error flow in the preset state space and the AUV error dynamics model, control signal constraints are performed to obtain the expected path and state variables, wherein the expected path is the expected path when the AUV to be optimized is on the horizontal plane, and the state variables are the state variables when the AUV to be optimized is on the horizontal plane;

[0046] Step S103, obtaining corresponding energy consumption according to the state variable of each unit step in the expected path;

[0047] Step S104, acquiring real-time ocean current interference data of the AUV to be optimized, and fitting the real-time navigation speed of the AUV to be optimized according to the ocean current interference data;

[0048] Step S105, determining the priorities of multiple optimization targets in the optimization process of the AUV to be optimized according to the preset LO-MOMPC;

[0049] Step S106, acquiring convergence conditions of the multiple optimization objectives according to the priorities, and performing energy optimization on the AUV to be optimized according to the convergence conditions.

[0050] In step S101, the AUV (Autonomous Underwater Vehicle) dynamic model in the ocean current environment is: where η = [x, y, ψ] T is the position and direction vector expressed in the inertial coordinate system, including position and yaw angle, v b Represents the velocity vector of the ocean current or wave in the inertial coordinate system.

[0051] v=[u,v,r] T is the relative velocity vector expressed in the hull coordinate system, including longitudinal, transverse and yaw velocities, and the control vector u=[F u ,F v ,F r ] T is the generalized thrust and torque, b represents the model uncertainty, and the state vector x = [x, y, ψ, u, v, r] T It consists of the position and velocity of the AUV, and J(ψ) is the rotation matrix from the vehicle coordinate system to the inertial coordinate system.

[0052] M=M RB +M A , C(v)=C RB (v)+C A (v), D(v), g(η) represent the mass matrix (M RB Rigid body mass matrix, C RB (v) Coriolis centripetal matrix, M A The hydrodynamic added mass matrix, C A (v) Added mass Coriolis centripetal matrix), Coriolis force matrix and damping matrix, restoring moment (approximately 0).

[0053]

[0054]

[0055] Where m is the mass of the vehicle, I o is the inertia tensor,

[0056] is the inertia term including the added mass, S(·) is the cross product operator, and the hydrodynamic coefficient is defined as the partial derivative of the added mass force with respect to the corresponding acceleration ωX u ,Y v ,N r is the linear resistance coefficient, D u ,D v ,D r is the secondary resistance coefficient.

[0057] In order to facilitate the application of LO-MOMPC controller, the system is discretized as follows:

[0058]

[0059] where x k+1,k 、x k,k 、u k,k and k,k are the k+1th step discrete time state variable, the kth step discrete time state variable, the input and output vectors starting from the initial conditions, respectively. k is the state variable matrix, B k is the input state matrix, C k is the output state matrix. According to the above prediction model, the future state variables can be predicted by the following formula:

[0060]

[0061] Where N P and N C are respectively the prediction time domain and the control time domain, satisfying N P ≤NC , according to the above state variables, the output is:

[0062]

[0063] The output can be rearranged into the following matrix form: k+1,k =ψx k,k +ΘU k,k , each vector matrix in the equation is expressed as follows:

[0064]

[0065] In step S102, the formula of the expected path is:

[0066]

[0067]

[0068] The mapping p is a mathematical expression of the path, which is smooth and bounded; the path parameter s Closed but possibly unbounded, i.e. Definition: For a function p(s), its derivative with respect to s is denoted by p′(s), and its time derivative is denoted by The meaning of the subscript under the mapping p: state vector x = [x, y, ψ, u, v, r] T , which consists of the position and velocity of the AUV.

[0069] As an optional embodiment of the present invention, in step S103, after obtaining the corresponding energy consumption according to the state variable of each unit step in the expected path, the method further includes:

[0070] Obtain secondary energy consumption, the formula of secondary energy consumption is: The percentage formula of the secondary energy consumption is: Among them, T sim is the navigation time, T is the control time, v s is the control input of the LO-MOMPC controller at time i, v s ′ is the control input of the conventional MPC in the prior art at time i.

[0071] In step S104, the real-time data of external ocean currents in the ocean current where the AUV to be optimized is located is obtained, and the optimal control force and torque of the thruster of the AUV to be optimized is obtained according to the real-time data of external ocean currents, and then the navigation speed of the AUV to be optimized is dynamically fitted: when there is no interference from ocean current data, the navigation speed of the AUV to be optimized is a fixed value, and when there is interference from external ocean currents, the navigation speed of the AUV to be optimized changes with the ocean current data.

[0072] The step of fitting the real-time navigation speed of the AUV to be optimized according to the ocean current disturbance data comprises:

[0073] According to the formula Get the real-time navigation speed, where F u is the thrust in the AUV forward direction, ρ is the fluid density, D is the propeller diameter, C p is the power coefficient, W is the ocean current speed, P h is the energy consumption used to support non-propulsion systems or auxiliary systems, E is the energy of the AUV to be optimized, P is the instantaneous power of the AUV to be optimized, R is the travel distance of the AUV to be optimized, is the instantaneous energy consumption of the AUV to be optimized, V is the navigation speed of the AUV to be optimized, and the formula of the navigation speed is: Among them, J is the speed coefficient.

[0074] In step S105, the LO-MOMPC determines the priorities of the multiple targets according to a lexicographic sorting method.

[0075] For example:

[0076] LO-MOMPC constructs three conflicting vector-valued objective functions through a lexicographic sorting method:

[0077] J(U,ξ)=[J1(U,ξ),J 21 (U,ξ),J2(U,ξ)] T ;

[0078] where U = col(ω0,ω1,...,ω N-1 ) is the control input sequence to be optimized, ξ=col(x,s) represents the discretized augmented system;

[0079] Each objective function has the following form:

[0080]

[0081] where ξ p =col(p x (s),s) is the reference convergence path, is the reference energy consumption, ξ t =col(p x (s r ),s r ) is the reference sailing speed, through Generate s from s0 r , Q i , R i and P iIs a positive definite weighted matrix. The change of the cost function must conform to the priority order of the target. The specific equation is as follows:

[0082]

[0083] Furthermore, the solution to the multi-objective problem is:

[0084]

[0085] Then, the priority of the target and whether it converges to the reference of each target can be visually verified through the solution curve.

[0086] To prevent the numerical algorithm from stalling and improve computational efficiency, the equality constraints The relaxation is as follows:

[0087]

[0088] in To tune the parameters, the mathematical expression is simplified as follows:

[0089]

[0090] where c i (U) is the equality constraint, including system dynamics and boundary conditions; j (U) represents inequality constraints, including state constraints and control constraints; and

[0091] The modified LO-MOMPC formula with terminal constraints is:

[0092]

[0093] Among them, the error dynamics adjustment formula Δ is:

[0094]

[0095] In the formula represents the error dynamics equation, in and Represents a system constraint.

[0096] In a specific embodiment, sufficient conditions for ensuring solution convergence under the multi-objective model predictive control algorithm are derived, and the closed-loop Lyapunov stability is verified. Specifically, the following assumptions are used to analyze the convergence of the LO-MOMPC algorithm solution:

[0097] Assumption 1: Function is continuous,

[0098]

[0099] Assumption 2: Dataset and is closed, It is continuous, and each data set contains the origin;

[0100] Assumption 3: There exists an error dynamics equation The invariant set It includes origin and local feedback control So that for any Established:

[0101]

[0102] Assuming that the above assumptions 1, 2, and 3 are all valid, Formula Λ is feasible at time t = 0;

[0103] set up Indicates the previous system state. represents the first element of the solution at the previous time. For t>0, assume and Then the error state converges to the origin, that is, when t→∞,

[0104] set up represents the previous time solution, represents the corresponding state prediction of i=0,1,...,N;

[0105] For formula Λ, it has been proved that the horizontal plane is feasible, and the following is preliminarily constructed:

[0106]

[0107] The solution of formula Λ is feasible for solving multi-objective problems, and the evaluation The optimal value function at these two consecutive sampling time instants is:

[0108]

[0109] For hypothesis 3, we have

[0110] Furthermore, by J1≥0, J 21 ≥0 can be inferred is a non-increasing sequence with a lower bound of 0. By contradiction, when t→∞,

[0111] Furthermore, the The convergence of the error state trajectory to the origin can be guaranteed, that is, when t→∞,

[0112] There are different ways to transform the path tracking problem into the regulation of error dynamics and satisfy the above assumptions, according to the AUV reference path: ψ (s) = atan2(p′ y ,p′ x ), We can further get:

[0113]

[0114] Horizontal motion error in the hull coordinate system Decompose it and get:

[0115]

[0116] Speed ​​error The specific expression is:

[0117]

[0118] definition in:

[0119]

[0120]

[0121]

[0122] The AUV error state equation can be further obtained:

[0123]

[0124] At the origin, it is further confirmed that:

[0125]

[0126] Further definition Error dynamics equation The specific expression of the error target cost function is:

[0127]

[0128]

[0129] Since the above calculation requires Therefore, the path dynamics second-order integrator is assigned, that is:

[0130]

[0131] in Forward movement Using the above AUV reference path p ψ (s) = atan2(p′ y ,p′ x ), As a terminal constraint, Satisfies κ1(0)=0.

[0132] In the embodiment of the present invention, a preset kinematic model of an autonomous underwater vehicle AUV and an AUV dynamic model are discretized to obtain an AUV error dynamic model; control signal constraints are performed according to the zero path error flow in the preset state space and the AUV error dynamic model to obtain an expected path and state variables, wherein the expected path is the expected path when the AUV to be optimized is in a horizontal plane, and the state variables are the state variables when the AUV to be optimized is in a horizontal plane; the corresponding energy consumption is obtained according to the state variables of each unit step in the expected path; the real-time ocean current interference data of the AUV to be optimized is obtained, and the real-time navigation speed of the AUV to be optimized is fitted according to the ocean current interference data; the priorities of multiple optimization targets in the optimization process of the AUV to be optimized are determined according to the preset LO-MOMPC; the convergence conditions of the multiple optimization targets are obtained according to the priorities, and the energy of the AUV to be optimized is optimized according to the convergence conditions. This allows the AUV operating in an underwater environment with multiple interference targets to fully consider multiple interference factors and quickly and accurately obtain the optimal energy consumption.

[0133] like Figure 2 FIG. 1 is a structural diagram of a system for AUV energy optimization provided by an embodiment of the present invention, wherein the system comprises:

[0134] The AUV error dynamics model acquisition module 201 is used to discretize the preset autonomous underwater vehicle AUV kinematic model and AUV dynamics model to acquire the AUV error dynamics model;

[0135] The expected path and state variable acquisition module 202 is used to perform control signal constraints according to the zero path error flow in the preset state space and the AUV error dynamics model, and acquire the expected path and state variables, wherein the expected path is the expected path when the AUV to be optimized is on the horizontal plane, and the state variables are the state variables when the AUV to be optimized is on the horizontal plane;

[0136] An energy consumption acquisition module 203, configured to acquire corresponding energy consumption according to a state variable of each unit step in the expected path;

[0137] The navigation speed acquisition module 204 is used to acquire the real-time ocean current interference data of the AUV to be optimized, and fit the real-time navigation speed of the AUV to be optimized according to the ocean current interference data;

[0138] A priority determination module 205, used to determine the priorities of multiple optimization targets in the optimization process of the AUV to be optimized according to a preset LO-MOMPC;

[0139] The energy optimization module 206 is used to obtain the convergence conditions of the multiple optimization objectives according to the priorities, and perform energy optimization on the AUV to be optimized according to the convergence conditions.

[0140] Figure 3 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 3 As shown, the electronic device may include: a processor (processor) 301, a communication interface (Communications Interface) 302, a memory (memory) 303 and a communication bus 304, wherein the processor, the communication interface and the memory communicate with each other through the communication bus. The processor can call the logic instructions in the memory to execute the AUV energy optimization method, which includes: discretizing a preset autonomous underwater vehicle AUV kinematic model and an AUV dynamic model to obtain an AUV error dynamic model; performing control signal constraints according to the zero path error flow in the preset state space and the AUV error dynamic model to obtain an expected path and state variables, wherein the expected path is the expected path when the AUV to be optimized is in a horizontal plane, and the state variables are the state variables when the AUV to be optimized is in a horizontal plane; obtaining the corresponding energy consumption according to the state variables of each unit step in the expected path; obtaining the real-time ocean current interference data of the AUV to be optimized, and fitting the real-time navigation speed of the AUV to be optimized according to the ocean current interference data; determining the priorities of multiple optimization targets in the optimization process of the AUV to be optimized according to the preset LO-MOMPC; obtaining the convergence conditions of the multiple optimization targets according to the priorities, and performing energy optimization on the AUV to be optimized according to the convergence conditions.

[0141] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0142] On the other hand, an embodiment of the present invention further provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, and when the program instructions are executed by a computer, the computer can execute the AUV energy optimization method provided by the above-mentioned method embodiments, the method comprising: discretizing a preset autonomous underwater vehicle AUV kinematic model and an AUV dynamic model to obtain an AUV error dynamic model; performing control signal constraints according to a zero path error manifold in a preset state space and the AUV error dynamic model to obtain an expected path and state. state variables, the expected path is the expected path when the AUV to be optimized is in a horizontal plane, and the state variables are the state variables when the AUV to be optimized is in a horizontal plane; according to the state variables of each unit step in the expected path, the corresponding energy consumption is obtained; the real-time ocean current interference data of the AUV to be optimized is obtained, and the real-time navigation speed of the AUV to be optimized is fitted according to the ocean current interference data; the priorities of multiple optimization targets in the optimization process of the AUV to be optimized are determined according to a preset LO-MOMPC; the convergence conditions of the multiple optimization targets are obtained according to the priorities, and the energy of the AUV to be optimized is optimized according to the convergence conditions.

[0143] On the other hand, an embodiment of the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the processor executes the method for AUV energy optimization provided by the above embodiments, the method comprising: discretizing a preset autonomous underwater vehicle AUV kinematic model and an AUV dynamic model to obtain an AUV error dynamic model; performing control signal constraints according to a preset zero path error flow in a state space and the AUV error dynamic model to obtain an expected path and state variables, wherein the expected path is the expected path when the AUV to be optimized is in a horizontal plane, and the state variables are the state variables when the AUV to be optimized is in a horizontal plane; obtaining corresponding energy consumption according to the state variables of each unit step in the expected path; obtaining real-time ocean current interference data of the AUV to be optimized, and fitting the real-time navigation speed of the AUV to be optimized according to the ocean current interference data; determining the priorities of multiple optimization targets in the optimization process of the AUV to be optimized according to a preset LO-MOMPC; obtaining convergence conditions of the multiple optimization targets according to the priorities, and performing energy optimization on the AUV to be optimized according to the convergence conditions.

[0144] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0145] The above is only a partial implementation of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for AUV energy optimization, characterized in that: The method comprises: Discrete the preset autonomous underwater vehicle AUV kinematic model and AUV dynamic model to obtain the AUV error dynamic model; According to the zero path error manifold in the preset state space and the AUV error dynamics model, control signal constraints are performed to obtain the expected path and state variables, wherein the expected path is the expected path when the AUV to be optimized is on a horizontal plane, and the state variables are the state variables when the AUV to be optimized is on a horizontal plane; Obtaining corresponding energy consumption according to the state variable of each unit step in the expected path; Acquire real-time ocean current disturbance data of the AUV to be optimized, and fit the real-time navigation speed of the AUV to be optimized according to the ocean current disturbance data; Determining the priorities of multiple optimization targets in the optimization process of the AUV to be optimized according to the preset LO-MOMPC; The convergence conditions of the multiple optimization objectives are acquired according to the priorities, and energy optimization is performed on the AUV to be optimized according to the convergence conditions.

2. The method according to claim 1, characterized in that The AUV dynamics model of the AUV in the ocean current environment is: where η = [x, y, ψ] T is the position and direction vector expressed in the inertial coordinate system, including position and yaw angle, v b Represents the velocity vector of the ocean current or wave in the inertial coordinate system. v=[u,v,r] T is the relative velocity vector expressed in the hull coordinate system, and the control vector u=[F u ,F v ,F r ] T is the generalized thrust and torque, b represents the model uncertainty, and the state vector x = [x, y, ψ, u, v, r] T It consists of the position and velocity of the AUV, and J(ψ) is the rotation matrix from the vehicle coordinate system to the inertial coordinate system. M=M RB +M A , C(v)=C RB (v)+C A (v), D(v), g(η) represent the mass matrix, M RB Rigid body mass matrix, C RB (v) Coriolis centripetal matrix, M A The hydrodynamic added mass matrix, C A (v)Added mass Coriolis centripetal matrix, Coriolis force matrix and damping matrix, restoring torque.

3. The method according to claim 1, characterized in that The formula for the expected path is: The mapping p is a mathematical expression of the path, which is smooth and bounded; the path parameter s is closed but may be unbounded, that is, Definition: For a function p(s), its derivative with respect to s is denoted by p′(s), and its time derivative is denoted by The meaning of the subscript under the mapping p: state vector x = [x, y, ψ, u, v, r] T , which consists of the position and velocity of the AUV.

4. The method according to claim 1, characterized in that After the step of acquiring the corresponding energy consumption according to the state variable of each unit step in the expected path, the method further includes: Obtain secondary energy consumption, the formula of secondary energy consumption is: The percentage formula of the secondary energy consumption is: Among them, T sim is the navigation time, T is the control time, v s is the control input of the LO-MOMPC controller at time i, v s ′ is the control input of the conventional MPC in the prior art at time i.

5. The method according to claim 1, characterized in that The step of fitting the real-time navigation speed of the AUV to be optimized according to the ocean current disturbance data comprises: According to the formula Get the real-time navigation speed, where F u is the thrust in the AUV forward direction, ρ is the fluid density, D is the propeller diameter, C p is the power coefficient, W is the ocean current speed, P h is the energy consumption used to support non-propulsion systems or auxiliary systems, E is the energy of the AUV to be optimized, P is the instantaneous power of the AUV to be optimized, R is the travel distance of the AUV to be optimized, is the instantaneous energy consumption of the AUV to be optimized, V is the navigation speed of the AUV to be optimized, and the formula of the navigation speed is: Among them, J is the speed coefficient.

6. The method according to claim 1, characterized in that The LO-MOMPC determines the priorities of the plurality of targets according to a lexicographic sorting method.

7. A system for AUV energy optimization, characterized in that: The system comprises: The AUV error dynamics model acquisition module is used to discretize the preset autonomous underwater vehicle AUV kinematic model and AUV dynamics model to obtain the AUV error dynamics model; An expected path and state variable acquisition module is used to perform control signal constraints according to the zero path error flow in the preset state space and the AUV error dynamics model, and acquire an expected path and state variables, wherein the expected path is the expected path when the AUV to be optimized is on a horizontal plane, and the state variables are the state variables when the AUV to be optimized is on a horizontal plane; An energy consumption acquisition module, used to acquire corresponding energy consumption according to the state variable of each unit step in the expected path; A navigation speed acquisition module, used to acquire real-time ocean current interference data of the AUV to be optimized, and fit the real-time navigation speed of the AUV to be optimized according to the ocean current interference data; A priority determination module, used to determine the priorities of multiple optimization objectives in the optimization process of the AUV to be optimized according to a preset model predictive control LO-MOMPC; An energy optimization module is used to obtain the convergence conditions of the multiple optimization objectives according to the priorities, and perform energy optimization on the AUV to be optimized according to the convergence conditions.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the AUV energy optimization method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for AUV energy optimization according to any one of claims 1 to 6 is implemented.

Images