Self-triggering model prediction hovering control method for underwater vehicle

Abstract

The invention particularly relates to an underwater vehicle self-triggering model prediction hovering control method, which comprises the following steps of: according to hovering motion characteristics of an AUV (Autonomous Underwater Vehicle), reasonably assuming and simplifying an AUV hovering model; according to hypothesis and simplification, establishing four-degree-of-freedom dynamics and kinematics equations in a vertical plane; analyzing the characteristics and actual constraints of the VBS according to the working requirements of the AUV on the VBS; according to the layout of the VBS on the AUV, force and torque changes caused by modeling water injection and drainage to the AUV are analyzed; designing an online minimization cost function and an optimization control problem required by the MPC, and designing an MPC controller of nonlinear mixed integer programming; designing a self-triggering mechanism, and calculating a next triggering moment and a prediction time domain according to the current state of the AUV; and the performance of the designed controller is verified through mathematical simulation. According to the method, the dynamic constraint of the VBS executing mechanism and the state constraint of the AUV are considered, the stable hovering control problem under switch input is solved, a self-triggering mechanism is introduced, and the optimal calculation burden of MPC is reduced.

Description

Technical Field

[0001] This invention relates to the field of underwater vehicle hovering control technology, specifically to a self-triggered model prediction hovering control method for underwater vehicles, which is applied to the field of marine engineering research. Background Technology

[0002] With the widespread application of AUVs in marine exploration, resource discovery, and environmental monitoring, the demand for precise control of AUVs in complex underwater environments is constantly increasing. AUV hovering control is one of its core technologies, especially crucial for tasks such as precision operations, fixed-point monitoring, and silent, latent detection, where hovering stability and accuracy are paramount. However, due to the characteristics of the underwater environment and AUVs themselves, such as large ocean current disturbances, weak adjustment capabilities, and uncertain dynamic characteristics, AUV hovering control faces significant challenges.

[0003] To achieve hovering maneuvers for underwater vehicles, two main methods are currently employed: auxiliary thrusters and variable buoyancy adjustment. The advantage of auxiliary thrusters for hovering control lies in their continuously adjustable thruster speed, relatively easy controller design, and good control performance. However, because underwater vehicles are typically configured with slight positive buoyancy before launch for safety, this imbalance between gravity and buoyancy necessitates continuous operation of the auxiliary thruster, resulting in high energy consumption. This is detrimental to long-term operational missions for underwater vehicles, and the thruster noise can affect sonar detection accuracy, reducing stealth capabilities.

[0004] Therefore, many scholars have already conducted research on AUVs, underwater gliders, underwater robots, and profiling buoys equipped with VBS (Variable Flow Control). VBS systems are classified into continuous and on / off types based on whether the flow rate can be continuously adjusted. In VBSAUV control, most systems employ direct control methods. Common methods include proportional-integral-derivative (PI-DI) control and its improved versions, linear quadratic control, fuzzy control, sliding mode control, and active disturbance rejection control.

[0005] However, these methods all have some drawbacks: (1) The proportional-integral-derivative control method is simple and practical, and can achieve good control effect through parameter tuning. However, when the research object is a nonlinear system with complex time-varying disturbances, it cannot achieve adaptive control of parameters. (2) The implementation of the linear quadratic control controller requires an accurate mathematical model. However, due to the highly coupled nonlinear characteristics of the hovering model of the AUV, it is generally difficult to obtain an accurate model and hydrodynamic parameters. (3) Sliding mode control generally suffers from the problem of "chattering", which requires the design of special methods to eliminate it; (4) Fuzzy control is essentially an empirical control method, and the subjective factors of the designer have a significant impact on the formulation of fuzzy rules.

[0006] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] This invention provides a self-triggering model prediction hovering control method for underwater vehicles, which considers the dynamic constraints of the VBS actuator and the state constraints of the AUV to solve the problem of stable hovering control under switch input, and introduces a self-triggering mechanism to reduce the optimization computation burden of MPC.

[0008] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0009] According to a first aspect of the present invention, a self-triggered model prediction hovering control method for an underwater vehicle is provided, the method comprising: Step 1: Based on the hovering motion characteristics of autonomous underwater vehicles (AUVs), make reasonable assumptions and simplifications to the AUV hovering model; Step 2: Based on the assumptions and simplifications in Step 1, establish the dynamic and kinematic equations of the AUV in the vertical plane with four degrees of freedom; Step 3: Based on the AUV's requirements for the variable buoyancy system (VBS), analyze the characteristics and actual constraints of the VBS; Step 4: Based on the layout of VBS on the AUV, analyze the force and torque changes caused by the modeling of water injection and drainage on the AUV; Step 5: Design the online minimization cost function and the optimization control problem required for Model Predictive Control (MPC), and construct a nonlinear mixed-integer programming MPC controller; Step 6: Design a self-triggering mechanism to calculate the next triggering time and prediction time domain based on the current state of the AUV.

[0010] In some exemplary embodiments, in step 1, the assumptions and simplifications include: no longitudinal velocity during hovering, and the main thruster and rudder are not working; hovering only occurs in the vertical plane, allowing horizontal drift; the types of disturbance forces acting on deep-water hovering are clearly defined, including the initial imbalance, the vertical force generated by the injection and discharge of hovering control tanks, the disturbance force of near-surface wave forces, and the resulting pitching moment; the characterization parameters of underwater hovering are defined, and the other vertical plane motion parameters are set to 0.

[0011] In some exemplary embodiments, in step 2, the dynamic equations involve the AUV's mass, seawater density, length, center of gravity position, moment of inertia, buoyancy, hydrodynamic parameters, and vertical drag parameters, while the kinematic equations relate to the AUV's velocity and attitude variables.

[0012] In some exemplary embodiments, in step 3, the specific characteristics of the VBS are: the pump used in the VBS is a metering pump, with only three modes: water injection, shutdown, and drainage. The specific constraints of the VBS are: the rate of change of water volume is only three constant values ​​and the volume of a single ballast water tank is 10L.

[0013] In some exemplary embodiments, in step 4, the changes in force and torque caused by the water injection and drainage on the AUV are specifically as follows:

[0014]

[0015]

[0016] in, The initial mass of the AUV before water injection, For seawater density, To adjust the weight of the water in the buoyancy tanks for bow buoyancy, To adjust the weight of the water in the stern buoyancy tank, The longitudinal distance between the bow buoyancy adjustment tank and the AUV's center of gravity is determined. The longitudinal distance between the stern weight buoyancy regulating tank and the AUV's center of gravity. This is the acceleration due to gravity.

[0017] In some exemplary embodiments, step 5, the optimization control problem, specifically refers to: At the trigger time The optimal control sequence found and optimal state sequence The following optimization problem is solved:

[0018] in, , , These are the system's predicted state, control input, and external disturbances. , , and It is a compact set that includes the origin as its interior. For local Lipschitz functions for The time domain for predicting time; By integrating the minimization of the cost function with the optimization control problem, a nonlinear MPC controller is formed.

[0019] In some exemplary embodiments, step 6, which involves designing a self-triggering mechanism to calculate the next triggering time and prediction time domain based on the current state of the AUV, specifically includes: The update timing should be determined in a self-triggered manner, i.e.

[0020] in, For trigger time; The prediction time domain should be updated to

[0021] in: To predict the decrease in the time domain, To ensure that the optimization control problem is feasible at the initial time, a given constant is used.

[0022] According to a second aspect of the present invention, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the underwater vehicle self-triggering model prediction hovering control method described in the first aspect above.

[0023] According to a third aspect of the present invention, a computer program product is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the underwater vehicle self-triggering model prediction hovering control method described in the first aspect is implemented.

[0024] According to a fourth aspect of the present invention, an electronic device is provided, comprising: Processor; and Memory for storing the executable instructions of the processor; The processor is configured to implement the underwater vehicle self-triggering model prediction hovering control method described in the first aspect above by executing the executable instructions.

[0025] The underwater vehicle self-triggering model prediction hovering control method provided by the embodiments of the present invention has the following advantages compared with the prior art: (1) In response to the constraints of input variables being switching quantities in hover control, such as the flow rate being unadjustable and the water tank volume being limited, a self-triggering mixed integer nonlinear programming MPC framework is proposed. This framework effectively handles the multi-constraint problem in the hovering process, realizes stable hovering under discontinuous input switching control, and solves the problems of depth overshoot and pitch oscillation in traditional AUV hover control. It greatly reduces the frequency of switching and extends the service life of valves and pumps.

[0026] (2) For the AUV hovering system with a slowly changing state, this invention introduces a self-triggering mechanism, which reduces the computational burden and saves the optimization computational resources of the MPC problem of mixed integer nonlinear programming.

[0027] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0028] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0029] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the inertial coordinate system {A} and the motion coordinate system {B} of an AUV; Figure 3 A schematic diagram of the layout of the bow and stern ballast tanks; Figure 4 A schematic diagram showing the working status of valves and pumps and the direction of water flow during VBS filling, draining and shutting down; Figure 5 The curves showing the changes in depth and vertical velocity during hovering control under four different methods are shown. Figure 6 The curves showing the changes in pitch angle and pitch rate during hovering control under four different methods are shown. Figure 7 The switching control inputs of the bow VBS and the changes in bow water volume during the MPC hovering control process are used. Figure 8 The switch control inputs of the bow VBS and the changes in water volume in the stern water tank are shown in the four methods of hovering control. Figure 9 For nonlinear MPC and , , Time-triggered moment versus prediction time-domain variation graph; Figure 10 For nonlinear MPC and , , Optimize the solution time at each trigger point. Detailed Implementation

[0030] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0031] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0032] To address the shortcomings and deficiencies of existing technologies, this example embodiment provides a Model Predictive Control (MPC) method for underwater vehicles, which utilizes Variable Buoyancy Systems (VBS) for hovering control. More specifically, it leverages the ability of VBS to fill and drain water to alter the net buoyancy of the AUV, thereby adjusting the AUV's pitch and buoyancy to achieve hovering control.

[0033] refer to Figure 1 As shown, the specific steps may include: Step 1: Based on the hovering motion characteristics of AUVs, make reasonable assumptions and simplifications to the AUV hovering model; Step 2: Based on the assumptions and simplifications in Step 1, establish the dynamic and kinematic equations for four degrees of freedom in the vertical plane; First, it serves as a predictive model in model predictive control, predicting the system state within a certain time step in the future; second, it serves as a controlled system model during simulation, realizing closed-loop control simulation.

[0034] Step 3: Based on the AUV's requirements for VBS, analyze the characteristics and actual constraints of VBS; Step 4: Based on the layout of VBS on the AUV, analyze and model the force and torque changes caused by water injection and drainage on the AUV; Step 5: Design the online minimization of the cost function and the optimization control problem required for MPC, and design an MPC controller based on nonlinear mixed integer programming; Step 6: Design a self-triggering mechanism to calculate the next triggering time and prediction time domain based on the current state of the AUV; Step 7: Verify the performance of the designed controller through mathematical simulation.

[0035] The steps in this exemplary embodiment will now be described in more detail with reference to the accompanying drawings and embodiments.

[0036] In step 1, the coordinate system of the AUV is defined as follows: Figure 2 As shown, based on the actual characteristics of hovering motion, the following simplifications and assumptions are made: 1) During underwater hovering motion, there is no longitudinal speed, and the main thruster and rudder are not working.

[0037] 2) Hovering motion occurs only in the vertical plane, with no control torque in the horizontal plane, thus allowing horizontal drift. Therefore, the state variables to be considered during hovering include velocity. These are respectively forward, longitudinal, and circumferential. Axis velocity / angular velocity; pose variables These are the forward position, depth, and pitch angle, respectively; input variables They are respectively along Axis, along Axis and winding Input force / torque of the shaft.

[0038] 3) The main types of disturbances acting on deep-water hovering are as follows: ① Initial imbalance. Mainly caused by errors during stopping and replenishing buoyancy, it is generally a constant value, i.e., the difference between the current buoyancy and the neutral buoyancy; ② The vertical force generated by the injection and drainage of the hovering control water tank ( and )in, To adjust the weight of the water in the buoyancy tanks for bow buoyancy, The layout of the water tanks is shown in the attached figure, which is designed to adjust the weight of the water in the stern buoyancy tanks. Figure 3 As shown; ③ The disturbance effect of near-surface wave forces; the calculation formula is shown below, assuming the AUV is a cylinder to calculate the wave forces and moments experienced by the vehicle.

[0039]

[0040] in and These represent the length and diameter of the AUV, respectively. The drag coefficient, For the additional quality coefficient, and These represent the vertical velocity and vertical acceleration at the integration point of the AUV, respectively. and These represent the velocity and acceleration of the long-peaked irregularity at the AUV integration point, respectively.

[0041] ④ , , :Depend on , and The resulting pitching moment.

[0042] 4) Underwater hovering motion is represented only by the following parameters; all other vertical plane motion parameters of the vehicle are set to 0, i.e. , , , and , where is the pitch angular velocity; That is, the vertical latency is equal to axial velocity .

[0043] In step S2, after the above assumptions and simplifications, the dynamics of the AUV in the vertical plane become: (1) (2) in: . For quality, The density of seawater, For length, The center of gravity position For rotational inertia, For AUV buoyancy, , These are all hydrodynamic parameters. This represents the vertical drag parameter.

[0044] The kinematic equations become: (3) In step S3, the AUV's requirements for using VBS are: changing depth without using a rudder and dynamically maintaining AUV balance when facing different load changes. To achieve these requirements, a variable buoyancy adjustment device is equipped at both the bow and stern of the AUV. The variable buoyancy adjustment device includes a ballast water tank, a metering pump, a reversing valve, related injection and drainage pipes, and control hardware and software.

[0045] To reduce economic costs, the pumps used in VBS are fixed displacement pumps with only three modes: filling, shutting off, and draining. 1) +1 (Water Injection): Inject water into the ballast water tank, as shown in the attached diagram. Figure 4 As shown in (a); 2) -1 (Drainage): Drainage to the outside of the cabin, as per attached Figure 4 As shown in (b); 3) 0 (Closed): No injection or discharge, close valves and pumps, as per attached. Figure 4 As shown in (c).

[0046] The metering pumps and valves are arranged in sequence to control the flow direction. Each VBS can inject (or drain) 1L of water per second. Therefore, the actual control system is subject to the following constraints: the rate of change of water volume can only be three constant values, and it can only inject or drain water at a fixed flow rate.

[0047] (4) in, For the rate of change of water volume in the bow water tank, The rate of change of water volume in the stern water tank.

[0048] Since the volume of a single ballast tank is 10L, assuming that the bow and stern ballast tanks of the AUV are each half full of water, the center of gravity of the AUV coincides with its center of buoyancy, and it is in a state of neutral buoyancy. , Therefore, the water volume in the bow and stern ballast tanks and ,in To adjust the weight of the water in the buoyancy tanks for bow buoyancy, The weight of the water in the stern buoyancy adjustment tank is subject to the following constraints: (5) In step S4, the force and torque changes caused by water injection and drainage on the aircraft are as follows: (6) (7) (8) Because the bow and stern ballast tanks are not located at the center of gravity. Therefore, the center of gravity It will move as the water volume in the two ballast tanks changes, and its moment of inertia... It will also change with the shift in the center of gravity and the moment of inertia. The change can be calculated using the following formula (9) (10) in, This refers to the initial mass of the AUV before it was filled with water. The density of seawater, To adjust the weight of the water in the buoyancy tanks for bow buoyancy, To adjust the weight of the water in the buoyancy tanks for bow buoyancy, The coordinates of the AUV's center of gravity before the bow and stern water tanks were filled with water. The coordinates of the center of gravity of the bow weight buoyancy adjustment tank. To adjust the center of gravity of the stern tank for buoyancy, It is the acceleration due to gravity. The longitudinal distance between the bow buoyancy adjustment tank and the AUV's center of gravity is determined. The longitudinal distance between the stern weight buoyancy regulating tank and the AUV's center of gravity. The initial moment of inertia of the bow and stern water tanks before they are emptied.

[0049] In step S5, consider the hovering nonlinear system established in step S2, and describe it as a difference equation in discrete time form as follows: (11) in, , , These are system status, control input, and external interference.

[0050] set up To predict the time domain, the online cost minimization function is as follows: (12) Wherein: State cost Terminal cost .here, , and This is the weight matrix.

[0051] The formula for the online solution of the optimization control problem is shown below. Optimize control issues: at the trigger time The optimal control sequence found and optimal state sequence The following optimization problem is solved: (13) By integrating the minimization of the cost function with the optimization of the control problem, a nonlinear MPC controller is formed. One of the key advantages of MPC is its ability to enforce control. , Formal state and input constraints, noteworthy here It is general enough to enforce integer constraints on subsets of the input. Therefore, the formula here already includes nonlinear mixed-integer MPC as a special case.

[0052] In step S6, to reduce the frequency of solving the optimization control problem, the update timing should be determined in a self-triggered manner, i.e. (14) in, This is the trigger time. Simultaneously, as the system state approaches the terminal invariant set, the prediction time domain should also adaptively shrink to reduce the computational burden of the optimization control problem. The prediction time domain should be updated to... (15) in: To predict the decrease in the time domain, These are given constants to ensure the feasibility of the optimization control problem at the initial time. For ease of representation, they will be used subsequently. represent ,use represent .

[0053] Calculated by the following formula (16) in (17) (18) in, This is the performance factor.

[0054] The prediction time domain is updated according to equation (15). It is calculated by the following formula: (19) in (20) The actual system in the interval The control sequence is given by equation (13).

[0055] In step S7, based on the above kinematic and dynamic models and the predictive control scheme of the nonlinear mixed integer programming model based on the self-triggering mechanism, simulation software is built to perform mathematical simulation analysis.

[0056] The following simulation analysis further verifies the invention: To verify the performance of the designed controller, a hovering task was conducted. Specifically, the task involved hovering from 0.5m to 15m, with the weight parameters in the cost function set to... , , Control cycle To demonstrate the effectiveness of the proposed control method, we compare its results with those of traditional Bang-Bang control, traditional switching control using PID + quantization switching, and fixed-period nonlinear MPC. We use metrics such as depth error, attitude error, switching frequency, and solution time of the optimization problem to illustrate the superiority of the proposed method.

[0057] like Figure 5 This study demonstrates two traditional switching control methods: Bang-Bang control and PID+quantization switching. As can be seen from the depth and velocity curves, these methods exhibit time-optimal characteristics, reaching the desired depth more quickly (e.g., in 104s). However, due to the non-adjustable pumping rate of the actuator, the neutral buoyancy condition and the zero vertical velocity condition cannot be simultaneously satisfied, causing the hovering depth to oscillate around the target depth, maintaining dynamic stability while the switch remains in a constantly switching state. This is because simple switching control methods typically do not consider the dynamic characteristics of the AUV and its significant system inertia, thus failing to handle the system's multi-objective optimization problem. In contrast, nonlinear mixed-integer programming (MPC) and self-triggering MPC methods predict future states to generate the optimal control sequence, thereby calculating the optimal switching time. This allows the hovering depth to stabilize at the desired depth, achieving stable hovering control under switching control.

[0058] Combination Figure 5 Vertical velocity curve and Figure 6 As can be seen from the pitch angle and pitch rate curves, the control method proposed in this invention achieves zero vertical velocity, pitch angle, and pitch rate after depth stabilization. This maintains depth stability in static hovering control while also ensuring pitch attitude stability. It can significantly reduce mechanical noise caused by the actuators, thereby increasing the stealth capability of the vehicle and enhancing the accuracy of sonar detection.

[0059] Figure 7 The demonstration showed the valve opening and closing of the bow ballast tank and the changes in water volume during hovering. Compared to traditional nonlinear MPC methods, the control method proposed in this invention requires fewer switching adjustments from the start of descent to depth stabilization. The proposed method switches four times within 138-153 seconds, which is less than the actual switching frequency of solenoid valves used in practical applications, making it suitable for real-world use.

[0060] Figure 8The invention demonstrates the valve opening and closing of the stern water tank and the changes in water volume in the stern water tank under four different control methods during hovering. The control method proposed in this invention involves the fewest switching operations compared to the other three methods, which is more beneficial for energy saving, increasing the AUV's duty cycle, and extending the service life of the actuators.

[0061] Figure 9 NMPC and NMPC with different performance factors were demonstrated. , and The parameters determine the triggering time and prediction time domain of the STMINLP MPC method. As the time approaches zero, the trigger time becomes close to periodic sampling, and the prediction range gradually decreases over time. This means that the complexity of the OCP problem decreases as the number of decision variables and constraints decreases. It can also be seen that in all cases, the trigger time eventually becomes periodic. This indicates that the system state has entered the terminal region, and the controller switches to periodic sampling control. Furthermore, the computational complexity is evaluated using the optimization solution time. Figure 10 NMPC and different The optimization time under the given value is reduced. Compared with NMPC, the optimization time at each step is reduced, indicating a decrease in computational complexity. Furthermore, the total optimization time is less than that of NMPC, demonstrating the computational advantage of the proposed algorithm.

[0062] It should be noted that, as another aspect, this application also provides a storage medium, which may be included in an electronic device or may exist independently without being assembled into the electronic device. The storage medium carries one or more programs, which, when executed by an electronic device, cause the electronic device to perform the methods described in the following embodiments.

[0063] In one embodiment, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0064] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0065] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0066] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is defined only by the appended claims.

Claims

1. An underwater vehicle self-triggered model predictive hover control method, characterized in that, The method comprises: Step 1: According to the hovering motion characteristics of the autonomous underwater vehicle (AUV), reasonable assumptions and simplifications are made for the AUV hovering model; Step 2: According to the assumptions and simplifications of step 1, the dynamics and kinematics equations of the AUV in the vertical plane are established; Step 3: According to the working requirements of the AUV on the variable buoyancy system (VBS), the characteristics and actual constraints of the VBS are analyzed; Step 4: According to the layout of the VBS on the AUV, the force and moment changes caused by the modeling and water discharge of the AUV are analyzed; Step 5: Design an online minimum cost function and an optimization control problem required by model predictive control (MPC), and construct a nonlinear mixed integer programming MPC controller; Step 6: Design a self-triggering mechanism to calculate the next triggering time and prediction horizon according to the current state of the AUV.

2. The method of claim 1, wherein, In step 1, the assumptions and simplifications include: no longitudinal velocity during hovering motion, main propeller and rudder do not work; hovering motion only occurs in the vertical plane, allowing horizontal drift; the types of disturbance forces acting on deep water hovering are specified, including initial unbalance, vertical force generated by hovering control water tank filling and draining, disturbance force of near-surface wave force and generated trim moment; the characterization parameters of underwater hovering motion are limited, and the remaining vertical plane motion parameters are 0.

3. The method of claim 1, wherein, In step 2, the dynamics equation involves AUV mass, seawater density, length, center of gravity position, moment of inertia, buoyancy, fluid dynamic parameters and vertical resistance parameters, and the kinematics equation relates to the speed and pose variables of the AUV.

4. The method of claim 1, wherein, In step 3, the characteristics of the VBS are: the pump used in the VBS is a constant pump, and there are only three modes of water injection, shutdown and water discharge; The actual constraints of the VBS are: the water volume change rate is only three constant values, and the single ballast tank volume is 10L.

5. The method of claim 4, wherein, In step 4, the force and moment changes caused by the water injection and discharge of the AUV are: wherein, is the initial mass of the AUV before water injection, is the density of seawater, is the weight of water in the bow weight-adjusting buoyancy tank, is the weight of water in the stern weight-adjusting buoyancy tank, is the longitudinal distance between the bow weight-adjusting buoyancy tank and the center of gravity of the AUV, is the longitudinal distance between the stern weight-adjusting buoyancy tank and the center of gravity of the AUV, is the acceleration of gravity.

6. The method of claim 5, wherein, In step 5, the optimization control problem is: At the triggering instant , the optimal control sequence and the optimal state sequence are found by solving the following optimization problem: wherein , , are the system predicted state, control input and external additive disturbance, respectively, , , and is a compact set containing the origin as an interior point, is a local Lipschitz function, is the prediction horizon at time instant By integrating the minimum cost function and the optimization control problem, a nonlinear MPC controller is constructed.

7. The method of claim 6, wherein, In step 6, the self-triggering mechanism is designed to calculate the next triggering time and prediction horizon according to the current state of the AUV, which is: The update time should be determined in a self-triggering manner, that is, wherein is the trigger time; The prediction horizon should be updated to wherein: is a reduction value for the prediction horizon, is a given constant that ensures the optimisation control problem is feasible at the initial time instant.

8. A storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to realize the autonomous underwater vehicle self-triggering model predictive hovering control method according to any one of claims 1 to 7.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the autonomous underwater vehicle self-triggering model predictive hovering control method according to any one of claims 1 to 7.

10. An electronic device, comprising: Comprise: A processor; And A memory for storing executable instructions of the processor; Wherein, the processor is configured to execute the autonomous underwater vehicle self-triggering model predictive hovering control method according to any one of claims 1 to 7 by executing the executable instructions.

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