Depth suspension control method and device for underwater vehicle
By using an extended state observer and PID predictive control, the vertical velocity and acceleration of the UUV are estimated in real time, and the future state is predicted. This solves the accuracy and lag problems in the hovering control of low-cost UUVs, and achieves high-precision and robust hovering control.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- INST OF ACOUSTICS CHINESE ACAD OF SCI
- Filing Date
- 2025-12-12
- Publication Date
- 2026-04-21
AI Technical Summary
Low-cost unmanned underwater vehicles (UUVs) lack speed sensors and have large measurement errors in their built-in accelerometers during hovering control, resulting in low depth adjustment accuracy. In addition, the buoyancy adjustment system is lagging, causing hovering oscillations.
An extended state observer (ESO) combined with PID predictive control is used to estimate the vertical velocity and acceleration of the UUV in real time through depth measurement values and control commands, predict the future state, and calculate control signals based on the predicted values to adjust the buoyancy pump's drainage, water intake, or shutdown operation.
It achieves high-precision and robust hovering control of UUVs at the target depth, reduces hovering oscillations, and improves the response speed and stability of the control system.
Smart Images
Figure CN121894129A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of underwater vehicle technology, and in particular to a method and apparatus for controlling the depth levitation of an underwater vehicle. Background Technology
[0002] Unmanned Underwater Vehicles (UUVs) are intelligent devices that autonomously navigate underwater and perform tasks through remote or automatic control. UUV depth hovering control mostly employs buoyancy adjustment systems, such as seawater pump-type buoyancy adjustment and oil bladder-type buoyancy adjustment devices, enabling the UUV to hover at a set depth. During the unpowered hovering adjustment process, relying solely on depth sensors is insufficient for precise control; vertical velocity and acceleration are also required. Low-cost UUVs lack velocity sensors and cannot detect vertical velocity; their onboard accelerometers measure acceleration in volume coordinates, requiring coordinate transformation to obtain depth-direction acceleration. However, due to the low attitude measurement accuracy of low-cost UUVs and the relatively small acceleration during hovering, using their own acceleration coordinate transformation method results in significant errors in the depth-direction acceleration obtained. Summary of the Invention
[0003] To address the problems existing in the prior art, embodiments of this application provide a method, apparatus, computing device, computer storage medium, and product containing a computer program for depth hovering control of an underwater vehicle, which can estimate the total disturbances affecting the system dynamics and estimate the original physical state, thereby achieving depth closed-loop control in the hovering state of the UUV.
[0004] In a first aspect, embodiments of this application provide a depth levitation control method for an underwater vehicle (UUV), comprising: acquiring a current depth measurement value and a preset target depth value of the UUV; inputting the current depth measurement value and a control command from the previous moment into an extended state observer to estimate the current state value of the UUV in real time, including an estimated current vertical velocity, an estimated current vertical acceleration, and an estimated current depth; predicting the state of the UUV at a future specified moment based on the current state value, including a predicted depth, a predicted velocity, and a predicted acceleration; calculating a control signal based on the predicted depth, the predicted velocity, the predicted acceleration, and the target depth value; and adjusting the buoyancy adjustment actuator of the UUV according to the control signal to control the UUV to drain water, suck water, or stop operation.
[0005] In some possible implementations, the construction of the extended state observer includes: characterizing the depth control model of the UUV as a third-order system with depth, vertical velocity, and total disturbance as state variables; and designing a Luneburger observer based on this third-order system, whose dynamic equations are as follows:
[0006]
[0007] In the formula, Characterization depth estimate, Characterizing the velocity estimate, Characterizing the acceleration estimate, Characterizes the input of control commands. Characterizing the error between the measured depth value and the estimated depth value. Characterizes the observer gain coefficient.
[0008] In some possible implementations, the observer gain coefficient is selected based on the following principle: the eigenvalues of the error system matrix of the extended state observer are all located in the left half of the complex plane, so as to ensure that the estimation error converges asymptotically.
[0009] In some possible implementations, predicting the UUV state at a specified future time based on the current state value specifically includes: prediction using a uniform motion model, with the specific formula being:
[0010]
[0011] In the formula, Characterization depth prediction value, Characterizing the current depth estimate, Characterizing the current speed estimate, Characterize the predicted time, Characterizing the current acceleration estimate, Characterizing the predicted velocity value, Characterizes the predicted acceleration value.
[0012] In some possible implementations, the control signal is calculated according to the following formula:
[0013]
[0014] In the formula, Indicates control signal, Indicates the target depth value. This is the gain coefficient for depth error. The gain coefficient for predicting velocity. This is the gain coefficient for predicting acceleration.
[0015] In some possible implementations, the buoyancy adjustment actuator of the UUV adjusts according to the control signal to control the UUV's drainage, water intake, or shutdown operation, including: if the control signal is greater than a threshold, outputting a command to control the actuator to drain water; if the absolute value of the control signal is not greater than the threshold, outputting a command to control the actuator to stop working; if the control signal is less than a negative value of the threshold, outputting a command to control the actuator to intake water.
[0016] In some possible implementations, the buoyancy adjustment actuator is a flow pump, and the flow pump operates in an on / off control mode.
[0017] Secondly, embodiments of this application provide a depth levitation control device for an underwater vehicle, comprising:
[0018] The system includes an acquisition module for acquiring the current depth measurement value and a preset target depth value of the underwater vehicle (UUV); a processing module for inputting the current depth measurement value and the control command from the previous moment into an extended state observer to estimate the current state value of the UUV in real time, including the current vertical velocity estimate, the current vertical acceleration estimate, and the current depth estimate; the processing module is also used to predict the UUV state at a specified future moment based on the current state value, including the predicted depth, predicted velocity, and predicted acceleration; the processing module is also used to calculate a control signal based on the predicted depth, the predicted velocity, the predicted acceleration, and the target depth value; and the processing module is also used to adjust the buoyancy adjustment actuator of the UUV according to the control signal to control the UUV's dewatering, water intake, or shutdown operation.
[0019] Thirdly, embodiments of this application provide a computer-readable storage medium including computer-readable instructions that, when read and executed by a computer, cause the computer to perform the method as described in any of the first aspects.
[0020] Fourthly, embodiments of this application provide a computing device, including a processor and a memory, wherein the memory stores computer program instructions, which, when executed by the processor, perform the method as described in any of the first aspects.
[0021] Fifthly, embodiments of this application provide a product comprising a computer program that, when the computer program product is run on a processor, causes the processor to perform the method as described in any of the first aspects.
[0022] In a sixth aspect, embodiments of this application provide an underwater vehicle including the apparatus described in the second aspect. Attached Figure Description
[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0024] Figure 1This is a schematic diagram of a depth levitation control system for an underwater vehicle provided in an embodiment of this application;
[0025] Figure 2 This is a schematic flowchart of a depth levitation control method for an underwater vehicle provided in an embodiment of this application;
[0026] Figure 3 This is a schematic diagram of force analysis of an underwater vehicle provided in an embodiment of this application;
[0027] Figure 4 This is a schematic diagram illustrating the change of key state variables of an underwater vehicle's depth channel over time, as provided in an embodiment of this application.
[0028] Figure 5 This is a schematic diagram of the actuator command output of a control system provided in an embodiment of this application;
[0029] Figure 6 This is a schematic diagram of the structure of a depth suspension control device for an underwater vehicle provided in an embodiment of this application. Detailed Implementation
[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0031] In this article, the term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A exists alone, A and B exist simultaneously, and B exists alone. The symbol " / " in this article indicates that the related objects are in an "or" relationship; for example, A / B means A or B.
[0032] The terms "first" and "second," etc., used in the specification and claims herein are used to distinguish different objects, not to describe a specific order of objects. For example, "first response message" and "second response message," etc., are used to distinguish different response messages, not to describe a specific order of response messages.
[0033] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.
[0034] In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, for example, multiple processing units means two or more processing units, multiple elements means two or more elements, etc.
[0035] To facilitate understanding of the embodiments of this application, the following will provide further explanation and description with reference to the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.
[0036] Low-cost UUVs lack velocity sensors, only knowing the current depth and unable to sense vertical velocity. Furthermore, their built-in accelerometers have significant measurement errors and require complex coordinate transformations, resulting in unreliable vertical acceleration data. Moreover, their flow pumps are controlled by an on / off mechanism, with three states—intake, discharge, and stop—to regulate buoyancy. Buoyancy is controlled by the pump's on / off switch; changes in buoyancy affect acceleration; the integral of acceleration is converted to velocity; and the integral of velocity is then used to determine depth change. This chain is very long; by the time depth deviations are detected and adjustments are made, it is too late, easily causing the UUV to oscillate repeatedly around the target depth.
[0037] In view of this, this application provides a depth levitation control method for an underwater vehicle, which adopts an extended state observation method, that is, using the second derivative of depth to obtain acceleration, combined with PID predictive control, that is, predicting the state of the UUV after a period of time based on the currently estimated acceleration, and then using the predicted state for PID control, which can achieve satisfactory depth control effect.
[0038] For example, Figure 1 A schematic diagram of a depth levitation control system for an underwater vehicle according to an embodiment of this application is shown. Figure 1As shown, this system comprises three core algorithm modules: a PID controller, an Extended State Observer (ESO), and a state predictor. The UUV system acquires its own depth setpoint and the actual measured depth value. Based on this data, it generates a control signal for a flow pump, controlling its on / off state. The ESO's input includes the PID controller's output signal and the actual depth value measured by the UUV. Based on these two data points, it estimates in real-time the internal state of the system, which cannot be directly measured by sensors, including the UUV's vertical velocity and acceleration. Furthermore, this module can estimate all unmodeled dynamics, such as model uncertainties and external water flow disturbances, as a "total disturbance." The state predictor employs feedforward control to address the inherent control lag problem in buoyancy regulation systems. Its input is the current state estimate provided by the ESO. Based on this current state estimate and the actual measured depth value, it predicts the system state a certain time window in the future. The PID controller uses a classic feedback control strategy, but its input is not the current state, but rather the predicted state provided by the state predictor. The PID controller calculates the error between the predicted depth and the target depth. Based on this error, predicted velocity, and predicted acceleration, a control signal is calculated. The flow pump then performs drainage, shutdown, and suction operations according to this control signal.
[0039] Next, we will introduce ESO separately.
[0040] In classical control theory, state observers use the system's output y and input u to estimate the system's internal state variables x, which cannot be directly measured. This requires a relatively accurate mathematical model of the system. However, typically, the established model is always a simplification or approximation of the real physical system, which is always subject to unknown disturbances from both the external and internal systems (such as resistance, load changes, friction, nonlinearity, etc.). Traditional observers cannot distinguish between state changes caused by the control input u and those caused by unknown disturbances / model errors. Therefore, when the model is inaccurate or disturbances are present, its estimation accuracy is significantly reduced.
[0041] The Extended State Observer ES0 treats all the total uncertainty of the system (including unknown parts of the model, nonlinearity, internal and external disturbances) as an additional "state", namely "extended state", and then designs an observer to simultaneously estimate the original state of the system and this "total disturbance".
[0042] The process can be described by formulas, which express the standard state-space description of a linear time-invariant system as follows:
[0043]
[0044] In the formula, State vector The derivative of represents the rate of change of the state over time. A system matrix representing the dynamic characteristics of the interactions between the internal states of a system. This represents the input matrix, indicating how the input u affects the rate of change of each state. Represents the state vector of the system. Represents the system's input variables. Represents the system's output variables. This represents the output matrix, indicating which states can be directly measured as output.
[0045] This model represents a precise, linear mathematical model, an ideal model, a model unaffected by unknown disturbances.
[0046]
[0047] In the formula, Represents state variables. This is a state variable representing the depth of the UUV. The vertical velocity of the UUV, This represents the known portion of acceleration produced by the control input u. For known or calibrable system parameters, Let x represent an unknown quantity, and let t represent an unknown function relating to state x and time t.
[0048] Now, the system state is estimated by measuring y. However, since the system equations are not precisely obtained, the traditional Luenberger observer cannot be used. An extended state method is adopted to incorporate the unknown variables... Set as a new state variable ,but have
[0049]
[0050] The expanded system can then be represented as
[0051]
[0052] in,
[0053] At this point, matrices A, B, and C are all known. As long as the system is observable, a standard Romberg state observer can be designed for this third-order extended system.
[0054]
[0055] Wherein, the observer gain matrix .
[0056] Expanding the matrix form of the extended state observer yields...
[0057]
[0058] Subtracting the standard Luneburg state observer from the expanded system formula yields: Among them, the estimation error Substituting into the previous equation, we get , .
[0059] A fundamental theorem for the stability of linear systems states that a system is asymptotically stable if and only if all eigenvalues of the system matrix have negative real parts. For errors... In general, if the eigenvalues can be guaranteed Since both are in the left half of the complex plane, under ideal conditions without any disturbance, the error e will converge exponentially to zero.
[0060] matrix The characteristic polynomial is As long as it is designed using the pole placement method This ensures the observer's stability by ensuring that all eigenvalues are located in the left half-plane.
[0061] Based on the system described above, the following section introduces a method for controlling the depth levitation of underwater vehicles. For example, Figure 2 A schematic flowchart of the depth levitation control method for an underwater vehicle provided in an embodiment of this application is shown. Figure 2 As shown, the method may include the following steps:
[0062] S21: Obtain the current depth measurement value and the preset target depth value of the underwater vehicle (UUV).
[0063] In this embodiment, the low-cost UUV lacks a speed sensor and cannot detect vertical velocity; it can only acquire its own depth measurement. Simultaneously, it acquires the target depth value of the underwater vehicle (UUV).
[0064] S22: Input the current depth measurement value and the control command of the previous moment into the extended state observer to estimate the current state value of the UUV in real time, including the current vertical velocity estimate, the current vertical acceleration estimate and the current depth estimate.
[0065] In this embodiment, after the UUV propulsion stops rotating, it loses power, and its movement in the depth direction depends entirely on gravity, buoyancy, and fluid resistance. Figure 3 A schematic diagram of the stress analysis of a UUV is shown. (For example...) Figure 3As shown, when a UUV is stationary in water, it experiences a neutral force perpendicularly downwards and a buoyant force perpendicularly upwards. Its fluid resistance is opposite to the direction of motion; the figure illustrates this with an upward force as an example.
[0066] Considering only the forces acting in the vertical plane, since the velocity v is relatively small, it is assumed that the resistance is proportional to the velocity (assuming the resistance is proportional to the square of the velocity does not affect the control process). Using a flow pump to control the UUV's intake or exhaust can change its own mass, thereby changing the buoyancy and causing the UUV to rise or submerge. Considering the change in mass per unit time caused by the flow pump, the following system of equations is established.
[0067]
[0068] In the formula, The derivative characterizing the current depth measurement value, Characterizing vertical velocity, The derivative characterizing vertical velocity, Characterizing the drag coefficient, Characterizing vertical acceleration, The derivative characterizing vertical acceleration, Characterizes the input of control commands.
[0069] By constructing a standard Romberg state observer for this system of equations, we can obtain the representation of the extended state observer:
[0070]
[0071] In the formula, Characterization depth estimate, Characterizing the velocity estimate, Characterizing the acceleration estimate, Characterizes the input of control commands. Characterizing the error between the measured depth value and the estimated depth value. Characterizes the observer gain coefficient. Note that in this application, the "·" above the letters all represent derivatives.
[0072] The rate of change used to represent the depth estimate should be equal to the current rate of estimation, plus a correction term due to depth error. The rate of change used to represent the velocity estimate should be equal to the current estimated acceleration, plus a correction term due to depth error. The rate of change used to represent the acceleration estimate should be equal to the known control input, plus a correction term due to depth error.
[0073] Choose the observer gain coefficient so that Negative eigenvalues ensure observer stability, allowing acceleration to be observed. In this step, a feedback mechanism allows the estimation of all states to be corrected using a single measurement error e, enabling the reconstruction of the entire system state—namely, the current vertical velocity estimate, the current vertical acceleration estimate, and the extended state estimate representing the total disturbance—based solely on depth measurement y and control input u.
[0074] In some possible embodiments, the value of the observer gain coefficient is specifically selected as follows: .
[0075] S23: Based on the current state value estimated by ESO, predict the state of the UUV at a specified future time, including predicting the depth, velocity and acceleration.
[0076] In this embodiment, since the control command requires multiple integrations of "acceleration → velocity → depth" to fully take effect, if the controller only reacts to the current state, its action will always lag, causing the UUV to oscillate repeatedly near the target depth. However, by extending the controller's control state to future moments, decisions can be made in advance. Therefore, based on the current vertical velocity estimate, current vertical acceleration estimate, and extended state estimate representing the total disturbance obtained from ESO estimation, the depth, velocity, and acceleration of the UUV at a future moment are inferred through forward prediction. The prediction is based on uniformly accelerated motion.
[0077]
[0078] In the formula, Characterization depth prediction value, Characterizing the current depth estimate, Characterizing the current speed estimate, Characterize the predicted time, Characterizing the current acceleration estimate, Characterizing the predicted velocity value, Characterizes the predicted acceleration value.
[0079] S24: Based on the predicted depth, predicted velocity, predicted acceleration, and target depth value, a control signal is calculated.
[0080] In this embodiment, a control signal is obtained based on the predicted depth, velocity, acceleration, and target depth value. This control signal can effectively eliminate the error between the predicted depth and the target depth. Its formula is:
[0081]
[0082] In the formula, This represents the PID control signal. Indicates the target depth value. This is the gain coefficient for depth error. The gain coefficient for predicting velocity. This is the gain coefficient for predicting acceleration.
[0083] S25: The buoyancy adjustment actuator of the UUV adjusts according to the control signal to control the UUV's drainage, water intake, or shutdown operation.
[0084] In this embodiment, after receiving the control signal, the buoyancy adjustment actuator of the UUV adjusts accordingly. The buoyancy adjustment actuator is a water pump, which can only drain, draw in water, or stop; therefore, the following formula is introduced:
[0085]
[0086] In the formula, The threshold value is a small positive number.
[0087] When the control signal is greater than the threshold, it indicates that the UUV will travel much deeper than the target depth in the future, and the controller will control the water pump to drain water. When the control signal is less than the threshold, it indicates that the UUV will travel shallower than the target depth in the future, and the controller will control the water pump to draw water. When the absolute value of the control signal is very small, it indicates that the current state is satisfactory or the deviation is very small, and no action from the actuator is required. Remaining stationary is the optimal strategy.
[0088] This invention verifies the effectiveness of the proposed underwater vehicle depth suspension control method based on an extended state observer and predictive control through simulation. In the simulation, the selected UUV had a weight of m = 15 kg, a maximum buoyancy adjustment of 150 g, and the flow pump could only be controlled via on / off switching, i.e., suction / discharge / stop, with a flow rate of 15 ml / s during suction / discharge. The drag coefficient (0.1), kp, kv, and ka were set to 0.1, 1, and 0.1, respectively. The simulation results are as follows: Figure 4 and Figure 5 As shown, the dynamic performance of the control system is fully presented from the perspectives of system state response and control command output. Figure 4 The changes of key state variables in the depth channel of the underwater vehicle over time are shown. For example... Figure 4As shown, the depth curve, starting from the initial value, converges rapidly to the target depth reference line with smooth and overshoot-free dynamic characteristics, and maintains extremely high steady-state accuracy thereafter. This response characteristic indicates that the control system possesses good setpoint tracking capability and robust stability. Meanwhile, the vertical velocity curve estimated in real time by the extended state observer exhibits reasonable dynamic changes: a peak appears in the transient phase to drive the vehicle's motion, followed by a smooth decay to zero, effectively suppressing the system's inertial overshoot. The estimated vertical acceleration curve also shows continuous and smooth characteristics, eventually converging to zero, confirming that gravity, buoyancy, and fluid resistance have reached a state of equilibrium. Figure 5 A schematic diagram of the actuator command output of the control system is shown. For example... Figure 5 As shown, in the initial control phase, to propel the vehicle towards the target depth, the controller outputs continuous unidirectional commands. As the system approaches the target, the control commands transition to a proactive, pulse-based switching mode, intelligently switching between three states: drainage, stopping, and water intake. This switching mode is not due to system oscillations, but rather an active strategy employed by the predictive control algorithm to compensate for system hysteresis and inertia. Particularly noteworthy is the presence of thresholds in the command sequence, which effectively prevents frequent actuator movements near the equilibrium point, demonstrating the engineering practicality and reliability of the control law design.
[0089] The above describes the depth hovering control method for underwater vehicles provided in this application. The method uses a depth sensor to obtain the current depth measurement of the vehicle, which, together with a preset target depth value, forms the input to the control system. The current depth value and the control command from the previous moment are input to an extended state observer to estimate key internal states of the vehicle that cannot be directly measured, including vertical velocity and vertical acceleration. Based on the currently estimated state, the state of the vehicle at a specified future moment is predicted, yielding predicted depth, predicted velocity, and predicted acceleration. Control signals are generated based on the prediction results to control the actuators to drain, stop, or suck water. This method, by integrating an extended state observer and predictive control, effectively overcomes the core challenges faced by low-cost UUVs in depth hovering control, such as unmeasurable state, model uncertainty, and control lag, achieving high-precision and robust autonomous hovering.
[0090] It is understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. Furthermore, in some possible implementations, each step in the above embodiments may be selectively executed according to actual circumstances; it may be partially or fully executed, without limitation here. All or part of any feature of any embodiment of this application can be freely and arbitrarily combined without contradiction. The combined technical solutions are also within the scope of this application.
[0091] Based on the methods in the above embodiments, this application also provides a depth levitation control device for an underwater vehicle. For example, Figure 6 A schematic diagram of the structure of a depth levitation control device for an underwater vehicle provided in an embodiment of this application is shown. Figure 6 As shown, the depth levitation control device 600 of the underwater vehicle includes an acquisition module 601 and a processing module 602.
[0092] The acquisition module 601 is used to acquire the current depth measurement value and the preset target depth value of the underwater vehicle (UUV).
[0093] The processing module 602 is used to input the current depth measurement value and the control command at the previous moment into the expansion state observer, and to estimate the current state value of the UUV in real time, including the current vertical velocity estimate, the current vertical acceleration estimate and the current depth estimate;
[0094] The processing module 602 is also used to predict the state of the UUV at a specified future time based on the current state value, including predicting the depth, predicting the velocity and predicting the acceleration.
[0095] The processing module 602 is further configured to calculate a control signal based on the predicted depth, the predicted velocity, the predicted acceleration, and the target depth value;
[0096] The processing module 602 is also used to adjust the buoyancy adjustment actuator of the UUV according to the control signal, and control the UUV to drain water, suck water or stop operation.
[0097] It should be understood that the above-described device is used to execute the methods in the above embodiments. The implementation principle and technical effect of the corresponding program modules in the device are similar to those described in the above methods. The working process of the device can be referred to the corresponding process in the above methods, and will not be repeated here.
[0098] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.
[0099] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.
[0100] It is understood that the processor in the embodiments of this application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor may be a microprocessor or any conventional processor.
[0101] The method steps in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.
[0102] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).
[0103] It is understood that the various numerical designations used in the embodiments of this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application.
Claims
1. A method for controlling the depth of an underwater vehicle's levitation, characterized in that, The method includes: Acquire the current depth measurement value and the preset target depth value of the underwater vehicle (UUV); The current depth measurement value and the control command from the previous moment are input to the extended state observer to estimate the current state value of the UUV in real time, including the current vertical velocity estimate, the current vertical acceleration estimate and the current depth estimate. Based on the current state value, predict the state of the UUV at a specified future time, including predicting the depth, velocity, and acceleration; Based on the predicted depth, the predicted velocity, the predicted acceleration, and the target depth value, a control signal is calculated. The buoyancy adjustment actuator of the UUV adjusts according to the control signal to control the UUV to drain water, suck water, or stop operation.
2. The method according to claim 1, characterized in that, The construction of the extended state observer includes: The depth control model of the UUV is characterized as a third-order system with depth, vertical velocity and total disturbance as state variables; Based on this third-order system, a Luneburger observer is designed, and its dynamic equations are as follows: In the formula, Characterization depth estimate, Characterizing the velocity estimate, Characterizing the acceleration estimate, Characterizes the input of control commands. Characterizing the error between the measured depth value and the estimated depth value. Characterizes the observer gain coefficient.
3. The method according to claim 2, characterized in that, The selection criteria for the observer gain coefficient are as follows: The eigenvalues of the error system matrix of the extended state observer are all located in the left half of the complex plane to ensure that the estimation error converges asymptotically.
4. The method according to claim 1, characterized in that, The prediction of the UUV state at a specified future time based on the current state value specifically includes: The prediction based on the uniform motion model is as follows: In the formula, Characterization depth prediction value, Characterizing the current depth estimate, Characterizing the current speed estimate, Characterize the predicted time, Characterizing the current acceleration estimate, Characterizing the predicted velocity value, Characterizes the predicted acceleration value.
5. The method according to claim 1, characterized in that, The control signal is calculated using the following formula: In the formula, Indicates control signal, Indicates the target depth value. This is the gain coefficient for depth error. The gain coefficient for predicting velocity. This is the gain coefficient for predicting acceleration.
6. The method according to claim 1, characterized in that, The buoyancy adjustment actuator of the UUV adjusts according to the control signal to control the UUV's drainage, water intake, or shutdown operation, including: If the control signal is greater than the threshold, an instruction is output to control the actuator to drain the water; If the absolute value of the control signal is not greater than the threshold, the output command controls the actuator to stop working; If the control signal is less than the negative value of the threshold, the output command controls the actuator to draw water.
7. The method according to claim 1, characterized in that, The buoyancy adjustment actuator is a flow pump, and the flow pump operates by on / off control.
8. A depth levitation control device for an underwater vehicle, characterized in that, The device includes: The acquisition module is used to acquire the current depth measurement value and the preset target depth value of the underwater vehicle (UUV). The processing module is used to input the current depth measurement value and the control command of the previous moment into the extended state observer, and to estimate the current state value of the UUV in real time, including the current vertical velocity estimate, the current vertical acceleration estimate and the current depth estimate; The processing module is also used to predict the UUV state at a specified future time based on the current state value, including predicting depth, predicting velocity and predicting acceleration. The processing module is further configured to calculate a control signal based on the predicted depth, the predicted velocity, the predicted acceleration, and the target depth value. The processing module is also used to adjust the buoyancy adjustment actuator of the UUV according to the control signal, and to control the UUV to drain water, suck water or stop operation.
9. An underwater vehicle, characterized in that, The device as described in claim 8 is deployed.
10. A computer-readable storage medium comprising computer-readable instructions that, when read and executed by a computer, cause the computer to perform the method as described in any one of claims 1-7.