Patent Application
20230286621
The present disclosure relates to a technology for estimating a ship state including a rudder angle used for ship control.
The rudder angle sensor is expensive, difficult to install, and it may be financially and technically burdensome.
The purpose of the present disclosure is to estimate the ship state including the rudder angle with high accuracy in a configuration where the rudder angle sensor is not required.
The ship state estimation device of the present disclosure is equipped with processing circuitry. The processing circuitry acquires an direction signal indicating an direction of the ship. The processing circuitry inputs the direction signal to a ship state estimator that outputs a ship state including at least a rudder angle of the ship when the direction signal of the ship is input, and estimates the ship state including the rudder angle of the ship. In this configuration, the rudder angle can be estimated with high accuracy without using a rudder angle sensor.
In the ship state estimation device of this disclosure, a turning signal for the ship is input to the ship state estimator. The processing circuitry uses the direction signal and the turning signal to estimate the ship state. In this configuration, the rudder angle can be estimated with high accuracy without using a rudder angle sensor.
In the ship state estimation device of this disclosure, the processing circuitry uses a equation of state to estimate a current ship state based on the ship state estimated previously. In this configuration, the ship state including the rudder angle can be estimated with high accuracy.
In the ship state estimation device of this disclosure, the processing circuitry estimates the ship state based on the equation of state and an observation equation which uses the direction signal. In this configuration, the ship state including the rudder angle can be estimated more accurately.
In the ship state estimation device of this disclosure, the processing circuitry further estimates at least one of a heading of the ship and a turnrate as the ship state. In this configuration, ship states other than the rudder angle can be estimated.
In the ship state estimation device of this disclosure, an initial value of the ship state estimation is based on the ship state when the ship is stopped or going straight. In this configuration, the initial value of the state estimator can be set with high accuracy.
In the ship state estimation device of this disclosure, the processing circuitry estimates the ship state based on a stochastic system as the ship state estimator. In this configuration, the ship state including the rudder angle can be estimated more accurately.
The ship state estimation device of this disclosure the processing circuitry acquires a command rudder angle relative to the ship and generates the turning signal based on the acquired command rudder angle and the rudder angle estimated by the ship state estimator. In this configuration, an accurate turning signal can be generated.
The ship state estimation device of the present disclosure the processing circuitry further acquires parameters relating to the characteristics of the ship and sets the state estimation model corresponding to the cruising state of the ship to the ship state estimator based on the parameters. In this configuration, the rudder angle can be estimated with high accuracy considering the cruising states.
The ship state estimation device of this disclosure the processing circuitry detects the direction of the ship and generates the direction signal. In this configuration, the ship state estimation device that estimates the ship state with high accuracy can be realized.
The illustrated embodiments of the subject matter will be best understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and processes that are consistent with the subject matter as claimed herein.
Example apparatus are described herein. Other example embodiments or features may further be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein. In the following detailed description, reference is made to the accompanying drawings, which form a part thereof.
The example embodiments described herein are not meant to be limiting. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the drawings, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations, all of which are explicitly contemplated herein.
A ship state estimation device, a ship state estimation system, a ship state estimation method, and a ship state estimation program according to a first embodiment of the present disclosure will be described with reference to the figures.
First, the configuration of the ship control system 10 is described using
Although not shown, propulsion generating devices such as screw propellers are also mounted on the ship. The controller 20 also controls the generation of propulsion to the propulsion generating devices, but a detailed explanation is omitted here.
The controller 20, the operating module 30, the observing value acquiring module 40, and the display module 50 are connected to each other by, for example, a data communication network 100 for ships. The controller 20 generates a turning signal (or “steering signal”) and outputs it to the rudder 90. The rudder 90 acquires the turning signal and controls the rudder in response to the turning signal. The specific generation method of the turning signal will be described later.
The operating module 30 is realized by, for example, a touch panel, physical buttons or switches. The operating module 30 accepts the operation of settings related to the autopilot control.
The observing value acquiring module 40 is a direction sensor, for example, and measures the heading ψ(psi). The observing value acquiring module 40 measures the heading ψ, and generates an direction signal of the measured heading ψ, at a predetermined sampling period. The observing value acquiring module 40 outputs the direction signal to the controller 20. However, the observing value acquiring module 40 is not limited to a direction sensor, as long as it generates a signal corresponding to the direction. For example, the observing value acquiring module 40 may generate a signal of the course over the ground (COG) measured by the measured data of GPS mounted on the ship.
The display module 50 is realized by, for example, a liquid crystal panel. When, for example, information related to the autopilot control is input from the controller 20, the display module 50 displays them. Although it is possible to omit the display module 50, it is preferable to have it, and the presence of the display module 50 allows the user to easily grasp the autopilot control status, etc.
The controller 20 includes, for example, an arithmetic processing device such as a CPU and a storage module such as a semiconductor memory. The storage module stores a program to be executed by the controller 20. In addition, the storage module is utilized when the CPU performs calculations.
As shown in
For example, the turning signal generator 23 calculates a difference value between the acquired command rudder angle and the estimated rudder angle δ, and generates the turning signal based on the difference value. The turning signal generator 23 outputs the turning signal to the rudder 90. For example, the turning signal generator 23 generates a turning signal of “PORT (+1)” when the difference value between the acquired command rudder angle and the estimated rudder angle is a positive value. The turning signal generator 23 generates a turning signal of “STBD (−1)” when the difference value between the acquired command rudder angle and the estimated rudder angle is a negative value. In this way, the controller 20 estimates the rudder angle using the direction signal indicating heading ψ. Therefore, the controller 20 can generate the turning signal without using a rudder angle sensor.
The ship state estimator 22 is equipped with a state estimator. The state estimator is set using a state estimation model based on the following concepts:
The turnrate (or “rate of turn”) r (t) and the rudder angle δ (t) can be represented by the following relationship, for example, by Nomoto's first-order delay model (estimation model).
The relationship between the turnrate r [k] and the heading ψ [k] is set appropriately by the relationship between the angular velocity and the direction. In addition, the relationship between the rudder angle ψ [k] and the turning signal r [k] is set appropriately by the relationship between the rudder angle, the turning signal, and the turnrate.
The state estimator is, for example, a Kalman filter. Based on Eq. (1), the equation of state of the Kalman filter is set as follows using heading ψ [k], turnrate r [k], and rudder angle δ [k].
The observation equation of the Kalman filter is set as follows.
The symbol s [k] is the turning signal and is set by 0, +1 and −1. The turning amount As [k] is set based on, for example, the turning signal and a turning amount coefficient based on the turnrate w and step time τ.
The symbol v [k] in Eq. (2) is system noise and is set accordingly. The symbol w [k] in Eq. (3) is observation noise and is set accordingly.
The symbol H is a transformation matrix, A is a coefficient vector for the turning signal s [k], and B is a coefficient vector for the system noise v [k]. Note that this example of setting is only an example, for example, the transformation matrix H can be set differently by using different models and discretization methods.
The ship state estimator 22 can estimate the heading ψ [k], the turnrate r [k] and the rudder angle δ [k] with high accuracy by sequentially computing the Kalman filter set in this way. That is, the ship state estimator 22 can estimate the ship state including the rudder angle δ [k] with high accuracy.
It is preferable that the estimator keeps (stores) each estimated ship state as data inside the ship state estimator 22. Thus, the ship state estimator 22 can use the data of each ship state held to estimate the next ship state including the rudder angle δ [k], which can be estimated with higher accuracy. The data retention is not limited to the above configuration, and the ship state estimator 22 may be configured to be able to communicate with an external server or cloud system. Also, it may be retained in the external server or cloud by communication.
In this case, the ship state estimator 22 can estimate the ship state including the rudder angle δ (k) with higher accuracy by appropriately setting the system noise v(k) based on, for example, the specifications of the ship and the rudder wheel. By appropriately setting the observation noise w(k) based on, for example, the measurement accuracy of the observation value acquisition module 40, the ship state estimator 22 can estimate the ship state including the rudder angle δ (k) with higher accuracy.
In addition, the ship state estimator 22 acquires the initial value of the equation of state when the ship is stationary or moving straight and utilizes the initial value. For example, while going straight, the initial value is set such that the direction signal is the value acquired by a sensor, etc. The turnrate is set to zero, the rudder angle is set to zero, and the turning signal is set to “STOP”. This enables to make the estimation process more stable. The ship state estimator 22 can estimate the ship state including the rudder angle δ [k] with higher accuracy.
The above processing is realized, for example, by processing of the flowchart shown in
The observation value receiver 21 receives a ship state observation value including the direction signal (S11). The ship state estimator 22 inputs the direction signal and the turning signal to the ship state estimator 22. The ship state estimator 22 estimates the ship state including the rudder angle δ of the ship (S12). The turning signal generator 23 uses the estimated rudder angle δ to generate the turning signal and outputs it to the rudder 90 (S13).
As shown in
Then, the controller 20 of the ship control system 10 can bring the heading of the ship closer to the set heading and hold the needle by incorporating measurement of the heading ψ, estimation of the rudder angle δ, turnrate r, and generation of the turning signal into a feedback controller (For example, a PD controller). At this time, by estimating the rudder angle δ with high accuracy, the ship control system 10 can realize highly accurate holding of the needle.
As shown in
In the ship control system 10, the Kalman filter, which is a linear stochastic system, is utilized as the state estimator. The state estimator may be a state estimator that estimates the ship state based on a stochastic system. For example, the state estimator may be an extended Kalman filter, which is a nonlinear stochastic system, an Unscented Kalman filter, a particle filter, etc.
The ship state estimation device, ship state estimation system, ship state estimation method, and ship state estimation program according to the second embodiment of the present disclosure may be described with reference to the figures.
As shown in
In the estimation model setting module 220, the ship characteristic estimation parameter value, which is the value of the parameter relating to the characteristics of the ship, is input from the observation value receiver 21. The ship characteristic parameter values include, for example, at least one of pitch angle, ground speed, and shift position. Using the ship characteristic parameter values, the estimation model setting module 220 sets a state estimation model to be used for the state estimator (
In the ship control system described in each of the above embodiment, the state estimator may be replaced with a learned model using artificial intelligence such as machine learning or deep learning. For example, in the state estimation model that outputs an estimation result of the rudder angle by inputting a direction signal, the state estimation model that can obtain the most probable estimation result may be learned as a learned learning model and the estimation may be performed by this learned model.
It is to be understood that not necessarily all objects or advantages may be achieved in accordance with any particular embodiment described herein. Thus, for example, those skilled in the art will recognize that certain embodiments may be configured to operate in a manner that achieves or optimizes one advantage or group of advantages as taught herein without necessarily achieving other objects or advantages as may be taught or suggested herein.
All of the processes described herein may be embodied in, and fully automated via, software code modules executed by a computing system that includes one or more computers or processors. The code modules may be stored in any type of non-transitory computer-readable medium or other computer storage device. Some or all the methods may be embodied in specialized computer hardware.
Many other variations than those described herein will be apparent from this disclosure. For example, depending on the embodiment, certain acts, events, or functions of any of the algorithms described herein can be performed in a different sequence, can be added, merged, or left out altogether (e.g., not all described acts or events are necessary for the practice of the algorithms). Moreover, in certain embodiments, acts or events can be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors or processor cores or on other parallel architectures, rather than sequentially. In addition, different tasks or processes can be performed by different machines and/or computing systems that can function together.
The various illustrative logical blocks and modules described in connection with the embodiments disclosed herein can be implemented or performed by a machine, such as a processor. A processor can be a microprocessor, but in the alternative, the processor can be a controller, microcontroller, or state machine, combinations of the same, or the like. A processor can include electrical circuitry configured to process computer-executable instructions. In another embodiment, a processor includes an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable device that performs logic operations without processing computer-executable instructions. A processor can also be implemented as a combination of computing devices, e.g., a combination of a digital signal processor (DSP) and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Although described herein primarily with respect to digital technology, a processor may also include primarily analog components. For example, some or all of the signal processing algorithms described herein may be implemented in analog circuitry or mixed analog and digital circuitry. A computing environment can include any type of computer system, including, but not limited to, a computer system based on a microprocessor, a mainframe computer, a digital signal processor, a portable computing device, a device controller, or a computational engine within an appliance, to name a few.
Conditional language such as, among others, “can,” “could,” “might” or “may,” unless specifically stated otherwise, are otherwise understood within the context as used in general to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment.
Disjunctive language such as the phrase “at least one of X, Y, or Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
Any process descriptions, elements or blocks in the flow diagrams described herein and/or depicted in the attached figures should be understood as potentially representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or elements in the process. Alternate implementations are included within the scope of the embodiments described herein in which elements or functions may be deleted, executed out of order from that shown, or discussed, including substantially concurrently or in reverse order, depending on the functionality involved as would be understood by those skilled in the art.
Unless otherwise explicitly stated, articles such as “a” or “an” should generally be interpreted to include one or more described items. Accordingly, phrases such as “a device configured to” are intended to include one or more recited devices. Such one or more recited devices can also be collectively configured to carry out the stated recitations. For example, “a processor configured to carry out recitations A, B and C” can include a first processor configured to carry out recitation A working in conjunction with a second processor configured to carry out recitations B and C. The same holds true for the use of definite articles used to introduce embodiment recitations. In addition, even if a specific number of an introduced embodiment recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations).
It will be understood by those within the art that, in general, terms used herein, are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.).
For expository purposes, the term “horizontal” as used herein is defined as a plane parallel to the plane or surface of the floor of the area in which the system being described is used or the method being described is performed, regardless of its orientation. The term “floor” can be interchanged with the term “ground” or “water surface.” The term “vertical” refers to a direction perpendicular to the horizontal as just defined. Terms such as “above,” “below,” “bottom,” “top,” “side,” “higher,” “lower,” “upper,” “over,” and “under,” are defined with respect to the horizontal plane.
As used herein, the terms “attached,” “connected,” “mated” and other such relational terms should be construed, unless otherwise noted, to include removable, moveable, fixed, adjustable, and/or releasable connections or attachments. The connections/attachments can include direct connections and/or connections having intermediate structure between the two components discussed.
Numbers preceded by a term such as “approximately,” “about,” and “substantially” as used herein include the recited numbers, and also represent an amount close to the stated amount that still performs a desired function or achieves a desired result. For example, the terms “approximately,” “about,” and “substantially” may refer to an amount that is within less than 10% of the stated amount. Features of embodiments disclosed herein preceded by a term such as “approximately,” “about,” and “substantially” as used herein represent the feature with some variability that still performs a desired function or achieves a desired result for that feature.
It should be emphasized that many variations and modifications may be made to the above-described embodiments, the elements of which are to be understood as being among other acceptable examples. All such modifications and variations are intended to be included herein within the scope of this disclosure and protected by the following claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2022-036792 | Mar 2022 | JP | national |
This application claims priority under 35 U.S.C. § 119 to Japanese Patent Application No. 2022-036792, which was filed on Mar. 10, 2022, the entire disclosure of which is hereby incorporated by reference.
