A distributed filtering method for moored dynamic positioning system under channel fading

Abstract

The present invention discloses a distributed filtering method for a moored dynamic positioning system under channel fading, and relates to the technical field of distributed filtering. The method comprises: obtaining the measured attitude data of a ship, and performing channel compensation on the measured attitude data to obtain the expected attitude data of the ship in the marine environment at the current moment; substituting the ship's observed attitude and expected attitude data at the current moment into the attitude filtering equation to obtain the ship's observed attitude at the next moment; obtaining another observed attitude of any other filter within the preset range of the filter through a rumor communication strategy, and determining the average value of the ship's observed attitude and another observed attitude as the ship's attitude at the next moment; the ship's attitude at the next moment is used to control the ship. The method can solve the channel fading situation during signal transmission to achieve precise control of the ship.

Description

Technical Field

[0001] The invention relates to the technical field of distributed filtering, and in particular to a distributed filtering method for a moored dynamic positioning system under channel fading. Background Art

[0002] At present, facing the complex and changeable environment of the marine system and unknown interference, how to safely and efficiently use the dynamic positioning system to reasonably obtain marine resources has always been a strategic goal of my country's maritime power, and it is also a hot issue in practical applications. Most of the related research focuses on improving the control performance of the dynamic positioning system and ensuring the efficient execution of dynamic positioning tasks. Such as: adaptive control, inversion control, model predictive control, dynamic surface control, neural network control, sliding mode control, fault-tolerant control and other control methods.

[0003] However, since the sensors are distributed in different corners of the ship, signal transmission is susceptible to multipath propagation, shielding effects of structural obstacles, and differences in propagation speeds in different media (on and below the water surface). During the transmission process, the intensity will fluctuate and change, leading to channel fading, which results in low precision in ship control. Summary of the invention

[0004] Based on this, it is necessary to provide a distributed filtering method for a moored dynamic positioning system under channel fading to address the above technical problems. This method can solve the channel fading during signal transmission to achieve precise control of the ship.

[0005] The present invention adopts the following technical solutions:

[0006] The present invention provides a distributed filtering method for a moored dynamic positioning system under channel fading, which is applied to any filter in the moored dynamic positioning system, and comprises:

[0007] Acquire the measured attitude data of the ship, and perform channel compensation on the measured attitude data to obtain the expected attitude data of the ship in the marine environment at the current moment; the measured attitude data is obtained after channel fading during the transmission process of the sensor sending the acquisition signal to the filter of the mooring dynamic positioning system; the sensor is installed on the ship;

[0008] Obtain the ship's observation attitude at the current moment, and substitute the ship's observation attitude and expected attitude data at the current moment into the ship's attitude filtering equation to obtain the ship's observation attitude at the next moment;

[0009] Obtain another observed attitude of any other filter within the preset range of the filter through a rumor communication strategy, and determine the average of the ship's observed attitude and the other observed attitude as the ship's attitude at the next moment; the ship's attitude at the next moment is used to control the ship;

[0010] The attitude filtering equation is:

[0011]

[0012] in, is the ship observation posture of the ith filter at s+1, is the ship observation posture of the ith filter at time s, L p,i is the gain of the ith filter, is the expected posture data of the i-th filter at time s, C i is the coefficient matrix, M is the system inertia matrix including the additional mass, u s is the control input of the ship's propeller at time s, D is the damping coefficient matrix, R(ψ) is the transformation matrix between the world coordinate system and the angular coordinate system, and ψ represents the displacement of the ship in the bow roll direction.

[0013] Preferably, channel compensation is performed on the measured attitude data to obtain the expected attitude data of the ship at the current moment, including:

[0014] Obtaining a time-varying channel matrix during the signal transmission process from the sensor to the filter;

[0015] The measured attitude data is compensated according to the expectation of the time-varying channel matrix to obtain the expected attitude data.

[0016] Preferably, the method for obtaining the gain of the filter includes:

[0017] According to Schur's complement theorem, a linear matrix inequality based on filter gain is established;

[0018] When the linear matrix is ​​not equal to or less than 0, the linear matrix inequality is solved to obtain the gain of the filter.

[0019] Preferably, the method further comprises:

[0020] Obtain the state equation and discrete error equation of the filter;

[0021] Define the closed-loop system through the state equation and discrete error equation;

[0022] According to the closed-loop system, the Lyapunov functional is constructed, and the mathematical expectation of the difference of the Lyapunov functional is calculated by introducing the fan-shaped boundedness condition of the nonlinear function.

[0023] According to Schur's complement theorem, it is determined that the mathematical expectation satisfies the preset conditions. The mathematical expectation satisfies the preset conditions, which means that the closed-loop system is ultimately exponentially bounded in the mean square sense.

[0024] The present invention provides a mooring dynamic positioning system, which includes a plurality of filters; each filter corresponds to a sensor; each sensor is installed at a different position of a ship in a marine environment;

[0025] For any filter, it is used to obtain the measured attitude data of the ship, and perform channel compensation on the measured attitude data to obtain the expected attitude data of the ship in the marine environment at the current moment; the measured attitude data is obtained after the channel fades during the transmission process of the corresponding sensor sending the acquisition signal to the filter; the ship observation attitude of the ship at the current moment is obtained, and the ship observation attitude and expected attitude data of the ship at the current moment are substituted into the ship's attitude filtering equation to obtain the ship observation attitude at the next moment; another observation attitude determined by any other filter within the preset range of the filter is obtained through a rumor communication strategy; and the average value of the ship observation attitude and another observation attitude is determined as the ship's attitude at the next moment;

[0026] A control system for controlling the ship according to the attitude of the ship at the next moment;

[0027] The attitude filtering equation is:

[0028]

[0029]

[0030] in, is the ship observation posture of the ith filter at s+1, is the ship observation posture of the ith filter at time s, L p,i is the gain of the ith filter, is the expected posture data of the i-th filter at time s, C i is the coefficient matrix, M is the system inertia matrix including the additional mass, u s is the control input of the ship's propeller at time s, D is the damping coefficient matrix, R(ψ) is the transformation matrix between the world coordinate system and the angular coordinate system, and ψ represents the displacement of the ship in the bow roll direction.

[0031] The present invention provides a distributed filtering device for a moored dynamic positioning system under channel fading, which is applied to any filter in the moored dynamic positioning system. The device comprises:

[0032] The compensation module is used to obtain the measured attitude data of the ship and perform channel compensation on the measured attitude data to obtain the expected attitude data of the ship in the marine environment at the current moment; the measured attitude data is obtained after channel fading during the transmission process of the sensor sending the acquisition signal to the filter of the mooring dynamic positioning system; the sensor is installed on the ship;

[0033] The observation module is used to obtain the ship's observation attitude at the current moment, and substitute the ship's observation attitude and expected attitude data at the current moment into the ship's attitude filtering equation to obtain the ship's observation attitude at the next moment;

[0034] The filtering module is used to obtain another observation posture of any other filter within the preset range of the filter through a rumor communication strategy, and determine the average value of the ship's observation posture and the other observation posture as the posture of the ship at the next moment; the posture of the ship at the next moment is used to control the ship;

[0035] The attitude filtering equation is:

[0036]

[0037]

[0038] in, is the ship observation posture of the ith filter at s+1, is the ship observation posture of the ith filter at time s, L p,i is the gain of the ith filter, is the expected posture data of the i-th filter at time s, C i is the coefficient matrix, M is the system inertia matrix including the additional mass, u s is the control input of the ship's propeller at time s, D is the damping coefficient matrix, R(ψ) is the transformation matrix between the world coordinate system and the angular coordinate system, and ψ represents the displacement of the ship in the bow roll direction.

[0039] The present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the distributed filtering method of the moored dynamic positioning system under channel fading is implemented.

[0040] The present invention provides a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the distributed filtering method of the moored dynamic positioning system under channel fading is implemented.

[0041] At least one of the above technical solutions adopted by the present invention can achieve the following beneficial effects:

[0042] In the present invention, the filter first performs channel compensation on the measured attitude data after channel fading, predicts the ship's observation attitude at the next moment with the expected attitude data at the current moment after channel compensation and the ship's observation attitude, and averages the ship's observation attitude at the next moment with the observation attitude of the ship by other filters through a rumor communication strategy. In this way, data interaction between different filters in the moored dynamic positioning system is realized, so that the moored dynamic positioning system can still effectively filter when the sensor signal suffers from fading, solves the channel fading problem during data transmission, and thus realizes precise control of ships in the marine environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0044] Figure 1 It is a structural schematic diagram of a mooring dynamic positioning system;

[0045] Figure 2 A schematic flow chart of a distributed filtering method for a moored dynamic positioning system under channel fading provided by the present invention;

[0046] Figure 3 A schematic diagram of the boundedness of a nonlinear function R(ψ) provided by the present invention at ρ(t) and v(t);

[0047] Figure 4 A schematic diagram of a rumor communication strategy provided by the present invention;

[0048] Figure 5 A schematic diagram of numerical simulation results of a filter provided by the present invention;

[0049] Figure 6 A schematic diagram of another filter numerical simulation result provided by the present invention;

[0050] Figure 7 A schematic diagram of another filter numerical simulation result provided by the present invention;

[0051] Figure 8 A schematic diagram of another filter numerical simulation result provided by the present invention;

[0052] Fig. 9 A schematic diagram of another filter numerical simulation result provided by the present invention;

[0053] Fig.10 A schematic structural diagram of another mooring dynamic positioning system provided by the present invention;

[0054] Fig.11 A schematic diagram of a distributed filtering device for a moored dynamic positioning system under channel fading provided by the present invention;

[0055] Fig.12 A schematic diagram of a computer device for implementing a distributed filtering method for a moored dynamic positioning system under channel fading provided by the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] With the growth of the world economy, the shortage of energy such as oil and natural gas is becoming increasingly serious. Land resources can no longer meet the needs of development. The ocean with rich oil and gas resources has become the focus of public attention. However, the ocean is different from the land. The water depth, water pressure, and complex and changeable wind, wave, and current environment have caused many obstacles to resource exploitation. Faced with the complex and changeable environment of the marine system and unknown interference, how to safely and efficiently use the dynamic positioning system to reasonably obtain marine resources has always been a strategic goal of my country's maritime power, and it is also a hot issue in practical applications.

[0058] The research on dynamic positioning systems should not only consider the robustness of control (model uncertainty and time-varying interference), but also the practical problems in the control process, such as constraints, fault handling, etc. Only by accurately solving the problems in actual engineering can better control effects be achieved and the smooth execution of the task be ensured. Therefore, the research on dynamic positioning systems has broad practical application prospects and potential application advantages.

[0059] At present, there are three main ways to position ships: mooring positioning technology, dynamic positioning technology and mooring dynamic positioning technology. These three positioning methods can all ensure the safe operation of ships, but their application scenarios are different and each has its own advantages and disadvantages. Mooring dynamic positioning technology combines the strengths of mooring positioning and dynamic positioning. The mooring system and the ship propulsion system are used in conjunction with each other, which can not only resist interference from the external environment, but also reduce energy consumption, while ensuring the safety of ships in severe sea conditions. Therefore, compared with separate mooring positioning technology and dynamic positioning technology, mooring dynamic positioning technology takes advantage of its strengths and avoids its weaknesses, combining the advantages of both. It is an indispensable all-weather, energy-efficient and safe marine resource development technology, such as Figure 1 As shown, Figure 1It is a structural diagram of a mooring dynamic positioning system, wherein the measurement system mainly includes a variety of sensors, which measure the attitude of the ship and send the attitude to the mooring dynamic positioning system. The signal processing unit of the mooring dynamic positioning system denoises the signal and sends it to the filter. The filter estimates the state of the ship for the received signal, and generates a control strategy according to the estimated state and the input desired position through the controller, and sends the compensation information output by the feedforward compensation unit generated by the control strategy and environmental interference to the thrust distribution unit, and sends the output of the thrust distribution unit to the propulsion system to control the ship through the propulsion system.

[0060] At present, most of the related research focuses on improving the control performance of the dynamic positioning system and ensuring the efficient execution of the dynamic positioning task. Such as: adaptive control, inversion control, model predictive control, dynamic surface control, neural network control, sliding mode control, fault-tolerant control and other control methods. However, these methods only regard the dynamic positioning system as a whole, and do not pay attention to the problems caused by the information interaction between different nodes in the dynamic positioning system, and ignore the research on distributed control and filtering.

[0061] In addition, since the sensors are distributed in different corners of the ship, signal transmission is susceptible to multipath propagation, shielding effects of structural obstacles, and differences in propagation speed in different media (above and below the water). During the transmission process, the intensity will fluctuate and change, resulting in channel fading. However, for practical engineering problems, researchers studying control methods focus more on the failures, constraints, saturation, and other phenomena of electronic hardware (such as sensors, controllers, thrusters, etc.), while researchers studying mechanical structures and marine engineering focus on structural damage and life prediction. There has been no research on the actual phenomenon of wireless signal transmission being affected by the marine environment.

[0062] Based on this, the present invention provides a distributed filtering method for a moored dynamic positioning system under channel fading. The method is applied to any filter in the moored dynamic positioning system. The method can solve the channel fading during signal transmission to achieve precise control of the ship.

[0063] The technical solutions provided by various embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0064] Figure 2 The present invention is a schematic flow chart of a distributed filtering method for a moored dynamic positioning system under channel fading, which specifically includes the following steps:

[0065] S201, acquiring the measured attitude data of the ship, and performing channel compensation on the measured attitude data to obtain the expected attitude data of the ship in the marine environment at the current moment; the measured attitude data is obtained after channel fading during the transmission process of the acquisition signal sent by the sensor to the filter of the mooring dynamic positioning system; the sensor is installed on the ship.

[0066] First, given the measurement equation:

[0067] y i,s =C i x s +ξ i,s (1)

[0068] Among them, y i，s Represents the measured posture data collected by the sensor, x s Indicates the actual attitude data of the ship, C i The coefficient matrix representing the measured output, ξ i,s is zero-mean Gaussian white noise, and

[0069] In a moored dynamic positioning system, multiple sensors are distributed in different corners of the ship. Signal transmission is easily affected by multipath propagation, shielding effects of structural obstacles, and differences in propagation speed in water or underwater. During the transmission process, intensity fluctuations and changes will occur, resulting in channel fading. In the present invention, the channel fading model suffered by the signal transmission from the sensor to the filter is assumed to be Rayleigh fading. Assume that the measured attitude data received by the filter is As shown in formula (2).

[0070]

[0071] in, The filter receives the measured attitude data sent by the sensor, H i,s is a known time-varying channel matrix, and is expected to be Channel Noise It obeys Gaussian distribution, with mean 0, variance greater than 0, and t is the time.

[0072] Therefore, in an exemplary embodiment, channel compensation is performed on the measured attitude data to obtain the expected attitude data of the ship at the current moment, including: acquiring the time-varying channel matrix in the signal transmission process from the sensor to the filter; compensating the measured attitude data according to the expectations of the time-varying channel matrix to obtain the expected attitude data.

[0073] For example, the channel fading model of the signal transmission from the sensor to the filter is assumed to be Rayleigh fading, then the time-varying channel matrix is ​​determined to be H i,sTherefore, due to the adverse effects of the Rayleigh fading channel, the transmitted signal may be distorted, which deteriorates the overall filtering performance. In order to restore the deviation that may be caused by the Rayleigh fading channel, we Compensate as follows:

[0074]

[0075] in, Represents the expected posture data.

[0076] S202, obtaining the ship observation posture at the current moment, and substituting the ship observation posture and expected posture data at the current moment into the ship's posture filtering equation to obtain the ship observation posture at the next moment.

[0077] First, the ship mooring dynamic positioning model is usually expressed in the form of a dynamic six-degree-of-freedom vector equation:

[0078]

[0079] Where η is the ship position attitude vector in the north-east coordinate system, is the first-order derivative of η; υ represents the linear velocity and angular velocity vector of the ship in the hull coordinate system, is the first-order derivative of υ; R(ψ) is the transformation matrix between the two coordinate systems (world coordinate system and angular coordinate system); M is the system inertia matrix including the additional mass; C(φ) is the Coriolis centripetal force matrix including the additional mass; D(φ) is the damping coefficient matrix; g(η) is the force and torque vector caused by gravity / buoyancy; τ represents the force and torque provided by the ship propulsion system; τ env Represents the forces and moments generated by environmental disturbances such as wind, waves, and currents; τ m It represents the mooring tension provided by the mooring dynamic positioning system; g0 is the equilibrium force vector provided by the ship's ballast water.

[0080] The moored dynamic positioning system generally only controls the position and heading of the surface ship or floating platform. Therefore, the six-degree-of-freedom equation can be simplified to a three-degree-of-freedom equation:

[0081]

[0082] Where η = [XY ψ] T and φ=[ρ vr] T They represent the displacement and velocity in the ship's longitudinal, transverse and yaw directions respectively.

[0083] Considering the system time-varying, and assuming the mooring tension τ m It is known that the above formula can be transformed into:

[0084]

[0085] Where t represents the time, u(t) represents the controller input, ω(t) represents the environmental disturbance, such as wind, waves and currents, G represents the mooring tension matrix of the mooring dynamic positioning system, ψ(t) represents the displacement in the bow roll direction, and Gψ(t) is the mooring tension provided by the mooring dynamic positioning system.

[0086] Through linear change we get:

[0087]

[0088] Then we can expand it to get:

[0089]

[0090] The discrete form is:

[0091] x s+1 =A p x s +f(x s )+B p u s +B p ω s (9)

[0092] in,

[0093] Furthermore, it can be proved that the nonlinear function R(ψ) is sector-bounded when ρ(t) and v(t) are bounded, as Figure 3 As shown, Figure 3 A schematic diagram of the boundedness of a nonlinear function R(ψ) provided by the present invention at ρ(t) and v(t), wherein: Figure 3 In (a), the horizontal axis is the yaw angle, and the vertical axis is cos(x 3,s )x 4,s -sin(x 3,s )x 5,s The value of Figure 3 In (b), the horizontal axis is the yaw angle, and the vertical axis is cos(x 3,s )x 5,s +sin(x 3,s )x 4,s The value of .

[0094] Based on the above description, the ship's attitude filtering equation can be expressed as:

[0095]

[0096] in, is the ship observation posture of the ith filter at time s+1, is the ship observation posture of the ith filter at time s, L p,i is the gain of the ith filter, is the expected posture data of the i-th filter at time s, u s is the controller input of the ship's thruster at time s; D is the damping coefficient matrix.

[0097] Therefore, the ship's observation attitude at the current moment can be obtained; then the ship's observation attitude and expected attitude data at the current moment are substituted into the attitude filtering equation to obtain the ship's observation attitude at the next moment.

[0098] S203, obtaining another observation posture of any other filter within the preset range of the filter through a rumor communication strategy, and determining the average value of the ship's observation posture and the other observation posture as the posture of the ship at the next moment; the posture of the ship at the next moment is used to control the ship.

[0099] It should be noted that each sensor corresponds to a filter, and other filters within the preset range of the filter represent filters corresponding to other sensors within the preset range of the sensor corresponding to the filter.

[0100] Among them, Figure 4 As shown, Figure 4 Schematic diagram of rumor communication strategy; at each moment, the rumor communication strategy rules are as follows:

[0101] ① Based on probability The i-th (i=1, 2, . . . , M) node is randomly selected among the M filters.

[0102] ②The i-th filter is The probability of n i The jth filter is randomly selected from the neighbors.

[0103] ③ The selected filters i and j average the measurement signals they receive and assign them back to themselves, and the remaining filters do not communicate with any filter except themselves.

[0104] Therefore, the rumor communication strategy can be used to obtain another observation posture of any other filter within the preset range of the filter, and the average value of the ship's observation posture and another observation posture is determined as the posture of the ship at the next moment; the posture of the ship at the next moment is used to control the ship.

[0105] Optionally, the effectiveness of the filtering method of the present filter can be verified, including: obtaining the state equation and discrete error equation of the filter; defining a closed-loop system through the state equation and discrete error equation; constructing a Lyapunov functional according to the closed-loop system, and calculating the mathematical expectation of the difference of the Lyapunov functional by introducing the fan-shaped boundedness condition of the nonlinear function; and then determining, according to the Schur complement theorem, whether the mathematical expectation satisfies a preset condition, and the mathematical expectation satisfying the preset condition indicates that the closed-loop system is ultimately exponentially bounded in the mean square sense.

[0106] Specifically, first, according to the ship observation posture of the ith filter at the time s+1, the error signal of the ith filter at the time s+1 is determined, as shown in formula (15).

[0107]

[0108] Among them, e i,s+1 Represents the error signal of the i-th filter at time s+1.

[0109] At each time instant, two filters are randomly selected for average data exchange:

[0110]

[0111] in, It represents the ship observation posture obtained after the average data exchange between the i-th filter and the j-th filter at time s+1. The ship observation posture is the ship observation posture of the i-th filter at time s+1; It represents the error signal obtained after the average data exchange between the i-th filter and the j-th filter at time s+1. The error signal is the error signal of the i-th filter at time s+1.

[0112] According to the above formula, the error signal can be augmented to obtain a discrete error equation, as shown in formula (17). The state equation of the filter can be found in formula (10).

[0113]

[0114] in,

[0115]

[0116] Independent and identically distributed random matrix W s By W ij Given, the probability is Define W s The expectation is Then we can get the following properties:

[0117]

[0118] in,

[0119]

[0120] Based on the above state equation and discrete error equation, define and Get a closed loop system.

[0121]

[0122] Then, according to the closed-loop system, the Lyapunov functional is constructed And introduce the fan-shaped boundedness condition of nonlinear function, and calculate the mathematical expectation of the difference of Lyapunov functional:

[0123]

[0124] in,

[0125] Then, according to Schur's complement theorem, it is determined that the mathematical expectation satisfies the preset conditions, as shown in formula (23).

[0126]

[0127] in,

[0128] In summary, it can be proved that the closed-loop system is ultimately exponentially bounded in the mean square sense.

[0129] In an exemplary embodiment, the method for obtaining the gain of the filter includes: establishing a linear matrix inequality based on the filter gain according to the Schur complement theorem; when the linear matrix is ​​not equal to or less than 0, solving the linear matrix inequality to obtain the gain of the filter.

[0130] According to Schur's complement theorem, the linear matrix inequality is obtained, as shown in formula (24).

[0131]

[0132] in,

[0133]

[0134] M is the number of filters, κ, P 22 , P 11 , and is a custom parameter.

[0135] The filter matrix gain can be obtained by the following formula. Optionally, the filter gain can be determined by exhaustive simulation using Matlab.

[0136]

[0137] Among them, L i is the gain of the ith filter, λ max represents the maximum eigenvalue, P 22 represents P to perform diag augmentation, I represents the identity matrix, Indicates custom and L i The unknown matrices of the same dimension. The purpose of this step is to solve the problem that the multiplication of two unknown matrices cannot be calculated in simulation. P represents a custom positive definite matrix, G1 and G2 are boundary matrices of fan-shaped bounded conditions, and μ1 and μ2 represent two randomly given numbers greater than zero.

[0138] In an exemplary embodiment, the present invention further provides a distributed filtering method for a moored dynamic positioning system under channel fading, comprising the following steps:

[0139] S1, according to the expression of the dynamic three-degree-of-freedom vector equation, the state space equation of the moored dynamic positioning system is obtained by linear transformation and augmentation.

[0140] S2, considering the sensor network measurement equations subject to channel fading, constructs a ship mooring dynamic positioning system model.

[0141] S3, introduce the rumor communication strategy to design a suitable distributed filtering algorithm.

[0142] S4, construct the Lyapunov functional and calculate the mathematical expectation of its difference.

[0143] S5, consider the nonlinearity of the system and analyze the filter performance.

[0144] S6, establish linear matrix inequality (LMI) and design the mean square exponential final bounded estimator gain.

[0145] S7, conduct numerical simulation experiments to verify the reliability of the results.

[0146] Specifically, in order to verify the effectiveness of the distributed filtering method of the present invention, a numerical simulation experiment is performed on the filter designed by the present invention. Figure 5-9 As shown, Figure 5-9 It is a schematic diagram of the numerical simulation results of the filter; Figure 5 It is a schematic diagram of the change of the horizontal axis displacement and velocity error trajectory. Figure 5 In (a) and (b), the abscissa represents time (k), and the ordinate represents the abscissa displacement and velocity error, respectively; Figure 6 It is a schematic diagram of the change of the vertical axis displacement and velocity error trajectory. Figure 6 In (a) and (b), the abscissa represents time (k), and the ordinate represents the ordinate displacement and velocity error, respectively; Figure 7 It is a schematic diagram of the change of the error trajectory of angular displacement and angular velocity. Figure 7 In (a) and (b), the horizontal axis represents time (k), and the vertical axis represents the errors of angular displacement and angular velocity, respectively; Figure 8 It is a schematic diagram of the consistency of the horizontal coordinate displacement, the vertical coordinate displacement and the elevation coordinate displacement. Figure 8 In (a), (b) and (c), the abscissa represents time (k), and the ordinate represents abscissa displacement, ordinate displacement and elevation displacement, respectively; Fig. 9 It is a schematic diagram of the consistency of the horizontal axis velocity, the vertical axis velocity and the angular velocity. Fig. 9 In (a), (b) and (c), the horizontal axis represents time (k), and the vertical axis represents the horizontal axis velocity, the vertical axis velocity and the angular velocity, respectively.

[0147] The present invention fills the gap in dynamic positioning of ships in marine engineering by studying the phenomenon of network induction in the wireless signal transmission process under the marine environment. Compared with the existing methods, this method establishes a new state space model of the moored dynamic positioning system, introduces a message communication strategy, considers the channel fading phenomenon caused by the difference in signal propagation speed in different medium environments (on and below the water surface), and designs a suitable distributed consistency filtering algorithm, so that the moored dynamic positioning system can still effectively filter when the sensor signal suffers from fading, and the filtering results of all sensors tend to be consistent.

[0148] When applying the distributed filtering method of the moored dynamic positioning system under channel fading provided by the present invention, it is not necessary to Figure 2 The steps are executed in the order shown. The specific execution order of the steps can be determined according to needs, and the present invention does not limit this.

[0149] Compared with traditional methods, the present invention pays more attention to the signal interaction of different nodes in the dynamic positioning system. In order to synchronize data of different nodes, load balance and effectively transmit, and save bandwidth, the rumor communication protocol is introduced, inspired by the social phenomenon of gossip. By applying the principles of Lyapunov stability theory, a set of linear matrix inequalities are established as the necessary conditions to ensure that the system is ultimately bounded in the mean square sense.

[0150] In an exemplary embodiment, the present invention provides a moored dynamic positioning system, such as Fig.10As shown, the mooring dynamic positioning system includes multiple filters; each filter corresponds to a sensor; and each sensor is installed at a different position of the ship in the marine environment.

[0151] For any filter, it is used to obtain the measured attitude data of the ship, and perform channel compensation on the measured attitude data to obtain the expected attitude data of the ship in the marine environment at the current moment; the measured attitude data is obtained after the channel fades during the transmission process of the corresponding sensor sending the acquisition signal to the filter; the ship observation attitude of the ship at the current moment is obtained, and the ship observation attitude and the expected attitude data of the ship at the current moment are substituted into the ship's attitude filtering equation to obtain the ship observation attitude at the next moment; another observation attitude determined by any other filter within the preset range of the filter is obtained through a rumor communication strategy; and the average value of the ship observation attitude and the other observation attitude is determined as the ship's attitude at the next moment.

[0152] A control system is used to control the ship according to the attitude of the ship at the next moment.

[0153] The ship's attitude filtering equation is as shown in the above formulas (10)-(14), which will not be described in detail in this embodiment.

[0154] It should be noted that Fig.10 In the figure, n filters are used for illustration. In actual application, the number of filters can be set according to the requirements. Fig.10 The control system may include a feedforward compensation unit, a controller, a thrust distribution unit, etc. The moored dynamic positioning system may also include a signal processing unit. The filter is connected to the sensor through the signal processing unit, that is, the measured attitude data of the ship obtained by the filter is essentially the measured attitude data processed by the signal processing unit. The implementation method of each part in the control system is the same as the description of the above embodiment, and this embodiment will not be repeated here.

[0155] For the specific definition of the filter in the moored dynamic positioning system, reference may be made to the definition of the distributed filtering method for the moored dynamic positioning system under channel fading in the above text, which will not be repeated here.

[0156] The above is a distributed filtering method for a moored dynamic positioning system under channel fading provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding distributed filtering device for a moored dynamic positioning system under channel fading, which is applied to any filter in the moored dynamic positioning system, such as Fig.11 shown.

[0157] In an exemplary embodiment, Fig.11 A schematic diagram of a distributed filtering device for a moored dynamic positioning system under channel fading provided by the present invention, the device 1100 comprises:

[0158] The compensation module 1101 is used to obtain the measured attitude data of the ship and perform channel compensation on the measured attitude data to obtain the expected attitude data of the ship in the marine environment at the current moment; the measured attitude data is obtained after channel fading during the transmission process of the sensor sending the acquisition signal to the filter of the mooring dynamic positioning system; the sensor is installed on the ship;

[0159] The observation module 1102 is used to obtain the ship observation attitude at the current moment, and substitute the ship observation attitude and expected attitude data at the current moment into the ship attitude filtering equation to obtain the ship observation attitude at the next moment;

[0160] The filtering module 1103 is used to obtain another observation posture of any other filter within the preset range of the filter through a rumor communication strategy, and determine the average value of the ship's observation posture and another observation posture as the posture of the ship at the next moment; the posture of the ship at the next moment is used to control the ship.

[0161] The ship's attitude filtering equation is as shown in the above formulas (10)-(14), which will not be described in detail in this embodiment.

[0162] The specific definition of the distributed filtering device of the moored dynamic positioning system under channel fading can be found in the definition of the distributed filtering method of the moored dynamic positioning system under channel fading in the above text, which will not be repeated here. Each module in the above-mentioned distributed filtering device of the moored dynamic positioning system under channel fading can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0163] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 2 A distributed filtering method for a moored dynamic positioning system under channel fading is provided.

[0164] The present invention also provides Fig.12 The structural diagram of the computer device shown in FIG. Fig.12 As shown in the figure, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 2 A distributed filtering method for a moored dynamic positioning system under channel fading is provided.

[0165] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0166] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. A distributed filtering method for a moored dynamic positioning system under channel fading, characterized in that: Applied to any filter in a moored dynamic positioning system, the method comprises: Acquire the measured attitude data of the ship, and perform channel compensation on the measured attitude data to obtain the expected attitude data of the ship in the marine environment at the current moment; the measured attitude data is obtained after channel fading during the transmission process of the sensor sending the acquisition signal to the filter of the mooring dynamic positioning system; the sensor is installed on the ship; Obtaining the ship observation attitude of the ship at the current moment, and substituting the ship observation attitude of the ship at the current moment and the expected attitude data into the ship attitude filtering equation to obtain the ship observation attitude at the next moment; Acquire another observation posture determined by any other filter within the preset range of the filter through a rumor communication strategy; and determine the average value of the ship observation posture and the other observation posture as the posture of the ship at the next moment; the posture of the ship at the next moment is used to control the ship; The attitude filtering equation is: in, is the ship observation posture of the ith filter at s+1, is the ship observation posture of the ith filter at time s, L p,i is the gain of the ith filter, is the expected posture data of the i-th filter at time s, C i is the coefficient matrix, M is the system inertia matrix including the additional mass, u s is the control input of the ship's propeller at time s, D is the damping coefficient matrix, R(ψ) is the conversion matrix between the world coordinate system and the angular coordinate system, ψ represents the displacement of the ship in the bow rolling direction, I is the unit matrix, and G represents the mooring tension matrix of the mooring dynamic positioning system; The rumor communication strategy is: based on probability The i-th node is randomly selected from the M filters, i = 1, 2, ..., M; the i-th filter is The probability of n i The jth filter is randomly selected from the neighbors; the selected filters i and j average the measurement signals they receive and reassign them to themselves, and the remaining filters do not communicate with any filter except themselves.

2. The method according to claim 1, characterized in that The performing channel compensation on the measured attitude data to obtain expected attitude data of the ship at the current moment includes: Acquire a time-varying channel matrix during signal transmission from the sensor to the filter; The measured attitude data is compensated according to the expectation of the time-varying channel matrix to obtain the expected attitude data.

3. The method according to claim 1, characterized in that The method for obtaining the gain of the filter includes: According to Schur's complement theorem, a linear matrix inequality based on filter gain is established; When the linear matrix is ​​not equal to or less than 0, the linear matrix inequality is solved to obtain the gain of the filter.

4. The method according to claim 1, characterized in that: The method further comprises: Obtaining a state equation and a discrete error equation of the filter; By means of the state equation and the discrete error equation, a closed-loop system is defined; According to the closed-loop system, a Lyapunov functional is constructed, and by introducing a nonlinear function sector-bounded condition, a mathematical expectation of a difference of the Lyapunov functional is calculated; According to Schur's complement theorem, it is determined that the mathematical expectation satisfies a preset condition. When the mathematical expectation satisfies the preset condition, it indicates that the closed-loop system is ultimately exponentially bounded in the mean square sense.

5. A mooring dynamic positioning system, characterized in that: The mooring dynamic positioning system includes a plurality of filters and a control system; each filter corresponds to a sensor; each sensor is installed at a different position of the ship; For any filter, it is used to obtain the measured attitude data of the ship, and perform channel compensation on the measured attitude data to obtain the expected attitude data of the ship in the marine environment at the current moment; the measured attitude data is obtained after the channel fades during the transmission process of the corresponding sensor sending the acquisition signal to the filter; the ship observation attitude of the ship at the current moment is obtained, and the ship observation attitude of the ship at the current moment and the expected attitude data are substituted into the ship's attitude filtering equation to obtain the ship observation attitude at the next moment; another observation attitude determined by any other filter within the preset range of the filter is obtained through a rumor communication strategy; and the average value of the ship observation attitude and the another observation attitude is determined as the attitude of the ship at the next moment; The control system is used to control the ship according to the posture of the ship at the next moment; The attitude filtering equation is: in, is the ship observation posture of the ith filter at s+1, is the ship observation posture of the ith filter at time s, L p,i is the gain of the ith filter, is the expected posture data of the i-th filter at time s, C i is the coefficient matrix, M is the system inertia matrix including the additional mass, u s is the control input of the ship's propeller at time s, D is the damping coefficient matrix, R(ψ) is the conversion matrix between the world coordinate system and the angular coordinate system, ψ represents the displacement of the ship in the bow rolling direction, I is the unit matrix, and G represents the mooring tension matrix of the mooring dynamic positioning system; The rumor communication strategy is: based on probability The i-th node is randomly selected from the M filters, i = 1, 2, ..., M; the i-th filter is The probability of n i The jth filter is randomly selected from the neighbors; the selected filters i and j average the measurement signals they receive and reassign them to themselves, and the remaining filters do not communicate with any filter except themselves.

6. A distributed filtering device for a moored dynamic positioning system under channel fading, characterized in that: Any filter used in moored dynamic positioning systems, including: A compensation module is used to obtain measured attitude data of the ship and perform channel compensation on the measured attitude data to obtain expected attitude data of the ship in the marine environment at the current moment; the measured attitude data is obtained after channel fading during the transmission process of the sensor sending the acquisition signal to the filter of the mooring dynamic positioning system; the sensor is installed on the ship; An observation module is used to obtain the ship observation posture of the ship at the current moment, and substitute the ship observation posture of the ship at the current moment and the expected posture data into the ship's posture filtering equation to obtain the ship observation posture at the next moment; A filtering module, used for acquiring another observation posture of any other filter within the preset range of the filter through a rumor communication strategy, and determining the average value of the ship observation posture and the other observation posture as the posture of the ship at the next moment; the posture of the ship at the next moment is used to control the ship; The attitude filtering equation is: in, is the ship observation posture of the ith filter at s+1, is the ship observation posture of the ith filter at time s, L p,i is the gain of the ith filter, is the expected posture data of the i-th filter at time s, C i is the coefficient matrix, M is the system inertia matrix including the additional mass, u s is the control input of the ship's propeller at time s, D is the damping coefficient matrix, R(ψ) is the conversion matrix between the world coordinate system and the angular coordinate system, ψ represents the displacement of the ship in the bow rolling direction, I is the unit matrix, and G represents the mooring tension matrix of the mooring dynamic positioning system; The rumor communication strategy is: based on probability The i-th node is randomly selected from the M filters, i = 1, 2, ..., M; the i-th filter is The probability of n i The jth filter is randomly selected from the neighbors; the selected filters i and j average the measurement signals they receive and reassign them to themselves, and the remaining filters do not communicate with any filter except themselves.

7. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

8. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 4 is implemented.

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