A method for power positioning control of an underactuated surface unmanned vehicle

Abstract

The application relates to a power positioning control method for an underactuated water unmanned vehicle, which comprises the following steps: at the current time, using Beidou II, combined navigation and AIS equipment carried by the unmanned vehicle to output real-time unmanned vehicle motion state information, and filtering the real-time unmanned vehicle motion state information through a nonlinear estimation filter; simultaneously, designing an environmental compensator of a nonlinear PID to compensate for marine environment interference received by the underactuated water unmanned vehicle; constructing a power positioning controller based on a model prediction control algorithm (MPC) to receive the filtered real-time unmanned vehicle motion state information and predict the unmanned vehicle motion state at a future time, so as to obtain an optimal command rudder angle and output the optimal command rudder angle to an unmanned vehicle motion system; the unmanned vehicle motion system continues to feed back an actual value to the controller for rolling optimization. The application can realize power positioning of the underactuated ocean measurement unmanned vehicle under complex sea conditions, and can achieve accurate ocean environment measurement in a designated coordinate sea area.

Description

Technical Field

[0001] This invention belongs to the field of unmanned surface vessel control technology, and in particular, a dynamic positioning control method for underactuated unmanned surface vessels. Background Technology

[0002] Unmanned surface vessels (USVs) are an increasingly widely used intelligent surface mission platform. Due to their characteristics such as unmanned operation, intelligence, low cost, long endurance, good economic efficiency, convenient payload carrying, and ability to replace manual labor in dangerous sea areas, they are used in the field of marine environmental surveying and mapping. For example... Figure 1 The unmanned marine surveying vessel shown is playing an increasingly important role in fields such as marine environmental surveying, seabed topography and geomorphology surveying, and marine resource development, and has broad application prospects.

[0003] Unmanned surface vessels (USVs) are equipped with marine surveying instruments such as multibeam sonar, single-beam sonar, sound velocity profilers, shallow seismic profilers, temperature, salinity, and depth (TDM) profilers, ocean temperature chains, gravimeters, magnetometers, and side-scan sonar to conduct three-dimensional marine environmental measurements at the surface, air, and seabed. Marine environmental measurements mainly include: marine geological element measurements, marine water body element measurements, and seawater acoustic environment element measurements. Among these, marine geological element measurements mainly include: marine topography, ocean depth measurement, seabed geomorphology measurement, ocean surface and bottom sediment measurement, shallow ocean profiling, marine magnetic measurements, marine gravity measurements, tidal correction measurements, and sound velocity profile measurements. These are primarily used for compiling various nautical charts and creating digital maps. Marine water body element measurements mainly include: seawater density measurement, seawater temperature measurement, seawater salinity measurement, ocean current and wave measurement, tidal measurement, and seawater transparency detection. Measurements of underwater acoustic environmental elements include: underwater sound propagation, background noise, ocean reverberation, seabed reflection, sound velocity in the seabed, and sound velocity profile, in order to understand the marine noise level and background noise.

[0004] When marine surveying unmanned surface vessels (USVs) use winches to deploy and recover temperature chains and sound velocity profilers to measure seawater temperature, salinity, and sound velocity profiles at different depths, the USV needs to be stationary at a specific work point for extended periods. Similarly, when conducting topographic mapping with USVs, it's necessary to measure seawater temperature and sound velocity profiles at different depths to correct data from multibeam sonar measurements. When measuring parameters such as seawater temperature, sound velocity, and salinity, the USV needs to remain at designated measurement points for extended periods, necessitating dynamic positioning technology that meets the functional requirements of marine surveying USVs.

[0005] On the sea surface, marine survey unmanned surface vessels are disturbed by marine environmental forces such as wind, waves, and currents. Moreover, marine survey unmanned surface vessels have underactuated characteristics, making it difficult to achieve the requirements of fixed-point operation. Therefore, there is an urgent need for dynamic positioning control technology that meets the requirements of marine operations and is suitable for underactuated marine survey unmanned surface vessels. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and propose a dynamic positioning control method for underactuated surface unmanned surface vessels, which can realize dynamic positioning of underactuated marine survey unmanned surface vessels under complex sea conditions, and achieve accurate marine environmental measurement in a specified sea area.

[0007] The technical problem solved by this invention is achieved through the following technical solution:

[0008] A dynamic positioning control method for underactuated unmanned surface vessels includes the following steps:

[0009] Step 1: At the current moment, the UAV's motion status information is output through the Beidou-2, inertial navigation and AIS equipment on board, and filtered through a nonlinear estimation filter to obtain accurate UAV motion status information: position information, heading information, speed information, etc.

[0010] Step 2: Build a model predictive controller to receive filtered real-time unmanned surface vessel (USV) motion state information and predict the USV motion state at future times to obtain the optimal command rudder angle, and output it to the USV motion system.

[0011] Step 3: The unmanned surface vessel motion system will continue to feed the actual values ​​back to the controller for rolling optimization.

[0012] Furthermore, the real-time motion status information of the unmanned surface vessel in step 1 includes location information, speed information, and heading information.

[0013] Furthermore, the nonlinear estimation filter in step 1 is:

[0014]

[0015] Where M is the inertia matrix of the unmanned surface vessel (USV), R(ψ) is the transformation matrix, D is the linear hydrodynamic matrix of the USV, m represents the vessel mass, and I z Indicates the moment of inertia. This represents the additional mass caused by the acceleration in the three directions of sway, roll, and pitch due to hydrodynamic forces, and is defined as a negative number in all of them. The additional mass is caused by the coupling of sway and yaw. Let A be the unmanned surface vessel operating state estimation matrix. ω It is a constant. The derivative of the low-frequency position and bow roll angle vectors, Let be the derivative of the unmanned surface vessel's lateral velocity. Let T be the three-dimensional state vector of the unmanned surface vessel, and R be a three-dimensional diagonal matrix containing the time constant. T(ψ) is the transformation matrix of R(ψ). Differentiate the three-dimensional state vector of the unmanned surface vessel. Transform the rank of the inertial matrix of the unmanned surface vessel. represents the inertial matrix variable of the unmanned surface vessel. It is the estimation error; W1∈R 6×3 W2, W3, W4∈R 3×3 The gain matrix is ​​used, and the nonlinear PID controller acts as an environmental compensator to compensate for the interference from ocean wind, waves, or currents experienced by the underactuated surface unmanned surface vessel when it is in a designated operating area at sea.

[0016]

[0017] τ=-K i R T (ψ)(θ-θ d )-K d vK p R T (ψ)(κ-κ d )

[0018] in, Let π be the matrix for estimating the operational state of an unmanned surface vessel (USV) subject to marine environmental disturbances. d For the marine environmental disturbance coefficient matrix, θ d K is the desired state variable. p K i K d These are the proportional, integral, and derivative control parameters of the PID controller. The nonlinear PID environmental compensation method mitigates the interference from ocean wind, waves, or currents experienced by the underactuated surface vessel (USV) when it is operating in a designated ocean area, outputting the USV control input command τ to improve its anti-interference capability.

[0019] Moreover, the specific implementation method of step 2 is as follows: a model predictive controller is constructed to receive real-time unmanned surface vessel (USV) motion state information and predict the USV motion state at future times. At the same time, the error between the predicted value and the reference value is fed back to the rolling optimization part to construct an optimization function for the USV position deviation. Finally, under the constraints of state variables and control input, the optimization function finds the optimal solution to obtain the optimal command rudder angle and outputs it to the USV motion system.

[0020] Furthermore, the model predictive controller is constructed using an unmanned surface vessel (USV) motion mathematical model, which is as follows:

[0021]

[0022] In the coordinate system of an unmanned surface vessel (USV), the x-axis is defined as pointing due north, and the angle between the bow and stern centerlines and the x-axis is called the bow angle. express. and V c These represent the flow direction and velocity, u. r v is the velocity relative to the water. r For the lateral velocity of water, for the combined velocity of water The velocity components of the unmanned surface vessel (USV) along the x-axis and y-axis in the oxyz coordinate system are u and v, respectively, where u is the forward velocity relative to the ground and v is the lateral velocity relative to the ground. The angular velocity of the bow rotating about the z-axis is r, and the resultant velocity relative to the ground is V = (u... 2 +v 2 ) 1 / 2 Drift angle β = arctan(v / u), δ is the rudder angle. r To command the rudder angle, K E For servo control gain, T E Let m be the servo time constant, and m be the mass of the unmanned surface vessel. x and m y For added mass, X H Y H and N H For viscous hydrodynamics acting on the hull, X P Y P and N P Let X be the propeller force in the X, Y, and Z directions of the coordinate system. W Y W and N W Let X represent the wind force in the X, Y, and Z directions of the coordinate system. Wave Y Wave and N Wave Let I represent the wave force in the X, Y, and Z directions of the coordinate system. ZZ Let J be the moment of inertia of the unmanned surface vessel about its vertical axis. ZZ To add the moment of inertia, X R Y R and N R Let t be the rudder force in the X, Y, and Z directions of the coordinate system. R It is the reduction in rudder drag, a H It is the ratio of the additional lateral force on the hull caused by steering to the lateral force of the rudder, x H It is the distance from the center of the lateral force acting on the steering-guided hull to the center of gravity of the unmanned surface vessel, F N It is the rudder positive pressure.

[0023] The method for calculating the rudder torque is as follows:

[0024]

[0025] Among them, t R It is the reduction in rudder drag, a H It is the ratio of the additional lateral force on the hull caused by steering to the lateral force of the rudder, xH It is the distance from the center of the lateral force acting on the steering-guided hull to the center of gravity of the unmanned surface vessel, F N It is the rudder positive pressure.

[0026] Discretize and predict the various states of the unmanned surface vessel's motion mathematical model:

[0027]

[0028] in, For the motion state of the unmanned surface vessel at time k+1 in the future, Let T be the motion state of the unmanned surface vessel at time k. c It refers to the prediction sampling time, which is the time interval between two consecutive predicted values. and These are the discretizations of the differential equations in the model predictive controller;

[0029] and These are the discretizations of the differential equations in the mathematical model of the unmanned surface vessel's motion:

[0030]

[0031] Where u(k) is the speed of the unmanned surface vessel at time k, ψ(k) is the heading angle of the unmanned surface vessel at time k, v(k) is the lateral velocity of the unmanned surface vessel at time k, r(k) is the angular velocity of the bow of the unmanned surface vessel about the z-axis at time k, and F u For, d 11 For, m 11 Let τ(k) be d uE (k) is, X is, Y is, F is r For, τ u Let δ(k) be the rudder angle at time k, and δ r (k) represents the commanded rudder angle at time k. This is the compensation value for the unknown term. It only considers lateral displacement for path tracking, calculating the compensation value for the unknown term based on the current k-times and the future N-times. P The output at each time point is used for prediction.

[0032] Furthermore, in step 2, the error between the predicted value and the reference value is fed back to the rolling optimization part. The specific implementation method for constructing the optimization function of the unmanned surface vessel's position deviation is as follows: the model predictive controller only considers lateral displacement to achieve dynamic positioning, and from the current k time to the future N... P Predict the output at each time step:

[0033]

[0034] Based on the prediction results and the reference lateral displacement yd Calculate the location prediction error Where j = 1, 2, ..., N P :

[0035]

[0036] Based on the prediction error, construct the optimization function:

[0037]

[0038] Where Q is the weight matrix, obtained by solving the quadratic form of QP or under the constraint δ min ≤δ≤δ max Solve the equation below Calculate the optimal control law.

[0039] Furthermore, step 2, which involves optimizing the function to find the optimal solution under constraints of state variables and control inputs, to obtain the optimal command rudder angle, includes the following steps:

[0040] Step 1: Initialize the chicken flock optimization algorithm parameters. Divide the entire chicken flock into several subgroups. Each subgroup consists of one rooster, several hens, and chicks. The number of subgroups is determined by the number of roosters.

[0041] Step 2: Evaluate the fitness value of each individual in the flock. Divide the flock into roosters, hens, and chicks according to the fitness value of each chicken. Roosters are those with good fitness values, chicks are those with poor fitness values, and the rest are hens. Set t=1.

[0042] Step 3: Determine if t is divisible by σ. If it is, update the flock hierarchy. If it is not divisible, update the hierarchy among roosters, hens, and chicks respectively.

[0043] Step 4: Update the hierarchy among the rooster, hen, and chicks;

[0044] Step 5: Update the fitness values ​​of roosters, hens, and chicks, and calculate and update the global optimal individual of the population;

[0045] Step 6: Compare whether t is greater than the maximum number of iterations. If it is greater than the maximum number of iterations, output the optimal value; otherwise, return to step (3).

[0046] The advantages and positive effects of this invention are:

[0047] This invention achieves real-time motion state information of an unmanned surface vessel (USV) using its onboard BeiDou-2 navigation system, integrated navigation system, and AIS equipment. This information is then filtered using a nonlinear estimation filter. A model predictive controller receives the filtered real-time motion state information and predicts the USV's motion state for future moments, obtaining the optimal command rudder angle, which is then output to the USV's motion system. The USV's motion system continues to feed the actual values ​​back to the controller for continuous optimization. This invention enables dynamic positioning of underactuated marine surveying USVs in complex sea conditions, achieving precise marine environmental measurements at specified coordinates. Attached Figure Description

[0048] Figure 1 The underactuated ocean survey unmanned surface vessel used in this invention;

[0049] Figure 2 This is a structural diagram of the dynamic positioning controller of the present invention;

[0050] Figure 3 This is a flowchart of the chicken flock optimization algorithm of the present invention. Detailed Implementation

[0051] The present invention will be further described in detail below with reference to the accompanying drawings.

[0052] like Figure 1 The image shows an underactuated unmanned surface vessel (USV). This USV is equipped with marine surveying instruments such as multibeam sonar, single-beam depth sounder, ADCP, CTD, sound velocity profiler, magnetometer, and meteorological instrument. It autonomously navigates to perform surveying and mapping of marine topography, marine magnetism, and marine hydrological and meteorological information. When performing ocean sound velocity profile and seabed topography measurements, the USV needs to perform fixed-point measurements. Therefore, a dynamic positioning control method for underactuated USVs based on marine surveying and mapping operations is designed. For example... Figure 2 The diagram illustrates the principle of the underactuated unmanned surface vessel (USV) dynamic positioning control method. The operator remotely sets the USV's position coordinates for fixed-point operations. Upon receiving the command, the USV proceeds to the designated coordinate point for measurement and mapping. Affected by wind, waves, and currents at sea, the USV may deviate from its designated coordinate position. An inertial navigation system continuously measures the USV's position in real time and compares it with the set position information, inputting the position error to a model predictive control-based dynamic positioning controller. After algorithm processing, control commands (throttle and steering commands) are output to the diesel engine, which in turn controls the USV's speed and heading, enabling automatic position adjustment.

[0053] Combination Figure 1 , Figure 2 , Figure 3 As shown, a dynamic positioning control method for underactuated unmanned surface vessels includes the following steps:

[0054] Step 1: At the current moment, the real-time motion status information of the unmanned surface vessel (USV) is output through the BeiDou-2 integrated navigation and AIS equipment onboard the USV, and filtered through a nonlinear estimation filter. The real-time motion status information includes position, speed, and heading.

[0055] The signals measured by the sensors contain a mixture of information about the motion of the unmanned surface vessel (USV) and some noise. Furthermore, the motion of the USV is a composite of high-frequency and low-frequency motions. The high-frequency motion of the USV manifests as periodic oscillations that do not change the average position of the USV. Therefore, in the actual control process, the high-frequency signal is essentially interference and requires a filter to remove it before control can be implemented.

[0056] The low-frequency model of the underactuated oceanographic unmanned surface vessel is as follows:

[0057]

[0058] Among them, E v It is a diagonal matrix, ω v It is zero-mean white noise.

[0059] The motion data of an underactuated oceanographic unmanned surface vessel (USV) measured by its sensors is generally composed of a superposition of low-frequency signals, high-frequency signals, and some measurement noise. Therefore, all of these factors must be considered during the modeling process, resulting in the system's measurement model:

[0060] y = M + M G +K y

[0061] Where M represents the low-frequency position and bow roll angle vector, M G K represents the high-frequency position and bow roll angle vector. y This represents the measurement noise vector.

[0062] Based on the characteristics of the underactuated oceanographic unmanned surface vessel system model, a nonlinear estimation filter is constructed:

[0063]

[0064] Where M is the inertia matrix of the unmanned surface vessel (USV), R(ψ) is the transformation matrix, D is the linear hydrodynamic matrix of the USV, m represents the vessel mass, and I z Indicates the moment of inertia. This represents the additional mass caused by the acceleration in the three directions of sway, roll, and pitch due to hydrodynamic forces, and is defined as a negative number in all of them. The additional mass is caused by the coupling of sway and yaw. Let A be the unmanned surface vessel operating state estimation matrix. ω It is a constant. The derivative of the low-frequency position and bow roll angle vectors, Let be the derivative of the unmanned surface vessel's lateral velocity. Let T be the three-dimensional state vector of the unmanned surface vessel, and R be a three-dimensional diagonal matrix containing the time constant. T (ψ) is the transformation matrix of R(ψ). Differentiate the three-dimensional state vector of the unmanned surface vessel. Transform the rank of the inertial matrix of the unmanned surface vessel. represents the inertial matrix variable of the unmanned surface vessel. It is the estimation error; W1∈R 6×3 W2, W3, W4∈R 3×3 The gain matrix is ​​used, and the nonlinear PID controller acts as an environmental compensator to compensate for the interference from ocean wind, waves, or currents experienced by the underactuated surface unmanned surface vessel when it is in a designated operating area at sea.

[0065]

[0066] τ=-K i R T (ψ)(θ-θ d )-K d vK p R T (ψ)(κ-κ d )

[0067] in, Let π be the matrix for estimating the operational state of an unmanned surface vessel (USV) subject to marine environmental disturbances. d For the marine environmental disturbance coefficient matrix, θ d K is the desired state variable. p K i K d These are the proportional, integral, and derivative control parameters of the PID controller. The nonlinear PID environmental compensation method mitigates the interference from ocean wind, waves, or currents experienced by the underactuated surface vessel (USV) when it is operating in a designated ocean area, outputting the USV control input command τ to improve its anti-interference capability.

[0068] Step 2: Build a model predictive controller to receive filtered real-time unmanned surface vessel (USV) motion state information and predict the USV motion state at future moments to obtain the optimal command rudder angle, and output it to the USV motion system.

[0069] The specific implementation method of step 2 is as follows: a model predictive controller is constructed to receive real-time unmanned surface vessel (USV) motion state information and predict the USV motion state at future times. At the same time, the error between the predicted value and the reference value is fed back to the rolling optimization part to construct an optimization function for the USV position deviation. Finally, under the constraints of state variables and control inputs, the optimization function finds the optimal solution to obtain the optimal command rudder angle and outputs it to the USV motion system.

[0070] The model predictive controller is constructed using an unmanned surface vessel (USV) motion mathematical model, which is as follows:

[0071]

[0072] In the coordinate system of an unmanned surface vessel (USV), the x-axis is defined as pointing due north, and the angle between the bow and stern centerlines and the x-axis is called the bow angle. express. and V c These represent the flow direction and velocity, u. r v is the velocity relative to the water. r For the lateral velocity of water, for the combined velocity of water The velocity components of the unmanned surface vessel (USV) along the x-axis and y-axis in the oxyz coordinate system are u and v, respectively, where u is the forward velocity relative to the ground and v is the lateral velocity relative to the ground. The angular velocity of the bow rotating about the z-axis is r, and the resultant velocity relative to the ground is V = (u... 2 +v 2 ) 1 / 2 Drift angle β = arctan(v / u), δ is the rudder angle. r To command the rudder angle, K E For servo control gain, T E Let m be the servo time constant, and m be the mass of the unmanned surface vessel. x and m y For added mass, X H Y H and N H For viscous hydrodynamics acting on the hull, X P Y P and N P Let X be the propeller force in the X, Y, and Z directions of the coordinate system. W Y W and N W Let X represent the wind force in the X, Y, and Z directions of the coordinate system. Wave Y Wave and N Wave Let I represent the wave force in the X, Y, and Z directions of the coordinate system. ZZ Let J be the moment of inertia of the unmanned surface vessel about its vertical axis. ZZ To add the moment of inertia, X R Y R and NR Let t be the rudder force in the X, Y, and Z directions of the coordinate system. R It is the reduction in rudder drag, a H It is the ratio of the additional lateral force on the hull caused by steering to the lateral force of the rudder, x H It is the distance from the center of the lateral force acting on the steering-guided hull to the center of gravity of the unmanned surface vessel, F N It is the rudder positive pressure.

[0073] The method for calculating the rudder torque is as follows:

[0074]

[0075] Where tR is the reduction in rudder drag, a H It is the ratio of the additional lateral force on the hull caused by steering to the lateral force of the rudder, x H It is the distance from the center of the lateral force acting on the steering-guided hull to the center of gravity of the unmanned surface vessel, F N It is the rudder positive pressure.

[0076] Discretize and predict the various states of the unmanned surface vessel's motion mathematical model:

[0077]

[0078] Among them, among them, For the motion state of the unmanned surface vessel at time k+1 in the future, Let T be the motion state of the unmanned surface vessel at time k. c It refers to the prediction sampling time, which is the time interval between two consecutive predicted values. and These are the discretizations of the differential equations in the model predictive controller.

[0079]

[0080] Where u(k) is the speed of the unmanned surface vessel at time k, ψ(k) is the heading angle of the unmanned surface vessel at time k, v(k) is the lateral velocity of the unmanned surface vessel at time k, r(k) is the angular velocity of the bow of the unmanned surface vessel about the z-axis at time k, and F u For, d 11 For, m 11 Let τ(k) be d uE (k) is, X is, Y is, F is r For, τ u Let δ(k) be the rudder angle at time k, and δ r (k) represents the commanded rudder angle at time k. This is the compensation value for the unknown term. It only considers lateral displacement for path tracking, calculating the compensation value for the unknown term based on the current k-times and the future N-times. PThe output at each time point is used for prediction.

[0081] Furthermore, in step 2, the error between the predicted value and the reference value is fed back to the rolling optimization part. The specific implementation method for constructing the optimization function of the unmanned surface vessel's position deviation is as follows: the model predictive controller only considers lateral displacement to achieve dynamic positioning, and from the current k time to the future N... P Predict the output at each time step:

[0082]

[0083] Based on the prediction results and the reference lateral displacement y d Calculate the location prediction error Where j = 1, 2, ..., N P :

[0084]

[0085] Based on the prediction error, construct the optimization function:

[0086]

[0087] Where Q is the weight matrix, obtained by solving the quadratic form of QP or under the constraint δ min ≤δ≤δ max Solve the equation below The optimal control law is calculated. Since model predictive control algorithms utilize online computation by the computer, the control law is calculated step-by-step online, rather than directly derived from its analytical expression. For the multiple disturbance terms included in the optimization function, a flocking optimization algorithm is used to solve for them.

[0088] The chicken flock optimization algorithm is based on the simulation of chicken flock behavior. The roles of roosters, hens, and chicks in the flock are determined by their fitness values. The chicken with the best fitness value is selected as a rooster, the chicken with the worst fitness value is a chick, and the remaining chickens are considered hens. The rooster with the best fitness value receives food first, and the higher the fitness value, the larger the attack search range.

[0089] x i,j (t+1)=x i,j (t)(1+Randn(0,σ 2 ))

[0090]

[0091] Where, x i,j (t) represents the position coordinates of individual i in the t-th iteration, Randn(0,σ) 2 ) is a standard value with a mean of 0 and a standard value of σ. 2f is a Gaussian distribution function. i Let ε be the fitness value of individual i. ε is a sufficiently small positive number to prevent... The denominator is 0. k is the number of any individual hen, and k ≠ i.

[0092] The hen follows the rooster in the search for food, and may also randomly steal good food found by other chickens. During this process, she is often restrained by the other chickens. In the competition for food, the dominant hen has an advantage over the docile one. The hen's position is updated as follows:

[0093] x i,j (t+1)=x i,j (t)+S1*Randn(x r1,j (t)-x i,j (t))+S1*Randn(x r2,j (t)-x i,j (t))

[0094] S1=exp((f i -f r1 ) / (abs(f i )+ε))

[0095] S2=exp((f r2 -f i )

[0096] In the formula, r1 is the number of the rooster that is the mate of the i-th hen, r2 is the number of any randomly selected rooster or hen, and r1≠r2, and Randn is a random number in [0,1].

[0097] The lower the hen's fitness value, the closer S1 is to 1, and the smaller the positional difference between her and the rooster. A more dominant hen is more likely to find food. Chicks search for food around the hen, and their positions are updated as follows:

[0098] x i,j (t+1)=x i,j (t)+FL*(x m,j (t)-x i,j (t))

[0099] In the formula, x m,j (t) represents the position of the mother of the i-th chick in the t-th iteration, and FL*() is a random number in [0,2].

[0100] like Figure 3 As shown, the chicken flock optimization algorithm includes the following steps:

[0101] Step 1: Initialize the chicken flock optimization algorithm parameters. Divide the entire chicken flock into several subgroups. Each subgroup consists of one rooster, several hens, and chicks. The number of subgroups is determined by the number of roosters.

[0102] Step 2: Evaluate the fitness value of each individual in the flock. Divide the flock into roosters, hens, and chicks according to the fitness value of each chicken. Roosters are those with good fitness values, chicks are those with poor fitness values, and the rest are hens. Set t=1.

[0103] Step 3: Determine if t is divisible by σ. If it is, update the flock hierarchy. If it is not divisible, update the hierarchy among roosters, hens, and chicks respectively.

[0104] Step 4: Update the hierarchy among the rooster, hen, and chicks;

[0105] Step 5: Update the fitness values ​​of roosters, hens, and chicks, and calculate and update the global optimal individual of the population;

[0106] Step 6: Compare whether t is greater than the maximum number of iterations. If it is greater than the maximum number of iterations, output the optimal value; otherwise, return to step (3).

[0107] Step 3: The unmanned surface vessel motion system will continue to feed the actual values ​​back to the controller for rolling optimization.

[0108] It should be emphasized that the embodiments described in this invention are illustrative rather than limiting. Therefore, this invention includes, but is not limited to, the embodiments described in the specific implementation. Any other implementations derived by those skilled in the art based on the technical solutions of this invention are also within the scope of protection of this invention.

Claims

1. A dynamic positioning control method for underactuated unmanned surface vessels, characterized in that: Includes the following steps: Step 1: At the current moment, output the real-time motion status information of the unmanned surface vessel through the Beidou-2, integrated navigation and AIS equipment carried by the unmanned surface vessel, and filter it through a nonlinear estimation filter; The real-time motion status information of the unmanned surface vessel in step 1 includes location information, speed information, and heading information; The nonlinear estimation filter in step 1 is: Where M is the inertia matrix of the unmanned surface vessel (USV), R(ψ) is the transformation matrix, D is the linear hydrodynamic matrix of the USV, m represents the vessel mass, and I z Indicates the moment of inertia. This represents the additional mass caused by the acceleration in the three directions of sway, roll, and pitch due to hydrodynamic forces, and is defined as a negative number in all of them. The additional mass is caused by the coupling of sway and yaw. Let A be the unmanned surface vessel operating state estimation matrix. ω It is a constant. The derivative of the low-frequency position and bow roll angle vectors, Let be the derivative of the unmanned surface vessel's lateral velocity. Let T be the three-dimensional state vector of the unmanned surface vessel, and R be a three-dimensional diagonal matrix containing the time constant. T (ψ) is the transformation matrix of R(ψ). Differentiate the three-dimensional state vector of the unmanned surface vessel. Transform the rank of the inertial matrix of the unmanned surface vessel. For the inertial matrix variables of the unmanned surface vessel, It is the estimation error; W1∈R 6×3 W2, W3, W4∈R 3×3 The gain matrix is ​​used, and the nonlinear PID controller acts as an environmental compensator to compensate for the interference from ocean wind, waves, or currents experienced by the underactuated surface unmanned surface vessel when it is in a designated operating area at sea. τ=-K i R T (ψ)(θ-θ d )-K d vK p R T (ψ)(k-k) d ) in, Let π be the matrix for estimating the operational state of an unmanned surface vessel (USV) subject to marine environmental disturbances. d For the marine environmental disturbance coefficient matrix, θ d K is the desired state variable. p K i K d The three control parameters of the PID controller are proportional, integral, and derivative. The nonlinear PID environmental compensation is used to mitigate the interference from ocean wind, waves, or currents when the underactuated surface unmanned surface vessel is in a designated ocean operating area. The output of the surface unmanned surface vessel control input command τ improves the anti-interference capability of the underactuated surface unmanned surface vessel. Step 2: Build a model predictive controller to receive filtered real-time unmanned surface vessel (USV) motion state information and predict the USV motion state at future times to obtain the optimal command rudder angle, and output it to the USV motion system. Step 3: The unmanned surface vessel motion system will continue to feed the actual values ​​back to the controller for rolling optimization.

2. The dynamic positioning control method for an underactuated unmanned surface vessel according to claim 1, characterized in that: The specific implementation method of step 2 is as follows: a model predictive controller is constructed to receive real-time unmanned surface vessel (USV) motion state information and predict the USV motion state at future times. At the same time, the error between the predicted value and the reference value is fed back to the rolling optimization part. An optimization function for the USV position deviation is constructed. Finally, under the constraints of state variables and control input, the optimization function finds the optimal solution, obtains the optimal command rudder angle, and outputs it to the USV motion system.

3. The dynamic positioning control method for underactuated unmanned surface vessels according to claim 2, characterized in that: The model predictive controller is constructed using an unmanned surface vessel (USV) motion mathematical model, which is as follows: In the coordinate system of an unmanned surface vessel (USV), the x-axis is defined as pointing due north, and the angle between the bow and stern centerlines and the x-axis is called the bow angle. express, and V c These represent the flow direction and velocity, u. r For the velocity relative to the water, v r For the lateral velocity of water, for the combined velocity of water The velocity components of the unmanned surface vessel (USV) along the x-axis and y-axis in the oxyz coordinate system are u and v, respectively, where u is the forward velocity relative to the ground and v is the lateral velocity relative to the ground. The angular velocity of the bow rotating about the z-axis is r, and the resultant velocity relative to the ground is V = (u... 2 +v 2 ) 1 / 2 Drift angle β = arctan(v / u), δ is the rudder angle, δ r To command the rudder angle, K E For servo control gain, T E Let m be the servo time constant, and m be the mass of the unmanned surface vessel. x and m y For added mass, X H Y H and N H For viscous hydrodynamics acting on the hull, X P Y P and N P Let X be the propeller force in the X, Y, and Z directions of the coordinate system. W Y W and N W Let X represent the wind force in the X, Y, and Z directions of the coordinate system. Wave Y Wave and N Wave Let I represent the wave force in the X, Y, and Z directions of the coordinate system. ZZ Let J be the moment of inertia of the unmanned surface vessel about its vertical axis. ZZ To add the moment of inertia, X R Y R and N R Let t be the rudder force in the X, Y, and Z directions of the coordinate system. R It is the reduction in rudder drag, a H It is the ratio of the additional lateral force on the hull caused by steering to the lateral force of the rudder, x H It is the distance from the center of the lateral force acting on the steering-guided hull to the center of gravity of the unmanned surface vessel, F N It is the rudder positive pressure. The method for calculating the rudder torque is as follows: Among them, t R It is the reduction in rudder drag, a H It is the ratio of the additional lateral force on the hull caused by steering to the lateral force of the rudder, x H It is the distance from the center of the lateral force acting on the steering-guided hull to the center of gravity of the unmanned surface vessel, F N It is the rudder positive pressure. Discretize and predict the various states of the unmanned surface vessel's motion mathematical model: in, For the motion state of the unmanned surface vessel at time k+1 in the future, Let T be the motion state of the unmanned surface vessel at time k. c It refers to the prediction sampling time, which is the time interval between two consecutive predicted values. and These are the discretizations of the differential equations in the model predictive controller; and The calculation method is as follows: in, This is the compensation value for the unknown term. It only considers lateral displacement for path tracking, and calculates the compensation value for the future N from the current k-time. P The output at each time point is used for prediction.

4. The dynamic positioning control method for an underactuated unmanned surface vessel according to claim 3, characterized in that: In step 2, the error between the predicted value and the reference value is fed back to the rolling optimization part. The specific implementation method for constructing the optimization function of the unmanned surface vessel's position deviation is as follows: the model predictive controller only considers lateral displacement to achieve dynamic positioning, and from the current k time to the future N... P Predict the output at each time step: Based on the prediction results and the reference lateral displacement y d Calculate the location prediction error Where j = 1, 2, ..., N P : Based on the prediction error, construct the optimization function: Where Q is the weight matrix, obtained by solving the quadratic form of QP or under the constraint δ min ≤δ≤δ max Solve the equation below Calculate the optimal control law.

5. The dynamic positioning control method for an underactuated unmanned surface vessel according to claim 4, characterized in that: Step 2, which optimizes the function to find the optimal solution under constraints of state variables and control inputs to obtain the optimal command rudder angle, includes the following steps: Step 1: Initialize the chicken flock optimization algorithm parameters. Divide the entire chicken flock into several subgroups. Each subgroup consists of one rooster, several hens, and chicks. The number of subgroups is determined by the number of roosters. Step 2: Evaluate the fitness value of each individual in the flock. Divide the flock into roosters, hens, and chicks according to the fitness value of each chicken. Roosters are those with good fitness values, chicks are those with poor fitness values, and the rest are hens. Set t=1. Step 3: Determine if t is divisible by σ. If it is, update the flock hierarchy. If it is not divisible, update the hierarchy among roosters, hens, and chicks respectively. Step 4: Update the hierarchy among the rooster, hen, and chicks; Step 5: Update the fitness values ​​of roosters, hens, and chicks, and calculate and update the global optimal individual of the population; Step 6: Compare whether t is greater than the maximum number of iterations. If it is greater than the maximum number of iterations, output the optimal value; otherwise, return to step (3).

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