Unmanned sailboat course control method and system based on MPC

Abstract

The invention relates to the technical field of ship course control, and particularly discloses an MPC-based unmanned sailing ship course control method and system, and the method comprises the steps: carrying out the stress analysis of a ship body, a sail and a rudder of an unmanned sailing ship based on a ship MMG separation modeling thought, and building a yawing dynamic model of the unmanned sailing ship; a second-order bow turning dynamical model of the unmanned sailboat about the bow turning angle and the bow turning angular speed serves as an MPC prediction model, the control increment of the rudder angle at the current moment is output, and the control quantity at the current moment is obtained through calculation; based on the wind field data, acquiring a yaw angle of the ship body course influenced by sail stress; the control quantity at the current moment is compensated through feedforward of the yaw angle, and the compensated control quantity is applied to a rudder of the unmanned sailboat. Accurate control of the course of the unmanned sailboat is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of ship heading control, and in particular to a method and system for controlling the heading of an unmanned sailboat based on MPC (Model Predictive Control). Background Art

[0002] An unmanned sailboat is an autonomous ocean vehicle that uses wind energy as its main power source. Compared with traditional surface unmanned vehicles, it has significant advantages in energy consumption and continuous operation.

[0003] At present, some unmanned sailboat heading control methods use model-free control methods to control the heading of the sailboat during its travel. However, it is difficult to handle the multi-constraint and strongly coupled nonlinear dynamic model of the unmanned sailboat. At the same time, the random wind and wave disturbances in the ocean environment and the uncertainty of the model parameters can easily lead to large heading deviations of the unmanned sailboat, slow convergence speed, and poor anti-disturbance ability.

[0004] For example, PID is used to control the heading. The current position and speed of the unmanned sailboat are obtained through sensors, and the heading is controlled according to the difference between the expected and actual values. This method has the advantages of simple structure and high stability. However, since the precise model of the unmanned sailboat is not considered, it cannot handle multiple constraint variables and cannot effectively deal with the nonlinear and time-varying characteristics of the unmanned sailboat in motion. The heading control has large overshoot and poor accuracy. For example, fuzzy control is used to realize the heading control of the sailboat. According to the sailor's navigation control experience, fuzzy control rules are written, and the unmanned sailboat controls the heading of the sailboat according to the fuzzy control rules. This method can effectively adapt to the dynamic model of the unmanned sailboat with nonlinear and time-varying characteristics, but its control effect depends on the writing of fuzzy rules, depends on various ship types and steering experience, does not consider the model of the sailboat, and has poor control accuracy and dynamic quality.

[0005] Some unmanned sailboat heading control methods that use unmanned sailboat models, for example, use the Nomoto second-order servo model. Its engineering calculations are relatively convenient, but its adaptability to environmental disturbances is poor. It is suitable for small rudder angle adjustments, but large steering errors are obvious. The interaction between the rudder, hull, and sail is not considered, and the robustness is poor. In addition, most of the unmanned sailboat models used are integral models with high model complexity, large amount of calculation (poor real-time performance), and weak physical interpretability, which restricts engineering applications. Summary of the Invention

[0006] To solve the above technical problems, one of the objectives of the present invention is to provide an MPC-based heading control method for an unmanned sailboat. This method is based on the MMG separated modeling of ships, comprehensively considers the coupling relationship between the parameters of the unmanned sailboat, uses the second-order bow turning dynamics model of the unmanned sailboat as the MPC prediction model, and optimizes the hull heading control in real time. In addition, the influence of the sail force on the heading is taken into account, and the feedforward control compensation and the MPC control method are coordinated to achieve high-performance heading control of the unmanned sailboat.

[0007] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions: This application relates to a heading control method for an unmanned sailboat based on MPC, comprising: Based on the MMG separation modeling concept of ships, the force analysis of the hull, sail and rudder of the unmanned sailboat was carried out, and the bow rolling dynamics model of the unmanned sailboat was established; The second-order bow turning dynamics model of the unmanned sailboat regarding the bow turning angle and bow turning angular velocity is used as the MPC prediction model to output the control increment of the rudder angle at the current moment, and the control amount at the current moment is obtained through calculation; Based on the wind field data, the yaw angle of the ship's heading is obtained due to the force exerted on the sail; The yaw angle feedforward compensates the control amount at the current moment, and the compensated control amount is applied to the rudder of the unmanned sailboat to control the heading of the unmanned sailboat.

[0008] In some embodiments of the present application, based on the concept of ship MMG separation modeling, the force analysis of the rudder, sail and hull of the unmanned sailboat is performed to establish the bow rolling dynamics model of the unmanned sailboat, as follows: ; ; ; ; in, is the bow angle, is the differential of the bow angle, is the bow angular velocity, is the differential of the bow angular velocity, is the moment of inertia of the hull rotation, 、 and They are the hull bow moment, sail moment and rudder moment respectively; is the ship speed, are the linear hydrodynamic forces and moments, are the nonlinear hydrodynamic forces and moments, 、 、 、 、 and All are coefficients; and are the sail lift and sail drag, is the relative wind direction angle, and They are respectively the sailboat heading thrust and the side thrust perpendicular to the sailboat heading. and are the positions of the sail force center in the ship coordinate system, is the positive pressure of the rudder, is the rudder angle, is the water density, is the rudder area, is the rudder efficiency coefficient, is the effective speed of the inflow rudder; , is the interference coefficient between the hull and the rudder, is the vertical distance between the center of rudder force and the center of gravity of the ship, is the correction coefficient of the rudder motion on the hull bow moment.

[0009] In some embodiments of the present application, the specific process of establishing the MPC prediction model is as follows: (1) Let the state quantity , control quantity ,Using the linearization processing method, the linearized state equation can obtain the system matrix A and control matrix B of the control system; (2) Equation of state After discretization, the state expression after discretization is obtained as follows: ; ; Among them, the coefficient matrix and are the discretized linear time-varying matrices, is the linear transformation matrix of the state mapping output, is the discrete time step, is the identity matrix; (3) Let the state quantity , obtain the new state-space model: ; in, ; To control the increment, is the output, Indicates the currentk The state quantity at the moment, Indicates the control quantity at the previous moment; (4) Set the prediction time domain to , the control time domain is ,and , based on the new state space model, the future state and output are predicted, and the predicted output expression can be obtained: ; in , , , ; (5) According to the expected heading and predicted output, the objective function is constructed using the state deviation and control increment of the unmanned sailboat: ; Among them, the matrix and are the weight matrices of state error and control increment, respectively. is the desired state on the desired course; (6) Define the performance indicator function: , ; (7) Apply constraints to the performance index function and take the minimum value of the performance index function to solve the optimal control increment sequence ; (8) According to the rolling optimization principle, obtain The first element of u *( k ) is applied to the control system to compensate for the control amount at the previous moment , get the control amount at the current moment =△ u *( k )+ .

[0010] In some embodiments of the present application, the following constraints are imposed on the performance indicator function: ; in, and Respectively represent the minimum value set and maximum value set of the control quantity in the control time domain, and They represent the minimum value set and maximum value set of the control increment in the control time domain respectively.

[0011] In some embodiments of the present application, based on wind field data, a yaw angle of the sail that is affected by the force on the hull is obtained; Based on the wind field data, obtaining the relative wind direction angle and relative speed of the unmanned sailboat during its travel; Perform open-loop control on the sail to obtain the sail attack angle ; use , decomposing the sail force into the lateral driving force : ; make , , calculate the yaw angle ; in, is the horizontal distance from the sail area to the center of the hull, K is the proportional gain coefficient, and the positive or negative sign of the yaw angle indicates the direction of the sail force.

[0012] In some embodiments of the present application, a wind field sensor is configured on the unmanned sailboat; The wind field sensor detects the wind field and obtains the wind field data.

[0013] In some embodiments of the present application, a magnetometer and an inertial measurement unit are configured on the unmanned sailboat; The real-time bow angle of the unmanned sailboat is identified by fusing the static bow angle obtained by the magnetometer and the data obtained by the inertial measurement unit.

[0014] The MPC-based heading control method for an unmanned sailboat provided in some embodiments of this application has the following advantages and beneficial effects: (1) Based on the MMG separation modeling concept of ships, the force analysis of the rudder, sail and hull of the unmanned sailboat is carried out, and the bow rolling dynamics model of the unmanned sailboat is established. The coupling relationship between the parameters of the unmanned sailboat is considered to improve the heading control accuracy; (2) The second-order bow turning dynamics model of the unmanned sailboat regarding the bow turning angle and bow turning angular velocity is used as the MPC prediction model to perform decoupling control on the sailboat, optimize the hull heading control in real time, and achieve precise heading control; (3) Considering the influence of sail force on heading, feedforward compensation is performed on the control quantity output by MPC predictive control to improve the heading response speed of the unmanned sailboat, realize the rapid heading convergence process, effectively reduce the number of servo movements, and at the same time, feedforward compensation and MPC predictive control work together to achieve high-performance heading control of the unmanned sailboat.

[0015] Some embodiments of the present application also relate to an MPC-based unmanned sailboat heading control system, comprising: The model building module is used to analyze the forces on the hull, sails, and rudder of the unmanned sailboat based on the MMG separation modeling concept of ships, and to establish the bow roll dynamics model of the unmanned sailboat; An MPC prediction module uses the second-order bow turning dynamics model of the unmanned sailboat regarding the bow angle and bow angular velocity as the MPC prediction model, outputs the control increment of the rudder angle at the current moment, and acts on the control variable at the previous moment to obtain the control variable at the current moment; The feedforward compensation module obtains the yaw angle of the ship's heading due to the sail force based on wind field data; The yaw angle feedforward compensates the control amount at the current moment, and the compensated control amount acts on the rudder of the unmanned sailboat to control the heading of the unmanned sailboat.

[0016] In some embodiments of the present application, the MPC prediction module includes: An MPC model building unit, which builds an MPC prediction model through linearization and discretization processing; A future output prediction unit predicts future state quantities and output quantities based on the MPC prediction model and obtains a predicted output expression; The rolling optimization unit solves the optimal control increment sequence in each control cycle and applies the first element in the optimal control increment sequence to the control system.

[0017] Other features and advantages of the present invention will become more apparent after reading the detailed description of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 This is a flow chart of an embodiment of the MPC-based unmanned sailboat heading control method proposed in this application; Figure 2 This is a control principle block diagram involved in an embodiment of the unmanned sailboat heading control method based on MPC proposed in this application; Figure 3 This is a flow chart of obtaining the yaw angle in an embodiment of the MPC-based unmanned sailboat heading control method proposed in this application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0021] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0022] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections. A person of ordinary skill in the art will understand the specific meanings of the above terms in the present invention in specific circumstances. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any appropriate manner in any one or more embodiments or examples.

[0023] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0024] In order to avoid the problems of large deviation and slow convergence speed in controlling the heading of unmanned sailboats in existing control methods, which lead to low heading control accuracy, see Figure 1 This application relates to an MPC-based unmanned sailboat heading control method, which realizes the precise heading control of the unmanned sailboat's multi-constraint, strongly coupled nonlinear dynamic model.

[0025] Figure 1 The flowchart of the MPC-based unmanned sailboat heading control method is shown. Figure 2 The control principle block diagram of the unmanned sailboat heading control method based on MPC is shown. Figure 2 The flowchart of obtaining the yaw angle in the MPC-based unmanned sailboat heading control method is shown.

[0026] Combine Figures 1 to 3 ,The heading control method of an unmanned sailboat based on MPC is described in detail as follows.

[0027] S1: Based on the MMG separation modeling concept of ships, the force analysis of the rudder, sail and hull of the unmanned sailboat is carried out to establish the bow roll dynamics model of the unmanned sailboat.

[0028] The MMG (mathematical modeling group model) separation modeling concept is a modeling method for ship motion. In some embodiments of the present application, the ship is an unmanned sailboat. Based on the MMG separation modeling concept, the forces and moments acting on the hull, sail and rudder, as well as their interaction, are analyzed and modeled separately to establish a bow roll dynamics model of the unmanned sailboat.

[0029] This modeling idea decouples the sailboat steering gear control, reduces the coupling error of various parameters of the unmanned sailboat, and facilitates the control of the steering gear.

[0030] In some embodiments of the present application, the main parts of the unmanned sailboat include the rudder, the sail and the hull. The forces on the rudder, the sail and the hull are analyzed separately, and the rolling motion of the unmanned sailboat around the X-axis of the ship coordinate is ignored. The bow rolling dynamic model of the unmanned sailboat is established as follows (1).

[0031] (1).

[0032] in, is the bow angle, is the differential of the bow angle, is the bow angular velocity, is the differential of the bow angular velocity, is the moment of inertia of the hull rotation, 、 and They are the hull bow moment, sail moment and rudder moment respectively.

[0033] As follows, we will 、 and Represent them separately.

[0034] In some embodiments of the present application, the hull bow moment It is expressed as follows: .

[0035] in, is the ship speed, are the linear hydrodynamic forces and moments, are the nonlinear hydrodynamic forces and moments, 、 、 、 、 and These coefficients can be identified from the actual operation data of the hull, or they can be approximately solved using empirical formulas.

[0036] In some embodiments of the present application, the sail turning moment It is expressed as follows: .

[0037] in, and are the sail lift and sail drag, is the relative wind direction angle, and They are respectively the sailboat heading thrust and the side thrust perpendicular to the sailboat heading. and are the positions of the sail force center in the ship coordinate system.

[0038] In some embodiments of the present application, during actual navigation, there is disturbance interference between the rudder and the hull, and the steering torque It is expressed as follows: .

[0039] in, is the positive pressure of the rudder, is the rudder angle, is the water density, is the rudder area, is the rudder efficiency coefficient, is the effective speed flowing into the rudder (can be the actual ship speed).

[0040] In the prior art (reference invention patent application number 202010969127.X), , is the interference coefficient between the hull and the rudder, is the vertical distance between the center of rudder force and the center of gravity of the ship, is the correction coefficient of the rudder motion on the hull bow moment.

[0041] Among them, the interference coefficient and The approximate calculation formula can be used as follows: .

[0042] in, C b is the ship design block coefficient, L For the captain.

[0043] S2: The second-order bow turning dynamics model of the unmanned sailboat regarding the bow turning angle and bow turning angular velocity is used as the MPC prediction model, the control increment of the rudder angle at the current moment is output, and the control amount at the current moment is obtained through calculation.

[0044] In some embodiments of the present application, an MPC prediction algorithm is used to predict the model first and then control, which is suitable for the multi-constraint, strongly coupled nonlinear model of the unmanned sailboat.

[0045] In some embodiments of the present application, the workflow of MPC prediction includes: establishing a prediction model, predicting future states, and rolling optimization.

[0046] In the following, each part of the MPC prediction workflow will be described separately.

[0047] (1) Establish a prediction model In order to facilitate the processing of the nonlinear model of the unmanned sailboat, the linearization method is adopted to process the nonlinear model into a linear model, and then the linear model predictive control theory is used to solve the optimal control sequence.

[0048] In some embodiments of the present application, the linearization processing method may use Taylor expansion or working point linearization to convert the original nonlinear model into a linear model.

[0049] In some embodiments of the present application, let the state quantity , control quantity , the state equation of the control system can be set as .

[0050] The Taylor expansion is used to linearize the nonlinear model. This can be done by selecting a point on the desired heading. Perform Taylor expansion, keep the first-order terms and ignore the higher-order terms, and we can get: .

[0051] is the deviation between the actual state and the expected state of the sailboat, is the deviation between the actual control amount and the expected control amount of the sailboat.

[0052] Based on the state space model, the system matrix A and control matrix B of the control system can be obtained: .

[0053] In this way, a linear model after linear processing can be obtained.

[0054] For ease of processing, in some embodiments of the present application, a bilinear transformation method is used to discretize the linearized model to obtain a discretized state space model: .

[0055] Among them, the coefficient matrix and are the discretized linear time-varying matrices, is the linear transformation matrix of the state mapping output, is the discrete time step (i.e. the time interval from one control cycle to the next), is the identity matrix, is the output quantity.

[0056] This model is used as the prediction model of the MPC prediction algorithm, and can then be used to perform multi-gait state prediction and output prediction.

[0057] (2) Predicting future states In view of the physical property constraints and actual operating rules in the heading control of the unmanned sailboat, the control quantity is restricted as follows: .

[0058] and are the minimum and maximum values ​​of the control quantity in the control time domain, and They are the minimum and maximum values ​​of the control increment in the control time domain respectively. The specific parameters can be determined according to the actual servo model.

[0059] In order to satisfy the constraints at the same time, define a new state quantity , obtain the new state-space model: .

[0060] in, , Indicates the current The state quantity at the moment, Indicates the control quantity at the previous moment, To control the increment, is the output quantity.

[0061] Set the prediction time domain to , the control time domain is ,and , based on the new state space model, the future state quantity and output quantity are predicted as follows: .

[0062] For the purpose of simplifying the calculation, it is assumed that the prediction time domain is Inside, , , i =1,2,..., .

[0063] Therefore, the new prediction output expression is: .

[0064] in, , , , .

[0065] (3) Rolling optimization The process of rolling optimization can be described as follows: After predicting the future output of the system as above, it is compared with the expected output of the control system. By solving an optimization problem, the future control increment is found to minimize the difference between the predicted output and the expected output. The first element of this future control increment is applied to the control quantity at the previous moment. For each control cycle, this control quantity is input to the servo and the current state is updated. The optimization problem is refreshed and solved again.

[0066] The specific process of rolling optimization can be found in the following implementation.

[0067] In some embodiments of the present application, an objective function is constructed based on the state deviation and control increment of the unmanned sailboat. The objective function is designed as follows: (2).

[0068] Among them, the matrix and are the weight matrices of state error and control increment respectively, and The weights in the weight matrix can be changed according to the actual sailboat parameters and heading environment. is the desired state on the desired heading.

[0069] In order to solve the objective function conveniently, for formula (2), remove and control the increment Irrelevant terms, resulting in a standard quadratic objective function As follows, it serves as a performance indicator function.

[0070] .

[0071] in, .

[0072] At this point, the heading control problem of the unmanned sailboat becomes a quadratic optimal value problem. The optimization goal is to reduce the tracking error and optimize the change range of the control input to ensure the smoothness of navigation.

[0073] In order to solve the optimal value problem, constraints are introduced. The constraints may be constraints on the control quantity, constraints on the control increment, constraints on the output variable, and / or constraints on the intermediate variable.

[0074] In some embodiments of the present application, as shown in the following formula (3), the constraint condition includes a constraint condition on the control amount and a constraint condition on the control increment, which is used to limit the rudder angle and the rudder angle change speed to avoid instability of the unmanned sailboat caused by excessive rudder angle and excessive rotation frequency.

[0075] (3).

[0076] in, and Respectively represent the minimum value set and maximum value set of the control quantity in the control time domain, and They represent the minimum value set and maximum value set of the control increment in the control time domain respectively.

[0077] Apply the constraints of formula (3) to the above performance index function, and take the minimum value of the performance index function to solve the optimal control increment sequence .

[0078] According to the rolling optimization principle, The first element of u *( k ) is applied to the input of the control system, and the control quantity at the previous moment Superposition to obtain the control amount at the current moment = +△ u *( k )(remember u ( k ) = ), as the control input of the control system.

[0079] In each control cycle, a , then input it into the steering gear and update the current state, refresh the optimization problem, and repeat this process to achieve dynamic real-time heading control of the unmanned sailboat.

[0080] In some embodiments of the present application, when solving the quadratic optimization problem, the objective function includes quadratic penalty terms of the bow angle φ and the bow angular velocity r, which can suppress the sailboat from turning violently and ensure its smooth sailing.

[0081] S3: Based on the wind field data, obtain the yaw angle of the ship's heading affected by the force on the sail.

[0082] In some embodiments of the present application, during the navigation of the unmanned sailboat, the force applied to the sail will also affect the heading. Therefore, considering the influence of the force applied to the sail on the rudder angle, the control input of the servo of the unmanned sailboat is compensated, which can enable the heading of the unmanned sailboat to converge quickly to the desired heading, reduce the heading deviation overshoot, and effectively reduce the number of movements of the servo.

[0083] See also Figure 2 In some embodiments of the present application, the yaw angle of the ship's heading due to the sail force is obtained by combining wind field data. , the yaw angle As the feedforward compensation angle, it compensates the control amount at the current moment and makes the ship heading converge quickly to the desired heading.

[0084] S31: Based on the wind field data, obtain the relative wind direction angle and relative speed of the unmanned sailboat during its travel.

[0085] Wind is the source of force acting on the sail, so it is necessary to obtain data related to the wind.

[0086] The wind field data can be detected by a wind field sensor, and the relative wind direction angle and relative speed of the unmanned sailboat during its travel can be obtained based on the detected wind field data.

[0087] Relative wind direction angle and relative speed are the data basis for open-loop control of the sail.

[0088] Open-loop sail control is a conventional sail control technology that controls the sail based on the relative wind direction angle and relative speed using existing sail control strategies (e.g., speed optimization method, optimal sail attack angle strategy).

[0089] S32: Open-loop control of the wind valve to obtain the sail attack angle .

[0090] According to the sail open-loop control rules, based on the relative wind direction angle and relative speed, the sail is open-loop controlled using existing sail control strategies (e.g., speed optimization method, optimal sail attack angle strategy) to obtain the sail attack angle. .

[0091] S33: Exploitation , decomposing the sail force into the lateral driving force : .

[0092] Sail lateral driving force It affects the heading of the steering gear, so the sail attack angle is used Get this power .

[0093] in, and are the sail lift and sail drag, It is the horizontal distance from the sail area to the center of the hull.

[0094] S34: Order , , calculate the yaw angle .

[0095] in, is the positive pressure of the rudder, is the rudder angle, is the water density, is the rudder area, K is the proportional gain coefficient, is the effective speed flowing into the rudder.

[0096] The sign of the calculated yaw angle indicates the direction of the sail force.

[0097] S4: The yaw angle feedforward compensates the control value at the current moment, and the compensated control value is applied to the rudder of the unmanned sailboat.

[0098] As mentioned above, the yaw angle As a feedforward compensation angle, it is used to compensate the MPC prediction output , that is, the control input of the control system for and sum ( δ = + ), this kind and The synergistic effect can realize high-precision heading control of the unmanned sailboat, and the control input after compensation can achieve fast heading control convergence speed.

[0099] During the actual heading control process of the unmanned sailboat, it is necessary to obtain the bow angle and bow angular velocity in real time. This data acquisition method is a conventional technology used by those skilled in the art and can be obtained using sensors configured on the unmanned sailboat (including magnetometers, inertial measurement units, and gyroscopes).

[0100] The magnetometer can obtain the static bow angle. However, the bow angle is also affected when the hull moves or is disturbed by wind and waves. Therefore, the data obtained by the inertial measurement unit is also integrated into the data fusion to identify the final bow angle, which is used as the real-time bow angle.

[0101] The bow angular velocity can be directly measured by a gyroscope (such as a FOG / MEMS gyroscope).

[0102] The MPC-based unmanned sailboat heading control method involved in this application adopts feedforward compensation and MPC prediction algorithm to collaboratively control the rudder angle of the servo, while optimizing the hull heading control in real time, improving the heading response speed of the unmanned sailboat, achieving rapid convergence of the heading to the desired heading, and improving the accuracy of heading control.

[0103] Some embodiments of the present application also relate to an MPC-based unmanned sailboat heading control system (not shown), which is used to implement the MPC-based unmanned sailboat heading control method as described above.

[0104] The unmanned sailboat heading control system includes a model building module (not shown), an MPC prediction module (not shown) and a feedforward compensation module (not shown). The control system is applied to the unmanned sailboat steering gear to achieve sailboat heading control.

[0105] The model building module is used to perform force analysis on the rudder, sail, and hull of the unmanned sailboat based on the ship MMG separation modeling concept, and to establish the bow roll dynamics model of the unmanned sailboat. The specific implementation process is as described above and will not be elaborated here.

[0106] The MPC prediction module is used to perform MPC prediction and output , specifically including MPC model building unit, future output prediction unit and rolling optimization unit.

[0107] The MPC model building unit establishes an MPC prediction model through linearization and discretization. The future output prediction unit predicts future state and output variables based on the MPC prediction model, obtaining the predicted output expression. The rolling optimization unit repeatedly solves the optimal control increment sequence during each control cycle and applies the first control increment to the control system. The specific implementation is described in the MPC prediction section above and is not detailed here.

[0108] The feedforward compensation module is used to obtain the feedforward compensation angle and compensate , used for the control input of the unmanned sailboat steering gear to achieve sailboat heading control. Feedforward compensation angle The acquisition process of is as described above and will not be described here in detail.

[0109] The MPC-based unmanned sailboat heading control system involved in this application adopts feedforward compensation and MPC prediction algorithm to collaboratively control the rudder angle of the servo, while optimizing the hull heading control in real time, improving the heading response speed of the unmanned sailboat, achieving rapid convergence of the heading to the desired heading, and improving the accuracy of heading control.

[0110] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for a person skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to replace some of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions claimed to be protected by the present invention.

Claims

1. A heading control method for an unmanned sailboat based on MPC, characterized in that: include: Based on the MMG separation modeling concept of ships, the force analysis of the hull, sail and rudder of the unmanned sailboat was carried out, and the bow rolling dynamics model of the unmanned sailboat was established; The second-order bow turning dynamics model of the unmanned sailboat regarding the bow turning angle and bow turning angular velocity is used as the MPC prediction model to output the control increment of the rudder angle at the current moment, and the control amount at the current moment is obtained through calculation. Based on the wind field data, the yaw angle of the ship's heading is obtained due to the force exerted on the sail; The yaw angle feedforward compensates the control amount at the current moment, and the compensated control amount is applied to the rudder of the unmanned sailboat to control the heading of the unmanned sailboat.

2. The MPC-based unmanned sailboat heading control method according to claim 1, characterized in that: Based on the idea of ​​ship MMG separation modeling, the force analysis of the hull, sail and rudder of the unmanned sailboat is carried out, and the bow rolling dynamic model of the unmanned sailboat is established. The details are as follows: ； ； ； ； in, is the bow angle, is the differential of the bow angle, is the bow angular velocity, is the differential of the bow angular velocity, is the moment of inertia of the hull rotation, 、 and They are the hull bow moment, sail moment and rudder moment respectively; is the ship speed, are the linear hydrodynamic forces and moments, are the nonlinear hydrodynamic forces and moments, 、 、 、 、 and All are coefficients; and are the sail lift and sail drag, is the relative wind direction angle, and They are respectively the sailboat heading thrust and the side thrust perpendicular to the sailboat heading. and are the positions of the sail force center in the ship coordinate system, is the positive pressure of the rudder, is the rudder angle, is the water density, is the rudder area, is the rudder efficiency coefficient, is the effective speed of the inflow rudder; , is the interference coefficient between the hull and the rudder, is the vertical distance between the center of rudder force and the center of gravity of the ship, is the correction coefficient of the rudder motion on the hull bow moment.

3. The MPC-based unmanned sailboat heading control method according to claim 1, characterized in that: The specific process of establishing the MPC prediction model is as follows: (1) Let the state quantity , control quantity ,Using the linearization processing method, the linearized state equation can obtain the system matrix A and control matrix B of the control system; (2) Equation of state After discretization, the state expression after discretization is obtained as follows: ； ； Among them, the coefficient matrix and are the discretized linear time-varying matrices, is the linear transformation matrix of the state mapping output, is the discrete time step, is the identity matrix; (3) Let the state quantity , obtain the new state-space model: ； in, ; To control the increment, is the output, Indicates the current The state quantity at the moment, Indicates the control quantity at the previous moment; (4) Set the prediction time domain to , the control time domain is ,and , based on the new state space model, the future state and output are predicted, and the predicted output expression can be obtained: ； in , , , ; (5) According to the expected heading and predicted output, the objective function is constructed using the state deviation and control increment of the unmanned sailboat: ； Among them, the matrix and are the weight matrices of state error and control increment, respectively. is the desired state on the desired course; (6) Define the performance indicator function: ， ； (7) Apply constraints to the performance index function and take the minimum value of the performance index function to solve the optimal control increment sequence ; (8) According to the rolling optimization principle, obtain The first element of u *( k ) is applied to the control system to compensate for the control amount at the previous moment , get the control amount at the current moment =△ u *( k )+ .

4. The MPC-based unmanned sailboat heading control method according to claim 3, characterized in that: The following constraints are imposed on the performance indicator function: ； in, and Respectively represent the minimum value set and maximum value set of the control quantity in the control time domain, and They represent the minimum value set and maximum value set of the control increment in the control time domain respectively.

5. The MPC-based unmanned sailboat heading control method according to claim 2, characterized in that: Based on the wind field data, the yaw angle of the ship's heading is obtained due to the force exerted on the sail; Based on the wind field data, obtaining the relative wind direction angle and relative speed of the unmanned sailboat during its travel; Perform open-loop control on the sail to obtain the sail attack angle ; use , decomposing the sail force into the lateral driving force : ; make , , calculate the yaw angle ; in, is the horizontal distance from the sail area to the center of the hull, K is the proportional gain coefficient, and the positive or negative sign of the yaw angle indicates the direction of the sail force.

6. The MPC-based unmanned sailboat heading control method according to claim 5, characterized in that: The unmanned sailboat is equipped with a wind field sensor; The wind field sensor detects the wind field and obtains the wind field data.

7. The MPC-based unmanned sailboat heading control method according to claim 1, characterized in that: The unmanned sailboat is equipped with a magnetometer and an inertial measurement unit; The real-time bow angle of the unmanned sailboat is identified by fusing the static bow angle obtained by the magnetometer and the data obtained by the inertial measurement unit.

8. An unmanned sailboat heading control system based on MPC, characterized in that: include: The model building module is used to analyze the forces on the hull, sails, and rudder of the unmanned sailboat based on the MMG separation modeling concept of ships, and to establish the bow roll dynamics model of the unmanned sailboat; An MPC prediction module uses the second-order bow turning dynamics model of the unmanned sailboat regarding the bow angle and bow angular velocity as the MPC prediction model, outputs the control increment of the rudder angle at the current moment, and acts on the control variable at the previous moment to obtain the control variable at the current moment; The feedforward compensation module obtains the yaw angle of the ship's heading due to the sail force based on wind field data; The yaw angle feedforward compensates the control amount at the current moment, and the compensated control amount is applied to the rudder of the unmanned sailboat to control the heading of the unmanned sailboat.

9. The MPC-based unmanned sailboat heading control system according to claim 8, characterized in that: The MPC prediction module includes: An MPC model building unit, which builds an MPC prediction model through linearization and discretization processing; A future output prediction unit predicts future state quantities and output quantities based on the MPC prediction model and obtains a predicted output expression; The rolling optimization unit solves the optimal control incremental sequence in each control cycle and applies the first element in the optimal control incremental sequence to the control system.

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