CN-121541637-B - Under-actuated unmanned ship obstacle avoidance method based on distributed model predictive control

CN121541637BCN 121541637 BCN121541637 BCN 121541637BCN-121541637-B

Abstract

The embodiment discloses an under-actuated unmanned aerial vehicle obstacle avoidance method based on distributed model predictive control, which comprises the steps of establishing a cost function considering the dynamic obstacle avoidance cost through a single-oar single-rudder under-actuated unmanned aerial vehicle kinematic model and a dynamic model, establishing an objective function and a constraint function based on the cost function, solving the objective function, and obtaining optimal control input of the unmanned aerial vehicle so as to realize obstacle avoidance of the under-actuated unmanned aerial vehicle. According to the invention, by fully utilizing the dynamic obstacle information (including the neighbor unmanned ship), the optimal control input of the unmanned ship is obtained, so that the unmanned ship has higher obstacle avoidance precision when executing tasks, and the autonomous obstacle avoidance capability of the multi-ship system in a complex environment is effectively improved.

Inventors

LIANG XIAO
Ma Chuanke
LIU DIANYONG
Sun Chenshuo
Cheng xinyuan
YU CHANGDONG
SONG YANKONG
LI WEI

Assignees

大连海事大学

Dates

Publication Date: 20260508
Application Date: 20251031

Claims (4)

1. The under-actuated unmanned ship obstacle avoidance method based on distributed model predictive control is characterized by comprising the following steps of: s1, establishing a single-oar single-rudder underactuated unmanned ship kinematic model and an error dynamic model to obtain a differential kinematic model according to the error dynamic model; S2, establishing a dynamics model of the single-oar single-rudder underactuated unmanned ship to obtain a dynamics model after difference; S3, establishing a cost function considering the avoidance cost of the dynamic barrier comprising the neighbor unmanned ship according to the kinematic model and the dynamic model; S4, establishing an objective function based on the cost function, and establishing a constraint function according to the kinematic model after difference and the dynamic model after difference so as to solve the objective function, obtain the optimal control input of the unmanned aerial vehicle, and further realize obstacle avoidance of the underactuated unmanned aerial vehicle; the cost function is expressed as follows: Wherein, the Wherein: Representing a tracking error cost; A prediction time domain representing model predictive control; index numbers representing discrete time steps; Representing a transverse tracking error weight coefficient; representation model predictive control A lateral tracking error for each discrete time step; Representing euclidean norms; representing a heading angle error weight coefficient; representing unmanned boat Heading angle errors of discrete time steps; representing a speed weight coefficient; representing unmanned ship under motion coordinate system Longitudinal speed of discrete time steps; Representing a desired longitudinal speed of the unmanned boat; Wherein: Representing a control input cost; representing a longitudinal thrust weighting factor; representation model predictive control Longitudinal thrust of discrete time steps; Representing a bow turning moment weight coefficient; representation model predictive control A yaw moment of discrete time steps; Wherein: representing a control smoothing cost; Representing a longitudinal thrust variation weight coefficient; representing a change weight coefficient of the turning bow moment; Wherein: Representing obstacle avoidance costs, including static obstacle avoidance costs and dynamic obstacle avoidance costs; Representing the obstacle avoidance weight coefficient of the static obstacle; representation model predictive control The distance between the unmanned ship and the static obstacle is a discrete time step; a positive number indicating that the numerical singular is avoided when the distance is 0; representing the obstacle avoidance weight coefficient of the dynamic obstacle; representation model predictive control Discrete time steps and prediction Euclidean distance of dynamic barrier of discrete time steps; Wherein: representing an inter-boat attraction cost for maintaining a spatial relationship between boats; representing the adjacent boat number of the current unmanned boat; representing the number of adjacent boats of the current unmanned boat; representing the current unmanned ship and the first Weight coefficients for attractive costs between unmanned boats; representing the current unmanned ship and the first The unmanned ship is at the first Euclidean distance of steps; representing the current unmanned ship and the first A desired distance between unmanned boats.
2. The method for avoiding an obstacle of an underactuated unmanned ship based on predictive control of a distributed model according to claim 1, wherein S1 comprises: S11, establishing a kinematic model of the single-oar single-rudder underactuated unmanned ship as follows: Wherein: representing the abscissa of the unmanned ship in a fixed coordinate system; Representation of Is the first derivative of (a); representing the ordinate of the unmanned ship in a fixed coordinate system; Representation of Is the first derivative of (a); representing heading angle of unmanned ship, i.e. bow and fixed coordinate system An included angle between the axes; Representation of Is the first derivative of (a); Representing the longitudinal speed of the unmanned ship under a motion coordinate system; Representing the lateral speed of the unmanned boat; Representing the heading angular velocity of the unmanned ship; s12, establishing an error dynamic model as follows: Wherein: representing unmanned boat A lateral tracking error for each discrete time step; representing unmanned boat Heading angle errors of discrete time steps; Representation and representation Corresponding ordinate values; representing unmanned boat The ordinate of the discrete time steps; Unmanned ship on track curve representing unmanned ship needing to be tracked under fixed coordinate system The abscissa of the discrete time steps; index numbers representing discrete time steps; The track curve representing the unmanned ship to be tracked under the fixed coordinate system is on the unmanned ship Tangential slope at the location of the discrete time steps; Indicating that unmanned ship is at the first Heading angle of discrete time steps; s13, according to the error dynamic model, acquiring a differential kinematic model as follows: Wherein: representing unmanned ship under motion coordinate system Longitudinal speed of discrete time steps; representing unmanned boat Heading angle of discrete time steps; representing a differential interval; representing unmanned ship under motion coordinate system A lateral velocity of the discrete time steps; representing unmanned boat The heading angular velocity of the discrete time steps.
3. The method for avoiding the obstacle of the underactuated unmanned ship based on the predictive control of the distributed model according to claim 2, wherein the dynamic model of the single-oar single-rudder underactuated unmanned ship is established as follows: Wherein: Representation of The first derivative of (a), namely the longitudinal acceleration of the unmanned ship under a motion coordinate system; Representing the mass of the unmanned boat; representing longitudinal speed of unmanned ship in motion coordinate system A corresponding linear damping derivative; Representing longitudinal acceleration A corresponding additional mass derivative; Representing the heading angular velocity of the unmanned ship; a nonlinear damping derivative representing a longitudinal velocity quadratic term; representing lateral speed of unmanned ship A corresponding linear lateral force derivative; representing lateral acceleration Corresponding lateral additional mass derivatives; a nonlinear lateral force derivative representing a lateral velocity quadratic term; representing the longitudinal thrust; Is that The first derivative of (a) i.e. the lateral acceleration of the unmanned boat; Representation of Absolute value of (2); representing a derivative of the turning moment corresponding to the heading angular velocity; representing an additional moment of inertia derivative corresponding to the angular acceleration of the heading; a nonlinear turning moment derivative representing a heading angular velocity quadratic term; representing the moment of inertia of the unmanned boat about a vertical axis; representing a turning bow moment; Representing the absolute value of the angular velocity of the heading; after the dynamics model is discretized, the dynamics model after difference is obtained as follows: 。
4. An under-actuated unmanned ship obstacle avoidance method based on distributed model predictive control as claimed in claim 3, wherein S4 comprises: s41, defining state variables and control variables to determine decision variables: Wherein: Indicating that unmanned ship is at the first A system state vector of discrete time steps; representing a transpose; Indicating that unmanned ship is at the first A system control vector of discrete time steps; Representing decision variables; representing an initial state vector; a state vector representing a predicted time domain end; Representing an initial control vector; A control vector representing a predicted temporal end; representation model predictive control A yaw moment of discrete time steps; representation model predictive control Longitudinal thrust of discrete time steps; S42, establishing an objective function according to the decision variable: Wherein: Representing decision variables; performance metrics that are desired to be minimized for the cost function to be minimized, i.e., under constraint conditions; Representing a constraint function; and (3) with Respectively a lower bound and an upper bound of the decision variable; Wherein, the In the formula, Representing initial predictive model constraints; A prediction model constraint representing a prediction time domain end; Represent the first A constraint function of discrete time steps; S43, introducing Lagrange multipliers, and constructing a Lagrange function based on a cost function comprising multi-boat cooperation and obstacle avoidance constraint: Wherein: Representing a lagrangian function; Is a Lagrangian multiplier; And S44, solving the Lagrangian function to obtain the optimal control input of the unmanned ship.

Description

Under-actuated unmanned ship obstacle avoidance method based on distributed model predictive control Technical Field The invention relates to the technical field of unmanned ship control, in particular to an under-actuated unmanned ship obstacle avoidance method based on distributed model predictive control. Background When an Unmanned Surface Vessel (USV) performs cruising or detecting tasks in the environment of ocean, river and lake, etc., various surrounding obstacles must be autonomously avoided when the navigation track is precisely controlled. Static obstacles (such as islands, submerged reefs, buoys, etc.) are fixed in position, and dynamic obstacles (such as other vessels) are changed in position with time. Therefore, unmanned ship track tracking control needs to consider information of static and dynamic obstacles at the same time so as to ensure navigation safety. For multi-boat formation control, the centralized method has large calculation amount and poor robustness, if a central calculation node is interfered, a cluster system is greatly affected, and distributed control is paid attention to. The Distributed Model Predictive Control (DMPC) divides the whole system into a plurality of subsystems, and each subsystem independently optimizes a control strategy based on the state of the subsystem and the information of the adjacent boats, and finally realizes global coordination. Compared with a centralized scheme, the distributed model predictive control has the advantages of good expansibility (the computational complexity linearly increases along with the number of vessels), stronger fault tolerance (single vessel faults do not affect the whole), high instantaneity (distributed computation reduces the burden of a single controller) and the like. In addition, in the multi-boat collaborative model prediction control, formation retention can be realized by introducing neighbor state consistency items into a cost function, and inter-boat collision is forcedly avoided through state constraint. However, in the prior art, tracking control of formation of multiple unmanned ships is realized by methods such as distributed model predictive control, but dynamic obstacle (especially other ships) information is not fully utilized, so that the unmanned ships have low predicted obstacle avoidance accuracy and unsatisfactory obstacle avoidance effect when performing tasks. Disclosure of Invention The invention discloses an under-actuated unmanned ship obstacle avoidance method based on distributed model predictive control, which aims to overcome the technical problems. In order to achieve the above object, the technical scheme of the present invention is as follows: An under-actuated unmanned ship obstacle avoidance method based on distributed model predictive control comprises the following steps: s1, establishing a single-oar single-rudder underactuated unmanned ship kinematic model and an error dynamic model to obtain a differential kinematic model according to the error dynamic model; S2, establishing a dynamics model of the single-oar single-rudder underactuated unmanned ship to obtain a dynamics model after difference; S3, establishing a cost function considering the avoidance cost of the dynamic barrier comprising the neighbor unmanned ship according to the kinematic model and the dynamic model; and S4, establishing an objective function based on the cost function, and establishing a constraint function according to the kinematic model after difference and the dynamic model after difference so as to solve the objective function, obtain the optimal control input of the unmanned aerial vehicle, and further realize obstacle avoidance of the underactuated unmanned aerial vehicle. The unmanned aerial vehicle under-actuated obstacle avoidance method based on the distributed model predictive control has the advantages that a cost function considering the dynamic obstacle avoidance cost is built through a single-oar single-rudder under-actuated unmanned aerial vehicle kinematic model and a dynamic model, an objective function and a constraint function based on the cost function are built, the objective function is solved, and optimal control input of the unmanned aerial vehicle is obtained, so that obstacle avoidance of the under-actuated unmanned aerial vehicle is achieved. According to the invention, by fully utilizing the dynamic obstacle information (including the neighbor unmanned ship), the optimal control input of the unmanned ship is obtained, so that the unmanned ship has higher obstacle avoidance precision when executing tasks, and the autonomous obstacle avoidance capability of the multi-ship system in a complex environment is effectively improved. Drawings In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it will b