CN107065569B - Ship dynamic positioning sliding mode control system and method based on RBF neural network compensation - Google Patents

Ship dynamic positioning sliding mode control system and method based on RBF neural network compensation Download PDF

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CN107065569B
CN107065569B CN201710396708.7A CN201710396708A CN107065569B CN 107065569 B CN107065569 B CN 107065569B CN 201710396708 A CN201710396708 A CN 201710396708A CN 107065569 B CN107065569 B CN 107065569B
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ship
sliding mode
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dynamic positioning
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CN107065569A (en
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夏国清
薛晶晶
陈兴华
刘彩云
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Harbin Engineering University
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    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
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Abstract

The invention provides a ship dynamic positioning sliding mode control system and method based on RBF neural network compensation. The dynamic positioning system comprises a display control computer (1), a guide system (2), an extended state observer (3), a sliding mode controller (4), an RBF neural network compensator (5), an executing mechanism (6), a dynamic positioning ship (7) and a sensor system (8). The ship dynamic positioning sliding mode control system and method based on RBF neural network compensation consider the problems of unmodeled dynamics, uncertain models, environmental interference and saturated input of an actuating mechanism in ship motion, improve the anti-interference capability and positioning accuracy of the ship dynamic positioning system, and can achieve better control effect under the condition that the input of actuating mechanisms such as a propeller and a propeller is limited.

Description

Ship dynamic positioning sliding mode control system and method based on RBF neural network compensation
Technical Field
The invention relates to a ship dynamic positioning sliding mode control system and a ship dynamic positioning sliding mode control method.
Background
The exploitation of marine resources such as offshore oil, natural gas and the like opens a new era for the development of ship dynamic positioning, the number of ships equipped with the dynamic positioning control system is from the first few to the present thousands or even more, and the wide application prospect of the dynamic positioning control system is fully displayed. The dynamic positioning control system is widely applied to operations such as offshore oil development, core drilling, submarine mining, cable laying, pipe laying, diving support, offshore fire fighting and the like. The dynamic positioning system is used for keeping the position and the heading of a ship only through the thrust generated by a propeller or a propeller of the dynamic positioning system. In the dynamic positioning motion control of the ship, the control effect of the ship motion is influenced by model uncertainty caused by hydrodynamic parameters and other factors, unmodeled dynamic terms in a mathematical model, slowly-varying environmental interference and the like, and the input of a control system is limited due to the amplitude limiting problem of actuating mechanisms such as a propeller and the like, so that the research on the problems has certain practical significance.
Chinese patent CN103760900A proposes a ship motion control system considering control input constraint, which utilizes the combination of a filtering backstepping method and a self-adaptive neural network to control the ship motion, and solves the input constraint problem and the unknown nonlinear problem of a ship model in the control process. Different from the method, the method mainly aims at the problems of uncertain models, unmodeled dynamic terms and environmental interference in the ship motion under the condition of saturated execution mechanisms.
Chinese patent CN105867382A proposes an equivalent interference compensation based ship dynamic positioning control system, which equates external environment interference, internal system uncertainty items and interference items into a state variable to respectively design extended state observers for three degrees of freedom of ship motion, thereby improving the anti-interference capability of the control system. Different from the method, the method not only researches uncertain models, unmodeled dynamic terms and environmental interference problems in ship motion, but also researches saturation problems caused by actuating mechanisms such as a propeller and a propeller in a control system.
Disclosure of Invention
The invention aims to provide a ship dynamic positioning sliding mode control system based on RBF neural network compensation, which has good anti-interference capability and high positioning precision and can achieve better control effect under the condition of limited input of an actuating mechanism. The invention also aims to provide a ship dynamic positioning sliding mode control method based on RBF neural network compensation.
The ship dynamic positioning sliding mode control system based on RBF neural network compensation comprises a display control computer 1, a guide system 2, an extended state observer 3, a sliding mode controller 4, an RBF neural network compensator 5, an execution mechanism 6, a dynamic positioning ship 7 and a sensor system 8; the method is characterized in that: the sensor system 8 collects position and angle information of the dynamic positioning ship 7 in real time, the position and angle information is called pose information eta for short, and the collected pose information is transmitted to the display control computer 1 and the extended state observer 3; the display and control computer 1 displays the actual pose signal of the ship in real time and sends an expected pose step signal etad0To the guidance system 2; the guidance system 2 carries out smoothing processing on the step signal of the expected pose to obtain continuous expected pose information etadAnd first and second derivatives thereof
Figure BDA0001308660160000021
And transmitted to the sliding mode controller 4; the extended state observer 3 expands unmodeled dynamics, model uncertainty and environmental interference in ship motion into an extended state vector d, and estimates attitude information and the extended state vector to obtain the extended state vectorState vector estimation
Figure BDA0001308660160000022
Pose information estimation value and first-order and second-order derivatives thereof
Figure BDA0001308660160000023
And transmitted to the sliding mode controller 4; the RBF neural network compensator 5 performs RBF approximation on the control quantity error under the control input saturation to obtain a control quantity compensation error
Figure BDA0001308660160000024
And transmitted to the sliding mode controller 4; the sliding mode controller 4 aims at the expected pose information and the first and second derivatives eta thereofd,
Figure BDA0001308660160000025
Pose information estimation value and first-order and second-order derivative and expansion state vector estimation value thereof
Figure BDA0001308660160000026
Control quantity error estimation under control input saturation
Figure BDA0001308660160000027
Performing sliding mode control to obtain a control quantity tau of the actuating mechanism 6 under an input saturation condition; the executing mechanism 6 controls the dynamic positioning ship 7 according to the control quantity tau output by the sliding mode controller 4, so that the ship moves to a desired pose state.
The ship dynamic positioning sliding mode control method based on RBF neural network compensation comprises the following steps:
(1) the sensor system 8 collects position and angle information of the dynamic positioning ship 7 in real time, the position and angle information is called pose information eta for short, and the collected pose information is transmitted to the display control computer 1 and the extended state observer 3;
(2) the display and control computer 1 displays the actual pose signal of the ship in real time and sends an expected pose step signal etad0To the guidance system 2;
(3) the guidance system 2 carries out smoothing processing on the step signal of the expected poseObtaining continuous expected pose information etadAnd first and second derivatives thereof
Figure BDA0001308660160000028
And transmitted to the sliding mode controller 4;
(4) the extended state observer 3 expands unmodeled dynamics, model uncertainty items and environmental interference in ship motion into an extended state vector d, and estimates attitude information and the extended state vector to obtain an extended state vector estimation value
Figure BDA0001308660160000029
Pose information estimation value and first-order and second-order derivatives thereof
Figure BDA00013086601600000210
And transmitted to the sliding mode controller 4;
(5) the RBF neural network compensator 5 performs RBF approximation on the control quantity error under the control input saturation to obtain a control quantity compensation error
Figure BDA00013086601600000211
And transmitted to the sliding mode controller 4;
(6) the sliding mode controller 4 aims at the expected pose information and the first and second derivatives eta thereofd,
Figure BDA00013086601600000212
Pose information estimation value and first-order and second-order derivative and expansion state vector estimation value thereof
Figure BDA00013086601600000213
Control quantity error estimation under control input saturation
Figure BDA00013086601600000214
Performing sliding mode control to obtain a control quantity tau of the actuating mechanism 6 under an input saturation condition;
(7) the executing mechanism 6 controls the dynamic positioning ship 7 according to the control quantity tau output by the sliding mode controller 4, so that the ship moves to a desired pose state.
The invention can improve the anti-interference capability and the positioning precision of the ship dynamic positioning system, and can achieve better control effect under the condition that the input of actuating mechanisms such as a propeller and a propeller is limited.
Drawings
FIG. 1 is a general structure diagram of a ship dynamic positioning sliding mode control system based on RBF neural network compensation;
FIG. 2 is a motion profile of a dynamically positioned vessel at saturation without regard to control inputs;
FIG. 3 is a motion profile of a dynamically positioned vessel under consideration of control input saturation;
FIG. 4 is a dynamic positioning vessel longitudinal control force curve;
FIG. 5 is a dynamic positioning vessel lateral control force curve;
FIG. 6 is a dynamic positioning vessel heading control moment curve.
Detailed Description
The invention is described in more detail below with reference to the accompanying drawings.
The ship dynamic positioning sliding mode control system based on RBF neural network compensation comprises a display control computer 1, a guide system 2, an extended state observer 3, a sliding mode controller 4, an RBF neural network compensator 5, an execution mechanism 6, a dynamic positioning ship 7 and a sensor system 8. The sensor system 8 collects position and angle information of the dynamic positioning ship 7 in real time, the position and angle information is called pose information eta for short, and the collected pose information is transmitted to the display control computer 1 and the extended state observer 3; the display and control computer 1 displays the actual pose signal of the ship in real time and sends an expected pose step signal etad0To the guidance system 2; the guidance system 2 carries out smoothing processing on the step signal of the expected pose to obtain continuous expected pose information etadAnd first and second derivatives thereof
Figure BDA0001308660160000031
And transmitted to the sliding mode controller 4; the extended state observer 3 expands unmodeled dynamics, model uncertainty and environmental disturbance in the ship motion into an extended state vector d, and aligns attitude information and extensionEstimating the tensor state vector to obtain an estimated value of the expansion state vector
Figure BDA0001308660160000032
Pose information estimation value and first-order and second-order derivatives thereof
Figure BDA0001308660160000033
And transmitted to the sliding mode controller 4; the RBF neural network compensator 5 performs RBF approximation on the control quantity error under the control input saturation to obtain a control quantity compensation error
Figure BDA0001308660160000034
And transmitted to the sliding mode controller 4; the sliding mode controller 4 aims at the expected pose information and the first and second derivatives eta thereofd,
Figure BDA0001308660160000035
Pose information estimation value and first-order and second-order derivative and expansion state vector estimation value thereof
Figure BDA0001308660160000036
Control quantity error estimation under control input saturation
Figure BDA0001308660160000037
Performing sliding mode control to obtain a control quantity tau of the actuating mechanism 6 under an input saturation condition; the executing mechanism 6 controls the dynamic positioning ship 7 according to the control quantity tau output by the sliding mode controller 4, so that the ship moves to a desired pose state.
The ship dynamic positioning sliding mode control method based on RBF neural network compensation of the invention is explained in more detail as follows:
the ship three-degree-of-freedom (surging, swaying and yawing) low-frequency motion model is as follows:
Figure BDA0001308660160000041
wherein the content of the first and second substances,
Figure BDA0001308660160000042
the position and attitude vectors of the ship under an inertial coordinate system (x, y are the transverse and longitudinal positions of the ship, psi is the ship heading angle),
Figure BDA0001308660160000043
is the first derivative of the attitude information of the ship in the inertial system,
Figure BDA0001308660160000044
is the pose vector of the ship under the attached coordinate system (u, v are the transverse and longitudinal speeds of the ship, r is the heading angular speed of the ship),
Figure BDA0001308660160000045
in the form of a ship mass matrix,
Figure BDA0001308660160000046
D(υ)＞03×3,D(υ)＝DΤ(v) is a matrix of damping coefficients,
Figure BDA0001308660160000047
is a matrix of coriolis and center forces,
Figure BDA0001308660160000048
in order to control the force and moment vectors,
Figure BDA0001308660160000049
force and moment vectors generated by slowly varying environmental disturbances and unmodeled dynamics.
Figure BDA00013086601600000410
Is a transformation matrix between the earth-fixed coordinate system and the satellite coordinate system, and the concrete representation form is
Figure BDA00013086601600000411
Step signal eta of guidance system for outputting expected pose to display control computerd0Smoothing to obtain expected pose information
Figure BDA00013086601600000412
And first and second derivatives of expected pose information
Figure BDA00013086601600000413
Figure BDA00013086601600000414
Figure BDA00013086601600000415
Wherein, TsAs cutoff time, ωnAnd zeta is the relative damping ratio of the oscillation link of the guide system and the design parameter of the longitudinal path generator.
In order to facilitate the control of the ship, the ship model (1) is transformed as follows:
Figure BDA00013086601600000416
wherein the content of the first and second substances,
Figure BDA00013086601600000417
for transforming the derivative of the matrix R (ψ) with respect to time, and
M*＝R(ψ)MRΤ(ψ)
D*＝R(ψ)D(υ)RΤ(ψ)
Figure BDA0001308660160000051
Figure BDA0001308660160000052
b*＝R(ψ)RΤ(ψ)b＝b
the specific form of the state observer is as follows:
Figure BDA0001308660160000053
wherein the content of the first and second substances,
Figure BDA0001308660160000054
is an estimated value of the position and orientation vector of the ship,
Figure BDA0001308660160000055
is an estimated value of a first-order derivative of the ship position and attitude signal,
Figure BDA0001308660160000056
is the first derivative of the estimated value of the ship position vector,
Figure BDA0001308660160000057
is the second derivative of the estimated value of the ship position vector,
Figure BDA0001308660160000058
in order to expand the estimated value of the state variable,
Figure BDA0001308660160000059
the first derivative of the estimate of the dilated state variable,
Figure BDA00013086601600000510
is a positive definite matrix and > 0 is a constant.
Setting the error between the actual control input and the control quantity as
Figure BDA00013086601600000511
Wherein sat (tau) is an input saturation function in the specific form
Figure BDA00013086601600000512
Wherein the content of the first and second substances,
Figure BDA00013086601600000513
is the maximum value of the control input.
The RBF neural network compensator approximates the error between the control quantities to obtain a compensation value
Figure BDA00013086601600000514
The RBF control algorithm is
Figure BDA00013086601600000515
Wherein the content of the first and second substances,
Figure BDA00013086601600000516
for neural network input, hjJ is 1,2, …, l is the output vector of the gaussian vector basis function, cjAs the center position of the jth hidden layer neuron, bjThe width of Gaussian distribution is less than or equal tomaxFor neural network error estimation, the weight matrix of the neural network is
Figure BDA00013086601600000517
Then the output of the RBF neural network
Figure BDA0001308660160000061
Is composed of
Figure BDA0001308660160000062
Wherein the content of the first and second substances,
Figure BDA0001308660160000063
for the weight estimation matrix, h (x) is a Gaussian basis vector.
Definition error
Figure BDA0001308660160000064
Is provided with
Figure BDA0001308660160000065
Then the sliding mode function s is
Figure BDA0001308660160000066
Wherein the content of the first and second substances,
Figure BDA0001308660160000067
υris the intermediate variable(s) of the variable,
Figure BDA0001308660160000068
is a gain matrix.
Therefore, the sliding mode control law is
Figure BDA0001308660160000069
Wherein the content of the first and second substances,
Figure BDA00013086601600000610
is a gain matrix.
Adaptation law of RBF neural network
Figure BDA00013086601600000611
Is composed of
Figure BDA00013086601600000612
Wherein, is a constant value square matrix, sΤIs the transpose of the sliding mode function vector.

Claims (2)

1. A ship dynamic positioning sliding-mode control system based on RBF neural network compensation comprises a display control computer (1), a guide system (2), an extended state observer (3), a sliding-mode controller (4), an RBF neural network compensator (5), an execution mechanism (6), a dynamic positioning ship (7) and a sensor system (8); the method is characterized in that: the sensor system (8) collects position and angle information of the dynamic positioning ship (7) in real time, the position and angle information is called pose information eta for short, and the collected pose information is transmitted to the display control computer (1) and the extended state observer (3); the display control computer (1) displays the actual pose signal of the ship in real time and sends an expected pose step signal etad0To the guidance system (2); the guidance system (2) carries out step signal processing on the expected posePerforming smoothing treatment to obtain continuous expected pose information etadAnd first and second derivatives thereof
Figure FDA0002673161060000011
And transmitted to the sliding mode controller (4); the extended state observer (3) expands unmodeled dynamics, model uncertainty and environmental interference in ship motion into an extended state vector d, and estimates attitude information and the extended state vector to obtain an extended state vector estimation value
Figure FDA0002673161060000012
Pose information estimation value and first-order and second-order derivatives thereof
Figure FDA0002673161060000013
And transmitted to the sliding mode controller (4); the RBF neural network compensator (5) carries out RBF approximation aiming at the control quantity error under the control input saturation to obtain the control quantity compensation error
Figure FDA0002673161060000014
And transmitted to the sliding mode controller (4); the sliding mode controller (4) aims at the expected pose information and the first-order and second-order derivatives eta thereofd,
Figure FDA0002673161060000015
Pose information estimation value and first-order and second-order derivative and expansion state vector estimation value thereof
Figure FDA0002673161060000016
Control quantity error estimation under control input saturation
Figure FDA0002673161060000017
Performing sliding mode control to obtain a control quantity tau of the actuating mechanism (6) under an input saturation condition; the executing mechanism (6) controls the dynamic positioning ship (7) according to the control quantity tau output by the sliding mode controller (4) so that the ship moves to a desired pose state;
the ship three-degree-of-freedom low-frequency motion model is as follows:
Figure FDA0002673161060000018
Figure FDA0002673161060000019
wherein the content of the first and second substances,
Figure FDA00026731610600000110
is the position and attitude vector of the ship under an inertial coordinate system, x and y are the transverse and longitudinal positions of the ship, psi is the ship heading angle,
Figure FDA00026731610600000111
is the first derivative of the attitude information of the ship in the inertial system,
Figure FDA00026731610600000112
is the pose vector of the ship in the attached coordinate system, u and v are the transverse and longitudinal speeds of the ship, r is the heading angular speed of the ship,
Figure FDA00026731610600000113
in the form of a ship mass matrix,
Figure FDA00026731610600000114
D(υ)＞03×3,D(υ)＝DΤ(v) is a matrix of damping coefficients,
Figure FDA00026731610600000115
is a matrix of coriolis and center forces,
Figure FDA00026731610600000116
in order to control the force and moment vectors,
Figure FDA00026731610600000117
for the force and moment vectors generated by slowly varying environmental disturbances and unmodeled dynamics,
Figure FDA00026731610600000118
the transformation matrix is a transformation matrix between a ground-fixed coordinate system and a random coordinate system, and the concrete representation form is as follows:
Figure FDA0002673161060000021
first and second derivatives of expected pose information
Figure FDA0002673161060000022
Satisfies the following conditions:
Figure FDA0002673161060000023
Figure FDA0002673161060000024
wherein, TsAs cutoff time, ωnThe natural frequency of the oscillation link of the guide system, zeta is the relative damping ratio of the oscillation link of the guide system, and is a design parameter of the longitudinal path generator;
the specific form of the state observer is as follows:
Figure FDA0002673161060000025
Figure FDA0002673161060000026
Figure FDA0002673161060000027
wherein the content of the first and second substances,
Figure FDA0002673161060000028
is an estimated value of the position and orientation vector of the ship,
Figure FDA0002673161060000029
is an estimated value of a first-order derivative of the ship position and attitude signal,
Figure FDA00026731610600000210
is the first derivative of the estimated value of the ship position vector,
Figure FDA00026731610600000211
is the second derivative of the estimated value of the ship position vector,
Figure FDA00026731610600000212
in order to expand the estimated value of the state variable,
Figure FDA00026731610600000213
the first derivative of the estimate of the dilated state variable,
Figure FDA00026731610600000214
i is 1,2 and 3 are positive definite matrixes, and more than 0 is a constant;
the RBF control algorithm is
Figure FDA00026731610600000215
＝{Wi *}Τ·{hi(x)}+
Wherein the content of the first and second substances,
Figure FDA00026731610600000216
for neural network input, hjJ is 1,2, …, l is the output vector of the gaussian vector basis function, cjAs the center position of the jth hidden layer neuron, bjThe width of Gaussian distribution is less than or equal tomaxFor neural network error estimation, the weight matrix of the neural network is
Figure FDA00026731610600000217
Then the output of the RBF neural network
Figure FDA00026731610600000218
Is composed of
Figure FDA00026731610600000219
Wherein the content of the first and second substances,
Figure FDA00026731610600000220
h (x) is a Gaussian base vector;
the sliding mode control law is as follows:
Figure FDA0002673161060000031
wherein the content of the first and second substances,
Figure FDA0002673161060000032
for the gain matrix, define M*＝R(ψ)MRΤ(ψ)；
Defining:
D*＝R(ψ)D(υ)RΤ(ψ)
Figure FDA0002673161060000033
b*＝R(ψ)RΤ(ψ)b＝b
Figure FDA0002673161060000034
for a transformation matrix, R, between the earth-fixed coordinate system and the satellite coordinate systemΤ(psi) is a transposed matrix of R (psi),
Figure FDA0002673161060000035
is the first derivative of R (ψ) with respect to time,
Figure FDA0002673161060000036
m is a ship mass matrix,
Figure FDA0002673161060000037
D(υ)＞03×3,D(υ)＝DΤ(upsilon), D (upsilon) is a damping coefficient matrix,
Figure FDA0002673161060000038
c (upsilon) is the coriolis and center force matrix,
Figure FDA0002673161060000039
force and moment vectors generated by slowly varying environmental disturbances and unmodeled dynamics;
υris the intermediate variable(s) of the variable,
Figure FDA00026731610600000310
for an intermediate variable, let
Figure FDA00026731610600000311
ηdDefining errors for expected pose information
Figure FDA00026731610600000312
Figure FDA00026731610600000313
As an estimated value of the pose information,
Figure FDA00026731610600000314
is a gain matrix, then
Figure FDA00026731610600000315
Is a medium variable upsilonrA first derivative with respect to time;
adaptation law of RBF neural network
Figure FDA00026731610600000316
Comprises the following steps:
Figure FDA00026731610600000317
wherein, is a constant value square matrix, sΤIs the transpose of the sliding mode function vector.
2. The control method of the ship dynamic positioning sliding-mode control system based on the RBF neural network compensation as claimed in claim 1, is characterized in that:
(1) the sensor system (8) collects position and angle information of the dynamic positioning ship (7) in real time, the position and angle information is called pose information eta for short, and the collected pose information is transmitted to the display control computer (1) and the extended state observer (3);
(2) the display control computer (1) displays the actual pose signal of the ship in real time and sends an expected pose step signal etad0To the guidance system (2);
(3) the guidance system (2) carries out smoothing processing on the step signal of the expected pose to obtain continuous expected pose information etadAnd first and second derivatives thereof
Figure FDA00026731610600000318
And transmitted to the sliding mode controller (4);
(4) the extended state observer (3) expands unmodeled dynamics, model uncertainty and environmental interference in ship motion into an extended state vector d, and estimates attitude information and the extended state vector to obtain an extended state vector estimation value
Figure FDA00026731610600000319
Pose information estimation value and first-order and second-order derivatives thereof
Figure FDA0002673161060000041
And transmitted to the sliding mode controller (4);
(5) the RBF neural network compensator (5) is used for controlling the error of the control quantity under the saturation of the control inputRBF approximation is carried out to obtain the compensation error of the control quantity
Figure FDA0002673161060000042
And transmitted to the sliding mode controller (4);
(6) the sliding mode controller (4) aims at the expected pose information and the first-order and second-order derivatives eta thereofd,
Figure FDA0002673161060000043
Pose information estimation value and first-order and second-order derivative and expansion state vector estimation value thereof
Figure FDA0002673161060000044
Control quantity error estimation under control input saturation
Figure FDA0002673161060000045
Performing sliding mode control to obtain a control quantity tau of the actuating mechanism (6) under an input saturation condition;
(7) and the executing mechanism (6) controls the dynamic positioning ship (7) according to the control quantity tau output by the sliding mode controller (4), so that the ship moves to a desired pose state.
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