CN109583144B - Dynamics optimization controller structure of unmanned marine vehicle and design method - Google Patents

Dynamics optimization controller structure of unmanned marine vehicle and design method Download PDF

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CN109583144B
CN109583144B CN201910016713.XA CN201910016713A CN109583144B CN 109583144 B CN109583144 B CN 109583144B CN 201910016713 A CN201910016713 A CN 201910016713A CN 109583144 B CN109583144 B CN 109583144B
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CN109583144A (en
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彭周华
吕光颢
王丹
刘陆
古楠
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Dalian Maritime University
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Abstract

The invention discloses a dynamic optimization controller structure of an unmanned marine vehicle and a design method. The invention combines the dimension reduction disturbance observer, the instruction optimization regulator and the dynamics controller, so that the internal and external disturbance and uncertainty information are accurately estimated and transmitted to the dynamics controller. The problem of internal and external disturbance of the unmanned aircraft and the problem of dynamic constraint of the unmanned marine aircraft are solved, and the method is independent of an accurate unmanned marine aircraft model and is easier for engineering realization. The method simultaneously considers input constraint and state constraint, adopts rolling time domain prediction to establish an optimized objective function, and obtains a guidance signal meeting constraint conditions by applying neurodynamic optimization solution, so that a control signal meets the actual physical constraint of the unmanned marine vehicle, and the performance of the unmanned marine vehicle is greatly improved.

Description

Dynamics optimization controller structure of unmanned marine vehicle and design method
Technical Field
The invention relates to the field of unmanned marine aircrafts, in particular to a dynamics optimization controller structure and a design method of an unmanned marine aircraft.
Background
The dynamics optimization control of the unmanned ocean vehicle has important significance for improving the autonomy and intelligence level of the unmanned ocean vehicle and promoting the industrial application of the unmanned ocean vehicle.
Chinese patent CN106773713A discloses a high-precision nonlinear path tracking control method for an under-actuated marine vehicle, which takes the change rate of the sideslip angle of the vehicle as an uncertain item, takes the parameter uncertainty, unmodeled dynamics and external environment disturbance in a dynamic model as lumped uncertainty, and adopts an observer to observe the kinematics uncertainty and the dynamics uncertainty in real time; calculating an expected line-of-sight angle by adopting a traditional line-of-sight angle guidance method; designing a nonlinear path tracking controller based on an observer, and compensating the observed values of kinematics and dynamics uncertainties; and the tracking differentiator is adopted to simplify the controller, so that the controller is more suitable for engineering application. The method eliminates the influence of model parameter uncertainty, unmodeled dynamics, external environment disturbance and the like on path tracking, and realizes accurate tracking control on the expected path of the aircraft.
Chinese patent CN108427414A proposes an autonomous underwater vehicle horizontal plane adaptive trajectory tracking control method, which estimates the speed and angular velocity of an AUV by using a high-gain state observer method, compensates model parameter uncertainty items and external interference items by using high-precision approximation functions of a Radial Basis Function (RBF) neural network, and converts the AUV trajectory tracking problem into the tracking problem under a polar coordinate system through coordinate transformation. Specifically, expected input of a kinematic model is designed firstly, then expected input of a dynamic model is designed, finally an RBF neural network is used for estimating an uncertainty item in the expected input, a neural network weight updating law is designed, and finally the AUV is enabled to track an expected track.
However, the conventional control method has the following problems:
firstly, in the existing control methods, the problem of dynamics constraint of the unmanned marine vehicle is ignored only by considering model uncertainty of the unmanned marine vehicle and the problem of external disturbance caused by marine environment, while the problem of dynamics constraint of the unmanned marine vehicle is only processed by some control methods, but the problem of external disturbance caused by marine environment is not processed by others, and the existing methods lack control methods capable of simultaneously solving the problems of dynamics constraint and external disturbance and uncertainty.
Second, constraints are ubiquitous in the control of motion of unmanned marine vehicles. Such as input constraints, state constraints. The existing methods for processing the constraint problem comprise barrier function design, auxiliary system design, model prediction control method and the like. However, the barrier function design only considers the output constraint but not the input and state constraint, the auxiliary system design only considers the input constraint but not the state constraint, and the model predictive control method considers the input and state constraint but highly depends on an accurate unmanned marine vehicle model, which is difficult to realize in engineering application. Even if an accurate unmanned marine vehicle model can be obtained, the model prediction control method cannot solve the problems of lumped uncertainty and poor anti-interference capability caused by uncertain hydrodynamic parameters, modeling errors and unknown disturbance brought by ocean currents.
Thirdly, the existing method for estimating uncertainty and external disturbance of a dynamic system of the unmanned marine vehicle mostly adopts a neural network method, and in practical application, the neural network method is not beneficial to engineering realization due to the defects of large calculation burden, multiple adjustment parameters and the like.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a structure and a design method of a dynamics optimization controller of an unmanned marine vehicle, which can realize the following purposes:
1. the dynamic constraint problem and the external disturbance and uncertainty problem can be solved simultaneously;
2. independent of accurate unmanned marine vehicle model and strong anti-interference capability
3. The method has the advantages of small calculation burden, few adjusting parameters and easy engineering realization.
In order to achieve the purpose, the technical scheme of the invention is as follows: the unmanned marine vehicle dynamics optimization controller structure comprises a disturbance observer, an instruction regulator and a dynamics controller, wherein the input end of the disturbance observer is respectively connected with the output end of the unmanned marine vehicle and the output end of the dynamics controller; the input end of the dynamic controller is respectively connected with the output end of the unmanned marine vehicle, the output end of the disturbance observer and the output end of the instruction regulator; and the input end of the instruction regulator is respectively connected with the output end of the dynamics controller and the outer ring controller.
A design method of a dynamics optimization controller structure of an unmanned marine vehicle is provided, wherein a six-degree-of-freedom dynamics model of the unmanned marine vehicle is represented by the following formula:
Figure BDA0001939320460000021
wherein:
Figure BDA0001939320460000022
representing position and attitude information of the unmanned marine vehicle, wherein x,
Figure BDA0001939320460000031
Phi, theta and psi respectively represent a longitudinal displacement component, a transverse displacement component, a vertical displacement component, a yaw angle, a roll angle and a pitch angle;
Figure BDA0001939320460000032
representing a volume.
Figure BDA0001939320460000033
Representing a velocity signal of the unmanned aircraft, wherein u, v, ω, p, q, and r represent a longitudinal velocity component, a lateral velocity component, a vertical velocity component, a heading angular velocity, a roll angular velocity, and a pitch angular velocity, respectively; satisfy unmanned ocean navigation ware state restraint v min ≤ν≤ν max Wherein v is min V and v max Respectively representing the upper and lower constraints on v.
Figure BDA0001939320460000034
Representing an inertia matrix.
Figure BDA0001939320460000035
Representing a coriolis centripetal matrix.
Figure BDA0001939320460000036
Representing a nonlinear damping matrix.
g (v, η) represents the restoring force caused by the combined action of buoyancy and gravity.
Figure BDA0001939320460000037
Representing control of unmanned marine vehicleA signal of which τ u 、τ v 、τ ω 、τ p 、τ q And τ r Respectively representing a transverse control component, a longitudinal control component, a vertical control component, a yawing angle control component and a pitching angle control component; satisfying control input constraint τ min ≤τ≤τ max In which τ is min And τ max Representing the constrained upper and lower bounds of τ, respectively.
Figure BDA0001939320460000038
Representing the external disturbance of a time-varying ocean current in a marine environment on an unmanned marine vehicle, where τ ωu 、τ ωv 、τ ωω 、τ ωp 、τ ωq And τ ωr Respectively representing a transverse external disturbance component, a longitudinal external disturbance component, a vertical external disturbance component, a yaw angle external disturbance component and a pitch angle external disturbance component.
Figure BDA0001939320460000039
Representing modeled errors for unmodeled fluid dynamics and unmanned marine vehicles.
The design method comprises the following steps:
A. design of disturbance observer
And (2) rewriting a six-degree-of-freedom dynamic model of the unmanned marine vehicle in the formula (1) into the following formula:
Figure BDA00019393204600000310
wherein
Figure BDA00019393204600000311
Is a known matrix;
B＝M -1 ；
σ(·)＝-C(ν)ν-D(ν)ν-Δ(ν,η)-g(η)+τ w (t)-M -1 avv represents the uncertainty and uncertain hydrodynamics parameters of the modelAnd lumped uncertainties due to external disturbances brought by time-varying ocean currents in marine environments.
Designing a disturbance observer to estimate the lumped uncertainty sigma, wherein input signals of the disturbance observer are a speed signal v and a control signal tau of the unmanned ocean vehicle, and the disturbance observer is expressed as follows:
Figure BDA0001939320460000041
wherein
Figure BDA0001939320460000042
Is the auxiliary state of the disturbance observer;
Figure BDA00019393204600000421
is an estimate of the lumped uncertainty σ;
Figure BDA0001939320460000043
representing a gain matrix.
B. Design of dynamic controller
The input signal of the dynamics controller comprises the output signal of the demand regulator
Figure BDA0001939320460000044
Lumped uncertainty estimation value output by unmanned ocean vehicle speed signal v and disturbance observer
Figure BDA0001939320460000045
The dynamics controller outputs signals tau and v m The design is as follows:
Figure BDA0001939320460000046
wherein
Figure BDA0001939320460000047
The representative system dynamic system response matrix satisfies the following conditions:
Figure BDA0001939320460000048
wherein
Figure BDA0001939320460000049
And
Figure BDA00019393204600000410
is a positive definite matrix.
Figure BDA00019393204600000411
Wherein
Figure BDA00019393204600000412
Satisfies A m ＝A+BK x ；
Figure BDA00019393204600000413
Satisfies B m ＝BK r (ii) a The output signals of the dynamics controller are tau and v m 。
C. Design of command regulator
The input signal of the command regulator is a reference speed signal v output by the dynamic controller m With outer loop given instruction signal v r . The output signal is an optimal guidance instruction signal under the condition of satisfying the state constraint and the control input constraint of the unmanned marine vehicle
Figure BDA00019393204600000414
The design method of the command governor is as follows.
Discretizing the unmanned marine vehicle dynamics reference model in the formula (4) to obtain a discretized dynamics prediction model in the command regulator:
Figure BDA00019393204600000415
wherein
Figure BDA00019393204600000416
And
Figure BDA00019393204600000417
are respectively A m And B m In a discrete form. Reference speed signal v in prediction time domain at k moment by adopting rolling time domain mode m The following formula is predicted:
Figure BDA00019393204600000418
wherein:
Figure BDA00019393204600000419
Figure BDA00019393204600000420
Figure BDA0001939320460000051
Figure BDA0001939320460000052
for reference speed signal v at time k m The predicted value of (2);
Figure BDA0001939320460000053
is the increment of the guidance signal at the time k; i is n Representing an n-dimensional identity matrix; n is a prediction time domain, and N is a prediction time domain,
Figure BDA0001939320460000054
to control the time domain. The instruction governor optimization objective function is represented as follows:
Figure BDA0001939320460000055
wherein:
Figure BDA0001939320460000056
the vector values are constant vectors which respectively represent the upper and lower limits of the increment of the guidance signal and the upper and lower limits of the guidance signal after considering the control input constraint;
Figure BDA0001939320460000057
representing the upper and lower bounds of the constraint after considering the state constraint,
Figure BDA0001939320460000058
is a normal number;
q and R represent the velocity state vector weight and the control input weight, respectively, and
Figure BDA0001939320460000059
converting formula (8) to the formula:
Figure BDA00019393204600000510
wherein:
W＝2(M T QM+R)
Figure BDA00019393204600000511
Figure BDA00019393204600000512
designing a neural dynamics optimization solving method to solve the objective function to obtain an optimal guidance signal meeting input constraint and state constraint, wherein the designed neural dynamics optimization solving method comprises the following steps:
Figure BDA0001939320460000061
wherein:
Figure BDA0001939320460000062
is a time constant;
Figure BDA0001939320460000063
is a normal number;
Figure BDA0001939320460000064
to represent
Figure BDA0001939320460000065
A gradient vector of (a);
Figure BDA0001939320460000066
to represent
Figure BDA0001939320460000067
The gradient vector of (a); projection function g [a,b] (ρ)＝[g [a,b] (ρ 1 ),...,g [a,b] (ρ n )]The design method comprises the following steps:
Figure BDA0001939320460000068
wherein: ρ = [ ρ ] 1 ,...,ρ n ] T ；a＝0；c＝1；i＝1,...,n。
Compared with the prior art, the invention has the following beneficial effects:
firstly, compared with the existing method which only considers the disturbance problem of the unmanned marine vehicle and ignores the dynamics constraint of the unmanned marine vehicle, the dynamics optimization controller structure and the design method provided by the invention combine the dimensionality reduction disturbance observer, the instruction optimization regulator and the dynamics controller, so that the internal and external disturbance and uncertainty information are accurately estimated and transmitted to the dynamics controller. Therefore, the problems of internal and external disturbance of the unmanned aircraft and dynamic constraint of the unmanned marine aircraft are solved, and the realization of the design method does not depend on an accurate unmanned marine aircraft model and is easier for engineering realization.
Secondly, the existing control methods such as barrier function design, auxiliary system design and the like do not fully consider some constraint conditions in unmanned marine vehicle motion control, such as input constraint and state constraint. The method simultaneously considers input constraint and state constraint, adopts rolling time domain prediction to establish an optimized objective function, and obtains a guidance signal meeting constraint conditions by applying neurodynamics optimization solution, so that a control signal meets the actual physical constraint of the unmanned marine vehicle, and the performance of the unmanned marine vehicle is greatly improved.
Thirdly, compared with the existing method for approximating uncertainty and disturbance by using a neural network, the disturbance observer of the unmanned marine vehicle is designed to estimate lumped uncertainty formed by modeling uncertainty of the unmanned marine vehicle and external disturbance brought by marine environment, and the method has the advantages of less required adjusting parameters and easiness in parameter adjustment. Meanwhile, the dynamics optimization control method of the unmanned ocean vehicle is not only suitable for the unmanned ocean vehicle on the water surface, but also suitable for the unmanned ocean vehicle under the water, and has important application value in the motion control occasions of target tracking, track path tracking and the like of the unmanned ocean vehicle on the water surface and the underwater.
Fourthly, the disturbance observer can estimate the internal and external disturbance and uncertainty information and send the estimated information to the dynamics controller, and the dynamics controller designs a control law considering the disturbance information, so that the anti-interference capability is improved.
Drawings
FIG. 1 is a schematic diagram of an unmanned marine vehicle dynamics optimization controller architecture;
FIG. 2 is a schematic view of the tracking effect of yaw rate;
FIG. 3 is a schematic diagram of the tracking effect of roll angular velocity;
FIG. 4 is a schematic illustration of the tracking effect of pitch angular velocity;
FIG. 5 is a schematic diagram of the tracking effect of longitudinal velocity;
FIG. 6 is a schematic diagram of the tracking effect of lateral velocity;
FIG. 7 is a schematic diagram of the tracking effect of vertical velocity;
FIG. 8 is a graph of lateral, longitudinal, and vertical lumped uncertainty observation estimates;
FIG. 9 is a block diagram of the effect of lumped uncertainty observation estimates on yaw, roll and pitch directions;
FIG. 10 is a schematic of longitudinal, lateral and vertical control components without kinetic optimization;
FIG. 11 is a schematic illustration of yaw, roll and pitch direction control components without dynamic optimization;
FIG. 12 is a schematic representation of kinetically optimized longitudinal, lateral and vertical control components;
FIG. 13 is a schematic representation of dynamically optimized yaw, roll and pitch directional control components.
Detailed Description
The invention is further described below with reference to the accompanying drawings. As shown in the figures 1-13 of the drawings,
the invention is further explained by taking a specific optimization control of the dynamics of the unmanned marine vehicle as an example, fig. 1 is a schematic structural diagram of the invention, the unmanned marine vehicle in the optimization control system of the dynamics of the unmanned marine vehicle satisfies a dynamics model in formula (1), and specific parameters of the model are as follows:
Figure BDA0001939320460000071
Figure BDA0001939320460000081
Figure BDA0001939320460000082
Figure BDA0001939320460000083
Figure BDA0001939320460000084
in this embodiment, the control objective of the dynamics optimization controller of the unmanned marine vehicle is to ensure that the unmanned marine vehicle accurately tracks an outer ring given command signal v r . The controller satisfies the controller structure described by the formulas (1) to (11), and the specific control parameters are as follows:
Figure BDA0001939320460000085
A m ＝diag(-3.5 -4 -4.5 -3 -4 -5)；
B m ＝diag(3.5 4 4.5 3 4 5)；
L＝diag(1000 1000 1000 500 500 500)；
K r ＝diag(204.4 95.2 107.1 10.14 4.72 13.35)；
ε＝0.00001；
τ ω = E sin (2 π ω t), where
Figure BDA0001939320460000086
ω is randomly generated.
The simulation results are shown in fig. 2-13. FIG. 2 is a schematic diagram of the tracking effect of the heading angular velocity, wherein a line p is a heading angular velocity component in an actual velocity signal of the unmanned ocean vehicle, and the line p is m For the yaw rate component in the reference rate signal, line p r The heading angular velocity component in the command velocity signal is given to the outer ring, and it can be seen from fig. 2 that the heading angular velocity component in the actual velocity signal can accurately track the heading angular velocity component in the command velocity signal given to the outer ring. FIG. 3 is a schematic diagram of the tracking effect of roll angular velocity, where a line q is a roll angular velocity component in an actual velocity signal of an unmanned marine vehicle, and a line q is a line m For the roll angular velocity component in the reference velocity signal, line q r Given the roll angular velocity component in the command velocity signal for the outer loop, it can be seen from fig. 3 that the roll angular velocity component in the actual velocity signal can be accurateAnd accurately tracking the roll angular velocity component in the given command velocity signal of the upper outer ring. FIG. 4 is a schematic diagram showing the tracking effect of the pitch angular velocity, wherein a line r is a pitch angular velocity component in an actual velocity signal of the unmanned ocean vehicle, and the line r m For the pitch angular velocity component in the reference velocity signal, line r r The pitching angular velocity component in the command velocity signal is given to the outer ring, and it can be seen from fig. 4 that the pitching angular velocity component in the actual velocity signal can accurately track the pitching angular velocity component in the command velocity signal given to the outer ring. FIG. 5 is a schematic diagram of the effect of tracking longitudinal velocity, where line u is the longitudinal velocity component of the actual velocity signal of the unmanned marine vehicle, line u m For the longitudinal velocity component in the reference velocity signal, line u r The longitudinal velocity component in the command velocity signal is given to the outer ring, and it can be seen from fig. 5 that the longitudinal velocity component in the actual velocity signal can accurately track the longitudinal velocity component in the command velocity signal given to the upper outer ring. FIG. 6 is a schematic diagram of the effect of tracking the lateral velocity, where line v is the lateral velocity component of the actual velocity signal of the unmanned marine vehicle, line v m For the transverse velocity component in the reference velocity signal, line v r The transverse velocity component in the command velocity signal is given to the outer loop, and it can be seen from fig. 6 that the transverse velocity component in the actual velocity signal can accurately track the transverse velocity component in the command velocity signal given to the outer loop. FIG. 7 is a schematic diagram of the tracking effect of the vertical velocity, where a line ω is a vertical velocity component in the actual velocity signal of the unmanned ocean vehicle, and the line ω is a line ω m For reference to the vertical velocity component of the velocity signal, line omega r The vertical velocity component in the command velocity signal is given to the outer ring, and it can be seen from fig. 7 that the vertical velocity component in the actual velocity signal can accurately track the vertical velocity component in the command velocity signal given to the outer ring. FIG. 8 is a graph of the effect of transverse, longitudinal, and vertical lumped uncertainty observations 1 Line sigma 2 Line sigma 3 Lumped uncertainty actual values for transverse, longitudinal and vertical directions, respectively, line in the graph
Figure BDA0001939320460000091
Line strip
Figure BDA0001939320460000092
Line strip
Figure BDA0001939320460000093
The transverse, longitudinal and vertical lumped uncertainty observation estimated values are respectively, and it can be seen from fig. 8 that the transverse, longitudinal and vertical lumped uncertainty can be accurately observed and estimated in real time. FIG. 9 is a graph of the effect of lumped uncertainty observation estimates on heading, roll and pitch directions, with line σ 4 Line sigma 5 Line sigma 6 Integrating uncertainty actual values of heading angle, roll angle and pitch angle directions respectively, and drawing lines
Figure BDA0001939320460000094
Line strip
Figure BDA0001939320460000095
Line strip
Figure BDA0001939320460000096
The collective uncertainty observation estimation values of the heading angle, the roll angle and the pitch angle are respectively, and it can be seen from fig. 9 that the collective uncertainty of the heading angle, the roll angle and the pitch angle can be accurately observed and estimated in real time. FIG. 10 is a schematic representation of the longitudinal, transverse and vertical control components without kinetic optimization, where line τ is u Line τ v Line τ ω The lateral, longitudinal and vertical control components are not dynamically optimized, and it can be seen from fig. 10 that at the time points 10S, 20S and 30S, the control components in the three directions exceed the upper and lower limits of the input constraint. FIG. 11 is a schematic representation of yaw, roll and pitch direction control components without dynamic optimization, where line τ is shown p Line τ q Line τ r The control components of the heading angle, the roll angle and the pitch angle which are not dynamically optimized are respectively shown in fig. 11, and it can be seen that the control components in the three directions have spike pulses and burrs at the time of 10S and 30S. FIG. 12 is kinetically optimizedLongitudinal, transverse and vertical control components, line τ u Line τ v Line τ ω The control components in the three directions satisfy the upper and lower limits of the input constraint, as can be seen from fig. 12. FIG. 13 is a schematic representation of the dynamically optimized yaw, roll and pitch direction control components, taken along line τ p Line τ q Line τ r The dynamically optimized control components of the heading angle, the roll angle and the pitch angle are respectively, and fig. 13 shows that the control components in the three directions have no spike and burr and are smoothly and continuously changed.
The present invention is not limited to the embodiment, and any equivalent idea or change within the technical scope of the present invention is to be regarded as the protection scope of the present invention.

Claims (1)

1. A design method of an unmanned ocean vehicle dynamics optimization controller structure comprises a disturbance observer, an instruction regulator and a dynamics controller, wherein the input end of the disturbance observer is connected with the output end of an unmanned ocean vehicle and the output end of the dynamics controller respectively; the input end of the dynamic controller is respectively connected with the output end of the unmanned marine vehicle, the output end of the disturbance observer and the output end of the instruction regulator; the input end of the instruction regulator is respectively connected with the output end of the dynamics controller and the outer ring controller;
the method is characterized in that: the six-degree-of-freedom dynamic model of the unmanned marine vehicle is represented by the following formula:
Figure FDA0003828389620000011
wherein:
Figure FDA0003828389620000012
indicating unmanned sea voyagePosition and attitude information of the device, wherein x, y, z, phi, theta and psi respectively represent a longitudinal displacement component, a transverse displacement component, a vertical displacement component, a yaw angle, a roll angle and a pitch angle;
Figure FDA0003828389620000013
represents a three-dimensional space;
Figure FDA0003828389620000014
representing a velocity signal of the unmanned aircraft, wherein u, v, w, p, q, and r represent a longitudinal velocity component, a lateral velocity component, a vertical velocity component, a heading angular velocity, a roll angular velocity, and a pitch angular velocity, respectively; satisfy unmanned ocean navigation ware state restraint v min ≤ν≤ν max Wherein v is min V and v max Respectively representing the upper and lower constraint bounds of v;
Figure FDA0003828389620000015
representing an inertia matrix;
Figure FDA0003828389620000016
represents a coriolis centripetal matrix;
Figure FDA0003828389620000017
represents a nonlinear damping matrix;
g (v, η) represents a restoring force caused by the combined action of buoyancy and gravity;
Figure FDA0003828389620000018
represents a control signal of the unmanned marine vehicle, where u 、τ v 、τ w 、τ p 、τ q And τ r Respectively representing the transverse control component, the longitudinal control component, the vertical control component,A yaw angle control component, a roll angle control component and a pitch angle control component; satisfying control input constraint τ min ≤τ≤τ max In which τ is min And τ max Respectively representing the constraint upper and lower bounds of tau;
Figure FDA0003828389620000019
representing the external disturbance of a time-varying ocean current in a marine environment on an unmanned marine vehicle, where τ wu 、τ wv 、τ ww 、τ wp 、τ wq And τ wr Respectively representing a transverse external disturbance component, a longitudinal external disturbance component, a vertical external disturbance component, a bow and roll angle external disturbance component, a roll angle external disturbance component and a pitch angle external disturbance component;
Figure FDA00038283896200000110
representing modeling errors of unmodeled fluid dynamics and unmanned marine vehicle;
the design method comprises the following steps:
A. design of disturbance observer
And (2) rewriting a six-degree-of-freedom dynamic model of the unmanned marine vehicle in the formula (1) into the following formula:
Figure FDA0003828389620000021
wherein
Figure FDA0003828389620000022
Is a known matrix;
B＝M -1 ；
σ(·)＝-C(ν)ν-D(ν)ν-Δ(ν,η)-g(η)+τ w (t)-M -1 avv represents lumped uncertainty caused by model uncertainty, uncertain hydrodynamic parameters and external disturbance brought by time-varying ocean currents in the ocean environment;
designing a disturbance observer to estimate the lumped uncertainty sigma, wherein input signals of the disturbance observer are a speed signal v and a control signal tau of the unmanned ocean vehicle, and the disturbance observer is expressed as follows:
Figure FDA0003828389620000023
wherein
Figure FDA0003828389620000024
Is the auxiliary state of the disturbance observer;
Figure FDA00038283896200000217
is an estimate of the lumped uncertainty σ;
Figure FDA0003828389620000025
represents a gain matrix;
B. design of dynamic controller
The input signal to the dynamics controller comprises the output signal of the demand regulator
Figure FDA0003828389620000026
Lumped uncertainty estimation value output by speed signal v and disturbance observer output by unmanned ocean vehicle
Figure FDA0003828389620000027
The dynamics controller outputs signals tau, v m The design is as follows:
Figure FDA0003828389620000028
wherein
Figure FDA0003828389620000029
The representative system dynamic system response matrix satisfies the following conditions:
Figure FDA00038283896200000210
wherein
Figure FDA00038283896200000211
And
Figure FDA00038283896200000212
is a positive definite matrix;
Figure FDA00038283896200000213
wherein
Figure FDA00038283896200000214
Satisfies A m ＝A+BK x ；
Figure FDA00038283896200000215
Satisfies B m ＝BK r (ii) a The output signals of the dynamics controller are tau and v m ；
C. Design of command regulator
The input signal of the command regulator is a reference speed signal v output by the dynamic controller m With outer loop given command signal v r (ii) a The output signal is an optimal guidance instruction signal under the condition of satisfying the state constraint and the control input constraint of the unmanned marine vehicle
Figure FDA00038283896200000216
The design method of the command regulator is as follows;
discretizing the unmanned marine vehicle dynamic reference model in the formula (4) to obtain a discretized dynamic prediction model in the command regulator:
Figure FDA0003828389620000031
wherein
Figure FDA0003828389620000032
And
Figure FDA0003828389620000033
are respectively A m And B m A discrete form of (a); reference speed signal v in prediction time domain at k moment by adopting rolling time domain mode m The following formula is predicted:
Figure FDA0003828389620000034
wherein:
Figure FDA0003828389620000035
Figure FDA0003828389620000036
Figure FDA0003828389620000037
Figure FDA0003828389620000038
for time k to reference velocity signal v m The predicted value of (2); delta - [k]＝ - [k]- - [k-1]Is the increment of the guidance signal at the time k; i is n Representing an n-dimensional identity matrix; n is a prediction time domain, and N is a prediction time domain,
Figure FDA00038283896200000314
is a control time domain; the instruction governor optimization objective function is represented as follows:
Figure FDA0003828389620000039
wherein:
Figure FDA00038283896200000310
the vector values are constant vectors which respectively represent the upper and lower limits of the increment of the guidance signal and the upper and lower limits of the guidance signal after considering the control input constraint;
Figure FDA00038283896200000311
representing the upper and lower bounds of the constraint after considering the state constraint,
Figure FDA00038283896200000312
is a normal number;
q and R represent the speed state vector weight and the control input weight respectively, and
Figure FDA00038283896200000313
converting formula (8) to the following formula:
Figure FDA0003828389620000041
wherein:
W＝2(M T QM+R)
Figure FDA0003828389620000042
Figure FDA0003828389620000043
designing a neural dynamics optimization solving method to solve the objective function to obtain an optimal guidance signal meeting input constraint and state constraint, wherein the designed neural dynamics optimization solving method comprises the following steps:
Figure FDA0003828389620000044
wherein:
Figure FDA0003828389620000045
is a time constant;
Figure FDA0003828389620000046
is a normal number;
Figure FDA0003828389620000047
represent
Figure FDA0003828389620000048
A gradient vector of (a);
Figure FDA0003828389620000049
to represent
Figure FDA00038283896200000410
A gradient vector of (a); projection function g [a,b] (ρ)＝[g [a,b] (ρ 1 ),...,g [a,b] (ρ n )]The design method comprises the following steps:
Figure FDA00038283896200000411
wherein: ρ = [ ρ ] 1 ,...,ρ n ] T ；a＝0；c＝1；i＝1,...,n。
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