CN102183889A - Heading robust intelligent coordination control method for pump-paddle hybrid propelled ship - Google Patents

Abstract

The invention provides a heading robust intelligent coordination control method for a pump-paddle hybrid propelled ship, wherein comprises the following steps: setting a command heading; acquiring the heading angle, heading angular velocity, swaying displacement and swaying speed of actual heading of the ship and feeding the information back to a robust controller by a heading detection device to; calculating a heading yawing torque required by the heading control by the robust controller by using a H2/H infinity control algorithm and combining with performance evaluation indexes of a control system; calculating and acquiring a helm angle value and a nozzle corner value by a pump paddle hybrid intelligent decision system by using a self-adaption genetic algorithm, and then respectively sending the helm angle value and the nozzle corner value to a steering engine servo system and a nozzle corner servo system; and changing the actual heading of the ship into the command heading under the combined control of a helm and a water spray propeller, thereby realizing the heading control for the pump-paddle hybrid propelled ship. By utilizing the robust control technology, the robustness of the control system is enhanced, the convenient engineering application is realized, the early-maturing problem and the slow evolution problem of the basic genetic algorithm are solved, and the energy consumption of driving system is reduced.

Description

Pump oar hybrid propulsion ship course robust intelligent coordination control method

Technical field

What the present invention relates to is a kind of ship's navigation attitude control method, particularly a kind of course heading control method of pump oar hybrid propulsion boats and ships.

Background technology

Cruise good economy performance, mobility of boats and ships is excellent, has become the target that Ship Design person pursues.On the one hand,, require boats and ships to cruise and improve the efficient of effectively cruising in the process, reduce ship resistance, thereby require boats and ships to have good and economic for saving fuel oil and arriving the destination as far as possible fast; On the other hand, boats and ships from alongside, dynamically positioning, turn around flexibly, barrier is evaded etc., and operating mode has the requirement boats and ships in time to change course and the speed of a ship or plane, thereby also require boats and ships to have good maneuverability, be turnability and acceleration and deceleration performance.Given this, traditional single thruster and array mode thereof can not meet the demands, and Ship Design person begins to consider to adopt the pattern of multiple different thruster hybrid propulsion, and wherein hydraulic propeller and screw propeller hybrid propulsion are the most typical.

Pump oar hybrid propulsion ship course control system is a kind of typical multivariate, non-coupling, uncertain Complex Nonlinear System, the course control law of appropriate design boats and ships, with hydraulic propeller and steering wheel organically combine, reasonable distribution, for improving the boats and ships handling, reducing system energy consumption and have important research and be worth.

Through existing technical literature retrieval is found, name in the CNKI database is called in the document of " mixed propulsion system hydraulic propeller and screw propeller repercussion study ", when finding the solution Reynolds equal RANS equation numerical simulation the flow field of mixed propulsion system, studied the interaction between two kinds of thrusters, but with the intelligent coordinated control of pump oar hybrid propulsion ship course robust of the present invention different.

Summary of the invention

The object of the present invention is to provide a kind of can under the prerequisite that guarantees direction stability, the raising course and control effect, reduce the pump oar hybrid propulsion ship course robust intelligent coordination control method of system energy consumption.

The object of the present invention is achieved like this:

The setting command course, the course pick-up unit is gathered course angle, course angle speed, swaying displacement and the swaying velocity information of boats and ships actual heading, and the information feedback of four amounts is arrived robust controller, and robust controller adopts H ²/ H ^∞Control algolithm, in conjunction with control system performance evaluation index, calculate the course and control needed course yawing moment, pump oar hybrid intelligent decision system adopts self-adapted genetic algorithm, with " system drive energy+course control performance index " minimum is objective function, satisfy the amplitude peak that steering and spout turn to, rate constraint, carrying out spout corner/rudder angle on-line intelligence distributes, calculate rudder angle and spout corner value, and rudder angle and spout corner value sent into steering wheel servo-drive system and spout corner servo-drive system respectively, drive steering wheel and spout angle station, boats and ships are under the combination control of rudder and hydraulic propeller, course change to command heading, is realized the course control to pump oar hybrid propulsion boats and ships.

The invention has the advantages that:

(1) course control system of pump oar hybrid propulsion boats and ships is the multiple-input and multiple-output coupled system, and operational configuration and marine environment is complicated and changeable, system exists random disturbance and model uncertainties such as sea wind, wave, ocean current, and the employing robust control technique has strengthened the control system robustness and engineering is used conveniently.

(2) decision-making of pump oar hybrid propulsion ship course intelligent adopts crossover probability and variation probability to carry out self-adjusting self-adapted genetic algorithm according to fitness value, the premature convergence problem of basic genetic algorithmic and the slow problem of evolving have been improved, guarantee is provided, has reduced the drive system energy consumption for seeking spout corner/rudder angle global optimum combination.

Description of drawings

Fig. 1 is the intelligent coordinated control structure figure of pump oar hybrid propulsion ship course robust;

Fig. 2 is a pump oar hybrid propulsion ship course intelligent decision making algorithm process flow diagram.

Embodiment

For a more detailed description to the present invention for example below in conjunction with accompanying drawing:

In conjunction with Fig. 1, the present invention realizes pump oar hybrid propulsion ship course design of robust controller by 5 steps, and is specific as follows:

The first step: set up boats and ships swaying/yawing coupled motions equation

Consider the coupling influence of boats and ships swaying/yawing campaign, ignore the influence of other degree of freedom motions, disturb the moment that produces to regard as rudder, hydraulic jet propulsion system and ocean, ignore the influence of rolling and the nonlinear terms in the equation simultaneously, and establish m the acting force of ship course motion _y=a _Yy, Y _v=b _Yy, Δ I _z=a _{ψ ψ}, N _r=b _{ψ ψ}, N _v=b _{ψ y}, order again

Wherein y represents the swaying displacement, obtains the linear coupled motions model of boats and ships swaying/yawing to be:

$(m+a_{\mathrm{yy}})\stackrel{\·\·}{y}+b_{\mathrm{yy}}\stackrel{\·}{y}+a_{\mathrm{y\ψ}}\stackrel{\·\·}{\mathrm{\ψ}}+b_{\mathrm{y\ψ}}\stackrel{\·}{\mathrm{\ψ}}=Y_{r+w}+Y_d$

$(I_z+a_{\mathrm{\ψ\ψ}})\stackrel{\·\·}{\mathrm{\ψ}}+b_{\mathrm{\ψ\ψ}}\stackrel{\·}{\mathrm{\ψ}}+a_{\mathrm{\ψy}}\stackrel{\·\·}{y}+b_{\mathrm{\ψy}}\stackrel{\·}{y}=N_{r+w}+N_d$

Wherein:

M-----boats and ships quality; m _y-----swaying additional mass;

Y-----swaying displacement; ψ-----course angle;

V-----swaying speed; R-----yaw angle speed;

a _{Y ψ}-----additional mass; a _{ψ ψ}, a _{ψ y}Represent the additional rotation moment of inertia;

b _Yy, b _{ψ ψ}, b _{ψ y}, b _{Y ψ}-----kinetic damping coefficient; I _z-----hull quality is to the moment of inertia of z axle;

y _R+w, N _R+wSwaying power, yawing moment that-----rudder, hydraulic jet propulsion system produce;

Y _d, N _dThe swaying perturbed force that-----boats and ships are subjected to, yawing disturbance torque.

Second step: consider parameter uncertainty

If m, I _zBe permanent, have probabilistic parameter and be mainly a _Yy, a _{ψ ψ}, a _{Y ψ}, a _{ψ y}, b _Yy, b _{Y ψ}, b _{ψ ψ}, b _{ψ y}, have:

$a_{\mathrm{yy}}={\stackrel{\‾}{a}}_{\mathrm{yy}}+\mathrm{\Δ}{a}_{\mathrm{yy}}{\mathrm{\δ}}_1;$ $a_{\mathrm{\ψ\ψ}}={\stackrel{\‾}{a}}_{\mathrm{\ψ\ψ}}+\mathrm{\Δ}{a}_{\mathrm{\ψ\ψ}}{\mathrm{\δ}}_2$

$a_{\mathrm{y\ψ}}={\stackrel{\‾}{a}}_{\mathrm{y\ψ}}+\mathrm{\Δ}{a}_{\mathrm{y\ψ}}{\mathrm{\δ}}_3;$ $a_{\mathrm{\ψy}}={\stackrel{\‾}{a}}_{\mathrm{\ψy}}+\mathrm{\Δ}{a}_{\mathrm{\ψy}}{\mathrm{\δ}}_4$

$b_{\mathrm{yy}}={\stackrel{\‾}{b}}_{\mathrm{yy}}+\mathrm{\Δ}{b}_{\mathrm{yy}}{\mathrm{\δ}}_5;$ $b_{\mathrm{\ψ\ψ}}={\stackrel{\‾}{b}}_{\mathrm{\ψ\ψ}}+\mathrm{\Δ}{b}_{\mathrm{\ψ\ψ}}{\mathrm{\δ}}_6$

$b_{\mathrm{y\ψ}}={\stackrel{\‾}{b}}_{\mathrm{y\ψ}}+\mathrm{\Δ}{b}_{\mathrm{y\ψ}}{\mathrm{\δ}}_7;$ $b_{\mathrm{\ψy}}={\stackrel{\‾}{b}}_{\mathrm{\ψy}}+\mathrm{\Δ}{b}_{\mathrm{\ψy}}{\mathrm{\δ}}_8$

Wherein: δ _iBe uncertain variable, and have || δ _i||≤1, i=1,2,, 8.

Make x=[x ₁x ₂x ₃x ₄] ^T, x ₁=y,

x ₃=ψ, U=P _y(α _r, β _w), w ₁=[Y _dN _d] ^T, the linear coupled motions of boats and ships swaying/yawing are write as the form of following state equation:

Wherein: α _HBe rudder, hydraulic jet propulsion system and hull hydrodynamic influence coefficient, x _R, x _WBe respectively the along slope coordinate of rudder, hydraulic jet propulsion system hydrodynamism point.

The 3rd step: carry out the design of robust control canonical form

Use H ²/ H ^∞Control technology designs robust controller, boats and ships swaying/yawing coupled motions equation is converted into can carries out H ²/ H ^∞The canonical form of controlling Design, order:

$A=\left[\begin{array}{cccc}1& 0& 0& 0\\ 0& m+a_{\mathrm{yy}}& 0& a_{\mathrm{y\ψ}}\\ 0& 0& 1& 0\\ 0& a_{\mathrm{\ψy}}& 0& I_z+a_{\mathrm{\ψ\ψ}}\end{array}\right]$

$=\left[\begin{array}{cccc}1& 0& 0& 0\\ 0& m+{\stackrel{\&OverBar;}{a}}_{\mathrm{yy}}& 0& {\stackrel{\&OverBar;}{a}}_{\mathrm{y\&psi;}}\\ 0& 0& 1& 0\\ 0& {\stackrel{\&OverBar;}{a}}_{\mathrm{\&psi;y}}& 0& I_z+{\stackrel{\&OverBar;}{a}}_{\mathrm{\&psi;\&psi;}}\end{array}\right]+\left[\begin{array}{cccc}0& 0& 0& 0\\ 0& {\mathrm{\&Delta;a}}_{\mathrm{yy}}{\mathrm{\&delta;}}_1& 0& \mathrm{\&Delta;}{a}_{\mathrm{y\&psi;}}{\mathrm{\&delta;}}_3\\ 0& 0& 0& 0\\ 0& \mathrm{\&Delta;}{a}_{\mathrm{\&psi;y}}{\mathrm{\&delta;}}_4& 0& \mathrm{\&Delta;}{a}_{\mathrm{\&psi;\&psi;}}{\mathrm{\&delta;}}_2\end{array}\right]={\stackrel{\&OverBar;}{A}}_1+\mathrm{\&Delta;}{\mathrm{<figure-callout\; id="1"\; label="A"\; filenames="HDA0000048889130000012.png"\; state="\{\{state\}\}">A</figure-callout>}}_1$

$B=\left[\begin{array}{cccc}0& 1& 0& 0\\ 0& -b_{\mathrm{yy}}& 0& {-b}_{\mathrm{y\&psi;}}\\ 0& 0& 0& 1\\ 0& -b_{\mathrm{\&psi;y}}& 0& -b_{\mathrm{\&psi;\&psi;}}\end{array}\right]$

$=\left[\begin{array}{cccc}0& 1& 0& 0\\ 0& -{\stackrel{\&OverBar;}{b}}_{\mathrm{yy}}& 0& -{\stackrel{\&OverBar;}{b}}_{\mathrm{y\&psi;}}\\ 0& 0& 0& 1\\ 0& -{\stackrel{\&OverBar;}{b}}_{\mathrm{\&psi;y}}& 0& -{\stackrel{\&OverBar;}{b}}_{\mathrm{\&psi;\&psi;}}\end{array}\right]+\left[\begin{array}{cccc}0& 0& 0& 0\\ 0& -{\mathrm{\&Delta;b}}_{\mathrm{yy}}{\mathrm{\&delta;}}_5& 0& -\mathrm{\&Delta;}{b}_{\mathrm{y\&psi;}}{\mathrm{\&delta;}}_7\\ 0& 0& 0& 0\\ 0& -\mathrm{\&Delta;}{b}_{\mathrm{\&psi;y}}{\mathrm{\&delta;}}_8& 0& -\mathrm{\&Delta;}{b}_{\mathrm{\&psi;\&psi;}}{\mathrm{\&delta;}}_6\end{array}\right]=\stackrel{\&OverBar;}{B}+\mathrm{\&Delta;B}$

$C=\left[\begin{array}{c}0\\ 1+a_H\\ 0\\ -(1+a_H)x_R\end{array}\right],$ $D=\left[\begin{array}{cc}0& 0\\ 1& 0\\ 0& 0\\ 0& 1\end{array}\right]$

Then boats and ships swaying/yawing coupled motions equation can turn to:

$({\stackrel{\&OverBar;}{A}}_1+{\mathrm{\&Delta;A}}_1)\stackrel{\&CenterDot;}{x}=(\stackrel{\&OverBar;}{B}+\mathrm{\&Delta;B})x+\mathrm{Cu}+D{w}_1$

Order

Definition broad sense is disturbed

Then have:

${\mathrm{Dw}}_1-\mathrm{\&Delta;}{A}_1\stackrel{\&CenterDot;}{x}=C_{0}w,$ $C_0=\left[\begin{array}{cc}0& 0\\ 1& 0\\ 0& 0\\ 0& 1\end{array}\right]$

Order again:

$A={\stackrel{\&OverBar;}{A}}_1^{-1}\stackrel{\&OverBar;}{B},$ $\mathrm{\&Delta;A}={\stackrel{\&OverBar;}{A}}_1^{-1}\mathrm{\&Delta;B},$ ${\mathrm{<figure-callout\; id="1"\; label="B"\; filenames="HDA0000048889130000012.png"\; state="\{\{state\}\}">B</figure-callout>}}_1={\stackrel{\&OverBar;}{A}}_1^{-1}C,$ $B_2={\stackrel{\&OverBar;}{A}}_1^{-1}{C}_0$

Finally obtain following form:

$\stackrel{\&CenterDot;}{x}=(A+\mathrm{\&Delta;A})x+B_{1}u+B_{2}w$

And have: [Δ A Δ B ₁]=HF[E ₁E ₂]

$H={\stackrel{\&OverBar;}{A}}_1^{-1}\left[\begin{array}{cccc}0& 0& 0& 0\\ 1& 0& 1& 0\\ 0& 0& 0& 0\\ 0& 1& 0& 1\end{array}\right],$ $F=\left[\begin{array}{cccc}{\mathrm{\&delta;}}_5& 0& 0& 0\\ 0& {\mathrm{\&delta;}}_8& 0& 0\\ 0& 0& {\mathrm{\&delta;}}_7& 0\\ 0& 0& 0& {\mathrm{\&delta;}}_6\end{array}\right]$

${\mathrm{<figure-callout\; id="1"\; label="E"\; filenames="HDA0000048889130000012.png"\; state="\{\{state\}\}">E</figure-callout>}}_1=\left[\begin{array}{cccc}0& -\mathrm{\&Delta;}{b}_{\mathrm{yy}}& 0& 0\\ 0& -{\mathrm{\&Delta;b}}_{\mathrm{\&psi;y}}& 0& 0\\ 0& 0& 0& -\mathrm{\&Delta;}{b}_{\mathrm{y\&psi;}}\\ 0& 0& 0& -\mathrm{\&Delta;}{b}_{\mathrm{\&psi;\&psi;}}\end{array}\right],$ E ₂＝0 _4×1

Wherein: F is uncertain matrix, obviously is to satisfy F ^TF≤I's, Δ A, Δ B ₁Satisfy H ²/ H ^∞The canonical form of robust control.

The 4th step: set up ship course control system performance evaluation signal

Pump oar hybrid propulsion ship course robust control system mathematical model is:

$\left\{\begin{array}{c}\stackrel{\&CenterDot;}{x}=(A+\mathrm{\&Delta;A})x+({\mathrm{<figure-callout\; id="1"\; label="B"\; filenames="HDA0000048889130000012.png"\; state="\{\{state\}\}">B</figure-callout>}}_1+\mathrm{\&Delta;}{B}_1)u+B_{2}w\\ z_{\&infin;}=C_{1}x+D_{12}u\\ z_2=C_{2}x+D_{22}u\\ y=\mathrm{Cx}+\mathrm{Du}\end{array}\right.$

System H ^∞Performance mainly strengthens the robustness of control system to wave interference and model parameter perturbation; For the H of system ²Performance then needs to take all factors into consideration course angle, course angle speed and to the constraint of controlled quentity controlled variable.The performance evaluation signal of definition pump oar hybrid propulsion ship course control system is:

z _∞＝ψ＝[0?0?10?]x

$z_2=\left[\begin{array}{c}\mathrm{\&psi;}\\ \stackrel{\&CenterDot;}{\mathrm{\&psi;}}\\ u\end{array}\right]=\left[\begin{array}{cccc}0& 0& 1& 0\\ 0& 0& 0& 1\\ 0& 0& 0& 0\end{array}\right]x+\left[\begin{array}{c}0\\ 0\\ 1\end{array}\right]u$

The 5th step: find the solution pump oar hybrid propulsion ship course robust controller

At the matrix of coefficients that obtains boats and ships swaying/yawing robust control system model, and after the performance evaluation signal of define system, find the solution the ship course robust controller, obtain u=[k ₁k ₂] ^TX.

In conjunction with Fig. 1, Fig. 2, calculate the required yawing uprighting force of course control by pump oar hybrid propulsion ship course robust controller, have multiple different spouts corner/rudder angle combination corresponding with it for the yawing uprighting force of determining, spout corner/rudder angle intelligent decision is followed " system drive energy+course control performance index " the minimum former min of being (Δ J _{α β}(k+1)+Δ J _{ψ δ}(k+1)), spout corner/rudder angle satisfies following allocation rule:

$\left\{\begin{array}{c}\left|{\mathrm{\&alpha;}}_r(k+1)\right|\&le;{\mathrm{\&alpha;}}_{r\max },\left|\frac{{\mathrm{\&alpha;}}_r(k+1)-{\mathrm{\&alpha;}}_r\left(k\right)}{\mathrm{\&Delta;t}}\right|\&le;{\stackrel{\&CenterDot;}{\mathrm{\&alpha;}}}_{r\max }\\ \left|{\mathrm{\&beta;}}_w(k+1)\right|\&le;{\mathrm{\&beta;}}_{w\max },\left|\frac{{\mathrm{\&beta;}}_w(k+1)-{\mathrm{\&beta;}}_w\left(k\right)}{\mathrm{\&Delta;t}}\right|\&le;{\stackrel{\&CenterDot;}{\mathrm{\&beta;}}}_{w\max }\\ \min (\mathrm{\&Delta;}{J}_{\mathrm{\&alpha;\&beta;}}(k+1)+\mathrm{\&Delta;}{J}_{\mathrm{\&psi;\&delta;}}(k+1))\end{array}\right.$

Wherein, α _Rmax,

Be respectively hard over angle, hard over angle speed, β _Wmax,

Be respectively maximum spout corner, maximum spout tarnsition velocity, Δ J _{α β}(k+1) be the driving energy that steering wheel and spout turn to, Δ J _{ψ δ}(k+1) be course control performance index.

Adopt self-adapted genetic algorithm to carry out spout corner/rudder angle intelligent decision, basic step is as follows:

Step 1: iterations t=0, select binary-coded initial population E:(k+1 at random) rudder angle α constantly _r(k+1) and spout corner β _w(k+1), coding range is respectively: $[{\mathrm{\&alpha;}}_r\left(k\right)-{\stackrel{\&CenterDot;}{\mathrm{\&alpha;}}}_{r\max }\&CenterDot;\mathrm{\&Delta;t},{\mathrm{\&alpha;}}_r\left(k\right)+{\stackrel{\&CenterDot;}{\mathrm{\&alpha;}}}_{r\max }\&CenterDot;\mathrm{\&Delta;t}],$ $[{\mathrm{\&beta;}}_w\left(k\right)-{\stackrel{\&CenterDot;}{\mathrm{\&beta;}}}_{w\max }\&CenterDot;\mathrm{\&Delta;t},{\mathrm{\&beta;}}_w\left(k\right)+{\stackrel{\&CenterDot;}{\mathrm{\&beta;}}}_{w\max }\&CenterDot;\mathrm{\&Delta;t}].$

Step 2: calculate ideal adaptation degree functional value at the individuality among the E:

$f({\mathrm{\&alpha;}}_r(k+1),{\mathrm{\&beta;}}_w(k+1))=\frac{1}{1+(\mathrm{\&Delta;}{J}_{\mathrm{\&alpha;\&beta;}}(k+1)+\mathrm{\&Delta;}{J}_{\mathrm{\&psi;\&delta;}}(k+1))}.$

Step 3: selection operation adopts the best method that keeps colony and select best several body combination from filial generation and parent, with the best individuality of previous generation and best individual comparison of a new generation, if the best individuality of a new generation is stronger than previous generation, then the poorest individual best individuality with previous generation with a new generation replaces; If a new generation is relatively poor, then replace the best individuality of a new generation with the best individuality of previous generation.

Step 4: intersect and the new population of generation that makes a variation, intersection and mutation operation are the main method that genetic algorithm simulating nature biological evolution process produces new individuality, and crossover probability Pc can change with fitness automatically with the probability P m that makes a variation in the self-adapted genetic algorithm.

$P_c=\left\{\begin{array}{cc}{P}_{c_1}-\frac{(P_{c_1}-P_{c_2})({\mathrm{fit}}_{\max }-{\mathrm{fit}}_i^{\&prime;})}{({\mathrm{fit}}_{\max }-{\mathrm{fit}}_{\mathrm{avg}})},& {\mathrm{fit}}_i^{\&prime;}\&GreaterEqual;{\mathrm{fit}}_{\mathrm{<figure-callout\; id="1"\; label="avg\; P\; c"\; filenames="HDA0000048889130000012.png"\; state="\{\{state\}\}">avg</figure-callout>}}& \\ P_{c_{}}{{}_1}_{},& {\mathrm{fit}}_i^{\&prime;}<{\mathrm{fit}}_{\mathrm{avg}}\end{array}\right.$

$P_m=\left\{\begin{array}{cc}{P}_{m_1}-\frac{(P_{m_1}-P_{m_2})({\mathrm{fit}}_{\max }-{\mathrm{fit}}_i^{\&prime;})}{({\mathrm{fit}}_{\max }-{\mathrm{fit}}_{\mathrm{avg}})},& {\mathrm{fit}}_i^{\&prime;}\&GreaterEqual;{\mathrm{fit}}_{\mathrm{<figure-callout\; id="1"\; label="avg\; P\; m"\; filenames="HDA0000048889130000012.png"\; state="\{\{state\}\}">avg</figure-callout>}}& \\ P_{m_{}}{{}_1}_{},& {\mathrm{fit}}_i^{\&prime;}<{\mathrm{fit}}_{\mathrm{avg}}\end{array}\right.$

Wherein: fit _Max, fit _AvgBe respectively maximum and average adaptation functional value when former generation glade body; Fit ' _iBe bigger adaptation functional value in two intersection individualities,

Be respectively maximum crossover probability, minimum crossover probability, maximum variation probability and minimum variation probability.

In the operating process, adapt to the little individuality of functional value and have bigger crossing-over rate and aberration rate, help accelerating search speed like this, and crossing-over rate and the aberration rate of maximum adaptation functional value homologue are non-vanishing, and are a smaller value.

Step 5: calculate the iterations that individual pairing fitness function value meets the demands or reaches setting? if then enter step 6, otherwise jump to step 2.

Step 6: provide rudder angle α _r(k+1) and hydraulic propeller spout corner β _w(k+1) best of breed finishes.

Boats and ships to command heading, are finished the intelligent coordinated control of pump oar hybrid propulsion ship course robust to course change under the combination control of rudder angle and spout corner, realize the effective control to pump oar hybrid propulsion ship course.

Claims (1)

1. pump oar hybrid propulsion ship course robust intelligent coordination control method, it is characterized in that: the setting command course, the course pick-up unit is gathered course angle, course angle speed, swaying displacement and the swaying velocity information of boats and ships actual heading, feeds back to robust controller, and robust controller adopts H ²/ H ^∞Control algolithm, in conjunction with control system performance evaluation index, calculate the course and control needed course yawing moment, pump oar hybrid intelligent decision system adopts self-adapted genetic algorithm, with " system drive energy+course control performance index " minimum is objective function, satisfy the amplitude peak that steering and spout turn to, rate constraint, carrying out spout corner and rudder angle on-line intelligence distributes, calculate rudder angle and spout corner value, and rudder angle and spout corner value sent into steering wheel servo-drive system and spout corner servo-drive system respectively, drive steering wheel and spout angle station, boats and ships are under the combination control of rudder and hydraulic propeller, course change to command heading, is realized the course control to pump oar hybrid propulsion boats and ships.

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