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CÁC FILE MATLAB ĐỂ THỰC HIỆN MÔ PHỎNG ĐIỀU KHIỂN AUV‌

PL1. File mô phỏng điều khiển hướng và độ sâu AUV

% Submarine control test

% Date: 2019/Apr/14th

% ANNAI control of heading and depth

% Author: PHAM VIET ANH – HCM Unv. Of Transport

%======================================================================

clear; % clear all variables

NN = 6000; % number of sample

h = 0.1; % sample time (sec) i = 0;

% -------------------------------------- z = 0;

delta = 0;

delta_s = 0;

max_delta = 20*(pi/180); max_delta_s = 20*(pi/180);

% Controller's parameters of the rudder

% rho = 1; lambda = 0.2; sigma1 = 0.2; sigma2 = 0.05;

rho = 0.5; lambda = 0.1; sigma1 = 0.2; sigma2 = 0;

% Controller's parameters of the P&S stern planes

rho_s = 0.2; lambda_s = 0.1; sigma1_s = 0.2; sigma2_s = 0;

% ui = [ delta_r delta_s delta_b delta_bp delta_bs n ]'

% initial states: x = [ u v w p q r x y z phi theta psi ]' x = [0.3 0 0 0 0 0 0 0 -10 0 0 0*pi/180]';

U = 0.3;

%*************** START SIMULATION ******************* disp('Running...')

% We start executing the controller with ralative small weights: W21 = rand(6,4)*0.0001; % Set random hidden layer weights

W32 = rand(1,6)*0.0001; % Set random output layer weights W21_s = rand(6,4)*0.0001; % Set random hidden layer weights W32_s = rand(1,6)*0.0001; % Set random output layer weights

% lr_rate(3) = 0.01; % Initial learning rate

% N(3) = 5; % Initial number of iterations

net_in = zeros(4,1); % Set initial inputs of neural network net_in_s = zeros(4,1);

while i < NN, i=i+1;

time = (i-1)*h; % simulation time in seconds clc;

fprintf('Running...n*Counter = %d (%d)n',round(i/10),round((NN- i)/10));

r = x(6);

w = x(3);

% Sensor noise for the heading sensor with a uniform distribution on

% [-0.1,+0.1] deg.

% s = 0.1*(pi/180)*(2*rand - 1);

s = 0; % This allow us to remove the noise psi = x(12) + s; % Heading of the ship with sensor noise Z = x(9);

% psi = x(6);

% control system

% Phuong an 1:

if i<2000, psi_ref(i) = 10*(pi/180); end if i>=2000, psi_ref(i) = 0*(pi/180); end if i>=4000, psi_ref(i) = 10*(pi/180); end

% Phuong an 2

% psi_ref(i) = 10*(pi/180); Z_ref(i) = -4;

err = psi-psi_ref(i); % Do lech huong di so voi huong dat net_in(1) = err;

err_s = Z-Z_ref(i); net_in_s(1) = err_s;

ii=round(i/10);

if round(i/10)==0,ii=2,end

% store data for presentation xout(i,:) = [time,x',U,delta,delta_s]; e_out(ii,:) = [err; err_s];

%----------------------------------------------------------------------

----

% Dau ra cua BDK duoc tinh toan 1 giay 1 lan (theo bien "ii") if ii>3

% Calculate rudder command:

delta = mlnnc(1,6,4,net_in,W21,W32); delta_s = mlnnc(1,6,4,net_in_s,W21_s,W32_s);

if abs(delta)>max_delta,delta=sign(delta)*max_delta;end if

abs(delta_s)>max_delta_s,delta_s=sign(delta_s)*max_delta_s;end

ui = [delta

delta_s 0

300 ];

[xdot,U] = npsauv(x,ui);

% numerical integration

x = euler2(xdot,x,h); % Euler integration

% z = z + h*err;

% z = z + h*err_s;

% Now update the weights of NN using adaptive interaction algorithm

% (intensive training):

[W21,W32] = annaiTrainIte(h,0.5,50,1,6,4,net_in,... err,delta,r,z,rho,lambda,sigma1,sigma2); % Add

integration action

[W21_s,W32_s] = annaiTrainIte(h,0.5,50,1,6,4,net_in_s,...

err_s,delta_s,w,z,rho_s,lambda_s,sigma1_s,sigma2_s); % Add integration action

net_in(2) = e_out(ii-1,1); net_in(3) = e_out(ii-2,1); net_in(4) = e_out(ii-3,1);

net_in_s(2) = e_out(ii-1,2); net_in_s(3) = e_out(ii-2,2); net_in_s(4) = e_out(ii-3,2);

end % end of "if ii>3"

%----------------------------------------------------------------------

end

% time-series

t = xout(:,1);

u = xout(:,2);

v = xout(:,3);

w = xout(:,4);

p = xout(:,5);

q = xout(:,6);

r = xout(:,7)*180/pi;

X = xout(:,8);

Y = xout(:,9);

Z = xout(:,10);

phi = xout(:,11); theta = xout(:,12);

psi = xout(:,13)*180/pi; U = xout(:,14);

delta = -xout(:,15)*180/pi; delta_s = xout(:,16)*180/pi; psi_ref = psi_ref*180/pi;

% PLOT THE RESULTS

figure(1) clf subplot(211)

plot(t,psi_ref,'LineWidth',2,'Color','r','LineStyle','--') hold on

plot(t,psi,'LineWidth',2,'Color','b','LineStyle','-')

title('NNC control - Yaw angle psi (deg)'),ylabel('[Deg]'),grid on legend('Set course','Heading','Location','Best')

subplot(212) hold on

plot(t,delta,'LineWidth',2,'Color','b','LineStyle','-') title('Rudder angle delta (deg)')

xlabel('Time (s)'),ylabel('[Deg]'),grid on

% legend('PID','NNC')

figure(2) clf

hold on subplot(211)

plot(t,Z_ref,'LineWidth',2,'Color','r','LineStyle','--') hold on

plot(t,Z,'LineWidth',2,'Color','b','LineStyle','-')

title('NNC control - Actual depth Z (m)'),ylabel('[m]'),grid on legend('Desired depth','Actual depth','Location','Best') subplot(212) plot(t,delta_s,'LineWidth',2,'Color','b','LineStyle','-') title('Plane angle delta_s (deg)')

xlabel('Time (s)'),ylabel('[Deg]'),grid on

figure(3) clf

plot3(X,Y,Z,'LineWidth',2,'Color','b','LineStyle','-') grid on

title('Simulation of the AUV track by NNC') xlabel('X(m)'),ylabel('Y(m)'),zlabel('Z(m)')

% >>>>>>END<<<<<<<<

PL2. File mô phỏng điều khiển hướng, độ sâu và tốc độ AUV

% Submarine control test

% Decoupled control of speed, depth and course

% Date: 2009/May/01st

% Author: PHAM VIET ANH – HCM Unv. Of Transport

%======================================================================

clear; % clear all variables

NN = 6000; % number of sample

h = 0.1; % sample time (sec) i = 0;

% -------------------------------------- z = 0;

z_s = 0;

z_n = 0;

delta = 0;

delta_s = 0;

n = 150;

u = 0.3;

max_delta = 20*(pi/180); max_delta_s = 20*(pi/180); max_n = 1500;

%----------------------------------------------------------------------

% Controller's parameters of the rudder

rho = 0.5; lambda = 0.1; sigma1 = 0.2; sigma2 = 0;

%----------------------------------------------------------------------

% Controller's parameters of the P&S stern planes

rho_s = 0.2; lambda_s = 0.1; sigma1_s = 0.2; sigma2_s = 0.01;

%----------------------------------------------------------------------

% Controller's parameters of the propeller

rho_n = 1.5; lambda_n = 0.1; sigma1_n = 0.2; sigma2_n = 0.05;

%----------------------------------------------------------------------

% ui = [ delta_r delta_s delta_b delta_bp delta_bs n ]'

% initial states: x = [ u v w p q r x y z phi theta psi ]' x = [0.3 0 0 0 0 0 0 0 -10 0 0 0*pi/180]';

U = 0.3;

%*************** START SIMULATION ******************* disp('Running...')

% We start executing the controller with ralative small weights: W21 = rand(6,4)*0.0001; % Set random hidden layer weights

W32 = rand(1,6)*0.0001; % Set random output layer weights W21_s = rand(6,4)*0.0001; % Set random hidden layer weights W32_s = rand(1,6)*0.0001; % Set random output layer weights W21_n = rand(6,4)*0.0001; % Set random hidden layer weights W32_n = rand(1,6)*0.0001; % Set random output layer weights

net_in = zeros(4,1); % Set initial inputs of neural network net_in_s = zeros(4,1);

net_in_n = zeros(4,1);

while i < NN, i=i+1;

time = (i-1)*h; % simulation time in seconds clc;

fprintf('Running...n*Counter = %d (%d)n',round(i/10),round((NN- i)/10));

r = x(6);

w = x(3);

udot = u-x(1);

u = x(1);

q = x(5);

theta= x(11);

% Sensor noise for the heading sensor with a uniform distribution on

% [-0.1,+0.1] deg.

% s = 0.1*(pi/180)*(2*rand - 1);

s = 0; % This allow us to remove the noise psi = x(12) + s; % Heading of the ship with sensor noise Z = x(9);

% psi = x(6);

% control system

% Phuong an 1:

if i<2000, psi_ref(i) = 10*(pi/180); end if i>=2000, psi_ref(i) = 0*(pi/180); end if i>=4000, psi_ref(i) = 15*(pi/180); end

% Phuong an 2

% psi_ref(i) = 10*(pi/180); Z_ref(i) = -2;

% if i<3000, ud(i) = 0.4; end

% if i>=3000, ud(i) = 0.5; end ud(i) = 0.4;

err = psi-psi_ref(i); % Do lech huong di so voi huong dat net_in(1) = err;

err_s = Z-Z_ref(i); net_in_s(1) = err_s;

err_n = -u+ud(i); net_in_n(1) = err_n;

ii=round(i/10);

if round(i/10)==0,ii=2,end

% store data for presentation

xout(i,:) = [time,x',U,delta,delta_s,n]; e_out(ii,:) = [err; err_s;err_n];

%---------------------------------------------------------------------

% Dau ra cua BDK duoc tinh toan 1 giay 1 lan (theo bien "ii") if ii>3

% Calculate rudder command:

delta = mlnnc(1,6,4,net_in,W21,W32); delta_s = mlnnc(1,6,4,net_in_s,W21_s,W32_s);

n = mlnnc(1,6,4,net_in_n,W21_n,W32_n)*1500;

if abs(delta)>max_delta,delta=sign(delta)*max_delta;end if

abs(delta_s)>max_delta_s,delta_s=sign(delta_s)*max_delta_s;end if abs(n)>max_n,n=sign(n)*max_n;end

ui = [delta

delta_s 0

n ];

[xdot,U] = npsauv(x,ui);

% numerical integration

x = euler2(xdot,x,h); % Euler integration z = z + h*err;

z_s = z_s + h*err_s; z_n = z_n + h*err_n;

% Now update the weights of NN using adaptive interaction algorithm

% (intensive training):

[W21,W32] = annaiTrainIte(h,0.5,50,1,6,4,net_in,... err,delta,r,z,rho,lambda,sigma1,sigma2);

% Add integration action

[W21_s,W32_s] = annaiTrainIte(h,0.5,50,1,6,4,net_in_s,...

err_s,delta_s,w,theta,rho_s,lambda_s,sigma1_s,sigma2_s); % Add integration action

[W21_n,W32_n] = annaiTrainIte(h,0.5,50,1,6,4,net_in_n,...

err_n,n/1500,udot,z_n,rho_n,lambda_n,sigma1_n,sigma2_n);% Add integration action

net_in(2) = e_out(ii-1,1); net_in(3) = e_out(ii-2,1); net_in(4) = e_out(ii-3,1);

net_in_s(2) = e_out(ii-1,2); net_in_s(3) = e_out(ii-2,2); net_in_s(4) = e_out(ii-3,2);

net_in_n(2) = e_out(ii-1,3); net_in_n(3) = e_out(ii-2,3); net_in_n(4) = e_out(ii-3,3);

end % end of "if ii>3"

%----------------------------------------------------------------------

----

end

% time-series

t = xout(:,1);

u = xout(:,2);

v = xout(:,3);

w = xout(:,4);

p = xout(:,5);

q = xout(:,6);

r = xout(:,7)*180/pi;

X = xout(:,8);

Y = xout(:,9);

Z = xout(:,10);

phi = xout(:,11);

theta = xout(:,12)*180/pi; psi = xout(:,13)*180/pi; U = xout(:,14);

delta = -xout(:,15)*180/pi; delta_s = xout(:,16)*180/pi; n = xout(:,17);

psi_ref = psi_ref*180/pi;

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