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Measurement 的代码
chap7_10f.m
%Discrete Kalman filter
%x=Ax+B(u+w(k));
%y=Cx+D+v(k)
function [u]=kalman(u1,u2,u3)
persistent A B C D Q R P x
yv=u2;
if u3==0
x=zeros(2,1);
ts=0.001;
a=25;b=133;
sys=tf(b,[1,a
chap7_10.m
%Discrete Kalman filter for PID control
%Reference kalman_2rank.m
%x=Ax+B(u+w(k));
%y=Cx+D+v(k)
clear all;
close all;
ts=0.001;
%Continuous Plant
a=25;b=133;
sys=tf(b,[1,a,0]);
dsys=c2d(sy
chap7_8.m
%Kalman filter
%x=Ax+B(u+w(k));
%y=Cx+D+v(k)
clear all;
close all;
ts=0.001;
M=3000;
%Continuous Plant
a=25;b=133;
sys=tf(b,[1,a,0]);
dsys=c2d(sys,ts,'z');
[num,den]=tfdata(dsys,'v');
chap7_11.m
%Discrete Kalman filter for PID control
%Reference kalman_2rank.m
%x=Ax+B(u+w(k));
%y=Cx+D+v(k)
clear all;
close all;
ts=0.001;
%Continuous Plant
a=25;b=133;
sys=tf(b,[1,a,0]);
dsys=c2d(sy
chap7_9.m
%Kalman filter
%x=Ax+B(u+w(k));
%y=Cx+D+v(k)
clear all;
close all;
ts=0.001;
M=3000;
%Continuous Plant
a=25;b=133;
sys=tf(b,[1,a,0]);
dsys=c2d(sys,ts,'z');
[num,den]=tfdata(dsys,'v');
chap7_10f.m
%Discrete Kalman filter
%x=Ax+B(u+w(k));
%y=Cx+D+v(k)
function [u]=kalman(u1,u2,u3)
persistent A B C D Q R P x
yv=u2;
if u3==0
x=zeros(2,1);
ts=0.001;
a=25;b=133;
sys=tf(b,[1,a
chap7_10.m
%Discrete Kalman filter for PID control
%Reference kalman_2rank.m
%x=Ax+B(u+w(k));
%y=Cx+D+v(k)
clear all;
close all;
ts=0.001;
%Continuous Plant
a=25;b=133;
sys=tf(b,[1,a,0]);
dsys=c2d(sy
chap7_8.m
%Kalman filter
%x=Ax+B(u+w(k));
%y=Cx+D+v(k)
clear all;
close all;
ts=0.001;
M=3000;
%Continuous Plant
a=25;b=133;
sys=tf(b,[1,a,0]);
dsys=c2d(sys,ts,'z');
[num,den]=tfdata(dsys,'v');
state_est.m
function [V, converged, i] = state_est(branch, Ybus, Yf, Yt, Sbus, V0, ref, pv, pq, mpopt)
%STATE_EST Solves a state estimation problem.
% [V, converged, i] = state_est(branch, Ybus, Yf, Yt, Sbus,
utf_smooth1.m
%UTF_SMOOTH1 Smoother based on two unscented Kalman filters
%
% Syntax:
% [M,P] = UTF_SMOOTH1(M,P,Y,[ia,Q,aparam,h,R,hparam,,alpha,beta,kappa,mat,same_p_a,same_p_h])
%
% In:
% M - NxK matrix of K