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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');
cov_e.m
function R=cov_w(obj,t);
% Returns the covariance matrix of the measurement noise, at time t.
%
% Syntax: (* = optional)
%
% R = cov_e(model, t);
%
% In arguments:
%
% 1. model
% Model objec
cov_e.m
function R=cov_w(obj,t);
% Returns the covariance matrix of the measurement noise, at time t.
%
% Syntax: (* = optional)
%
% R = cov_e(model, t);
%
% In arguments:
%
% 1. model
% Model objec
cov_e.m
function R=cov_w(obj,t);
% Returns the covariance matrix of the measurement noise, at time t.
%
% Syntax: (* = optional)
%
% R = cov_e(model, t);
%
% In arguments:
%
% 1. model
% Model objec
chap7_9.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');