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Measurement 的代码
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');
josephb.m
function [K,Pout] = josephb(z,R,H,P)
%
% Joseph "stabilized" Kalman filter measurement
% update as modified by Bierman.
%
T1 = sqrt(R);
T2 = H/T1;
T4 = P*T2';
T5 = T2*T4 + 1;
K = T4/T5;
T7
josephdv.m
function [K,Pout] = josephdv(z,R,H,P)
%
% Joseph "stabilized" Kalman filter measurement
% update as implemented by De Vries.
%
T1 = P*H';
T2 = H*T1 + R;
K = T1/T2;
T3 = .5*K*T2 - T1;
T4 = T3
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
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,
bc.h
/*********************************** MODULE INFO ************************************
Device : AT90S2333
File name : BC.h
Ver nr. : 1.0
Description : Header file fo
bc.h
/*********************************** MODULE INFO ************************************
Device : AT90S2333
File name : BC.h
Ver nr. : 1.0
Description : Header file fo