fig3_49.m

包含了控制理论的各种仿真程序

%  Figure 3.49      Feedback Control of Dynamic Systems, 4e
%                        Franklin, Powell, Emami
%

%  Example 3.28     

clf;
t=[0 .1 .2 .3 .4 .5 1 1.5 2 2.5 3 4 10];
y=[0 .005 .034 .085 .140 .215 .510 .7 .817 .890 .932 .975 .9999];
A=-1.33;   % these parameters are not the best fit   
alpha=1;   % but they are certainly reasonable 
B=.33;     % based on the eyeball fit with logs.
beta=5.8;  % 

axis([0 6 -.1 1])
fity1=1+A*exp(-alpha*t)+B*exp(-beta*t);

A=-1.37;   % these parameters are better
alpha=1;   % they were obtained by using 
B=.37;     % some iteration including
beta=4.3;  % looking at final fit plot
fity2=1+A*exp(-alpha*t)+B*exp(-beta*t);

plot(t,y,'o',t,fity1,'-',t,fity2,'--'),grid
title('Fig. 3.49 Response data vs. fit')
ylabel('y(t)')
xlabel('Time (sec)')
text(1.3,.5,'--------- A=-1.33, \alpha=1, B=.33, \beta = 5.8')
text(1.3,.4,'- - - - - A=-1.37, \alpha=1, B=.37, \beta = 4.3')
text(1,-.1,'o  o  o Data','Color','b');