Section 8–4 / Design of PID Controllers with Computational Optimization Approach 585
sortsolution =
2.0000 0.5000 0.9002
2.2000 0.5000 0.9114
2.4000 0.5000 0.9207
2.6000 0.5000 0.9283
2.8000 0.5000 0.9348
3.0000 0.5000 0.9402
2.0000 0.7000 0.9807
2.2000 0.7000 0.9837
2.4000 0.7000 0.9859
2.6000 0.7000 0.9877
2.8000 0.7000 1.0024
3.0000 0.7000 1.0177
2.0000 0.9000 1.0614
2.2000 0.9000 1.0772
2.4000 0.9000 1.0923
% Plot the response with the largest overshoot that is less than 10%
K = sortsolution(k,1)
K =
2.4000
a = sortsolution(k,2)
a =
0.9000
gc = tf(K[1 2a a^2], [1 0]);
G = gcg/(1 + gcg);
step(G,t)
grid % See Figure 8–20
% If you wish to plot the response with the smallest overshoot that is
% greater than 0%, then enter the following values of 'K' and 'a'
K = sortsolution(11,1)
K =
2.8000
a = sortsolution(11,2)
a =
0.7000
gc = tf(K[1 2a a^2], [1 0]);
G = gcg/(1 + gcg);
step(G,t)
grid % See Figure 8–21
