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180 lines (147 loc) · 4.88 KB
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"""Demonstrate mixed-sensitivity H-infinity design for a MIMO plant.
Based on Example 3.8 from Multivariable Feedback Control, Skogestad
and Postlethwaite, 1st Edition.
"""
import numpy as np
import matplotlib.pyplot as plt
from control import tf, ss, mixsyn, feedback, step_response
def weighting(wb,m,a):
"""weighting(wb,m,a) -> wf
wb - design frequency (where |wf| is approximately 1)
m - high frequency gain of 1/wf; should be > 1
a - low frequency gain of 1/wf; should be < 1
wf - SISO LTI object
"""
s = tf([1,0],[1])
return (s/m+wb)/(s+wb*a)
def plant():
"""plant() -> g
g - LTI object; 2x2 plant with a RHP zero, at s=0.5.
"""
den = [0.2,1.2,1]
gtf=tf([[[1],[1]],
[[2,1],[2]]],
[[den,den],
[den,den]])
return ss(gtf)
# as of this writing (2017-07-01), python-control doesn't have an
# equivalent to Matlab's sigma function, so use a trivial stand-in.
def triv_sigma(g,w):
"""triv_sigma(g,w) -> s
g - LTI object, order n
w - frequencies, length m
s - (m,n) array of singular values of g(1j*w)"""
m,p,_ = g.freqresp(w)
sjw = (m * np.exp(1j*p*np.pi/180)).transpose(2,0,1)
sv = np.linalg.svd(sjw,compute_uv=False)
return sv
def analysis():
"""Plot open-loop responses for various inputs"""
g=plant()
t = np.linspace(0,10,101)
_, yu1 = step_response(g,t,input=0)
_, yu2 = step_response(g,t,input=1)
yu1 = yu1
yu2 = yu2
# linear system, so scale and sum previous results to get the
# [1,-1] response
yuz = yu1 - yu2
plt.figure(1)
plt.subplot(1,3,1)
plt.plot(t,yu1[0],label='$y_1$')
plt.plot(t,yu1[1],label='$y_2$')
plt.xlabel('time')
plt.ylabel('output')
plt.ylim([-1.1,2.1])
plt.legend()
plt.title('o/l response to input [1,0]')
plt.subplot(1,3,2)
plt.plot(t,yu2[0],label='$y_1$')
plt.plot(t,yu2[1],label='$y_2$')
plt.xlabel('time')
plt.ylabel('output')
plt.ylim([-1.1,2.1])
plt.legend()
plt.title('o/l response to input [0,1]')
plt.subplot(1,3,3)
plt.plot(t,yuz[0],label='$y_1$')
plt.plot(t,yuz[1],label='$y_2$')
plt.xlabel('time')
plt.ylabel('output')
plt.ylim([-1.1,2.1])
plt.legend()
plt.title('o/l response to input [1,-1]')
def synth(wb1,wb2):
"""synth(wb1,wb2) -> k,gamma
wb1: S weighting frequency
wb2: KS weighting frequency
k: controller
gamma: H-infinity norm of 'design', that is, of evaluation system
with loop closed through design
"""
g = plant()
wu = ss([],[],[],np.eye(2))
wp1 = ss(weighting(wb=wb1, m=1.5, a=1e-4))
wp2 = ss(weighting(wb=wb2, m=1.5, a=1e-4))
wp = wp1.append(wp2)
k,_,info = mixsyn(g,wp,wu)
return k, info.gamma
def step_opposite(g,t):
"""reponse to step of [-1,1]"""
_, yu1 = step_response(g,t,input=0)
_, yu2 = step_response(g,t,input=1)
return yu1 - yu2
def design():
"""Show results of designs"""
# equal weighting on each output
k1, gam1 = synth(0.25,0.25)
# increase "bandwidth" of output 2 by moving crossover weighting frequency 100 times higher
k2, gam2 = synth(0.25,25)
# now weight output 1 more heavily
# won't plot this one, just want gamma
_, gam3 = synth(25,0.25)
print('design 1 gamma {:.3g} (Skogestad: 2.80)'.format(gam1))
print('design 2 gamma {:.3g} (Skogestad: 2.92)'.format(gam2))
print('design 3 gamma {:.3g} (Skogestad: 6.73)'.format(gam3))
# do the designs
g = plant()
w = np.logspace(-2,2,101)
I = ss([],[],[],np.eye(2))
s1 = I.feedback(g*k1)
s2 = I.feedback(g*k2)
# frequency response
sv1 = triv_sigma(s1,w)
sv2 = triv_sigma(s2,w)
plt.figure(2)
plt.subplot(1,2,1)
plt.semilogx(w, 20*np.log10(sv1[:,0]), label=r'$\sigma_1(S_1)$')
plt.semilogx(w, 20*np.log10(sv1[:,1]), label=r'$\sigma_2(S_1)$')
plt.semilogx(w, 20*np.log10(sv2[:,0]), label=r'$\sigma_1(S_2)$')
plt.semilogx(w, 20*np.log10(sv2[:,1]), label=r'$\sigma_2(S_2)$')
plt.ylim([-60,10])
plt.ylabel('magnitude [dB]')
plt.xlim([1e-2,1e2])
plt.xlabel('freq [rad/s]')
plt.legend()
plt.title('Singular values of S')
# time response
# in design 1, both outputs have an inverse initial response; in
# design 2, output 2 does not, and is very fast, while output 1
# has a larger initial inverse response than in design 1
time = np.linspace(0,10,301)
t1 = (g*k1).feedback(I)
t2 = (g*k2).feedback(I)
y1 = step_opposite(t1,time)
y2 = step_opposite(t2,time)
plt.subplot(1,2,2)
plt.plot(time, y1[0], label='des. 1 $y_1(t))$')
plt.plot(time, y1[1], label='des. 1 $y_2(t))$')
plt.plot(time, y2[0], label='des. 2 $y_1(t))$')
plt.plot(time, y2[1], label='des. 2 $y_2(t))$')
plt.xlabel('time [s]')
plt.ylabel('response [1]')
plt.legend()
plt.title('c/l response to reference [1,-1]')
analysis()
design()
plt.show()