# MAE 143B - Homework 9

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1 MAE 143B - Homework a) We have stable first-order poles at p 1 = 1 and p 2 = 1. For small values of ω, we recover the DC gain K = lim ω G(jω) = 1 1 = 2dB. Having this finite limit, our straight-line approximation for the magnitude plot must start with a horizontal line at 2dB. The resulting straight-line approximation and Bode plot are shown in Fig. 1, where the exact plot can be reconstructed in Matlab using the commands zpk (you could also use tf or ss, whichever you prefer) and bode: >> sys = zpk([],[-1-1],1) sys = (s+1) (s+1) Continuous-time zero/pole/gain model. >> bode(sys); Mag(dB) ω Phased(deg) ω Figure 1: Bode plot for part a), straight-line approximation in red. 1

2 c) We have a simple zero at z 1 = 1, stable second-order poles with ω n = 1 rad/s and ζ =.5 and DC gain K = lim ω G(jω) = 1 = 2dB. Resulting straight-line approximation and Bode plot are shown in Fig Mag(dB) 5 1 ω Phased(deg) ω Figure 2: Bode plot for part c), straight-line approximation in red. g) In this case, we have a simple zero at z 1 = 1 and a pole of multiplicity 2 at p 1 = 1. The DC gain is K = lim ω G(jω) = 1 = 2dB. Resulting straight-line approximation and Bode plot are shown in Fig. 3 below. 2

3 5 Mag(dB) 5 1 ω Phased(deg) ω Figure 3: Bode plot for part g), straight-line approximation in red. 3

4 7.2 The three plots are shown in Figures 4-6. You can reconstruct these using the Matlab command nyquist. None of the plots have encirclements of the point 1, implying stability under negative unit feedback by the Nyquist stability criterion..6 Nyquist Diagram.4 Imaginary Axis Real Axis Figure 4: Polar plot for part a). 4

5 15 Nyquist Diagram 1 Imaginary Axis Real Axis Figure 5: Polar plot for part c). 8 Nyquist Diagram 6 4 Imaginary Axis Real Axis Figure 6: Polar plot for part g). 5

7 2 Bode Diagram Magnitude (db) Phase (deg) Frequency (rad/s) Figure 7: Bode plot for P7.8. response to a rectangular pothole traversed at different velocities. The Fourier transform of a rectangle of height d and width T centered at the origin is a scaled sinc function, i.e. ( ) ωt F{d rect(t/t )} = dt sinc. 2π Figure 8 shows the Fourier transform of the pothole traversed at the two velocities. Notice that a time shift does not change the magnitude of our Fourier transform, only the phase. As expected, it does not matter when we hit the pothole, provided our velocity does not change. Zooming into the Fourier transform plots reveals approximately the same frequency content at the resonance frequency of approximately 15.7 rad/s. Given that the system is a high-pass filter, the large peak of the low-velocity Fourier transform at low frequencies does not affect the output significantly. At high frequencies, the two signals have similar total frequency content. Based on these observations, it seems reasonable that we did not observe a fundamental difference in the responses computed in assignment 7 for these two velocities. 7

8 2 x 1 3 v = 1km/h 2 x 1 3 v = 1km/h Fourier transform of d*rect(t/t) Fourier transform of d*rect(t/t) ω ω Figure 8: Fourier transform of centered pothole with d = 1m traversed at given velocities. 8

9 7.9 As we know from previous assignments, the transfer function of this plant model is G(s) = Ω 2(s) Y (s) = r 1Ω 1 (s) r 2 T (s) = r 1r 2 /J r, s + b r /J r with I-controller K(s) = K i /s for some K i. Independent of our design, the closed-loop system will be able to track a constant reference due to the controller pole at the origin. Also, using Bode or Nyquist plots and positivity of all parameters, it is easily verified that the closed-loop system is asymptotically stable for any K i >, which is not surprising after P6.16 on assignment 7. The plots are shown in Figure 9. Bode Diagram Nyquist Diagram 1 Magnitude (db) Phase (deg) Imaginary Axis Frequency (rad/s) Real Axis Figure 9: Plots for P7.9. 9

10 7.13 Given our parameters, we can use Matlab to compute the transfer function of our system. One possible controller achieving closed-loop stability can be constructed by placing complex conjugate zeros on the imaginary axis at approximately half the imaginary parts of the complex system poles on the axis. The two poles required to make the controller proper can be placed on the negative part of the real axis, for instance at p c,1/2 = 1. Bode and Nyquist plots corresponding to this setup are displayed in Figure 1, where the Bode plot includes gain and phase margins and the Nyquist plot has information on several points for positive frequencies to help track the behavior. We clearly have positive margins and no encirclements of the point at s = 1, implying asymptotic stability of the closed-loop transfer function with the controller described above. The plots can be reconstructed using the following Matlab code, where the Nyquist plot requires some zooming due to the very large and very small gains of our transfer function at different frequencies. % P 7.13 m = 16; T = 11*36; W = 2*pi/T; A = [ 1 ; 3*W^2 2*W; -2*W ]; B = [ ; ; 1/m ]; C = [ 1 ]; D = ; sys = ss(a,b,c,d); sys = zpk(sys); sys.p = {zeros(3,1) + j*imag(sys.p{:})}; % To correct numerical errors in computing the transfer function C = zpk([-j*.15e-3/2,j*.15e-3/2],[-1,-1],1); figure; subplot(1,2,1) margin(sys*c); grid on; subplot(1,2,2) nyquist(sys*c,{1e-5,1}); 1

11 Bode Diagram Gm = 2 db (at 1 rad/s), Pm = 9 deg (at.159 rad/s) 2 4 x 1 5 Nyquist Diagram Magnitude (db) Phase (deg) Imaginary Axis 3 2 System: untitled1 1Real: 2.71e 1 Imag: 2.35e 1 Frequency (rad/s): 4.58 System: untitled1 1 Real: 9.59e 1 Imag: 2.42e 5 Frequency (rad/s): System: untitled1 Real: 5.2e 1 Imag: 1.99e 5 Frequency (rad/s):.126 System: untitled1 Real: 8.32e 11 Imag: 2e 5 Frequency (rad/s): 2.9e Frequency (rad/s) Real Axis x 1 9 Figure 1: Plots for P

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