# ROOT LOCUS. Consider the system. Root locus presents the poles of the closed-loop system when the gain K changes from 0 to. H(s) H ( s) = ( s)

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1 C1 ROOT LOCUS Consider the system R(s) E(s) C(s) + K G(s) - H(s) C(s) R(s) = K G(s) 1 + K G(s) H(s) Root locus presents the poles of the closed-loop system when the gain K changes from 0 to 1+ K G ( s) H ( s) = 0 { K G( s) H ( s) = 1 Magnitude Condition, G( s) H ( s) =± 180 (2k + 1) k= 0,1,2, Angle Condition

2 C2 Example: K G(s) H(s) = K s (s + 1) 1 + K G(s) H(s) = 0 s 2 + s + K = 0 s 1,2 = 1 2 ± K Angle condition: K = s s + 1= (180 θ ) θ2 = s ( s + 1) 1 ±, 180 s-plane K=1 θ 2 θ 1 K=0-1 K=1/4 K=0

3 C3 Magnitude and Angle Conditions K G(s) H(s) = K (s + z 1 ) (s + p 1 ) (s + p 2 ) (s + p 3 ) (s + p 4 ) K G(s) H(s) = K B 1 A 1 A 2 A 3 A 4 = 1 G(s) H(s) = ϕ 1 θ 1 θ 2 θ 3 θ 4 = ±180, (2 k +1) for k = 0, 1, 2,

4 C4 Construction Rules for Root Locus Open-loop transfer function: K H(s) G(s) = K B(s) A(s) m: order of open-loop numerator polynomial n: order of open-loop denominator polynomial Rule 1: Number of branches The number of branches is equal to the number of poles of the open-loop transfer function. Rule 2: Real-axis root locus If the total number of poles and zeros of the open-loop system to the right of the s-point on the real axis is odd, then this point lies on the locus. Rule 3: Root locus end-points The locus starting point ( K=0 ) are at the open-loop poles and the locus ending points ( K= ) are at the open loop zeros and n-m branches terminate at infinity.

5 C5 Rule 4: Slope of asymptotes of root locus as s approaches infinity I m Asymptote γ Real σ a γ = ±180 (2k + 1), k = 0,1,2,... n m Rule 5: Abscissa of the intersection between asymptotes of root locus and real-axis σ a= n ( pi ) n m m i= 1 i= 1 ( zi ) (- p i ) = poles of open-loop transfer function (- z i ) = zeros of open-loop transfer function

6 C6 Rule 6: Break-away and break-in points From the characteristic equation f()= s A(s) + K B(s) = 0 the break-away and -in points can be found from: dk ds = A' (s) B(s) A(s) B ' (s) B 2 (s) = 0 Rule 7: Angle of departure from complex poles or zeros Subtract from 180 the sum of all angles from all other zeros and poles of the open-loop system to the complex pole (or zero) with appropriate signs. Rule 8: Imaginary-axis crossing points Find these points by solving the characteristic equation for s=jω or by using the Routh s table.

7 C7 Rule 9: Conservation of the sum of the system roots If the order of numerator is lower than the order of denominator by two or more, then the sum of the roots of the characteristic equation is constant. Therefore, if some of the roots more towards the left as K is increased, the other roots must more toward the right as K in increased.

8 C8 Discussion of Root Locus Construction Rules Consider: R(s) E(s) C(s) + K G(s) - H(s) K H ( s) G( s) = K B( s) A( s) = K m i= 0 n i= 0 b i s α s i m i n i m: number of zeros of open-loop KH(s)G(s) n: number of poles of open-loop KH(s)G(s) Characteristic Equation: f()= s A(s) + K B(s) = 0

9 C9 Rule 1: Number of branches The characteristic equation has n zeros the root locus has n branches Rule 2: Real-axis root locus ϕ 4 ϕ 3 s 2 ϕ 2 s 1 ϕ 1 ϕ 5 Consider two points s 1 and s 2 : s 1 { ϕ1 =180, ϕ2 = 0 = ϕ3 ϕ1+ϕ2 ϕ3+ϕ4 +ϕ5 = 3 180, ϕ 4+ϕ5 = s ϕ1 =180, ϕ2 = 180, ϕ3=0, ϕ4 +ϕ5 = { ϕ1+ϕ2 ϕ3+ϕ4+ϕ5 = Therefore, s 1 is on the root locus; s 2 is not.

10 C10 Rule 3: Root locus end-points Magnitude condition: B(s) A(s) K=0 open loop poles = K= m open loop zeros 1 K = m-n branches approach infinity m i = 1 n i = 1 (s + z i ) (s + p i ) Rule 4: Slope of asymptotes of root locus as s approaches infinity K B(s) A(s) = 1 lim K B(s) s A(s) = lim K s s n m = 1 s n m Using the angle condition: s m n, = K = ± 180 (2 k + 1) = K for s, k = 1, 2, 3, or

11 C11, ( n m) s = ± 180 (2 k + 1) leading to s = γ = ±180, (2 k +1) n m Rule 5: Abscissa of the intersection between asymptotes of root-locus and real axis A(s) B(s) = s n + s n 1 p i n i = 1 m s m + s m 1 z i i = 1 m p i i = 1 n z i i = 1 = K Dividing numerator by denominator yields: s n m m z i i = 1 n p i i = 1 s n m = K For large values of s this can be approximated by: m n z i i = 1 s n m p i i = 1 n m = K The equation for the asymptote (for s ) was found in Rule 4 as

12 C12 s n m = K this implies σ a = m zi + n pi pi i= 1 i= 1 i= 1 i= 1 = n m n m n m zi Rule 6: Break-away and break-in points At break-away (and break-in) points the characteristic equation: f(s) = A(s) + K B(s) = 0 has multiple roots such that: df (s) ds = 0 A ' (s) + K B ' (s) = 0 A ' (s) = da(s) ds for K = A ' (s) B ' (s), f(s) has multiple roots Substituting the above equation into f(s) gives: A(s) B ' (s) A ' (s) B(s) = 0

13 C13 Another approach is using: This gives: K = A(s) B(s) from f()= s 0 dk (s) B(s) A(s) B ' (s) ds = A' B 2 (s) and break-away, break-in points are obtained from: dk ds = 0 Extended Rule 6: Consider and f(s) = A(s) + K B(s) = 0 K = A(s) B(s) If the first (y-1) derivatives of A(s)/B(s) vanish at a given point on the root locus, then there will be y branches approaching and y branches leaving this point. The angle between two adjacent approaching branches is given by: θ y = ± 360, y

14 C14 The angle between a leaving branch and an adjacent approaching branch is: θ y = ± 180, y Rule 7: Angle of departure from complex pole or zero θ 3 θ 2 = 90, ϕ 1 θ 1 θ 3 = 180, (θ 1 +θ 2 ϕ 1 ) θ 2 Rule 8: Imaginary-axis crossing points Example: f (s) = s 3 + b s 2 + c s + K d = 0 s 3 1 c s 2 b Kd s 1 (bc-kd)/b s 0 Kd s 1,2 = ± j K d b For crossing points on the Imaginary axis: b c K d = 0 K = bc d Further, b s 2 + K d = 0 leading to = ± jω

15 C15 The same result is obtained by solving f(jω) = 0. Rule 9: Conservation of the sum of the system roots From = + = + n i r i s s B K s A 1 ) ( ) ( ) ( we have ) ( ) ( ) ( = = = + = n i m i n i i i i r s r s K p s with = + = n i p i s s A 1 ) ( ) ( and = + = m i z i s s B 1 ) ( ) ( By equating coefficients of s n 1 for n m + 2, we obtain the following: = = = n i i n i p i r 1 1 i.e. the sum of closed-loop poles is independent of K! Sum of openloop poles Sum of closedloop poles

16 C16

17 C17 Effect of Derivative Control and Velocity Feedback Consider the following three systems: Positional servo. Closed-loop poles: s = 0.1 ± j Positional servo with derivative control. Closed-loop poles: s = 0.5 ± j Positional servo with velocity feedback. Closed-loop poles: s = 0.5 ± j Open-loop of system I: G I (s) = 5 s ( 5 s + 1) Open-loop of systems II and III: G(s) = 5 (1+ 0.8s) s ( 5 s + 1)

18 C18 Root locus for the three systems jω j X X 0 -j1 σ a) System I Closed-loop zero jω j X X 0 -j1 σ b) System II

19 C19 Open-loop zero jω j X X 0 -j1 σ c) System III Closed-loop zeros: System I: System II: System III: none 1+0.8s=0 none Observations: The root locus presents the closed loop poles but gives no information about closed-loop zeros. Two system with same root locus (same closed-loop poles) may have different responses due to different closed-loop zeros.

20 C20 Unit-step response curves for systems I,II and III : The unit-step response of system II is the fastest of the three. This is due to the fact that derivative control responds to the rate of change of the error signal. Thus, it can produce a correction signal before the error becomes large. This leads to a faster response.

21 C21 Conditionally Stable Systems System which can be stable or unstable depending on the value of gain K. R(s) + - K G(s) C(s) O unstable X X X X O stable X unstable Minimum Phase Systems All poles and zeros are in the left half plane.

22 C22 Frequency Response Methods x(t) G(s) y(t) x(t) = X sin(ω t) X(s) = ω X s 2 + ω 2 Y(s) = G(s) X(s) = G(s) a = a = X(s) y(t) = ae j ω t Y(s) ω X s 2 + ω 2 = + a e j ω t a s + jω + a s jω + + b i e s i t i stable system Re( s i ) < 0 for all i for t y(t) = ae jω t + a e jω t ω X G(s) s 2 +ω 2 s+ jω ( ) = ω X G(s) s 2 +ω 2(s jω) = ( s= jω) (s= j ω) XG( j ω) 2j XG(jω) 2j i b i s + s i

23 C23 and G( jω ) = G( j ω) e j ϕ G( jω ) = G(j ω) e j ϕ ϕ = tan 1 Im(G( j ω)) Re( G( j ω)) y(t) = X G( j ω) ej(ωt+ϕ) e j(ωt+ϕ) = Y sin(ωt +ϕ 2j ϕ > 0 phase lag ϕ < 0 phase lead G( jω ) = Y( j ω) X( jω ) G( j ω) = Y(j ω) X( j ω) Magnitude response ϕ = (G(j ω)) = Y( j ω) X( jω ) Phase response

24 C24 Connection between pole locations and Frequency Response G(s) = K(s + z) s(s + p) G( jω) = K jω + z jω jω + p G(jω ) = ϕ θ 1 θ 2 Frequency Response Plots Bode Diagrams Polar Plots (Nyquist Plots) Log-Magnitude-Versus-Phase Plots (Nichols Plots)

25 C25 Bode Diagrams Magnitude response G( jω) 20 log G( jω) in db Phase response G(jω) in degrees Basic factors of G(jω): Gain K Integral or derivative factors ( jω) ±1 First-order factors Quadratic factors (1 + jωt ) ± 1 ± 1 2 jω jω 1+ 2 j + ω n n ω 1. Gain Factor K Horizontal straight line at magnitude 20 log(k) db Phase is zero

26 C26 2. Integral or derivative factors ( jω) ± 1 ( jω ) 1 ( jω ) 20 log 1 jω = 20 logω magnitude: straight line with slope 20 db/decade phase: -90 o 20 log jω = 20logω magnitude: straight line with slope 20 db/decade phase: +90 o

27 C27 3. First order factors (1+ jω T) ± 1 (1+ jω T) 1 Magnitude: 1 2 T 2 20 log = 20 log 1 + ω 1 + jωt db for ω << T 1 0 db magnitude for ω >> T 1 20log( ω T) db magnitude Approximation of the magnitude: for ω between 0 and ω = 1 T 0 db for ω >> 1 T straight line with slope 20 db /decade Phase: (1+ jω T) 1 = tan 1 (ωt ) for ω = 0 ϕ = 0, 1 1 T for ω = tan = 1 ϕ = 45, T T for ω = ϕ = 90,

28 C28 (1+ jω T) + 1 Using 20 log 1+ jω T = 20log jω T (1+ jω T) = tan 1 ( ω T) = jω T

29 C29 4. Quadratic Factors ) ( + + = n n j j j G ω ω ω ω ς ω 1 0 < <ς Magnitude: 20 log G( jω) = 20 log 1 ω 2 ω 2 n 2 2 j ω ω n 2 for ω << ω n 0 db for ω >> ω n 20 log ω 2 ω 2 n = 40log ω ω n db Phase: = = tan ) ( tan n n j G ω ω ω ω ς ω ϕ Resonant Frequency: ς ω ω = n T Resonant Peak Value: 2 max ) ( ς ξς ω = = j G M T

30 C30 Example: G( jω ) = 10( jω + 3) jω ( ) jω + 2 ( ) jω ( ) 2 + jω + 2 ( ) ( ) ( ) = ) ( 2 ω ω ω ω ω ω j j j j j j G

31 C31

32 C32 Frequency Response of non-minimum Phase systems Minimum phase systems have all poles and zeros in the left half s-plane and were discussed before Consider then A 1 (s) = 1+ Ts A 2 (s) = 1 Ts A 3 (s)= Ts 1 A 1 (jω) = A 2 (jω) = A 3 (jω) A 2 (jω) = A 1 (jω) A 3 (jω) = 180 A 1 (jω) We know that phase of A 1 (jω) from 0 o to +90 o Æ phase of A 2 (jω) from 0 o to -90 o phase of A 3 (jω) from 180 o to +90 o

33 C33 Phase-angle characteristic of the two systems G 1 (s) and G 2 (s) having the same magnitude response but G 1 (s) is minimum phase while G 2 (s) is not. Frequency Response of Unstable Systems Consider then 1 G 1 (s) = 1 + Ts G 2 (s) = 1 1 Ts 1 G 3 (s) = Ts 1 G 1 (jω) = G 2 (jω) = G 3 (jω) G 2 (jω) = G 1 (jω) G 3 (jω) = 180 o G 1 (jω) We know that phase of G 1 (jω) from 0 o to -90 o Æ phase of G 2 (jω) from 0 o to +90 o phase of G 3 (jω) from -180 o to -90 o

34 C34 Relationship between System Type and Log- Magintude curve Type of system determines: the slope of the log-magnitude curve at low frequencies for minimum phase, also the phase at low frequencies Type 0 Position Error Coefficient K p 0 Slope at low frequencies: 0 db/decade Phase at low frequencies (minimum phase): 0 o lim G( jω) = K p ω 0

35 C35 Type 1 Velocity Error Coefficient K v 0 (K p = ) K v = lim jω G( jω ) ω 0 G(jω ) = K v jω for ω << 20log K v = 20log K v jω for ω = 1 Slope at low frequencies: -20 db/decade Phase at low frequencies (minimum phase): -90 o

36 C36 Type 2 Acceleration Error Coefficient K a K a 2 ( jω ) G( j ) 0 = lim ω ω 0 (K p = K v = ) K G(jω ) = a (jω ) 2 for ω << 1 20logK a = 20log K a ( jω ) 2 for ω = 1 Slope at low frequencies: -40 db/decade Phase at low frequencies (minimum phase): -180 o

37 C37 Polar Plots (Nyquist Plots) G( jω ) = G( jω ) G( jω ) = Re[ G( jω )]+ Im[ G( jω )] Advantage over Bode plots: only one plot Disadvantage : Polar plot of G( jω) = G1 ( jω) G2( jω) is more difficult to construct than its Bode plot. Basic factors of G(jω): Integral or derivative factors ( jω) ± 1 G( jω ) = 1 jω = j 1 ω = G(jω ) = jω = ω 90, 1 ω 90,

38 C38 First order factors (1+ jω T) ± 1 X = G( jω ) = jω T = X + jy 1 1+ ω 2 T 2, Y = ω T 1 +ω 2 T 2 It can be show that (X 0.5) 2 + Y 2 = (0.5) 2 Polar plot is a circle with Center (1/2, 0) and Radius 0.5.

39 C39 Quadratic Factors G( jω) = 1+ 2ζ 1 ω jω j + ωn ωn 2 1 >ζ >0 lim ω 0 G( jω ) = 1 0, lim G(jω) = 0 180, ω 1, G( jωn) = 90 j2 ς

40 C n n j j ω ω ω ω ς lim ω 0 G( jω ) = 1 0, lim ω G(jω) = 180,

41 C41 General shapes of polar plots K(1 + jωt )...(1 ) ( ) 1 + jωt G jω = m λ ( jω) (1 + jωtλ+ 1)...(1 + jωtn ) n = order of the system (denominator) λ = type of system λ > 0 m = order of numerator n > m For low frequencies: The phase at ω 0 is λ ( 90 ), For system type 1, the low frequency asymptote is obtained by taking: Re[ G( jω )] for ω 0 For high frequencies: The phase is: (n - m) (- 90 o )

42 C42

43 Log-Magnitude-Versus-Phase Plots (Nichols Plots) C43

44 C44 Example: Frequency Response of a quadratic factor The same information presented in three different ways: Bode Diagram Polar Plot Log-Magnitude-Versus-Phase Plots

45 Nyquist Stability Criterion C45 The Nyquist stability criterion relates the stability of the closed loop system to the frequency response of the openloop system. R(s) + - G(s) C(s) H(s) Open-loop: Closed-loop: G() s H() s G() s 1+ Gs () Hs () Advantages of the Nyquist Stability Criterion: Simple graphical procedure to determine whether a system is stable or not The degree of stability can be easily obtained Easy for compensator design The response for steady-state sinusoidal inputs can be easily obtained from measurements

46 C46 Preview Mathematical Background Mapping theorem Nyquist path Nyquist stability criterion Z=N+P Z: Number of zeros of (1+H(s)G(s)) in the right half plane = number of unstable poles of the closed-loop system N: Number of clockwise encirclements of the point 1+j0 P: Number of poles of G(s)H(s) in the right half plane Application of the Stability Criterion Sketch the Nyquist plot for ω (0 +, ) Extend to ω (-,+ ) Apply the stability criterion (find N and P and compute Z).

47 C47 Mapping Theorem The total number N of clockwise encirclements of the origin of the F(s) plane, as a representative point s traces out the entire contour in the clockwise direction, is equal to Z P. F(s) = A(s) Q(s) P: Number of poles, Q(s) = 0 Z: Number of zeros, A(s) = 0 VSODQH )V SODQH FRQWRXU [ )V R [ R R [ [ ]HURV \$V SROHV 4V

48 C48 Mapping for F(s) = s/(s+0.5), (Z = P =1) Example of Mapping theorem (Z P = 2). Example of Mapping theorem (Z P = - 1).

49 C49 Application of the mapping theorem to stability analysis Mω VSODQH )V SODQH 5 1\TXLVW SDWK WKH FRPSOHWH ULJKW KDOI SODQH LV HQFORVHG LQVLGH WKH SDWK Mω )V *V+ V Mapping theorem: The number of clockwise encirclements of the origin is equal to the difference between the zeros and poles of F(s) = 1+ G(s)H(s). Zeros of F(s) = poles of closed-loop system Poles of F(s) = poles of open-loop system

50 C50 Frequency response of open-loop system: G(jω)H(jω) +jω s-plane F(s)(=1+G(s)H(s))-plane G(s)H(s)-plane Im R= 1+G(jω)H(jω) Re G(jω)H(jω) -jω F(s)=1+G(s)H(s) Frequency response of a type 1 system

51 C51 Nyquist stability criterion Consider R(s) + - G(s) C(s) H(s) The Nyquist stability criterion states that: Z = N + P Z: Number of zeros of 1+H(s)G(s) in the right half s-plane = number of poles of closed-loop system in right half s- plane. N: Number of clockwise encirclements of the point 1+j0 (when tracing from ω = - to ω = + ). P: Number of poles of G(s)H(s) in the right half s-plane Thus: if Z = 0 closed-loop system is stable if Z > 0 closed-loop system has Z unstable poles if Z < 0 impossible, a mistake has been made

52 Alternative form for the Nyquist stability criterion: C52 If the open-loops system G(s)H(s) has k poles in the right half s-plane, then the closed-loop system is stable if and only if the G(s)H(s) locus for a representative point s tracing the modified Nyquist path, encircles the 1+j0 point k times in the counterclockwise direction. Frequency Response of G(jω)H(jω) for ω : (-,+ ) a) ω : (0 +,+ ) : using the rules discussed earlier b) ω : (0 -,- ) : G(-jω)H(-jω) is symmetric with G(jω)H(jω) (real axis is symmetry axis) c) ω : (0 -,0 + ) : next page

53 Poles at the origin for G(s)H(s): C53 G() s Hs ()= (...) s λ (...) If G(s)H(s) involves a factor 1, then the plot of G(jω)H(jω), s λ for ω between 0 - and 0 +, has λ clockwise semicircles of infinite radius about the origin in the GH plane. These semicircles correspond to a representative point s moving along the Nyquist path with a semicircle of radius ε around the origin in the s plane.

54 Relative Stability C54 Consider a modified Nyquist path which ensures that the closed-loop system has no poles with real part larger than -σ 0 : Another possible modified Nyquist path:

55 Phase and Gain Margins C55 A measure for relative stability of the closed-loop system is how close G(jω), the frequency response of the open-loop system, comes to 1+j0 point. This is represented by phase and gain margins. Phase margin: The amount of additional phase lag at the Gain Crossover Frequency ω o required to bring the system to the verge of instability. Gain Crossover Frequency: ω o for which G jωo = 1 Phase margin: γ = G(jω o ) = φ Gain margin: The reciprocal of the magnitude G jω at the 1 Phase Crossover Frequency ω 1 required to bring the system to the verge of instability. Phase Crossover Frequency: ω 1 where G jω 1 = 180, Gain margin: K 1 g = G( jω ) 1 Gain margin in db: K g in db= 20logG jω 1 K g in db > 0 = stable (for minimum phase systems) K g in db < 0 = unstable (for minimum phase systems)

56 C56 Figure: Phase and gain margins of stable and unstable systems (a) Bode diagrams; (b) Polar plots; (c) Logmagnitude-versus-phase plots.

57 C57 If the open-loop system is minimum phase and has both phase and gain margins positive, Æ then the closed-loop system is stable. For good relative stability both margins are required to be positive. Good values for minimum phase system: Phase margin : Gain margin: above 6 db

58 C58 Correlation between damping ratio and frequency response for 2 nd order systems R(s) + - s ω n ( s + 2ζω ) n C(s) C R 2 () s ωn () s 2 2 Phase margin: = C jω s + 2ζωns + ωn R jω ( ) ( ) = M ω jα ω ( ) e ( ) γ = G(jω) G(jω): open loop transfer function G ( jω) = jω ω 2 n ( jω + 2ζω ) n becomes unity for ω = ω n 1 + 4ζ 2ζ and 1 2ζω 1 γ = tan n = tan ω1 2ζ ζ 2ζ Æ γ depends only on ζ

59 C59 Performance specifications in the frequency domain: 0dB M r -3dB ω r ωc ω c : ω c : Cutoff Frequency 0 ω ω c : Bandwidth Slope of log-magnitude curve: Cutoff Rate ability to distinguish between signal and noise ω r : Resonant Frequency indicative of transient response speed ω r Æ increase, transient response faster (dominant complex conjugate poles assumed) M r = max G(jω) : Resonant Peak

60 C60 Closed-Loop Frequency Response Open-loop system: G(s) Stable closed-loop system: C( s) = G( s) R( s) 1+ G( s) R(s) + - G(s) C(s) Gs () 1+Gs = () OA PA

61 Closed-Loop Frequency Response: C61 ( ) ( ) = G jω C jω Rjω ( ) 1+G( jω ) = M ejα Constant Magnitude Loci: ( jω ) = X jy G + X + jy M = = const 1 + X + jy X + M2 M Y 2 = M 2 M Constant Phase-Angle Loci G ( jω ) = X + jy Æ e jα = X + jy 1+ X + jy = const 2 2 X Y + 1 2N = N 2, N = tanα

62 Figure: A family of constant M circles. C62

63 C63 Figure: (a) G(jω) locus superimposed on a family of M circles; (b) G(jω) locus superimposed om a family of N circles; (c) Closed-loop frequency-response curves

64 C64 Experimental Determination of Transfer Function Derivation of mathematical model is often difficult and may involve errors. Frequency response can be obtained using sinusoidal signal generators. Measure the output and obtain: Magnitudes (quite accurate) Phase (not as accurate) Use the Magnitude data and asymptotes to find: Type and error coefficients Corner frequencies Orders of numerator and denominator If second order terms are involved, ζ is obtained from the resonant peak. Use phase to determine if system is minimum phase or not: Minimum phase: ωæ phase = -90 (n - m) (n-m) difference in the order of denominator and numerator.

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