# Lecture 5 Classical Control Overview III. Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore

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1 Lecture 5 Classical Control Overview III Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore

2 A Fundamental Problem in Control Systems Poles of open loop transfer function are easy to find and they do not change with gain variation either. Poles of closed loop transfer function, which dictate stability characteristics, are more difficult to find and change with gain open loop transfer function = KG() s H () s closed loop transfer function KG() s T() s = 1 + KG( s) H ( s) let NG () s NH () s Gs () = and Hs () = D () s D () s G H T() s = KNG() s DH() s D () s D () s + KN () s N () s G H G H 2

3 Example ( s+ 1) ( s+ 3) Let Gs ( ) =, Hs ( ) = ss ( + 2) ( s+ 4) Ks ( + 1)( s+ 3) Then KG( s) H ( s) = ss ( + 2)( s+ 4) Poles of open loop Transfer Function KG( s) H ( s) are (0, 2, 4) Now Closed Loop Transfer Function T( s) T() s = Ks ( + 1)( s+ 4) + (6 + ) + (8 + 4 ) s K s K s K Poles of T( s) are not immediately known; they depends on K as well. System stability and transient response depends on poles of T( s) Root Locus gives vivid picture of the poles of T( s) as Kvaries 3

4 Vector Representation of Complex Numbers and Complex Functions Regular representation: s = σ + jω Fs () = s+ a= ( σ + a) + jω Alternate representation: ( ()) = ( + ) = ( ) 5+ 2 zero F s zero s a a 7 s j We can conclude that (s + a) is a complex number and can be represented by a vector drawn from the zero of the function (- a) to the point s. s + 4

5 Magnitude and Angle of Complex Functions m ( s+ z ) F s = m number of zeros n number of poles ( s+ p ) i i= 1 Let ( ),, n j= 1 The magnitude M of F( s) at any point s is M zero lengths = = pole lengths i= 1 n i= 1 ( s+ z ) ( s+ p ) The angle, θ, of Fs ( ) at any point s is j m θ = zero angles pole angles = ( s + zi) ( s + p j) i j m i= 1 j= 1 n 5

6 Example ( s + 1) Fs () = ss ( + 2) Vector originating at 1: Vector originating at 0: Vector originating at 2: M θ of F( s) 20 = =

7 Root Locus Analysis Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore

8 Facts Introduced by W. R. Evans (1948) Graphical representation of the closed loop poles in s-plane as the system parameter (typically controller gain) is varied. Gives a graphic representation of a system s stability characteristics Contains both qualitative and quantitative information Holds good for higher order systems 8

9 A Motivating Example Ref: N. S. Nise: Control Systems Engineering, 4 th Ed., Wiley, 2004 Pole location as a function of gain for the system. over damped under damped Note: Typically K > 0 for negative feedback systems 9

10 A Motivating Example Poles are always negative: system is always stable Real parts are same: same settling time As gain increases, damping ratio decreases %age overshoot increases 10

11 Properties of Root Locus The closed loop Transfer Function KG() s T() s = 1 + KG( s) H ( s) Characteristics equation: 1 + KG( s) H ( s) = 0 Closed loop poles are solution of characteristics equation. However, 1 + KG( s) H ( s) = 0 is a complex quantity. Hence, it can be expressed as KG s H s k k 0 ( ) ( ) = 1 = 1 (2 + 1)180 = 0, ± 1, ± 2, ± 3,... The above condition (Evan's condition)can be written as KG( s) H ( s) = 1 ( Magnitude criterion) KG s H s = + 0 ( ) ( ) (2k 1)180 (Angle criterion) 11

12 Example 0 Fact : If the angle of a complex number is an odd multiple of 180 for an open loop transfer function, then it is a pole of the closed loop system with 1 1 K = = GsHs () () Gs () Hs () Example: Let us consider a system as in the Figure and consider two points: P : 2 + j3 1 P 2 2 : 2 + j 2 12

13 Example For P: θ = θ + θ θ θ = i = (2k + 1) Therefore 2 + j3 is not a point on root locus For P : θ = i 0 2 Hence 2 + j is on root locus for some value of K. 2 2 (1.22) LL 3 4 Moreover, K = = 2 = 0.33 LL (2.12) (1.22)

14 Summary Given the poles and zeros of the open loop transfer function KG( s) H ( s), a point in the s - plane is on the root locus for a particular value of gain K, if the angles of the zeros minus the 0 angles of the poles add up to (2k + 1)180. Furthermore, the gain K at that point can be found by dividing the product of the lengths of the poles by the product of the lengths of the zeros. 14

15 Sketching the Root Locus: Basic Five Rules 1. Number of Branches: The number of branches of the root locus equals the number of closed loop poles (since each pole should move as the gain varies). 2. Symmetricity: A root locus is always symmetric about the real axis, since complex poles must always appear in conjugate pairs. 3. Real-axis segments: On the real axis, for K > 0, the root locus exists to the left of an odd number of real-axis finite open-loop poles and zeros. 15

16 Sketching the Root Locus: Basic Five Rules 4. Starting and ending points: The root locus begins at the finite and infinite poles of G(s)H(s) and ends at the finite and infinite zeros of G(s)H(s) 5. Behavior at Infinity: The root locus approaches straight lines as asymptotes as the locus approaches infinity The equation of asymptotes is given by the real-axis intercept σ a θ and angle a as follows: finite poles finite zeros σ a = No. of finite poles No. of finite zeros (2k + 1) Π θa = No. of finite poles No. of finite zeros where k = 0, ± 1, ± 2, ± 3,... For additional rules, refer to: N. S. Nise: Control Systems Engineering, 4 th Ed., Wiley, and the angle is given in radians wrt. positive extension of real axis 16

17 Example 17

18 Control Tuning via Gain Adjustment using Root Locus Sample root locus: A. Possible design point via gain adjustment B. Desired design point that cannot be met via simple gain adjustment (needs dynamic compensators) 18

19 PID Control Design Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore

20 Introduction The PID controller involves three components: Proportional feedback Integral feedback Derivative feedback By tuning the three components (gains), a suitable control action is generated that leads to desirable closed loop response of the output. Ref : en.wikipedia.org/wiki/pid_controller E ( s) U( s) u( t) Ref: N. S. Nise: Control Systems Engineering, 4 th Ed., Wiley,

21 Philosophy of PID Design The proportional component determines the reaction to the current value of the output error. It serves as a all pass block. The integral component determines the reaction based on the integral (sum) of recent errors. In a way, it accounts for the history of the error and serves as a low pass block. The derivative component determines the reaction based on the rate of change of the error. In a way, it accounts for the future value of the error and serves as a high pass block. 21

22 PID Controller The final form of the PID algorithm is: d ut ()= Ket p () + K i e( τ) dτ + Kd et () dt o U() s = Kp + Ki / s+ Kds Es () The tuning parameters are: Proportional gain, K Integral gain, K = K / T Derivative gain, p i p i K = K T d p d t 22

23 Effect of Proportional Term Proportional Term: P P K out out p = K e() t p : Proportional term of output : Proportional gain (a tuning parameter) If the gain K p is low, then control action may be too small when responding to system disturbances. Hence, it need not lead to desirable performance. If the proportional gain K p is too high, the system can become unstable. It may also lead to noise amplification 23

24 Effect of Integral Term Integral term : I out = Ki e( τ) dτ I K out i t 0 : Integral term of output : Integral gain, a tuning parameter The integral term (when added to the proportional term) accelerates the movement of the process towards setpoint and eliminates the residual steady-state error that occurs with a proportional only controller. 24

25 Effect of Integral Term Destabilizing effects of the Integral term : It can be seen that adding an integral term to a pure proportional term increases the gain by a factor of = 1+ > 1, for all ω. 2 2 jωti ω T i and simultaneously increases the phase-lag since tan = < 0 for all ω. jωti ωt i Because of this, both the gain margin (GM) and phase margin (PM) are reduced, and the closed- loop system becomes more oscillatory and potentially unstable. Ref : Li, Y., Ang, K.H. and Chong, G.C.Y. (2006) PID Control System Analysis and Design. IEEE Control Systems Magazine 26(1):pp

26 Effect of Integral Term Integrator Windup: If the actuator that realizes control action has saturated and if it is neglected, this causes low frequency oscillations and leads to instability. Remedies: Automatic Resetting, Explicit Anti-windup. 26

27 Effect of Derivative Term Derivative term : d Dout = Kd e() t dt K : Derivative gain (a tuning parameter) d The derivative term speeds up the transient behaviour. In general, it has negligible effect on the steady state performance (for step inputs, the effect on steady state response is zero). Differentiation of a signal amplifies noise. Hence, this term in the controller is highly sensitive to noise in the error term! Because of this, the derivative compensation should be used with care. 27

28 Effect of Derivative Term Adding a derivative term to pure proportional term reduces the phase lag by 1 ωtd ( 1+ jωtd ) = tan [ 0, π /2] for allω 1 which tends to increase the Phase Margin. In the meantime, however, the gain increases by a factor of 2 1+ jωt = 1+ ω T d 2 d > 1, for all ω. which decreases the Gain Margin. Hence the overall stability may be improved or degraded. Additional requirement : Low pass filter, Set-point filter, Pre-filter etc. 28

29 Effect of Increasing Gains on Output Performance Parameter Rise time Overshoot Settling time Error at equilibrium K p Decreases Increases Small Decreases change K i Decreases Increases Increases Eliminated K d Indefinite (can either decrease or increase) Decreases Decreases No Effect 29

30 Tuning of PID Design Method Advantages Disadvantages Manual Tuning Cohen-Coon No math required. Largely trial-and-error approach (based on general observations) Results in good tuning in general Requires experienced personnel Offline method. Good for first-order processes Ziegler Nichols Proven online method. Very aggressive tuning, May upset some inherent advantages of the process Software Tools Online or offline methods. Some cost and training of personnel is involved 30

31 Ziegler Nichols Method for Gain Tuning The K i and K d gains are first set to zero. The P gain is increased until it reaches the critical gain, K c, at which the output of the loop starts to oscillate. Next, K c and the oscillation period P c are used to set the gains as per the following rule. Control Type K p K i K d P 0.50K c - - PI 0.45K c 1.2K p / P c - PID 0.60K c 2K p / P c K p P c / 8 Note: The constants used may vary depending on the application. 31

32 Limitations of PID control It is a SISO design approach and hence can effectively handle only such system System should behave in a fairly linear manner. Hence, it is valid in close proximity of an operating point (about which the linearized system is valid) Does not take into account the limitations of the actuators Techniques to overcome: Gain scheduling, Cascading controllers, Filters in loop etc. 32

33 33

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