The Complete Set of PID Controllers with Guaranteed Gain and Phase Margins

Size: px
Start display at page:

Download "The Complete Set of PID Controllers with Guaranteed Gain and Phase Margins"

Transcription

1 Proceedings of the th IEEE onference on Decision and ontrol, and the European ontrol onference 5 Seville, Spain, December -5, 5 ThA7.5 The omplete Set of PID ontrollers with Guaranteed Gain and Phase Margins Keunsik Kim and Young hol Kim Abstract The problem of determining the entire set of PID controllers that stabilize a linear time invariant(lti) plant has been recently solved in [], []. In this paper, we extend these results to the problem of obtaining the complete set of PID parameters that attains prescribed gain and phase margins. An example is given for illustration. I. INTRODUTION The basic approach in modern control theories is first to obtain the entire set of stabilizing controllers as in the Youla parameterization, and then to search over the set to get a controller that meets given performance criteria. Using this approach, Datta et al. [] have recently reported very important results about computation of all stabilizing P, PI and PID controllers for a LTI system of arbitrary order. This complete solution is based on the generalized Hermite- Biehler theorem. They have shown that this problem is equivalent to the problem of linear programming. In [], the result has been extended to an arbitrary order LTI plant with time delay. In this paper, we consider a problem of computing PID controllers, (s) = ( + k p s + s )/s, along with an arbitrary LTI plant, P (s) = N(s)/D(s)e T ds, in the unit feedback configuration, that meet the stability margin specifications of gain and/or phase margins. We will show that this problem can be solved by extending the results from [], []. Here let A s denote the complete set of PID controllers which stabilizes a given LTI plant. If we wish to search a PID set over A s which attains merely a phase margin requirement, it is straightforwardly solved by using the method in []. However, it will be shown that more conditions should be considered for the case the gain margin specification is imposed. For this problem, a different computation method based on the characteristic polynomial with frequency sweeping has been proposed in []. Applying this method to PID controllers, we need to search three kinds of -D sets. Those shall be computed on (k p, ), (k p, ) and (, ) planes respectively. The final set is determined by taking the intersections of those sets. As a similar result, a problem of determining the complete set of stabilizing first order controllers that guarantees prespecified gain and phase margins has been solved in []. This research was supported in part by grant R from the Basic Research Program of the Korea Science & Engineering Foundation and in part by grant N-55 from the KOTEF. Keunsik Kim is with the School of Automotive Eng., Ajou ollege, Boryong, , Korea kskim@motor.ac.kr Young hol Kim is with the School of Elec.&omputer Eng., hungbuk National University, heongju, 6-76, Korea yckim@cbu.ac.kr In the present paper, we start by finding the set of all stabilizing PID controllers, by means of the methods in [], []. We also use the important property that at a fixed k p, a subset of A s,kp on the (, ) plane appears only in the form of a polygon. Due to the fact that there must exist at least one characteristic root on the imaginary axis if one chooses the PID gains at the boundary of the stabilizing region, the magnitude and phase conditions of the Nyquist plot derived at the point +j are used to map the A s,kp onto the set A R,kp in the (, ) plane. The A R,kp represents all (, ) parameters that satisfy the prescribed stability margins at a fixed k p A s. Then the complete set of PID controllers attaining the given gain and/or phase margin will be determined by sweeping over k p. All these sets can be displayed using and -D graphics. An example is given for illustration. II. PROBLEM FORMULATION AND PRELIMINARIES In this section, we present several definitions and preliminaries regarding the relationship between the Nyquist plot and the boundary of the stability region in controller parameter space. onsider the unit feedback system given by a LTI plant and PID controller: P (s) := N(s) D(s) e T ds, (s) := ( + k p s + s ) s The open loop transfer function is written as G (s) =(s)p (s) =( + k p s + s ) G s (s), () G s (s) := N(s) sd(s) e T ds. We first clarify the definitions of gain and phase margins. Let us replace the plant P (s) by KP(s) for gain margin. If the loop transfer function G (s) has N unstable poles in the right half plane(rhp) of the s-plane, the Nyquist plot has to encircle the point +j as much by N times in order for the overall system to be stable. Thus, there may exist two intervals of gain K for stability, which are [K, ] and [,K + ]. K and K + are referred to as lower gain margin and upper gain margin, respectively. For simplicity, the prespecified gain margin is denoted by [K,K + ]. Similarly, let us replace the plant P (s) by e jθ P (s) for the phase margin. In the case that G (s) has zeros in RHP(or the plant is of nonminimum phase or time delay), the phase margin /5/$. 5 IEEE 65

2 is also characterized by lower phase margin θ and upper phase margin θ +, which are written simply as [θ,θ + ]. Let A s be the set of parameters of all PID controllers that stabilizes the given plant P (s). A s can be computed using the results from [], []. It is important to note that the stabilizing region in the (, ) plane for a fixed value k p is a convex polygon as shown in Fig.. Let A s,kp denote a subset of A s with k p fixed at some value. A s is then the boundary of A s. Fig.. Stabilizing PID set: A s,kp, A K,kp, A θ,kp forafixedk p. For any value of A s, the Nyquist curve of the corresponding G (s) should cross (,j) for some ω. At this point, we have G (jω)=. This, in turn, is equivalent to the following two conditions for (k p,, ) A s : arg[( ω + jk p ω)g s (jω)] = π, () ( ω + jk p ω)g s (jω) =. () Here arg( ) [, π). Obviously, every PID controller with (k p,, ) A s gives rise to the gain/phase margin of [db] and o. On the other hand, any values inside A s provide a larger stability margin than that of the stability boundary. Therefore it is natural to assume that there exists a subset of A s attaining the given gain margin [K,K + ] and/or phase margin [θ,θ + ]. The problem of interest is to find the complete set of (k p,, ) from A s. The approach developed here to solve this problem proceeds as follows: (i) ompute all stabilizing PID gains using the result of [], []. (ii) ompute the set of PID parameters attaining the given gain margins [K,K + ]. The set is denoted by A K.Todo this, replace the plant P (s) in () by KP(s). ompute the set A c K : A c K := {(k p,, ) (k p,, ) A s and L(jω):=K( ω + jk p ω)g s (jω) =, for K [K,K + ],ω Ω K }, () Ω K will be defined in the next section. By the definition above, A c K is the subset of A s which makes KG (s) have a minimal destabilizing K. Then A K is determined by excluding A c K from A s. That is, A K = A s \A c K. This is a PID design with guaranteed gain margin. (iii) ompute the set of PID parameters attaining the given phase margins [θ,θ + ]. The set is denoted by A θ. To do this, replace the plant P (s) in () by e jθ P (s). ompute the set A c θ := {(k p,, ) (k p,, ) A s and G(jω):=e jθ ( ω + jk p ω)g s (jω) =, for θ [θ,θ + ],ω Ω θ }, (5) Ω θ will be defined in section IV. From the definition above, A c θ is the subset of A s which makes e jθ G (s) have a minimal destabilizing θ. Then A θ is determined by excluding A c θ from A s. That is, A θ = A s \A c θ. This is a PID design with guaranteed phase margin. (iv) If A K A θ Ø, it is clear that the set of PID controllers simultaneously satisfying both guaranteed gain and phase margins shall be obtained by A R = A s A K A θ. To proceed, we first investigate another equivalent condition for () as follows. Proposition The phase condition () yields the following identity. ω k p ω = tan{arg[g s (jω)]}, (6) with (a) <arg[g s (jω)] <π if k p >, (b) arg[g s (jω)] = or π if k p =, (7) (c) π < arg[g s (jω)] < π if k p <. Proof: The following equation necessarily implies the phase condition (). arg[( ω + jk p ω]= arg[g s (jω)] + iπ, (i =, ). (8) Expressing the left hand side of (8) in terms of arctangent, tan k p ω ( ω ) ± lπ = arg[g s(jω)] + iπ, (l =, ). (9) Using tan{θ +(i ± l)π} = tan(θ) and tan( θ) = tan(θ), (9) becomes k p ω ω = tan{ arg[g s (jω)]+(i ± l)π} = tan{arg[g s (jω)]}. () Equation () results in (6). This means that (6) is identical to two cases of (); arg[g (jω)] = and arg[g (jω)] = π. Thus, we need to impose other conditions on (6) to establish (). Rewriting (8) with i =, arg[g s (jω)] = π arg[ ω + jk p ω]. () learly, <arg[ ω + jk p ω] <πif k p > and π < arg[ ω + jk p ω] < π if k p <. Furthermore, if k p =, arg[ ω ]=or π. Therefore, the conditions (a), (b) and (c) must hold. 65

3 "! ( ' & % III. ALL PID SET WITH GUARANTEED GAIN MARGIN Since we have assumed that A s is known, solving problem (i) described in the previous section is equivalent to the problem of finding A c K in (). onsider a gain margin specification of [,K + ] for a stable plant and [K,K + ] for unstable plant. According to the definition of stability margins, the Nyquist plot of G (jω) should cross over the negative real axis at least [ K ]g L and [K + ]g U from (,j) in order for the PID controllers to guarantee the gain margin. This is shown in Fig..! " # ω + i := {ω K(ω) =K + }, for a stable or unstable P (s) ω i := {ω K(ω) =} for a stable P (s), := {ω K(ω) =K } for an unstable P (s). We see that it is sufficient to search over ω Ω K when we ) * + ' (, % & (a) G (s) stable (a) G (s) stable (b) G (s) unstable Fig.. Nyquist plots attaining the given gain margin The Nyquist curve corresponding to the boundary A s shown in Fig. must go through the point (,j) for some ω. For example, when the upper gain margin K + and the lower gain margin K are given, a PID controller that meets the stability margins should make the Nyquist curve of L(jω) cross over the negative real axis outside the interval [g L,g U ] in Fig.. From these, we know that computing such all PID set can be achieved by finding all regions in A s which provide gain margins [K, ] and [,K + ] reversely. The subset has been defined by A c K. In the sequel, the solution to problem (i) will be obtained by extruding A c K from A s. Therefore it is sufficient to compute A c K for the guaranteed gain margin problem. Let us define (6) again as follows: For afixedkp A s, kp ω M(ω) := tan{arg [G s (jω)]} =( ω ). () From the magnitude condition KG (jω) =,wehave K(ω) := = ( ω ) +(kp ω) G s (jω). () M(ω) +(kp ω) G s (jω) The frequency plot of K(ω) for ω [, ) as shown in Fig.. It is worth comparing the regions [,K + ] and [K,K + ] in Fig. with those in Fig.. Here we define a set of ω as follows: Ω K := {ω ω {[ω,ω+ ] [ω m,ω + m]}} () Fig.. #! " $ (b) G (s) unstable K(ω) vs. ω forafixedk p find the set A c K from A s with a fixed k p. Note that we need not consider the case of =because the corresponding point does not exist in the Nyquist plane []. However, we need to deal with two cases, k p and k p =, separately. A. k p Since K(ω) is the function for which the phase is π (see Proposition ), () can be represented by a set of straight lines in the (, ) plane. That is, for a fixed kp, using (), we have A c K,k p A c K,k p := {(, ) (, ) A s and ωj = M(ω j ), for ω j Ω K }. (5) denotes the subset of A c K with a fixed k p. Once we determine the set A c K,k p at a kp, we can compute A K,kp = A s,kp \A c K,k p. B. k p = If k p =, then, according to condition (b) in Proposition, (6) can not be occupied in (5). The plant P (s) can be written in terms of its even and odd parts. N(s) = N e (s )+sn o (s ), D(s) = D e (s )+sd o (s ). ondition (b) is equivalent to the condition that the imaginary part of G s (jω) equals zero. This is derived from the 655

4 definition of G s (s) and substituting s = jω as follows: (N e( ω ) D e( ω )+ω N o( ω ) D o( ω ))cosωt d + ω(n o( ω )D e( ω ) N e( ω )D o( ω ))sinωt d = (6) We solve the identity (6) for both cases of arg[g s (jω)] = and π. Let the solution be ω i for i =,,. Let s define Ω := { ω i arg[g s (j ω i )] = }, Ω π := { ω i arg[g s (j ω i )] = π} for i =,,. At these points, the magnitude condition () yields ω k = ± K G s ( ω k ), for ω k [Ω Ω π ]. (7) In the sequel, we can compute A c K,k p with k p =as follows: A c K,k p := {(, ) (, ) A s and ω j = K G s ( ω j ), for ω j Ω, ω k = K G s ( ω k ), for ω k Ω π }, (8) K [K,K + ]. Finally, by sweeping over k p, we will have the entire set of PID gains that guarantees the given gain margin: A K = k p A K,kp. (9) IV. ALL PID SET WITH GUARANTEED PHASE MARGIN To implement problem (ii) formulated in section II, we first replace plant P (s) by e jθ P (s). Then, G(s) :=e jθ G (s) =e jθ ( +k p s+ s ) G s (s). () Similar to the guaranteed gain margin, we consider a phase margin specification of θ + for a stable plant and [θ,θ + ] for a nonminimum phase plant. According to the definition of stability margins, the Nyquist plot of G(jω) should cross over the unit circle at least θ + or [θ,θ + ] from (,j) in order for the PID controllers to guarantee the phase margin. This is shown in Fig.. margin either θ + or [θ,θ + ]. The gain and phase conditions for G(jω) can be written as arg[( ω + jk p ω)g s (jω)] θ = π, () G(jω) = ( ω + jk p ω)g s (jω) =. () For a fixed k p, () becomes ω = ± Π(ω), () Π(ω) := G s (jω) (k p ω). () Note that Π(ω) should be greater than or equal to zero since ω R. Furthermore, rewriting (), we have θ(ω) =arg[(± Π(ω)+jω k p) G s (jω)] π. (5) Fig. 5 shows a typical plot of θ(ω). From these figures, we define Ω θ := {ω ω {[ω,ω+ ] [ω t,ω + t ]}} (6) ω + i := {ω θ(ω) =θ + }, for any G (s) ω i := {ω θ(ω) =}, for stable G (s), := {ω θ(ω) =θ }, for other G (s). (a) G (s) stable (a) G (s) stable (b) G (s) nonminimum phase Fig.. Nyquist plots attaining the given phase margin It is clear that if one chooses PID gains on the boundary A s, there exists at least one ω such that the Nyquist curve of G(jω) goes through the point (,j). That is, those PID gains give a phase margin of. Fig. shows that A c θ in (5) is nothing but the subset of A s that makes the phase (b) G (s) nonminimum phase or time delay Fig. 5. θ(ω) vs. ω forafixedkp Therefore it is sufficient to search over only ω Ω θ for the sake of finding A c θ from A s. ombining this set and (), we can compute A c θ with fixed k p. Let s define A c θ,k p := {(, ) (, ) A s and ωj = ± Π(ω)(ω j ), for ω j Ω θ }. (7) 656

5 Here A c θ,k p denotes the subset of A c θ for the fixed k p, which is a set of straight lines. After determining A c θ,k p at a kp,we compute A θ,kp = A s,kp \A c θ,k p. Finally sweeping over k p, we will have the complete set of PID gains that guarantees the given phase margin. That is, A θ = k p A θ,kp. (8) K(ω) arg[g(jω)]=π arg[g(jω)]= This is the solution to problem (ii) in section II. Remark PI and PD controllers are special cases of a PID controller with either =or =. Thus the above algorithm can be extended to these cases as well. V. ILLUSTRATIVE EXAMPLES In this section, we will give two examples; a time delayed plant and the same plant with delay free. Example onsider the following nonminimum phase plant in []: s s + s + P (s) = s 5 +8s +s +6s +6s +7 e T ds (9) with time delay T d =[sec]. We wish to find the entire set of PID controllers that guarantees the following gain and phase margins: Gain margin : K + = (about 6 [db]), Phase margin : [θ,θ + ]=[ o, 6 o ]. To begin with, we use the method proposed in [], [] to obtain the complete set of PID controllers A s that stabilize the closed loop system in the plant above. Fig. 6 shows the set. 6 K ω Fig. 7. K(ω) vs. ω for Example From Fig. 6 k p =.88 has been chosen. Fig. 7 shows the K(ω) plot over ω [, ][rad/sec]. Solving () with K(ω) =K + =, we obtain Ω K = {[.59,.56] [.86,.] [.567,.67] [.8,.] [6.597, 6.67] [9.65, 9.75] [.9,.87] [.97,.98] [7.958, ]}. Next we compute a set of straight lines on the (, ) plane, which is obtained by (5) at every ω = {ω + i,ω i }, as shown in Fig. 8. Then has been determined, and appear in the same figure. A K,kp 5 A K, Kp A K, Kp K p 6 8 Kp=.56 Kp=.89 Kp=. Kp=.55 Kp=.88 Kp=. Kp=.6 Kp=. Kp=.79 Kp=.6 Kp=. Kp=.8 Kp=.8 Kp= 5.5 Kp= 5.8 Kp= Kp= 7.6 Kp= 7.8 Fig. 6. All stabilizing PID controllers in the Example A. Stabilizing Region for Guaranteed Gain Margin Based on the discussion in section III, the following steps can be used for computing A K : ) hoose a k p from the results shown in Fig. 6. ) ompute Ω K using () after setting K(ω) =K +. ) ompute A c K,k p using (5) if k p or (8) if k p =. ) Find A K,kp by extruding A c K,k p from A s,kp k i Fig. 8. The region for guaranteed gain margin at k p =.88 B. Stabilizing Region for Guaranteed Phase Margin The procedures computing A θ from A s are as follows: ) hoose a k p from the result shown in Fig. 6. ) ompute Ω θ using (6) after setting θ(ω) = θ and θ +. ) ompute A c θ,k p using (7). ) Find A θ,kp by extruding A c θ,k p from A s,kp. Similar to the previous step, we choose k p =.88 from Fig. 6. The θ(ω) plot over ω [, ] [rad/sec] is shown in Fig. 9. By solving (6) with θ + = 6 o and θ = o, we obtain Ω θ = {[.96,.57] [.77,.655] [5.875, 6.7] [.56,.77] [6.97, 8.] [.985,.899] [.656,.6] [8.78, 9.7] [.95, 5.8] [9.976,.]}. 657

6 By means of (7), we constitute a set of straight lines at 5 every ω = {ω +,ω }. Fig. shows A θ,kp, A c θ,k p and A s,kp respectively. Region A θ,kp indicates the complete set of all PID parameters that guarantees the phase margin requirement when k p =.88. Fig. shows A K,kp, A θ,kp, A s,kp and their intersection, A R,kp. Thus any point (, ) A R,kp constitutes a controller that guarantees the previously specified gain and phase margins. To verify this, let s pick four points out on the Fig.. The gain and phase margins corresponding to these PID gains are given in Table I. # A R, Kp # # A K, Kp # θ(ω) 5 +Γ Π ΓΠ A θ, Kp θ + 5 Fig.. The region for guaranteed gain and phase margins at k p =.88 θ 5 Kp=. Kp=.55 Kp=.88 5 Kp= ω 5 A θ,kp Fig. 9. θ(ω) vs. ω at k p =.88 A θ,kp A S,KP A S,KP Fig.. The region for guaranteed phase margin at k p =.88 TABLE I GAIN AND PHASE MARGINS OF THE PID GAINS SELETED IN FIG. No. k p,, Gm[dB] Pm[deg] Remark #.88,.5, A K,kp #.88,.6, A θ,kp #.88,.7, A K,kp A θ,kp #.88,.9, inside A R,kp If we repeat this procedure over the whole range of k p A s, we obtain the complete set A R of PID controllers which guarantees both gain and phase margins simultaneously, as shown in Fig.. k p Kp=. Kp=.6 Kp=.79 Kp=. Kp=.6 Fig.. The complete set of PID gains with guaranteed gain and phase margins in Example VI. ONLUDING REMARKS In this paper, we have determined for a given LTI plant, the complete set of stabilizing PID controllers that attains the given gain and phase margins. This computing method extends the results of [], [] in all stabilizing PID controllers for a LTI system were found and also for a LTI plant with time delay. The procedures are simple and constitutional. -D and -D graphics of the PID set will be very useful for computer-aided design because they allow for much better designs than those for obtaining a single controller, as mentioned in []. REFERENES [] A. Datta, M.T. Ho, and S.P. Bhattacharyya, Structure and Synthesis of PID ontrollers, Springer-Verlag, London, UK.,. [] H. Xu, A. Datta and S.P. Bhattacharyya, PID Stabilization of LTI Plants with Time-Delay, Proc. of the nd IEEE conf. on Decision and ontrol, Maui, Hawaii,, pp 8-. [] M. Tan, I. Kaya and D.P. Atherton, omputation of Stabilizing PI and PID ontrollers, Proc. of the IEEE conf. on ontrol Applications,, pp [] R.N. Tantaris, L.H. Keel and S.P. Bhattacharyya, Gain/Phase Margin Design with First Order ontrollers, Proc. of the American ontrol onf., Denver, olorado,, pp

PID CONTROLLER SYNTHESIS FREE OF ANALYTICAL MODELS

PID CONTROLLER SYNTHESIS FREE OF ANALYTICAL MODELS PID CONTROLLER SYNTHESIS FREE OF ANALYTICAL MODELS L.H. Keel,1 S.P. Bhattacharyya, Tennessee State University, Nashville, Tennessee, USA Texas A&M University, College Station, Texas, USA Abstract: In this

More information

Stability Margin Based Design of Multivariable Controllers

Stability Margin Based Design of Multivariable Controllers Stability Margin Based Design of Multivariable Controllers Iván D. Díaz-Rodríguez Sangjin Han Shankar P. Bhattacharyya Dept. of Electrical and Computer Engineering Texas A&M University College Station,

More information

Data Based Design of 3Term Controllers. Data Based Design of 3 Term Controllers p. 1/10

Data Based Design of 3Term Controllers. Data Based Design of 3 Term Controllers p. 1/10 Data Based Design of 3Term Controllers Data Based Design of 3 Term Controllers p. 1/10 Data Based Design of 3 Term Controllers p. 2/10 History Classical Control - single controller (PID, lead/lag) is designed

More information

Robust fixed-order H Controller Design for Spectral Models by Convex Optimization

Robust fixed-order H Controller Design for Spectral Models by Convex Optimization Robust fixed-order H Controller Design for Spectral Models by Convex Optimization Alireza Karimi, Gorka Galdos and Roland Longchamp Abstract A new approach for robust fixed-order H controller design by

More information

Systems Analysis and Control

Systems Analysis and Control Systems Analysis and Control Matthew M. Peet Illinois Institute of Technology Lecture 23: Drawing The Nyquist Plot Overview In this Lecture, you will learn: Review of Nyquist Drawing the Nyquist Plot Using

More information

A Note on Bode Plot Asymptotes based on Transfer Function Coefficients

A Note on Bode Plot Asymptotes based on Transfer Function Coefficients ICCAS5 June -5, KINTEX, Gyeonggi-Do, Korea A Note on Bode Plot Asymptotes based on Transfer Function Coefficients Young Chol Kim, Kwan Ho Lee and Young Tae Woo School of Electrical & Computer Eng., Chungbuk

More information

Nyquist Criterion For Stability of Closed Loop System

Nyquist Criterion For Stability of Closed Loop System Nyquist Criterion For Stability of Closed Loop System Prof. N. Puri ECE Department, Rutgers University Nyquist Theorem Given a closed loop system: r(t) + KG(s) = K N(s) c(t) H(s) = KG(s) +KG(s) = KN(s)

More information

Systems Analysis and Control

Systems Analysis and Control Systems Analysis and Control Matthew M. Peet Arizona State University Lecture 23: Drawing The Nyquist Plot Overview In this Lecture, you will learn: Review of Nyquist Drawing the Nyquist Plot Using the

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Mechanical Engineering Dynamics and Control II Fall 2007

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Mechanical Engineering Dynamics and Control II Fall 2007 MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Mechanical Engineering.4 Dynamics and Control II Fall 7 Problem Set #9 Solution Posted: Sunday, Dec., 7. The.4 Tower system. The system parameters are

More information

Frequency methods for the analysis of feedback systems. Lecture 6. Loop analysis of feedback systems. Nyquist approach to study stability

Frequency methods for the analysis of feedback systems. Lecture 6. Loop analysis of feedback systems. Nyquist approach to study stability Lecture 6. Loop analysis of feedback systems 1. Motivation 2. Graphical representation of frequency response: Bode and Nyquist curves 3. Nyquist stability theorem 4. Stability margins Frequency methods

More information

PID Controllers for S ystems Systems with with Time- Time Delay

PID Controllers for S ystems Systems with with Time- Time Delay PID Controllers for Systems with Time-Delay 1 General Considerations CHARACTERISTIC EQUATIONS FOR DEALY SYSTEMS u(t) Delay T y(t) =u(t T ) u(t) _y(t) Integrator y(t) _y(t)+ay(t T )=u(t) a Delay T 2 u(t)

More information

Homework 7 - Solutions

Homework 7 - Solutions Homework 7 - Solutions Note: This homework is worth a total of 48 points. 1. Compensators (9 points) For a unity feedback system given below, with G(s) = K s(s + 5)(s + 11) do the following: (c) Find the

More information

Control Systems I. Lecture 9: The Nyquist condition

Control Systems I. Lecture 9: The Nyquist condition Control Systems I Lecture 9: The Nyquist condition Readings: Åstrom and Murray, Chapter 9.1 4 www.cds.caltech.edu/~murray/amwiki/index.php/first_edition Jacopo Tani Institute for Dynamic Systems and Control

More information

DESIGN USING TRANSFORMATION TECHNIQUE CLASSICAL METHOD

DESIGN USING TRANSFORMATION TECHNIQUE CLASSICAL METHOD 206 Spring Semester ELEC733 Digital Control System LECTURE 7: DESIGN USING TRANSFORMATION TECHNIQUE CLASSICAL METHOD For a unit ramp input Tz Ez ( ) 2 ( z ) D( z) G( z) Tz e( ) lim( z) z 2 ( z ) D( z)

More information

1 (s + 3)(s + 2)(s + a) G(s) = C(s) = K P + K I

1 (s + 3)(s + 2)(s + a) G(s) = C(s) = K P + K I MAE 43B Linear Control Prof. M. Krstic FINAL June 9, Problem. ( points) Consider a plant in feedback with the PI controller G(s) = (s + 3)(s + )(s + a) C(s) = K P + K I s. (a) (4 points) For a given constant

More information

Control Systems I. Lecture 9: The Nyquist condition

Control Systems I. Lecture 9: The Nyquist condition Control Systems I Lecture 9: The Nyquist condition adings: Guzzella, Chapter 9.4 6 Åstrom and Murray, Chapter 9.1 4 www.cds.caltech.edu/~murray/amwiki/index.php/first_edition Emilio Frazzoli Institute

More information

Uncertainty and Robustness for SISO Systems

Uncertainty and Robustness for SISO Systems Uncertainty and Robustness for SISO Systems ELEC 571L Robust Multivariable Control prepared by: Greg Stewart Outline Nature of uncertainty (models and signals). Physical sources of model uncertainty. Mathematical

More information

Exact Tuning of PID Controllers in Control Feedback Design

Exact Tuning of PID Controllers in Control Feedback Design Exact Tuning of PID Controllers in Control Feedback Design Lorenzo Ntogramatzidis Augusto Ferrante Department of Mathematics and Statistics, Curtin University, Perth WA 6845, Australia. E-mail: L.Ntogramatzidis@curtin.edu.au.

More information

MULTILOOP PI CONTROLLER FOR ACHIEVING SIMULTANEOUS TIME AND FREQUENCY DOMAIN SPECIFICATIONS

MULTILOOP PI CONTROLLER FOR ACHIEVING SIMULTANEOUS TIME AND FREQUENCY DOMAIN SPECIFICATIONS Journal of Engineering Science and Technology Vol. 1, No. 8 (215) 113-1115 School of Engineering, Taylor s University MULTILOOP PI CONTROLLER FOR ACHIEVING SIMULTANEOUS TIME AND FREQUENCY DOMAIN SPECIFICATIONS

More information

AN AUTOMATED VIRTUAL TOOL TO COMPUTE THE ENTIRE SET OF PROPORTIONAL INTEGRAL DERIVATIVE CONTROLLERS FOR A CONTINUOUS LINEAR TIME INVARIANT SYSTEM

AN AUTOMATED VIRTUAL TOOL TO COMPUTE THE ENTIRE SET OF PROPORTIONAL INTEGRAL DERIVATIVE CONTROLLERS FOR A CONTINUOUS LINEAR TIME INVARIANT SYSTEM AN AUTOMATED VIRTUAL TOOL TO COMPUTE THE ENTIRE SET OF PROPORTIONAL INTEGRAL DERIVATIVE CONTROLLERS FOR A CONTINUOUS LINEAR TIME INVARIANT SYSTEM A Thesis by BHARAT NARASIMHAN Submitted to the Office of

More information

1 (20 pts) Nyquist Exercise

1 (20 pts) Nyquist Exercise EE C128 / ME134 Problem Set 6 Solution Fall 2011 1 (20 pts) Nyquist Exercise Consider a close loop system with unity feedback. For each G(s), hand sketch the Nyquist diagram, determine Z = P N, algebraically

More information

Robust Loop Shaping Controller Design for Spectral Models by Quadratic Programming

Robust Loop Shaping Controller Design for Spectral Models by Quadratic Programming Robust Loop Shaping Controller Design for Spectral Models by Quadratic Programming Gorka Galdos, Alireza Karimi and Roland Longchamp Abstract A quadratic programming approach is proposed to tune fixed-order

More information

On Stabilization of First-Order Plus Dead-Time Unstable Processes Using PID Controllers

On Stabilization of First-Order Plus Dead-Time Unstable Processes Using PID Controllers On Stabilization of First-Order Plus Dead-Time Unstable Processes Using PID Controllers Chyi Hwang Department of Chemical Engineering National Chung Cheng University Chia-Yi 621, TAIWAN E-mail: chmch@ccu.edu.tw

More information

Analysis of SISO Control Loops

Analysis of SISO Control Loops Chapter 5 Analysis of SISO Control Loops Topics to be covered For a given controller and plant connected in feedback we ask and answer the following questions: Is the loop stable? What are the sensitivities

More information

Control Systems I Lecture 10: System Specifications

Control Systems I Lecture 10: System Specifications Control Systems I Lecture 10: System Specifications Readings: Guzzella, Chapter 10 Emilio Frazzoli Institute for Dynamic Systems and Control D-MAVT ETH Zürich November 24, 2017 E. Frazzoli (ETH) Lecture

More information

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)

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) 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)

More information

Lecture 6 Classical Control Overview IV. Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore

Lecture 6 Classical Control Overview IV. Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore Lecture 6 Classical Control Overview IV Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore Lead Lag Compensator Design Dr. Radhakant Padhi Asst.

More information

A NEW APPROACH TO MIXED H 2 /H OPTIMAL PI/PID CONTROLLER DESIGN

A NEW APPROACH TO MIXED H 2 /H OPTIMAL PI/PID CONTROLLER DESIGN Copyright 2002 IFAC 15th Triennial World Congress, Barcelona, Spain A NEW APPROACH TO MIXED H 2 /H OPTIMAL PI/PID CONTROLLER DESIGN Chyi Hwang,1 Chun-Yen Hsiao Department of Chemical Engineering National

More information

Systems Analysis and Control

Systems Analysis and Control Systems Analysis and Control Matthew M. Peet Arizona State University Lecture 21: Stability Margins and Closing the Loop Overview In this Lecture, you will learn: Closing the Loop Effect on Bode Plot Effect

More information

ECSE 4962 Control Systems Design. A Brief Tutorial on Control Design

ECSE 4962 Control Systems Design. A Brief Tutorial on Control Design ECSE 4962 Control Systems Design A Brief Tutorial on Control Design Instructor: Professor John T. Wen TA: Ben Potsaid http://www.cat.rpi.edu/~wen/ecse4962s04/ Don t Wait Until The Last Minute! You got

More information

Topic # Feedback Control Systems

Topic # Feedback Control Systems Topic #19 16.31 Feedback Control Systems Stengel Chapter 6 Question: how well do the large gain and phase margins discussed for LQR map over to DOFB using LQR and LQE (called LQG)? Fall 2010 16.30/31 19

More information

QFT design for uncertain non-minimum phase and unstable plants revisited

QFT design for uncertain non-minimum phase and unstable plants revisited Loughborough University Institutional Repository QFT design for uncertain non-minimum phase and unstable plants revisited This item was submitted to Loughborough University's Institutional Repository by

More information

Qualitative Graphical Representation of Nyquist Plots

Qualitative Graphical Representation of Nyquist Plots Qualitative Graphical presentation of Nyquist Plots R. Zanasi a, F. Grossi a,, L. Biagiotti a a Department of Engineering Enzo Ferrari, University of Modena and ggio Emilia, via Pietro Vivarelli 0, 425

More information

FEL3210 Multivariable Feedback Control

FEL3210 Multivariable Feedback Control FEL3210 Multivariable Feedback Control Lecture 5: Uncertainty and Robustness in SISO Systems [Ch.7-(8)] Elling W. Jacobsen, Automatic Control Lab, KTH Lecture 5:Uncertainty and Robustness () FEL3210 MIMO

More information

MAS107 Control Theory Exam Solutions 2008

MAS107 Control Theory Exam Solutions 2008 MAS07 CONTROL THEORY. HOVLAND: EXAM SOLUTION 2008 MAS07 Control Theory Exam Solutions 2008 Geir Hovland, Mechatronics Group, Grimstad, Norway June 30, 2008 C. Repeat question B, but plot the phase curve

More information

Systems Analysis and Control

Systems Analysis and Control Systems Analysis and Control Matthew M. Peet Illinois Institute of Technology Lecture 22: The Nyquist Criterion Overview In this Lecture, you will learn: Complex Analysis The Argument Principle The Contour

More information

CDS 101/110a: Lecture 10-1 Robust Performance

CDS 101/110a: Lecture 10-1 Robust Performance CDS 11/11a: Lecture 1-1 Robust Performance Richard M. Murray 1 December 28 Goals: Describe how to represent uncertainty in process dynamics Describe how to analyze a system in the presence of uncertainty

More information

Design and Tuning of Fractional-order PID Controllers for Time-delayed Processes

Design and Tuning of Fractional-order PID Controllers for Time-delayed Processes Design and Tuning of Fractional-order PID Controllers for Time-delayed Processes Emmanuel Edet Technology and Innovation Centre University of Strathclyde 99 George Street Glasgow, United Kingdom emmanuel.edet@strath.ac.uk

More information

Introduction. Performance and Robustness (Chapter 1) Advanced Control Systems Spring / 31

Introduction. Performance and Robustness (Chapter 1) Advanced Control Systems Spring / 31 Introduction Classical Control Robust Control u(t) y(t) G u(t) G + y(t) G : nominal model G = G + : plant uncertainty Uncertainty sources : Structured : parametric uncertainty, multimodel uncertainty Unstructured

More information

Course Outline. Closed Loop Stability. Stability. Amme 3500 : System Dynamics & Control. Nyquist Stability. Dr. Dunant Halim

Course Outline. Closed Loop Stability. Stability. Amme 3500 : System Dynamics & Control. Nyquist Stability. Dr. Dunant Halim Amme 3 : System Dynamics & Control Nyquist Stability Dr. Dunant Halim Course Outline Week Date Content Assignment Notes 1 5 Mar Introduction 2 12 Mar Frequency Domain Modelling 3 19 Mar System Response

More information

Root Locus. Signals and Systems: 3C1 Control Systems Handout 3 Dr. David Corrigan Electronic and Electrical Engineering

Root Locus. Signals and Systems: 3C1 Control Systems Handout 3 Dr. David Corrigan Electronic and Electrical Engineering Root Locus Signals and Systems: 3C1 Control Systems Handout 3 Dr. David Corrigan Electronic and Electrical Engineering corrigad@tcd.ie Recall, the example of the PI controller car cruise control system.

More information

Robust Performance Example #1

Robust Performance Example #1 Robust Performance Example # The transfer function for a nominal system (plant) is given, along with the transfer function for one extreme system. These two transfer functions define a family of plants

More information

80DB A 40DB 0DB DB

80DB A 40DB 0DB DB Stability Analysis On the Nichols Chart and Its Application in QFT Wenhua Chen and Donald J. Ballance Centre for Systems & Control Department of Mechanical Engineering University of Glasgow Glasgow G12

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Mechanical Engineering Dynamics and Control II Fall K(s +1)(s +2) G(s) =.

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Mechanical Engineering Dynamics and Control II Fall K(s +1)(s +2) G(s) =. MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Mechanical Engineering. Dynamics and Control II Fall 7 Problem Set #7 Solution Posted: Friday, Nov., 7. Nise problem 5 from chapter 8, page 76. Answer:

More information

Computation of Stabilizing PI and PID parameters for multivariable system with time delays

Computation of Stabilizing PI and PID parameters for multivariable system with time delays Computation of Stabilizing PI and PID parameters for multivariable system with time delays Nour El Houda Mansour, Sami Hafsi, Kaouther Laabidi Laboratoire d Analyse, Conception et Commande des Systèmes

More information

School of Mechanical Engineering Purdue University. DC Motor Position Control The block diagram for position control of the servo table is given by:

School of Mechanical Engineering Purdue University. DC Motor Position Control The block diagram for position control of the servo table is given by: Root Locus Motivation Sketching Root Locus Examples ME375 Root Locus - 1 Servo Table Example DC Motor Position Control The block diagram for position control of the servo table is given by: θ D 0.09 See

More information

Raktim Bhattacharya. . AERO 422: Active Controls for Aerospace Vehicles. Frequency Response-Design Method

Raktim Bhattacharya. . AERO 422: Active Controls for Aerospace Vehicles. Frequency Response-Design Method .. AERO 422: Active Controls for Aerospace Vehicles Frequency Response- Method Raktim Bhattacharya Laboratory For Uncertainty Quantification Aerospace Engineering, Texas A&M University. ... Response to

More information

2.004 Dynamics and Control II Spring 2008

2.004 Dynamics and Control II Spring 2008 MT OpenCourseWare http://ocw.mit.edu.004 Dynamics and Control Spring 008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. Massachusetts nstitute of Technology

More information

ECE 486 Control Systems

ECE 486 Control Systems ECE 486 Control Systems Spring 208 Midterm #2 Information Issued: April 5, 208 Updated: April 8, 208 ˆ This document is an info sheet about the second exam of ECE 486, Spring 208. ˆ Please read the following

More information

Control for. Maarten Steinbuch Dept. Mechanical Engineering Control Systems Technology Group TU/e

Control for. Maarten Steinbuch Dept. Mechanical Engineering Control Systems Technology Group TU/e Control for Maarten Steinbuch Dept. Mechanical Engineering Control Systems Technology Group TU/e Motion Systems m F Introduction Timedomain tuning Frequency domain & stability Filters Feedforward Servo-oriented

More information

EECE 460 : Control System Design

EECE 460 : Control System Design EECE 460 : Control System Design SISO Pole Placement Guy A. Dumont UBC EECE January 2011 Guy A. Dumont (UBC EECE) EECE 460: Pole Placement January 2011 1 / 29 Contents 1 Preview 2 Polynomial Pole Placement

More information

Lecture 5: Linear Systems. Transfer functions. Frequency Domain Analysis. Basic Control Design.

Lecture 5: Linear Systems. Transfer functions. Frequency Domain Analysis. Basic Control Design. ISS0031 Modeling and Identification Lecture 5: Linear Systems. Transfer functions. Frequency Domain Analysis. Basic Control Design. Aleksei Tepljakov, Ph.D. September 30, 2015 Linear Dynamic Systems Definition

More information

Plan of the Lecture. Goal: wrap up lead and lag control; start looking at frequency response as an alternative methodology for control systems design.

Plan of the Lecture. Goal: wrap up lead and lag control; start looking at frequency response as an alternative methodology for control systems design. Plan of the Lecture Review: design using Root Locus; dynamic compensation; PD and lead control Today s topic: PI and lag control; introduction to frequency-response design method Goal: wrap up lead and

More information

Robustness Analysis and Controller Synthesis with Non-Normalized Coprime Factor Uncertainty Characterisation

Robustness Analysis and Controller Synthesis with Non-Normalized Coprime Factor Uncertainty Characterisation 211 5th IEEE onference on Decision and ontrol and European ontrol onference (D-E) Orlando, FL, USA, December 12-15, 211 Robustness Analysis and ontroller Synthesis with Non-Normalized oprime Factor Uncertainty

More information

Radar Dish. Armature controlled dc motor. Inside. θ r input. Outside. θ D output. θ m. Gearbox. Control Transmitter. Control. θ D.

Radar Dish. Armature controlled dc motor. Inside. θ r input. Outside. θ D output. θ m. Gearbox. Control Transmitter. Control. θ D. Radar Dish ME 304 CONTROL SYSTEMS Mechanical Engineering Department, Middle East Technical University Armature controlled dc motor Outside θ D output Inside θ r input r θ m Gearbox Control Transmitter

More information

STABILITY ANALYSIS TECHNIQUES

STABILITY ANALYSIS TECHNIQUES ECE4540/5540: Digital Control Systems 4 1 STABILITY ANALYSIS TECHNIQUES 41: Bilinear transformation Three main aspects to control-system design: 1 Stability, 2 Steady-state response, 3 Transient response

More information

Didier HENRION henrion

Didier HENRION  henrion GRADUATE COURSE ON POLYNOMIAL METHODS FOR ROBUST CONTROL PART II.1 ROBUST STABILITY ANALYSIS: POLYTOPIC UNCERTAINTY Didier HENRION www.laas.fr/ henrion henrion@laas.fr Cité de l espace with Ariane launcher

More information

Topic # Feedback Control Systems

Topic # Feedback Control Systems Topic #20 16.31 Feedback Control Systems Closed-loop system analysis Bounded Gain Theorem Robust Stability Fall 2007 16.31 20 1 SISO Performance Objectives Basic setup: d i d o r u y G c (s) G(s) n control

More information

Control System Design

Control System Design ELEC ENG 4CL4: Control System Design Notes for Lecture #11 Wednesday, January 28, 2004 Dr. Ian C. Bruce Room: CRL-229 Phone ext.: 26984 Email: ibruce@mail.ece.mcmaster.ca Relative Stability: Stability

More information

Chapter 6 - Solved Problems

Chapter 6 - Solved Problems Chapter 6 - Solved Problems Solved Problem 6.. Contributed by - James Welsh, University of Newcastle, Australia. Find suitable values for the PID parameters using the Z-N tuning strategy for the nominal

More information

Lecture 5: Frequency domain analysis: Nyquist, Bode Diagrams, second order systems, system types

Lecture 5: Frequency domain analysis: Nyquist, Bode Diagrams, second order systems, system types Lecture 5: Frequency domain analysis: Nyquist, Bode Diagrams, second order systems, system types Venkata Sonti Department of Mechanical Engineering Indian Institute of Science Bangalore, India, 562 This

More information

INTRODUCTION TO DIGITAL CONTROL

INTRODUCTION TO DIGITAL CONTROL ECE4540/5540: Digital Control Systems INTRODUCTION TO DIGITAL CONTROL.: Introduction In ECE450/ECE550 Feedback Control Systems, welearnedhow to make an analog controller D(s) to control a linear-time-invariant

More information

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 53, NO. 9, OCTOBER

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 53, NO. 9, OCTOBER IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL 53, NO 9, OCTOBER 2008 2033 Mixed Deterministic/Randomized Methods for Fixed Order Controller Design Yasumasa Fujisaki, Member, IEEE, Yasuaki Oishi, Member,

More information

ECE 380: Control Systems

ECE 380: Control Systems ECE 380: Control Systems Winter 2011 Course Notes, Part II Section 002 Prof. Shreyas Sundaram Department of Electrical and Computer Engineering University of Waterloo ii Acknowledgments Parts of these

More information

r + - FINAL June 12, 2012 MAE 143B Linear Control Prof. M. Krstic

r + - FINAL June 12, 2012 MAE 143B Linear Control Prof. M. Krstic MAE 43B Linear Control Prof. M. Krstic FINAL June, One sheet of hand-written notes (two pages). Present your reasoning and calculations clearly. Inconsistent etchings will not be graded. Write answers

More information

Digital Control Systems

Digital Control Systems Digital Control Systems Lecture Summary #4 This summary discussed some graphical methods their use to determine the stability the stability margins of closed loop systems. A. Nyquist criterion Nyquist

More information

Frequency domain analysis

Frequency domain analysis Automatic Control 2 Frequency domain analysis Prof. Alberto Bemporad University of Trento Academic year 2010-2011 Prof. Alberto Bemporad (University of Trento) Automatic Control 2 Academic year 2010-2011

More information

Control of integral processes with dead time Part IV: various issues about PI controllers

Control of integral processes with dead time Part IV: various issues about PI controllers Control of integral processes with dead time Part IV: various issues about PI controllers B. Wang, D. Rees and Q.-C. Zhong Abstract: Various issues about integral processes with dead time controlled by

More information

Linear Control Systems Lecture #3 - Frequency Domain Analysis. Guillaume Drion Academic year

Linear Control Systems Lecture #3 - Frequency Domain Analysis. Guillaume Drion Academic year Linear Control Systems Lecture #3 - Frequency Domain Analysis Guillaume Drion Academic year 2018-2019 1 Goal and Outline Goal: To be able to analyze the stability and robustness of a closed-loop system

More information

An Internal Stability Example

An Internal Stability Example An Internal Stability Example Roy Smith 26 April 2015 To illustrate the concept of internal stability we will look at an example where there are several pole-zero cancellations between the controller and

More information

CHAPTER 7 : BODE PLOTS AND GAIN ADJUSTMENTS COMPENSATION

CHAPTER 7 : BODE PLOTS AND GAIN ADJUSTMENTS COMPENSATION CHAPTER 7 : BODE PLOTS AND GAIN ADJUSTMENTS COMPENSATION Objectives Students should be able to: Draw the bode plots for first order and second order system. Determine the stability through the bode plots.

More information

The loop shaping paradigm. Lecture 7. Loop analysis of feedback systems (2) Essential specifications (2)

The loop shaping paradigm. Lecture 7. Loop analysis of feedback systems (2) Essential specifications (2) Lecture 7. Loop analysis of feedback systems (2). Loop shaping 2. Performance limitations The loop shaping paradigm. Estimate performance and robustness of the feedback system from the loop transfer L(jω)

More information

Frequency Response Techniques

Frequency Response Techniques 4th Edition T E N Frequency Response Techniques SOLUTION TO CASE STUDY CHALLENGE Antenna Control: Stability Design and Transient Performance First find the forward transfer function, G(s). Pot: K 1 = 10

More information

Control Systems 2. Lecture 4: Sensitivity function limits. Roy Smith

Control Systems 2. Lecture 4: Sensitivity function limits. Roy Smith Control Systems 2 Lecture 4: Sensitivity function limits Roy Smith 2017-3-14 4.1 Input-output controllability Control design questions: 1. How well can the plant be controlled? 2. What control structure

More information

The Frequency-response Design Method

The Frequency-response Design Method Chapter 6 The Frequency-response Design Method Problems and Solutions for Section 6.. (a) Show that α 0 in Eq. (6.2) is given by α 0 = G(s) U 0ω = U 0 G( jω) s jω s= jω 2j and α 0 = G(s) U 0ω = U 0 G(jω)

More information

Software Engineering/Mechatronics 3DX4. Slides 6: Stability

Software Engineering/Mechatronics 3DX4. Slides 6: Stability Software Engineering/Mechatronics 3DX4 Slides 6: Stability Dr. Ryan Leduc Department of Computing and Software McMaster University Material based on lecture notes by P. Taylor and M. Lawford, and Control

More information

Automatic Control 2. Loop shaping. Prof. Alberto Bemporad. University of Trento. Academic year

Automatic Control 2. Loop shaping. Prof. Alberto Bemporad. University of Trento. Academic year Automatic Control 2 Loop shaping Prof. Alberto Bemporad University of Trento Academic year 21-211 Prof. Alberto Bemporad (University of Trento) Automatic Control 2 Academic year 21-211 1 / 39 Feedback

More information

Analysis of Discrete-Time Systems

Analysis of Discrete-Time Systems TU Berlin Discrete-Time Control Systems 1 Analysis of Discrete-Time Systems Overview Stability Sensitivity and Robustness Controllability, Reachability, Observability, and Detectabiliy TU Berlin Discrete-Time

More information

Theory of Machines and Automatic Control Winter 2018/2019

Theory of Machines and Automatic Control Winter 2018/2019 Theory of Machines and Automatic Control Winter 2018/2019 Lecturer: Sebastian Korczak, PhD, Eng. Institute of Machine Design Fundamentals - Department of Mechanics http://www.ipbm.simr.pw.edu.pl/ Lecture

More information

COMPUTER AIDED SYNTHESIS AND DESIGN OF PID CONTROLLERS. A Thesis SANDIPAN MITRA

COMPUTER AIDED SYNTHESIS AND DESIGN OF PID CONTROLLERS. A Thesis SANDIPAN MITRA COMPUTER AIDED SYNTHESIS AND DESIGN OF PID CONTROLLERS A Thesis by SANDIPAN MITRA Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the

More information

Übersetzungshilfe / Translation aid (English) To be returned at the end of the exam!

Übersetzungshilfe / Translation aid (English) To be returned at the end of the exam! Prüfung Regelungstechnik I (Control Systems I) Prof. Dr. Lino Guzzella 5. 2. 2 Übersetzungshilfe / Translation aid (English) To be returned at the end of the exam! Do not mark up this translation aid -

More information

MEM 355 Performance Enhancement of Dynamical Systems

MEM 355 Performance Enhancement of Dynamical Systems MEM 355 Performance Enhancement of Dynamical Systems Frequency Domain Design Harry G. Kwatny Department of Mechanical Engineering & Mechanics Drexel University 5/8/25 Outline Closed Loop Transfer Functions

More information

Iterative Controller Tuning Using Bode s Integrals

Iterative Controller Tuning Using Bode s Integrals Iterative Controller Tuning Using Bode s Integrals A. Karimi, D. Garcia and R. Longchamp Laboratoire d automatique, École Polytechnique Fédérale de Lausanne (EPFL), 05 Lausanne, Switzerland. email: alireza.karimi@epfl.ch

More information

Module 3F2: Systems and Control EXAMPLES PAPER 2 ROOT-LOCUS. Solutions

Module 3F2: Systems and Control EXAMPLES PAPER 2 ROOT-LOCUS. Solutions Cambridge University Engineering Dept. Third Year Module 3F: Systems and Control EXAMPLES PAPER ROOT-LOCUS Solutions. (a) For the system L(s) = (s + a)(s + b) (a, b both real) show that the root-locus

More information

Robust Control 3 The Closed Loop

Robust Control 3 The Closed Loop Robust Control 3 The Closed Loop Harry G. Kwatny Department of Mechanical Engineering & Mechanics Drexel University /2/2002 Outline Closed Loop Transfer Functions Traditional Performance Measures Time

More information

Intro to Frequency Domain Design

Intro to Frequency Domain Design Intro to Frequency Domain Design MEM 355 Performance Enhancement of Dynamical Systems Harry G. Kwatny Department of Mechanical Engineering & Mechanics Drexel University Outline Closed Loop Transfer Functions

More information

H(s) = s. a 2. H eq (z) = z z. G(s) a 2. G(s) A B. s 2 s(s + a) 2 s(s a) G(s) 1 a 1 a. } = (z s 1)( z. e ) ) (z. (z 1)(z e at )(z e at )

H(s) = s. a 2. H eq (z) = z z. G(s) a 2. G(s) A B. s 2 s(s + a) 2 s(s a) G(s) 1 a 1 a. } = (z s 1)( z. e ) ) (z. (z 1)(z e at )(z e at ) .7 Quiz Solutions Problem : a H(s) = s a a) Calculate the zero order hold equivalent H eq (z). H eq (z) = z z G(s) Z{ } s G(s) a Z{ } = Z{ s s(s a ) } G(s) A B Z{ } = Z{ + } s s(s + a) s(s a) G(s) a a

More information

Definition of Stability

Definition of Stability Definition of Stability Transfer function of a linear time-invariant (LTI) system Fs () = b 2 1 0+ b1s+ b2s + + b m m m 1s - - + bms a0 + a1s+ a2s2 + + an-1sn- 1+ ansn Characteristic equation and poles

More information

Response to a pure sinusoid

Response to a pure sinusoid Harvard University Division of Engineering and Applied Sciences ES 145/215 - INTRODUCTION TO SYSTEMS ANALYSIS WITH PHYSIOLOGICAL APPLICATIONS Fall Lecture 14: The Bode Plot Response to a pure sinusoid

More information

K(s +2) s +20 K (s + 10)(s +1) 2. (c) KG(s) = K(s + 10)(s +1) (s + 100)(s +5) 3. Solution : (a) KG(s) = s +20 = K s s

K(s +2) s +20 K (s + 10)(s +1) 2. (c) KG(s) = K(s + 10)(s +1) (s + 100)(s +5) 3. Solution : (a) KG(s) = s +20 = K s s 321 16. Determine the range of K for which each of the following systems is stable by making a Bode plot for K = 1 and imagining the magnitude plot sliding up or down until instability results. Verify

More information

Root Locus Techniques

Root Locus Techniques 4th Edition E I G H T Root Locus Techniques SOLUTIONS TO CASE STUDIES CHALLENGES Antenna Control: Transient Design via Gain a. From the Chapter 5 Case Study Challenge: 76.39K G(s) = s(s+50)(s+.32) Since

More information

Exercise 1 (A Non-minimum Phase System)

Exercise 1 (A Non-minimum Phase System) Prof. Dr. E. Frazzoli 5-59- Control Systems I (HS 25) Solution Exercise Set Loop Shaping Noele Norris, 9th December 26 Exercise (A Non-minimum Phase System) To increase the rise time of the system, we

More information

Recitation 11: Time delays

Recitation 11: Time delays Recitation : Time delays Emilio Frazzoli Laboratory for Information and Decision Systems Massachusetts Institute of Technology November, 00. Introduction and motivation. Delays are incurred when the controller

More information

Software Engineering 3DX3. Slides 8: Root Locus Techniques

Software Engineering 3DX3. Slides 8: Root Locus Techniques Software Engineering 3DX3 Slides 8: Root Locus Techniques Dr. Ryan Leduc Department of Computing and Software McMaster University Material based on Control Systems Engineering by N. Nise. c 2006, 2007

More information

Topic # Feedback Control

Topic # Feedback Control Topic #4 16.31 Feedback Control Stability in the Frequency Domain Nyquist Stability Theorem Examples Appendix (details) This is the basis of future robustness tests. Fall 2007 16.31 4 2 Frequency Stability

More information

PD, PI, PID Compensation. M. Sami Fadali Professor of Electrical Engineering University of Nevada

PD, PI, PID Compensation. M. Sami Fadali Professor of Electrical Engineering University of Nevada PD, PI, PID Compensation M. Sami Fadali Professor of Electrical Engineering University of Nevada 1 Outline PD compensation. PI compensation. PID compensation. 2 PD Control L= loop gain s cl = desired closed-loop

More information

Design Methods for Control Systems

Design Methods for Control Systems Design Methods for Control Systems Maarten Steinbuch TU/e Gjerrit Meinsma UT Dutch Institute of Systems and Control Winter term 2002-2003 Schedule November 25 MSt December 2 MSt Homework # 1 December 9

More information

Classify a transfer function to see which order or ramp it can follow and with which expected error.

Classify a transfer function to see which order or ramp it can follow and with which expected error. Dr. J. Tani, Prof. Dr. E. Frazzoli 5-059-00 Control Systems I (Autumn 208) Exercise Set 0 Topic: Specifications for Feedback Systems Discussion: 30.. 208 Learning objectives: The student can grizzi@ethz.ch,

More information

The Nyquist criterion relates the stability of a closed system to the open-loop frequency response and open loop pole location.

The Nyquist criterion relates the stability of a closed system to the open-loop frequency response and open loop pole location. Introduction to the Nyquist criterion The Nyquist criterion relates the stability of a closed system to the open-loop frequency response and open loop pole location. Mapping. If we take a complex number

More information

Exercise 1 (A Non-minimum Phase System)

Exercise 1 (A Non-minimum Phase System) Prof. Dr. E. Frazzoli 5-59- Control Systems I (Autumn 27) Solution Exercise Set 2 Loop Shaping clruch@ethz.ch, 8th December 27 Exercise (A Non-minimum Phase System) To decrease the rise time of the system,

More information