Tuning of PID Controllers Based on Sensitivity Margin Specification

Size: px
Start display at page:

Download "Tuning of PID Controllers Based on Sensitivity Margin Specification"

Transcription

1 Tuning of D Controllers ased on Sensitivity Margin Specification S. Dormido and F. Morilla Dpto. de nformática y Automática-UNED c/ Juan del Rosal 6, 84 Madrid, Spain {sdormido,fmorilla}@dia.uned.es Abstract This paper presents an alternative method to the one proposed by Åström and Hägglund (995) for the tuning of D controllers with specification of sensitivity margin M s. The method is based on the following steps: º) to delimit the range of frequencies in which it is possible to contact with a specific point of a circle of radius M s - centered at -, º) to explore the frequencies where this contact takes place tangentially, because these frequencies will only be possible solutions, and 3º) to choose one of the possible solutions. Some examples have been selected in order to show the main features of the proposed method. ntroduction n D control design, one often needs to go beyond the issue of closed loop stability. n particular, it is common to specify some quantitative measures of how far from instability the nominal loop is. This is attained by introducing measures that describe the distance from the nominal open-loop frequency response to the critical stability point (-;). Two indicators of relative stability for the case in which the open loop has no poles in the open RH are the gain margin, A m, and the phase margin, m. Figure shows an alternative measure for relative stability. The radius of the circle tangent to the polar plot of L(j) = G p (j)g c (j) is the reciprocal of the nominal sensitivity peak M s. The larger the sensitivity peak is, the closer the loop will be to instability. The sensitivity peak is a more reliable indicator of relative stability than the gain and phase margin. t is possible to find situations where the gain and phase margin are good, yet a very large sensitivity peak informs us of a very subtle stability condition. The reverse is not true: sensitivity performance gives always minimal value for the gain and phase margins. Minimum bounds on both the gain margin and the phase margin of a system characterized by a minimum phase L(j) can be expressed directly as function of M - s, as shown in Figure. Note that any loop gain point will lie outside the circle of radius M - s so that L(j cg ) = A - m d. Therefore, the distance d = -M s - shown in Figure represents a lower bound on the gain margin of the system in the sense that - m L(j) Am M s M s d M s () s cp cg d A m mag Figure : Open loop gain and M s - circle Furthermore, the angle depicted in Figure represents a lower bound on the phase margin of the system in the sense that - m sin () M s To ensure, for example, an acceptable nominal design that attains A m and m 3º we may now require that j.5 - Ms 6 d or that Ms min L (3) so that A m and m 9º 3º, in view of () and (), respectively. However, such a requirement may be

2 conservative, since larger values of M - s can imply both A m > and m > 3º. Note that the single sensitivity function requirement defined by (3) can be used to replace both the A m and the m requirements not only in the minimum phase cases but also in the unstable and non minimum phase cases as well. n particular, (3) will ensure that L(j) remains an acceptable, marginal distance away from the critical - point, irrespective of the number and the direction of encirclements required for closed loop stability; that is, (3) will ensure robust stability with respect to plant parameter variations once nominal closed-loop stability has been obtained. The paper is organized as follows. Section is devoted to introduce basic concepts of tuning of D controllers in the frequency domain. n section 3 the tuning of D controller when the design criterion is to obtain a specified sensitivity M s is explained in a detailed way. Some illustrative examples are described in section 4. Finally section 5 presents some conclusions. Tuning of D in the frequency domain Some frequency domain tuning methods of D controllers (Åström and Hägglund (995), Morilla and Dormido ()) can be interpreted in terms of moving one point A in the frequency response of the process G p (j) to an arbitrary point. This point is the specification or is determined by the specification (phase margin, gain margin). Therefore, once point has been fixed there is a range of frequencies in the Nyquist plot of G p (j) where it is possible to find a solution. This range can be determined from the following angular condition: c min ( - ()) c max where and () are the phase of point and G p (j), with respect to the negative real axis, and c min and c max are the minimum and maximum angular contributions of the controller. The range of frequencies depends therefore on the type of controller to be tuned. Figure shows an example, where the controller is a D controller. oint belongs to the bisector of the third quadrant, and the possible points A are represented in continuous trace ( min and max delimitate the corresponding range of frequencies). The example includes as a particular case a specification of phase margin equal to 45º (point is located on the unit circle). Another important consideration in Morilla and Dormido () was the convenience of using an auxiliary criterion (the maximum integral gain) together with the specification of phase or gain margin to choose the design frequency among all the possible solutions. However this constraint is not necessary when the tuning is made by combining both specifications. (4) - 7º - 8º ossible points mag max G p (j) ossible points A min - 9º Figure : Range for the design frequency Figure 3 shows the interpretation of the tuning by combined phase-gain margin. The tuning procedure can be seen as the change, in the complex plane, of two points (A and D) on G p (j) into points ( and E) on L(j). Figure 4 shows the interpretation of this tuning procedure in the parametric plane K (proportional gain) - K (integral gain), (Shafiei and Shenton, 997). m E D L(j) mag A m A G p (j) Figure 3: D tuning by combined phase-gain margin K d min a max d max a min Figure 4: D tuning in the parametric plane a d K

3 3 Tuning of D with specification of M s. n (Aström and Hägglund (995)) the design criterion is to obtain a specified sensitivity M s (see Figure 5) with good rejection of load disturbances. From the numerical point of view the procedure is reduced to the resolution of a system of three non-linear equations with five unknowns: the three D controller parameters K, K and K D, the frequency in which the specified sensitivity will take place and the angle where the contact with the circle M s will happen. For the resolution of these equations they propose to use an optimization procedure, combined either with the maximum integral gain criterion or the maximum frequency; the value of is bounded to the interval [ /]. r s r L(j) mag G p (j) Figure 5: Nyquist plot of G p (j) and L(j) The system of equations, with certain changes with respect to the formulation of Aström and Hägglund (995), is the following: - K - cos K cos D - (5) Ms K sin r K K sin cos r K sin D - (6) M a, K b, K c, K (7) (5) and (6) equations impose the contact of L(j) with the circle M s, with an angle, while (7) imposes the tangency condition. r() and () are the magnitude and the phase of G p (j), a(,), b(,) and c(,) are three functions used to abbreviate equation (7) that depend on the derivatives r (), () of the magnitude and phase of G p (j) as shown in the following expressions: D s r', ' r r', r r', r a b c ' ' cotan cotan cotan 3. New tuning procedure of D controllers The method for tuning consists of the following steps: a) To specify exactly the point where the contact with the circle M s will take place. b) To determine the family of controllers able to make L(j) pass through the specified point. c) To use the tangency condition (the proposal given by Aström and Hägglund (995) or a simpler one given in this paper), to determine those controllers that guarantee the tangential contact with the specified point on circle M s. d) To choose one of the possible controllers. Next it is described how the contact point can be specified, how the family of controllers can be determined and represented, and how the tangency condition can be formulated. Other aspects, related with practical application of the tuning method and the choice of the controllers, are discussed in the next section together with the different steps of the proposed algorithm. oint can be specified completely if specification M s is accompanied by angle in which the contact will take place (see Figure 5). oint is perfectly located in the complex plane by means of its real and imaginary part (X, Y ) or its magnitude and phase (r, ) whose dependence with respect to r s = M - s and is given in the following expressions X = - + r s cos = - r cos Y = - r s sen = - r sen rs sen tg (9) r cos - s rs sen r () sen The parameters of the D controllers that are able to make L(j) pass through point can be obtained by applying the following expressions (Morilla and Dormido, ): T K tg r r cos () + 4 +tg ()

4 TD = T (3) in the range of frequencies that verify the angular condition (4). Equation (9) admits this other equivalent expression: r sen tg (4) - r cos The tangent to circle M s in a point (X s, Y s ) is a function of the angle that can be obtained as follows: d - rs sen d Ys d - rs cos d X d - r cos s s - rs sen tg d The tangent to L(j) is a function of the frequency that can be obtained as follows: d rl d L - sen - r cos d mag L j L L L d d d Lj d rl d L - cos L rl senl d d The equality of both tangents in the point give us the following expression for : tg r cos - r sen (5) r sen r cos where r () and () represent the value of the derivative of the magnitude and phase of L(j) in the point respectively. Equaling equations (4) and (5) gives the following condition that must fulfill the magnitude and phase of point and the respective derivatives of L(j) in point. r r cos - r r sen (6) 3. Tuning algorithm of D controllers Given the plant G p (j), the D parameters can be tuned to meet a specified M s in the following way: Step : nitialization. The user specifies the value of M s and the angle in order to locate point and chooses the controller's type, or D. n the last case specifies ( = T D /T ). Step : Calculate and r by using (9) and (). Step 3: Calculate the range of frequencies, using the expression (4), where L(j) may pass through point. Step 4: Sweep the whole range of frequencies in order to determine in which frequencies the tangency condition is fulfilled. This sweeping makes the following calculations for all and each one of the frequencies: Calculate the control parameters by means of (), () and (3). Calculate the magnitude r L () and phase L () of L(j) in the vicinity of the design frequency, in particular in the five points -, -,, + y +, due to the following approximation for the calculation of the derivative r L () and L () in point (Savitzky and Golay, 964) f -8 f 8 f - f d f (7) d it is also convenient to make sure that r L () and L () coincide with values r and determined in Step. Calculate the derivatives r () and (), using the generic approximation (7) Evaluate the tangency condition, using the following quadratic function: FCT r r r cos r (8) sen Step 5: Choose the frequency and the corresponding parameters assigned to the controller. This selection is not trivial, because, as it will be seen in the examples, the tuning for a specified sensitivity can have a unique or a multiple solution. n the case of a unique solution, the condition of minimum of function FCT is sufficient in order to determine it. However in the case of a multiple solution the search inside a tolerance is the only way to find several zones that contain the different solutions. t is also necessary to use an auxiliary criterion (i.e. the absolute minimum FCT, the maximum integral gain, or the relative minimum FCT in the high frequencies zone) to select the frequency solution. f the tangency condition of Aström and Hägglund (995) is used it is only necessary to modify step 4 lightly calculating the magnitude r() and the phase () of G p (j) and their derivatives in the vicinity of the design frequency, by applying the following quadratic function: FCT a, K b, K c, K (9) where a(,), b(,) and c(,) are evaluated using the expressions (8). 4 Examples Let be the third order process evaluated by ersson (99): G (s) p.3 s.3s.6s The problem consists in tuning a controller with the following specifications: M s = and = 3º. Figure 6 shows how the tangency condition FCT varies in the whole range of frequencies where there is contact with point. With a logarithmic sweeping of points, only in the range of.53 to.57 rd/s does the tangency condition remain below the chosen tolerance (.). The minimum of FCT is presented at the frequency c =.553 rd/s and the controller parameters K =.36, T =.69 and T D = are obtained. D

5 FCT Figure 6: Tangency condition with the controller Figure 7 shows a graph in the parametric plane (K,K ) with all the couples that allow contact with the circle M s = at the point = 3º. Those couples that also allow tangential contact with a tolerance less than. are marked with circles. n the crossing point of the horizontal and vertical lines we find the couple of parameters that is able to minimize the tangency condition..7 K K Figure 7: Tuning for maximum sensitivity Figure 8 shows the Nyquist diagram of the process and the result of the tuning. The circle corresponds to the specification of sensitivity and the radius is associated to point in which the contact and the tangency with this circle take place. A more accurate numeric analysis allows to check that the desired sensitivity specification has been obtained. Figure 9 shows the process output and the control signal responses to steps in the set point and load disturbances. Figures and show the results for a D controller with ratio = T D /T =.. Due to the form of the FCT tangency condition, it is observed that now it is possible to contact with point in a logically bigger range of frequencies, and that two sub ranges exist where the tangency condition remains below the tolerance chosen (.); these sub ranges are [.54.58] and [.8.]. The circles marked in the c parametric plane (K, K ) allow to observe where the two families of controllers meet the specifications. maginary axis Nyquist Diagram axis Figure 8: Tuning for maximum sensitivity Setpoint and output process Control signal FCT Figure 9: rocess output and control signal response Figure : Tangency condition for the D controller c

6 .7 K K D Figure : Tangency condition for the D controller Figure shows a comparative graph between two possible D tuning; the first one corresponds to the absolute minimum of FCT at c =.56 rd/s, and the second to the relative minimum of the other subrange at c =. rd/s. maginary axis c =.56 Nyquist Diagram c= axis Figure : Tangency condition for the D controller With the controller parameters of the first tuning, K =.37, T =.7 and T D =.7, almost identical to the one obtained with the controller, the specified sensitivity is obtained. With the controller parameters of the second tuning, K =.6, T = 8. and T D =.8 the specified sensitivity is also obtained. ut by observing the comparative graph of the time response in Figure 3 it can be concluded that the first tuning is preferable to the second, because it allows to reach the reference and to reject the load disturbance in a shorter time and with a smaller control effort. 5. Conclusions The objective of this paper is to present an alternative method to the one proposed by Åström and Hägglund (995) for the tuning of D controllers with specification of sensitivity margin M s. The method is based on the following steps: º) to delimit the range of frequencies in which it is possible to contact with a specific point of a - circle of radius M s centered at -, º) to explore the frequencies where this contact takes place tangentially, because these frequencies will only be possible solutions and 3º) to choose one of the possible solutions. Some examples have been selected in order to show the main features of the proposed method c =.56 c=. Setpoint and output process Figure 3: Set point and load disturbances responses Acknowledgements This work has been supported by the Spanish CCYT under grant D -. References [] Aström, K.J. and T. Hägglund (995). D Controllers: Theory, Design, and Tuning (nd Edition). Research Triangle ark, NC: nstrument Society of America. [] Dormido, S. (). Control Learning: resent and Future. lenary lecture. reprints of b 5 th World Congress of FAC, arcelona -6 july. [3] Morilla, F. and S. Dormido (). Methodologies for the tuning of D controllers in the frequency domain. D' FAC Workshop on Digital Control: ast, present and future of D Control, 55-6, Terrassa. [4] Morilla, F., A. Fernández and S. Dormido Canto (). Control systems analysis & design server. CE' FAC Workshop on nternet ased Control Education, 5-56, Madrid. [5] ersson,. (99). Towards Autonomous D Control. Doctoral Dissertation. Department of Automatic Control, Lund nstitute of Technology. [6] Savitzky and Golay (964). Smoothing and differentiation of dates by simplified least squares procedures. Analytical Chemistry, vol. 36, nº8, july 964. [7] Shafiei, Z. and A.T. Shenton (997). Frequencydomain Design of D Controllers for Stable and Unstable Systems with Time Delay. Automatic, 3-3.

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

Model-based PID tuning for high-order processes: when to approximate

Model-based PID tuning for high-order processes: when to approximate Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 25 Seville, Spain, December 2-5, 25 ThB5. Model-based PID tuning for high-order processes: when to approximate

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

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

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

Chapter 9 Robust Stability in SISO Systems 9. Introduction There are many reasons to use feedback control. As we have seen earlier, with the help of a

Chapter 9 Robust Stability in SISO Systems 9. Introduction There are many reasons to use feedback control. As we have seen earlier, with the help of a Lectures on Dynamic Systems and Control Mohammed Dahleh Munther A. Dahleh George Verghese Department of Electrical Engineering and Computer Science Massachuasetts Institute of Technology c Chapter 9 Robust

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

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

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

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

(Continued on next page)

(Continued on next page) (Continued on next page) 18.2 Roots of Stability Nyquist Criterion 87 e(s) 1 S(s) = =, r(s) 1 + P (s)c(s) where P (s) represents the plant transfer function, and C(s) the compensator. The closedloop characteristic

More information

QFT VERSUS CLASSICAL GAIN SCHEDULING: STUDY FOR A FAST FERRY

QFT VERSUS CLASSICAL GAIN SCHEDULING: STUDY FOR A FAST FERRY Copyright 00 IFAC 5th Triennial World Congress, Barcelona, Spain QFT VERSUS CLASSICAL GAIN SCHEDULING: STUDY FOR A FAST FERRY J. Aranda, J. M. de la Cruz, J.M. Díaz, S. Dormido Canto Dpt. De Informática

More information

PID control of FOPDT plants with dominant dead time based on the modulus optimum criterion

PID control of FOPDT plants with dominant dead time based on the modulus optimum criterion Archives of Control Sciences Volume 6LXII, 016 No. 1, pages 5 17 PID control of FOPDT plants with dominant dead time based on the modulus optimum criterion JAN CVEJN The modulus optimum MO criterion can

More information

Control Systems. Frequency Method Nyquist Analysis.

Control Systems. Frequency Method Nyquist Analysis. Frequency Method Nyquist Analysis chibum@seoultech.ac.kr Outline Polar plots Nyquist plots Factors of polar plots PolarNyquist Plots Polar plot: he locus of the magnitude of ω vs. the phase of ω on polar

More information

ADAPTIVE PID CONTROLLER WITH ON LINE IDENTIFICATION

ADAPTIVE PID CONTROLLER WITH ON LINE IDENTIFICATION Journal of ELECTRICAL ENGINEERING, VOL. 53, NO. 9-10, 00, 33 40 ADAPTIVE PID CONTROLLER WITH ON LINE IDENTIFICATION Jiří Macháče Vladimír Bobál A digital adaptive PID controller algorithm which contains

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

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

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

Analysis and Synthesis of Single-Input Single-Output Control Systems

Analysis and Synthesis of Single-Input Single-Output Control Systems Lino Guzzella Analysis and Synthesis of Single-Input Single-Output Control Systems l+kja» \Uja>)W2(ja»\ um Contents 1 Definitions and Problem Formulations 1 1.1 Introduction 1 1.2 Definitions 1 1.2.1 Systems

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

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

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

Design of Measurement Noise Filters for PID Control

Design of Measurement Noise Filters for PID Control Preprints of the 9th World Congress The International Federation of Automatic Control Design of Measurement Noise Filters for D Control Vanessa R. Segovia Tore Hägglund Karl J. Åström Department of Automatic

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

Chapter Stability Robustness Introduction Last chapter showed how the Nyquist stability criterion provides conditions for the stability robustness of

Chapter Stability Robustness Introduction Last chapter showed how the Nyquist stability criterion provides conditions for the stability robustness of Lectures on Dynamic Systems and Control Mohammed Dahleh Munther A Dahleh George Verghese Department of Electrical Engineering and Computer Science Massachuasetts Institute of Technology c Chapter Stability

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

Additional Closed-Loop Frequency Response Material (Second edition, Chapter 14)

Additional Closed-Loop Frequency Response Material (Second edition, Chapter 14) Appendix J Additional Closed-Loop Frequency Response Material (Second edition, Chapter 4) APPENDIX CONTENTS J. Closed-Loop Behavior J.2 Bode Stability Criterion J.3 Nyquist Stability Criterion J.4 Gain

More information

A sub-optimal second order sliding mode controller for systems with saturating actuators

A sub-optimal second order sliding mode controller for systems with saturating actuators 28 American Control Conference Westin Seattle Hotel, Seattle, Washington, USA June -3, 28 FrB2.5 A sub-optimal second order sliding mode for systems with saturating actuators Antonella Ferrara and Matteo

More information

Controls Problems for Qualifying Exam - Spring 2014

Controls Problems for Qualifying Exam - Spring 2014 Controls Problems for Qualifying Exam - Spring 2014 Problem 1 Consider the system block diagram given in Figure 1. Find the overall transfer function T(s) = C(s)/R(s). Note that this transfer function

More information

I. D. Landau, A. Karimi: A Course on Adaptive Control Adaptive Control. Part 9: Adaptive Control with Multiple Models and Switching

I. D. Landau, A. Karimi: A Course on Adaptive Control Adaptive Control. Part 9: Adaptive Control with Multiple Models and Switching I. D. Landau, A. Karimi: A Course on Adaptive Control - 5 1 Adaptive Control Part 9: Adaptive Control with Multiple Models and Switching I. D. Landau, A. Karimi: A Course on Adaptive Control - 5 2 Outline

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

IMC based automatic tuning method for PID controllers in a Smith predictor configuration

IMC based automatic tuning method for PID controllers in a Smith predictor configuration Computers and Chemical Engineering 28 (2004) 281 290 IMC based automatic tuning method for PID controllers in a Smith predictor configuration Ibrahim Kaya Department of Electrical and Electronics Engineering,

More information

Comparative analysis of decoupling control methodologies and multivariable robust control for VS-VP wind turbines

Comparative analysis of decoupling control methodologies and multivariable robust control for VS-VP wind turbines Comparative analysis of decoupling control methodologies and multivariable robust control for VS-VP wind turbines Sergio Fragoso, Juan Garrido, Francisco Vázquez Department of Computer Science and Numerical

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

Should we forget the Smith Predictor?

Should we forget the Smith Predictor? FrBT3. Should we forget the Smith Predictor? Chriss Grimholt Sigurd Skogestad* Abstract: The / controller is the most used controller in industry. However, for processes with large time delays, the common

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

Chapter 2. Classical Control System Design. Dutch Institute of Systems and Control

Chapter 2. Classical Control System Design. Dutch Institute of Systems and Control Chapter 2 Classical Control System Design Overview Ch. 2. 2. Classical control system design Introduction Introduction Steady-state Steady-state errors errors Type Type k k systems systems Integral Integral

More information

THE ANNALS OF "DUNAREA DE JOS" UNIVERSITY OF GALATI FASCICLE III, 2000 ISSN X ELECTROTECHNICS, ELECTRONICS, AUTOMATIC CONTROL, INFORMATICS

THE ANNALS OF DUNAREA DE JOS UNIVERSITY OF GALATI FASCICLE III, 2000 ISSN X ELECTROTECHNICS, ELECTRONICS, AUTOMATIC CONTROL, INFORMATICS ELECTROTECHNICS, ELECTRONICS, AUTOMATIC CONTROL, INFORMATICS ON A TAKAGI-SUGENO FUZZY CONTROLLER WITH NON-HOMOGENOUS DYNAMICS Radu-Emil PRECUP and Stefan PREITL Politehnica University of Timisoara, Department

More information

Unit 11 - Week 7: Quantitative feedback theory (Part 1/2)

Unit 11 - Week 7: Quantitative feedback theory (Part 1/2) X reviewer3@nptel.iitm.ac.in Courses» Control System Design Announcements Course Ask a Question Progress Mentor FAQ Unit 11 - Week 7: Quantitative feedback theory (Part 1/2) Course outline How to access

More information

Robust discrete time control

Robust discrete time control Robust discrete time control I.D. Landau Emeritus Research Director at C.N.R. Laboratoire d utomatique de Grenoble, INPG/CNR, France pril 2004, Valencia I.D. Landau course on robust discrete time control,

More information

MAT1035 Analytic Geometry

MAT1035 Analytic Geometry MAT1035 Analytic Geometry Lecture Notes R.A. Sabri Kaan Gürbüzer Dokuz Eylül University 2016 2 Contents 1 Review of Trigonometry 5 2 Polar Coordinates 7 3 Vectors in R n 9 3.1 Located Vectors..............................................

More information

A Simple PID Control Design for Systems with Time Delay

A Simple PID Control Design for Systems with Time Delay Industrial Electrical Engineering and Automation CODEN:LUTEDX/(TEIE-7266)/1-16/(2017) A Simple PID Control Design for Systems with Time Delay Mats Lilja Division of Industrial Electrical Engineering and

More information

MRAGPC Control of MIMO Processes with Input Constraints and Disturbance

MRAGPC Control of MIMO Processes with Input Constraints and Disturbance Proceedings of the World Congress on Engineering and Computer Science 9 Vol II WCECS 9, October -, 9, San Francisco, USA MRAGPC Control of MIMO Processes with Input Constraints and Disturbance A. S. Osunleke,

More information

Robust and Gain-Scheduled PID Controller Design for Condensing Boilers by Linear Programming

Robust and Gain-Scheduled PID Controller Design for Condensing Boilers by Linear Programming Robust and Gain-Scheduled PID Controller Design for Condensing Boilers by Linear Programming Vinicius de Oliveira and Alireza Karimi Laboratoire d Automatque École Polytechnique Fédérale de Lausanne (EPFL)

More information

Robust QFT-based PI controller for a feedforward control scheme

Robust QFT-based PI controller for a feedforward control scheme Integral-Derivative Control, Ghent, Belgium, May 9-11, 218 ThAT4.4 Robust QFT-based PI controller for a feedforward control scheme Ángeles Hoyo José Carlos Moreno José Luis Guzmán Tore Hägglund Dep. of

More information

CHAPTER ONE FUNCTIONS AND GRAPHS. In everyday life, many quantities depend on one or more changing variables eg:

CHAPTER ONE FUNCTIONS AND GRAPHS. In everyday life, many quantities depend on one or more changing variables eg: CHAPTER ONE FUNCTIONS AND GRAPHS 1.0 Introduction to Functions In everyday life, many quantities depend on one or more changing variables eg: (a) plant growth depends on sunlight and rainfall (b) speed

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

Decentralised control of a quadruple tank plant with a decoupled event-based strategy

Decentralised control of a quadruple tank plant with a decoupled event-based strategy Decentralised control of a quadruple tank plant with a decoupled event-based strategy Jesús Chacón Sombría José Sánchez Moreno Antonio Visioli Sebastián Dormido Bencomo Universidad Nacional de Educación

More information

Here represents the impulse (or delta) function. is an diagonal matrix of intensities, and is an diagonal matrix of intensities.

Here represents the impulse (or delta) function. is an diagonal matrix of intensities, and is an diagonal matrix of intensities. 19 KALMAN FILTER 19.1 Introduction In the previous section, we derived the linear quadratic regulator as an optimal solution for the fullstate feedback control problem. The inherent assumption was that

More information

CHAPTER 5 ROBUSTNESS ANALYSIS OF THE CONTROLLER

CHAPTER 5 ROBUSTNESS ANALYSIS OF THE CONTROLLER 114 CHAPTER 5 ROBUSTNESS ANALYSIS OF THE CONTROLLER 5.1 INTRODUCTION Robust control is a branch of control theory that explicitly deals with uncertainty in its approach to controller design. It also refers

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

Find the rectangular coordinates for each of the following polar coordinates:

Find the rectangular coordinates for each of the following polar coordinates: WORKSHEET 13.1 1. Plot the following: 7 3 A. 6, B. 3, 6 4 5 8 D. 6, 3 C., 11 2 E. 5, F. 4, 6 3 Find the rectangular coordinates for each of the following polar coordinates: 5 2 2. 4, 3. 8, 6 3 Given the

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

Lecture 10: Proportional, Integral and Derivative Actions

Lecture 10: Proportional, Integral and Derivative Actions MCE441: Intr. Linear Control Systems Lecture 10: Proportional, Integral and Derivative Actions Stability Concepts BIBO Stability and The Routh-Hurwitz Criterion Dorf, Sections 6.1, 6.2, 7.6 Cleveland State

More information

STABILITY OF CLOSED-LOOP CONTOL SYSTEMS

STABILITY OF CLOSED-LOOP CONTOL SYSTEMS CHBE320 LECTURE X STABILITY OF CLOSED-LOOP CONTOL SYSTEMS Professor Dae Ryook Yang Spring 2018 Dept. of Chemical and Biological Engineering 10-1 Road Map of the Lecture X Stability of closed-loop control

More information

Definition (Polar Coordinates) Figure: The polar coordinates (r, )ofapointp

Definition (Polar Coordinates) Figure: The polar coordinates (r, )ofapointp Polar Coordinates Acoordinatesystemusesapairofnumberstorepresentapointontheplane. We are familiar with the Cartesian or rectangular coordinate system, (x, y). It is not always the most convenient system

More information

Tuning and robustness analysis of event-based PID controllers under different event generation strategies

Tuning and robustness analysis of event-based PID controllers under different event generation strategies Tuning and robustness analysis of event-based PID controllers under different event generation strategies Julio Ariel Romero Pérez and Roberto Sanchis Llopis Departament of Industrial Systems Engineering

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

Topic 1: Fractional and Negative Exponents x x Topic 2: Domain. 2x 9 2x y x 2 5x y logg 2x 12

Topic 1: Fractional and Negative Exponents x x Topic 2: Domain. 2x 9 2x y x 2 5x y logg 2x 12 AP Calculus BC Summer Packet Name INSTRUCTIONS: Where applicable, put your solutions in interval notation. Do not use any calculator (ecept on Topics 5:#5 & :#6). Please do all work on separate paper and

More information

CHAPTER # 9 ROOT LOCUS ANALYSES

CHAPTER # 9 ROOT LOCUS ANALYSES F K א CHAPTER # 9 ROOT LOCUS ANALYSES 1. Introduction The basic characteristic of the transient response of a closed-loop system is closely related to the location of the closed-loop poles. If the system

More information

Control Systems I. Lecture 2: Modeling. Suggested Readings: Åström & Murray Ch. 2-3, Guzzella Ch Emilio Frazzoli

Control Systems I. Lecture 2: Modeling. Suggested Readings: Åström & Murray Ch. 2-3, Guzzella Ch Emilio Frazzoli Control Systems I Lecture 2: Modeling Suggested Readings: Åström & Murray Ch. 2-3, Guzzella Ch. 2-3 Emilio Frazzoli Institute for Dynamic Systems and Control D-MAVT ETH Zürich September 29, 2017 E. Frazzoli

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

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

CHAPTER 3 TUNING METHODS OF CONTROLLER

CHAPTER 3 TUNING METHODS OF CONTROLLER 57 CHAPTER 3 TUNING METHODS OF CONTROLLER 3.1 INTRODUCTION This chapter deals with a simple method of designing PI and PID controllers for first order plus time delay with integrator systems (FOPTDI).

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

Variable-gain output feedback control

Variable-gain output feedback control 7. Variable-gain output feedback control 7.1. Introduction PUC-Rio - Certificação Digital Nº 611865/CA In designing control laws, the usual first step is to describe the plant at a given operating point

More information

Today (10/23/01) Today. Reading Assignment: 6.3. Gain/phase margin lead/lag compensator Ref. 6.4, 6.7, 6.10

Today (10/23/01) Today. Reading Assignment: 6.3. Gain/phase margin lead/lag compensator Ref. 6.4, 6.7, 6.10 Today Today (10/23/01) Gain/phase margin lead/lag compensator Ref. 6.4, 6.7, 6.10 Reading Assignment: 6.3 Last Time In the last lecture, we discussed control design through shaping of the loop gain GK:

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

QFT Framework for Robust Tuning of Power System Stabilizers

QFT Framework for Robust Tuning of Power System Stabilizers 45-E-PSS-75 QFT Framework for Robust Tuning of Power System Stabilizers Seyyed Mohammad Mahdi Alavi, Roozbeh Izadi-Zamanabadi Department of Control Engineering, Aalborg University, Denmark Correspondence

More information

A unified double-loop multi-scale control strategy for NMP integrating-unstable systems

A unified double-loop multi-scale control strategy for NMP integrating-unstable systems Home Search Collections Journals About Contact us My IOPscience A unified double-loop multi-scale control strategy for NMP integrating-unstable systems This content has been downloaded from IOPscience.

More information

CDS 101/110a: Lecture 8-1 Frequency Domain Design

CDS 101/110a: Lecture 8-1 Frequency Domain Design CDS 11/11a: Lecture 8-1 Frequency Domain Design Richard M. Murray 17 November 28 Goals: Describe canonical control design problem and standard performance measures Show how to use loop shaping to achieve

More information

Inertia Identification and Auto-Tuning. of Induction Motor Using MRAS

Inertia Identification and Auto-Tuning. of Induction Motor Using MRAS Inertia Identification and Auto-Tuning of Induction Motor Using MRAS Yujie GUO *, Lipei HUANG *, Yang QIU *, Masaharu MURAMATSU ** * Department of Electrical Engineering, Tsinghua University, Beijing,

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

CONTROL OF DIGITAL SYSTEMS

CONTROL OF DIGITAL SYSTEMS AUTOMATIC CONTROL AND SYSTEM THEORY CONTROL OF DIGITAL SYSTEMS Gianluca Palli Dipartimento di Ingegneria dell Energia Elettrica e dell Informazione (DEI) Università di Bologna Email: gianluca.palli@unibo.it

More information

Chapter 2: Circle. Mr. Migo M. Mendoza. SSMth1: Precalculus. Science and Technology, Engineering and Mathematics (STEM)

Chapter 2: Circle. Mr. Migo M. Mendoza. SSMth1: Precalculus. Science and Technology, Engineering and Mathematics (STEM) Chapter 2: Circle SSMth1: Precalculus Science and Technology, Engineering and Mathematics (STEM) Mr. Migo M. Mendoza Chapter 2: Circles Lecture 2: Introduction to Circles Lecture 3: Form of a Circle Lecture

More information

Remember that : Definition :

Remember that : Definition : Stability This lecture we will concentrate on How to determine the stability of a system represented as a transfer function How to determine the stability of a system represented in state-space How to

More information

ADAPTIVE TEMPERATURE CONTROL IN CONTINUOUS STIRRED TANK REACTOR

ADAPTIVE TEMPERATURE CONTROL IN CONTINUOUS STIRRED TANK REACTOR INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY (IJEET) International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 6545(Print), ISSN 0976 6545(Print) ISSN 0976 6553(Online)

More information

PARETO OPTIMAL SENSITIVITY BOUNDS

PARETO OPTIMAL SENSITIVITY BOUNDS ARETO OTIMA SENSITIVITY OUNDS FOR A STRONGY INTERACTING -OO RAZING TEMERATURE CONTRO SYSTEM Eduard Eitelberg ORT raude College, Karmiel, Israel & University of KwaZulu-Natal, Durban, South Africa Eitelberg@braude.ac.il

More information

Control of Electromechanical Systems

Control of Electromechanical Systems Control of Electromechanical Systems November 3, 27 Exercise Consider the feedback control scheme of the motor speed ω in Fig., where the torque actuation includes a time constant τ A =. s and a disturbance

More information

ECE317 : Feedback and Control

ECE317 : Feedback and Control ECE317 : Feedback and Control Lecture : Routh-Hurwitz stability criterion Examples Dr. Richard Tymerski Dept. of Electrical and Computer Engineering Portland State University 1 Course roadmap Modeling

More information

8.3 GRAPH AND WRITE EQUATIONS OF CIRCLES

8.3 GRAPH AND WRITE EQUATIONS OF CIRCLES 8.3 GRAPH AND WRITE EQUATIONS OF CIRCLES What is the standard form equation for a circle? Why do you use the distance formula when writing the equation of a circle? What general equation of a circle is

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

COMS 4771 Introduction to Machine Learning. Nakul Verma

COMS 4771 Introduction to Machine Learning. Nakul Verma COMS 4771 Introduction to Machine Learning Nakul Verma Announcements HW1 due next lecture Project details are available decide on the group and topic by Thursday Last time Generative vs. Discriminative

More information

Lecture 6. Chapter 8: Robust Stability and Performance Analysis for MIMO Systems. Eugenio Schuster.

Lecture 6. Chapter 8: Robust Stability and Performance Analysis for MIMO Systems. Eugenio Schuster. Lecture 6 Chapter 8: Robust Stability and Performance Analysis for MIMO Systems Eugenio Schuster schuster@lehigh.edu Mechanical Engineering and Mechanics Lehigh University Lecture 6 p. 1/73 6.1 General

More information

Feedback Control of Linear SISO systems. Process Dynamics and Control

Feedback Control of Linear SISO systems. Process Dynamics and Control Feedback Control of Linear SISO systems Process Dynamics and Control 1 Open-Loop Process The study of dynamics was limited to open-loop systems Observe process behavior as a result of specific input signals

More information

STABILITY ANALYSIS. Asystemmaybe stable, neutrallyormarginallystable, or unstable. This can be illustrated using cones: Stable Neutral Unstable

STABILITY ANALYSIS. Asystemmaybe stable, neutrallyormarginallystable, or unstable. This can be illustrated using cones: Stable Neutral Unstable ECE4510/5510: Feedback Control Systems. 5 1 STABILITY ANALYSIS 5.1: Bounded-input bounded-output (BIBO) stability Asystemmaybe stable, neutrallyormarginallystable, or unstable. This can be illustrated

More information

CHAPTER 10: STABILITY &TUNING

CHAPTER 10: STABILITY &TUNING When I complete this chapter, I want to be able to do the following. Determine the stability of a process without control Determine the stability of a closed-loop feedback control system Use these approaches

More information

Learn2Control Laboratory

Learn2Control Laboratory Learn2Control Laboratory Version 3.2 Summer Term 2014 1 This Script is for use in the scope of the Process Control lab. It is in no way claimed to be in any scientific way complete or unique. Errors should

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

Lecture 1: Feedback Control Loop

Lecture 1: Feedback Control Loop Lecture : Feedback Control Loop Loop Transfer function The standard feedback control system structure is depicted in Figure. This represend(t) n(t) r(t) e(t) u(t) v(t) η(t) y(t) F (s) C(s) P (s) Figure

More information

6.241 Dynamic Systems and Control

6.241 Dynamic Systems and Control 6.241 Dynamic Systems and Control Lecture 17: Robust Stability Readings: DDV, Chapters 19, 20 Emilio Frazzoli Aeronautics and Astronautics Massachusetts Institute of Technology April 6, 2011 E. Frazzoli

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

Control Systems. EC / EE / IN. For

Control Systems.   EC / EE / IN. For Control Systems For EC / EE / IN By www.thegateacademy.com Syllabus Syllabus for Control Systems Basic Control System Components; Block Diagrammatic Description, Reduction of Block Diagrams. Open Loop

More information

Fall 2003 BMI 226 / CS 426 AUTOMATIC SYNTHESIS OF IMPROVED TUNING RULES FOR PID CONTROLLERS

Fall 2003 BMI 226 / CS 426 AUTOMATIC SYNTHESIS OF IMPROVED TUNING RULES FOR PID CONTROLLERS Notes LL-1 AUTOMATIC SYNTHESIS OF IMPROVED TUNING RULES FOR PID CONTROLLERS Notes LL-2 AUTOMATIC SYNTHESIS OF IMPROVED TUNING RULES FOR PID CONTROLLERS The PID controller was patented in 1939 by Albert

More information

EECE Adaptive Control

EECE Adaptive Control EECE 574 - Adaptive Control Overview Guy Dumont Department of Electrical and Computer Engineering University of British Columbia Lectures: Thursday 09h00-12h00 Location: PPC 101 Guy Dumont (UBC) EECE 574

More information

Quantitative Feedback Theory based Controller Design of an Unstable System

Quantitative Feedback Theory based Controller Design of an Unstable System Quantitative Feedback Theory based Controller Design of an Unstable System Chandrima Roy Department of E.C.E, Assistant Professor Heritage Institute of Technology, Kolkata, WB Kalyankumar Datta Department

More information

PARAMETRIC SYNTHESIS OF QUALITATIVE ROBUST CONTROL SYSTEMS USING ROOT LOCUS FIELDS. A.A. Nesenchuk

PARAMETRIC SYNTHESIS OF QUALITATIVE ROBUST CONTROL SYSTEMS USING ROOT LOCUS FIELDS. A.A. Nesenchuk Copyright 2002 IFAC 15th Triennial World Congress, Barcelona, pain PARAMETRIC YNTHEI OF QUALITATIVE ROBUT CONTROL YTEM UIN ROOT LOCU FIELD A.A. Nesenchuk Institute of Engineering Cybernetics of Belarusian

More information

Internal Model Control of A Class of Continuous Linear Underactuated Systems

Internal Model Control of A Class of Continuous Linear Underactuated Systems Internal Model Control of A Class of Continuous Linear Underactuated Systems Asma Mezzi Tunis El Manar University, Automatic Control Research Laboratory, LA.R.A, National Engineering School of Tunis (ENIT),

More information

Chapter 11 Parametric Equations, Polar Curves, and Conic Sections

Chapter 11 Parametric Equations, Polar Curves, and Conic Sections Chapter 11 Parametric Equations, Polar Curves, and Conic Sections ü 11.1 Parametric Equations Students should read Sections 11.1-11. of Rogawski's Calculus [1] for a detailed discussion of the material

More information

IDENTIFICATION FOR CONTROL

IDENTIFICATION FOR CONTROL IDENTIFICATION FOR CONTROL Raymond A. de Callafon, University of California San Diego, USA Paul M.J. Van den Hof, Delft University of Technology, the Netherlands Keywords: Controller, Closed loop model,

More information