POLE PLACEMENT. Sadegh Bolouki. Lecture slides for ECE 515. University of Illinois, Urbana-Champaign. Fall S. Bolouki (UIUC) 1 / 19

Size: px
Start display at page:

Download "POLE PLACEMENT. Sadegh Bolouki. Lecture slides for ECE 515. University of Illinois, Urbana-Champaign. Fall S. Bolouki (UIUC) 1 / 19"

Transcription

1 POLE PLACEMENT Sadegh Bolouki Lecture slides for ECE 515 University of Illinois, Urbana-Champaign Fall 2016 S. Bolouki (UIUC) 1 / 19

2 Outline 1 State Feedback 2 Observer 3 Observer Feedback 4 Reduced Order Observers S. Bolouki (UIUC) 2 / 19

3 General objective: Stabilization of LTI systems via feedback. Example. Consider the following LTI system: ẋ = Ax + Bu = x + 1 u [ ] y = Cx = x System modes are 1, 2, 1 (check the eigenvalues of A). Modes 1, 2 are controllable (check the rank of [ λi A B ] ). Modes 1, 1 are observable (check the rank of [ λi A T C T] ). Consider a linear state feedback u = Kx + r, where K = [ k 1 k 2 k 3 ]. Thus, {ẋ = (A BK)x + Br = Acl x + Br y = Cx Challenge: Find K such that A cl is asymptotically stable.

4 (Example cont.) ẋ = A cl x + Br = y = Cx = [ ] x 1 k 1 k 2 k 3 1 k 1 k 2 k 3 x + 1 r Controllable subspace (therefore controllability) is invariant. Uncontrollable modes are invariant. Controllable modes are not invariant. Observability is not invariant. Stability is not invariant.

5 State Feedback State Feedback S. Bolouki (UIUC) 5 / 19

6 State Feedback State Feedback General objective: Stabilization via state feedback. {ẋ = Ax + Bu y = Cx Considering the state feedback u = Kx, we have ẋ = A cl x = (A BK)x Determine K to stabilize the system, i.e., to place all of eigenvalues of A cl in the left half-plane. Theorem Any controllable LTI system can be stabilized. Controllability is not necessary for stabilizability. Like controllability, stabilizability is usually associated with the pair (A, B). S. Bolouki (UIUC) 6 / 19

7 State Feedback State Feedback Theorem Given the eigenvalues of A are distinct, a pair (A, B) is stabilizable if and only if all uncontrollable modes of A are in the left half-plane. sketch of proof: We show that (i) Controllable modes of A can be placed arbitrarily via state feedback, respecting complex conjugate constraints. (ii) Uncontrollable modes of A are invariant under state feedback. We use the KCCF to prove (i) and (ii). ] ] [Āc [ B Ā =, B = c 0 Theorem 0 Ā c A pair (A, B) is stabilizable if and only if all eigenvalues of Ā c are in the left half-plane. Stabilizability Unstable subspace Controllable subspace. S. Bolouki (UIUC) 7 / 19

8 Observer Observer S. Bolouki (UIUC) 8 / 19

9 Observer Observer General objective: Estimating the states (x) via partial observation (y). Why do we estimate the states? What is a good estimation? How do we do it? S. Bolouki (UIUC) 9 / 19

10 Observer Observer {ẋ = Ax + Bu y = Cx + Du Example. Ignoring the observations y (terrible idea!), let ˆx = Aˆx + Bu, where ˆx is an estimation of x. Will e(t) x(t) ˆx(t) converge to 0 as t grows? ė = Ae e(t) = exp{at}e(0). Thus, e(t) will vanish if A is stable. S. Bolouki (UIUC) 10 / 19

11 Observer Observer Better idea: where ŷ = Cˆx + Du. ˆx = Aˆx + Bu + L(y ŷ), Will e(t) = x(t) ˆx(t) converge to 0 as t grows? } ẋ = Ax + Bu ˆx = LCx + (A LC)ˆx + Bu ė = (A LC)e lim t e(t) = 0 A LC is stable Convergence occurs at an exponential rate. A LC is stable for some L if and only if (A T, C T ) is stabilizable. Given the eigenvalues of A are distinct, (A T, C T ) is stabilizable if and only if all unobservable modes of A are in the left half-plane. (A T, C T ) is stabilizable if and only if all eigenvalues of Āō of the KOCF are in the left half-plane. S. Bolouki (UIUC) 11 / 19

12 Observer Feedback Observer Feedback S. Bolouki (UIUC) 12 / 19

13 Observer Feedback Observer Feedback General objective: Stabilization via observer feedback. ẋ = Ax BKˆx ˆx = LCx + (A BK LC)ˆx [ẋ ] ˆx = [ [ ] A BK LC A BK LC] xˆx S. Bolouki (UIUC) 13 / 19

14 Observer Feedback Observer Feedback [ ] [ [ ] x xˆx] I 0 = P, where P = e I I ẋ = Ax BKˆx = (A BK)x + BKe ė = (A LC)e [ẋ ] [ ] [ A BK BK x = ė 0 A LC e] }{{} A cl ( ) si (A LC) det(si A cl ) = det ( si (A BK) ) }{{} arbitrary pp if (A, B) is controllable Seperation Principle }{{} arbitrary pp if (A, C) is observable S. Bolouki (UIUC) 14 / 19

15 Reduced Order Observers Reduced Order Observers S. Bolouki (UIUC) 15 / 19

16 Reduced Order Observers Reduced Order Observers {ẋ = Ax + Bu Let C p n be full-row-rank. Define [ P = y = Cx C n p rows to make P non-singular ] ] [ x1, x = = Px x 2 S. Bolouki (UIUC) 16 / 19

17 Reduced Order Observers Reduced Order Observers Let C p n be full-row-rank. Define [ P = {ẋ = Ax + Bu y = Cx C n p rows to make P non-singular ] ] [ x1, x = = Px x 2 Therefore x = ] [ x 1 = x 2 ] ] ] [Ā11 Ā 12 [ x1 [ B + 1 u Ā 21 Ā 22 x 2 B 2 }{{}}{{} PAP 1 PB y = [ I 0 ] [ x ] 1 x 2 S. Bolouki (UIUC) 16 / 19

18 Reduced Order Observers Reduced Order Observers x 2 = Ā 22 x 2 + Ā 21 y + B 2 u }{{} ū ẏ Ā 11 y B 1 u = Ā }{{} 12 x 2 ȳ S. Bolouki (UIUC) 17 / 19

19 Reduced Order Observers Reduced Order Observers Thus, to estimate x 2, we employ x 2 = Ā 22 x 2 + Ā 21 y + B 2 u }{{} ū ẏ Ā 11 y B 1 u = Ā }{{} 12 x 2 ȳ observer ˆx2 = Ā 22ˆx 2 + ū + L(ȳ ŷ) where ŷ = Ā 12ˆx 2 S. Bolouki (UIUC) 17 / 19

20 Reduced Order Observers Reduced Order Observers Thus, to estimate x 2, we employ x 2 = Ā 22 x 2 + Ā 21 y + B 2 u }{{} ū ẏ Ā 11 y B 1 u = Ā }{{} 12 x 2 ȳ observer ˆx2 = Ā 22ˆx 2 + ū + L(ȳ ŷ) Then, if e = x 2 ˆx 2, we have where ŷ = Ā 12ˆx 2 ė = (Ā 22 LĀ 12 )e Remember: Arbitrary pole placement Observability of (Ā 22, Ā 12 ). S. Bolouki (UIUC) 17 / 19

21 Reduced Order Observers Reduced Order Observers Reduced Order Observer Estimation of x S. Bolouki (UIUC) 18 / 19

22 Reduced Order Observers Reduced Order Observers Reduced Order Observer No derivative of y is required! S. Bolouki (UIUC) 19 / 19

TRACKING AND DISTURBANCE REJECTION

TRACKING AND DISTURBANCE REJECTION TRACKING AND DISTURBANCE REJECTION Sadegh Bolouki Lecture slides for ECE 515 University of Illinois, Urbana-Champaign Fall 2016 S. Bolouki (UIUC) 1 / 13 General objective: The output to track a reference

More information

5. Observer-based Controller Design

5. Observer-based Controller Design EE635 - Control System Theory 5. Observer-based Controller Design Jitkomut Songsiri state feedback pole-placement design regulation and tracking state observer feedback observer design LQR and LQG 5-1

More information

CONTROL DESIGN FOR SET POINT TRACKING

CONTROL DESIGN FOR SET POINT TRACKING Chapter 5 CONTROL DESIGN FOR SET POINT TRACKING In this chapter, we extend the pole placement, observer-based output feedback design to solve tracking problems. By tracking we mean that the output is commanded

More information

Stability, Pole Placement, Observers and Stabilization

Stability, Pole Placement, Observers and Stabilization Stability, Pole Placement, Observers and Stabilization 1 1, The Netherlands DISC Course Mathematical Models of Systems Outline 1 Stability of autonomous systems 2 The pole placement problem 3 Stabilization

More information

SYSTEMTEORI - ÖVNING 5: FEEDBACK, POLE ASSIGNMENT AND OBSERVER

SYSTEMTEORI - ÖVNING 5: FEEDBACK, POLE ASSIGNMENT AND OBSERVER SYSTEMTEORI - ÖVNING 5: FEEDBACK, POLE ASSIGNMENT AND OBSERVER Exercise 54 Consider the system: ẍ aẋ bx u where u is the input and x the output signal (a): Determine a state space realization (b): Is the

More information

EL 625 Lecture 10. Pole Placement and Observer Design. ẋ = Ax (1)

EL 625 Lecture 10. Pole Placement and Observer Design. ẋ = Ax (1) EL 625 Lecture 0 EL 625 Lecture 0 Pole Placement and Observer Design Pole Placement Consider the system ẋ Ax () The solution to this system is x(t) e At x(0) (2) If the eigenvalues of A all lie in the

More information

Control Systems Design, SC4026. SC4026 Fall 2010, dr. A. Abate, DCSC, TU Delft

Control Systems Design, SC4026. SC4026 Fall 2010, dr. A. Abate, DCSC, TU Delft Control Systems Design, SC426 SC426 Fall 2, dr A Abate, DCSC, TU Delft Lecture 5 Controllable Canonical and Observable Canonical Forms Stabilization by State Feedback State Estimation, Observer Design

More information

6 OUTPUT FEEDBACK DESIGN

6 OUTPUT FEEDBACK DESIGN 6 OUTPUT FEEDBACK DESIGN When the whole sate vector is not available for feedback, i.e, we can measure only y = Cx. 6.1 Review of observer design Recall from the first class in linear systems that a simple

More information

1 Continuous-time Systems

1 Continuous-time Systems Observability Completely controllable systems can be restructured by means of state feedback to have many desirable properties. But what if the state is not available for feedback? What if only the output

More information

University of Toronto Department of Electrical and Computer Engineering ECE410F Control Systems Problem Set #3 Solutions = Q o = CA.

University of Toronto Department of Electrical and Computer Engineering ECE410F Control Systems Problem Set #3 Solutions = Q o = CA. University of Toronto Department of Electrical and Computer Engineering ECE41F Control Systems Problem Set #3 Solutions 1. The observability matrix is Q o C CA 5 6 3 34. Since det(q o ), the matrix is

More information

Intro. Computer Control Systems: F9

Intro. Computer Control Systems: F9 Intro. Computer Control Systems: F9 State-feedback control and observers Dave Zachariah Dept. Information Technology, Div. Systems and Control 1 / 21 dave.zachariah@it.uu.se F8: Quiz! 2 / 21 dave.zachariah@it.uu.se

More information

Topic # Feedback Control. State-Space Systems Closed-loop control using estimators and regulators. Dynamics output feedback

Topic # Feedback Control. State-Space Systems Closed-loop control using estimators and regulators. Dynamics output feedback Topic #17 16.31 Feedback Control State-Space Systems Closed-loop control using estimators and regulators. Dynamics output feedback Back to reality Copyright 21 by Jonathan How. All Rights reserved 1 Fall

More information

Decentralized control with input saturation

Decentralized control with input saturation Decentralized control with input saturation Ciprian Deliu Faculty of Mathematics and Computer Science Technical University Eindhoven Eindhoven, The Netherlands November 2006 Decentralized control with

More information

OPTIMAL CONTROL. Sadegh Bolouki. Lecture slides for ECE 515. University of Illinois, Urbana-Champaign. Fall S. Bolouki (UIUC) 1 / 28

OPTIMAL CONTROL. Sadegh Bolouki. Lecture slides for ECE 515. University of Illinois, Urbana-Champaign. Fall S. Bolouki (UIUC) 1 / 28 OPTIMAL CONTROL Sadegh Bolouki Lecture slides for ECE 515 University of Illinois, Urbana-Champaign Fall 2016 S. Bolouki (UIUC) 1 / 28 (Example from Optimal Control Theory, Kirk) Objective: To get from

More information

Module 03 Linear Systems Theory: Necessary Background

Module 03 Linear Systems Theory: Necessary Background Module 03 Linear Systems Theory: Necessary Background Ahmad F. Taha EE 5243: Introduction to Cyber-Physical Systems Email: ahmad.taha@utsa.edu Webpage: http://engineering.utsa.edu/ taha/index.html September

More information

Module 07 Controllability and Controller Design of Dynamical LTI Systems

Module 07 Controllability and Controller Design of Dynamical LTI Systems Module 07 Controllability and Controller Design of Dynamical LTI Systems Ahmad F. Taha EE 5143: Linear Systems and Control Email: ahmad.taha@utsa.edu Webpage: http://engineering.utsa.edu/ataha October

More information

Control engineering sample exam paper - Model answers

Control engineering sample exam paper - Model answers Question Control engineering sample exam paper - Model answers a) By a direct computation we obtain x() =, x(2) =, x(3) =, x(4) = = x(). This trajectory is sketched in Figure (left). Note that A 2 = I

More information

Control Systems I. Lecture 4: Diagonalization, Modal Analysis, Intro to Feedback. Readings: Emilio Frazzoli

Control Systems I. Lecture 4: Diagonalization, Modal Analysis, Intro to Feedback. Readings: Emilio Frazzoli Control Systems I Lecture 4: Diagonalization, Modal Analysis, Intro to Feedback Readings: Emilio Frazzoli Institute for Dynamic Systems and Control D-MAVT ETH Zürich October 13, 2017 E. Frazzoli (ETH)

More information

Chapter 3. State Feedback - Pole Placement. Motivation

Chapter 3. State Feedback - Pole Placement. Motivation Chapter 3 State Feedback - Pole Placement Motivation Whereas classical control theory is based on output feedback, this course mainly deals with control system design by state feedback. This model-based

More information

Module 08 Observability and State Estimator Design of Dynamical LTI Systems

Module 08 Observability and State Estimator Design of Dynamical LTI Systems Module 08 Observability and State Estimator Design of Dynamical LTI Systems Ahmad F. Taha EE 5143: Linear Systems and Control Email: ahmad.taha@utsa.edu Webpage: http://engineering.utsa.edu/ataha November

More information

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science : MULTIVARIABLE CONTROL SYSTEMS by A.

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science : MULTIVARIABLE CONTROL SYSTEMS by A. Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.245: MULTIVARIABLE CONTROL SYSTEMS by A. Megretski Q-Parameterization 1 This lecture introduces the so-called

More information

ECE 388 Automatic Control

ECE 388 Automatic Control Controllability and State Feedback Control Associate Prof. Dr. of Mechatronics Engineeering Çankaya University Compulsory Course in Electronic and Communication Engineering Credits (2/2/3) Course Webpage:

More information

ECEEN 5448 Fall 2011 Homework #4 Solutions

ECEEN 5448 Fall 2011 Homework #4 Solutions ECEEN 5448 Fall 2 Homework #4 Solutions Professor David G. Meyer Novemeber 29, 2. The state-space realization is A = [ [ ; b = ; c = [ which describes, of course, a free mass (in normalized units) with

More information

MEM 355 Performance Enhancement of Dynamical Systems

MEM 355 Performance Enhancement of Dynamical Systems MEM 355 Performance Enhancement of Dynamical Systems State Space Design Harry G. Kwatny Department of Mechanical Engineering & Mechanics Drexel University 11/8/2016 Outline State space techniques emerged

More information

State Feedback and State Estimators Linear System Theory and Design, Chapter 8.

State Feedback and State Estimators Linear System Theory and Design, Chapter 8. 1 Linear System Theory and Design, http://zitompul.wordpress.com 2 0 1 4 State Estimator In previous section, we have discussed the state feedback, based on the assumption that all state variables are

More information

Controllability, Observability, Full State Feedback, Observer Based Control

Controllability, Observability, Full State Feedback, Observer Based Control Multivariable Control Lecture 4 Controllability, Observability, Full State Feedback, Observer Based Control John T. Wen September 13, 24 Ref: 3.2-3.4 of Text Controllability ẋ = Ax + Bu; x() = x. At time

More information

Intro. Computer Control Systems: F8

Intro. Computer Control Systems: F8 Intro. Computer Control Systems: F8 Properties of state-space descriptions and feedback Dave Zachariah Dept. Information Technology, Div. Systems and Control 1 / 22 dave.zachariah@it.uu.se F7: Quiz! 2

More information

ẋ n = f n (x 1,...,x n,u 1,...,u m ) (5) y 1 = g 1 (x 1,...,x n,u 1,...,u m ) (6) y p = g p (x 1,...,x n,u 1,...,u m ) (7)

ẋ n = f n (x 1,...,x n,u 1,...,u m ) (5) y 1 = g 1 (x 1,...,x n,u 1,...,u m ) (6) y p = g p (x 1,...,x n,u 1,...,u m ) (7) EEE582 Topical Outline A.A. Rodriguez Fall 2007 GWC 352, 965-3712 The following represents a detailed topical outline of the course. It attempts to highlight most of the key concepts to be covered and

More information

Lecture 2: Discrete-time Linear Quadratic Optimal Control

Lecture 2: Discrete-time Linear Quadratic Optimal Control ME 33, U Berkeley, Spring 04 Xu hen Lecture : Discrete-time Linear Quadratic Optimal ontrol Big picture Example onvergence of finite-time LQ solutions Big picture previously: dynamic programming and finite-horizon

More information

Modern Control Systems

Modern Control Systems Modern Control Systems Matthew M. Peet Arizona State University Lecture 09: Observability Observability For Static Full-State Feedback, we assume knowledge of the Full-State. In reality, we only have measurements

More information

There are none. Abstract for Gauranteed Margins for LQG Regulators, John Doyle, 1978 [Doy78].

There are none. Abstract for Gauranteed Margins for LQG Regulators, John Doyle, 1978 [Doy78]. Chapter 7 Output Feedback There are none. Abstract for Gauranteed Margins for LQG Regulators, John Doyle, 1978 [Doy78]. In the last chapter we considered the use of state feedback to modify the dynamics

More information

Linear System Theory

Linear System Theory Linear System Theory Wonhee Kim Chapter 6: Controllability & Observability Chapter 7: Minimal Realizations May 2, 217 1 / 31 Recap State space equation Linear Algebra Solutions of LTI and LTV system Stability

More information

Observability. It was the property in Lyapunov stability which allowed us to resolve that

Observability. It was the property in Lyapunov stability which allowed us to resolve that Observability We have seen observability twice already It was the property which permitted us to retrieve the initial state from the initial data {u(0),y(0),u(1),y(1),...,u(n 1),y(n 1)} It was the property

More information

Problem 2 (Gaussian Elimination, Fundamental Spaces, Least Squares, Minimum Norm) Consider the following linear algebraic system of equations:

Problem 2 (Gaussian Elimination, Fundamental Spaces, Least Squares, Minimum Norm) Consider the following linear algebraic system of equations: EEE58 Exam, Fall 6 AA Rodriguez Rules: Closed notes/books, No calculators permitted, open minds GWC 35, 965-37 Problem (Dynamic Augmentation: State Space Representation) Consider a dynamical system consisting

More information

LMIs for Observability and Observer Design

LMIs for Observability and Observer Design LMIs for Observability and Observer Design Matthew M. Peet Arizona State University Lecture 06: LMIs for Observability and Observer Design Observability Consider a system with no input: ẋ(t) = Ax(t), x(0)

More information

1 (30 pts) Dominant Pole

1 (30 pts) Dominant Pole EECS C8/ME C34 Fall Problem Set 9 Solutions (3 pts) Dominant Pole For the following transfer function: Y (s) U(s) = (s + )(s + ) a) Give state space description of the system in parallel form (ẋ = Ax +

More information

Linear State Feedback Controller Design

Linear State Feedback Controller Design Assignment For EE5101 - Linear Systems Sem I AY2010/2011 Linear State Feedback Controller Design Phang Swee King A0033585A Email: king@nus.edu.sg NGS/ECE Dept. Faculty of Engineering National University

More information

4F3 - Predictive Control

4F3 - Predictive Control 4F3 Predictive Control - Discrete-time systems p. 1/30 4F3 - Predictive Control Discrete-time State Space Control Theory For reference only Jan Maciejowski jmm@eng.cam.ac.uk 4F3 Predictive Control - Discrete-time

More information

16.31 Fall 2005 Lecture Presentation Mon 31-Oct-05 ver 1.1

16.31 Fall 2005 Lecture Presentation Mon 31-Oct-05 ver 1.1 16.31 Fall 2005 Lecture Presentation Mon 31-Oct-05 ver 1.1 Charles P. Coleman October 31, 2005 1 / 40 : Controllability Tests Observability Tests LEARNING OUTCOMES: Perform controllability tests Perform

More information

Full State Feedback for State Space Approach

Full State Feedback for State Space Approach Full State Feedback for State Space Approach State Space Equations Using Cramer s rule it can be shown that the characteristic equation of the system is : det[ si A] 0 Roots (for s) of the resulting polynomial

More information

CALIFORNIA INSTITUTE OF TECHNOLOGY Control and Dynamical Systems. CDS 110b

CALIFORNIA INSTITUTE OF TECHNOLOGY Control and Dynamical Systems. CDS 110b CALIFORNIA INSTITUTE OF TECHNOLOGY Control and Dynamical Systems CDS 110b R. M. Murray Kalman Filters 25 January 2006 Reading: This set of lectures provides a brief introduction to Kalman filtering, following

More information

State Space Design. MEM 355 Performance Enhancement of Dynamical Systems

State Space Design. MEM 355 Performance Enhancement of Dynamical Systems State Space Design MEM 355 Performance Enhancement of Dynamical Systems Harry G. Kwatny Department of Mechanical Engineering & Mechanics Drexel University Outline State space techniques emerged around

More information

Topic # /31 Feedback Control Systems. Analysis of Nonlinear Systems Lyapunov Stability Analysis

Topic # /31 Feedback Control Systems. Analysis of Nonlinear Systems Lyapunov Stability Analysis Topic # 16.30/31 Feedback Control Systems Analysis of Nonlinear Systems Lyapunov Stability Analysis Fall 010 16.30/31 Lyapunov Stability Analysis Very general method to prove (or disprove) stability of

More information

Lecture 2 and 3: Controllability of DT-LTI systems

Lecture 2 and 3: Controllability of DT-LTI systems 1 Lecture 2 and 3: Controllability of DT-LTI systems Spring 2013 - EE 194, Advanced Control (Prof Khan) January 23 (Wed) and 28 (Mon), 2013 I LTI SYSTEMS Recall that continuous-time LTI systems can be

More information

Robotics. Control Theory. Marc Toussaint U Stuttgart

Robotics. Control Theory. Marc Toussaint U Stuttgart Robotics Control Theory Topics in control theory, optimal control, HJB equation, infinite horizon case, Linear-Quadratic optimal control, Riccati equations (differential, algebraic, discrete-time), controllability,

More information

State Regulator. Advanced Control. design of controllers using pole placement and LQ design rules

State Regulator. Advanced Control. design of controllers using pole placement and LQ design rules Advanced Control State Regulator Scope design of controllers using pole placement and LQ design rules Keywords pole placement, optimal control, LQ regulator, weighting matrixes Prerequisites Contact state

More information

EEE582 Homework Problems

EEE582 Homework Problems EEE582 Homework Problems HW. Write a state-space realization of the linearized model for the cruise control system around speeds v = 4 (Section.3, http://tsakalis.faculty.asu.edu/notes/models.pdf). Use

More information

ME Fall 2001, Fall 2002, Spring I/O Stability. Preliminaries: Vector and function norms

ME Fall 2001, Fall 2002, Spring I/O Stability. Preliminaries: Vector and function norms I/O Stability Preliminaries: Vector and function norms 1. Sup norms are used for vectors for simplicity: x = max i x i. Other norms are also okay 2. Induced matrix norms: let A R n n, (i stands for induced)

More information

Course Outline. Higher Order Poles: Example. Higher Order Poles. Amme 3500 : System Dynamics & Control. State Space Design. 1 G(s) = s(s + 2)(s +10)

Course Outline. Higher Order Poles: Example. Higher Order Poles. Amme 3500 : System Dynamics & Control. State Space Design. 1 G(s) = s(s + 2)(s +10) Amme 35 : System Dynamics Control State Space Design Course Outline Week Date Content Assignment Notes 1 1 Mar Introduction 2 8 Mar Frequency Domain Modelling 3 15 Mar Transient Performance and the s-plane

More information

EE C128 / ME C134 Fall 2014 HW 9 Solutions. HW 9 Solutions. 10(s + 3) s(s + 2)(s + 5) G(s) =

EE C128 / ME C134 Fall 2014 HW 9 Solutions. HW 9 Solutions. 10(s + 3) s(s + 2)(s + 5) G(s) = 1. Pole Placement Given the following open-loop plant, HW 9 Solutions G(s) = 1(s + 3) s(s + 2)(s + 5) design the state-variable feedback controller u = Kx + r, where K = [k 1 k 2 k 3 ] is the feedback

More information

State Feedback and State Estimators Linear System Theory and Design, Chapter 8.

State Feedback and State Estimators Linear System Theory and Design, Chapter 8. 1 Linear System Theory and Design, http://zitompul.wordpress.com 2 0 1 4 2 Homework 7: State Estimators (a) For the same system as discussed in previous slides, design another closed-loop state estimator,

More information

CDS Solutions to the Midterm Exam

CDS Solutions to the Midterm Exam CDS 22 - Solutions to the Midterm Exam Instructor: Danielle C. Tarraf November 6, 27 Problem (a) Recall that the H norm of a transfer function is time-delay invariant. Hence: ( ) Ĝ(s) = s + a = sup /2

More information

Kalman Decomposition B 2. z = T 1 x, where C = ( C. z + u (7) T 1, and. where B = T, and

Kalman Decomposition B 2. z = T 1 x, where C = ( C. z + u (7) T 1, and. where B = T, and Kalman Decomposition Controllable / uncontrollable decomposition Suppose that the controllability matrix C R n n of a system has rank n 1

More information

Topics in control Tracking and regulation A. Astolfi

Topics in control Tracking and regulation A. Astolfi Topics in control Tracking and regulation A. Astolfi Contents 1 Introduction 1 2 The full information regulator problem 3 3 The FBI equations 5 4 The error feedback regulator problem 5 5 The internal model

More information

Lec 6: State Feedback, Controllability, Integral Action

Lec 6: State Feedback, Controllability, Integral Action Lec 6: State Feedback, Controllability, Integral Action November 22, 2017 Lund University, Department of Automatic Control Controllability and Observability Example of Kalman decomposition 1 s 1 x 10 x

More information

Steady State Kalman Filter

Steady State Kalman Filter Steady State Kalman Filter Infinite Horizon LQ Control: ẋ = Ax + Bu R positive definite, Q = Q T 2Q 1 2. (A, B) stabilizable, (A, Q 1 2) detectable. Solve for the positive (semi-) definite P in the ARE:

More information

Control Systems Design

Control Systems Design ELEC4410 Control Systems Design Lecture 18: State Feedback Tracking and State Estimation Julio H. Braslavsky julio@ee.newcastle.edu.au School of Electrical Engineering and Computer Science Lecture 18:

More information

Controllability. Chapter Reachable States. This chapter develops the fundamental results about controllability and pole assignment.

Controllability. Chapter Reachable States. This chapter develops the fundamental results about controllability and pole assignment. Chapter Controllability This chapter develops the fundamental results about controllability and pole assignment Reachable States We study the linear system ẋ = Ax + Bu, t, where x(t) R n and u(t) R m Thus

More information

Multivariable Control. Lecture 03. Description of Linear Time Invariant Systems. John T. Wen. September 7, 2006

Multivariable Control. Lecture 03. Description of Linear Time Invariant Systems. John T. Wen. September 7, 2006 Multivariable Control Lecture 3 Description of Linear Time Invariant Systems John T. Wen September 7, 26 Outline Mathematical description of LTI Systems Ref: 3.1-3.4 of text September 7, 26Copyrighted

More information

Nonlinear Observers. Jaime A. Moreno. Eléctrica y Computación Instituto de Ingeniería Universidad Nacional Autónoma de México

Nonlinear Observers. Jaime A. Moreno. Eléctrica y Computación Instituto de Ingeniería Universidad Nacional Autónoma de México Nonlinear Observers Jaime A. Moreno JMorenoP@ii.unam.mx Eléctrica y Computación Instituto de Ingeniería Universidad Nacional Autónoma de México XVI Congreso Latinoamericano de Control Automático October

More information

1. Find the solution of the following uncontrolled linear system. 2 α 1 1

1. Find the solution of the following uncontrolled linear system. 2 α 1 1 Appendix B Revision Problems 1. Find the solution of the following uncontrolled linear system 0 1 1 ẋ = x, x(0) =. 2 3 1 Class test, August 1998 2. Given the linear system described by 2 α 1 1 ẋ = x +

More information

Control System Design

Control System Design ELEC4410 Control System Design Lecture 19: Feedback from Estimated States and Discrete-Time Control Design Julio H. Braslavsky julio@ee.newcastle.edu.au School of Electrical Engineering and Computer Science

More information

Control Systems Design, SC4026. SC4026 Fall 2009, dr. A. Abate, DCSC, TU Delft

Control Systems Design, SC4026. SC4026 Fall 2009, dr. A. Abate, DCSC, TU Delft Control Systems Design, SC4026 SC4026 Fall 2009, dr. A. Abate, DCSC, TU Delft Lecture 4 Controllability (a.k.a. Reachability) vs Observability Algebraic Tests (Kalman rank condition & Hautus test) A few

More information

CALIFORNIA INSTITUTE OF TECHNOLOGY Control and Dynamical Systems. CDS 110b

CALIFORNIA INSTITUTE OF TECHNOLOGY Control and Dynamical Systems. CDS 110b CALIFORNIA INSTITUTE OF TECHNOLOGY Control and Dynamical Systems CDS 110b R. M. Murray Kalman Filters 14 January 2007 Reading: This set of lectures provides a brief introduction to Kalman filtering, following

More information

Control Systems Design, SC4026. SC4026 Fall 2010, dr. A. Abate, DCSC, TU Delft

Control Systems Design, SC4026. SC4026 Fall 2010, dr. A. Abate, DCSC, TU Delft Control Systems Design, SC4026 SC4026 Fall 2010, dr. A. Abate, DCSC, TU Delft Lecture 4 Controllability (a.k.a. Reachability) and Observability Algebraic Tests (Kalman rank condition & Hautus test) A few

More information

Lecture 18 : State Space Design

Lecture 18 : State Space Design UCSI University Kuala Lumpur, Malaysia Faculty of Engineering Department of Mechatronics Lecture 18 State Space Design Mohd Sulhi bin Azman Lecturer Department of Mechatronics UCSI University sulhi@ucsi.edu.my

More information

Stability of Parameter Adaptation Algorithms. Big picture

Stability of Parameter Adaptation Algorithms. Big picture ME5895, UConn, Fall 215 Prof. Xu Chen Big picture For ˆθ (k + 1) = ˆθ (k) + [correction term] we haven t talked about whether ˆθ(k) will converge to the true value θ if k. We haven t even talked about

More information

Balanced Truncation 1

Balanced Truncation 1 Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.242, Fall 2004: MODEL REDUCTION Balanced Truncation This lecture introduces balanced truncation for LTI

More information

MEM 355 Performance Enhancement of Dynamical Systems MIMO Introduction

MEM 355 Performance Enhancement of Dynamical Systems MIMO Introduction MEM 355 Performance Enhancement of Dynamical Systems MIMO Introduction Harry G. Kwatny Department of Mechanical Engineering & Mechanics Drexel University 11/2/214 Outline Solving State Equations Variation

More information

Power Systems Control Prof. Wonhee Kim. Ch.3. Controller Design in Time Domain

Power Systems Control Prof. Wonhee Kim. Ch.3. Controller Design in Time Domain Power Systems Control Prof. Wonhee Kim Ch.3. Controller Design in Time Domain Stability in State Space Equation: State Feeback t A t B t t C t D t x x u y x u u t Kx t t A t BK t A BK x t x x x K shoul

More information

Linear Quadratic Gausssian Control Design with Loop Transfer Recovery

Linear Quadratic Gausssian Control Design with Loop Transfer Recovery Linear Quadratic Gausssian Control Design with Loop Transfer Recovery Leonid Freidovich Department of Mathematics Michigan State University MI 48824, USA e-mail:leonid@math.msu.edu http://www.math.msu.edu/

More information

Full-State Feedback Design for a Multi-Input System

Full-State Feedback Design for a Multi-Input System Full-State Feedback Design for a Multi-Input System A. Introduction The open-loop system is described by the following state space model. x(t) = Ax(t)+Bu(t), y(t) =Cx(t)+Du(t) () 4 8.5 A =, B =.5.5, C

More information

Iterative methods to compute center and center-stable manifolds with application to the optimal output regulation problem

Iterative methods to compute center and center-stable manifolds with application to the optimal output regulation problem Iterative methods to compute center and center-stable manifolds with application to the optimal output regulation problem Noboru Sakamoto, Branislav Rehak N.S.: Nagoya University, Department of Aerospace

More information

Identification Methods for Structural Systems

Identification Methods for Structural Systems Prof. Dr. Eleni Chatzi System Stability Fundamentals Overview System Stability Assume given a dynamic system with input u(t) and output x(t). The stability property of a dynamic system can be defined from

More information

EE C128 / ME C134 Final Exam Fall 2014

EE C128 / ME C134 Final Exam Fall 2014 EE C128 / ME C134 Final Exam Fall 2014 December 19, 2014 Your PRINTED FULL NAME Your STUDENT ID NUMBER Number of additional sheets 1. No computers, no tablets, no connected device (phone etc.) 2. Pocket

More information

Solution of Linear State-space Systems

Solution of Linear State-space Systems Solution of Linear State-space Systems Homogeneous (u=0) LTV systems first Theorem (Peano-Baker series) The unique solution to x(t) = (t, )x 0 where The matrix function is given by is called the state

More information

Control Systems Design

Control Systems Design ELEC4410 Control Systems Design Lectre 16: Controllability and Observability Canonical Decompositions Jlio H. Braslavsky jlio@ee.newcastle.ed.a School of Electrical Engineering and Compter Science Lectre

More information

Systems and Control Theory Lecture Notes. Laura Giarré

Systems and Control Theory Lecture Notes. Laura Giarré Systems and Control Theory Lecture Notes Laura Giarré L. Giarré 2017-2018 Lesson 17: Model-based Controller Feedback Stabilization Observers Ackerman Formula Model-based Controller L. Giarré- Systems and

More information

SYSTEMTEORI - KALMAN FILTER VS LQ CONTROL

SYSTEMTEORI - KALMAN FILTER VS LQ CONTROL SYSTEMTEORI - KALMAN FILTER VS LQ CONTROL 1. Optimal regulator with noisy measurement Consider the following system: ẋ = Ax + Bu + w, x(0) = x 0 where w(t) is white noise with Ew(t) = 0, and x 0 is a stochastic

More information

Robust Control 2 Controllability, Observability & Transfer Functions

Robust Control 2 Controllability, Observability & Transfer Functions Robust Control 2 Controllability, Observability & Transfer Functions Harry G. Kwatny Department of Mechanical Engineering & Mechanics Drexel University /26/24 Outline Reachable Controllability Distinguishable

More information

Control Systems. Laplace domain analysis

Control Systems. Laplace domain analysis Control Systems Laplace domain analysis L. Lanari outline introduce the Laplace unilateral transform define its properties show its advantages in turning ODEs to algebraic equations define an Input/Output

More information

Exam. 135 minutes, 15 minutes reading time

Exam. 135 minutes, 15 minutes reading time Exam August 6, 208 Control Systems II (5-0590-00) Dr. Jacopo Tani Exam Exam Duration: 35 minutes, 5 minutes reading time Number of Problems: 35 Number of Points: 47 Permitted aids: 0 pages (5 sheets) A4.

More information

Comparison of four state observer design algorithms for MIMO system

Comparison of four state observer design algorithms for MIMO system Archives of Control Sciences Volume 23(LIX), 2013 No. 2, pages 131 144 Comparison of four state observer design algorithms for MIMO system VINODH KUMAR. E, JOVITHA JEROME and S. AYYAPPAN A state observer

More information

Lecture 19 Observability and state estimation

Lecture 19 Observability and state estimation EE263 Autumn 2007-08 Stephen Boyd Lecture 19 Observability and state estimation state estimation discrete-time observability observability controllability duality observers for noiseless case continuous-time

More information

Robust Control 5 Nominal Controller Design Continued

Robust Control 5 Nominal Controller Design Continued Robust Control 5 Nominal Controller Design Continued Harry G. Kwatny Department of Mechanical Engineering & Mechanics Drexel University 4/14/2003 Outline he LQR Problem A Generalization to LQR Min-Max

More information

Lecture 15: H Control Synthesis

Lecture 15: H Control Synthesis c A. Shiriaev/L. Freidovich. March 12, 2010. Optimal Control for Linear Systems: Lecture 15 p. 1/14 Lecture 15: H Control Synthesis Example c A. Shiriaev/L. Freidovich. March 12, 2010. Optimal Control

More information

DESIGN OF OBSERVERS FOR SYSTEMS WITH SLOW AND FAST MODES

DESIGN OF OBSERVERS FOR SYSTEMS WITH SLOW AND FAST MODES DESIGN OF OBSERVERS FOR SYSTEMS WITH SLOW AND FAST MODES by HEONJONG YOO A thesis submitted to the Graduate School-New Brunswick Rutgers, The State University of New Jersey In partial fulfillment of the

More information

Quadratic Stability of Dynamical Systems. Raktim Bhattacharya Aerospace Engineering, Texas A&M University

Quadratic Stability of Dynamical Systems. Raktim Bhattacharya Aerospace Engineering, Texas A&M University .. Quadratic Stability of Dynamical Systems Raktim Bhattacharya Aerospace Engineering, Texas A&M University Quadratic Lyapunov Functions Quadratic Stability Dynamical system is quadratically stable if

More information

High-Gain Observers in Nonlinear Feedback Control. Lecture # 2 Separation Principle

High-Gain Observers in Nonlinear Feedback Control. Lecture # 2 Separation Principle High-Gain Observers in Nonlinear Feedback Control Lecture # 2 Separation Principle High-Gain ObserversinNonlinear Feedback ControlLecture # 2Separation Principle p. 1/4 The Class of Systems ẋ = Ax + Bφ(x,

More information

Control Systems. Internal Stability - LTI systems. L. Lanari

Control Systems. Internal Stability - LTI systems. L. Lanari Control Systems Internal Stability - LTI systems L. Lanari outline LTI systems: definitions conditions South stability criterion equilibrium points Nonlinear systems: equilibrium points examples stable

More information

EECS C128/ ME C134 Final Wed. Dec. 15, am. Closed book. Two pages of formula sheets. No calculators.

EECS C128/ ME C134 Final Wed. Dec. 15, am. Closed book. Two pages of formula sheets. No calculators. Name: SID: EECS C28/ ME C34 Final Wed. Dec. 5, 2 8- am Closed book. Two pages of formula sheets. No calculators. There are 8 problems worth points total. Problem Points Score 2 2 6 3 4 4 5 6 6 7 8 2 Total

More information

LQR, Kalman Filter, and LQG. Postgraduate Course, M.Sc. Electrical Engineering Department College of Engineering University of Salahaddin

LQR, Kalman Filter, and LQG. Postgraduate Course, M.Sc. Electrical Engineering Department College of Engineering University of Salahaddin LQR, Kalman Filter, and LQG Postgraduate Course, M.Sc. Electrical Engineering Department College of Engineering University of Salahaddin May 2015 Linear Quadratic Regulator (LQR) Consider a linear system

More information

Semidefinite Programming Duality and Linear Time-invariant Systems

Semidefinite Programming Duality and Linear Time-invariant Systems Semidefinite Programming Duality and Linear Time-invariant Systems Venkataramanan (Ragu) Balakrishnan School of ECE, Purdue University 2 July 2004 Workshop on Linear Matrix Inequalities in Control LAAS-CNRS,

More information

Multi-Model Adaptive Regulation for a Family of Systems Containing Different Zero Structures

Multi-Model Adaptive Regulation for a Family of Systems Containing Different Zero Structures Preprints of the 19th World Congress The International Federation of Automatic Control Multi-Model Adaptive Regulation for a Family of Systems Containing Different Zero Structures Eric Peterson Harry G.

More information

Observability and state estimation

Observability and state estimation EE263 Autumn 2015 S Boyd and S Lall Observability and state estimation state estimation discrete-time observability observability controllability duality observers for noiseless case continuous-time observability

More information

STRUCTURE MATTERS: Some Notes on High Gain Observer Design for Nonlinear Systems. Int. Conf. on Systems, Analysis and Automatic Control 2012

STRUCTURE MATTERS: Some Notes on High Gain Observer Design for Nonlinear Systems. Int. Conf. on Systems, Analysis and Automatic Control 2012 Faculty of Electrical and Computer Engineering Institute of Control Theory STRUCTURE MATTERS: Some Notes on High Gain Observer Design for Nonlinear Systems Klaus Röbenack Int. Conf. on Systems, Analysis

More information

NPTEL Online Course: Control Engineering

NPTEL Online Course: Control Engineering NPTEL Online Course: Control Engineering Ramkrishna Pasumarthy Assignment-11 : s 1. Consider a system described by state space model [ ] [ 0 1 1 x + u 5 1 2] y = [ 1 2 ] x What is the transfer function

More information

ECE 680 Linear Matrix Inequalities

ECE 680 Linear Matrix Inequalities ECE 680 Linear Matrix Inequalities Stan Żak School of Electrical and Computer Engineering Purdue University zak@purdue.edu October 11, 2017 Stan Żak (Purdue University) ECE 680 Linear Matrix Inequalities

More information

ECEN 605 LINEAR SYSTEMS. Lecture 8 Invariant Subspaces 1/26

ECEN 605 LINEAR SYSTEMS. Lecture 8 Invariant Subspaces 1/26 1/26 ECEN 605 LINEAR SYSTEMS Lecture 8 Invariant Subspaces Subspaces Let ẋ(t) = A x(t) + B u(t) y(t) = C x(t) (1a) (1b) denote a dynamic system where X, U and Y denote n, r and m dimensional vector spaces,

More information

Chap. 3. Controlled Systems, Controllability

Chap. 3. Controlled Systems, Controllability Chap. 3. Controlled Systems, Controllability 1. Controllability of Linear Systems 1.1. Kalman s Criterion Consider the linear system ẋ = Ax + Bu where x R n : state vector and u R m : input vector. A :

More information