1 Controllability and Observability

Size: px
Start display at page:

Download "1 Controllability and Observability"

Transcription

1 1 Controllability and Observability 1.1 Linear Time-Invariant (LTI) Systems State-space: Dimensions: Notation Transfer function: ẋ = Ax+Bu, x() = x, y = Cx+Du. x R n, u R m, y R p. Note that H(s) is always proper! [ ] A B C D H(s) = C(sI A) 1 B +D Similarity transformation: [ T 1 AT T 1 ] B CT D Similarity does not change transfer function [ ] A B C D H(s) = CT(sI T 1 AT) 1 T 1 B +D = C(sI A) 1 B +D System response: Y(s) = H(s)U(s) }{{} + C(sI A) 1 x() }{{} Input Initial conditions MIMO comes for free! MAE 28 B 5 Maurício de Oliveira

2 1.2 Concepts from MAE 28 A Controllability Matrix: C(A,B) = [ B AB A n 1 B ]. Controllability Gramian: X(t) = t e Aξ BB T e ATξ dξ. Observability Matrix: Observability Gramian: O(A,C) = C CA. CA n 1. Y(t) = t e ATξ C T Ce Aξ dξ. MAE 28 B 6 Maurício de Oliveira

3 1.3 Controllability Problem: Given x() = and any x, can one compute u(t) such that x( t) = x for some t >? Theorem: The following are equivalent a) The pair (A,B) is controllable; b) The Controllability Matrix C(A, B) has full-row rank; c) There exists no z such that z A = λz, z B = ; d) The Controllability Gramian X(t) is positive definite for some t. 1.4 Observability Problem: Given y(t) over t [, t] with t > can one compute x(t) for all t [, t]? Theorem: The following are equivalent a) The pair (A,C) is observable; b) The Observability Matrix O(A, C) has full-column rank; c) There exists no x such that Ax = λx, Cx = ; d) The Observability Gramian Y = Y(t) is positive definite for some t. 1.5 Things you should already know 1. Why a) and b) are equivalent. 2. Why can we stop C(A,B) at A n 1 B and O(A,C) at CA n 1? 3. Kalman canonical forms. E.g. if (A,C) is not observable then [ ] A A B o B o Aōo Aō Bō C C o where A o R r r and (A o,c o ) is observable. MAE 28 B 7 Maurício de Oliveira

4 1.6 The Popov-Belevitch-Hautus Test Theorem: The pair (A,C) is observable if and only if there exists no x such that Ax = λx, Cx =. (1) Proof: Sufficiency: Assume there exists x such that (1) holds. Then CAx = λcx =, CA 2 x = λcax =,. CA n 1 x = λca n 2 x = so that O(A,C)x =, which implies that the pair (A,C) is not observable. Necessity: Assume that (A, C) is not observable. Then transform it into the equivalent non observable realization where [ ] Ao [ Ā =, C = Co ]. Aōo Aō Chose x such that Aōx = λx. Then [ Ao Aōo Aō ]( ) = λ x ( ), x [ Co ]( ) =. x MAE 28 B 8 Maurício de Oliveira

5 1.7 Controllability Gramian Problem: Given x() = and any x, compute u(t) such that x( t) = x for some t >. Solution: We know that x = x( t) = t e A( t τ) Bu(τ)dτ. If we limit our search to controls u of the form u(t) = B T e ( t t) z AT we have x = = = t ( t e A( t τ) BB T e ( t τ) zdτ, AT ) e A( t τ) BB T e AT ( t τ) dτ ( ) t e Aξ BB T e ATξ dξ z, z, ξ = t τ and ( 1 t z = e Aξ BB T e dξ) ATξ x, ( 1 t u(t) = B T e AT ( t t) e Aξ BB T e dξ) ATξ x The symmetric matrix X(t) := is the Controllability Gramian. t e Aξ BB T e ATξ dξ MAE 28 B 9 Maurício de Oliveira

6 1.8 Stabilizability Problem: Given any x() = x can one compute u(t) such that x( t) = for some t >? Theorem: The following are equivalent a) The pair (A,B) is stabilizable; b) There exists no z and λ such that z A = λz, z B = with λ+λ. 1.9 Detectability Problem: Given y(t) over t [, t] with t > can one compute x( t)? Theorem: The following are equivalent a) The pair (A,C) is detectable; b) There exists no x and λ such that Ax = λx, Cx = with λ+λ. MAE 28 B 1 Maurício de Oliveira

7 1.1 Example: satellite in circular orbit u r θ m u 1 e Satellite of mass m with thrust in the radial direction u 1 and in the tangential direction u 2. From Skelton, DSC, p. 11. Newton s law where f g is the gravitational force m r = u 1 + u 2 + f g, f g = km r 2 r r. Using cylindrical coordinates ( cosθ e 1 = sinθ we have ), e 2 = ( ) sinθ, cosθ r = r e 1, u 1 = u 1 e 1, u 2 = u 2 e 2, fg = km r 2 e 1. MAE 28 B 11 Maurício de Oliveira

8 We need to compute where That is r = d2 dt 2(r e 1) = d dt (ṙ e 1 +r e 1 ) = r e 1 +2ṙ e 1 +r e 1, θ( ) sinθ e 1 = = cosθ θ e 2, θ( ) ( ) sinθ e 1 = + cosθ θ cosθ 2 sinθ r = ( r r θ 2 ) e 1 +(2ṙ θ +r θ) e 2, = θ e 2 θ 2 e 1. so that Newton s law can be rewritten as m( r r θ 2 ) e 1 +m(2ṙ θ +r θ) e 2 = u 1 e 1 +u 2 e 2 km r 2 e 1, or, equivalently, as the two scalar differential equations m( r r θ 2 ) = u 1 km r 2, m(2ṙ θ+r θ) = u 2. In state space r x = θ ṙ, ẋ = θ ṙ θ r = θ x 3 x 4 x 1 x 2 4 k/x2 1 2x 3 x 4 /x 1 + 1/m 1/(mx 1 ) ( u1 u 2 ). This is a nonlinear system and we look for equilibrium ( r = θ = ) when u 1 = u 2 =. This can be stated as x 1 x 2 4 k/x 2 1 =, 2x 3 x 4 /x 1 =. The second condition implies x 3 = ṙ and/or x 4 = θ must be zero. We choose x 3 = ṙ = which implies x 1 = r = r constant and x 4 = θ k k = = r = ω k = r3 ω 2. 3 Note also that x 2 = θ = ωt. x 3 1 MAE 28 B 12 Maurício de Oliveira

9 This nonlinear system is in the form ẋ = f(x,t)+g(x)u. We will linearize f(x,t) and g(x)u around the equilibrium point ( x,ū) to obtain the linearized system ẋ = ( f x ) T [x(t) x(t)]+( g x ) T [x(t) x(t)]ū+g( x)u. For this problem r x(t) = ωt, ū =, f(x,t) = ω and ( f x ) T = ẋ = 1 1 x 3 x 4 x 1 x 2 4 k/x 2 1 2x 3 x 4 /x 1 2k/ r 3 + ω 2 2 r ω 2 ω/ r This produces the linearized system ω 2 2 r ω 2 ω/ r = x+, g(x) = 1/m 1/(mx 1 ) ω 2 2 r ω 2 ω/ r 1/m 1/(m r) ( u1 u 2 ). If we looking at the satellite (from the earth) we can say that we can observe r and θ (how?), that is [ ] 1 y = x. 1 Questions: 1) Can we estimate the state of the satellite by measuring only r? 2) Can we estimate the state of the satellite by measuring only θ? 3) Can we estimate the state of the satellite by measuring r and θ? 4) Can the system be controlled to remain in circular orbit using radial thrusting (u 1 ) alone? 5) Can the system be controlled using tangential thrusting (u 2 ) alone?., MAE 28 B 13 Maurício de Oliveira

10 Question: Can we estimate the state of the satellite by measuring only r? Answer: Is the system observable when C = [ 1 ]? Compute the observability matrix C O(A,C) = CA CA 2, CA 3 1 = 1 3 ω 2 2 r ω ω 2 Physical interpretation: measuring r does not give any information on θ or θ! Note that if we that know the satellite is in equilibrium and measure k then k θ = ω = r 3. But we still do not know θ since and we do not know θ()! θ(t) = θ()+ωt, MAE 28 B 14 Maurício de Oliveira

11 Question: Can we estimate the state of the satellite by observing θ only? Answer: Is the system observable when C = [ 1 ]? Compute the observability matrix C O(A,C) = CA CA 2, CA 3 1 = 2 ω/ r 6 ω 3 / r 4 ω 2 2 ω 3 / r Physical interpretation: again, if we try to reconstruct θ from θ we still need to know θ at some t! From that point on θ = θ( t)+ t t θ(τ)dτ. MAE 28 B 15 Maurício de Oliveira

12 Question: Can we estimate the state of the satellite by measuring r and θ? [ ] 1 Answer: Is the system observable when C =? Compute the 1 observability matrix C O(A,C) = CA CA 2, CA ω/ r = 3 ω 2 2 r ω 6 ω 3 / r 4 ω 2 ω 2 2 ω 3 / r Physical interpretation: can we estimate θ at all? MAE 28 B 16 Maurício de Oliveira

13 Question: Can the system be controlled to remain in circular orbit using radial thrusting (u 1 ) alone? Answer: Is the system controllable when B = controllability matrix Note that 1/m C(A,B) = [ B AB A 2 B A 3 B ], = 1 m ω 2 1 ω 2 2 ω/ r 1 ω 2 2 ω/ r 2 ω 3 / r 1 2 ω/ r = ω 2 2 ω 3 / r which implies that the system is not controllable from u 1!? Compute the Physical interpretation: there must be a change in the angular velocity θ if one changes the radius! MAE 28 B 17 Maurício de Oliveira

14 Question: Can the system be controlled using tangential thrusting (u 2 ) alone? Answer: Is the system controllable when B =? Compute the 1/(m r) controllability matrix C(A,B) = [ B AB A 2 B A 3 B ], = 1 m r 2 r ω 1 4 ω 2 2 r ω 2 r ω ω 2 The above matrix has full rank, so the system is controllable from u 2! Physical interpretation: we can change the radius by changing the angular velocity! MAE 28 B 18 Maurício de Oliveira

15 1.11 More on Gramians Theorem: The Controllability Gramian X(t) = t is the solution to the differential equation If X = lim t X(t) exists then e Aξ BB T e ATξ dξ, d dt X(t) = AX(t)+X(t)AT +BB T. AX +XA T +BB T =. Theorem: The Observability Gramian Y(t) = t is the solution to the differential equation If Y = lim t X(t) exists then e ATξ C T Ce Aξ dξ, d dt Y(t) = AT Y(t)+Y(t)A+C T C. A T Y +YA+C T C =. MAE 28 B 19 Maurício de Oliveira

16 Proof (Controllability): For the first part, compute t d dt X(t) = d e Aξ BB T e ATξ dξ = d e A(t τ) BB T e AT (t τ) dτ, dt dt t d = dt ea(t τ) BB T e AT (t τ) + e A(t τ) BB T e AT (t τ), τ=t ( t ) = A e A(t τ) BB T e AT (t τ) dτ ( t ) + e A(t τ) BB T e AT (t τ) dτ A T +BB T, = AX(t)+X(t)A T +BB T. For the second part, use the fact that X(t) is smooth and therefore t d lim X(t) = X lim X(t) =. t t dt MAE 28 B 2 Maurício de Oliveira

17 Lemma: Consider the Lyapunov Equation where A C n n and C C m n. A T X +XA+C T C = 1. A solution X C n n exists and is unique if and only if λ j (A)+λ i (A) for all i,j = 1,...,n. Furthermore X is symmetric. 2. If A is Hurwitz then X is positive semidefinite. 3. If (A,C) is detectable and X is positive semidefinite then A is Hurwitz. 4. If (A,C) is observable and A is Hurwitz then X is positive definite. Proof: Item 1. The Lyapunov Equation is a linear equation and it has a unique solution if and only if the homogeneous equation associated with the Lyapunov equation admits only the trivial solution. Assume it does not, that is, there X such that A T X + XA = Then, multiplication of the above on the right by x i, the ith eigenvector of A and on the right by x j yields = x i AT Xxj +x i XAx j = [λ j (A)+λ i (A)]x i Xx j. Since λ i (A)+λ j (B) by hypothesis we must have x i Xx j = for all i,j. One can show that this indeed implies X =, establishing a contradiction. That X is symmetric follows from uniqueness since so that X T X =. = (A T X +XA+C T C) T (A T X +XA+C T C) = A T (X T X)+(X T X)A Item 2. If A is Hurwitz then lim t e At =. But and X = A T X +XA = lim t e ATt C T Ce At dt d dt eatt C T Ce At dt = e ATt C T Ce At = CT C. MAE 28 B 21 Maurício de Oliveira

18 Item 3. Assume (A,C) is detectable, X and that A is not Hurwitz. Then there exists λ and x such that Ax = λx with λ+λ. But if X solves the Lyapunov equation x C T Cx = x A T Xx+x XAx = (λ+λ )x Xx which implies Cx =, hence (A,C) not detectable. Item 4. Assume that (A,C) is observable and A is Hurwitz. From Item 2. X. Assume X is not positive definite, that is, there exists x such that X x =. It follows that = x X x = x e ATt C T Ce At xdt = y (t)y(t)dt which implies that response y(t) = Ce At x = to a non null initial condition x() = x is null, which contradicts the hypothesis that (A,C) is observable. MAE 28 B 22 Maurício de Oliveira

1 Some Facts on Symmetric Matrices

1 Some Facts on Symmetric Matrices 1 Some Facts on Symmetric Matrices Definition: Matrix A is symmetric if A = A T. Theorem: Any symmetric matrix 1) has only real eigenvalues; 2) is always iagonalizable; 3) has orthogonal eigenvectors.

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

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

2 The Linear Quadratic Regulator (LQR)

2 The Linear Quadratic Regulator (LQR) 2 The Linear Quadratic Regulator (LQR) Problem: Compute a state feedback controller u(t) = Kx(t) that stabilizes the closed loop system and minimizes J := 0 x(t) T Qx(t)+u(t) T Ru(t)dt where x and u are

More information

7.1 Linear Systems Stability Consider the Continuous-Time (CT) Linear Time-Invariant (LTI) system

7.1 Linear Systems Stability Consider the Continuous-Time (CT) Linear Time-Invariant (LTI) system 7 Stability 7.1 Linear Systems Stability Consider the Continuous-Time (CT) Linear Time-Invariant (LTI) system ẋ(t) = A x(t), x(0) = x 0, A R n n, x 0 R n. (14) The origin x = 0 is a globally asymptotically

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

8 A First Glimpse on Design with LMIs

8 A First Glimpse on Design with LMIs 8 A First Glimpse on Design with LMIs 8.1 Conceptual Design Problem Given a linear time invariant system design a linear time invariant controller or filter so as to guarantee some closed loop indices

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

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

4 Optimal State Estimation

4 Optimal State Estimation 4 Optimal State Estimation Consider the LTI system ẋ(t) = Ax(t)+B w w(t), y(t) = C y x(t)+d yw w(t), z(t) = C z x(t) Problem: Compute (Â,F) such that the output of the state estimator ˆx(t) = ˆx(t) Fy(t),

More information

Nonlinear Control. Nonlinear Control Lecture # 6 Passivity and Input-Output Stability

Nonlinear Control. Nonlinear Control Lecture # 6 Passivity and Input-Output Stability Nonlinear Control Lecture # 6 Passivity and Input-Output Stability Passivity: Memoryless Functions y y y u u u (a) (b) (c) Passive Passive Not passive y = h(t,u), h [0, ] Vector case: y = h(t,u), h T =

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

9 The LQR Problem Revisited

9 The LQR Problem Revisited 9 he LQR Problem Revisited Problem: Compute a state feedback controller u(t) = Kx(t) that stabilizes the closed loop system and minimizes J := lim E [ z(t) z(t) ] t for the LI system Assumptions: a) D

More information

Modeling and Analysis of Dynamic Systems

Modeling and Analysis of Dynamic Systems Modeling and Analysis of Dynamic Systems Dr. Guillaume Ducard Fall 2017 Institute for Dynamic Systems and Control ETH Zurich, Switzerland G. Ducard c 1 / 54 Outline 1 G. Ducard c 2 / 54 Outline 1 G. Ducard

More information

Observability. Dynamic Systems. Lecture 2 Observability. Observability, continuous time: Observability, discrete time: = h (2) (x, u, u)

Observability. Dynamic Systems. Lecture 2 Observability. Observability, continuous time: Observability, discrete time: = h (2) (x, u, u) Observability Dynamic Systems Lecture 2 Observability Continuous time model: Discrete time model: ẋ(t) = f (x(t), u(t)), y(t) = h(x(t), u(t)) x(t + 1) = f (x(t), u(t)), y(t) = h(x(t)) Reglerteknik, ISY,

More information

ME 234, Lyapunov and Riccati Problems. 1. This problem is to recall some facts and formulae you already know. e Aτ BB e A τ dτ

ME 234, Lyapunov and Riccati Problems. 1. This problem is to recall some facts and formulae you already know. e Aτ BB e A τ dτ ME 234, Lyapunov and Riccati Problems. This problem is to recall some facts and formulae you already know. (a) Let A and B be matrices of appropriate dimension. Show that (A, B) is controllable if and

More information

Introduction to Nonlinear Control Lecture # 4 Passivity

Introduction to Nonlinear Control Lecture # 4 Passivity p. 1/6 Introduction to Nonlinear Control Lecture # 4 Passivity È p. 2/6 Memoryless Functions ¹ y È Ý Ù È È È È u (b) µ power inflow = uy Resistor is passive if uy 0 p. 3/6 y y y u u u (a) (b) (c) Passive

More information

6.241 Dynamic Systems and Control

6.241 Dynamic Systems and Control 6.241 Dynamic Systems and Control Lecture 24: H2 Synthesis Emilio Frazzoli Aeronautics and Astronautics Massachusetts Institute of Technology May 4, 2011 E. Frazzoli (MIT) Lecture 24: H 2 Synthesis May

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science : Dynamic Systems Spring 2011

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science : Dynamic Systems Spring 2011 MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science 6.4: Dynamic Systems Spring Homework Solutions Exercise 3. a) We are given the single input LTI system: [

More information

ECEEN 5448 Fall 2011 Homework #5 Solutions

ECEEN 5448 Fall 2011 Homework #5 Solutions ECEEN 5448 Fall 211 Homework #5 Solutions Professor David G. Meyer December 8, 211 1. Consider the 1-dimensional time-varying linear system ẋ t (u x) (a) Find the state-transition matrix, Φ(t, τ). Here

More information

Control Systems. Frequency domain analysis. L. Lanari

Control Systems. Frequency domain analysis. L. Lanari Control Systems m i l e r p r a in r e v y n is o Frequency domain analysis L. Lanari outline introduce the Laplace unilateral transform define its properties show its advantages in turning ODEs to algebraic

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

Lecture 8. Chapter 5: Input-Output Stability Chapter 6: Passivity Chapter 14: Passivity-Based Control. Eugenio Schuster.

Lecture 8. Chapter 5: Input-Output Stability Chapter 6: Passivity Chapter 14: Passivity-Based Control. Eugenio Schuster. Lecture 8 Chapter 5: Input-Output Stability Chapter 6: Passivity Chapter 14: Passivity-Based Control Eugenio Schuster schuster@lehigh.edu Mechanical Engineering and Mechanics Lehigh University Lecture

More information

In the presence of viscous damping, a more generalized form of the Lagrange s equation of motion can be written as

In the presence of viscous damping, a more generalized form of the Lagrange s equation of motion can be written as 2 MODELING Once the control target is identified, which includes the state variable to be controlled (ex. speed, position, temperature, flow rate, etc), and once the system drives are identified (ex. force,

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

Model reduction for linear systems by balancing

Model reduction for linear systems by balancing Model reduction for linear systems by balancing Bart Besselink Jan C. Willems Center for Systems and Control Johann Bernoulli Institute for Mathematics and Computer Science University of Groningen, Groningen,

More information

Lecture 4. Chapter 4: Lyapunov Stability. Eugenio Schuster. Mechanical Engineering and Mechanics Lehigh University.

Lecture 4. Chapter 4: Lyapunov Stability. Eugenio Schuster. Mechanical Engineering and Mechanics Lehigh University. Lecture 4 Chapter 4: Lyapunov Stability Eugenio Schuster schuster@lehigh.edu Mechanical Engineering and Mechanics Lehigh University Lecture 4 p. 1/86 Autonomous Systems Consider the autonomous system ẋ

More information

Nonlinear systems. Lyapunov stability theory. G. Ferrari Trecate

Nonlinear systems. Lyapunov stability theory. G. Ferrari Trecate Nonlinear systems Lyapunov stability theory G. Ferrari Trecate Dipartimento di Ingegneria Industriale e dell Informazione Università degli Studi di Pavia Advanced automation and control Ferrari Trecate

More information

Introduction to Nonlinear Control Lecture # 3 Time-Varying and Perturbed Systems

Introduction to Nonlinear Control Lecture # 3 Time-Varying and Perturbed Systems p. 1/5 Introduction to Nonlinear Control Lecture # 3 Time-Varying and Perturbed Systems p. 2/5 Time-varying Systems ẋ = f(t, x) f(t, x) is piecewise continuous in t and locally Lipschitz in x for all t

More information

Prof. Krstic Nonlinear Systems MAE281A Homework set 1 Linearization & phase portrait

Prof. Krstic Nonlinear Systems MAE281A Homework set 1 Linearization & phase portrait Prof. Krstic Nonlinear Systems MAE28A Homework set Linearization & phase portrait. For each of the following systems, find all equilibrium points and determine the type of each isolated equilibrium. Use

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

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

1 Relative degree and local normal forms

1 Relative degree and local normal forms THE ZERO DYNAMICS OF A NONLINEAR SYSTEM 1 Relative degree and local normal orms The purpose o this Section is to show how single-input single-output nonlinear systems can be locally given, by means o a

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

MCE693/793: Analysis and Control of Nonlinear Systems

MCE693/793: Analysis and Control of Nonlinear Systems MCE693/793: Analysis and Control of Nonlinear Systems Introduction to Nonlinear Controllability and Observability Hanz Richter Mechanical Engineering Department Cleveland State University Definition of

More information

M2A2 Problem Sheet 3 - Hamiltonian Mechanics

M2A2 Problem Sheet 3 - Hamiltonian Mechanics MA Problem Sheet 3 - Hamiltonian Mechanics. The particle in a cone. A particle slides under gravity, inside a smooth circular cone with a vertical axis, z = k x + y. Write down its Lagrangian in a) Cartesian,

More information

Linearization problem. The simplest example

Linearization problem. The simplest example Linear Systems Lecture 3 1 problem Consider a non-linear time-invariant system of the form ( ẋ(t f x(t u(t y(t g ( x(t u(t (1 such that x R n u R m y R p and Slide 1 A: f(xu f(xu g(xu and g(xu exist and

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

Theorem 1. ẋ = Ax is globally exponentially stable (GES) iff A is Hurwitz (i.e., max(re(σ(a))) < 0).

Theorem 1. ẋ = Ax is globally exponentially stable (GES) iff A is Hurwitz (i.e., max(re(σ(a))) < 0). Linear Systems Notes Lecture Proposition. A M n (R) is positive definite iff all nested minors are greater than or equal to zero. n Proof. ( ): Positive definite iff λ i >. Let det(a) = λj and H = {x D

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

ECE557 Systems Control

ECE557 Systems Control ECE557 Systems Control Bruce Francis Course notes, Version.0, September 008 Preface This is the second Engineering Science course on control. It assumes ECE56 as a prerequisite. If you didn t take ECE56,

More information

Control Systems Design

Control Systems Design ELEC4410 Control Systems Design Lecture 14: Controllability Julio H. Braslavsky julio@ee.newcastle.edu.au School of Electrical Engineering and Computer Science Lecture 14: Controllability p.1/23 Outline

More information

Control, Stabilization and Numerics for Partial Differential Equations

Control, Stabilization and Numerics for Partial Differential Equations Paris-Sud, Orsay, December 06 Control, Stabilization and Numerics for Partial Differential Equations Enrique Zuazua Universidad Autónoma 28049 Madrid, Spain enrique.zuazua@uam.es http://www.uam.es/enrique.zuazua

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

PH.D. PRELIMINARY EXAMINATION MATHEMATICS

PH.D. PRELIMINARY EXAMINATION MATHEMATICS UNIVERSITY OF CALIFORNIA, BERKELEY SPRING SEMESTER 207 Dept. of Civil and Environmental Engineering Structural Engineering, Mechanics and Materials NAME PH.D. PRELIMINARY EXAMINATION MATHEMATICS Problem

More information

Nonlinear Control Systems

Nonlinear Control Systems Nonlinear Control Systems António Pedro Aguiar pedro@isr.ist.utl.pt 7. Feedback Linearization IST-DEEC PhD Course http://users.isr.ist.utl.pt/%7epedro/ncs1/ 1 1 Feedback Linearization Given a nonlinear

More information

Raktim Bhattacharya. . AERO 632: Design of Advance Flight Control System. Norms for Signals and Systems

Raktim Bhattacharya. . AERO 632: Design of Advance Flight Control System. Norms for Signals and Systems . AERO 632: Design of Advance Flight Control System Norms for Signals and. Raktim Bhattacharya Laboratory For Uncertainty Quantification Aerospace Engineering, Texas A&M University. Norms for Signals ...

More information

Time-Invariant Linear Quadratic Regulators Robert Stengel Optimal Control and Estimation MAE 546 Princeton University, 2015

Time-Invariant Linear Quadratic Regulators Robert Stengel Optimal Control and Estimation MAE 546 Princeton University, 2015 Time-Invariant Linear Quadratic Regulators Robert Stengel Optimal Control and Estimation MAE 546 Princeton University, 15 Asymptotic approach from time-varying to constant gains Elimination of cross weighting

More information

Discrete and continuous dynamic systems

Discrete and continuous dynamic systems Discrete and continuous dynamic systems Bounded input bounded output (BIBO) and asymptotic stability Continuous and discrete time linear time-invariant systems Katalin Hangos University of Pannonia Faculty

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

Dynamical Systems & Lyapunov Stability

Dynamical Systems & Lyapunov Stability Dynamical Systems & Lyapunov Stability Harry G. Kwatny Department of Mechanical Engineering & Mechanics Drexel University Outline Ordinary Differential Equations Existence & uniqueness Continuous dependence

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

Decay rates for partially dissipative hyperbolic systems

Decay rates for partially dissipative hyperbolic systems Outline Decay rates for partially dissipative hyperbolic systems Basque Center for Applied Mathematics Bilbao, Basque Country, Spain zuazua@bcamath.org http://www.bcamath.org/zuazua/ Numerical Methods

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

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

3 Gramians and Balanced Realizations

3 Gramians and Balanced Realizations 3 Gramians and Balanced Realizations In this lecture, we use an optimization approach to find suitable realizations for truncation and singular perturbation of G. It turns out that the recommended realizations

More information

Grammians. Matthew M. Peet. Lecture 20: Grammians. Illinois Institute of Technology

Grammians. Matthew M. Peet. Lecture 20: Grammians. Illinois Institute of Technology Grammians Matthew M. Peet Illinois Institute of Technology Lecture 2: Grammians Lyapunov Equations Proposition 1. Suppose A is Hurwitz and Q is a square matrix. Then X = e AT s Qe As ds is the unique solution

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

Nonlinear Control. Nonlinear Control Lecture # 3 Stability of Equilibrium Points

Nonlinear Control. Nonlinear Control Lecture # 3 Stability of Equilibrium Points Nonlinear Control Lecture # 3 Stability of Equilibrium Points The Invariance Principle Definitions Let x(t) be a solution of ẋ = f(x) A point p is a positive limit point of x(t) if there is a sequence

More information

w T 1 w T 2. w T n 0 if i j 1 if i = j

w T 1 w T 2. w T n 0 if i j 1 if i = j Lyapunov Operator Let A F n n be given, and define a linear operator L A : C n n C n n as L A (X) := A X + XA Suppose A is diagonalizable (what follows can be generalized even if this is not possible -

More information

Review: control, feedback, etc. Today s topic: state-space models of systems; linearization

Review: control, feedback, etc. Today s topic: state-space models of systems; linearization Plan of the Lecture Review: control, feedback, etc Today s topic: state-space models of systems; linearization Goal: a general framework that encompasses all examples of interest Once we have mastered

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

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

Robot Control Basics CS 685

Robot Control Basics CS 685 Robot Control Basics CS 685 Control basics Use some concepts from control theory to understand and learn how to control robots Control Theory general field studies control and understanding of behavior

More information

Contents. 1 State-Space Linear Systems 5. 2 Linearization Causality, Time Invariance, and Linearity 31

Contents. 1 State-Space Linear Systems 5. 2 Linearization Causality, Time Invariance, and Linearity 31 Contents Preamble xiii Linear Systems I Basic Concepts 1 I System Representation 3 1 State-Space Linear Systems 5 1.1 State-Space Linear Systems 5 1.2 Block Diagrams 7 1.3 Exercises 11 2 Linearization

More information

Nonlinear Control Systems

Nonlinear Control Systems Nonlinear Control Systems António Pedro Aguiar pedro@isr.ist.utl.pt 5. Input-Output Stability DEEC PhD Course http://users.isr.ist.utl.pt/%7epedro/ncs2012/ 2012 1 Input-Output Stability y = Hu H denotes

More information

Module 09 From s-domain to time-domain From ODEs, TFs to State-Space Modern Control

Module 09 From s-domain to time-domain From ODEs, TFs to State-Space Modern Control Module 09 From s-domain to time-domain From ODEs, TFs to State-Space Modern Control Ahmad F. Taha EE 3413: Analysis and Desgin of Control Systems Email: ahmad.taha@utsa.edu Webpage: http://engineering.utsa.edu/

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

2 Lyapunov Stability. x(0) x 0 < δ x(t) x 0 < ɛ

2 Lyapunov Stability. x(0) x 0 < δ x(t) x 0 < ɛ 1 2 Lyapunov Stability Whereas I/O stability is concerned with the effect of inputs on outputs, Lyapunov stability deals with unforced systems: ẋ = f(x, t) (1) where x R n, t R +, and f : R n R + R n.

More information

Cyber-Physical Systems Modeling and Simulation of Continuous Systems

Cyber-Physical Systems Modeling and Simulation of Continuous Systems Cyber-Physical Systems Modeling and Simulation of Continuous Systems Matthias Althoff TU München 29. May 2015 Matthias Althoff Modeling and Simulation of Cont. Systems 29. May 2015 1 / 38 Ordinary Differential

More information

Topic # Feedback Control

Topic # Feedback Control Topic #7 16.31 Feedback Control State-Space Systems What are state-space models? Why should we use them? How are they related to the transfer functions used in classical control design and how do we develop

More information

Nonlinear Systems and Control Lecture # 12 Converse Lyapunov Functions & Time Varying Systems. p. 1/1

Nonlinear Systems and Control Lecture # 12 Converse Lyapunov Functions & Time Varying Systems. p. 1/1 Nonlinear Systems and Control Lecture # 12 Converse Lyapunov Functions & Time Varying Systems p. 1/1 p. 2/1 Converse Lyapunov Theorem Exponential Stability Let x = 0 be an exponentially stable equilibrium

More information

Chap 3. Linear Algebra

Chap 3. Linear Algebra Chap 3. Linear Algebra Outlines 1. Introduction 2. Basis, Representation, and Orthonormalization 3. Linear Algebraic Equations 4. Similarity Transformation 5. Diagonal Form and Jordan Form 6. Functions

More information

A nemlineáris rendszer- és irányításelmélet alapjai Relative degree and Zero dynamics (Lie-derivatives)

A nemlineáris rendszer- és irányításelmélet alapjai Relative degree and Zero dynamics (Lie-derivatives) A nemlineáris rendszer- és irányításelmélet alapjai Relative degree and Zero dynamics (Lie-derivatives) Hangos Katalin BME Analízis Tanszék Rendszer- és Irányításelméleti Kutató Laboratórium MTA Számítástechnikai

More information

2.10 Saddles, Nodes, Foci and Centers

2.10 Saddles, Nodes, Foci and Centers 2.10 Saddles, Nodes, Foci and Centers In Section 1.5, a linear system (1 where x R 2 was said to have a saddle, node, focus or center at the origin if its phase portrait was linearly equivalent to one

More information

Module 02 CPS Background: Linear Systems Preliminaries

Module 02 CPS Background: Linear Systems Preliminaries Module 02 CPS Background: Linear Systems Preliminaries Ahmad F. Taha EE 5243: Introduction to Cyber-Physical Systems Email: ahmad.taha@utsa.edu Webpage: http://engineering.utsa.edu/ taha/index.html August

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

Symmetries 2 - Rotations in Space

Symmetries 2 - Rotations in Space Symmetries 2 - Rotations in Space This symmetry is about the isotropy of space, i.e. space is the same in all orientations. Thus, if we continuously rotated an entire system in space, we expect the system

More information

Lecture 9 Nonlinear Control Design

Lecture 9 Nonlinear Control Design Lecture 9 Nonlinear Control Design Exact-linearization Lyapunov-based design Lab 2 Adaptive control Sliding modes control Literature: [Khalil, ch.s 13, 14.1,14.2] and [Glad-Ljung,ch.17] Course Outline

More information

ECEN 420 LINEAR CONTROL SYSTEMS. Lecture 6 Mathematical Representation of Physical Systems II 1/67

ECEN 420 LINEAR CONTROL SYSTEMS. Lecture 6 Mathematical Representation of Physical Systems II 1/67 1/67 ECEN 420 LINEAR CONTROL SYSTEMS Lecture 6 Mathematical Representation of Physical Systems II State Variable Models for Dynamic Systems u 1 u 2 u ṙ. Internal Variables x 1, x 2 x n y 1 y 2. y m Figure

More information

Modern Optimal Control

Modern Optimal Control Modern Optimal Control Matthew M. Peet Arizona State University Lecture 19: Stabilization via LMIs Optimization Optimization can be posed in functional form: min x F objective function : inequality constraints

More information

Time-Invariant Linear Quadratic Regulators!

Time-Invariant Linear Quadratic Regulators! Time-Invariant Linear Quadratic Regulators Robert Stengel Optimal Control and Estimation MAE 546 Princeton University, 17 Asymptotic approach from time-varying to constant gains Elimination of cross weighting

More information

Two-Body Problem. Central Potential. 1D Motion

Two-Body Problem. Central Potential. 1D Motion Two-Body Problem. Central Potential. D Motion The simplest non-trivial dynamical problem is the problem of two particles. The equations of motion read. m r = F 2, () We already know that the center of

More information

MCE693/793: Analysis and Control of Nonlinear Systems

MCE693/793: Analysis and Control of Nonlinear Systems MCE693/793: Analysis and Control of Nonlinear Systems Lyapunov Stability - I Hanz Richter Mechanical Engineering Department Cleveland State University Definition of Stability - Lyapunov Sense Lyapunov

More information

Denis ARZELIER arzelier

Denis ARZELIER   arzelier COURSE ON LMI OPTIMIZATION WITH APPLICATIONS IN CONTROL PART II.2 LMIs IN SYSTEMS CONTROL STATE-SPACE METHODS PERFORMANCE ANALYSIS and SYNTHESIS Denis ARZELIER www.laas.fr/ arzelier arzelier@laas.fr 15

More information

Balanced realization and model order reduction for nonlinear systems based on singular value analysis

Balanced realization and model order reduction for nonlinear systems based on singular value analysis Balanced realization and model order reduction for nonlinear systems based on singular value analysis Kenji Fujimoto a, and Jacquelien M. A. Scherpen b a Department of Mechanical Science and Engineering

More information

I ml. g l. sin. l 0,,2,...,n A. ( t vs. ), and ( vs. ). d dt. sin l

I ml. g l. sin. l 0,,2,...,n A. ( t vs. ), and ( vs. ). d dt. sin l Advanced Control Theory Homework #3 Student No.597 Name : Jinseong Kim. The simple pendulum dynamics is given by g sin L A. Derive above dynamic equation from the free body diagram. B. Find the equilibrium

More information

STABILITY ANALYSIS OF DYNAMIC SYSTEMS

STABILITY ANALYSIS OF DYNAMIC SYSTEMS C. Melchiorri (DEI) Automatic Control & System Theory 1 AUTOMATIC CONTROL AND SYSTEM THEORY STABILITY ANALYSIS OF DYNAMIC SYSTEMS Claudio Melchiorri Dipartimento di Ingegneria dell Energia Elettrica e

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

Advanced Control Theory

Advanced Control Theory State Space Solution and Realization chibum@seoultech.ac.kr Outline State space solution 2 Solution of state-space equations x t = Ax t + Bu t First, recall results for scalar equation: x t = a x t + b

More information

NECESSARY AND SUFFICIENT CONDITIONS FOR STATE-SPACE NETWORK REALIZATION. Philip E. Paré Masters Thesis Defense

NECESSARY AND SUFFICIENT CONDITIONS FOR STATE-SPACE NETWORK REALIZATION. Philip E. Paré Masters Thesis Defense NECESSARY AND SUFFICIENT CONDITIONS FOR STATE-SPACE NETWORK REALIZATION Philip E. Paré Masters Thesis Defense INTRODUCTION MATHEMATICAL MODELING DYNAMIC MODELS A dynamic model has memory DYNAMIC MODELS

More information

CDS Solutions to Final Exam

CDS Solutions to Final Exam CDS 22 - Solutions to Final Exam Instructor: Danielle C Tarraf Fall 27 Problem (a) We will compute the H 2 norm of G using state-space methods (see Section 26 in DFT) We begin by finding a minimal state-space

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

Transfer function and linearization

Transfer function and linearization Transfer function and linearization Daniele Carnevale Dipartimento di Ing. Civile ed Ing. Informatica (DICII), University of Rome Tor Vergata Corso di Controlli Automatici, A.A. 24-25 Testo del corso:

More information

Solutions. Problems of Chapter 1

Solutions. Problems of Chapter 1 Solutions Problems of Chapter 1 1.1 A real square matrix A IR n n is invertible if and only if its determinant is non zero. The determinant of A is a polynomial in the entries a ij of A, whose set of zeros

More information

Introduction to Modern Control MT 2016

Introduction to Modern Control MT 2016 CDT Autonomous and Intelligent Machines & Systems Introduction to Modern Control MT 2016 Alessandro Abate Lecture 2 First-order ordinary differential equations (ODE) Solution of a linear ODE Hints to nonlinear

More information

Feedback Linearization Lectures delivered at IIT-Kanpur, TEQIP program, September 2016.

Feedback Linearization Lectures delivered at IIT-Kanpur, TEQIP program, September 2016. Feedback Linearization Lectures delivered at IIT-Kanpur, TEQIP program, September 216 Ravi N Banavar banavar@iitbacin September 24, 216 These notes are based on my readings o the two books Nonlinear Control

More information

H 2 Optimal State Feedback Control Synthesis. Raktim Bhattacharya Aerospace Engineering, Texas A&M University

H 2 Optimal State Feedback Control Synthesis. Raktim Bhattacharya Aerospace Engineering, Texas A&M University H 2 Optimal State Feedback Control Synthesis Raktim Bhattacharya Aerospace Engineering, Texas A&M University Motivation Motivation w(t) u(t) G K y(t) z(t) w(t) are exogenous signals reference, process

More information