On perturbations in the leading coefficient matrix of time-varying index-1 DAEs
|
|
- Abner Craig
- 5 years ago
- Views:
Transcription
1 On perturbations in the leading coefficient matrix of time-varying index-1 DAEs Institute of Mathematics, Ilmenau University of Technology Darmstadt, March 27, 2012 Institute of Mathematics, Ilmenau University of Technology Page 1 / 11
2 Perturbations in the leading coefficient of DAEs E(t)ẋ = A(t)x, E, A C(R 0 ; R n n ) Institute of Mathematics, Ilmenau University of Technology Page 2 / 11
3 Perturbations in the leading coefficient of DAEs E(t)ẋ = A(t)x, E, A C(R 0 ; R n n ) [März 1991]: (E, A) is index-1 Q C 1 : Q(t) 2 = Q(t) im Q(t) = ker E(t), and D C 0 : Eẋ = Ax d dt (P x) = ( P + P D)P x, Qx = QDP x P = I Q Institute of Mathematics, Ilmenau University of Technology Page 2 / 11
4 Perturbations in the leading coefficient of DAEs E(t)ẋ = A(t)x, E, A C(R 0 ; R n n ) [März 1991]: (E, A) is index-1 Q C 1 : Q(t) 2 = Q(t) im Q(t) = ker E(t), and D C 0 : Eẋ = Ax d dt (P x) = ( P + P D)P x, Qx = QDP x P = I Q ẋ 1 = x 2 0 = x 1 ẋ1 = D 11 x 1 x 2 = D 21 x 1 Institute of Mathematics, Ilmenau University of Technology Page 2 / 11
5 Eẋ = Ax d dt (P x) = ( P + P D)P x, Qx = QDP x Institute of Mathematics, Ilmenau University of Technology Page 3 / 11
6 Eẋ = Ax d dt (P x) = ( P + P D)P x, Qx = QDP x Eẋ = Ax d dt x = P x + Qx = (I + QD)P x, (P x) = ( P + P D)P x Institute of Mathematics, Ilmenau University of Technology Page 3 / 11
7 Eẋ = Ax d dt (P x) = ( P + P D)P x, Qx = QDP x Eẋ = Ax d dt x = P x + Qx = (I + QD)P x, (P x) = ( P + P D)P x ẏ = ( P + P D)y, y(t 0 ) = P (t 0 )x(t 0 ) uniqueness = y(t) = P (t)x(t) Institute of Mathematics, Ilmenau University of Technology Page 3 / 11
8 crucial: E = EP Eẋ = Ax EP ẋ = Ax d dt (P x) = ( P + P D)P x, Qx = QDP x Institute of Mathematics, Ilmenau University of Technology Page 4 / 11
9 crucial: E = EP Eẋ = (A + A )x EP ẋ = (A + A )x d dt (P x) = ( P + P D)P x + P G A x, Qx = QDP x + QG A x G 1 Institute of Mathematics, Ilmenau University of Technology Page 4 / 11
10 crucial: E = EP i.g. (E + E )ẋ = Ax (E + E )P ẋ = Ax Institute of Mathematics, Ilmenau University of Technology Page 4 / 11
11 crucial: E = EP i.g. (E + E )ẋ = Ax (E + E )P ẋ = Ax (A): E C(R 0 ; R n n ) s.t. (E + E, A) is index-1 and ker E(t) = ker(e(t) + E (t)) Institute of Mathematics, Ilmenau University of Technology Page 4 / 11
12 crucial: E = EP i.g. (E + E )ẋ = Ax (E + E )P ẋ = Ax (A): E C(R 0 ; R n n ) s.t. (E + E, A) is index-1 and ker E(t) = ker(e(t) + E (t)) (E + E )ẋ = Ax d dt (P x) = ( P + P D)P x + P G P x, Qx = QDP x + QG P x Institute of Mathematics, Ilmenau University of Technology Page 4 / 11
13 [ ] ) [ ] ( ) In1 0 (ẋ1 A11 (t) A = 12 (t) x1 0 0 A 21 (t) A 22 (t) ẋ 2 x 2 Institute of Mathematics, Ilmenau University of Technology Page 5 / 11
14 [ ] ) [ ] ( ) In1 0 (ẋ1 A11 (t) A = 12 (t) x1 (t) 0 A 21 (t) A 22 (t) ẋ 2 0 = (A 21 A 11 )(t)x 1 + (A 22 A 12 )(t)x 2 x 2 Institute of Mathematics, Ilmenau University of Technology Page 5 / 11
15 [ ] ) [ ] ( ) In1 0 (ẋ1 A11 (t) A = 12 (t) x1 (t) 0 A 21 (t) A 22 (t) ẋ 2 0 = (A 21 A 11 )(t)x 1 + (A 22 A 12 )(t)x 2 [ ] ) 1 0 (ẋ1 = 0 0 ẋ 2 [ ] ( ) x1 x 2 x 2 x 1 (t) = e t x 0 1 x 2 (t) = 0 exp. stable Institute of Mathematics, Ilmenau University of Technology Page 5 / 11
16 [ ] ) [ ] ( ) In1 0 (ẋ1 A11 (t) A = 12 (t) x1 (t) 0 A 21 (t) A 22 (t) ẋ 2 0 = (A 21 A 11 )(t)x 1 + (A 22 A 12 )(t)x 2 [ ] ) 1 0 (ẋ1 = 0 ε ẋ 2 [ ] ( ) x1 x 2 x 2 x 1 (t) = e t x 0 1 x 2 (t) = e t/ε x 0 2 not exp. stable Institute of Mathematics, Ilmenau University of Technology Page 5 / 11
17 Bohl exponent and perturbation operator E(t) d dt Φ(t, t 0) = A(t)Φ(t, t 0 ), P (t 0 )(Φ(t 0, t 0 ) I) = 0. (E, A) exp. stab. µ, M > 0 t t 0 : Φ(t, t 0 ) Me µ(t t 0) Perturbation theory Institute of Mathematics, Ilmenau University of Technology Page 6 / 11
18 Bohl exponent and perturbation operator E(t) d dt Φ(t, t 0) = A(t)Φ(t, t 0 ), P (t 0 )(Φ(t 0, t 0 ) I) = 0. (E, A) exp. stab. µ, M > 0 t t 0 : Φ(t, t 0 ) Me µ(t t 0) k B (E, A) = inf ρ R N ρ > 0 t s : Φ(t, s) N ρ e ρ(t s) } Perturbation theory Institute of Mathematics, Ilmenau University of Technology Page 6 / 11
19 Bohl exponent and perturbation operator E(t) d dt Φ(t, t 0) = A(t)Φ(t, t 0 ), P (t 0 )(Φ(t 0, t 0 ) I) = 0. (E, A) exp. stab. µ, M > 0 t t 0 : Φ(t, t 0 ) Me µ(t t 0) k B (E, A) = inf ρ R N ρ > 0 t s : Φ(t, s) N ρ e ρ(t s) } Rem.: k B (E, A) < 0 (E, A) exp. stable Perturbation theory Institute of Mathematics, Ilmenau University of Technology Page 6 / 11
20 Bohl exponent and perturbation operator E(t) d dt Φ(t, t 0) = A(t)Φ(t, t 0 ), P (t 0 )(Φ(t 0, t 0 ) I) = 0. (E, A) exp. stab. µ, M > 0 t t 0 : Φ(t, t 0 ) Me µ(t t 0) k B (E, A) = inf ρ R N ρ > 0 t s : Φ(t, s) N ρ e ρ(t s) } Rem.: k B (E, A) < 0 (E, A) exp. stable L t0 : L 2 ([t 0, ); R n ) L 2 ([t 0, ); R n ), f( ) x( ), x solves E(t)ẋ = A(t)x + f(t), P (t 0 )x(t 0 ) = 0 Perturbation theory Institute of Mathematics, Ilmenau University of Technology Page 6 / 11
21 Bohl exponent and perturbation operator E(t) d dt Φ(t, t 0) = A(t)Φ(t, t 0 ), P (t 0 )(Φ(t 0, t 0 ) I) = 0. (E, A) exp. stab. µ, M > 0 t t 0 : Φ(t, t 0 ) Me µ(t t 0) k B (E, A) = inf ρ R N ρ > 0 t s : Φ(t, s) N ρ e ρ(t s) } Rem.: k B (E, A) < 0 (E, A) exp. stable L t0 : L 2 ([t 0, ); R n ) L 2 ([t 0, ); R n ), f( ) x( ), x solves E(t)ẋ = A(t)x + f(t), P (t 0 )x(t 0 ) = 0 Lemma [Du et al. 2006]: (E, A) exp. stable and (BC) hold = L t0 is linear bd. operator and t 0 L t0 is mon. nonincreasing Perturbation theory Institute of Mathematics, Ilmenau University of Technology Page 6 / 11
22 Theorem (Robustness of Bohl exponent) (E, A) index-1, Q bounded, given ε > 0: E satisfies (A), E suff. small = k B (E + E, A) k B (E, A) + ε Perturbation theory Institute of Mathematics, Ilmenau University of Technology Page 7 / 11
23 Theorem (Robustness via perturbation operator) (E, A) index-1 and exp. stable, (BC) hold, E satisfies (A): } κ i = κ i (E, A, Q), i = 1, 2, 3, α := min lim L t t 0 0 1, κ 3, lim E [t0, ) < t 0 = (E + E, A) is exponentially stable α κ 1 + κ 2 α Perturbation theory Institute of Mathematics, Ilmenau University of Technology Page 8 / 11
24 Stability radius P := [ E, A ] B(R 0 ; R n 2n ) inf [ E, A ] P (E + E, A + A ) is index-1, ker E(t) = ker(e(t) + E (t)) S := (E, A) C(R 0 ; R n n ) } 2 (E, A) is exponentially stable, [ r(e, A) := [ E, A ] E, A ] P or (E + E, A + A ) S }, } Stability radius Institute of Mathematics, Ilmenau University of Technology Page 9 / 11
25 Proposition (Properties of the stability radius) r(e, A) = 0 (E, A) S r(α(e, A)) = r(αe, αa) = α r(e, A) for all α 0 V(t) time-varying subspace of R n with constant dimension, K V := [E, A] B(R 0 ; R n 2n ) (E, A) is index-1, ker E(t) = V(t) = K V [E, A] r(e, A) is continuous }, Stability radius Institute of Mathematics, Ilmenau University of Technology Page 10 / 11
26 Theorem (Lower bound for the stability radius) (E, A) index-1 and exp. stable, (BC) holds = } κ i = κ i (E, A, Q), i = 1, 2, 3, α := min lim L t t 0 0 1, κ 3, α r(e, A) κ 1 + κ 2 α Stability radius Institute of Mathematics, Ilmenau University of Technology Page 11 / 11
27 Theorem (Lower bound for the stability radius) (E, A) index-1 and exp. stable, (BC) holds = } κ i = κ i (E, A, Q), i = 1, 2, 3, α := min lim L t t 0 0 1, κ 3, α r(e, A) κ 1 + κ 2 α Cor.: V(t) time-varying subspace of R n with constant dimension, S V := [E, A] B(R 0 ; R n 2n ) (E, A) is index-1 and exp. stable, ker E(t) = V(t) for all t R 0 } = S V is open in K V Stability radius Institute of Mathematics, Ilmenau University of Technology Page 11 / 11
Bohl exponent for time-varying linear differential-algebraic equations
Bohl exponent for time-varying linear differential-algebraic equations Thomas Berger Institute of Mathematics, Ilmenau University of Technology, Weimarer Straße 25, 98693 Ilmenau, Germany, thomas.berger@tu-ilmenau.de.
More informationStability analysis of differential algebraic systems
Stability analysis of differential algebraic systems Volker Mehrmann TU Berlin, Institut für Mathematik with Vu Hoang Linh and Erik Van Vleck DFG Research Center MATHEON Mathematics for key technologies
More informationNonlinear Control Systems
Nonlinear Control Systems António Pedro Aguiar pedro@isr.ist.utl.pt 3. Fundamental properties IST-DEEC PhD Course http://users.isr.ist.utl.pt/%7epedro/ncs2012/ 2012 1 Example Consider the system ẋ = f
More information1. The Transition Matrix (Hint: Recall that the solution to the linear equation ẋ = Ax + Bu is
ECE 55, Fall 2007 Problem Set #4 Solution The Transition Matrix (Hint: Recall that the solution to the linear equation ẋ Ax + Bu is x(t) e A(t ) x( ) + e A(t τ) Bu(τ)dτ () This formula is extremely important
More informationLinear Differential Equations. Problems
Chapter 1 Linear Differential Equations. Problems 1.1 Introduction 1.1.1 Show that the function ϕ : R R, given by the expression ϕ(t) = 2e 3t for all t R, is a solution of the Initial Value Problem x =
More information6 Linear Equation. 6.1 Equation with constant coefficients
6 Linear Equation 6.1 Equation with constant coefficients Consider the equation ẋ = Ax, x R n. This equating has n independent solutions. If the eigenvalues are distinct then the solutions are c k e λ
More informationLecture 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 informationFirst and Second Order Differential Equations Lecture 4
First and Second Order Differential Equations Lecture 4 Dibyajyoti Deb 4.1. Outline of Lecture The Existence and the Uniqueness Theorem Homogeneous Equations with Constant Coefficients 4.2. The Existence
More informationModel 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 information1. 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 informationChapter 2: First Order DE 2.4 Linear vs. Nonlinear DEs
Chapter 2: First Order DE 2.4 Linear vs. Nonlinear DEs First Order DE 2.4 Linear vs. Nonlinear DE We recall the general form of the First Oreder DEs (FODE): dy = f(t, y) (1) dt where f(t, y) is a function
More informationLINEAR EQUATIONS OF HIGHER ORDER. EXAMPLES. General framework
Differential Equations Grinshpan LINEAR EQUATIONS OF HIGHER ORDER. EXAMPLES. We consider linear ODE of order n: General framework (1) x (n) (t) + P n 1 (t)x (n 1) (t) + + P 1 (t)x (t) + P 0 (t)x(t) = 0
More informationCalculating the domain of attraction: Zubov s method and extensions
Calculating the domain of attraction: Zubov s method and extensions Fabio Camilli 1 Lars Grüne 2 Fabian Wirth 3 1 University of L Aquila, Italy 2 University of Bayreuth, Germany 3 Hamilton Institute, NUI
More informationLMI Methods in Optimal and Robust Control
LMI Methods in Optimal and Robust Control Matthew M. Peet Arizona State University Lecture 15: Nonlinear Systems and Lyapunov Functions Overview Our next goal is to extend LMI s and optimization to nonlinear
More informationThe goal of this chapter is to study linear systems of ordinary differential equations: dt,..., dx ) T
1 1 Linear Systems The goal of this chapter is to study linear systems of ordinary differential equations: ẋ = Ax, x(0) = x 0, (1) where x R n, A is an n n matrix and ẋ = dx ( dt = dx1 dt,..., dx ) T n.
More informationNonuniform in time state estimation of dynamic systems
Systems & Control Letters 57 28 714 725 www.elsevier.com/locate/sysconle Nonuniform in time state estimation of dynamic systems Iasson Karafyllis a,, Costas Kravaris b a Department of Environmental Engineering,
More informationPoincaré Map, Floquet Theory, and Stability of Periodic Orbits
Poincaré Map, Floquet Theory, and Stability of Periodic Orbits CDS140A Lecturer: W.S. Koon Fall, 2006 1 Poincaré Maps Definition (Poincaré Map): Consider ẋ = f(x) with periodic solution x(t). Construct
More information2. Metric Spaces. 2.1 Definitions etc.
2. Metric Spaces 2.1 Definitions etc. The procedure in Section for regarding R as a topological space may be generalized to many other sets in which there is some kind of distance (formally, sets with
More informationApproximation of spectral intervals and associated leading directions for linear DAEs via smooth SVDs*
Approximation of spectral intervals and associated leading directions for linear DAEs via smooth SVDs* (supported by Alexander von Humboldt Foundation) Faculty of Mathematics, Mechanics and Informatics
More informationOrdinary Differential Equations II
Ordinary Differential Equations II February 9 217 Linearization of an autonomous system We consider the system (1) x = f(x) near a fixed point x. As usual f C 1. Without loss of generality we assume x
More informationDifferential-algebraic equations. Control and Numerics V
Differential-algebraic equations. Control and Numerics V Volker Mehrmann Institut für Mathematik Technische Universität Berlin with V.H. Linh, P. Kunkel, E. Van Vleck DFG Research Center MATHEON Mathematics
More informationChapter III. Stability of Linear Systems
1 Chapter III Stability of Linear Systems 1. Stability and state transition matrix 2. Time-varying (non-autonomous) systems 3. Time-invariant systems 1 STABILITY AND STATE TRANSITION MATRIX 2 In this chapter,
More informationPROBABILITY: LIMIT THEOREMS II, SPRING HOMEWORK PROBLEMS
PROBABILITY: LIMIT THEOREMS II, SPRING 15. HOMEWORK PROBLEMS PROF. YURI BAKHTIN Instructions. You are allowed to work on solutions in groups, but you are required to write up solutions on your own. Please
More informationExponential stability of families of linear delay systems
Exponential stability of families of linear delay systems F. Wirth Zentrum für Technomathematik Universität Bremen 28334 Bremen, Germany fabian@math.uni-bremen.de Keywords: Abstract Stability, delay systems,
More informationNonlinear Control. Nonlinear Control Lecture # 8 Time Varying and Perturbed Systems
Nonlinear Control Lecture # 8 Time Varying and Perturbed Systems Time-varying Systems ẋ = f(t,x) f(t,x) is piecewise continuous in t and locally Lipschitz in x for all t 0 and all x D, (0 D). The origin
More informationIntroduction 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 informationNonlinear Control. Nonlinear Control Lecture # 8 Time Varying and Perturbed Systems
Nonlinear Control Lecture # 8 Time Varying and Perturbed Systems Time-varying Systems ẋ = f(t,x) f(t,x) is piecewise continuous in t and locally Lipschitz in x for all t 0 and all x D, (0 D). The origin
More informationZ i Q ij Z j. J(x, φ; U) = X T φ(t ) 2 h + where h k k, H(t) k k and R(t) r r are nonnegative definite matrices (R(t) is uniformly in t nonsingular).
2. LINEAR QUADRATIC DETERMINISTIC PROBLEM Notations: For a vector Z, Z = Z, Z is the Euclidean norm here Z, Z = i Z2 i is the inner product; For a vector Z and nonnegative definite matrix Q, Z Q = Z, QZ
More information1. Stochastic Processes and filtrations
1. Stochastic Processes and 1. Stoch. pr., A stochastic process (X t ) t T is a collection of random variables on (Ω, F) with values in a measurable space (S, S), i.e., for all t, In our case X t : Ω S
More information= m(0) + 4e 2 ( 3e 2 ) 2e 2, 1 (2k + k 2 ) dt. m(0) = u + R 1 B T P x 2 R dt. u + R 1 B T P y 2 R dt +
ECE 553, Spring 8 Posted: May nd, 8 Problem Set #7 Solution Solutions: 1. The optimal controller is still the one given in the solution to the Problem 6 in Homework #5: u (x, t) = p(t)x k(t), t. The minimum
More informationEntrance Exam, Differential Equations April, (Solve exactly 6 out of the 8 problems) y + 2y + y cos(x 2 y) = 0, y(0) = 2, y (0) = 4.
Entrance Exam, Differential Equations April, 7 (Solve exactly 6 out of the 8 problems). Consider the following initial value problem: { y + y + y cos(x y) =, y() = y. Find all the values y such that the
More informationPresenter: Noriyoshi Fukaya
Y. Martel, F. Merle, and T.-P. Tsai, Stability and Asymptotic Stability in the Energy Space of the Sum of N Solitons for Subcritical gkdv Equations, Comm. Math. Phys. 31 (00), 347-373. Presenter: Noriyoshi
More informationLogistic Map, Euler & Runge-Kutta Method and Lotka-Volterra Equations
Logistic Map, Euler & Runge-Kutta Method and Lotka-Volterra Equations S. Y. Ha and J. Park Department of Mathematical Sciences Seoul National University Sep 23, 2013 Contents 1 Logistic Map 2 Euler and
More informationJordan Canonical Form
October 26, 2005 15-1 Jdan Canonical Fm Suppose that A is an n n matrix with characteristic polynomial. p(λ) = (λ λ 1 ) m 1... (λ λ s ) ms and generalized eigenspaces V j = ker(a λ j I) m j. Let L be the
More informationEcon 508B: Lecture 5
Econ 508B: Lecture 5 Expectation, MGF and CGF Hongyi Liu Washington University in St. Louis July 31, 2017 Hongyi Liu (Washington University in St. Louis) Math Camp 2017 Stats July 31, 2017 1 / 23 Outline
More informationTheorem 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 informationProblem List MATH 5173 Spring, 2014
Problem List MATH 5173 Spring, 2014 The notation p/n means the problem with number n on page p of Perko. 1. 5/3 [Due Wednesday, January 15] 2. 6/5 and describe the relationship of the phase portraits [Due
More informationChap. 1. Some Differential Geometric Tools
Chap. 1. Some Differential Geometric Tools 1. Manifold, Diffeomorphism 1.1. The Implicit Function Theorem ϕ : U R n R n p (0 p < n), of class C k (k 1) x 0 U such that ϕ(x 0 ) = 0 rank Dϕ(x) = n p x U
More informationStabilization of persistently excited linear systems
Stabilization of persistently excited linear systems Yacine Chitour Laboratoire des signaux et systèmes & Université Paris-Sud, Orsay Exposé LJLL Paris, 28/9/2012 Stabilization & intermittent control Consider
More informationDifferential equations
Differential equations Math 27 Spring 2008 In-term exam February 5th. Solutions This exam contains fourteen problems numbered through 4. Problems 3 are multiple choice problems, which each count 6% of
More informationContinuous Distributions
A normal distribution and other density functions involving exponential forms play the most important role in probability and statistics. They are related in a certain way, as summarized in a diagram later
More informationController Design for Robust Output Regulation of Regular Linear Systems
Controller Design for Robust Output Regulation of Regular Linear Systems L. Paunonen We present three dynamic error feedbac controllers for robust output regulation of regular linear systems. These controllers
More informationOn a general definition of transition waves and their properties
On a general definition of transition waves and their properties Henri Berestycki a and François Hamel b a EHESS, CAMS, 54 Boulevard Raspail, F-75006 Paris, France b Université Aix-Marseille III, LATP,
More informationAlgebraic Properties of Solutions of Linear Systems
Algebraic Properties of Solutions of Linear Systems In this chapter we will consider simultaneous first-order differential equations in several variables, that is, equations of the form f 1t,,,x n d f
More informationNonlinear 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 informationMath Ordinary Differential Equations
Math 411 - Ordinary Differential Equations Review Notes - 1 1 - Basic Theory A first order ordinary differential equation has the form x = f(t, x) (11) Here x = dx/dt Given an initial data x(t 0 ) = x
More informationGeneralized transition waves and their properties
Generalized transition waves and their properties Henri Berestycki a and François Hamel b a EHESS, CAMS, 54 Boulevard Raspail, F-75006 Paris, France & University of Chicago, Department of Mathematics 5734
More informationBackward Stochastic Differential Equations with Infinite Time Horizon
Backward Stochastic Differential Equations with Infinite Time Horizon Holger Metzler PhD advisor: Prof. G. Tessitore Università di Milano-Bicocca Spring School Stochastic Control in Finance Roscoff, March
More informationODE Homework 1. Due Wed. 19 August 2009; At the beginning of the class
ODE Homework Due Wed. 9 August 2009; At the beginning of the class. (a) Solve Lẏ + Ry = E sin(ωt) with y(0) = k () L, R, E, ω are positive constants. (b) What is the limit of the solution as ω 0? (c) Is
More informationNonlinear 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 information6.241 Dynamic Systems and Control
6.241 Dynamic Systems and Control Lecture 8: Solutions of State-space Models Readings: DDV, Chapters 10, 11, 12 (skip the parts on transform methods) Emilio Frazzoli Aeronautics and Astronautics Massachusetts
More informationON THE STABILITY OF THE QUASI-LINEAR IMPLICIT EQUATIONS IN HILBERT SPACES
Khayyam J. Math. 5 (219), no. 1, 15-112 DOI: 1.2234/kjm.219.81222 ON THE STABILITY OF THE QUASI-LINEAR IMPLICIT EQUATIONS IN HILBERT SPACES MEHDI BENABDALLAH 1 AND MOHAMED HARIRI 2 Communicated by J. Brzdęk
More informationDeterministic Dynamic Programming
Deterministic Dynamic Programming 1 Value Function Consider the following optimal control problem in Mayer s form: V (t 0, x 0 ) = inf u U J(t 1, x(t 1 )) (1) subject to ẋ(t) = f(t, x(t), u(t)), x(t 0
More informationExamples include: (a) the Lorenz system for climate and weather modeling (b) the Hodgkin-Huxley system for neuron modeling
1 Introduction Many natural processes can be viewed as dynamical systems, where the system is represented by a set of state variables and its evolution governed by a set of differential equations. Examples
More information8 Periodic Linear Di erential Equations - Floquet Theory
8 Periodic Linear Di erential Equations - Floquet Theory The general theory of time varying linear di erential equations _x(t) = A(t)x(t) is still amazingly incomplete. Only for certain classes of functions
More informationWeak Singular Optimal Controls Subject to State-Control Equality Constraints
Int. Journal of Math. Analysis, Vol. 6, 212, no. 36, 1797-1812 Weak Singular Optimal Controls Subject to State-Control Equality Constraints Javier F Rosenblueth IIMAS UNAM, Universidad Nacional Autónoma
More informationEDP with strong anisotropy : transport, heat, waves equations
EDP with strong anisotropy : transport, heat, waves equations Mihaï BOSTAN University of Aix-Marseille, FRANCE mihai.bostan@univ-amu.fr Nachos team INRIA Sophia Antipolis, 3/07/2017 Main goals Effective
More informationNonlinear Control Lecture 5: Stability Analysis II
Nonlinear Control Lecture 5: Stability Analysis II Farzaneh Abdollahi Department of Electrical Engineering Amirkabir University of Technology Fall 2010 Farzaneh Abdollahi Nonlinear Control Lecture 5 1/41
More informationSequential State Estimation (Crassidas and Junkins, Chapter 5)
Sequential State Estimation (Crassidas and Junkins, Chapter 5) Please read: 5.1, 5.3-5.6 5.3 The Discrete-Time Kalman Filter The discrete-time Kalman filter is used when the dynamics and measurements are
More informationSection , #5. Let Q be the amount of salt in oz in the tank. The scenario can be modeled by a differential equation.
Section.3.5.3, #5. Let Q be the amount of salt in oz in the tank. The scenario can be modeled by a differential equation dq = 1 4 (1 + sin(t) ) + Q, Q(0) = 50. (1) 100 (a) The differential equation given
More information(2) Let f(x) = a 2 x if x<2, 4 2x 2 ifx 2. (b) Find the lim f(x). (c) Find all values of a that make f continuous at 2. Justify your answer.
(1) Let f(x) = x x 2 9. (a) Find the domain of f. (b) Write an equation for each vertical asymptote of the graph of f. (c) Write an equation for each horizontal asymptote of the graph of f. (d) Is f odd,
More informationEquations with Separated Variables on Time Scales
International Journal of Difference Equations ISSN 973-669, Volume 12, Number 1, pp. 1 11 (217) http://campus.mst.edu/ijde Equations with Separated Variables on Time Scales Antoni Augustynowicz Institute
More informationLast lecture: Recurrence relations and differential equations. The solution to the differential equation dx
Last lecture: Recurrence relations and differential equations The solution to the differential equation dx = ax is x(t) = ce ax, where c = x() is determined by the initial conditions x(t) Let X(t) = and
More informationAsymptotic Stability by Linearization
Dynamical Systems Prof. J. Rauch Asymptotic Stability by Linearization Summary. Sufficient and nearly sharp sufficient conditions for asymptotic stability of equiiibria of differential equations, fixed
More informationDISCRETE GRONWALL LEMMA AND APPLICATIONS
DISCRETE GRONWALL LEMMA AND APPLICATIONS JOHN M. HOLTE Variations of Gronwall s Lemma Gronwall s lemma, which solves a certain kind of inequality for a function, is useful in the theory of differential
More informationBefore you begin read these instructions carefully.
MATHEMATICAL TRIPOS Part IA Friday, 1 June, 2012 1:30 pm to 4:30 pm PAPER 2 Before you begin read these instructions carefully. The examination paper is divided into two sections. Each question in Section
More informationMath 273, Final Exam Solutions
Math 273, Final Exam Solutions 1. Find the solution of the differential equation y = y +e x that satisfies the condition y(x) 0 as x +. SOLUTION: y = y H + y P where y H = ce x is a solution of the homogeneous
More informationAdvanced Mechatronics Engineering
Advanced Mechatronics Engineering German University in Cairo 21 December, 2013 Outline Necessary conditions for optimal input Example Linear regulator problem Example Necessary conditions for optimal input
More informationNOTES ON CALCULUS OF VARIATIONS. September 13, 2012
NOTES ON CALCULUS OF VARIATIONS JON JOHNSEN September 13, 212 1. The basic problem In Calculus of Variations one is given a fixed C 2 -function F (t, x, u), where F is defined for t [, t 1 ] and x, u R,
More information1+t 2 (l) y = 2xy 3 (m) x = 2tx + 1 (n) x = 2tx + t (o) y = 1 + y (p) y = ty (q) y =
DIFFERENTIAL EQUATIONS. Solved exercises.. Find the set of all solutions of the following first order differential equations: (a) x = t (b) y = xy (c) x = x (d) x = (e) x = t (f) x = x t (g) x = x log
More information4. Conditional risk measures and their robust representation
4. Conditional risk measures and their robust representation We consider a discrete-time information structure given by a filtration (F t ) t=0,...,t on our probability space (Ω, F, P ). The time horizon
More information{ } is an asymptotic sequence.
AMS B Perturbation Methods Lecture 3 Copyright by Hongyun Wang, UCSC Recap Iterative method for finding asymptotic series requirement on the iteration formula to make it work Singular perturbation use
More informationSome results in Floquet theory, with application to periodic epidemic models
Applicable Analysis, 214 http://dx.doi.org/1.18/36811.214.91866 Some results in Floquet theory, with application to periodic epidemic models Jianjun Paul Tian a,1 and Jin Wang b a Department of Mathematics,
More informationFunctions of a Complex Variable (S1) Lecture 11. VII. Integral Transforms. Integral transforms from application of complex calculus
Functions of a Complex Variable (S1) Lecture 11 VII. Integral Transforms An introduction to Fourier and Laplace transformations Integral transforms from application of complex calculus Properties of Fourier
More informationImpulse free solutions for switched differential algebraic equations
Impulse free solutions for switched differential algebraic equations Stephan Trenn Institute of Mathematics, Ilmenau University of Technology, Weimerarer Str. 25, 98693 Ilmenau, Germany Abstract Linear
More informationA note on linear differential equations with periodic coefficients.
A note on linear differential equations with periodic coefficients. Maite Grau (1) and Daniel Peralta-Salas (2) (1) Departament de Matemàtica. Universitat de Lleida. Avda. Jaume II, 69. 251 Lleida, Spain.
More informationPeriodic Linear Systems
Periodic Linear Systems Lecture 17 Math 634 10/8/99 We now consider ẋ = A(t)x (1) when A is a continuous periodic n n matrix function of t; i.e., when there is a constant T>0 such that A(t + T )=A(t) for
More informationJournal of Computational and Applied Mathematics
Journal of Computational and Applied Mathematics 234 (2) 538 544 Contents lists available at ScienceDirect Journal of Computational and Applied Mathematics journal homepage: www.elsevier.com/locate/cam
More information11.2 Basic First-order System Methods
112 Basic First-order System Methods 797 112 Basic First-order System Methods Solving 2 2 Systems It is shown here that any constant linear system u a b = A u, A = c d can be solved by one of the following
More informationOn Controllability of Linear Systems 1
On Controllability of Linear Systems 1 M.T.Nair Department of Mathematics, IIT Madras Abstract In this article we discuss some issues related to the observability and controllability of linear systems.
More informationPROBABILITY: LIMIT THEOREMS II, SPRING HOMEWORK PROBLEMS
PROBABILITY: LIMIT THEOREMS II, SPRING 218. HOMEWORK PROBLEMS PROF. YURI BAKHTIN Instructions. You are allowed to work on solutions in groups, but you are required to write up solutions on your own. Please
More informationFunctional Analysis. Martin Brokate. 1 Normed Spaces 2. 2 Hilbert Spaces The Principle of Uniform Boundedness 32
Functional Analysis Martin Brokate Contents 1 Normed Spaces 2 2 Hilbert Spaces 2 3 The Principle of Uniform Boundedness 32 4 Extension, Reflexivity, Separation 37 5 Compact subsets of C and L p 46 6 Weak
More informationObservability 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 informationNumerical Treatment of Unstructured. Differential-Algebraic Equations. with Arbitrary Index
Numerical Treatment of Unstructured Differential-Algebraic Equations with Arbitrary Index Peter Kunkel (Leipzig) SDS2003, Bari-Monopoli, 22. 25.06.2003 Outline Numerical Treatment of Unstructured Differential-Algebraic
More informationCourse 212: Academic Year Section 1: Metric Spaces
Course 212: Academic Year 1991-2 Section 1: Metric Spaces D. R. Wilkins Contents 1 Metric Spaces 3 1.1 Distance Functions and Metric Spaces............. 3 1.2 Convergence and Continuity in Metric Spaces.........
More informationSolution of Additional Exercises for Chapter 4
1 1. (1) Try V (x) = 1 (x 1 + x ). Solution of Additional Exercises for Chapter 4 V (x) = x 1 ( x 1 + x ) x = x 1 x + x 1 x In the neighborhood of the origin, the term (x 1 + x ) dominates. Hence, the
More informationStability of Stochastic Differential Equations
Lyapunov stability theory for ODEs s Stability of Stochastic Differential Equations Part 1: Introduction Department of Mathematics and Statistics University of Strathclyde Glasgow, G1 1XH December 2010
More informationChap. 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 informationLinear ODEs. Existence of solutions to linear IVPs. Resolvent matrix. Autonomous linear systems
Linear ODEs p. 1 Linear ODEs Existence of solutions to linear IVPs Resolvent matrix Autonomous linear systems Linear ODEs Definition (Linear ODE) A linear ODE is a differential equation taking the form
More informationA Bang-Bang Principle of Time Optimal Internal Controls of the Heat Equation
arxiv:math/6137v1 [math.oc] 9 Dec 6 A Bang-Bang Principle of Time Optimal Internal Controls of the Heat Equation Gengsheng Wang School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei, 437,
More informationProblem 1 In each of the following problems find the general solution of the given differential
VI Problem 1 dt + 2dy 3y = 0; dt 9dy + 9y = 0. Problem 2 dt + dy 2y = 0, y(0) = 1, y (0) = 1; dt 2 y = 0, y( 2) = 1, y ( 2) = Problem 3 Find the solution of the initial value problem 2 d2 y dt 2 3dy dt
More informationEML Spring 2012
Home http://vdol.mae.ufl.edu/eml6934-spring2012/ Page 1 of 2 1/10/2012 Search EML6934 - Spring 2012 Optimal Control Home Instructor Anil V. Rao Office Hours: M, W, F 2:00 PM to 3:00 PM Office: MAE-A, Room
More informationLecture 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 informationOrdinary Differential Equation Theory
Part I Ordinary Differential Equation Theory 1 Introductory Theory An n th order ODE for y = y(t) has the form Usually it can be written F (t, y, y,.., y (n) ) = y (n) = f(t, y, y,.., y (n 1) ) (Implicit
More informationNonlinear Systems and Control Lecture # 19 Perturbed Systems & Input-to-State Stability
p. 1/1 Nonlinear Systems and Control Lecture # 19 Perturbed Systems & Input-to-State Stability p. 2/1 Perturbed Systems: Nonvanishing Perturbation Nominal System: Perturbed System: ẋ = f(x), f(0) = 0 ẋ
More informationSTAT/MATH 395 A - PROBABILITY II UW Winter Quarter Moment functions. x r p X (x) (1) E[X r ] = x r f X (x) dx (2) (x E[X]) r p X (x) (3)
STAT/MATH 395 A - PROBABILITY II UW Winter Quarter 07 Néhémy Lim Moment functions Moments of a random variable Definition.. Let X be a rrv on probability space (Ω, A, P). For a given r N, E[X r ], if it
More informationNonlinear Systems Theory
Nonlinear Systems Theory Matthew M. Peet Arizona State University Lecture 2: Nonlinear Systems Theory Overview Our next goal is to extend LMI s and optimization to nonlinear systems analysis. Today we
More information1 Lyapunov theory of stability
M.Kawski, APM 581 Diff Equns Intro to Lyapunov theory. November 15, 29 1 1 Lyapunov theory of stability Introduction. Lyapunov s second (or direct) method provides tools for studying (asymptotic) stability
More informationPREPRINT 2009:10. Continuous-discrete optimal control problems KJELL HOLMÅKER
PREPRINT 2009:10 Continuous-discrete optimal control problems KJELL HOLMÅKER Department of Mathematical Sciences Division of Mathematics CHALMERS UNIVERSITY OF TECHNOLOGY UNIVERSITY OF GOTHENBURG Göteborg
More informationThe Kalman-Yakubovich-Popov Lemma for Differential-Algebraic Equations with Applications
MAX PLANCK INSTITUTE Elgersburg Workshop Elgersburg February 11-14, 2013 The Kalman-Yakubovich-Popov Lemma for Differential-Algebraic Equations with Applications Timo Reis 1 Matthias Voigt 2 1 Department
More information