Decay rates for partially dissipative hyperbolic systems
|
|
- Reginald Chambers
- 5 years ago
- Views:
Transcription
1 Outline Decay rates for partially dissipative hyperbolic systems Basque Center for Applied Mathematics Bilbao, Basque Country, Spain Numerical Methods for Hyperbolic Problem, Chevaleret, November 2009 Joint work with Karine BEAUCHARD, ENS-Cachan, France
2 Outline Outline
3 Outline Outline 1 Introduction 2 Preliminaries in control theory 3 Systems of balance laws 4 Partially dissipative linear hyperbolic systems: (SK) and beyond 5 Back to nonlinear 6 Connections with hypoellipticity & hypocoercivity 7 Conclusions
4 Outline Outline 1 Introduction 2 Preliminaries in control theory 3 Systems of balance laws 4 Partially dissipative linear hyperbolic systems: (SK) and beyond 5 Back to nonlinear 6 Connections with hypoellipticity & hypocoercivity 7 Conclusions
5 Outline Outline 1 Introduction 2 Preliminaries in control theory 3 Systems of balance laws 4 Partially dissipative linear hyperbolic systems: (SK) and beyond 5 Back to nonlinear 6 Connections with hypoellipticity & hypocoercivity 7 Conclusions
6 Outline Outline 1 Introduction 2 Preliminaries in control theory 3 Systems of balance laws 4 Partially dissipative linear hyperbolic systems: (SK) and beyond 5 Back to nonlinear 6 Connections with hypoellipticity & hypocoercivity 7 Conclusions
7 Outline Outline 1 Introduction 2 Preliminaries in control theory 3 Systems of balance laws 4 Partially dissipative linear hyperbolic systems: (SK) and beyond 5 Back to nonlinear 6 Connections with hypoellipticity & hypocoercivity 7 Conclusions
8 Outline Outline 1 Introduction 2 Preliminaries in control theory 3 Systems of balance laws 4 Partially dissipative linear hyperbolic systems: (SK) and beyond 5 Back to nonlinear 6 Connections with hypoellipticity & hypocoercivity 7 Conclusions
9 Outline Outline 1 Introduction 2 Preliminaries in control theory 3 Systems of balance laws 4 Partially dissipative linear hyperbolic systems: (SK) and beyond 5 Back to nonlinear 6 Connections with hypoellipticity & hypocoercivity 7 Conclusions
10 Introduction Partially dissipative linear hyperbolic systems may develop a complex asymptotic behavior as t depending on the space dimension, the dimension of the system, and the interaction between the free dynamics and the damping operator. The understanding of this issue is relevant, in particular, for analyzing global existence of solutions for nonlinear problems, near constant equilibria. The issue turns out to be closely linked to the classical Kalman rank condition for the control of finite-dimensional linear systems, the (SK) condition and other notions such as hypoellipticity and hypocoercivity of partially diffusive PDEs.
11 Introduction Partially dissipative linear hyperbolic systems may develop a complex asymptotic behavior as t depending on the space dimension, the dimension of the system, and the interaction between the free dynamics and the damping operator. The understanding of this issue is relevant, in particular, for analyzing global existence of solutions for nonlinear problems, near constant equilibria. The issue turns out to be closely linked to the classical Kalman rank condition for the control of finite-dimensional linear systems, the (SK) condition and other notions such as hypoellipticity and hypocoercivity of partially diffusive PDEs.
12 Introduction Partially dissipative linear hyperbolic systems may develop a complex asymptotic behavior as t depending on the space dimension, the dimension of the system, and the interaction between the free dynamics and the damping operator. The understanding of this issue is relevant, in particular, for analyzing global existence of solutions for nonlinear problems, near constant equilibria. The issue turns out to be closely linked to the classical Kalman rank condition for the control of finite-dimensional linear systems, the (SK) condition and other notions such as hypoellipticity and hypocoercivity of partially diffusive PDEs.
13 The Kalman rank condition Controllability of finite dimensional linear systems Let n, m N and T > 0. Consider the following finite dimensional system: { x (t) = Ax(t)+Bu(t), t (0, T ), x(0) = x 0. Here A is a real n n matrix, B is a real n m matrix, x : [0, T ] R n represents the state and u : [0, T ] R m the control. System (1) is controllable in time T > 0 if given any initial and final one x 0, x 1 R n there exists u L 2 (0, T, R m ) such that the solution of (1) satisfies x(t ) = x 1. Obviously, in practice m n. Of particular interest is the case where m is as small as possible. (1)
14 The control property does not only depend on the dimensions m and n but on how the matrices A and B interact x (t) = Ax(t) + Bu(t) Theorem (Kalman, 1958) The system (A, B) is controllable in some time T if and only if rank [B, AB,, A n 1 B] = n. (2) Consequently, if system (1) is controllable in some time T > 0 it is controllable in any time. Note that, the so-called controllability matrix [B, AB,, A n 1 B] is of dimension n nm. Thus, in the limit case where m = 1 (one single control), it is a n n matrix. In this case the Kalman rank condition requires it to be invertible.
15 Proof of Theorem 1: Suppose that rank([b, AB,, A n 1 B]) < n. From Cayley-Hamilton Theorem we deduce that rank([b, AB,, A n 1 B,...]) < n. Thus, the rank of e At B is confined in a vector space of dimension < n for all t > 0. From the variation of constants formula, the solution x of (1) satisfies t x(t) = e At x 0 + e A(t s) Bu(s)ds. 0 But t 0 ea(t s) Bu(s)ds is confined to a subspace of dimension < n. Consequently, there are non trivial directions v R n such that v, x(t ) = v, e AT x 0 + T 0 v, e A(T s) Bu(s) ds = v, e AT x 0.
16 Stabilization of finite dimensional linear systems The controls we have obtained so far are the so called open loop controls. In practice, it is interesting to get closed loop or feedback controls, so that its value is related in real time with the state itself. Assume, to fix ideas, that A is a skew-adjoint matrix, i. e. A = A. In this case, < Ax, x >= 0. Consider the system { x = Ax+Bu x(0) = x 0 (3). When u 0, the energy of the solution of (3) is conserved. Indeed, by multiplying (3) by x, if u 0, one obtains Hence, d dt x(t) 2 = 0. x(t) = x 0, t 0.
17 We then look for a matrix L such that the solution of system (3) with the feedback control law u(t) = Lx(t) has a uniform exponential decay, i.e. there exist c > 0 and ω > 0 such that x(t) ce ωt x 0 for any solution. In other words, we are looking for matrices L such that the solution of the system x = (A + BL)x = Dx has an uniform exponential decay rate.
18 Theorem If A is skew-adjoint and the pair (A, B) is controllable then L = B stabilizes the system, i.e. the solution of { x = Ax BB x x(0) = x 0 (4) has an uniform exponential decay. Proof: With L = B we obtain that 1 d 2 dt x(t) 2 = < BB x(t), x(t) >= B x(t) 2 0. Hence, the norm of the solution decreases in time. Then, use the same ideas as before in the way you prefer to conclude.
19 An example: The harmonic oscillator Consider the damped harmonic oscillator: mx + Rx+kx = 0, (5) where m, k and R are positive constants. It is easy to see that the solutions of this equation have an exponential decay property. Indeed, it is sufficient to remark that the two characteristic roots have negative real part. Indeed, and therefore mr 2 + R + kr = 0 r ± = k ± k 2 4mR 2m Re r ± = { k 2m k 2m ± k 2 4m R 2m if k 2 4mR if k 2 4mR.
20 We observe here the classical overdamping phenomenon. Contradicting a first intuition it is not true that the decay rate increases when the value of the damping parameter k increases.
21 Systems of balance laws We consider nonlinear hyperbolic systems of the form w m t + j=1 F j (w) x j = Q(w), x R m, t > 0, w : R R m R n (t, x) w(t, x) arising in so many applications: fluid mechanics, gas dynamics, traffic flow, ralaxation,... Local (in time) smooth solutions are known to exist (C. Dafermos, L. Hsiao-T.-P. Liu, A. Majda, D. Serre,...) But possible singularities (i.e. shock waves) may arise in finite time. The nonlinear term Q may play the role of a partial dissipation and help to the existence of global smooth solutions.
22 Global smooth solutions in a neighborhood of a constant equilibrium: Q(w ) = 0. THE METHOD = LINEARIZATION + FIXED POINT. If the linearized dynamics exhibits solutions that decay as t, a perturbation argument may hopefully allow showing that, locally around the constant equilibrium, solutions are global and decay as well. One has to distinguish: Total dissipation : Q(w) = Bw, ( B > ) Partial dissipation: Q(w) = w. 0 D Y. Shizuta & S. Kawashima (85), Y. Zeng (99), W. A. Yong (04), S. Bianchini, B. Hanouzet & R. Natalini (03, m = 1),... Example : Isentropic Euler equations with damping u t v x = 0, v t + f (u) x = v
23 Partially dissipative linear hyperbolic systems A 1,..., A m symmetric w m t + w A j = Bw, x R m, w R n (6) x j j=1 B = ( D ) n1 n 2 X t DX > 0 X R n 2 {0} Goal: Understand the asymptotic behavior as t. Apply Fourier transform: ŵ m t = ( B ia(ξ))ŵ where A(ξ) := ξ j A j Lack coercivity : [B + ia(ξ)]x, X = BX, X = DX 2, X 2 c X 2 But possible decay depending on ξ: exp[( B ia(ξ))t] Ce λ(ξ)t j=1
24 PARTIALLY DISSIPATIVE LINEAR HYPERBOLIC SYSTEM m-parameter (ξ) FAMILY OF FINITE-DIMENSIONAL PARTIALLY DISSIPATIVE n-dimensional SYSTEMS. The asymptotic behavior of solutions is determined by the behavior of the function ξ λ(ξ) giving the decay rate as a function of ξ. Example: The dissipative wave equation u tt u xx + u t = 0. u t = v x u; v t = u x. Solutions may be decomposed as A high frequency component tending to zero exponentially fast as t 0; A low frequency component with the same decay rate as the heat kernel t 1/2 decay in L for L 1 data. This corresponds to a function λ(ξ) min( ξ 2, 1).
25 (SK) for partially dissipative linear hyperbolic systems The pioneering and well-known result by Shizuta-Kawashima (85) may be viewed as a generalization of the example above on the dissipative wave equation. Under the condition (SK) below (SK) : ξ R m, Ker(B) {eigenvectors of A(ξ)} = {0} exp[( B ia(ξ))t] Ce λ(ξ)t, λ(ξ) = c min(1, ξ 2 ) Their proof is based on a (long) linear algebra computation. Consequences : (1) w 0 L 1 L 2 (R m, R n ), w = w l + w w h w h (t) L 2 (R m,r n ) Ce γt 0 L 2 (R m,r n ) w l (t) L (R m,r n ) Ct m 2 w 0 L 1 (R m,r n ) (2) Global smooth solutions for the NL system for initial data close to a constant equilibrium.
26 Our contributions (K. Beauchard & E. Z., ARMA, to appear) 1st step : (SK) and Kalman rank condition 2nd step : Measure of the decay rate for B + ia(ξ) 3rd step : Classification of the asymptotic behavior for linear hyperbolic systems (with/without (SK)) 4th step : Nonlinear systems of balance laws : global smooth solutions without (SK)
27 (SK) and the Kalman rank condition
28 (SK) and Kalman rank conditions Lemma The (SK) condition is equivalent to imposing the Kalman rank condition to the pairs (A(ξ), B) for all values of ξ R m. A symmetric B = ( D (SK) : Ker(B) {eigenvectors of A} = {0} ) n1 n 2 X t DX > 0 X R n 2 {0} The pair (A, B) satisfies the Kalman rank condition Be At X 0 for all t > 0 = X 0 n 1 k=0 BA k X 2 is a norm on R n (the Kalman quadratic form)
29 The (SK) condition requires the conditions above to be satisfied for all ξ R m. In 1 d (m = 1) the (SK) condition is sharp. Whenever it fails, travelling wave solutions with L 2 -profiles exist, thus making the decay of solutions impossible. This is so because, in 1 d, the Kalman condition holds for all ξ R if and only if the pair (A, B) sastifies the Kalman condition. This is not true in the muti-dimensional case since A(ξ) = A(ξ) := m ξ j A j depends on ξ in a non-trivial way. j=1 In view of the analysis above it is more natural to analyse the positive definiteness of the quadratic form n 1 BA(ξ) k X 2, as k=0 function of ξ. This will illustrate the existence of many other scenarios that (SK) excludes, except in one space dimension (m = 1). In the multi-dimensional case (SK) is a sufficient condition for decay, but is far from being necessary.
30 Decay rate for B + ia(ξ) as a function of ξ
31 A measure of the decay rate as a function of ξ ( ) A 1,..., A m 0 0 n1 B = A(ξ) := m ξ symmetric 0 D n j A j 2 ξ = ρω R m ρ > 0 ω S m 1 (m k ) well chosen Theorem n 1 N,ɛ (ω) := min{ ɛ m k BA(ω) k x 2 ; x S n 1 }. k=0 ɛ > 0, c > 0 such that ɛ (0, ɛ ), exp[( B iρa(ω))t] 2e cn,ɛ(ω)min{1,ρ2 }t. Remark : (SK) N,ɛ (ω) N,ɛ > 0, ω S m 1. In general, N,ɛ (ω) may vanish for some values of ω S m 1, in which case the decomposition of solutions and its asymptotic form is more complex. j=1
32 Our proof: Is based on energy arguments and the construction of a suitable Lyapunov functional that allows exhibiting the exponential decay rate. In that sense it is similar to the techniques employed for proving decay rates for dissipative wave equations, and the works by C. Villani et al. on the decay for kinetic equations and hypocoercivity. Yields the result by Shizuta-Kawashima in a simpler way, but shows that it only covers one of the many possible behaviors one may encounter for m 2. Provides quantitative estimates on the decay rate as a function of ξ that can be used for a better understanding of the nonlinear problem.
33 An example: the dissipative wave equation. u tt u xx + u t = 0. w tt + ξ 2 w + w t = 0. e ξ (t) = 1 2 [ w t 2 + ξ 2 w 2 ]. de ξ (t) = w t 2. dt The exponential decay rate does not come directly out of this. But it is easy to check that is, for ε small enough, such that f ξ (t) = e ξ (t) + εww t, and, on the other hand, df ξ (t) dt f ξ e ξ c(ξ, ε)f ξ (t).
34 Indeed, df ξ (t) dt = w t 2 + ε w t 2 + εww tt = (1 ε) w t 2 εξ 2 w 2 εww t (1 ε ε 2ξ 2 ) w t 2 εξ2 2 w 2. Then, it suffices to take: ε = min ( ξ 2, 1 ). 4 Note that there is an extensive literature on the extensions of this result to nonlinear problems (M. Nakao, A. Haraux,... ).
35 Decay rate for B + ia(ξ) : ρ < 1 ω S m 1 : n 1 k=0 ẋ = ( B iρa(ω))x, ɛ m k BA(ω) k x 2 N,ɛ > 0, x S n 1. Goal : x(t) 2 x 0 e cn,ɛρ2 t Strategy : find L(x) x 2 such that dl dt cρ 2 N,ɛ L n 1 L(x) = x 2 + ρ ɛ m k Im ( A(ω) k BBA(ω) k 1 x, x ) k=1 dl dt = 2Re ( (B + iρa ω )x, x ) ρ n 1 k=1 ɛm k Im ( (A t ω) k B t BA k 1 ω (B + iρa ω )x, x ) ρ n 1 k=1 ɛm k Im ( (A t ω) k B t BAω k 1 x, (B + iρa ω )x ) 2C 1 Bx 2 ρ 2 n 1 k=1 ɛm k BA k ωx 2 +ρ n 1 k=1 ɛm k Bx [ BA k 1 ω BA k ωx + BA k ω BA k 1 ω x ] +ρ 2 n 1 k=1 ɛm k BA k 1 ω x BA k+1 ω x
36 Asymptotic behavior for linear hyperbolic systems (with or without (SK))
37 The set of degeneracy The minimum of Kalman s quadratic form N,ɛ (ω) := min{ n 1 k=0 ɛm k BA(ω) k x 2 ; x S n 1 } measures the decay rate for B + iρa(ω) N,ɛ (ω) > 0 Ker(B) {eigenvectors of A(ω)} = {0} (A(ω), B) satisfies the Kalman rank condition. The set of degeneracy : D(B + ia(ξ)) = {ξ R m ; rank[b BA(ξ)... BA(ξ) n 1 ] < n} is an algebraic submanifold either D = 0 N,ɛ > 0 a.e. strong L 2 stability or D = R m : non dissipated solutions Decomposition?
38 D = 0, n 1 = 1 (B is effective in all but one components) Theorem When n 1 = 1, D is a vector subspace of R m and N,ɛ (ω) c min{1, dist(ω, D) 2 }, ω S m 1. As a consequence we have the following new decomposition w = w h + w l + w new with wh w (t) L 2 (R m,r n ) Ce t 0 L 2 (R m,r n ) w l (t) L (R m,r n ) Ct m 2 w 0 L 1 (R m,r n ) w new (t) L (R m,r n ) Ct 1 2 w 0 Example: n = m = 2; D = {(ξ 1, ξ 2 ) : a 1 21 ξ 1 + a 2 21 ξ 2 = 0}.
39 Classification of the asymptotic behaviors w m t + w A j = Bw, x R m, w R n x j j=1 (SK) D L 2 stability decomposition m = 1 (SK) {0} yes e t + 1 t n no (SK) R no e t + 1 t +trav. waves n = 2 (SK) {0} yes m no (SK) hyperplane yes e t + 1 t e t + 1 t + 1 t no (SK) R m no e t + trav. wave n (SK) {0} yes e t + 1 t m/2 m no &n 1 = 1 vect. subsp. yes e t t m/2 t no (SK) submanifold yes open R m no open
40 Existence of global smooth solutions with (SK) (Yong, 04) w m t + j=1 ( F j (w) 0 0 = Bw B := x j 0 I n2 W e constant equilibrium with (SK) on its linearized system. ) n1 n 2 Theorem Let s [m/2] + 2 be an integer. There exists δ > 0 such that, w 0 W e + H s (R m, R n ) with H w 0 W e δ s there exists a unique global solution w C 0 ((0, + ) t, W e + H s ). Proof : Local existence + continuation argument. It uses the decay rate of the linearized system around W e.
41 Existence of global smooth solutions without (SK) inspired by Coron s return method The same result of global existence holds under the following assumptions: (H1) : A j := df j (w) symmetric (H2) : W p W in R n constant equilibria with N (W p ) > 0, N (W ) = 0 and N (W p ) >> W p W C(W p ) n 1 C(W p ) := sup{ ɛ m k A Wp (ω) k B t BA Wp (ω) k 1 ; ω S m 1 } k=1
42 Example w t + F (w) x = Bw B := ( ) F = (H1) F 1 w 2 = F 2 w 1 (H2) W = (0, 0) with F 1 w 2 (0, 0) = 0 no (SK) W p = (a p, 0) with F 1 w 2 (a p, 0) 0 (SK) We assume that M > 0 such that F 1 w 1 + F 2 w 2 M F 1 Then C(W p ) thus the theorem applies if N (W p ) F 1 w 2 (a p, 0) w 2. F 1 2 F 1 (a p, 0) >> a p (0, 0) >> 1 w 2 w 2 w 1 ( F1 F 2 )
43 Connections with hypoellipticity & hypocoercivity Hypoellipticity The fundamental solution is C away form the diagonal (...L. Hörmander, ) t u n a jk jk 2 u + j,k=1 n b jk x j k u = 0. j,k=1 Example: Kolmogorov equation: u t u xx xu y = 0. After applying Fourier transform: U t A(ξ, ξ)u n b jk j (ξ k U) = 0. j,k=1 Hypoellipticity fails iff A(e Bsξ, e Bsξ ) = 0 for all s > for some ξ 0.
44 Hypocoercivity (...C. Villani, 2006,...) L = A A + B, f t + Lf = 0 where B = B. Example: Similar models as above but in the context of kinetic equations: Fokker-Planck equation: f t v f + v x f V (x) v f v (vf ) = 0. Despite the lack of coercivity of L, under suitable assumptions on the commutators of A and B, one can build a modified energy or suitable Lyapunov functional (similar to our construction of the functional L) in which the time exponential decay can be proved.
45 Null control of the Kolmogorov equation: f f +v t x 2 f v 2 = u(t, x, v)1 ω(x, v), (x, v) R x R v, t (0, + ), [ ] (7) where ω = R x R v [a, b]. Equivalently, one may address the following observability inequality for the adjoint system: { g t v g x 2 g = 0, (x, v) R v 2 x R v, t (0, T ), (8) g(0, x, v) = g 0 (x, v), (x, v) R x R v, satisfies R x R v g(t, x, v) 2 dxdv C T 0 ω g(t, x, v) 2 dxdvdt.
46 Ideas of the proof: Fourier transform in v: { ˆf t (t, ξ, v) + iξvˆf (t, ξ, v) 2ˆf (t, ξ, v) = û(t, ξ, v)1 v 2 R [a,b] (v), ˆf (0, ξ, v) = ˆf 0 (ξ, v). (9) Decay: ˆf (t, ξ,.) ˆf t 0 (ξ,.) 3 /12 L 2 (R) L 2 (R) e ξ2, ξ R, t R +. Control depending on the parameter ξ with cost e C(T ) max{1, ξ }. (10)
47 Conclusion Control theoretical tools applied to analyze the decay rate for B + ia(ξ) yield: Goals: For linear hyperbolic systems : a (yet non complete) classification for the asymptotic behaviors, with and without (SK), for nonlinear systems of conservation laws : existence of global smooth solutions around some degenerate constant equilibria. To make the classification of linear systems complete; Significantly enlarge the class of nonlinear systems for which global existence holds.
48 Existence of global smooth solutions : proof w m t + A j (w) w = Bw x j j=1 Strategy : local existence + continuation argument 2 T w(t ) W e + 2 w 2 + C 1N (W p H s H s e ) 2 x w 2 w0 H W e H s 1 s 1st step : 2nd step : w(t ) 0 L 2 -estimate w(t ) W e 2 L H s -estimate 2 H s + 2 T 0 T 0 w 2 2 L 2 w0 2 L 2 w 2 2 H s w0 2 H s + CN s(t ) H N s (T ) := sup{ w(t) W e ; t (0, T )} s T 0 w 2 H s 1
49 3rd step : estimate on T 0 w 2 L 2 w m t + A j (We p ) w = Bw + h p x j j=1 N (W p e )= hypocoercivity for the linear system 2 w(t ) W e w 0 W e 2 + T H s 0 w N (W p H s e ) 2 w 2 x w + (N s(t ) + W e W p H s e 2 ) T 0 If N s (T ) + W e W p e 2 ) < N (W p e ) 2 then w(t ) W e H s < w0 W e H s. H s 1 2 H s 1 This always holds when H w 0 W e < 1 s 2 N (We p ) 2 W e We p < 1 2 N (We p )
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 informationSwitching, sparse and averaged control
Switching, sparse and averaged control Enrique Zuazua Ikerbasque & BCAM Basque Center for Applied Mathematics Bilbao - Basque Country- Spain zuazua@bcamath.org http://www.bcamath.org/zuazua/ WG-BCAM, February
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 informationHypocoercivity for kinetic equations with linear relaxation terms
Hypocoercivity for kinetic equations with linear relaxation terms Jean Dolbeault dolbeaul@ceremade.dauphine.fr CEREMADE CNRS & Université Paris-Dauphine http://www.ceremade.dauphine.fr/ dolbeaul (A JOINT
More informationUne approche hypocoercive L 2 pour l équation de Vlasov-Fokker-Planck
Une approche hypocoercive L 2 pour l équation de Vlasov-Fokker-Planck Jean Dolbeault dolbeaul@ceremade.dauphine.fr CEREMADE CNRS & Université Paris-Dauphine http://www.ceremade.dauphine.fr/ dolbeaul (EN
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 informationStability of an abstract wave equation with delay and a Kelvin Voigt damping
Stability of an abstract wave equation with delay and a Kelvin Voigt damping University of Monastir/UPSAY/LMV-UVSQ Joint work with Serge Nicaise and Cristina Pignotti Outline 1 Problem The idea Stability
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 informationEN Nonlinear Control and Planning in Robotics Lecture 3: Stability February 4, 2015
EN530.678 Nonlinear Control and Planning in Robotics Lecture 3: Stability February 4, 2015 Prof: Marin Kobilarov 0.1 Model prerequisites Consider ẋ = f(t, x). We will make the following basic assumptions
More informationAn introduction to Birkhoff normal form
An introduction to Birkhoff normal form Dario Bambusi Dipartimento di Matematica, Universitá di Milano via Saldini 50, 0133 Milano (Italy) 19.11.14 1 Introduction The aim of this note is to present an
More informationNonlinear stabilization via a linear observability
via a linear observability Kaïs Ammari Department of Mathematics University of Monastir Joint work with Fathia Alabau-Boussouira Collocated feedback stabilization Outline 1 Introduction and main result
More informationdegenerate Fokker-Planck equations with linear drift
TECHNISCHE UNIVERSITÄT WIEN Entropy method for hypocoercive & non-symmetric Fokker-Planck equations with linear drift Anton ARNOLD with Jan Erb; Franz Achleitner Paris, April 2017 Anton ARNOLD (TU Vienna)
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 informationStability of Feedback Solutions for Infinite Horizon Noncooperative Differential Games
Stability of Feedback Solutions for Infinite Horizon Noncooperative Differential Games Alberto Bressan ) and Khai T. Nguyen ) *) Department of Mathematics, Penn State University **) Department of Mathematics,
More information1 Math 241A-B Homework Problem List for F2015 and W2016
1 Math 241A-B Homework Problem List for F2015 W2016 1.1 Homework 1. Due Wednesday, October 7, 2015 Notation 1.1 Let U be any set, g be a positive function on U, Y be a normed space. For any f : U Y let
More informationStabilization and Passivity-Based Control
DISC Systems and Control Theory of Nonlinear Systems, 2010 1 Stabilization and Passivity-Based Control Lecture 8 Nonlinear Dynamical Control Systems, Chapter 10, plus handout from R. Sepulchre, Constructive
More informationMinimal time issues for the observability of Grushin-type equations
Intro Proofs Further Minimal time issues for the observability of Grushin-type equations Karine Beauchard (1) Jérémi Dardé (2) (2) (1) ENS Rennes (2) Institut de Mathématiques de Toulouse GT Contrôle LJLL
More informationME 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 informationSemigroup factorization and relaxation rates of kinetic equations
Semigroup factorization and relaxation rates of kinetic equations Clément Mouhot, University of Cambridge Analysis and Partial Differential Equations seminar University of Sussex 24th of february 2014
More informationMathematical modelling of collective behavior
Mathematical modelling of collective behavior Young-Pil Choi Fakultät für Mathematik Technische Universität München This talk is based on joint works with José A. Carrillo, Maxime Hauray, and Samir Salem
More informationChap 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 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 information1 Controllability and Observability
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
More informationThroughout these notes we assume V, W are finite dimensional inner product spaces over C.
Math 342 - Linear Algebra II Notes Throughout these notes we assume V, W are finite dimensional inner product spaces over C 1 Upper Triangular Representation Proposition: Let T L(V ) There exists an orthonormal
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 Assignment 1: Nonlinear dynamics (due September
Assignment : Nonlinear dynamics (due September 4, 28). Consider the ordinary differential equation du/dt = cos(u). Sketch the equilibria and indicate by arrows the increase or decrease of the solutions.
More informationSome recent results on controllability of coupled parabolic systems: Towards a Kalman condition
Some recent results on controllability of coupled parabolic systems: Towards a Kalman condition F. Ammar Khodja Clermont-Ferrand, June 2011 GOAL: 1 Show the important differences between scalar and non
More informationMEM 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 informationRobust 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 informationASYMPTOTIC STRUCTURE FOR SOLUTIONS OF THE NAVIER STOKES EQUATIONS. Tian Ma. Shouhong Wang
DISCRETE AND CONTINUOUS Website: http://aimsciences.org DYNAMICAL SYSTEMS Volume 11, Number 1, July 004 pp. 189 04 ASYMPTOTIC STRUCTURE FOR SOLUTIONS OF THE NAVIER STOKES EQUATIONS Tian Ma Department of
More informationControl 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 informationDecay profiles of a linear artificial viscosity system
Decay profiles of a linear artificial viscosity system Gene Wayne, Ryan Goh and Roland Welter Boston University rwelter@bu.edu July 2, 2018 This research was supported by funding from the NSF. Roland Welter
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 informationRelaxation methods and finite element schemes for the equations of visco-elastodynamics. Chiara Simeoni
Relaxation methods and finite element schemes for the equations of visco-elastodynamics Chiara Simeoni Department of Information Engineering, Computer Science and Mathematics University of L Aquila (Italy)
More informationFinite time horizon versus stationary control
Finite time horizon versus stationary control Enrique Zuazua BCAM Basque Center for Applied Mathematics & Ikerbasque Bilbao, Basque Country, Spain zuazua@bcamath.org http://www.bcamath.org/zuazua/ WG,
More informationSolution 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 informationWeek 6 Notes, Math 865, Tanveer
Week 6 Notes, Math 865, Tanveer. Energy Methods for Euler and Navier-Stokes Equation We will consider this week basic energy estimates. These are estimates on the L 2 spatial norms of the solution u(x,
More informationHypocoercivity and exponential time decay for the linear inhomogeneous relaxation Boltzmann equation
Hypocoercivity and exponential time decay for the linear inhomogeneous relaxation Boltzmann equation Frédéric Hérau Université de Reims March 16, 2005 Abstract We consider an inhomogeneous linear Boltzmann
More informationMath 46, Applied Math (Spring 2008): Final
Math 46, Applied Math (Spring 2008): Final 3 hours, 80 points total, 9 questions, roughly in syllabus order (apart from short answers) 1. [16 points. Note part c, worth 7 points, is independent of the
More informationExplicit approximate controllability of the Schrödinger equation with a polarizability term.
Explicit approximate controllability of the Schrödinger equation with a polarizability term. Morgan MORANCEY CMLA, ENS Cachan Sept. 2011 Control of dispersive equations, Benasque. Morgan MORANCEY (CMLA,
More informationFrom Boltzmann Equations to Gas Dynamics: From DiPerna-Lions to Leray
From Boltzmann Equations to Gas Dynamics: From DiPerna-Lions to Leray C. David Levermore Department of Mathematics and Institute for Physical Science and Technology University of Maryland, College Park
More informationIncoming and disappearaing solutions of Maxwell s equations. Université Bordeaux I
1 / 27 Incoming and disappearaing solutions of Maxwell s equations Vesselin Petkov (joint work with F. Colombini and J. Rauch) Université Bordeaux I MSRI, September 9th, 2010 Introduction 2 / 27 0. Introduction
More informationDamped harmonic motion
Damped harmonic motion March 3, 016 Harmonic motion is studied in the presence of a damping force proportional to the velocity. The complex method is introduced, and the different cases of under-damping,
More informationLinear systems the finite dimensional case
Linear systems the finite dimensional case Johann Baumeister Goethe University, Frankfurt, Germany Rio de Janeiro / October 2017 Outline Controlled and observed systems Stability Observability Controllablity
More informationOn the Stability of the Best Reply Map for Noncooperative Differential Games
On the Stability of the Best Reply Map for Noncooperative Differential Games Alberto Bressan and Zipeng Wang Department of Mathematics, Penn State University, University Park, PA, 68, USA DPMMS, University
More informationSemidefinite 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 informationDynamical 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 informationFeedback Linearization
Feedback Linearization Peter Al Hokayem and Eduardo Gallestey May 14, 2015 1 Introduction Consider a class o single-input-single-output (SISO) nonlinear systems o the orm ẋ = (x) + g(x)u (1) y = h(x) (2)
More informationMATH 205C: STATIONARY PHASE LEMMA
MATH 205C: STATIONARY PHASE LEMMA For ω, consider an integral of the form I(ω) = e iωf(x) u(x) dx, where u Cc (R n ) complex valued, with support in a compact set K, and f C (R n ) real valued. Thus, I(ω)
More informationStabilization of Distributed Parameter Systems by State Feedback with Positivity Constraints
Stabilization of Distributed Parameter Systems by State Feedback with Positivity Constraints Joseph Winkin Namur Center of Complex Systems (naxys) and Dept. of Mathematics, University of Namur, Belgium
More informationPutzer s Algorithm. Norman Lebovitz. September 8, 2016
Putzer s Algorithm Norman Lebovitz September 8, 2016 1 Putzer s algorithm The differential equation dx = Ax, (1) dt where A is an n n matrix of constants, possesses the fundamental matrix solution exp(at),
More informationWEYL S LEMMA, ONE OF MANY. Daniel W. Stroock
WEYL S LEMMA, ONE OF MANY Daniel W Stroock Abstract This note is a brief, and somewhat biased, account of the evolution of what people working in PDE s call Weyl s Lemma about the regularity of solutions
More information4. Complex Oscillations
4. Complex Oscillations The most common use of complex numbers in physics is for analyzing oscillations and waves. We will illustrate this with a simple but crucially important model, the damped harmonic
More informationTopic # /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 informationGrammians. 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 informationGreedy Control. Enrique Zuazua 1 2
Greedy Control Enrique Zuazua 1 2 DeustoTech - Bilbao, Basque Country, Spain Universidad Autónoma de Madrid, Spain Visiting Fellow of LJLL-UPMC, Paris enrique.zuazua@deusto.es http://enzuazua.net X ENAMA,
More informationin Bounded Domains Ariane Trescases CMLA, ENS Cachan
CMLA, ENS Cachan Joint work with Yan GUO, Chanwoo KIM and Daniela TONON International Conference on Nonlinear Analysis: Boundary Phenomena for Evolutionnary PDE Academia Sinica December 21, 214 Outline
More informationA LOWER BOUND ON BLOWUP RATES FOR THE 3D INCOMPRESSIBLE EULER EQUATION AND A SINGLE EXPONENTIAL BEALE-KATO-MAJDA ESTIMATE. 1.
A LOWER BOUND ON BLOWUP RATES FOR THE 3D INCOMPRESSIBLE EULER EQUATION AND A SINGLE EXPONENTIAL BEALE-KATO-MAJDA ESTIMATE THOMAS CHEN AND NATAŠA PAVLOVIĆ Abstract. We prove a Beale-Kato-Majda criterion
More informationAsymptotic Behavior of a Hyperbolic-parabolic Coupled System Arising in Fluid-structure Interaction
International Series of Numerical Mathematics, Vol. 154, 445 455 c 2006 Birkhäuser Verlag Basel/Switzerland Asymptotic Behavior of a Hyperbolic-parabolic Coupled System Arising in Fluid-structure Interaction
More informationNumerical Methods for Conservation Laws WPI, January 2006 C. Ringhofer C2 b 2
Numerical Methods for Conservation Laws WPI, January 2006 C. Ringhofer ringhofer@asu.edu, C2 b 2 2 h2 x u http://math.la.asu.edu/ chris Last update: Jan 24, 2006 1 LITERATURE 1. Numerical Methods for Conservation
More informationInput-to-state stability and interconnected Systems
10th Elgersburg School Day 1 Input-to-state stability and interconnected Systems Sergey Dashkovskiy Universität Würzburg Elgersburg, March 5, 2018 1/20 Introduction Consider Solution: ẋ := dx dt = ax,
More information6.241 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 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 informationFinite difference method for elliptic problems: I
Finite difference method for elliptic problems: I Praveen. C praveen@math.tifrbng.res.in Tata Institute of Fundamental Research Center for Applicable Mathematics Bangalore 560065 http://math.tifrbng.res.in/~praveen
More informationA COUNTEREXAMPLE TO AN ENDPOINT BILINEAR STRICHARTZ INEQUALITY TERENCE TAO. t L x (R R2 ) f L 2 x (R2 )
Electronic Journal of Differential Equations, Vol. 2006(2006), No. 5, pp. 6. ISSN: 072-669. URL: http://ejde.math.txstate.edu or http://ejde.math.unt.edu ftp ejde.math.txstate.edu (login: ftp) A COUNTEREXAMPLE
More informationRANDOM PROPERTIES BENOIT PAUSADER
RANDOM PROPERTIES BENOIT PAUSADER. Quasilinear problems In general, one consider the following trichotomy for nonlinear PDEs: A semilinear problem is a problem where the highest-order terms appears linearly
More informationEntropy-dissipation methods I: Fokker-Planck equations
1 Entropy-dissipation methods I: Fokker-Planck equations Ansgar Jüngel Vienna University of Technology, Austria www.jungel.at.vu Introduction Boltzmann equation Fokker-Planck equations Degenerate parabolic
More information7 Planar systems of linear ODE
7 Planar systems of linear ODE Here I restrict my attention to a very special class of autonomous ODE: linear ODE with constant coefficients This is arguably the only class of ODE for which explicit solution
More informationOn Smoothness of Suitable Weak Solutions to the Navier-Stokes Equations
On Smoothness of Suitable Weak Solutions to the Navier-Stokes Equations G. Seregin, V. Šverák Dedicated to Vsevolod Alexeevich Solonnikov Abstract We prove two sufficient conditions for local regularity
More informationGeometry and the Kato square root problem
Geometry and the Kato square root problem Lashi Bandara Centre for Mathematics and its Applications Australian National University 7 June 2013 Geometric Analysis Seminar University of Wollongong Lashi
More informationA Minimal Uncertainty Product for One Dimensional Semiclassical Wave Packets
A Minimal Uncertainty Product for One Dimensional Semiclassical Wave Packets George A. Hagedorn Happy 60 th birthday, Mr. Fritz! Abstract. Although real, normalized Gaussian wave packets minimize the product
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 informationSpring, 2012 CIS 515. Fundamentals of Linear Algebra and Optimization Jean Gallier
Spring 0 CIS 55 Fundamentals of Linear Algebra and Optimization Jean Gallier Homework 5 & 6 + Project 3 & 4 Note: Problems B and B6 are for extra credit April 7 0; Due May 7 0 Problem B (0 pts) Let A be
More informationStabilization of second order evolution equations with unbounded feedback with delay
Stabilization of second order evolution equations with unbounded feedback with delay S. Nicaise and J. Valein snicaise,julie.valein@univ-valenciennes.fr Laboratoire LAMAV, Université de Valenciennes et
More informationDepartment of Mathematics, University of California, Berkeley. GRADUATE PRELIMINARY EXAMINATION, Part A Fall Semester 2016
Department of Mathematics, University of California, Berkeley YOUR 1 OR 2 DIGIT EXAM NUMBER GRADUATE PRELIMINARY EXAMINATION, Part A Fall Semester 2016 1. Please write your 1- or 2-digit exam number on
More informationASYMPTOTICALLY NONEXPANSIVE MAPPINGS IN MODULAR FUNCTION SPACES ABSTRACT
ASYMPTOTICALLY NONEXPANSIVE MAPPINGS IN MODULAR FUNCTION SPACES T. DOMINGUEZ-BENAVIDES, M.A. KHAMSI AND S. SAMADI ABSTRACT In this paper, we prove that if ρ is a convex, σ-finite modular function satisfying
More information16.31 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 informationENGI 9420 Lecture Notes 8 - PDEs Page 8.01
ENGI 940 ecture Notes 8 - PDEs Page 8.0 8. Partial Differential Equations Partial differential equations (PDEs) are equations involving functions of more than one variable and their partial derivatives
More informationLec 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 informationStabilization for the Wave Equation with Variable Coefficients and Balakrishnan-Taylor Damping. Tae Gab Ha
TAIWANEE JOURNAL OF MATHEMATIC Vol. xx, No. x, pp., xx 0xx DOI: 0.650/tjm/788 This paper is available online at http://journal.tms.org.tw tabilization for the Wave Equation with Variable Coefficients and
More informationARCS IN FINITE PROJECTIVE SPACES. Basic objects and definitions
ARCS IN FINITE PROJECTIVE SPACES SIMEON BALL Abstract. These notes are an outline of a course on arcs given at the Finite Geometry Summer School, University of Sussex, June 26-30, 2017. Let K denote an
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 informationConverse Lyapunov theorem and Input-to-State Stability
Converse Lyapunov theorem and Input-to-State Stability April 6, 2014 1 Converse Lyapunov theorem In the previous lecture, we have discussed few examples of nonlinear control systems and stability concepts
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 informationOn continuous time contract theory
Ecole Polytechnique, France Journée de rentrée du CMAP, 3 octobre, 218 Outline 1 2 Semimartingale measures on the canonical space Random horizon 2nd order backward SDEs (Static) Principal-Agent Problem
More informationHypocoercivity and Sensitivity Analysis in Kinetic Equations and Uncertainty Quantification October 2 nd 5 th
Hypocoercivity and Sensitivity Analysis in Kinetic Equations and Uncertainty Quantification October 2 nd 5 th Department of Mathematics, University of Wisconsin Madison Venue: van Vleck Hall 911 Monday,
More informationNormal form for the non linear Schrödinger equation
Normal form for the non linear Schrödinger equation joint work with Claudio Procesi and Nguyen Bich Van Universita di Roma La Sapienza S. Etienne de Tinee 4-9 Feb. 2013 Nonlinear Schrödinger equation Consider
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 informationPage 404. Lecture 22: Simple Harmonic Oscillator: Energy Basis Date Given: 2008/11/19 Date Revised: 2008/11/19
Page 404 Lecture : Simple Harmonic Oscillator: Energy Basis Date Given: 008/11/19 Date Revised: 008/11/19 Coordinate Basis Section 6. The One-Dimensional Simple Harmonic Oscillator: Coordinate Basis Page
More informationEN Nonlinear Control and Planning in Robotics Lecture 10: Lyapunov Redesign and Robust Backstepping April 6, 2015
EN530.678 Nonlinear Control and Planning in Robotics Lecture 10: Lyapunov Redesign and Robust Backstepping April 6, 2015 Prof: Marin Kobilarov 1 Uncertainty and Lyapunov Redesign Consider the system [1]
More informationDenis 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 information5 Quiver Representations
5 Quiver Representations 5. Problems Problem 5.. Field embeddings. Recall that k(y,..., y m ) denotes the field of rational functions of y,..., y m over a field k. Let f : k[x,..., x n ] k(y,..., y m )
More informationALGEBRA QUALIFYING EXAM PROBLEMS LINEAR ALGEBRA
ALGEBRA QUALIFYING EXAM PROBLEMS LINEAR ALGEBRA Kent State University Department of Mathematical Sciences Compiled and Maintained by Donald L. White Version: August 29, 2017 CONTENTS LINEAR ALGEBRA AND
More informationarxiv: v2 [math.fa] 17 May 2016
ESTIMATES ON SINGULAR VALUES OF FUNCTIONS OF PERTURBED OPERATORS arxiv:1605.03931v2 [math.fa] 17 May 2016 QINBO LIU DEPARTMENT OF MATHEMATICS, MICHIGAN STATE UNIVERSITY EAST LANSING, MI 48824, USA Abstract.
More informationCLASSIFICATIONS OF THE FLOWS OF LINEAR ODE
CLASSIFICATIONS OF THE FLOWS OF LINEAR ODE PETER ROBICHEAUX Abstract. The goal of this paper is to examine characterizations of linear differential equations. We define the flow of an equation and examine
More informationQuantum Fokker-Planck models: kinetic & operator theory approaches
TECHNISCHE UNIVERSITÄT WIEN Quantum Fokker-Planck models: kinetic & operator theory approaches Anton ARNOLD with F. Fagnola (Milan), L. Neumann (Vienna) TU Vienna Institute for Analysis and Scientific
More informationConvergence of Particle Filtering Method for Nonlinear Estimation of Vortex Dynamics
Convergence of Particle Filtering Method for Nonlinear Estimation of Vortex Dynamics Meng Xu Department of Mathematics University of Wyoming February 20, 2010 Outline 1 Nonlinear Filtering Stochastic Vortex
More informationSome solutions of the written exam of January 27th, 2014
TEORIA DEI SISTEMI Systems Theory) Prof. C. Manes, Prof. A. Germani Some solutions of the written exam of January 7th, 0 Problem. Consider a feedback control system with unit feedback gain, with the following
More informationFrom the Newton equation to the wave equation in some simple cases
From the ewton equation to the wave equation in some simple cases Xavier Blanc joint work with C. Le Bris (EPC) and P.-L. Lions (Collège de France) Université Paris Diderot, FRACE http://www.ann.jussieu.fr/
More informationA conjecture on sustained oscillations for a closed-loop heat equation
A conjecture on sustained oscillations for a closed-loop heat equation C.I. Byrnes, D.S. Gilliam Abstract The conjecture in this paper represents an initial step aimed toward understanding and shaping
More information